Split (1)

train · 5k rows

path stringlengths 8 204	content_id stringlengths 40 40	detected_licenses list	license_type stringclasses 2 values	repo_name stringlengths 8 100	repo_url stringlengths 27 119	star_events_count int64 0 6.26k	fork_events_count int64 0 3.52k	gha_license_id stringclasses 10 values	gha_event_created_at timestamp[ns]	gha_updated_at timestamp[ns]	gha_language stringclasses 12 values	language stringclasses 1 value	is_generated bool 1 class	is_vendor bool 1 class	conversion_extension stringclasses 6 values	size int64 172 10.2M	script stringlengths 367 7.46M	script_size int64 367 7.46M
/Day23/.ipynb_checkpoints/作業解答23-checkpoint.ipynb	05e29e3e269fed80b24a9bfcff55db41dd4d80b4	[]	no_license	hochinchang/python60days	https://github.com/hochinchang/python60days	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	3,640,417	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] colab_type="text" id="xp4Wh9IFK_Ps" # 目標: 學習並了解Bokeh 如何應用 # # 重點: # # 初步了解 BOKEH 互動式GUIDE LINE # # 自定義和組織資料數據可視化 # # 為可視化添加交互姓 --- CustomJS/Render # # 作業: # # 1.建立簡單的水果資料集 # fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] # # counts = [5, 3, 4, 2, 4, 6] # # 2. 利用 Source 建立字典, 再用figure 輸出 BAR 圖 # source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6)) # # 3. Bokeh官方有提供sample_data給大家練習，gallery豐富的範例都取自sample_data，對比官方的資料格式就能輕鬆模仿應用, 下載股市資料 # # 4. 使用HoverTool(游標滑過時顯示資料); Click_policy (藉由標籤控制數值顯示) # # + [markdown] colab_type="text" id="A7MvttnXP40Z" # # 利用 Bokeh 與 Python 製作網頁互動視覺化 # # 設定資料與輸出檔案 # output_file(“out.html”) # 利用 Bokeh 繪製圖表 # p = figure() # p.line([1,2,3,4,5], [5,4,3,2,1]) # 開啟產生的 HTML 檔 ( HTML + JavaScript ，自動生成 ) # show(p) # + colab={} colab_type="code" id="fE_sd_KzP40Z" from bokeh.plotting import figure, output_file, show from bokeh.models import widgets from bokeh.io import output_notebook import numpy as np # 讓網頁直接輸出在NOTEBOOK output_notebook() # + colab={"base_uri": "https://localhost:8080/", "height": 852} colab_type="code" id="yiUh9I2kP40f" outputId="a96b2eaf-b1f4-4ef4-d0e9-2339ba2b75d5" #Bokeh官方有提供sample_data給大家練習，gallery豐富的範例都取自sample_data。 #下載sample_data指令為bokeh.sampledata.download()，直接貼在jupyter執行。檔案會下載到bokeh module裡。 import bokeh.sampledata bokeh.sampledata.download() # + colab={"base_uri": "https://localhost:8080/", "height": 267} colab_type="code" id="eUGPEX5pV1jA" outputId="0d31a441-a236-4528-9d1e-d14d6dce4b19" from bokeh.models import ColumnDataSource from bokeh.palettes import Spectral6 from bokeh.models import ColumnDataSource, HoverTool from bokeh.plotting import figure fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] counts = [5, 3, 4, 2, 4, 6] source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6)) p = figure(x_range=fruits, plot_height=250, y_range=(0, 9), title="Fruit Counts") p.vbar(x='fruits', top='counts', width=0.9, color='color', legend_field="fruits", source=source) p.xgrid.grid_line_color = None p.legend.orientation = "horizontal" p.legend.location = "top_center" show(p) # + colab={"base_uri": "https://localhost:8080/", "height": 267} colab_type="code" id="ejwc4XFJt4V4" outputId="0a00d34c-98e7-4bd7-8b12-8074995866e2" from bokeh.models import FactorRange from bokeh.transform import factor_cmap fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries'] years = ['2015', '2016', '2017'] data = {'fruits' : fruits, '2015' : [2, 1, 4, 3, 2, 4], '2016' : [5, 3, 3, 2, 4, 6], '2017' : [3, 2, 4, 4, 5, 3]} # this creates [ ("Apples", "2015"), ("Apples", "2016"), ("Apples", "2017"), ("Pears", "2015), ... ] x = [ (fruit, year) for fruit in fruits for year in years ] counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) # like an hstack source = ColumnDataSource(data=dict(x=x, counts=counts)) p = figure(x_range=FactorRange(x), plot_height=250, title="Fruit Counts by Year") #p.vbar(x='x', top='counts', width=0.9, source=source) p.vbar(x='x', top='counts', width=0.9, source=source, line_color="white", # use the palette to colormap based on the the x[1:2] values fill_color=factor_cmap('x', palette=['firebrick', 'olive', 'navy'], factors=years, start=1, end=2)) p.y_range.start = 0 p.x_range.range_padding = 0.1 p.xaxis.major_label_orientation = 1 p.xgrid.grid_line_color = None show(p) # + colab={"base_uri": "https://localhost:8080/", "height": 417} colab_type="code" id="FIsyxA8hvYOd" outputId="01790929-23d8-48eb-90b4-70454b7211d9" import bokeh.io from bokeh.resources import INLINE from bokeh.models import HoverTool from bokeh.palettes import Spectral4 from bokeh.plotting import figure, output_file, show, output_notebook, ColumnDataSource from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT import pandas as pd # 環境 settings bokeh.io.reset_output() bokeh.io.output_notebook(INLINE) # set hover ## HoverTool # 游標滑過時顯示資料,date格式需要轉換，不然會是timestamp hover = HoverTool( tooltips = [ ("date", "@date"), ("close", "@open"), ("close", "@close"), ("high", "@high"), ("low", "@low"), ("volume","@volume") ], formatters={"@date":"datetime"} ) # set figure p = figure( plot_width=1000, plot_height=400, x_axis_type="datetime", tools=[hover,"pan,box_zoom,reset,save"], ) p.title.text = 'Stock_Price--Click on legend entries to mute the corresponding lines and show daily details in hover' # use ColumnDataSource to control # click_policy # 藉由標籤控制數值顯示 # hide為隱藏，mute為切換自訂顯示模式 # 可在muted_color控制顏色, muted_alpha控制顏色濃淡 for data, name, color in zip([AAPL, IBM, MSFT, GOOG], ["AAPL", "IBM", "MSFT", "GOOG"], Spectral4): df = pd.DataFrame(data) df['date'] = pd.to_datetime(df['date']) source = ColumnDataSource(df) p.line(x="date",y="close", line_width=2, color=color, alpha=0.8, muted_color=color, muted_alpha=0.2, legend_label=name,source=source) p.legend.location = "top_left" # use hide or mute p.legend.click_policy="mute" # output_file("interactive_legend.html", title="interactive_legend.py example") show(p) output_notebook() n=lon, lat=lat, name=name, units=units) # append to list gmobj_m_mon_nlt.append(gmobj_tmp_nlt) gmobj_m_mon_lt.append(gmobj_tmp_lt) gmobj_m_mon_kpp.append(gmobj_tmp_kpp) gmobj_m_mon_epbl.append(gmobj_tmp_epbl) gmobj_m_mon_smc.append(gmobj_tmp_smc) # tmp list gmobj_stat_tmp = [] # loop over turbulent methods for i in np.arange(nm): if diag_type == 'D': # difference diag1 = tmp[i,:] - diag0_nlt elif diag_type == 'R': # ratio diag1 = tmp[i,:] / diag0_nlt # create GOTMMap object gmobj_tmp = GOTMMap(data=diag1, lon=lon, lat=lat, name=name, units=units) # append to list gmobj_stat_tmp.append(gmobj_tmp) # append to list gmobj_stat_arr.append(gmobj_stat_tmp) # + # figure 1: Statistics of the ratio to the median or mean f = plt.figure() f.set_size_inches(6, 4) pbdata0 = [] xshift = list((np.arange(nm)-nm/2)1) for k in np.arange(nm): pdata = np.concatenate([gmobj_stat_arr[i][k].data for i in np.arange(12)]) tmp = pdata pdata0 = pdata[~np.isnan(tmp)] pbdata0.append(pdata0) # all the non-Langmuir cases pdata1 = np.concatenate(pbdata0[0:5]) # all the Langmuir cases pdata2 = np.concatenate(pbdata0[5:]) pbdata0.append(pdata1) pbdata0.append(pdata2) # add color for non-Lanmguir and Langmuir cases pbcolor = bcolor pbcolor.append('lightgray') pbcolor.append('darkgray') # add label for non-Lanmguir and Langmuir cases label_list = legend_list label_list.append('Non-Langmuir') label_list.append('Langmuir') # reference line plt.axhline(y=y_ref, linewidth=1, color='gray') # plot xx = np.arange(nm+2)+1 position_arr = xx pbox0 = plt.boxplot(pbdata0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.3, patch_artist=True) tmp_arr = [item.get_ydata() for item in pbox0['whiskers']] label_b = tmp_arr[20][1] label_a = tmp_arr[15][1] plt.xlim([0,nm+3]) ax = plt.gca() if diag_type == 'D': ylims = [-13,32] panel = '(a)' label_a_str = r'$\uparrow${:4.1f}'.format(label_a) label_b_str = r'$\downarrow${:4.1f}'.format(label_b) elif diag_type == 'R': ylims = [0.75,1.4] panel = '(c)' label_a_str = r'$\uparrow${:4.2f}'.format(label_a) label_b_str = r'$\downarrow${:4.2f}'.format(label_b) plt.ylim(ylims) if label_a > ylims[1]: plt.text(0.58, 0.96, label_a_str, transform=ax.transAxes, fontsize=10, color='k', va='top') if label_b < ylims[0]: plt.text(0.79, 0.07, label_b_str, transform=ax.transAxes, fontsize=10, color='k', va='top') plt.text(0.06, 0.91, panel, transform=ax.transAxes, fontsize=14, color='k', va='top') # color for the boxes for patch, color in zip(pbox0['boxes'], pbcolor): patch.set_facecolor(color) # x- and y-labels plt.setp(ax, xticks=xx, xticklabels=label_list) plt.ylabel(y_label) # reduce margin plt.tight_layout() # auto adjust the x-axis label plt.gcf().autofmt_xdate() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_all.png' plt.savefig(figname, dpi = 300) # print info print('Max for KPPLT-R16: {:4.2f}'.format(label_a)) print('Min for OSMOSIS: {:4.2f}'.format(label_b)) print('Standard deviation of non-Langmuir: {:6.4f}'.format(np.std(pdata1))) print('Standard deviation of Langmuir: {:6.4f}'.format(np.std(pdata2))) # + # figure 1b: Statistics of the ratio to the median or mean # mean of all non-Langmuir and Langmuir cases, respectivly f = plt.figure() f.set_size_inches(6, 4) pbdata0 = [] xshift = list((np.arange(nm)-nm/2)1) for k in np.arange(nm): pdata = np.concatenate([gmobj_stat_arr[i][k].data for i in np.arange(12)]) tmp = pdata pdata0 = pdata[~np.isnan(tmp)] pbdata0.append(pdata0) # mean of the non-Langmuir cases pdata = np.concatenate([gmobj_m_mon_nlt[i].data for i in np.arange(12)]) tmp = pdata pdata1 = pdata[~np.isnan(tmp)] # mean of the Langmuir cases pdata = np.concatenate([gmobj_m_mon_lt[i].data for i in np.arange(12)]) tmp = pdata pdata2 = pdata[~np.isnan(tmp)] pbdata0.append(pdata1) pbdata0.append(pdata2) # add color for non-Lanmguir and Langmuir cases pbcolor = bcolor pbcolor.append('lightgray') pbcolor.append('darkgray') # add label for non-Lanmguir and Langmuir cases label_list = legend_list label_list.append('Non-Langmuir') label_list.append('Langmuir') # reference line plt.axhline(y=y_ref, linewidth=1, color='gray') # plot xx = np.arange(nm+2)+1 position_arr = xx pbox0 = plt.boxplot(pbdata0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.3, patch_artist=True) plt.ylim([ymin,ymax]) plt.xlim([0,nm+3]) # color for the boxes for patch, color in zip(pbox0['boxes'], pbcolor): patch.set_facecolor(color) # x- and y-labels ax = plt.gca() plt.setp(ax, xticks=xx, xticklabels=label_list) plt.ylabel(y_label) # reduce margin plt.tight_layout() # auto adjust the x-axis label plt.gcf().autofmt_xdate() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_all_b.png' plt.savefig(figname, dpi = 300) # + # figure 1c: Statistics of the ratio to the median or mean # Ratio of Langmuir over non-Langmuir for KPP, ePBL and SMC, and mean of all f = plt.figure() f.set_size_inches(2.5, 4) pbdata0 = [] xshift = list((np.arange(4)1)) # Langmuir effects in KPP pdata = np.concatenate([gmobj_m_mon_kpp[i].data for i in np.arange(12)]) tmp = pdata pdata1 = pdata[~np.isnan(tmp)] # Langmuir effects in ePBL pdata = np.concatenate([gmobj_m_mon_epbl[i].data for i in np.arange(12)]) tmp = pdata pdata2 = pdata[~np.isnan(tmp)] # Langmuir effects in SMC pdata = np.concatenate([gmobj_m_mon_smc[i].data for i in np.arange(12)]) tmp = pdata pdata3 = pdata[~np.isnan(tmp)] # mean of the Langmuir cases pdata = np.concatenate([gmobj_m_mon_lt[i].data for i in np.arange(12)]) tmp = pdata pdata4 = pdata[~np.isnan(tmp)] pbdata0.append(pdata1) pbdata0.append(pdata2) pbdata0.append(pdata3) pbdata0.append(pdata4) # add color for non-Lanmguir and Langmuir cases pbcolor = ['deepskyblue','forestgreen','firebrick','darkgray'] # add label for non-Lanmguir and Langmuir cases label_list = ['KPPLT-LF17', 'ePBL-LT', 'SMCLT-H15', 'Langmuir'] # reference line plt.axhline(y=y_ref, linewidth=1, color='gray') # plot xx = np.arange(4)+1 position_arr = xx pbox0 = plt.boxplot(pbdata0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.3, patch_artist=True) plt.xlim([0,5]) ax = plt.gca() if diag_type == 'D': plt.ylim([-12,32]) plt.text(0.14, 0.91, '(b)', transform=ax.transAxes, fontsize=14, color='k', va='top') # label elif diag_type == 'R': plt.ylim([0.75,1.4]) plt.text(0.14, 0.91, '(d)', transform=ax.transAxes, fontsize=14, color='k', va='top') # color for the boxes for patch, color in zip(pbox0['boxes'], pbcolor): patch.set_facecolor(color) # x- and y-labels plt.setp(ax, xticks=xx, xticklabels=label_list) # plt.ylabel(y_label+'$^\prime$') # reduce margin plt.tight_layout() # auto adjust the x-axis label plt.gcf().autofmt_xdate() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_all_c.png' plt.savefig(figname, dpi = 300) # print info medians_arr = [item.get_ydata() for item in pbox0['medians']] whiskers_arr = [item.get_ydata() for item in pbox0['whiskers']] for i, var in enumerate(label_list): print('{}:'.format(var)) print(' Mean: {:4.2f}'.format(pbdata0[i].mean())) print(' Median: {:4.2f}'.format(medians_arr[i][0])) ii = i2 print(' 50% range: {:4.2f} - {:4.2f}'.format(whiskers_arr[ii][0], whiskers_arr[ii+1][0])) print(' 90% range: {:4.2f} - {:4.2f}'.format(whiskers_arr[ii][1], whiskers_arr[ii+1][1])) # + # figure 1d: Legend f = plt.figure() f.set_size_inches(6, 1.2) # add color for non-Lanmguir and Langmuir cases pbcolor = bcolor pbcolor.append('lightgray') pbcolor.append('darkgray') # add label for non-Lanmguir and Langmuir cases label_list = legend_list label_list.append('Non-Langmuir') label_list.append('Langmuir') # plot xx = np.arange(nm+2)+1 yy = np.ones(xx.size) sct = plt.scatter(xx, yy, s=60, c=pbcolor, edgecolors='k') plt.xlim([0,nm+3]) for i in np.arange(nm+2): plt.text(xx[i], yy[i]-0.05, label_list[i], color='black', fontsize=11, rotation=30, va='top', ha='right') # x- and y-labels ax = plt.gca() ax.spines['bottom'].set_color('white') ax.spines['top'].set_color('white') ax.spines['right'].set_color('white') ax.spines['left'].set_color('white') ax.axes.get_yaxis().set_visible(False) ax.axes.get_xaxis().set_visible(False) # plt.setp(ax, xticks=xx, xticklabels=label_list) # reduce margin plt.tight_layout() # auto adjust the x-axis label plt.gcf().autofmt_xdate() plt.gcf().subplots_adjust(bottom=0.65) plt.gcf().subplots_adjust(left=0.1) # save figure figname = fig_root+'/legend.png' plt.savefig(figname, dpi = 300) # + # figure 1e: Legend2 f = plt.figure() f.set_size_inches(6, 1.2) # plot xx = np.arange(nm)+1 yy = np.ones(xx.size) sct = plt.scatter(xx, yy, s=80, c=bcolor, edgecolors='k', linewidth=1) plt.xlim([0,nm+1]) for i in np.arange(nm): plt.text(xx[i], yy[i]-0.05, legend_list[i], color='black', fontsize=11, rotation=30, va='top', ha='right') # x- and y-labels ax = plt.gca() ax.spines['bottom'].set_color('white') ax.spines['top'].set_color('white') ax.spines['right'].set_color('white') ax.spines['left'].set_color('white') ax.axes.get_yaxis().set_visible(False) ax.axes.get_xaxis().set_visible(False) # reduce margin plt.tight_layout() # auto adjust the x-axis label plt.gcf().autofmt_xdate() plt.gcf().subplots_adjust(bottom=0.65) plt.gcf().subplots_adjust(left=0.1) # save figure figname = fig_root+'/legend_2.png' plt.savefig(figname, dpi = 300) # - def mask_forcing_regime(pdata, pfreg): hist = np.zeros(14) pdata_S0= pdata[pfreg==1] pdata_S1 = pdata[pfreg==-1] hist[0] = pdata_S0.size hist[1] = pdata_S1.size pdata_L0= pdata[pfreg==2] pdata_L1 = pdata[pfreg==-2] hist[2] = pdata_L0.size hist[3] = pdata_L1.size pdata_C0= pdata[pfreg==3] pdata_C1 = pdata[pfreg==-3] hist[4] = pdata_C0.size hist[5] = pdata_C1.size pdata_SL0= pdata[pfreg==4] pdata_SL1 = pdata[pfreg==-4] hist[6] = pdata_SL0.size hist[7] = pdata_SL1.size pdata_SC0 = pdata[pfreg==5] pdata_SC1 = pdata[pfreg==-5] hist[8] = pdata_SC0.size hist[9] = pdata_SC1.size pdata_LC0 = pdata[pfreg==6] pdata_LC1 = pdata[pfreg==-6] hist[10] = pdata_LC0.size hist[11] = pdata_LC1.size pdata_SLC0 = pdata[pfreg==7] pdata_SLC1 = pdata[pfreg==-7] hist[12] = pdata_SLC0.size hist[13] = pdata_SLC1.size masked_data = [pdata_S0, pdata_S1, pdata_L0, pdata_L1, pdata_C0, pdata_C1, \ pdata_SL0, pdata_SL1, pdata_SC0, pdata_SC1, pdata_LC0, pdata_LC1, \ pdata_SLC0, pdata_SLC1] hist_pct = hist/np.sum(hist)100 return masked_data, hist_pct # + # figure 2: sort the differences by forcing regime # forcing regimes pfreg = np.concatenate([gmobj_freg_mon[i].data for i in np.arange(12)]) f, axarr = plt.subplots(2) f.set_size_inches(8, 6) # reference line axarr[0].axhline(y=y_ref, linewidth=1, color='gray') axarr[1].axhline(y=y_ref, linewidth=1, color='gray') xshift = list((np.arange(nm)-nm/2)0.07+0.035) for k in np.arange(nm): pdata = np.concatenate([gmobj_stat_arr[i][k].data for i in np.arange(12)]) tmp = pdata pfreg0 = pfreg[~np.isnan(tmp)] pdata0 = pdata[~np.isnan(tmp)] if k == 0: pbdata0, hist_pct = mask_forcing_regime(pdata0, pfreg0) else: pbdata0, tmp = mask_forcing_regime(pdata0, pfreg0) xx = np.arange(hist_pct.size/2)+1 position_arr = xx+xshift[k] pbox0 = axarr[0].boxplot(pbdata0[0::2], whis=[5, 95], showfliers=False, positions=position_arr, widths=0.05, patch_artist=True) pbox1 = axarr[1].boxplot(pbdata0[1::2], whis=[5, 95], showfliers=False, positions=position_arr, widths=0.05, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(bcolor[k]) for patch in pbox1['boxes']: patch.set_facecolor(bcolor[k]) # x- and y-labels plt.setp(axarr[0], xticks=xx, xticklabels=['S', 'L', 'C', 'SL', 'SC', 'LC', 'SLC']) plt.setp(axarr[1], xticks=xx, xticklabels=['S','L','C','SL','SC','LC', 'SLC']) axarr[0].set_ylabel(y_label) axarr[1].set_ylabel(y_label) axarr[0].set_xlim([0.5,np.max(xx)+0.5]) axarr[1].set_xlim([0.5,np.max(xx)+0.5]) axarr[0].set_ylim([ymin,ymax]) axarr[1].set_ylim([ymin,ymax]) # frequency of occurrence par1 = axarr[0].twinx() par1.bar(xx, hist_pct[0::2], width=0.4, color='lightgray') par1.set_ylabel('$\%$') par1.set_ylim([0, 25]) axarr[0].set_zorder(par1.get_zorder()+1) axarr[0].patch.set_visible(False) par2 = axarr[1].twinx() par2.bar(xx, hist_pct[1::2], width=0.4, color='lightgray') par2.set_ylabel('$\%$') par2.set_ylim([0, 25]) axarr[1].set_zorder(par2.get_zorder()+1) axarr[1].patch.set_visible(False) # reduce margin plt.tight_layout() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_forc_reg.png' plt.savefig(figname, dpi = 300) print(hist_pct) # + # figure 2b: sort the differences by forcing regime, only including L LC and C # forcing regimes pfreg = np.concatenate([gmobj_freg_mon[i].data for i in np.arange(12)]) f, axarr = plt.subplots(2) f.set_size_inches(8, 8) # reference line axarr[0].axhline(y=y_ref, linewidth=1, color='gray') axarr[1].axhline(y=y_ref, linewidth=1, color='gray') xshift = list((np.arange(nm+2)-nm/2-1)0.06+0.03) pbdata0s0_nlt = [] pbdata0s1_nlt = [] pbdata0s0_lt = [] pbdata0s1_lt = [] for k in np.arange(nm): pdata = np.concatenate([gmobj_stat_arr[i][k].data for i in np.arange(12)]) tmp = pdata pfreg0 = pfreg[~np.isnan(tmp)] pdata0 = pdata[~np.isnan(tmp)] if k == 0: pbdata0, hist_pct = mask_forcing_regime(pdata0, pfreg0) else: pbdata0, tmp = mask_forcing_regime(pdata0, pfreg0) xx = np.arange(3)+1 position_arr = xx+xshift[k] pbdata0s0 = [pbdata0[i] for i in [2,10,4]] pbox0 = axarr[0].boxplot(pbdata0s0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.04, patch_artist=True) pbdata0s1 = [pbdata0[i] for i in [3,11,5]] pbox1 = axarr[1].boxplot(pbdata0s1, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.04, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(bcolor[k]) for patch in pbox1['boxes']: patch.set_facecolor(bcolor[k]) # save the data for non-Langmuir and Langmuir groups if k < 5: pbdata0s0_nlt.append(pbdata0s0) pbdata0s1_nlt.append(pbdata0s1) else: pbdata0s0_lt.append(pbdata0s0) pbdata0s1_lt.append(pbdata0s1) # process data for non-Langmuir and Langmuir cases pbdata1s0_nlt = [] pbdata1s1_nlt = [] pbdata1s0_lt = [] pbdata1s1_lt = [] for k in np.arange(3): pbdata0s0 = np.concatenate([pbdata0s0_nlt[i][k] \ for i in np.arange(len(pbdata0s0_nlt))]) pbdata1s0_nlt.append(pbdata0s0) pbdata0s1 = np.concatenate([pbdata0s1_nlt[i][k] \ for i in np.arange(len(pbdata0s1_nlt))]) pbdata1s1_nlt.append(pbdata0s1) pbdata0s0 = np.concatenate([pbdata0s0_lt[i][k] \ for i in np.arange(len(pbdata0s0_lt))]) pbdata1s0_lt.append(pbdata0s0) pbdata0s1 = np.concatenate([pbdata0s1_lt[i][k] \ for i in np.arange(len(pbdata0s1_lt))]) pbdata1s1_lt.append(pbdata0s1) pbcolor = ['lightgray', 'darkgray'] # plot non-Langmuir cases position_arr = xx+xshift[nm] pbox0 = axarr[0].boxplot(pbdata1s0_nlt, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.04, patch_artist=True) pbox1 = axarr[1].boxplot(pbdata1s1_nlt, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.04, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(pbcolor[0]) for patch in pbox1['boxes']: patch.set_facecolor(pbcolor[0]) # plot Langmuir cases position_arr = xx+xshift[nm+1] pbox0 = axarr[0].boxplot(pbdata1s0_lt, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.04, patch_artist=True) pbox1 = axarr[1].boxplot(pbdata1s1_lt, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.04, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(pbcolor[1]) for patch in pbox1['boxes']: patch.set_facecolor(pbcolor[1]) # x- and y-labels plt.setp(axarr[0], xticks=xx, xticklabels=['L', 'LC', 'C']) plt.setp(axarr[1], xticks=xx, xticklabels=['L','LC','C']) axarr[0].set_ylabel(y_label) axarr[1].set_ylabel(y_label) axarr[0].set_xlim([0.5,np.max(xx)+0.5]) axarr[1].set_xlim([0.5,np.max(xx)+0.5]) axarr[0].set_ylim([ymin,ymax]) axarr[1].set_ylim([ymin,ymax]) # frequency of occurrence par1 = axarr[0].twinx() hist_pcts1 = [hist_pct[i] for i in [2,10,4]] par1.bar(xx, hist_pcts1, width=0.4, color='lightgray') par1.set_ylabel('$\%$') par1.set_ylim([0, 45]) axarr[0].set_zorder(par1.get_zorder()+1) axarr[0].patch.set_visible(False) # label axarr[0].text(0.02, 0.12, '(a) Destablizing', fontsize=13, color='k', va='top', transform=axarr[0].transAxes) par2 = axarr[1].twinx() hist_pcts2 = [hist_pct[i] for i in [3,11,5]] par2.bar(xx, hist_pcts2, width=0.4, color='lightgray') par2.set_ylabel('$\%$') par2.set_ylim([0, 45]) axarr[1].set_zorder(par2.get_zorder()+1) axarr[1].patch.set_visible(False) # label axarr[1].text(0.02, 0.12, '(b) Stabilizing', fontsize=13, color='k', va='top', transform=axarr[1].transAxes) # reduce margin plt.tight_layout() # add legend plt.subplots_adjust(bottom=0.2) # add color for non-Lanmguir and Langmuir cases pbcolor = bcolor pbcolor.append('lightgray') pbcolor.append('darkgray') # add label for non-Lanmguir and Langmuir cases label_list = legend_list label_list.append('Non-Langmuir') label_list.append('Langmuir') # plot xshift = 0.15 xx = np.arange(nm+2)0.06+xshift yy = -np.ones(xx.size)0.3 for i in np.arange(nm+2): axarr[1].text(xx[i], yy[i], legend_list[i], color='black', transform=axarr[1].transAxes, fontsize=11, rotation=30, va='top', ha='right') axarr[1].scatter(xx[i], yy[i]+0.07, s=60, c=bcolor[i], edgecolors='k', linewidth=1, transform=axarr[1].transAxes, clip_on=False) # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_forc_reg_s.pdf' plt.savefig(figname, dpi = 300) # print info print('Percentage of each regime:') print(' L: {:6.2f}%, LC: {:6.2f}%, C: {:6.2f}%'.format(hist_pcts1[0], hist_pcts1[1], hist_pcts1[2])) print(' L: {:6.2f}%, LC: {:6.2f}%, C: {:6.2f}%'.format(hist_pcts2[0], hist_pcts2[1], hist_pcts2[2])) print('Total: {:6.2f}%'.format(sum(hist_pcts1)+sum(hist_pcts2))) # + # figure 2c: sort the effects of LT by forcing regime, only including L LC and C f, axarr = plt.subplots(2) f.set_size_inches(5, 6) # add color for non-Lanmguir and Langmuir cases pbcolor = ['deepskyblue','forestgreen','firebrick','darkgray'] # add label for non-Lanmguir and Langmuir cases label_list = ['KPPLT-LF17', 'ePBL-LT', 'SMCLT-H15', 'Langmuir'] # reference line axarr[0].axhline(y=y_ref, linewidth=1, color='gray') axarr[1].axhline(y=y_ref, linewidth=1, color='gray') xshift = list((np.arange(4)-2)0.10+0.05) # forcing regimes pfreg = np.concatenate([gmobj_freg_mon[i].data for i in np.arange(12)]) gmobj_list = [] # Langmuir effects in KPP gmobj_list.append(gmobj_m_mon_kpp) # Langmuir effects in ePBL gmobj_list.append(gmobj_m_mon_epbl) # Langmuir effects in SMC gmobj_list.append(gmobj_m_mon_smc) # mean of the Langmuir cases gmobj_list.append(gmobj_m_mon_lt) for k in np.arange(4): pdata = np.concatenate([gmobj_list[k][i].data for i in np.arange(12)]) tmp = pdata pfreg0 = pfreg[~np.isnan(tmp)] pdata0 = pdata[~np.isnan(tmp)] pbdata0, tmp = mask_forcing_regime(pdata0, pfreg0) xx = (np.arange(3)+1)0.7 position_arr = xx+xshift[k] pbdata0s0 = [pbdata0[i] for i in [2,10,4]] pbox0 = axarr[0].boxplot(pbdata0s0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.06, patch_artist=True) pbdata0s1 = [pbdata0[i] for i in [3,11,5]] pbox1 = axarr[1].boxplot(pbdata0s1, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.06, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(pbcolor[k]) for patch in pbox1['boxes']: patch.set_facecolor(pbcolor[k]) # x- and y-labels plt.setp(axarr[0], xticks=xx, xticklabels=['L', 'LC', 'C']) plt.setp(axarr[1], xticks=xx, xticklabels=['L','LC','C']) axarr[0].set_ylabel(y_label) axarr[1].set_ylabel(y_label) axarr[0].set_xlim([0.3,np.max(xx)+0.4]) axarr[1].set_xlim([0.3,np.max(xx)+0.4]) if diag_type == 'R': ymin1 = 0.9 ymax1 = 1.4 elif diag_type == 'D': ymin1 = -5 ymax1 = 35 axarr[0].set_ylim([ymin1,ymax1]) axarr[1].set_ylim([ymin1,ymax1]) # frequency of occurrence par1 = axarr[0].twinx() hist_pcts1 = [hist_pct[i] for i in [2,10,4]] par1.bar(xx, hist_pcts1, width=0.4, color='lightgray') par1.set_ylabel('$\%$') par1.set_ylim([0, 45]) axarr[0].set_zorder(par1.get_zorder()+1) axarr[0].patch.set_visible(False) # label axarr[0].text(0.05, 0.92, '(a) Destablizing', transform=axarr[0].transAxes, fontsize=13, color='k', va='top') par2 = axarr[1].twinx() hist_pcts2 = [hist_pct[i] for i in [3,11,5]] par2.bar(xx, hist_pcts2, width=0.4, color='lightgray') par2.set_ylabel('$\%$') par2.set_ylim([0, 45]) axarr[1].set_zorder(par2.get_zorder()+1) axarr[1].patch.set_visible(False) # label axarr[1].text(0.05, 0.92, '(b) Stablizing', transform=axarr[1].transAxes, fontsize=13, color='k', va='top') # legend xshift = 0.63 xx = np.arange(4) xx = xx0.1+xshift yy = np.ones(xx.size)0.90 for i in np.arange(4): axarr[1].text(xx[i], yy[i], label_list[i], color='black', transform=axarr[1].transAxes, fontsize=10, rotation=30, va='top', ha='right') axarr[1].scatter(xx[i], 0.92, s=60, c=pbcolor[i], edgecolors='k', linewidth=1, transform=axarr[1].transAxes) # reduce margin plt.tight_layout() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_forc_reg_s_c.pdf' plt.savefig(figname, dpi = 300) # print info print('Destablizing:') medians_arr = [item.get_ydata() for item in pbox0['medians']] whiskers_arr = [item.get_ydata() for item in pbox0['whiskers']] for i, var in enumerate(['L', 'LC', 'C']): print('{}:'.format(var)) print(' Mean: {:4.2f}'.format(pbdata0s0[i].mean())) print(' Median: {:4.2f}'.format(medians_arr[i][0])) ii = i2 print(' 50% range: {:4.2f} - {:4.2f}'.format(whiskers_arr[ii][0], whiskers_arr[ii+1][0])) print(' 90% range: {:4.2f} - {:4.2f}'.format(whiskers_arr[ii][1], whiskers_arr[ii+1][1])) print('Stablizing:') medians_arr = [item.get_ydata() for item in pbox1['medians']] whiskers_arr = [item.get_ydata() for item in pbox1['whiskers']] for i, var in enumerate(['L', 'LC', 'C']): print('{}:'.format(var)) print(' Mean: {:4.2f}'.format(pbdata0s1[i].mean())) print(' Median: {:4.2f}'.format(medians_arr[i][0])) ii = i2 print(' 50% range: {:4.2f} - {:4.2f}'.format(whiskers_arr[ii][0], whiskers_arr[ii+1][0])) print(' 90% range: {:4.2f} - {:4.2f}'.format(whiskers_arr[ii][1], whiskers_arr[ii+1][1])) # - def mask_latitude(pdata, lat): hist = np.zeros(7) pdata_S3 = pdata[lat<=-50] hist[0] = pdata_S3.size pdata_S2 = pdata[(lat>-50) & (lat<=-30)] hist[1] = pdata_S2.size pdata_S1 = pdata[(lat>-30) & (lat<=-10)] hist[2] = pdata_S1.size pdata_EQ = pdata[(lat>-10) & (lat<10)] hist[3] = pdata_EQ.size pdata_N1 = pdata[(lat>=10) & (lat<30)] hist[4] = pdata_N1.size pdata_N2 = pdata[(lat>=30) & (lat<50)] hist[5] = pdata_N2.size pdata_N3 = pdata[lat>=50] hist[6] = pdata_N3.size masked_data = [pdata_S3, pdata_S2, pdata_S1, pdata_EQ, pdata_N1, pdata_N2, pdata_N3] hist_pct = hist/np.sum(hist)100 return masked_data, hist_pct # + # figure 3: sort the differences by latitude plat = np.concatenate([gmobj_m_mon_nlt[i].lat for i in np.arange(12)]) f = plt.figure() f.set_size_inches(8, 3.5) # reference line plt.axhline(y=y_ref, linewidth=1, color='gray') xshift = list((np.arange(nm)-nm/2)0.07+0.035) for k in np.arange(nm): pdata = np.concatenate([gmobj_stat_arr[i][k].data for i in np.arange(12)]) tmp = pdata plat0 = plat[~np.isnan(tmp)] pdata0 = pdata[~np.isnan(tmp)] pbdata0, hist_pct = mask_latitude(pdata0, plat0) xx = np.arange(hist_pct.size)+1 position_arr = xx+xshift[k] pbox0 = plt.boxplot(pbdata0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.05, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(bcolor[k]) # x- and y-labels ax = plt.gca() plt.setp(ax, xticks=xx, xticklabels=['70$^\circ$S-50$^\circ$S', '50$^\circ$S-30$^\circ$S', '30$^\circ$S-10$^\circ$S', '10$^\circ$S-10$^\circ$N', '10$^\circ$N-30$^\circ$N', '30$^\circ$N-50$^\circ$N', '50$^\circ$N-70$^\circ$N']) plt.ylabel(y_label) plt.xlim([0.5,np.max(xx)+0.5]) plt.ylim([ymin,ymax]) # frequency of occurrence par1 = ax.twinx() par1.bar(xx, hist_pct, width=0.4, color='lightgray') par1.set_ylabel('$\%$') par1.set_ylim([0, 25]) ax.set_zorder(par1.get_zorder()+1) ax.patch.set_visible(False) # reduce margin plt.tight_layout() # auto adjust the x-axis label plt.gcf().autofmt_xdate() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_lat.png' plt.savefig(figname, dpi = 300) # + # figure 4: sort the differences by month f, axarr = plt.subplots(3) f.set_size_inches(12, 9) # reference line axarr[0].axhline(y=y_ref, linewidth=1, color='gray') axarr[1].axhline(y=y_ref, linewidth=1, color='gray') axarr[2].axhline(y=y_ref, linewidth=1, color='gray') xx = np.arange(12)+1 xshift = list((np.arange(nm)-nm/2)*0.07+0.035) for k in np.arange(nm): pbdata0 = [] pbdata1 = [] pbdata2 = [] for i in np.arange(12): plat = gmobj_stat_arr[i][k].lat # south of 30S pdata = gmobj_stat_arr[i][k].data[plat<=-30] pdata = pdata[~np.isnan(pdata)] pbdata0.append(pdata) # 30S-30N pdata = gmobj_stat_arr[i][k].data[(plat>-30) & (plat<30)] pdata = pdata[~np.isnan(pdata)] pbdata1.append(pdata) # north of 30N pdata = gmobj_stat_arr[i][k].data[plat>=30] pdata = pdata[~np.isnan(pdata)] pbdata2.append(pdata) position_arr = xx+xshift[k] pbox0 = axarr[0].boxplot(pbdata0, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.05, patch_artist=True) pbox1 = axarr[1].boxplot(pbdata1, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.05, patch_artist=True) pbox2 = axarr[2].boxplot(pbdata2, whis=[5, 95], showfliers=False, positions=position_arr, widths=0.05, patch_artist=True) for patch in pbox0['boxes']: patch.set_facecolor(bcolor[k]) for patch in pbox1['boxes']: patch.set_facecolor(bcolor[k]) for patch in pbox2['boxes']: patch.set_facecolor(bcolor[k]) # x- and y-labels month_labels = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', \ 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC'] plt.setp(axarr[0], xticks=xx, xticklabels=month_labels) plt.setp(axarr[1], xticks=xx, xticklabels=month_labels) plt.setp(axarr[2], xticks=xx, xticklabels=month_labels) axarr[0].set_ylabel(y_label) axarr[1].set_ylabel(y_label) axarr[2].set_ylabel(y_label) axarr[0].set_xlim([0.5,np.max(xx)+0.5]) axarr[1].set_xlim([0.5,np.max(xx)+0.5]) axarr[2].set_xlim([0.5,np.max(xx)+0.5]) axarr[0].set_ylim([ymin,ymax]) axarr[1].set_ylim([ymin,ymax]) axarr[2].set_ylim([ymin,ymax]) # reduce margin plt.tight_layout() # save figure figname = fig_root+'/'+fig_prefix+'_'+diagname+'_mon.png' plt.savefig(figname, dpi = 300) # -	34,059
/analysis/average_project_length/brian/Census_Exploration-All-Data.ipynb	54f18993ada0513a63b8de5f5bf0de53e81ecc33	[]	no_license	brgoggin/datasci-housing-pipeline	https://github.com/brgoggin/datasci-housing-pipeline	2	0	null	2017-08-28T18:15:41	2017-05-18T03:14:47	null	Jupyter Notebook	false	false	.py	2,263,596	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns pd.set_option('display.max_columns', 100) pd.set_option('display.max_rows', 100) # + #Loading and compiling dataset df_schools = pd.read_csv("Masterlist of Schools.csv", index_col="school.id") df_location = pd.read_csv("Schools Location Data.csv", encoding = "latin-1", index_col="School ID", usecols=["School ID", "Enrolment", "Latitude", "Longitude"]) df_rooms = pd.read_csv('Rooms data.csv', index_col="School ID") df_teachers = pd.read_csv("Teachers data.csv", index_col="school.id") df_elementary = pd.read_csv("Enrollment Master Data_2015_E.csv")[:-1].astype(int).set_index("School ID") df_secondary = (pd.read_csv('Enrollment Master Data_2015_S.csv')[:-1] .replace(",", "", regex=True) .astype(int) .replace("SPED NG Male", "SPED NG Male SS") .replace("SPED NG Female", "SPED NG Female SS") .set_index("School ID")) df_mooe = (pd.read_csv('MOOE data.csv', index_col="school.id", usecols=["school.id", " school.mooe "]) .replace(",", "", regex=True).astype(float)) # - #Saving all datasets into one data frame df_all = pd.concat([df_schools, df_location, df_rooms, df_teachers, df_elementary, df_secondary, df_mooe], axis=1) df_all #Checking the shape df_all.shape #Checking for missing values df_all.isna().sum() #Checking for duplicates df_all[df_all.index.duplicated(keep=False)] #Checking the columns df_all.columns # Let's try to explore the data for both elementary and secondary school # Obtain all numeric features and school.classification df_numeric = df_all[['school.region', 'school.cityincome','rooms.standard.academic', 'rooms.standard.unused', 'rooms.nonstandard.academic', 'rooms.nonstandard.unused', 'teachers.instructor', 'teachers.mobile', 'teachers.regular', 'teachers.sped','Enrolment', ' school.mooe ', 'school.classification']] # + # Combine all rooms and all teachers df_numeric["rooms_total"] = (df_numeric['rooms.standard.academic'] + df_numeric['rooms.standard.unused'] + df_numeric['rooms.nonstandard.academic'] + df_numeric['rooms.nonstandard.unused']) df_numeric["teachers_total"] = (df_numeric['teachers.instructor'] + df_numeric['teachers.mobile'] + df_numeric['teachers.regular'] + df_numeric['teachers.sped']) df_numeric['student_teacher_ratio'] = df_numeric['Enrolment']/df_numeric["teachers_total"] df_numeric['student_room_ratio'] = df_numeric['Enrolment']/df_numeric["rooms_total"] df_numeric['student_mooe_ratio'] = df_numeric['Enrolment']/df_numeric[' school.mooe '] df_numeric = df_numeric.dropna() # Removing (statistical) outliers for MOOE Q1 = df_numeric[' school.mooe '].quantile(0.25) Q3 = df_numeric[' school.mooe '].quantile(0.75) IQR = Q3 - Q1 df_outlier_removed = (df_numeric[(df_numeric[' school.mooe '] >= Q1 - 1.5IQR) & (df_numeric[' school.mooe '] <= Q3 + 1.5IQR)]) df_outlier_removed.columns # - #Checking the dataset df_numeric ["school.cityincome"] = df_numeric["school.cityincome"].replace(['P 55 M or more'],'P 55 M or more but less than P 80 M') df_numeric #reordering categories df_numeric["school.cityincome"] = df_numeric["school.cityincome"].astype('category') df_numeric["school.cityincome"].cat.reorder_categories(['Below P 15 M', 'P 15 M or more but less than P 25 M', 'P 25 M or more but less than P 35 M', 'P 35 M or more but less than P 45 M', 'P 45 M or more but less than P 55 M', 'P 55 M or more but less than P 80 M', 'P 80 M or more but less than P 160 M', 'P 160 M or more but less than P 240 M', 'P 240 M or more but less than P 320 M', 'P 320 M or more but less than P 400 M', 'P 400 M or more', 'Special Class']) #Getting the no. of students per city classified by income students_per_city_income = df_numeric.groupby("school.cityincome").agg(Enrolment=("Enrolment", sum)) students_per_city_income #Plotting the bar graph for students per city classified by income plt.figure(figsize=(12,6), dpi = 80) plt.barh(students_per_city_income.index, students_per_city_income["Enrolment"].values) plt.title("Students") plt.ticklabel_format(axis="x", style="plain") plt.show #Getting the no. of teachers per city classified by income teachers_per_city_income = df_numeric.groupby("school.cityincome").agg(Teachers=("teachers_total", sum)) teachers_per_city_income #Plotting the bar graph for teachers per city classified by income plt.figure(figsize=(12,6), dpi = 80) plt.barh(teachers_per_city_income.index, teachers_per_city_income["Teachers"].values) plt.title("Teachers") plt.ticklabel_format(axis="x", style="plain") plt.show #Getting the no. of rooms per city classified by income rooms_per_city_income = df_numeric.groupby("school.cityincome").agg(Rooms=("rooms_total", sum)) rooms_per_city_income #Plotting the bar graph for rooms per city classified by income plt.figure(figsize=(12,6), dpi=80) plt.barh(rooms_per_city_income.index, rooms_per_city_income["Rooms"].values) plt.title("Rooms") plt.ticklabel_format(axis="x", style="plain") plt.show #Getting the total MOOE per city classified by income mooe_per_city_income = df_numeric.groupby("school.cityincome").agg(MOOE_per_city_income=(" school.mooe ",sum)) mooe_per_city_income #Plotting the bar graph for total MOOE per city classified by income plt.figure(figsize=(12,6), dpi = 80) plt.barh(mooe_per_city_income.index, mooe_per_city_income ["MOOE_per_city_income"].values) plt.title("Total MOOE") plt.ticklabel_format(axis="x", style="plain") plt.show rs','21 years', '22 to 24 years', '25 to 29 years','30 to 34 years','35 to 39 years'] middle=['40 to 44 years','45 to 49 years','50 to 54 years','55 to 59 years'] old=['60 and 61 years','62 to 64 years','65 and 66 years','67 to 69 years', '70 to 74 years','75 to 79 years','80 to 84 years','85 years and over'] teen_cats=[] adult_cats=[] middle_cats=[] old_cats=[] for cat in teen: teen_cats.append(male+cat) teen_cats.append(female+cat) for cat in adult: adult_cats.append(male+cat) adult_cats.append(female+cat) for cat in middle: middle_cats.append(male+cat) middle_cats.append(female+cat) for cat in old: old_cats.append(male+cat) old_cats.append(female+cat) tracts['teen']=tracts[teen_cats].sum(axis=1) tracts['adult']=tracts[adult_cats].sum(axis=1) tracts['middle']=tracts[middle_cats].sum(axis=1) tracts['old']=tracts[old_cats].sum(axis=1) # + #Consolidate "moved-in" categories times=['Moved in 2015 or later', 'Moved in 2010 to 2014', 'Moved in 2000 to 2009','Moved in 1990 to 1999', 'Moved in 1980 to 1989', 'Moved in 1979 or earlier'] for period in times: tracts['total: '+period]=tracts['Estimate; Owner occupied: - '+ period]+tracts['Estimate; Renter occupied: - '+ period] # - #Create variables to explore tracts['perc_old']=(tracts['old']/tracts['Estimate; Total:_B01001'])100 tracts['perc_white']=(tracts['Estimate; Total: - White alone']/tracts['Estimate; Total:_B02001'])100 tracts['perc_rich']=(tracts['>100']/tracts['Estimate; Total:_B01001'])100 tracts['pop_density']=tracts['Estimate; Total:_B01001']/(tracts['ALAND'](0.000000386102)) #convert to pop per square miles (from square meters) tracts['perc_owner']=(tracts['Estimate; Total: - Owner occupied']/tracts['Estimate; Total:_B25003'])100 tracts['median_age']=tracts['Total; Estimate; SUMMARY INDICATORS - Median age (years)'] tracts['median_income']=tracts['Households; Estimate; Median income (dollars)'] tracts.loc[(tracts['median_age']=='-'), 'median_age'] = 'nan' tracts.loc[(tracts['median_income']=='-'), 'median_income'] = 'nan' tracts.loc[(tracts['median_income']=='(X)'), 'median_income'] = 'nan' tracts['median_age']=tracts['median_age'].astype(float) tracts['median_income']=tracts['median_income'].astype(float) tracts['median_homevalue']=tracts['Estimate; Median value (dollars)'] tracts.loc[(tracts['median_homevalue']=='-'), 'median_homevalue'] = 'nan' tracts.loc[(tracts['median_homevalue']=='2,000,000+'), 'median_homevalue'] = '2000000' #round down to 2 million for these tracts['median_homevalue']=tracts['median_homevalue'].astype(float) #percentage pre-2000 and pre-1990 move-in tracts['perc_pre_2000']=((tracts['total: Moved in 1980 to 1989']+tracts['total: Moved in 1990 to 1999']+tracts['total: Moved in 1979 or earlier'])/tracts['Estimate; Total:_B25038'])100 tracts['perc_pre_1990']=((tracts['total: Moved in 1980 to 1989']+tracts['total: Moved in 1979 or earlier'])/tracts['Estimate; Total:_B25038'])*100 #tracts['perc_pre_2000'].describe(percentiles=[.1, .2, .3, .4, .5, .6, .7, .8,.9]) #tracts['perc_pre_1990'].describe(percentiles=[.1, .2, .3, .4, .5, .6, .7, .8,.9]) #make series of scatter plots, correlation graphs. Population density, age, income, tenure, race, home age. varlist=['GEOID', 'geometry', 'perc_owner', 'pop_density', 'perc_old', 'perc_rich', 'perc_white', 'median_age', 'median_income', 'median_homevalue', 'perc_pre_2000', 'perc_pre_1990'] #keep only certain variables tracts=tracts[varlist] # + #Check plots #tracts.plot(column='perc_rich', cmap='OrRd') #plt.xlim([-122.58, -122.18]) #plt.ylim([37.7, 37.82]) # - #First, spatial join between points and neighborhood boundaries. Set 'how' to 'left' to preserve all developments df = gpd.sjoin(devs, tracts, how = 'inner', op='within') # + # a few projects appear to be outside tract boundaries so they are dropped here. not too many so I'm not worried about them too much. #base=tracts.plot(color='white', linewidth=.1) #devs.plot(ax=base) #plt.xlim([-122.58, -122.18]) #plt.ylim([37.7, 37.82]) # - df.head() # # Joint Scatter Plot Graphs df['years_per_unit']=df['project_time_years']/df['units'] df[pd.notnull(df['project_time_years'])].shape df_export.head() # # Interuption: Export Shapefile # + def permit_time(value): returnval=np.nan if pd.notnull(value['BP_date']) & pd.notnull(value['first_date']): returnval=((dateutil.parser.parse(value['BP_date']) - dateutil.parser.parse(value['first_date'])).days)/365 return returnval def bp_time(value): returnval=np.nan if pd.notnull(value['con_date']) & pd.notnull(value['BP_date']): returnval=((dateutil.parser.parse(value['con_date']) - dateutil.parser.parse(value['BP_date'])).days)/365 return returnval def con_time(value): returnval=np.nan if pd.notnull(value['comp_date']) & pd.notnull(value['con_date']): returnval=((dateutil.parser.parse(value['comp_date']) - dateutil.parser.parse(value['con_date'])).days)/365 return returnval df_export['permit_time']=df_export.apply(permit_time, axis=1) df_export['bp_time']=df_export.apply(bp_time, axis=1) df_export['con_time']=df_export.apply(con_time, axis=1) # - df_export['years_per_unit']=df_export['project_time_years']/df_export['units'] df_export['ptime_per_unit']=df_export['permit_time']/df_export['units'] #df_export=df[pd.notnull(df['project_time_years'])] df_export=df_export[['address', 'apn', 'x', 'y', 'geometry', 'years_per_unit', 'ptime_per_unit']] df_export.to_file(driver='ESRI Shapefile',filename=output+"dots.shp") df_export.to_csv(output+"dots.csv") # # End Interuption # + def joint_scatter(variable, title, yaxis,file, df=df, color='blue'): fig, ax = plt.subplots(4, 2, figsize=(14,20), sharey=True) sample_size=df[pd.notnull(df[variable])][variable].count() plt.suptitle(title+ " n="+str(sample_size), fontsize=20) #plt.ylim([0,25]) for m in range(4): for n in range(2): ax[m, n].tick_params(axis='both', which='major', labelsize=13) ax[m, n].get_xaxis().set_major_formatter(mpl.ticker.FuncFormatter(lambda x, p: format(int(x), ','))) dotsize=50 ax[0, 0].scatter(df['pop_density'], df[variable], s=dotsize, color=color, edgecolors='black') ax[0, 0].set_xlabel('Population Density', fontsize=18) ax[0, 0].set_ylabel(yaxis, fontsize=14) ax[1, 0].scatter(df['perc_owner'], df[variable], s=dotsize, color=color, edgecolors='black') ax[1, 0].set_xlabel('Percentage Owner', fontsize=18) ax[1, 0].set_ylabel(yaxis, fontsize=14) ax[2, 0].scatter(df['perc_white'], df[variable], s=dotsize, color=color, edgecolors='black') ax[2, 0].set_xlabel('Percentage White', fontsize=18) ax[2, 0].set_ylabel(yaxis, fontsize=14) ax[0, 1].scatter(df['median_age'], df[variable], s=dotsize, color=color, edgecolors='black') ax[0, 1].set_xlabel('Median Age', fontsize=18) ax[1, 1].scatter(df['median_income'], df[variable], s=dotsize, color=color, edgecolors='black') ax[1, 1].set_xlabel('Median Income', fontsize=18) ax[2, 1].scatter(df['median_homevalue'], df[variable], s=dotsize, color=color, edgecolors='black') ax[2, 1].set_xlabel('Median Home Value', fontsize=18) ax[3, 0].scatter(df['perc_pre_2000'], df[variable], s=dotsize, color=color, edgecolors='black') ax[3, 0].set_xlabel('Percentage Pre 2000', fontsize=18) ax[3, 0].set_ylabel(yaxis, fontsize=14) ax[3, 1].scatter(df['perc_pre_1990'], df[variable], s=dotsize, color=color, edgecolors='black') ax[3, 1].set_xlabel('Percentage Pre 1990', fontsize=18) export_folder = output+"/census_scatterplots" plt.savefig(export_folder+'/'+file+'.png') #ax[0, 1].scatter(df['perc_old'], df[variable], s=60) #ax[0, 1].set_xlabel('Percentage Old', fontsize=18) #ax[1, 1].scatter(df['perc_rich'], df[variable], s=60) #ax[1, 1].set_xlabel('Percentage Rich', fontsize=18) joint_scatter('project_time_years','Scatter Plots: Time to Completion (Years)', 'Time (Years)', 'all_years') # - joint_scatter('years_per_unit', 'Scatter Plots: Time to Completion (Years per Unit)', 'Time (Years per Unit)', 'all_years_per_unit') # ## Scatter Entitlement Times df2 = df[pd.notnull(df['BP_date'])] #scatter plots by entitlement times df2['permit_time']=df2.apply(lambda x: ((dateutil.parser.parse(x['BP_date']) - dateutil.parser.parse(x['first_date'])).days)/365, axis=1) df2['permit_time_per_unit']=df2['permit_time']/df2['units'] joint_scatter('permit_time', 'Scatter Plots: Permit Time (Years)', 'Time (Years)','all_ent_years', df=df2, color='red') joint_scatter('permit_time_per_unit', 'Scatter Plots: Permit Time (Years per Unit)', 'Time (Years per Unit)','all_ent_years_per_unit', df=df2, color='red') # ## Big Project Scatter Plots df_big=df[df['units']>=10] joint_scatter('project_time_years', 'Big Projects: Time to Completion (Years)', 'Time (Years)', 'big', df=df_big, color='green') df_big=df_big[pd.notnull(df_big['BP_date'])] #drop all without BP date for permitting time graphs df_big['permit_time']=df_big.apply(lambda x: ((dateutil.parser.parse(x['BP_date']) - dateutil.parser.parse(x['first_date'])).days)/365, axis=1) joint_scatter('permit_time', 'Big Projects: Permit Time (Years)', 'Time (Years)', 'big_ent', df=df_big, color='green') # # Regressions import statsmodels.formula.api as smf import numpy as np from sklearn import linear_model df.head() mod = smf.ols(formula='project_time_years ~ units + pop_density + median_age + median_income + median_homevalue + perc_pre_1990', data=df) res = mod.fit() print(res.summary()) mod = smf.ols(formula='permit_time ~ units + pop_density + median_age + median_income + median_homevalue + perc_pre_1990', data=df2) res = mod.fit() print(res.summary()) mod = smf.ols(formula='permit_time ~ units + median_age', data=df2) res = mod.fit() print(res.summary()) mod = smf.ols(formula='years_per_unit ~ pop_density + median_age + median_income + median_homevalue + perc_pre_1990', data=df) res = mod.fit() print(res.summary())	16,247
/hw5/hw5.ipynb	cedd282325bfb5fb80d6932aa79f4add61acff11	[]	no_license	JialiangZJU/csmath	https://github.com/JialiangZJU/csmath	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	6,966	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # Collect 10 sentences as a corpus # Make a word vectors of this corpus corpus = ['king is a strong man', 'queen is a wise woman', 'boy is a young man', 'girl is a young woman', 'prince is a young king', 'princess is a young queen', 'man is strong', 'woman is pretty', 'prince is a boy will be king', 'princess is a girl will be queen'] # + # Remove Stop word. The words which are more frequent in sentence are called stop word, i.e. is,a,the,will def remove_stop_words(corpus): stop_words = ['is','a','will','be'] results = [] #print(corpus) for sentence in corpus: words_in_sentence = sentence.split(' ') for stp_w in stop_words: if stp_w in words_in_sentence: words_in_sentence.remove(stp_w) results.append(" ".join(words_in_sentence)) return results # + corpus = remove_stop_words(corpus) corpus # make a word list with unique value word_list = [] for sentence in corpus: for word in sentence.split(' '): word_list.append(word) word_list = set(word_list) #word_list # + # Generation of data. word2int = {} for i,word in enumerate(word_list): word2int[word] = i #word2int # generate multidimentional array. it contains word of each sentence in a array. sentences = [] for sentence in corpus: sentences.append(sentence.split()) # Findout neighbors of window size 2. WINDOW_SIZE = 2 data = [] for sentence in sentences: for idx, word in enumerate(sentence): # print(idx,word) # print(sentence[max(idx - WINDOW_SIZE, 0) : min(idx + WINDOW_SIZE, len(sentence)) + 1]) for neighbor in sentence[max(idx - WINDOW_SIZE, 0) : min(idx + WINDOW_SIZE, len(sentence)) + 1] : if neighbor != word: data.append([word, neighbor]) #data # + #preparing Dataframe using panda import pandas as pd # for text in corpus: # print(text) df = pd.DataFrame(data, columns = ['input', 'label']) df # + # Gererate one hot encoding for every piece of data (input, label) import tensorflow as tf import numpy as np ONE_HOT_ENCODE = len(word_list) # function for generating one hot encoding def one_hot_encoding(word_index): one_hot_encoding = np.zeros(ONE_HOT_ENCODE) one_hot_encoding[word_index] = 1 #place 1 for the specified word return one_hot_encoding X = [] # input array Y = [] # target array for x,y in zip(df['input'],df['label']): X.append(one_hot_encoding(word2int[x])) Y.append(one_hot_encoding(word2int[y])) # convert them to numpy arrays X_train = np.asarray(X) Y_train = np.asarray(Y) # making placeholders for X_train and Y_train x = tf.placeholder(tf.float32, shape=(None, ONE_HOT_ENCODE)) y_label = tf.placeholder(tf.float32, shape=(None, ONE_HOT_ENCODE)) # word embedding will be 2 dimension for 2d visualization EMBEDDING_DIM = 2 # hidden layer: which represents word vector eventually W1 = tf.Variable(tf.random_normal([ONE_HOT_ENCODE, EMBEDDING_DIM])) b1 = tf.Variable(tf.random_normal([1])) #bias hidden_layer = tf.add(tf.matmul(x,W1), b1) # output layer W2 = tf.Variable(tf.random_normal([EMBEDDING_DIM, ONE_HOT_ENCODE])) b2 = tf.Variable(tf.random_normal([1])) prediction = tf.nn.softmax(tf.add( tf.matmul(hidden_layer, W2), b2)) # loss function: cross entropy loss = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(prediction), axis=[1])) # training operation train_op = tf.train.GradientDescentOptimizer(0.05).minimize(loss) # + sess = tf.Session() init = tf.global_variables_initializer() sess.run(init) iteration = 20000 for i in range(iteration): # input is X_train which is one hot encoded word # label is Y_train which is one hot encoded neighbor word sess.run(train_op, feed_dict={x: X_train, y_label: Y_train}) if i % 3000 == 0: print('iteration '+str(i)+' loss is : ', sess.run(loss, feed_dict={x: X_train, y_label: Y_train})) # - # Now the hidden layer (W1 + b1) is actually the word look up table vectors = sess.run(W1 + b1) print(vectors) w2v_df = pd.DataFrame(vectors, columns = ['x1', 'x2']) w2v_df['word'] = word_list w2v_df = w2v_df[['word', 'x1', 'x2']] w2v_df #word vector in 2d chart. Graphically showing how words are similar to each other	4,576
/Visualization Data.ipynb	9e558c386b53e7754e2420b96b81d2223aaf183b	[]	no_license	mominur0rabbi/MyProject	https://github.com/mominur0rabbi/MyProject	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	282,391	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import seaborn as sns import numpy as np import matplotlib.pyplot as plt # %matplotlib inline # showign graph in notebook housing = pd.read_csv('house_prices.csv') housing.shape housing.head() housing.columns # Analyze the spread of the 'lotArea' column sns.distplot(housing['LotArea'], kde=False) sns.distplot(housing['LotArea'], kde=True) plot = sns.countplot(x='Exterior1st', data=housing) plot = sns.countplot(x='Exterior1st', data=housing) plot.set_xticklabels(plot.get_xticklabels(), rotation=40); sns.regplot(x="LotArea", y = "SalePrice", data= housing) # + # Are outliers hewing the relationship. Redraw the relationship after removing very large value housing['LotArea'].quantile([0.5, 0.95, 0.99]) # - # Plot after Removeing highest and lowest values housing_sub = housing.loc[housing['LotArea']< housing['LotArea'].quantile(0.95)] sns.regplot(x="LotArea", y = "SalePrice", data= housing_sub) # # Plotting multiple graphs # # Q: Analyze the relationship between SalesPrice and all "Square Feet(SF)" related Columns # # sf_cols =[col_name for col_name in housing.columns if "SF" in col_name] len(sf_cols) sf_cols fig, axs = plt.subplots(nrows = 3, ncols = 3, figsize=(10,10)) for i in range(0, len(sf_cols)): rows = i//3 cols = i%3 print(rows, cols) fig, axs = plt.subplots(nrows = 3, ncols = 3, figsize=(10,10)) for i in range(0, len(sf_cols)): rows = i//3 cols = i%3 ax = axs[rows, cols] plot = sns.regplot(x=sf_cols[i], y ='SalePrice', data = housing, ax=ax) ### Is the price of the house impacted by the Exterior covering on house housing['Exterior1st'].value_counts() fig, axs = plt.subplots(figsize = (10,10)) sns.boxplot(Data = housing, x ="Exterior1st", y = "SalePrice", ax=axs); plot.set_xticklabels(plot.get_xticklabels(), rotation=40);	2,099
/testing/main.ipynb	3c2856e97371a930fefdaca26c185c96c0a70f07	[]	no_license	Dark417/Kaggle_titanic	https://github.com/Dark417/Kaggle_titanic	2	0	null	null	null	null	Jupyter Notebook	false	false	.py	21,128	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + from matplotlib.colors import ListedColormap from sklearn import model_selection, datasets, linear_model, metrics import numpy as np # - # %pylab def plot_2d_dataset(data, colors): pyplot.figure(figsize(8, 8)) pyplot.scatter(list(map(lambda x: x[0], data[0])), list(map(lambda x: x[1], data[0])), c = data[1], cmap = colors) # + #генерация датаседа для регрессии reg_data, reg_target = datasets.make_regression(n_features = 2, n_informative = 1, n_targets = 1, noise = 5., random_state = 7) plot_2d_dataset([reg_data, reg_target], ListedColormap(['red', 'blue'])) # - train_data, test_data, train_labels, test_labels = model_selection.train_test_split(reg_data, reg_target, test_size = 0.3, random_state = 1) #Линейная регрессия (минимум среднеквадратичной ошибки) linear_regressor = linear_model.LinearRegression() linear_regressor.fit(train_data, train_labels) predictions = linear_regressor.predict(test_data) #Линейная регрессия (минимум асолютной ошибки) -- отсеивает признаки, некритично реагирует на выбросы lasso_regressor = linear_model.Lasso(random_state = 3) lasso_regressor.fit(train_data, train_labels) lasso_predictions = lasso_regressor.predict(test_data) # + #Линейная регрессия (минимум асолютной ошибки) со встроенной кросс-валидацией alphas = np.arange(1, 100, 5)# вектор коэффициентов регуляризации lasso_cv_regressor = linear_model.LassoCV(alphas=alphas, cv=3)# кросс-валидация с 3 фолдами lasso_cv_regressor.fit(train_data, train_labels) alphas_p = lasso_cv_regressor.alphas_ #лучше брать вектор коэффициентов из модели, он может измениться mse_p = lasso_cv_regressor.mse_path_.mean(axis=1) #среднеквадратичная ошибка # - #Регрессия стохастический градиентный спуск sgd_regressor = linear_model.SGDRegressor(random_state = 0) sgd_regressor.fit(train_data, train_labels) predictions = sgd_regressor.predict(test_data) 009E": "Population 25 and Over w/ 5th Grade", "B15003_010E": "Population 25 and Over w/ 6th Grade", "B15003_011E": "Population 25 and Over w/ 7th Grade", "B15003_012E": "Population 25 and Over w/ 8th Grade", "B15003_013E": "Population 25 and Over w/ 9th Grade", "B15003_014E": "Population 25 and Over w/ 10th Grade", "B15003_015E": "Population 25 and Over w/ 11th Grade", "B15003_016E": "Population 25 and Over w/ 12th Grade, no diploma", "B15003_017E": "Population 25 and Over w/ Regular High School diploma", "B15003_018E": "Population 25 and Over w/ GED or alternative credential", "B15003_019E": "Population 25 and Over w/ Some college, less than 1 year", "B15003_020E": "Population 25 and Over w/ Some college, 1 or more years", "B15003_021E": "Population 25 and Over w/ Associate's degree", "B15003_022E": "Population 25 and Over w/ Bachelor's degree", "B15003_023E": "Population 25 and Over w/ Master's degree", "B15003_024E": "Population 25 and Over w/ Professional school degree", "B15003_025E": "Population 25 and Over w/ Doctorate degree", }) # Dataframe to be cleaned is put to screen census_raw.head() # - # Check for duplicate zip codes len(census_raw[census_raw.duplicated(["Zip Code Tabulation Area"])]) # Rows with empty values are dropped census_no_na = census_raw.dropna() census_no_na.head() # Force all dtypes to float census_no_na = census_no_na.astype(float) census_no_na.dtypes # + # All rows containing a negative value are dropped # Empty array for indeces to drop is initialized indeces_to_drop = [] # Every row is checked for a negative value # Iterate through the rows for index, row in census_no_na.iterrows(): # Ensure that only 1 index is given per row unique = True # Loop through columns for col in row: # If a value is negative, add its index to the array be dropped and flip boolean to ensure only 1 entry if ((col < 0) & unique): indeces_to_drop.append(index) unique = False # Delete each row whose for index in indeces_to_drop: census_no_na.drop(index, inplace=True) census_no_na.head() # + # All rates are calculated # Rate Poverty census_no_na["Poverty Rate"] = census_no_na["Poverty Count"] / census_no_na["Population"] # Rate over 25 less than 1st grade census_no_na["Rate 25 and Over w/ less than 1st grade"] =\ (census_no_na["Population 25 and Over w/ No Schooling"] +\ census_no_na["Population 25 and Over w/ Nursery School"] +\ census_no_na["Population 25 and Over w/ Kindergarten"]) / census_no_na["Population 25 and Over"] # Rate over 25 with some or completed elementary school (1st through 6th grade) census_no_na["Rate 25 and Over w/ Some or Completed Elementary School"] =\ (census_no_na["Population 25 and Over w/ 1st Grade"] +\ census_no_na["Population 25 and Over w/ 2nd Grade"] +\ census_no_na["Population 25 and Over w/ 3rd Grade"] +\ census_no_na["Population 25 and Over w/ 4th Grade"] +\ census_no_na["Population 25 and Over w/ 5th Grade"] +\ census_no_na["Population 25 and Over w/ 6th Grade"]) / census_no_na["Population 25 and Over"] # Rate over 25 with some or completed middle school (7th and 8th grade) census_no_na["Rate 25 and Over w/ Some or Completed Middle School"] =\ (census_no_na["Population 25 and Over w/ 7th Grade"] +\ census_no_na["Population 25 and Over w/ 8th Grade"]) / census_no_na["Population 25 and Over"] # Rate over 25 with some high school (9th through 12th grade) census_no_na["Rate 25 and Over w/ Some High School"] =\ (census_no_na["Population 25 and Over w/ 9th Grade"] +\ census_no_na["Population 25 and Over w/ 10th Grade"] +\ census_no_na["Population 25 and Over w/ 11th Grade"] +\ census_no_na["Population 25 and Over w/ 12th Grade, no diploma"]) / census_no_na["Population 25 and Over"] # Rate over 25 with completed high school or equivalent census_no_na["Rate 25 and Over w/ Completed High School or Equivalent"] =\ (census_no_na["Population 25 and Over w/ Regular High School diploma"] +\ census_no_na["Population 25 and Over w/ GED or alternative credential"]) / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Some college, less than 1 year census_no_na["Rate 25 and Over w/ Some college, less than 1 year"] =\ census_no_na["Population 25 and Over w/ Some college, less than 1 year"] / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Some college, 1 or more years census_no_na["Rate 25 and Over w/ Some college, 1 or more years"] =\ census_no_na["Population 25 and Over w/ Some college, 1 or more years"] / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Associate's degree census_no_na["Rate 25 and Over w/ Associate's degree"] =\ census_no_na["Population 25 and Over w/ Associate's degree"] / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Bachelor's degree census_no_na["Rate 25 and Over w/ Bachelor's degree"] =\ census_no_na["Population 25 and Over w/ Bachelor's degree"] / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Master's degree census_no_na["Rate 25 and Over w/ Master's degree"] =\ census_no_na["Population 25 and Over w/ Master's degree"] / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Professional school degree census_no_na["Rate 25 and Over w/ Professional school degree"] =\ census_no_na["Population 25 and Over w/ Professional school degree"] / census_no_na["Population 25 and Over"] # Rate 25 and Over w/ Doctorate degree census_no_na["Rate 25 and Over w/ Doctorate degree"] =\ census_no_na["Population 25 and Over w/ Doctorate degree"] / census_no_na["Population 25 and Over"] # + # Final DataFrame is made census_df = census_no_na[["Zip Code Tabulation Area", "Population", "Median Age", "Household Income", "Per Capita Income", "Poverty Rate", "Population 25 and Over", "Rate 25 and Over w/ less than 1st grade", "Rate 25 and Over w/ Some or Completed Elementary School", "Rate 25 and Over w/ Some or Completed Middle School", "Rate 25 and Over w/ Some High School", "Rate 25 and Over w/ Completed High School or Equivalent", "Rate 25 and Over w/ Some college, less than 1 year", "Rate 25 and Over w/ Some college, 1 or more years", "Rate 25 and Over w/ Associate's degree", "Rate 25 and Over w/ Bachelor's degree", "Rate 25 and Over w/ Master's degree", "Rate 25 and Over w/ Professional school degree", "Rate 25 and Over w/ Doctorate degree"]] # Output Dataframe to csv and screen census_df.to_csv("acs5_2018.csv", index=False) census_df.head() # - beta_1, beta_2)) criterion = nn.BCEWithLogitsLoss() cur_step = 0 classifier_losses = [] # classifier_val_losses = [] for epoch in range(n_epochs): # Dataloader returns the batches for real, labels in tqdm(dataloader): real = real.to(device) labels = labels[:, label_indices].to(device).float() class_opt.zero_grad() class_pred = classifier(real) class_loss = criterion(class_pred, labels) class_loss.backward() # Calculate the gradients class_opt.step() # Update the weights classifier_losses += [class_loss.item()] # Keep track of the average classifier loss ## Visualization code ## if cur_step % display_step == 0 and cur_step > 0: class_mean = sum(classifier_losses[-display_step:]) / display_step print(f"Step {cur_step}: Classifier loss: {class_mean}") step_bins = 20 x_axis = sorted([i * step_bins for i in range(len(classifier_losses) // step_bins)] * step_bins) sns.lineplot(x_axis, classifier_losses[:len(x_axis)], label="Classifier Loss") plt.legend() plt.show() torch.save({"classifier": classifier.state_dict()}, filename) cur_step += 1 # Uncomment the last line to train your own classfier - this line will not work in Coursera. # If you'd like to do this, you'll have to download it and run it, ideally using a GPU # train_classifier("filename") # + [markdown] colab_type="text" id="Iu1TcEA3aSSI" # ## Loading the Pretrained Models # You will then load the pretrained generator and classifier using the following code. (If you trained your own classifier, you can load that one here instead.) # + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="OgrLujk_tYDu" outputId="57924502-e734-46fc-da2e-df18dd807fb3" import torch gen = Generator(z_dim).to(device) gen_dict = torch.load("pretrained_celeba.pth", map_location=torch.device(device))["gen"] gen.load_state_dict(gen_dict) gen.eval() n_classes = 40 classifier = Classifier(n_classes=n_classes).to(device) class_dict = torch.load("pretrained_classifier.pth", map_location=torch.device(device))["classifier"] classifier.load_state_dict(class_dict) classifier.eval() print("Loaded the models!") opt = torch.optim.Adam(classifier.parameters(), lr=0.01) # + [markdown] colab_type="text" id="_aq53cc1nZgq" # ## Training # Now you can start implementing a method for controlling your GAN! # + [markdown] colab_type="text" id="ZJuga5nC-b3a" # #### Update Noise # For training, you need to write the code to update the noise to produce more of your desired feature. You do this by performing stochastic gradient ascent. You use stochastic gradient ascent to find the local maxima, as opposed to stochastic gradient descent which finds the local minima. Gradient ascent is gradient descent over the negative of the value being optimized. Their formulas are essentially the same, however, instead of subtracting the weighted value, stochastic gradient ascent adds it; it can be calculated by `new = old + (∇ old * weight)`, where ∇ is the gradient of `old`. You perform stochastic gradient ascent to try and maximize the amount of the feature you want. If you wanted to reduce the amount of the feature, you would perform gradient descent. However, in this assignment you are interested in maximize your feature using gradient ascent, since many features in the dataset are not present much more often than they're present and you are trying to add a feature to the images, not remove. # # Given the noise with its gradient already calculated through the classifier, you want to return the new noise vector. # # <details> # # <summary> # <font size="3" color="green"> # <b>Optional hint for <code><font size="4">calculate_updated_noise</font></code></b> # </font> # </summary> # # 1. Remember the equation for gradient ascent: `new = old + (∇ old * weight)`. # # </details> # + colab={} colab_type="code" id="U9WLR8Oy1rxU" # UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT) # GRADED FUNCTION: calculate_updated_noise def calculate_updated_noise(noise, weight): ''' Function to return noise vectors updated with stochastic gradient ascent. Parameters: noise: the current noise vectors. You have already called the backwards function on the target class so you can access the gradient of the output class with respect to the noise by using noise.grad weight: the scalar amount by which you should weight the noise gradient ''' #### START CODE HERE #### new_noise = noise + ( noise.grad * weight) #### END CODE HERE #### return new_noise # + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="8s2RbF5F3_lL" outputId="e165d0bb-a937-4ce0-9f78-3a094513487c" # UNIT TEST # Check that the basic function works opt.zero_grad() noise = torch.ones(20, 20) * 2 noise.requires_grad_() fake_classes = (noise ** 2).mean() fake_classes.backward() new_noise = calculate_updated_noise(noise, 0.1) assert type(new_noise) == torch.Tensor assert tuple(new_noise.shape) == (20, 20) assert new_noise.max() == 2.0010 assert new_noise.min() == 2.0010 assert torch.isclose(new_noise.sum(), torch.tensor(0.4) + 20 * 20 * 2) print("Success!") # - # Check that it works for generated images opt.zero_grad() noise = get_noise(32, z_dim).to(device).requires_grad_() fake = gen(noise) fake_classes = classifier(fake)[:, 0] fake_classes.mean().backward() noise.data = calculate_updated_noise(noise, 0.01) fake = gen(noise) fake_classes_new = classifier(fake)[:, 0] assert torch.all(fake_classes_new > fake_classes) print("Success!") # + [markdown] colab_type="text" id="tj-c9LT5lIRC" # #### Generation # Now, you can use the classifier along with stochastic gradient ascent to make noise that generates more of a certain feature. In the code given to you here, you can generate smiling faces. Feel free to change the target index and control some of the other features in the list! You will notice that some features are easier to detect and control than others. # # The list you have here are the features labeled in CelebA, which you used to train your classifier. If you wanted to control another feature, you would need to get data that is labeled with that feature and train a classifier on that feature. # + colab={"base_uri": "https://localhost:8080/", "height": 597} colab_type="code" id="kASNj6nLz7kh" outputId="50c4dfce-5925-4c85-e601-fb92c4ed5299" # First generate a bunch of images with the generator n_images = 8 fake_image_history = [] grad_steps = 10 # Number of gradient steps to take skip = 2 # Number of gradient steps to skip in the visualization feature_names = ["5oClockShadow", "ArchedEyebrows", "Attractive", "BagsUnderEyes", "Bald", "Bangs", "BigLips", "BigNose", "BlackHair", "BlondHair", "Blurry", "BrownHair", "BushyEyebrows", "Chubby", "DoubleChin", "Eyeglasses", "Goatee", "GrayHair", "HeavyMakeup", "HighCheekbones", "Male", "MouthSlightlyOpen", "Mustache", "NarrowEyes", "NoBeard", "OvalFace", "PaleSkin", "PointyNose", "RecedingHairline", "RosyCheeks", "Sideburn", "Smiling", "StraightHair", "WavyHair", "WearingEarrings", "WearingHat", "WearingLipstick", "WearingNecklace", "WearingNecktie", "Young"] ### Change me! ### target_indices = feature_names.index("Smiling") # Feel free to change this value to any string from feature_names! noise = get_noise(n_images, z_dim).to(device).requires_grad_() for i in range(grad_steps): opt.zero_grad() fake = gen(noise) fake_image_history += [fake] fake_classes_score = classifier(fake)[:, target_indices].mean() fake_classes_score.backward() noise.data = calculate_updated_noise(noise, 1 / grad_steps) plt.rcParams['figure.figsize'] = [n_images * 2, grad_steps * 2] show_tensor_images(torch.cat(fake_image_history[::skip], dim=2), num_images=n_images, nrow=n_images) # + [markdown] colab_type="text" id="PmETsfun7bLc" # ## Entanglement and Regularization # You may also notice that sometimes more features than just the target feature change. This is because some features are entangled. To fix this, you can try to isolate the target feature more by holding the classes outside of the target class constant. One way you can implement this is by penalizing the differences from the original class with L2 regularization. This L2 regularization would apply a penalty for this difference using the L2 norm and this would just be an additional term on the loss function. # # Here, you'll have to implement the score function: the higher, the better. The score is calculated by adding the target score and a penalty -- note that the penalty is meant to lower the score, so it should have a negative value. # # For every non-target class, take the difference between the current noise and the old noise. The greater this value is, the more features outside the target have changed. You will calculate the magnitude of the change, take the mean, and negate it. Finally, add this penalty to the target score. The target score is the mean of the target class in the current noise. # # <details> # # <summary> # <font size="3" color="green"> # <b>Optional hints for <code><font size="4">get_score</font></code></b> # </font> # </summary> # # 1. The higher the score, the better! # 2. You want to calculate the loss per image, so you'll need to pass a dim argument to [`torch.norm`](https://pytorch.org/docs/stable/generated/torch.norm.html). # 3. Calculating the magnitude of the change requires you to take the norm of the difference between the classifications, not the difference of the norms. # # </details> # + colab={} colab_type="code" id="qabLcvEL7X-J" # UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT) # GRADED FUNCTION: get_score def get_score(current_classifications, original_classifications, target_indices, other_indices, penalty_weight): ''' Function to return the score of the current classifications, penalizing changes to other classes with an L2 norm. Parameters: current_classifications: the classifications associated with the current noise original_classifications: the classifications associated with the original noise target_indices: the index of the target class other_indices: the indices of the other classes penalty_weight: the amount that the penalty should be weighted in the overall score ''' # Steps: 1) Calculate the change between the original and current classifications (as a tensor) # by indexing into the other_indices you're trying to preserve, like in x[:, features]. # 2) Calculate the norm (magnitude) of changes per example. # 3) Multiply the mean of the example norms by the penalty weight. # This will be your other_class_penalty. # Make sure to negate the value since it's a penalty! # 4) Take the mean of the current classifications for the target feature over all the examples. # This mean will be your target_score. #### START CODE HERE #### other_distances = current_classifications[:,other_indices] - original_classifications[:,other_indices] # Calculate the norm (magnitude) of changes per example and multiply by penalty weight other_class_penalty = -torch.norm(other_distances, dim=1).mean() * penalty_weight # Take the mean of the current classifications for the target feature target_score = current_classifications[:, target_indices].mean() #### END CODE HERE #### return target_score + other_class_penalty # + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="5-vTjn__EKQT" outputId="f48d4b7e-f9bc-403f-822d-a222f868ebd4" # UNIT TEST assert torch.isclose( get_score(torch.ones(4, 3), torch.zeros(4, 3), [0], [1, 2], 0.2), 1 - torch.sqrt(torch.tensor(2.)) * 0.2 ) rows = 10 current_class = torch.tensor([[1] * rows, [2] * rows, [3] * rows, [4] * rows]).T.float() original_class = torch.tensor([[1] * rows, [2] * rows, [3] * rows, [4] * rows]).T.float() # Must be 3 assert get_score(current_class, original_class, [1, 3] , [0, 2], 0.2).item() == 3 current_class = torch.tensor([[1] * rows, [2] * rows, [3] * rows, [4] * rows]).T.float() original_class = torch.tensor([[4] * rows, [4] * rows, [2] * rows, [1] * rows]).T.float() # Must be 3 - 0.2 * sqrt(10) assert torch.isclose(get_score(current_class, original_class, [1, 3] , [0, 2], 0.2), -torch.sqrt(torch.tensor(10.0)) * 0.2 + 3) print("Success!") # + [markdown] colab_type="text" id="CkrGr-NUGwC8" # In the following block of code, you will run the gradient ascent with this new score function. You might notice a few things after running it: # # 1. It may fail more often at producing the target feature when compared to the original approach. This suggests that the model may not be able to generate an image that has the target feature without changing the other features. This makes sense! For example, it may not be able to generate a face that's smiling but whose mouth is NOT slightly open. This may also expose a limitation of the generator. # Alternatively, even if the generator can produce an image with the intended features, it might require many intermediate changes to get there and may get stuck in a local minimum. # # 2. This process may change features which the classifier was not trained to recognize since there is no way to penalize them with this method. Whether it's possible to train models to avoid changing unsupervised features is an open question. # + colab={"base_uri": "https://localhost:8080/", "height": 597} colab_type="code" id="l3SshFjn-soX" outputId="4d97c409-589c-46b7-97b3-8d0483e968d5" fake_image_history = [] ### Change me! ### target_indices = feature_names.index("Smiling") # Feel free to change this value to any string from feature_names from earlier! other_indices = [cur_idx != target_indices for cur_idx, _ in enumerate(feature_names)] noise = get_noise(n_images, z_dim).to(device).requires_grad_() original_classifications = classifier(gen(noise)).detach() for i in range(grad_steps): opt.zero_grad() fake = gen(noise) fake_image_history += [fake] fake_score = get_score( classifier(fake), original_classifications, target_indices, other_indices, penalty_weight=0.1 ) fake_score.backward() noise.data = calculate_updated_noise(noise, 1 / grad_steps) plt.rcParams['figure.figsize'] = [n_images * 2, grad_steps * 2] show_tensor_images(torch.cat(fake_image_history[::skip], dim=2), num_images=n_images, nrow=n_images) # -	24,806
/Python/Numpy_basics.ipynb	84367c1cb97a0a4276d9f094fdb7aedc96d0d066	[]	no_license	rachelrliu/Machine_Learning_Notes	https://github.com/rachelrliu/Machine_Learning_Notes	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	16,120	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # #Python NumPy: Arrays and Vectorized Computation # # This is a personal study note for Data Wrangling. It is meant to be a both a quick guide and reference for further research into these topics. # # Reference: Python for Data Analysis by Wes McKinny # # ##Array # # A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers indicating the size of each dimension. Arrays also have a "size" attribute. For a 1-dimensional array this is equivalent to its length. It is essentially a product of the dimensions. # # The easiest way to create an array is to use the array function. This accepts any se- quence-like object (including other arrays) and produces a new NumPy array containing the passed data. # import numpy as np a = np.array([1, 2, 3]) # Create a rank 1 array print type(a), a.ndim, a.shape, a.size a[0] = 5 # Change an element of the array print a b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array print b print b.ndim, b.shape, b.size # More on array creation: [Array creation routine](http://docs.scipy.org/doc/numpy/reference/routines.array-creation.html) # ##Datatypes # # Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. # + x = np.array([1, 2]) # Let numpy choose the datatype y = np.array([1, 2], dtype=np.int64) # Force a particular datatype z = x.astype(np.float64) #Cast an array from one dtype to another print x.dtype, y.dtype, z.dtype # - # More on dtype: [documentation](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html) # ##Array Indexing # # ###Slicing # Similar to Python lists, numpy arrays can be sliced. A slice of an array is a view into the same data. Since arrays may be multidimensional, you must specify a slice for each dimension of the array. # + # Create the following rank 2 array with shape (3, 4) # [[ 1 2 3 4] # [ 5 6 7 8] # [ 9 10 11 12]] a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) # Use slicing to pull out the subarray consisting of the first 2 rows # and columns 1 and 2; b is the following array of shape (2, 2): # [[2 3] # [6 7]] b = a[:2, 1:3] print b # A slice of an array is a view into the same data, so modifying it # will modify the original array. print a[0, 1] b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1] print a[0, 1] # + c = np.array([[[1],[2],[3]], [[4],[5],[6]]]) print c.shape #If the number of objects in the selection tuple is less than ndim , #then : is assumed for any subsequent dimensions. d = c[1:2,0:2] print d,d.shape # - # ###Integer array indexing # When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array. In contrast, integer array indexing allows you to construct arbitrary arrays using the data from another array. Here is an example: # + a = np.array([[1,2], [3, 4], [5, 6]]) # An example of integer array indexing. # The returned array will have shape (3,) and print a[[0, 1, 2], [0, 1, 0]] # The above example of integer array indexing is equivalent to this: print np.array([a[0, 0], a[1, 1], a[2, 0]]) # When using integer array indexing, you can reuse the same # element from the source array: print a[[0, 0], [1, 1]] # Equivalent to the previous integer array indexing example print np.array([a[0, 1], a[0, 1]]) # - # We can also mix integer indexing with slice indexing. However, doing so will yield an array of lower rank than the original array. # + # Create the following rank 2 array with shape (3, 4) # [[ 1 2 3 4] # [ 5 6 7 8] # [ 9 10 11 12]] a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) # Two ways of accessing the data in the middle row of the array. # Mixing integer indexing with slices yields an array of lower rank, # while using only slices yields an array of the same rank as the # original array: row_r1 = a[1,:] # Rank 1 view of the second row of a row_r2 = a[1:2,:] # Rank 2 view of the second row of a print row_r1, row_r1.shape, row_r1.ndim print row_r2, row_r2.shape, row_r2.ndim # - # ###Boolean array indexing # # Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example: # # a = np.array([[1,2], [3, 4], [5, 6]]) bool_idx = (a > 2) # Find the elements of a that are bigger than 2; # this returns a numpy array of Booleans of the same # shape as a, where each slot of bool_idx tells # whether that element of a is > 2. bool_idx # We can use boolean array indexing to construct a rank 1 array consisting of the elements of a corresponding to the True values of bool_idx: # + print a[bool_idx] # We can do all of the above in a single concise statement: print a[a > 2] # - # We can use what NumPy calls "Boolean indexing", combined with the sum function, to count the number of True values in the array: #number of elements in array that are greater than 2 print ((a > 2) == True).sum() # ##Array Math # # Basic mathematical functions (universal functions) operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module. # # [Universal functions documentation](http://docs.scipy.org/doc/numpy/reference/ufuncs.html) # ##Sorting # # Sorting works much like it does with built-in lists. The np.sort() function is a pure function that returns a sorted copy of the array while leaving the original array untouched, whereas the .sort() method is a modifier that sorts the array in place. # # # + int_arr = np.random.randint(0,10,8) #generate 8 interger from range(10) print int_arr np.sort(int_arr) print np.sort(int_arr) print int_arr # - int_arr.sort() print int_arr # We can sort multidimensional arrays by passing in the axis along which you want to sort. For a 2D array, this means passing in axis 0 if you want to sort by columns and axis 1 if you want to sort by rows: # + twod_int_arr = np.random.randint(0,10,(4,4)) print twod_int_arr print np.sort(twod_int_arr,0) #sort by column print np.sort(twod_int_arr,1) #sort by row # - # np.argsort returns the indices that would sort an array. # + arr = np.random.randint(0,10,10) print arr print arr.argsort() print arr.argsort()[::-1] #reverse # - # ##Some Useful NumPy Functions # # numpy.where(condition[, x, y]) return elements, either from x or y, depending on condition. When True, yield x, otherwise yield y. # + a = np.array([[1,2],[3,4]]) b = np.array([[9,8],[7,6]]) c = np.array([[True,False],[True,False]]) print np.where(c,a,b) # - # in1d() function tests a set of input values for membership in a given array or set it returns an array of Booleans indicating which of the input set can be found in the target: # + arr = np.random.randint(0,10,10) print arr print np.in1d([3,9,6],arr) # - # unique() function returns a sorted list of unique values found in the input array: # + arr = np.random.randint(0,10,10) print arr print np.unique(arr) # - # More: # [NumPy Reference](http://docs.scipy.org/doc/numpy/reference/routines.html)	7,682
/Functions_2.ipynb	b2b6e8e5ce5b07a5502cf3f6254102647c933386	[]	no_license	vincedlbr/ort-ms2i-vdelabre	https://github.com/vincedlbr/ort-ms2i-vdelabre	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,839	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + mylistVeloEuro=[ 500, 650, 800, 950, 1100, 1250, 1400, 1550, 1700, 1850, 2000, 2150, 2300, 2450, 2600, 2750, 2900, 3050, 3200, 3350, 3500, 3650, 3800, 3950, 4100, 4250, 4400, 4550, 4700, 4850, 5000, 5150, 5300, 5450, 5600, 5750, 5900, 6050, 6200, 6350, 6500, 6650, 6800, 6950, 7100, 7250, 7400, 7550, 7700, 7850, 8000, 8150, 8300, 8450, 8600, 8750, 8900, 9050, 9200, 9350 ] def countVelo(tab, sup, c=None) : nb_velo=0 for euro in tab : if (euro > sup) : nb_velo=nb_velo+1 return len(tab), nb_velo, c print(countVelo) NbTotalVelo, NbVeloSup, test = countVelo(mylistVeloEuro, 4000) print(NbTotalVelo) print(NbVeloSup) print(test) NbTotalVelo, NbVeloSup, test = countVelo(mylistVeloEuro, 4000, "blop") print(NbTotalVelo) print(NbVeloSup) print(test)	1,082
/analysis/EWAS/blood/modeling_linear_XGboost/blood_testing_models_by_sex.ipynb	bb293656718dab11a5aed001a93fd0a828713e12	[]	no_license	AC297rDNAMethylation2021/Healthy-Aging	https://github.com/AC297rDNAMethylation2021/Healthy-Aging	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,459,229	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import requests from bs4 import BeautifulSoup resp = requests.get("http://blog.castman.net/py-scraping-analysis-book/ch1/connect.html") soup = BeautifulSoup(resp.text, "html.parser") print(soup.find("h1").text) # + import requests from bs4 import BeautifulSoup def main(): url01 = 'http://blog.castman.net/py-scraping-analysis-book/ch1/connect.html' bad_url = 'http://non-existed.domin/connect.html' text01 = get_tag_text(url01,'h1') print(text01) text02 = get_tag_text(url01,'h2') print(text02) text03 = get_tag_text(bad_url,'h1') print(text03) def get_tag_text(url,tag): try: resp = requests.get(url) if resp.status_code == 200: soup = BeautifulSoup(resp.text, 'html.parser') return soup.find(tag).text except Exception as e: print('Exception: %s' %(e)) return None if __name__ =='__main__': main() # - the we created installed. import age_predict.Loading_EWAS_Aging_Data as le import age_predict.Regression as rg import age_predict.Pickle_unpickle as pu # View working directory os.getcwd() # #### Set paths data_path = '../../data/' cpg_path = '../saved_features/' save_models_path = '../saved_models/' # #### Get Saved imputed whole blood data 2263 cpgs # * The dataframes imported here were created with the "blood_feature_selection_by_XGBoost_importance_scores_gender" notebook df_train = pd.read_csv(data_path + 'df_train_ranked_gen.csv', index_col=0) df_test = pd.read_csv(data_path + 'df_test_ranked_gen.csv', index_col=0) # #### Get Saved blood cpg rankings # * The list of top ranked cpgs imported here was created with the "blood_feature_selection_by_XGBoost_importance_scores_gender" notebook and pickled cpgs_XGboost_blood_ranked_gender = pu.get_pickled_object(cpg_path + 'cpgs_XGboost_blood_ranked_gender') top_100 = cpgs_XGboost_blood_ranked_gender[:100] top_1000 = cpgs_XGboost_blood_ranked_gender[:1000] s = pd.Series(list(df_train.age) + list(df_test.age)) # #### Looking at the age distributions in the data # Histogram of ages in train + test data plt.figure(figsize=(6,4)) s.hist(bins=20,histtype='bar', ec='black' ) plt.xlabel('Age', fontsize=14) plt.xlim(0,120) plt.ylabel('Count', fontsize=14) plt.grid(True, lw=1, ls = '--', alpha=0.2) plt.title('Blood DNA meythylation dataset', fontsize=16) plt.show() # Histogram of ages in train data plt.figure(figsize=(6,4)) df_train.age.hist(bins=20,histtype='bar', ec='black' ) plt.xlabel('Age', fontsize=14) plt.xlim(0,120) plt.ylabel('Count', fontsize=14) plt.grid(True, lw=1, ls = '--', alpha=0.2) plt.title('Histogram of Ages Train data', fontsize=16) plt.show() # Histogram of ages in test data plt.figure(figsize=(6,4)) df_test.age.hist(bins=20, histtype='bar', ec='black') plt.xlabel('Age') plt.ylabel('Count') plt.title('Histogram of Ages Test data') plt.show() # ## 1 = Male, 0 = Female df_train df_test # #### Training Linear, XGboost, Ridge, Lasso models using the top 100 ranked cpgs from sklearn.model_selection import train_test_split X = df_train[top_100] y = df_train.age X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state = 2021) wb_mod_100, rms_train, rms_test, r2_train, r2_test = rg.linear_regress(X_train, y_train, X_test, y_test, plot=True) wb_mod_XG_100, rms_train, rms_test, r2_train, r2_test, feature_importances_ = rg.xgboost_regress(X_train, y_train, X_test, y_test, early_stopping_rounds=10) wb_mod_lasso_100, rms_train, rms_test, r2_train, r2_test = rg.lassoCV_regress(X_train, y_train, X_test, y_test, plot=True, alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4, 1e5],cv=5) wb_mod_ridge_100, rms_train, rms_test, r2_train, r2_test = rg.ridgeCV_regress(X_train, y_train, X_test, y_test, plot=True, alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4, 1e5],cv=5) wb_mod_kNN_100, rms_train, rms_test, r2_train, r2_test = rg.kNN_regress(X_train, y_train, X_test, y_test, plot=True, ks=[1, 2, 3, 5, 10, 15, 20, 30, 50],cv=5) # #### Testing Linear, XGboost, Ridge, Lasso models using the top 100 ranked cpgs on both sexes separately. df_male = df_test[df_test.sex == 1] df_female = df_test[df_test.sex == 0 # + X_test_male = df_male[top_100] y_test_male = df_male.age X_test_female = df_female[top_100] y_test_female = df_female.age # - preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_male, y_test_male, wb_mod_100, mtype='Linear Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_female, y_test_female, wb_mod_100, mtype='Linear Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_male, y_test_male, wb_mod_XG_100, mtype='XGboost Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_female, y_test_female, wb_mod_XG_100, mtype='XGboost Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_male, y_test_male, wb_mod_lasso_100, mtype='Lasso Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_female, y_test_female, wb_mod_lasso_100, mtype='Lasso Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_male, y_test_male, wb_mod_ridge_100, mtype='Ridge Regression', figsize=(8,4)) preds, MSE, rms, r2, MAE, r_corr = rg.test_model_on_heldout_data(X_test_female, y_test_female, wb_mod_ridge_100, mtype='Ridge Regression', figsize=(8,4))	5,802
/.ipynb_checkpoints/day_3-checkpoint.ipynb	fa0f16afd158e7995ca92cccaa00b9a0934e7bd8	[]	no_license	appdulrahman/60DaysofUdacity	https://github.com/appdulrahman/60DaysofUdacity	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	6,991	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # import the necessary libraries import pickle import numpy as np import pandas as pd import matplotlib.pyplot as plt import scipy.sparse as sparse import skimage.io as skio import copy as cp # ### Creating Training Data # open the training data array tempfile = open('Variables/trainImagePatchArray.pckl', 'rb') # load through the pickle library trainImagePatchArray = pickle.load(tempfile) tempfile.close() # convert all values to integer trainingDataIn= trainImagePatchArray.astype(int) # open the output image obtained from the k-means++ clustering tempfile = open('Variables/trainingOutputImage.pckl', 'rb') # load the output image through the pickle library trainingOutputImage = pickle.load(tempfile) tempfile.close() # taking the entire left half of the input image except the first pixel trainingOutputImage = trainingOutputImage[1:300,1:224] # reshaping the image trainout = trainingOutputImage.reshape(((trainingOutputImage.shape[0]trainingOutputImage.shape[1]),3)) trainout # open the file containing the centers of the clusters tempfile = open('Variables/clusters.pckl', 'rb') # load the clusters using pickle clusters = pickle.load(tempfile) tempfile.close() # from the clusters dictionary, get the list of centers of the colors colorlist = list(clusters.keys()) # and convert to a numpy array colors = np.asarray(colorlist,dtype=int) colors trainingDataIn # initialize an array of zeros with the length of trainout trainingDataOut = np.zeros((len(trainout),5),dtype=int) # for all pixels in the training image for index,pixel in enumerate(trainout): # reassign the values to the array to 1 if the colors array is equal to the pixel's column trainingDataOut[index,np.where((colors == pixel).all(axis=1))] = 1 trainingDataOut # convert the training input data into a dataframe Trainingdataframe = pd.DataFrame(data=trainingDataIn,index=range(1,len(trainingDataIn)+1), columns= ["px"+str(i) for i in range(1,10)] ) # converting the training output data to a coordinate matrix, detailing the colors from the clusters arr = sparse.coo_matrix(trainingDataOut) # and adding that to the dataframe Trainingdataframe['Color'] = arr.toarray().tolist() Trainingdataframe # ### Creating Testing Data. # open the test data from the pickle file tempfile = open('Variables/testImagePatchArray.pckl', 'rb') # loading it testImagePatchArray = pickle.load(tempfile) tempfile.close() # converting it into integer data type testDataX = testImagePatchArray.astype(int) # load the expected test output image tempfile = open('Variables/expectedtestingOutputImage.pckl', 'rb') testOutputImage = pickle.load(tempfile) tempfile.close() # taking the entire test image except the first pixel testOutputImage = testOutputImage[1:300,1:224] # and reshaping it to incorporate the R, G, B channels testout = testOutputImage.reshape(((testOutputImage.shape[0]testOutputImage.shape[1]),3)) testDataX testout # initialize an array of zeros with the length of the test output data testDataY = np.zeros((len(testout),5),dtype=int) # for all pixels in the test output image for index,pixel in enumerate(testout): # assign a value of 1 for all the indices where the column of the pixel represents the color from the clusters testDataY[index,np.where((colors == pixel).all(axis=1))] = 1 testDataY # create a dataframe for the test data with each pixel from the above Testingdataframe = pd.DataFrame(data=testDataX,index=range(1,len(testDataX)+1), columns= ["px"+str(i) for i in range(1,10)] ) # convert the array into a sparse coordinate matrix arr = sparse.coo_matrix(testDataY) # and add that to the dataframe Testingdataframe['Color'] = arr.toarray().tolist() Testingdataframe # ### Defining Functions for Multiclass Logistic Regression # + def softmax(XW): """ implementing the softmax function for logistic regression """ XW -= np.max(XW) # returns the softmax value based on teh formula for the softmax function prob = (np.exp(XW).T/ np.sum(np.exp(XW),axis=1)).T return prob def loss(W,X,Y,lambd): """ implements the loss function """ # compute the softmax function by taking the dot product of input data and the weights prob = softmax(np.dot(X,W)) N = len(Y) # compute the loss based on the formula for loss of logistic regression lvalue = ((-1 / N) * np.sum(Y * np.log(prob))) + lambd(0.5)np.sum(np.dot(W,W.T)) # return the loss value return lvalue def gradient(W,X,Y,lambd): """ implements gradient descent """ # get the probability from the softmax function prob = softmax(np.dot(X,W)) N = len(Y) # compute gradient of the loss function galue = ((-1 / N) * np.dot(X.T,(Y - prob))) + lambdW return galue def predict(prob): """ predict the colors """ # get indices of max probabilities in the columns preds = np.argmax(prob,axis=1) # get the color predictions based on the indices obtained above predcolors = [colors[index] for index in preds] # and return the predictions as an array return np.asarray(predcolors) def accuracy(predicted,actual): """ computes the accuracy of the prediction """ # get the average of all values that are same as the actual value accuracy = (np.sum(np.equal(predicted,actual).all(axis=1))/len(actual))100 # and return return accuracy def minibatch(X,Y,batchSize): """ returns mini batches """ # choose a random data point for Y initialpoint = np.random.randint(0, Y.shape[0] - batchSize - 1) # get all X points from the initial point until the length of the batch size Xbatch = X[initialpoint:(initialpoint + batchSize)] # get all Y points from the initial point until the length of the batch size Ybatch = Y[initialpoint:(initialpoint + batchSize)] return Xbatch,Ybatch def getbestparameters(dataDictionary): """ gets the best parameters (weights, test accuracy and hyperparameters) for the model """ # set test accuracy to 0 initially test_accuracy = 0 # initialize an array of zeros for the weights initially W = np.zeros((9,5)) # set hyperparameters to 0 initially hyperparameters = (0,0,0) for key in dataDictionary.keys(): # if the updated test accuracy is more than the previous test accuracy obtained if dataDictionary.get(key).get("test_accuracy") > test_accuracy: # update weights W = dataDictionary.get(key).get("W") # update hyperparameters hyperparameters = dataDictionary.get(key).get("hyper_parameters") # update test accuracy test_accuracy = dataDictionary.get(key).get("test_accuracy") return(W,test_accuracy,hyperparameters) # + # dictionary for the data data = {} # list of batch sizes for the data batchsizes = [1, 10 , 100, 1000] # list of values for alpha (learning rate) alphas = [10, 1, 0.1] # values for lambda (regularization constant) lambds = [0, 5, 10] times = 0 # run for 10 iterations for iteration in range(0,10): # for all lambda values for lambd in lambds: # for all batch sizes for batchSize in batchsizes: # for all alpha values for ogalpha in alphas: # randomly choose an index from the training input data shuffledindex = np.random.choice(range(0,len(trainingDataIn)),len(trainingDataIn),replace=False) # take 80% of these samples as the training data trainingindices = shuffledindex[:int(.80len(trainingDataIn))] # take remaining samples as the validation data validationindices = shuffledindex[int(.80len(trainingDataIn)):] # normalize the training data trainingDataInNorm = (trainingDataIn - np.mean(trainingDataIn,axis=0))/np.std(trainingDataIn,axis=0) # add a column of ones to the normalized training data trainingDataInNorm = np.column_stack((np.ones((len(trainingDataInNorm),1),dtype=int),trainingDataInNorm)) # get the normalized validation data using the indices defined above validationDataXnorm = trainingDataInNorm[validationindices] # get the output validation data from the expected training data output validationDataY = trainingDataOut[validationindices] # get the normalized training input data from the above trainingDataXnorm = trainingDataInNorm[trainingindices] # get the output training data trainingDataY = trainingDataOut[trainingindices] # reshape the data using a normal Gaussian distribution W = np.reshape(np.random.normal(0, 1/50, 50),(10,5)) # initiaize previous validation loss to 0 previousValLoss = 0 # maximum number of iterations is 100 maxiter = 100 i = 1 # keep a copy of the current value of alpha alpha = cp.copy(ogalpha) # run for 100 iterations while (i < maxiter): # get the mini batch from the normalized data minibatchX,minibatchY = minibatch(trainingDataXnorm,trainingDataY,batchSize) # run gradient descent on this mini batch gred = gradient(W,minibatchX,minibatchY,lambd) alpha = alpha/np.sqrt(i+1) # update the weights W = W - (alphagred) # get the validation loss valLoss = loss(W,validationDataXnorm,validationDataY,lambd) # get the accuracy of the prediction valaccuracy = accuracy(predict(softmax(np.dot(validationDataXnorm,W))),trainout[validationindices]) # checks if difference between the previous validation loss and current validation loss is not significant # or if the validation loss is NaN if (np.abs(previousValLoss - valLoss) < 0.00001previousValLoss) or (np.isnan(valLoss)): # if so, break out of the loop break # otherwise, update the validation loss previousValLoss = valLoss # inrease i for the next iteration i = i+1 # print both validation loss and validation accuracy print("validation loss :: " + str(valLoss) + " ; Validation accuracy :: " + str(valaccuracy)) # normalize the test data testDataX1 = (testDataX - np.mean(testDataX,axis=0))/np.std(testDataX,axis=0) # append a column of 1s to the test data testDataX1 = np.column_stack((np.ones((len(testDataX1),1),dtype=int),testDataX1)) # get the test accuracy testaccuracy = accuracy(predict(softmax(np.dot(testDataX1,W))),testout) # and print it print("Test accuracy :: " + str(testaccuracy)) times = times + 1 print((ogalpha,batchSize,lambd)) # update the data dictionary for the next batch size data.update({times:{"W" : W, "test_accuracy" : testaccuracy, "hyper_parameters" : (ogalpha,batchSize,lambd)}}) # - # getting the test accuracy from the model W,test_accuracy,parameters = getbestparameters(data) test_accuracy # get the prediction prediction = predict(softmax(np.dot(testDataX1,W))) prediction.shape # get the predicted image predictionImage = prediction.reshape((299,223,3)) # display the predicted image plt.imshow(predictionImage) # get expected output test image expectedtestingOutputImage = skio.imread(fname="expectedtestingOutputImage.jpg") # display it plt.imshow(expectedtestingOutputImage)	12,275
/.ipynb_checkpoints/Data by Asset Class-checkpoint.ipynb	1db7246577f2fe497b60469987cd40f6339c3cfc	[]	no_license	snhuber/iblocal	https://github.com/snhuber/iblocal	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	158,021	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/resh1604/SIT742/blob/master/Part2half.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="Vn6NE6IBT02O" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 33} outputId="039fc790-ee7b-408b-e6c2-3a0a47076ad0" # !pip install wget # + id="urEVJeYIT3w6" colab_type="code" colab={} import wget link_to_data = 'https://github.com/tulip-lab/sit742/raw/master/Assessment/2019/data/wine.json' DataSet = wget.download(link_to_data) link_to_data = 'https://github.com/tulip-lab/sit742/raw/master/Assessment/2019/data/stopwords.txt' DataSet = wget.download(link_to_data) # + id="KVj3VLr5T9vn" colab_type="code" colab={} import json import pandas as pd import matplotlib.pyplot as plt # + id="0UsZmmxzT_x6" colab_type="code" colab={} import re import nltk from nltk.tokenize import RegexpTokenizer from nltk.probability import * from itertools import chain #from tqdm import tqdm import codecs # + id="14tTLtKCT5vC" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 100} outputId="0a72aab4-ee54-4322-d90f-b445582e5ef7" nltk.download('punkt') nltk.download('reuters') # + id="7XuGu7UHUH-Q" colab_type="code" colab={} file = 'wine.json' # + id="xM9Q2ZmkUKnz" colab_type="code" colab={} df = pd.read_json(file, orient='columns') #df.head(10) #print(df) #print(df.to_json(orient='index', lines='True')) df # + id="9o6IBIsIUpj8" colab_type="code" colab={} data1 = df[['description']] data1 # + id="cW3X3Z6-Usgf" colab_type="code" colab={} data2 =data1.loc[:,].tail(100) data2 # + id="Ucc2d4MeUuxX" colab_type="code" colab={} data2.iloc[0,0] # + id="xQaRkH0eU6Ws" colab_type="code" colab={} data3 = [] for index, row in data2.iterrows(): data3.append(row['description'].lower()) #print(ans) with open('your_file1.txt', 'w') as f: for item in data3: f.write("%s\n" % item) #print(row['description']) # + id="P6SIpq5EXkbY" colab_type="code" colab={} data4 = ''.join(map(str, data3)) data4 # + id="bYuX5Px0afBZ" colab_type="code" colab={} data5 = re.sub(r'[^\w]', ' ', data4) data5 # + id="Ozb5iFiYX2aF" colab_type="code" colab={} with open('stopwords.txt') as f: stop_words = f.read().splitlines() stop_words = set(stop_words) stop_words # + id="9zmTc3orX5gN" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 70} outputId="fcc5bd8e-9a1c-4772-8641-e4eea5be67e9" from nltk.corpus import stopwords from nltk.tokenize import word_tokenize word_tokens = word_tokenize(data5) data6 = [w for w in word_tokens if not w in stop_words] data6 = [] for w in word_tokens: if w not in stop_words: data6.append(w) print(word_tokens) print(data6) # + id="eEU_7i_zfZya" colab_type="code" colab={} data6.sort() # + id="aAJt3ZRsevk-" colab_type="code" colab={} fd_1 = FreqDist(data6) fd_2 = fd_1.most_common(50) # + id="rH-rfQBelLP1" colab_type="code" colab={} with open("top.txt", "w") as output: output.write(str(fd_2)) # + id="UMsR2_7RFlAP" colab_type="code" colab={} from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer(min_df=1) X = vectorizer.fit_transform(data6) idf = vectorizer._tfidf.idf_ data7 = dict(zip(vectorizer.get_feature_names(), idf)) data7 grid(True) plt.title('GAMMA = 10') fig.colorbar(cax, ticks=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, .75,.8,.85,.90,.95,1]) plt.show()	3,780
/atv03/3.1 entrega/03_entrega.ipynb	26e8b7d23a9919d3340345a289048fd32baebe73	[]	no_license	adrielnardi/mestrado-renear	https://github.com/adrielnardi/mestrado-renear	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	42,477	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="1-aLH0rksl7A" # ### Mestrado Profissional em Computação Aplicada # # #### Disciplina: Redes Neurais Artificiais # #### Professor: Dr. Francisco de Assis Boldt # #### Aluno: Adriel Monti De Nardi # # ------ # # # + [markdown] id="OZOvm7fzhOH_" # ### Trabalho 03: Plotar a região de decisão de uma ELM # # Para concluir a atividade proposta devemos plotar a região de decisão da ELM mostrada no vídeo. # # # + [markdown] id="TFBi7oQRiagi" # #Função gera dataset, plota dataset e hiperplano # + colab={"base_uri": "https://localhost:8080/", "height": 298} id="-z7A1LpojWfm" outputId="ef926e73-2c11-44a4-ca49-e45d9b4d7334" import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import make_blobs from sklearn.preprocessing import label_binarize def geradataset(tamanho=20, centros=[[0,0],[1,0],[1,1],[0,1]]): X, y = make_blobs(n_samples=tamanho, centers=centros, cluster_std=0.2) y = np.array(y%2, dtype=int) return X, y def plotadataset(X, y): plt.xlabel('X1') plt.ylabel('X2') for k in set(y): plt.plot(X[:,0][y==k],X[:,1][y==k], "o", alpha=0.3) def plotahiperplano(vetor, bias=0, xmin=0, xmax=1): xs = np.linspace(xmin, xmax, num=2) ys = (-vetor[0] / vetor[1]) * xs - bias / vetor[1] plt.plot(xs,ys) X, y = geradataset() plotadataset(X, y) plt.show() y # + [markdown] id="jgaFAqyAjg76" # #Funções de Custo # # + id="6qhwsxToji-P" class CustoPerceptron(): @staticmethod def erro(y, ypred): return y - ypred @staticmethod def custo(y, ypred): return np.sum(CustoPerceptron.erro(y, ypred)2) @staticmethod def gradiente(y, ypred, X): return np.matmul(X.T, CustoPerceptron.erro(y, ypred)) class Adaline(): def __init__(self): self.preactivated = True @staticmethod def erro(y, ypred): return y - ypred @staticmethod def custo(y, ypred): return np.sum((1 - Adaline.erro(y, ypred))2) @staticmethod def gradiente(y, ypred, X): return np.matmul(X.T, Adaline.erro(y, ypred)) # + [markdown] id="R0Ht-X56jn8Y" # #Algoritmos # + id="FpihARbxjpd1" class DescidaGradiente(): def __init__(self, custo=Adaline(), maxiter=1000, alpha=0.005): self.custo = custo self.maxiter = maxiter self.alpha = alpha def getW(self, X, y, activation=lambda a: a): w = np.random.uniform(-1, -1, size=(X.shape[1], y.shape[1])) for _ in range(self.maxiter): ypred = activation(np.matmul(X, w)) custo = self.custo.custo(y, ypred) if custo == 0: break w = w + self.alpha * self.custo.gradiente(y, ypred, X) return w class PseudoInversa(): def __init__(self): pass def getW(self, X, y): pinv = np.linalg.pinv(X) w = np.matmul(pinv, y) return w # + [markdown] id="bnI4_plKjvNi" # #Extreme Learning Machine # + id="82VNzSFQjzJ8" from sklearn.base import BaseEstimator, ClassifierMixin from scipy.special import expit def tanh(a): return expit(a) * 2 - 1 class ExtremeLearningMachine(BaseEstimator, ClassifierMixin): def __init__(self, algoritmo=PseudoInversa()): self.wih = None self.w = None self.threshold = 0 self.activation = tanh self.algoritmo = algoritmo @staticmethod def includebias(X): bias = np.ones((X.shape[0],1)) Xb = np.concatenate((bias,X), axis=1) return Xb def fit(self, X, y): self.wih = np.random.uniform(-1, 1, size=(X.shape[1],X.shape[0]//3)) Xh = np.matmul(X, self.wih) Xho = self.activation(Xh) X = ExtremeLearningMachine.includebias(Xho) self.labels = list(set(y)) y = label_binarize(y, classes=self.labels)*2-1 if len(self.labels) == 2 : y = y[:,0:1] # treinamento if hasattr(self.algoritmo, 'custo') and not (hasattr(self.algoritmo.custo, 'preactivated') and self.algoritmo.custo.preactivated): self.w = self.algoritmo.getW(X, y, self.activation) else: self.w = self.algoritmo.getW(X, y) def predict(self, X): Xh = np.matmul(X, self.wih) Xho = self.activation(Xh) Xb = ExtremeLearningMachine.includebias(Xho) a = np.matmul(Xb, self.w) if self.w.shape[1] > 1: idx = np.argmax(a, axis=1) else: idx = np.array(self.activation(a) > self.threshold, dtype=int)[:,0] ypred = np.array([self.labels[i] for i in idx]) return ypred # + colab={"base_uri": "https://localhost:8080/"} id="wy9i2-e5kXWy" outputId="fceeec36-ca83-4dc2-b0d2-bbc0dfcb79a2" elm = ExtremeLearningMachine() elm.fit(X, y) ypred = elm.predict(X) print(sum(y == ypred)/len(y)) # + [markdown] id="qQ9Tv9M9kjT6" # #Plotting Decision Regions # # Aqui será plotado a região de decisão da ELM # + colab={"base_uri": "https://localhost:8080/", "height": 587} id="amx1C7cknsey" outputId="19f73d83-d9c8-4611-81ec-94cafa2e6c1f" from mlxtend.plotting import plot_decision_regions # !pip install mlxtend plot_decision_regions(X, ypred, clf=elm, legend=len(set(y))) plt.xlabel('X1') plt.ylabel('X2') plt.title("Plotando Decisão de Região") plt.show() # + [markdown] id="ngeLd99OicIQ" # #Referência: # # - [Como faço para instalar pacotes Python no Colab do Google?](https://qastack.com.br/programming/51342408/how-do-i-install-python-packages-in-googles-colab) (data de acesso: 01.09.2021) # - [Installing mlxtend](http://rasbt.github.io/mlxtend/installation/) (data de acesso: 01.09.2021) # - [Plotting Decision Regions](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/) # (data de acesso: 01.09.2021) # #	6,011
/Mini 3/.ipynb_checkpoints/Untitled-checkpoint.ipynb	6a40ad54e409bb716365cdccc7f8ea0e1fdb68e9	[]	no_license	jannesgg/statistical-learning-big-data	https://github.com/jannesgg/statistical-learning-big-data	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	492,933	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Viewing Example Augmentations # %reload_ext autoreload # %autoreload 2 # + import sys sys.path.append("..") import numpy as np from src.data.prepare_data import * from src.models.model import * from src.visualization.exploration import grid_augmentations_show from PIL import Image from torchvision.transforms import ToPILImage # - SEED = 1 np.random.seed(SEED) train = pd.read_csv("../data/internal/train.csv") test = pd.read_csv("../data/internal/test.csv") sub = pd.read_csv("../data/internal/sample_submission.csv") transform = ImageTransform(128, True) dataset_train = MelanomaDataset("../data/internal/train", train, transform=transform, phase='visualize_augmentations') sample_images = [] for i, idx in enumerate(np.random.randint(0, len(train), size=20)): print(i + 1) sample_images.append(dataset_train[idx]['inputs']) fig = grid_augmentations_show(sample_images, 16, 16, 4, 5) fig.savefig('../images/augmentation_example.png') random((5, 3)) random_array_2 random_array_2.shape random_array_3 = np.random.rand(5, 3) random_array_3 # Pseudo-random numbers np.random.seed(seed = 99999) random_array_4 = np.random.randint(10, size = (5, 3)) random_array_4 np.random.seed(7) random_array_5 = np.random.random((5, 3)) random_array_5 random_array_5 = np.random.random((5, 3)) random_array_5 random_array_4.shape # ## 3. Viewing arrays and matrices np.unique(random_array_4) a1 a2 a3 a1[0] a2[0] a3[0] a2 a2[1] a3[:2, :2, :2] a4 = np.random.randint(10, size = (2, 3, 4, 5)) a4 a4.shape, a4.ndim # Get the first 4 numbers of the inner most arrays a4[:, :, :, :1] # ## 4. Manipulating & comparing arrays # ### Arithmetic a1 ones = np.ones(3) ones a1 + ones a1 - ones a1 * ones a2 a1 * a2 a3 # How can you reshape a2 to be compatible with a3? # Search: "How to reshape numpy array" a1 / ones a2 / a1 # Floor division removes the decimales (rounds down) a2 // a1 a2 ** 2 np.square(a2) np.add(a1, ones) a1 % 2 a1 / 2 a2 % 2 np.exp(a1) np.log(a1) # ## Aggregation # # Aggregation = performing the same operation on a number of things listy_list = [1, 2, 3] type(listy_list) sum(listy_list) sum(a1) np.sum(a1) # Use Python's methods (`sum()`) on Python datatypes and use Numpy's methods on Numpy arrays (`np.sum()`). #Creative a massive Numpy array massive_array = np.random.random(100000) massive_array.size massive_array[:10] # %timeit sum(massive_array) # Python's sum() # %timeit np.sum(massive_array) # Numpy's np.sum() a2 np.mean(a2) np.max(a2) np.min(a2) # Standart deviation = a measure o how spread out a group of numbers is from the mean np.std(a2) # Variance = measure of the average degree to which each number is different to the mean # Higher variance = wider range of numbers # Lower variance = lower range of numbers np.var(a2) # Standard deviation = squareroot of variance np.sqrt(np.var(a2)) # Demo of std and var high_var_array = np.array([1, 100, 200, 300, 4000, 5000]) low_var_array = np.array([2, 4, 6, 8, 10]) np.var(high_var_array), np.var(low_var_array) np.std(high_var_array), np.std(low_var_array) np.mean(high_var_array), np.mean(low_var_array) # %matplotlib inline import matplotlib.pyplot as plt plt.hist(high_var_array) plt.show() plt.hist(low_var_array) plt.show() # ### Reshaping & transposing a2 a2.shape a3 a3.shape a2 * a3 a2.reshape(2, 3, 1) a2_reshape = a2.reshape(2, 3, 1) a2_reshape a2_reshape * a3 a2 # Transpose a2.shape # Transpose = switches the axis' a2.T a2.T.shape a3 a3.shape a3.T a3.T.shape # ## Dot product # + np.random.seed(0) mat1 = np.random.randint(10, size = (5, 3)) mat2 = np.random.randint(10, size = (5, 3)) mat1 # - mat2 mat1.shape, mat2.shape # Element-wise multiplication (Hadamard product) mat1 * mat2 # Dot product np.dot(mat1, mat2) # Transpose mat1 mat1.T mat1.shape, mat2.T.shape mat3 = np.dot(mat1, mat2.T) mat3 mat3.shape	4,169
/notebooks/utkrisht44sharma/a-study-of-personality-using-nlp-techniques.ipynb	dbf9677a0f13ae9344becfe424e3bdc083694a29	[]	no_license	Sayem-Mohammad-Imtiaz/kaggle-notebooks	https://github.com/Sayem-Mohammad-Imtiaz/kaggle-notebooks	5	6	null	null	null	null	Jupyter Notebook	false	false	.py	6,976	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] _uuid="b6ae14c08e1660d5be189ead001c6b2c7d7162e4" # A study of personality using NLP techniques # # I am trying to find out whether personality is a factor determining how a person writes. I am using the Word2Vec model by [Mikolov et. al. ](https://github.com/svn2github/word2vec) to calculate word similarites by getting the cosine similarites ( dot product ) of the word vectors trained by the Word2Vec model. The [four temperament model by David Kiersey](https://keirsey.com/temperament-overview/) takes temperamance as a factor and divides people into 4 groups (Artisan , Guardian , Idealist, Rational) . In the given article he draws parallel between Jungian types and his temperaments as follows (Artisan -SP , Guardian - SJ , Idealist - NF , Rational -NT). I have performed an one way anova with four groups with the following results. # + _uuid="6fa1b3f88439be97d977946cd2f7fb6c5936965a" import pandas as pd import numpy as np from matplotlib import pyplot as plt # + _uuid="6d5eb033a91ba5a3b8f974e74cfd02bffc59717a" import pandas as pd text=pd.read_csv("../input/mbti_1.csv") # + _uuid="ee0d26642c00dfa6546bddf4eabdb8e9a190b2b3" posts=text.values.tolist() # + _uuid="c0b8ab63ff7222eee3d31d177410a92005a83db6" mbti_list=['ENFJ','ENFP','ENTJ','ENTP','ESFJ','ESFP','ESTJ','ESTP','INFJ','INFP','INTJ','INTP','ISFJ','ISFP','ISFP','ISTP'] values = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] index = np.arange(len(mbti_list)) # + _uuid="01c94fead6ea9b6ac446ea987bcaf61b2dacb6a4" for post in posts: for i in range(0,len(mbti_list)): if post[0] == mbti_list[i]: values[i]=values[i]+1 # + _uuid="a1d4eb272fafeeeda181bc8cb012ed656cb65fda" plt.bar(index,values) plt.xlabel('Personality Type',fontsize=3) plt.ylabel('No of persons',fontsize=5) plt.xticks(index,mbti_list,fontsize=8,rotation=35) plt.title('Distribution of types among Dataset(1 person=50 tweets)') plt.show() # + [markdown] _uuid="5f29b1169a17366574fc19675078e4506128855d" # This plot shows the number of people present for each type present in the dataset. # + [markdown] _uuid="db0e4ea16e37a7532f3805ebf26179b6f037b6a7" # This function performs the various pre-processing tasks and trains the Word2Vec model and then saves it in a binary file. # + _uuid="20b49a3ccacaa71945b329da86aedb22d1160fbf" from nltk.tokenize import word_tokenize from gensim.models import Word2Vec import re def train_w2v_using_key(temp):# I'm too lazy to learn regex in python perlist=list() if temp=="SJ": for i in posts: if i[0]=='ISFJ' or i[0]=='ISTJ' or i[0]=='ESFJ' or i[0]=='ESTJ': perlist.append(i[1]) if temp == 'SP': for i in posts: if i[0]=='ISFP' or i[0]=='ISTP' or i[0]=='ESFP' or i[0]=='ESTP': perlist.append(i[1]) else: for i in posts: if temp in i[0]: perlist.append(i[1]) for i in range(0,len(perlist)): # using some code https://www.kaggle.com/prnvk05/rnn-mbti-predictor for filtering out links and numbers from the text tempstr = ''.join(str(e) for e in perlist[i]) post=tempstr.lower() post=post.replace('\|\|\|',"") post = re.sub(r'''(?i)\b((?:https?://\|www\d{0,3}[.]\|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+\|$([^\s()<>]+\|(\([^\s()<>]+$))\))+(?:$([^\s()<>]+\|(\([^\s()<>]+$))\)\|[^\s`!()\[\]{};:'".,<>?«»“”‘’]))''', '', post, flags=re.MULTILINE) puncs1=['@','#','$','%','^','&','','(',')','-','_','+','=','{','}','[',']','\|','\\','"',"'",';',':','<','>','/'] for punc in puncs1: post=post.replace(punc,'') puncs2=[',','.','?','!','\n'] for punc in puncs2: post=post.replace(punc,' ') post=re.sub( '\s+', ' ', post ).strip() perlist[i]=post word_tokens=[] for i in range(0,len(perlist)): word_tokens.append(word_tokenize(perlist[i])) model = Word2Vec(word_tokens, min_count=1) model.save(temp+".bin") # + _uuid="f3c621cad5ff87da5496d9a64fa5f65365896516" train_w2v_using_key("NT") # + _uuid="aae641fa80754da63c473b847e2c6db9b7597f96" train_w2v_using_key("NF") # + _uuid="fcccd8e77a36791f8d6306b9ed89e528d588580e" train_w2v_using_key("SP") # + _uuid="fcef7b7a052d69aa1063d5824f37b732bbe2f9e5" train_w2v_using_key("SJ") # + [markdown] _uuid="ca162b05e3a4ce2e5afd0882f1cadedd742a7def" # Similarities* # # model.wv.similarity('word1','word2') returns the cosine similarity of the vectors of the words word1 and word2. # The higher the cosine similarity between the more similar the words. # + _uuid="a49c01a9abfe8b6a55eb5f33147f7f26bf713508" model=Word2Vec.load("NT.bin") model.wv.similarity("defend","justify") # + [markdown] _uuid="2b64516eda5f5e7c98122b686dc8ea403bb5f863" # The following shows that temperamannce is a factor that determines writing as the F-value obtained is larger than the critical value with the p-value being much smaller than the significance level for the hypothesis test. # ![](https://i.imgur.com/iJtapj3.jpg) #	5,294
/tutorials/3_03_Example_qixiang_topop_output.ipynb	041948f2a6dd784f03bb8323bca991c2795e44eb	[ "MIT" ]	permissive	NengLu/UWG_QiXiang	https://github.com/NengLu/UWG_QiXiang	3	0	null	null	null	null	Jupyter Notebook	false	false	.py	678,807	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: "py37\u3000" # language: python # name: py37 # --- # # A work example for qixiang.TopoGrid, qixiang.Flow, qixiang.Stream # # These codes (topogrid.py, flow.py, stream.py) are written by reorganizing the codes (grid.py, flow.py, network.py) from repo [geolovic/topopy](https://github.com/geolovic/topopy) by [José Vicente Pérez Peña](https://scholar.google.es/citations?user=by5rTEUAAAAJ&hl=es) from Universidad de Granada, Granada, Spain, and adding some functions which function like klargestconncomps.m and trunk.m from TopoToolbox matlab codes by Wolfgang Schwanghart. # # Cite: # # Schwanghart, W., Scherler, D., 2014. Short Communication: TopoToolbox 2 - MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surf. Dyn. 2, 1–7. https://doi.org/10.5194/esurf-2-1-2014 # # + import sys sys.path.append("..") from qixiang import TopoGrid from qixiang import Flow from qixiang import Stream # + import numpy as np import matplotlib.pyplot as plt from matplotlib import cm # %matplotlib inline # - # # 1. TopoGrid # # Class for holding and manipulating gridded data. # # This class is reorganized the Class PRaster(), Grid(), DEM() in grid.py from repo [geolovic/topopy](https://github.com/geolovic/topopy) input_file = "../data/dem/TRR_Earth2014.TBI2014.1min.order10800.tif" topo = TopoGrid(input_file) topo.plot() # # 2. Flow # # Class that define a network object (topologically sorted giver-receiver cells). # # This class is reorganized the Class Flow() in flow.py from repo [geolovic/topopy](https://github.com/geolovic/topopy) # # The algoritm to created the topologically sorted network has been adapted to Python from FLOWobj.m by Wolfgang Schwanghart (version of 17. August, 2017) included in TopoToolbox matlab codes. # ### How the pixels are sorted in flow.py from repo [geolovic/topopy](https://github.com/geolovic/topopy) # # Sort the pixels and get _ix (giver) and _ixc (receiver) # # ```python # class Flow(PRaster): # # def __init__(self, dem="", auxtopo=False, filled=False, verbose=False, verb_func=print): # # self._ix, self._ixc = sort_pixels(dem, auxtopo=auxtopo, filled=filled, verbose=verbose, verb_func=verb_func) # ``` # # 1. Fill the sinks # # Algoritm: skimage.morphology.reconstruction # # # 2. Get the flats and sills # # Flats are defined as cells without downward neighboring cells. Sills are cells where flat regions spill over into lower terrain. # # Algoritm: identifyflats.m in TopoToolbox (ndimage.morphology.grey_erosion, ndimage.morphology.grey_dilation) # # # 3. Get presills # # presills are pixels immediately upstream to sill pixels. # # ```python # presills_pos = get_presills(dem_arr, flats, sills) # ``` # # Algoritm: from TopoToolbox # # # 4. Get the auxiliar topography for the flats areas # # ```python # topodiff = get_aux_topography(topodiff.astype(np.float32), flats.astype(np.int8)) # ``` # # 5. Get the weights inside the flat areas # # Calculate weights in the flats areas by doing a cost-distance analysis.It uses presill positions as seed locations, and an auxiliar topography as friction surface. # # ```python # weights = get_weights(flats, topodiff, presills_pos) # ``` # # Algoritm: from TopoToolbox (skimage.graph.MCP_Geometric) # # # 6. Sort pixels (givers) # # ```python # ix = sort_dem(dem_arr, weights) # ``` # # Algoritm: from TopoToolbox # # # 7. Get receivers # # ```python # ixc = get_receivers(ix, dem_arr, cellsize) # ``` # Algoritm: from TopoToolbox flow = Flow(topo, auxtopo=False, filled=False, verbose=True, verb_func=print) #flow = Flow(topo, auxtopo=False, filled=True, verbose=True, verb_func=print) # + flowcc = flow.get_flow_accumulation() extent = flowcc.get_extent() res_area = 2 xticks_area = np.arange(extent[0],extent[1]+res_area/2,res_area) yticks_area = np.arange(extent[2],extent[3]+res_area,res_area) # + #flow_arr = flowcc.read_array() if flowcc._nodata: mask = flowcc._array == flowcc._nodata flow_arr = flowcc._array.copy() flow_arr[mask] = flow_arr.min() #flow_arr = np.ma.array(flowcc._array, mask = mask) fig = plt.figure(figsize=(8, 6)) ax = plt.subplot(111) ax.set(xlabel='Longitude', ylabel='Latitude', yticks=yticks_area, xticks=xticks_area) ax.set_title('The flow path of the rivers in the TRR-topopy') #im = ax.imshow(flow_arr,extent=extent,cmap=cm.Blues,vmin=0, vmax=2000) im = ax.imshow(np.log10(flow_arr),extent=extent,cmap=cm.Blues,vmin=0, vmax=5) cbar= plt.colorbar(im,fraction=0.046, pad=0.04) cbar.set_label('Discharge (log10)') plt.savefig(('TRR_Flowpath_nofill.png'),dpi=300) plt.show() # - # # 3. Stream # # Class that define a stream network object, defined by applying a threshold to a flow accumulation raster derived from the Flow (Flow.get_flow_accumulation()). # # This class is based on the Class Network() in network.py from repo [geolovic/topopy](https://github.com/geolovic/topopy), and added some new funtions which can function as klargestconncomps.m and trunk.m from TopoToolbox matlab codes by Wolfgang Schwanghart. # # + # min_area = 0.0005 # threshold = int(flow._ncells * min_area) threshold = 500 stream = Stream(dem=topo, flow=flow, threshold=threshold, verbose=False, thetaref=0.45, npoints=5) #streams = stream.get_streams(asgrid=False) #streams_seg = stream.get_stream_segments(asgrid=False) #streams_or = stream.get_stream_order(kind="shreeve", asgrid=False) str_or0 = stream.get_stream_order(kind="strahler", asgrid=False) # - print(threshold) print(flow._ix.shape) print(stream._ix.shape) # ## Test get_klargestconncomps ccs_arr,ccs_id = stream.get_klargestconncomps(k=5,asgrid=False) #ccs_arr = ccs._array.copy() #extent = stream.get_extent() ccs_id # + data_img = ccs_arr fig = plt.figure(figsize=(8, 6)) ax = plt.subplot(111) ax.set(xlabel='Longitude', ylabel='Latitude', yticks=yticks_area, xticks=xticks_area) ax.set_title('The biggest basin of the TRR') ax.imshow(data_img,extent=extent,cmap=cm.tab20b) # - # ## Test get_trunk ccs_arr,ccs_id = stream.get_klargestconncomps(k=5,asgrid=False) ccs_id trunk_arr2 = stream.get_trunk(ccs_arr,ccs_id) stream.get_trunk_output(trunk_arr2,path='../data/data_rivers/qixiang/') # + data_img = trunk_arr2 fig = plt.figure(figsize=(8, 6)) ax = plt.subplot(111) ax.set(xlabel='Longitude', ylabel='Latitude', yticks=yticks_area, xticks=xticks_area) ax.set_title('The Basins of the TRR') ax.imshow(data_img,extent=extent,cmap=cm.tab20b) # + import cartopy import cartopy.crs as ccrs import cartopy.feature as cfeature # cartopy parameters rivers = cfeature.NaturalEarthFeature('physical', 'rivers_lake_centerlines', '50m', edgecolor='Blue', facecolor="none") coastline = cfeature.NaturalEarthFeature('physical', 'coastline', '50m', edgecolor=(0.0,0.0,0.0),facecolor="none") lakes = cfeature.NaturalEarthFeature('physical', 'lakes', '50m', edgecolor="blue", facecolor="blue") prj_base = ccrs.PlateCarree() # w = ccs_arr # flow_arr2 = flow_arr.copy() # flow_arr2[np.where(w==0)]=0 # data_img = np.flipud(flow_arr2) w = trunk_arr2 flow_arr2 = flow_arr.copy() flow_arr2[np.where(w==0)]=0 data_img2 = np.flipud(flow_arr2) fig = plt.figure(figsize=(8, 6)) ax = plt.subplot(111, projection=prj_base) ax.set_extent(extent) ax.set(xlabel='Longitude', ylabel='Latitude', yticks=yticks_area, xticks=xticks_area) ax.set_title('The flow path of the rivers in the TRR') #ax.imshow(data_img,extent=extent,cmap=cm.Blues,vmin=0, vmax=2000,transform=prj_base) ax.imshow(data_img2,extent=extent,cmap=cm.Blues,vmin=0, vmax=2000,transform=prj_base,alpha=0.5) ax.add_feature(coastline, linewidth=1.5, edgecolor="Black", zorder=5) ax.add_feature(rivers, linewidth=0.5, edgecolor="r", zorder=6) ax.add_feature(lakes, linewidth=0, edgecolor="Blue", facecolor="#4477FF", zorder=7, alpha=0.5) plt.show() #plt.savefig(('TRR_Flowpath.png'),dpi=300) # - fig = plt.figure(figsize=(8, 6)) ax = plt.subplot(111) ax = plt.axes(projection=prj_base) ax.set(xlabel='Longitude', ylabel='Latitude', yticks=yticks_area, xticks=xticks_area) ax.set_title('The flow path of the rivers in the TRR') labels = np.arange(1,len(ccs_id)+1) colors =['b','y','c','r','g'] for i in range(0,len(labels)): fname_load = '../data/data_rivers/qixiang/river'+str(labels[i])+'.txt' river = np.loadtxt(fname_load) ax.scatter(river[:,0],river[:,1],label=labels[i],color=colors[i],s =5) plt.legend(loc = 'lower right',prop = {'size':8}) ax.add_feature(coastline, linewidth=1.5, edgecolor="Black", zorder=5) ax.add_feature(rivers, linewidth=1.0, edgecolor="r", zorder=6) ax.add_feature(lakes, linewidth=0, edgecolor="Blue", facecolor="#4477FF", zorder=7, alpha=0.5)	9,111
/DSE210x/HW_9_Weather Data.ipynb	0d1fa3ca10663314d8f4d5f9f5d6ea5049f03d60	[]	no_license	FayeAlangi/UCSanDiegoX	https://github.com/FayeAlangi/UCSanDiegoX	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	106,458	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import numpy as np import scipy.stats import matplotlib.pyplot as plt # + # # This sets the size and properties of the plots when using matplotlib.pyplot # plt.style.use([{"figure.figsize":(30,5), # # "xtick.labelsize": "medium", # # "ytick.labelsize": "medium", # # "legend.fontsize": "large", # # "axes.labelsize": "large", # # "axes.titlesize": "large", # # "axes.spines.top": False, # # "axes.spines.right": False, # # "ytick.major.right":False, # # "xtick.major.top":False # },'seaborn-poster']) # - #Loading data data=pd.read_csv("temperature.csv") data.head() Detroit_data=data[['datetime','Detroit']].copy() Detroit_data.head() rows_before=Detroit_data.shape[0] rows_before Detroit_data=Detroit_data.dropna() rows_after=Detroit_data.shape[0] rows_after plt.hist(Detroit_data['Detroit'],bins=100,density=True) plt.show() scipy.stats.norm.cdf(Detroit_data['Detroit']) Detroit_data['Detroit'].describe() hist = np.histogram(Detroit_data['Detroit'], bins=100) #hist_dist = scipy.stats.rv_histogram.mean(hist) hist_dist=scipy.stats.rv_histogram(hist) hist_dist Detroit_data['Detroit'].plot.density(bw_method=0.1) Detroit_data['Detroit'].plot.kde() 0.50.7794+0.50.0228 (291-276)/6.5 (291-293)/6 0.50.9893+0.50.3707 0.68-0.4011	1,574
/python/modis_et/et_extract.ipynb	c1eb22defad5774f0640ec0bff7f35830c4c7a2a	[]	no_license	hydrosense-uva/codes	https://github.com/hydrosense-uva/codes	0	2	null	2023-04-17T14:20:15	2022-09-24T00:50:04	Python	Jupyter Notebook	false	false	.py	17,868	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/neerajthandayan/Tensorflow-2.0/blob/main/CNN_CIFAR.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="-RBrKGon-DXX" # importing Libraries import numpy as np import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, Input # + id="zeTEhrxl_Ki3" # Fetching Dataset data = tf.keras.datasets.cifar10 (J_train,k_train), (J_test,k_test) = data.load_data() J_train, J_test = J_train/255, J_test/255 k_train, k_test = k_train.flatten(), k_test.flatten() # + colab={"base_uri": "https://localhost:8080/"} id="6N8NYHPt_0hq" outputId="419cfc89-3246-491a-a38c-eed81fd76616" # Checking Shape of Data J_train.shape # + colab={"base_uri": "https://localhost:8080/"} id="oWZw2AfBALmd" outputId="0d8843bd-8256-4083-88f1-b7881eeba69b" # Finding number of classes print(f'Number of classes: {len(set(k_train))}') # + id="_BTnymS9AghG" # Constructing Model i = Input(shape=J_train[0].shape) x = Conv2D(32, (3,3), strides=2, activation='relu')(i) x = Conv2D(64, (3,3), strides=2, activation='relu')(x) x = Conv2D(128, (3,3), strides=2, activation='relu')(x) x = Flatten()(x) x = Dropout(0.5)(x) x = Dense(512, activation='relu')(x) x = Dropout(0.2)(x) x = Dense(1024, activation='relu')(x) x = Dropout(0.2)(x) x = Dense(10, activation='softmax')(x) clf = Model(i,x) # + colab={"base_uri": "https://localhost:8080/"} id="FnmgXW1gCZIL" outputId="0f736ec2-0819-4cc2-95eb-9926406ec973" # Compiling Data clf.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) result = clf.fit(J_train,k_train, validation_data=(J_test,k_test), epochs=15) # + colab={"base_uri": "https://localhost:8080/", "height": 282} id="wzj9yOvqD1I4" outputId="f0f1fa65-f375-405d-ee50-39d62267e473" # Checking Loss import matplotlib.pyplot as plt plt.plot(result.history['loss'], label='loss') plt.plot(result.history['val_loss'], label='val_loss') plt.legend() # + colab={"base_uri": "https://localhost:8080/", "height": 282} id="Z48xh5hAFz9-" outputId="60ebde19-311f-4b20-a225-782292f47adc" # Checking Accuracy plt.plot(result.history['accuracy'], label='Accuracy') plt.plot(result.history['val_accuracy'], label='val_accuracy') plt.legend() # + colab={"base_uri": "https://localhost:8080/", "height": 496} id="TjZsvpJuGZmO" outputId="7bae94cb-d7f0-4a48-ad88-fedc38df8762" # Plot confusion matrix from sklearn.metrics import confusion_matrix import itertools def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print("Normalized confusion matrix") else: print('Confusion matrix, without normalization') print(cm) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.show() p_test = clf.predict(J_test).argmax(axis=1) cm = confusion_matrix(k_test, p_test) plot_confusion_matrix(cm, list(range(10))) # + id="PvBgBfsiGeRE" # label mapping labels = '''airplane automobile bird cat deer dog frog horse ship truck'''.split() # + colab={"base_uri": "https://localhost:8080/", "height": 281} id="QK7bs4LSGg8g" outputId="5d4587a6-b09e-4bdb-c207-b870bb14af71" # Show some misclassified examples # TODO: add label names misclassified_idx = np.where(p_test != k_test)[0] i = np.random.choice(misclassified_idx) plt.imshow(J_test[i], cmap='gray') plt.title("True label: %s Predicted: %s" % (labels[k_test[i]], labels[p_test[i]])); random variables with mean of 0, stddev of 0.01 # b is initialized to 0 # shape of w depends on the dimension of X and Y so that Y = tf.matmul(X, w) # shape of b depends on Y w = tf.Variable(tf.random_normal(shape=[784, 10], stddev=0.01), name='weights') b = tf.Variable(tf.zeros([1, 10]), name="bias") # Step 4: build model # the model that returns the logits. # this logits will be later passed through softmax layer logits = tf.matmul(X, w) + b # Step 5: define loss function # use cross entropy of softmax of logits as the loss function entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y, name='loss') loss = tf.reduce_mean(entropy) # computes the mean over all the examples in the batch # Step 6: define training op # using gradient descent with learning rate of 0.01 to minimize loss optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) with tf.Session() as sess: # to visualize using TensorBoard writer = tf.summary.FileWriter('./logistic_reg', sess.graph) start_time = time.time() sess.run(tf.global_variables_initializer()) n_batches = int(mnist.train.num_examples/batch_size) for i in range(n_epochs): # train the model n_epochs times total_loss = 0 for _ in range(n_batches): X_batch, Y_batch = mnist.train.next_batch(batch_size) _, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch}) total_loss += loss_batch print ('Average loss epoch {0}: {1}'.format(i, total_loss/n_batches)) print ('Total time: {0} seconds'.format(time.time() - start_time)) print('Optimization Finished!') # should be around 0.35 after 25 epochs # test the model n_batches = int(mnist.test.num_examples/batch_size) total_correct_preds = 0 for i in range(n_batches): X_batch, Y_batch = mnist.test.next_batch(batch_size) _, loss_batch, logits_batch = sess.run([optimizer, loss, logits], feed_dict={X: X_batch, Y:Y_batch}) preds = tf.nn.softmax(logits_batch) correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_batch, 1)) accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32)) total_correct_preds += sess.run(accuracy) print ('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples)) writer.close() # - # Answer: # --- # Learning Rate: 0.1 # # Batch Size : 150 # # Total Time: 12.00 # # Accuracy : 0.915 ax, lat_roi_min]) dst_ds = None print(mos_file_name) # - # _______________________________________________________________________________________________________________________________________________________________________ # Step 5: Seperate the individual files corresponding to each month jan_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 1] feb_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 2] mar_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 3] apr_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 4] may_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 5] jun_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 6] jul_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 7] aug_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 8] sep_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 9] oct_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 10] nov_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 11] dec_files = [name for name in os.listdir('.') if name.endswith('.tif') and 'ctd' not in name and pd.to_datetime(name[date_start:date_end],format='%Y%j').month == 12] # _______________________________________________________________________________________________________________________________________________________________________ # Step 6: Create function to convert 8-day to monthly value def non_empty_month(): if len(jan_files) != 0: return jan_files if len(feb_files) != 0: return feb_files if len(mar_files) != 0: return mar_files if len(apr_files) != 0: return apr_files if len(may_files) != 0: return may_files if len(jun_files) != 0: return jun_files if len(jul_files) != 0: return jul_files if len(aug_files) != 0: return aug_files if len(sep_files) != 0: return sep_files if len(oct_files) != 0: return oct_files if len(nov_files) != 0: return nov_files if len(dec_files) != 0: return dec_files # + profile = rasterio.open(non_empty_month()[0]).profile raster_shape = rasterio.open(non_empty_month()[0]).shape def monthly(file_list): data = np.empty((raster_shape[0],raster_shape[1],1)) if len(file_list) > 0: for i in range(len(file_list)): file_open = rasterio.open(file_list[i]) file = file_open.read(1) file = np.reshape(file,(file.shape[0], file.shape[1], 1)) data = np.append(data,file, axis=2) data_new = np.delete(data, 0, axis=2) data_new[data_new > nodata] = np.nan data_mean = np.nanmean(data_new, axis=2) data_final = (data_mean/8)scale_factorpd.to_datetime(file_list[0][date_start:date_end],format='%Y%j').days_in_month with rasterio.open('MODIS_ET_'+ str(pd.to_datetime(file_list[0][date_start:date_end],format='%Y%j').year) + str(pd.to_datetime(file_list[0][date_start:date_end],format='%Y%j').month).zfill(2) + '.tif', 'w', **profile) as dst: dst.write(data_final.astype(rasterio.int16), 1) # - # _______________________________________________________________________________________________________________________________________________________________________ # Step 7: Use the function to create monthly files for the whole time series monthly(jan_files) monthly(feb_files) monthly(mar_files) monthly(apr_files) monthly(may_files) monthly(jun_files) monthly(jul_files) monthly(aug_files) monthly(sep_files) monthly(oct_files) monthly(nov_files) monthly(dec_files)	11,693
/NLP_Job_Descriptions.ipynb	473720e8c41b8e96b41a763401f95122180a9c7f	[]	no_license	larzeitlin/JupyterNotebooks	https://github.com/larzeitlin/JupyterNotebooks	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	595,997	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Applying various NLP processes to Reed.com job description dataset # ...to identify categorized key terms # # Note: various cells have been commented out because they are time consuming. To rerun these processes simply uncomment them. Their work should be saved to disk, so they can be safely recommented to avoid repeating work import pandas as pd import spacy from spacy.matcher import Matcher from pprint import pprint import math import string import warnings from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS import re import gensim from gensim.corpora import Dictionary from gensim.corpora import MmCorpus from gensim.utils import simple_preprocess import pyLDAvis import pyLDAvis.gensim import pickle warnings.filterwarnings('ignore') # ## First some preprocessing # # We want to have all the job descriptions lemmatized, with common n-gram terms identified. # # Load up the file containing job posting and create a list of all the job description contents... df = pd.read_csv("reed_uk.csv") raw_jds = df['job_description'].tolist() # %%capture # Lemmatization, bigram, trigram, stopword removal, etc. Uncomment to rerun ''' def jd_to_words(jds): for jd in jds: yield(gensim.utils.simple_preprocess(str(jd), deacc=True)) data_words = list(jd_to_words(raw_jds)) bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) bigram.save("bg_model") trigram = gensim.models.Phrases(bigram[data_words], threshold=100) trigram.save("tg_model") ''' bigram = gensim.models.phrases.Phrases.load("bg_model") trigram = gensim.models.phrases.Phrases.load("tg_model") bigram_mod = gensim.models.phrases.Phraser(bigram) trigram_mod = gensim.models.phrases.Phraser(trigram) # + def remove_stopwords(text): return([word for word in simple_preprocess(str(text)) if word not in ENGLISH_STOP_WORDS]) def lemmatization(text, allowed_postags): doc = nlp_1(" ".join(text)) text_out = [token.lemma_ for token in doc if token.pos_ in allowed_postags] text_out = " ".join(text_out) return(text_out) # - # %%capture ''' nlp_1 = spacy.load('en', disable=['parser', 'ner']) df['lemmatized'] = '' total_rows = df.shape[0] for index, row in df.iterrows(): jd = row['job_description'].encode('ascii', errors='ignore').decode() jd_no_stops = remove_stopwords(jd) jd_tg = trigram_mod[bigram_mod[jd_no_stops]] lemmatized = lemmatization(jd_tg, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']) df.at[index, "lemmatized"] = lemmatized df.to_csv("reed_jobs_jd_lemmatized.csv") ''' df = pd.read_csv("reed_jobs_jd_lemmatized.csv") # ## LDA Modeling # # Now let's build a LDA model to identify parts of job descriptions that do not pertain to relevent skills information. Then we can then safely ignore these sentences. # + # %%capture # uncomment this to rerun ''' jds_list_lemmatized = df['lemmatized'].tolist() split_jds = [jd.split() for jd in jds_list_lemmatized] gensim_dict = Dictionary(split_jds) gensim_dict.save("jd_gensim.dict") corpus = [gensim_dict.doc2bow(text) for text in split_jds] MmCorpus.serialize("mmcorpus.mm", corpus) ''' # + # load up the "here's some I made earlier" components gensim_dict = Dictionary.load("jd_gensim.dict") corpus = MmCorpus("mmcorpus.mm") # + # %%capture # uncomment this to rerun ''' lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=gensim_dict, num_topics=25, random_state=100, update_every=1, chunksize=100, passes=10, alpha='auto', per_word_topics=True) lda_model.save("reed_jd_lda_1") ''' # - lda_model = gensim.models.ldamodel.LdaModel.load("reed_jd_lda_1") # We can load this up into LDAvis to have a look at the topics that have been identified. # We are mainly interested in the general job description ones here (so that we can exclude them) but we'll name as many as possible anyway # + # %%capture # uncomment this to rerun ''' LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, corpus, gensim_dict) with open("lda_vis_prep", 'wb') as f: pickle.dump(LDAvis_prepared, f) ''' # + with open("lda_vis_prep", 'rb') as f: LDAvis_prepared = pickle.load(f) pyLDAvis.display(LDAvis_prepared) pyLDAvis.display(LDAvis_prepared) # - topic_tags = { 1 : "exclude", 2 : "Customer Service", 3 : "exclude", 4 : "Project Managment", 5 : "managment", 6 : "exclude", 7 : "exclude", 8 : "sales", 9 : "Finance Administration", 10 : "exclude", 11 : "Health and Safety", 12 : "Manufacturing", 13 : "Recruitment", 14 : "Finance Regulatory", 15 : "Digital Marketing", 16 : "Technician", 17 : "Charity / Fundraising", 18 : "Graduate", 19 : "Hospitality", 20 : "Care", 21 : "Catering", 22 : "Transport", 23 : "Education", 24 : "Unknown Topic 1", 25 : "exclude"} bigram = gensim.models.phrases.Phrases.load("bg_model") trigram = gensim.models.phrases.Phrases.load("tg_model") bigram_mod = gensim.models.phrases.Phraser(bigram) trigram_mod = gensim.models.phrases.Phraser(trigram) nlp_1 = spacy.load('en', disable=['parser', 'ner']) def is_exclude_sent(sent): sent = sent.encode('ascii', errors='ignore').decode() sent_no_stops = remove_stopwords(sent) sent_tg = trigram_mod[bigram_mod[sent_no_stops]] lemmatized = lemmatization(sent_tg, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']) bow = gensim_dict.doc2bow(lemmatized.split()) vector = lda_model[bow][0] topics_df = pd.DataFrame(vector, columns=['topic', 'freq']) topics_df = topics_df.sort_values('freq', ascending=False) # topics_df = topics_df[topics_df["freq"] > min_topic_freq] topics_df['topic'] = topics_df['topic'].apply(lambda x : topic_tags[x]) topics_df = topics_df.set_index('topic') return(topics_df) is_exclude_sent("Key Accountabilities & Responsibilities In association with content editors, support the online delivery of marketing campaigns to drive interest and salesTake ownership of on-site journey, identifying design problems and devise elegant solutions, driving the implementation of these initiatives to continually increase conversion and overall revenue") # Now we'll make some helper functions to identify term frequency in a job description, inverse document frequency accross the corpus, and industry key terms (per industry) # ## Term Frequency # Calculates the ratio of the number of times a word appears to the total length of the job description: # # $f_{t, d}$ def TF(description): description = description.split() # splits on whitespace desc_length = len(description) lemmas_dict = {} for token in description: if token not in lemmas_dict: lemmas_dict[token] = 1 else: lemmas_dict[token] += 1 TF_dict = {k:v/desc_length for k, v in lemmas_dict.items()} return(TF_dict) # ## Inverse Document Frequency # Calculates the natural log of the ratio of the number of descriptions a word appears in to the total number of descriptions. # # # $\log \frac{N} {n_{t}}$ # # Uncomment the below 3 cells to re-process (lengthy) # %%capture ''' def IDF(desc_list): idf_dict = {} for ind, desc in enumerate(desc_list): desc = desc.split() lemmas = [] for token in desc: lemmas.append(token) lemmas = list(set(lemmas)) for l in lemmas: if l not in idf_dict: idf_dict[l] = 1 else: idf_dict[l] += 1 idf_dict = {k:math.log(len(desc_list) / v) for k, v in idf_dict.items()} return(idf_dict) idf_dict = IDF(df['lemmatized'].tolist()) print(len(idf_dict.keys())) IDF_df = pd.DataFrame.from_dict(idf_dict, orient="index", columns=['IDF']) IDF_df = IDF_df.sort_values(by=['IDF'], ascending=False) IDF_df.to_csv("IDF.csv") ''' IDF_df = pd.read_csv("IDF.csv", index_col=0) # ## TF-IDF # The product of the two functions above, roughly represents the importance of the word in that JD # # $f_{t, d} \times \log \frac{N} {n_{t}}$ def TFIDF(JD): JD_TF = TF(JD) # note this assumed a preprocessed job description TF_df = pd.DataFrame.from_dict(JD_TF, orient="index", columns=["TF"]) for index, row in TF_df.iterrows(): try: TF_df.at[index, 'TFIDF'] = row['TF'] * IDF_df.at[row.name, "IDF"] except KeyError as e: # print("KeyError, ", e) TF_df.at[index, 'IFIDF'] = 0.0 TF_df = TF_df.sort_values(by=['TFIDF'], ascending=False) return(TF_df) # ## Identify Key Terms by Industry # # We want to have a list of key terms that are especially relevent to each industry. # We can use the same principles as TF-IDF, but simply treat all the job descriptions from a specific industry as a single "document" for the TF component. # eg: Term Frequency accross industry * Inverse Document Frequency # # $f_{t, i} \times \log \frac{N} {n_{t}}$ # + def TFI(list_jds): lemmas_dict = {} total_words = 0 total_jds = len(list_jds) for ind, jd in enumerate(list_jds): desc = jd.split() # splits on whitespace desc_length = len(desc) total_words += desc_length for token in desc: if token not in lemmas_dict: lemmas_dict[token] = 1 else: lemmas_dict[token] += 1 TF_dict = {k:v/total_words for k, v in lemmas_dict.items()} return(TF_dict) def TFI_IDF(JDs_list): TFI_dict = TFI(JDs_list) TFI_df = pd.DataFrame.from_dict(TFI_dict, orient="index", columns=["TF"]) total_rows = TFI_df.shape[0] for index, row in TFI_df.iterrows(): try: TFI_df.at[index, 'TFI_IDF'] = row['TF'] * IDF_df.at[row.name, "IDF"] except KeyError as e: # print("KeyError, ", e) TFI_df.at[index, 'IFI_IDF'] = 0.0 # If something goes wrong here, we just give it a value of zero output_df = TFI_df.sort_values(by=['TFI_IDF'], ascending=False) return(output_df) # - # ## Make the industry word lists # # We'll run this process on each industry and save it to disk as a csv # + industries_list = list(set(df['category'].tolist())) for ind in industries_list: ind_df = df[df['category'] == ind] ind_JDs = ind_df['lemmatized'].tolist() tfi_idf_df = TFI_IDF(ind_JDs) tfi_idf_df.to_csv("key_terms_for_{}.csv".format(ind.replace(' ', '_'))) # - # ## Compound Terms # # We've found some n-gram terms with the preprocessing above. We can also consider noun chunks and pattern matching to itentify key terms indise the job descriptions # + nlp = spacy.load("en") def noun_chunks(JD, threshold): desc = nlp(JD) exclude_list = [] for ent in desc.ents: if ent.label_ in ['PERSON', 'GPE', 'DATE', 'TIME', 'MONEY', 'LOC']: exclude_list.append(ent.text) tfidf = TFIDF(JD) noun_chunks = [chunk for chunk in desc.noun_chunks if len(chunk) > 1] nc_df = pd.DataFrame() nc_df['chunks'] = pd.Series(noun_chunks) for index, row in nc_df.iterrows(): tfidf_score = 0 for i in row['chunks']: try: tfidf_score += tfidf.at[i.lemma_, 'TFIDF'] except KeyError: pass nc_df.at[index, 'tfidf_score'] = tfidf_score / len(row['chunks']) nc_df = nc_df.sort_values(by=['tfidf_score'], ascending=False) exc_tags = ['DT', 'PP', 'SYM', 'ADP', 'PRP', 'PRP$', 'POS' ] nc_df['chunks'] = nc_df['chunks'].apply(lambda x : '_'.join([w.text for w in x if (not w.is_punct and w.tag_ not in exc_tags)])) nc_df = nc_df[(nc_df['tfidf_score'] > threshold)] noun_chunks = set(list(nc_df['chunks'].tolist())) chunks = ' '.join(noun_chunks) return(chunks) # - threshold = 0.01 df['noun_chunks'] = '' num_rows = df.shape[0] for index, row in df.iterrows(): # print("working on row {} of {}".format(index, num_rows)) jd = row['job_description'].encode('ascii', errors='ignore').decode() df.at[index, 'noun_chunks'] = noun_chunks(jd, threshold) df.to_csv("job_lem_noun_chunks.csv") def pattern_match(JD, threshold): tfidf = TFIDF(JD) matcher = Matcher(nlp.vocab) pattern = [{'TAG' : 'NN'}, {'TAG' : 'VBG'}] matcher.add("noun_verb_pair", None, pattern) desc = nlp(JD) matches = matcher(desc) score_dict = {} for match_id, start, end in matches: string_id = nlp.vocab.strings[match_id] span = desc[start:end] word_list = nlp(span.text) score = 0 for token in word_list: try: score = tfidf.at[token.lemma_, 'TFIDF'] except KeyError: pass score = score / len(word_list) score_dict[span.text] = score return_list = [k for k, v in score_dict.items() if v > threshold] return(return_list) threshold = 0.01 df['pattern_matches'] = '' num_rows = df.shape[0] for index, row in df.iterrows(): # print("working on row {} of {}".format(index, num_rows)) jd = row['job_description'].encode('ascii', errors='ignore').decode() df.at[index, 'noun_chunks'] = pattern_match(jd, threshold) df.to_csv("job_lem_chunks_patterns.csv") # ## Filter out unwanted tags # such as peoples names, dates, times, etc def single_word_filter(JD, threshold): desc = nlp(JD) single_word_tfidf = TFIDF(JD) exclude_list = [] for ent in desc.ents: if ent.label_ in ['PERSON', 'GPE', 'DATE', 'TIME', 'MONEY', 'LOC']: exclude_list.append(ent.text) exclude_list = [x.split() for x in exclude_list] exclude_list = [item for sublist in exclude_list for item in sublist] tag_include = ['NN', 'NNS', 'VB', 'VBS', 'VBP', 'VBN', 'VBG'] token_list = [t for t in desc if t.text not in exclude_list] token_list = [t for t in desc if t.tag_ in tag_include] return_list = [] for t in token_list: try: tfidf = single_word_tfidf.at[t.lemma_, 'TFIDF'] except KeyError: continue if tfidf > threshold: return_list.append(t.lemma_) return(list(set(return_list))) def key_tags(JD): threshold = 0.01 JD = JD.encode('ascii', errors='ignore').decode() chunks = noun_chunks(JD, threshold) pattern_matches = pattern_match(JD, threshold) single_word_tags = single_word_filter(JD, threshold) tags = chunks + pattern_matches + single_word_tags tags = [t for t in tags if t.lower() not in global_exclude] tags = [t for t in tags if len(t) > 1] tags = [t for t in tags if len(t.split()) < 5] return(tags) # ## Let's test it out on some JDs... # # uncomment to run. # %%capture ''' for index, row in df.iterrows(): if row['category'] == 'hr jobs': print(row['job_title']) print(key_tags(row['job_description'])) ''' # Run cell below to add TFIDF tags to all the job postings # %%capture ''' for index, row in df.iterrows(): print(index) if index % 50 == 0: df.to_csv("jobs_jd_tags_temp.csv") tags = key_tags(row['job_description']) tags = [x.lower().replace(' ', '_') for x in tags] tags_str = ' '.join(tags) df.at[index, 'tags'] = tags_str df.to_csv("jobs_jd_tags.csv") ''' # ## Outcome # # This method has produced some interesting tags, with a lot that are clearly skills. However, there is also a lot of noise, and no clear way to filter it out. This might be improved by training a new Spacy classifier on a labeled dataset. [DataTurks](https://dataturks.com/) is a potential tool for this # ## Second Pass: Latent Dirichlet Allocation / Topic Modeling # # Using topic modeling to identify key areas of the corpus and the accociated topics might yield valuble results / help with the process of categorizing job descriptions. raw_jds = df['job_description'].tolist() def sent_to_words(sentences): for sentence in sentences: yield(gensim.utils.simple_preprocess(str(sentence), deacc=True)) data_words = list(sent_to_words(raw_jds)) # Rather than using the more complicated noun clustering approach to compound tags, we'll simply use statistical bigram and trigram identification. Any tags longer than three words will not be caught in this process bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) trigram = gensim.models.Phrases(bigram[data_words], threshold=100) bigram_mod = gensim.models.phrases.Phraser(bigram) trigram_mod = gensim.models.phrases.Phraser(trigram) # Some utility functions for processing the text # + def remove_stopwords(texts): return([[word for word in simple_preprocess(str(doc)) if word not in ENGLISH_STOP_WORDS] for doc in texts]) def make_bigrams(texts): return([bigram_mod[doc] for doc in texts]) def make_trigrams(texts): return([trigram_mod[bigram_mod[doc]] for doc in texts]) def lemmatization(texts, allowed_postags): texts_out = [] n_texts = len(texts) for en, sent in enumerate(texts): print("{} out of {} lemmatized".format(en, n_texts), end='\r') doc = nlp_1(" ".join(sent)) texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags]) return(texts_out) # - # NB: The below is a somewhat lengthy process, once run, comment it out and rely on the saved data that id loaded up two cells below data_words_nostops = remove_stopwords(data_words) # data_words_bigrams = make_bigrams(data_words_nostops) data_words_trigrams = make_trigrams(data_words_nostops) nlp_1 = spacy.load('en', disable=['parser', 'ner']) data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']) # Create a dictionary of terms from th job descriptions. Corpus is a matrix of the descriptions in which they appear gensim_dict = Dictionary(data_lemmatized) gensim_dict.save("jd_gensim.dict") corpus = [gensim_dict.doc2bow(text) for text in data_lemmatized] MmCorpus.serialize("mmcorpus.mm", corpus) gensim_dict = Dictionary.load("jd_gensim.dict") corpus = MmCorpus("mmcorpus.mm") mallet_path = "mallet-2.0.8" lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=gensim_dict, num_topics=25, random_state=100, update_every=1, chunksize=100, passes=10, alpha='auto', per_word_topics=True) lda_model.save("reed_jd_lda") # Using LDAVis we can have a look at the clusters which represent topics # + LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, corpus, gensim_dict) with open("lda_vis_prep", 'wb') as f: pickle.dump(LDAvis_prepared, f) # - with open("lda_vis_prep", 'rb') as f: LDAvis_prepared = pickle.load(f) pyLDAvis.display(LDAvis_prepared) pyLDAvis.display(LDAvis_prepared) def lda_desc(text, min_topic_freq=0.05): parsed_text = nlp(text) ug_parsed_text = [t.lemma_ for t in parsed_text if not t.is_punct] tg_parsed_text = trigram_mod[ug_parsed_text] tg_parsed_text = [t for t in tg_parsed_text if t not in ENGLISH_STOP_WORDS] text_bow = gensim_dict.doc2bow(tg_parsed_text) text_lda = lda_model[text_bow][0] topics_df = pd.DataFrame(text_lda, columns=['topic', 'freq']) topics_df = topics_df.sort_values('freq', ascending=False) topics_df = topics_df[topics_df["freq"] > min_topic_freq] topics_df['topic'] = topics_df['topic'].apply(lambda x : topic_names[x]) topics_df = topics_df.set_index('topic') return(topics_df) import textract text = textract.process("test.docx").decode() lda_desc(text)	20,503
/second_dataset_python_code/.ipynb_checkpoints/Prediction - EU Sales-checkpoint.ipynb	d353a4f615ce26b4c0d840152e22da6ff671342f	[]	no_license	akulisek/game_sales_information_retrieval	https://github.com/akulisek/game_sales_information_retrieval	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	105,870	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # + import pandas as pd from sklearn import linear_model from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import numpy as np import operator ITERATION_FAKTOR = 20 GROUP_OF_PREDICTION = 'EU_Sales' def get_whole_dataset(): column_names = ['Name', 'Series', 'Platform', 'Year_of_Release', 'Genre', 'Publisher', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales', 'Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Rating', 'Predecessors_Count', 'Predecessors_Global_Sales_Mean', 'Predecessors_JP_Sales_Mean', 'Predecessors_EU_Sales_Mean', 'Predecessors_NA_Sales_Mean', 'Predecessors_Other_Sales_Mean'] raw_data = pd.read_csv('../dataset/video_games_sales_with_predecessors2.csv', usecols = column_names, sep = ';') return raw_data show_data = get_whole_dataset() #show_data.head() show_data # - # ## Prepare data # Niektoré časti pre spracovanie ako spracovanie predchodcov boli vyhodnotené za pomoci ElasticSearch. #remove not used columns def remove_unused_columns(dataset): dataset = dataset.drop('Series', axis = 1) dataset = dataset.drop('Rating', axis = 1) dataset = dataset.drop('Publisher', axis = 1) return dataset # + # Years in dataset # min(prepared_data.Year_of_Release) # 1985 # max(prepared_data.Year_of_Release) # 2016 genres = ['Sports', 'Platform', 'Racing', 'Role-Playing', 'Puzzle', 'Misc','Shooter', 'Simulation', 'Action', 'Fighting', 'Adventure','Strategy'] platforms = ['Wii', 'DS', 'X360', 'PS3', 'PS2', 'PS4', '3DS','PS', 'X', 'PC', 'PSP', 'WiiU', 'GC', 'GBA', 'XOne', 'PSV', 'DC'] def get_decade(row): if row['Year_of_Release'] <= 1990: return 1980 elif row['Year_of_Release'] <= 2000: return 1990 elif row['Year_of_Release'] <= 2010: return 2000 else: return 2010 def label_genres(row, genre): if(row['Genre'] == genre): return 1 else: return 0 def label_platforms(row, platform): if(row['Platform'] == platform): return 1 else: return 0 def set_means(row, means): return means[row['Genre']] def calculate_means(data, group): # get mean of sales for each genre in group(NA,EU,...) means = {} for genre in genres: genre_only = data[data['Genre'] == genre] means[genre] = genre_only[group].mean() return means def mean_sales_of_genre_for_group(data): means = calculate_means(data,'Global_Sales') data['Global_Mean_Sale_For_Genre'] = data.apply(lambda row: set_means(row,means), axis=1) means = calculate_means(data,'NA_Sales') data['NA_Mean_Sale_For_Genre'] = data.apply(lambda row: set_means(row,means), axis=1) means = calculate_means(data,'EU_Sales') data['EU_Mean_Sale_For_Genre'] = data.apply(lambda row: set_means(row,means), axis=1) means = calculate_means(data,'JP_Sales') data['JP_Mean_Sale_For_Genre'] = data.apply(lambda row: set_means(row,means), axis=1) means = calculate_means(data,'Other_Sales') data['Other_Mean_Sale_For_Genre'] = data.apply(lambda row: set_means(row,means), axis=1) def get_filtered_data(): data = get_whole_dataset() data = remove_unused_columns(data) # remove data with no user count with score data = data[data.User_Count != 0] # remove data with no critic count with score data = data[data.Critic_Count != 0] # remove all null columns data = data.dropna() # add Decade column data = data.copy() data['Decade'] = data.apply(get_decade,axis=1) # add count of genre for each part #add 0/1 to for genre in genres: data[genre] = data.apply(lambda row: label_genres(row,genre), axis=1) # remove genre column mean_sales_of_genre_for_group(data) data = data.drop('Genre', axis = 1) #add 0/1 to for platform in platforms: data[platform] = data.apply(lambda row: label_platforms(row,platform), axis=1) # remove platform column data = data.drop('Platform', axis = 1) # remove name column data = data.drop('Name', axis = 1) return data # - # ## Linear Regression - Global Sales # + def data_for_global(): data = get_filtered_data() data = data.drop('NA_Sales', axis = 1) # data = data.drop('EU_Sales', axis = 1) data = data.drop('JP_Sales', axis = 1) data = data.drop('Other_Sales', axis = 1) data = data.drop('Global_Sales', axis = 1) data = data.drop('NA_Mean_Sale_For_Genre', axis = 1) # data = data.drop('EU_Mean_Sale_For_Genre', axis = 1) data = data.drop('JP_Mean_Sale_For_Genre', axis = 1) data = data.drop('Other_Mean_Sale_For_Genre', axis = 1) data = data.drop('Global_Mean_Sale_For_Genre', axis = 1) data = data.drop('Predecessors_JP_Sales_Mean', axis = 1) # data = data.drop('Predecessors_EU_Sales_Mean', axis = 1) data = data.drop('Predecessors_NA_Sales_Mean', axis = 1) data = data.drop('Predecessors_Other_Sales_Mean', axis = 1) data = data.drop('Predecessors_Global_Sales_Mean', axis = 1) return data def print_model(coef, labels, rsq_error, mean_error, alpha = None): # View the R-Squared score alpha_str = '' if alpha is None else 'Alpha = ' + str(alpha) + ' ' print alpha_str + 'R-Squared score: ' + str(rsq_error) + '\n' df = pd.DataFrame(coef, index = labels, columns = ['Coefficient']) df = df[df.Coefficient != 0] df = df.sort_values(by = 'Coefficient', ascending = False) print df alpha_str = '\n' if alpha is None else '\nAlpha = ' + str(alpha) + ' ' print alpha_str + 'Mean squared error: ' + str(mean_error), print '\n__________________________________________________\n' # View the R-Squared score def get_rsq_error(model,X_test,Y_test): return model.score(X_test, Y_test) # View the mean square error score def get_mean_sq_error(model,X_test,Y_test): Y_pred = model.predict(X_test) return mean_squared_error(y_true = Y_test, y_pred = Y_pred) # + data = data_for_global() count_of_coef = (len(data.columns)-1) # -1, one column(y) will be deleted coef_array = [0] * count_of_coef rsq_error = 0 mean_error = 0 for _ in range(ITERATION_FAKTOR): # create train set 80% and train set 20% train_set, test_set = train_test_split(data, test_size = 0.2) # training set Y_train = train_set[GROUP_OF_PREDICTION] X_train = train_set.drop(GROUP_OF_PREDICTION, axis = 1) # test set Y_test = test_set[GROUP_OF_PREDICTION] X_test = test_set.drop(GROUP_OF_PREDICTION, axis = 1) lin_regresion = linear_model.LinearRegression() model = lin_regresion.fit(X_train, Y_train) coef_array = map(operator.add,coef_array,model.coef_) rsq_error += get_rsq_error(model,X_test,Y_test) mean_error += get_mean_sq_error(model,X_test,Y_test) coef_array = map(operator.truediv,coef_array,[ITERATION_FAKTOR]count_of_coef) rsq_error = rsq_error / ITERATION_FAKTOR mean_error = mean_error / ITERATION_FAKTOR print_model(coef_array, list(X_train), rsq_error, mean_error) # # Run the model on X_test and show the first five results # print list(model.predict(X_test)[0:5]) # # View the first five test Y values # print list(Y_test)[0:5] # - # ## Lasso - Global Sales # + data = data_for_global() for alpha in [.0001, .1, 10]: count_of_coef = (len(data.columns)-1) # -1, one column(y) will be deleted coef_array = [0] count_of_coef rsq_error = 0 mean_error = 0 for _ in range(ITERATION_FAKTOR): # create train set 80% and train set 20% train_set, test_set = train_test_split(data, test_size = 0.2) # training set Y_train = train_set[GROUP_OF_PREDICTION] X_train = train_set.drop(GROUP_OF_PREDICTION, axis = 1) # test set Y_test = test_set[GROUP_OF_PREDICTION] X_test = test_set.drop(GROUP_OF_PREDICTION, axis = 1) lasso = linear_model.Lasso(alpha = alpha) model = lasso.fit(X_train, Y_train) coef_array = map(operator.add,coef_array,model.coef_) rsq_error += get_rsq_error(model,X_test,Y_test) mean_error += get_mean_sq_error(model,X_test,Y_test) coef_array = map(operator.truediv,coef_array,[ITERATION_FAKTOR]count_of_coef) rsq_error = rsq_error / ITERATION_FAKTOR mean_error = mean_error / ITERATION_FAKTOR print_model(coef_array, list(X_train), rsq_error, mean_error, alpha) # - # ## Ridge - Global Sales # + data = data_for_global() count_of_coef = (len(data.columns)-1) # -1, one column(y) will be deleted coef_array = [0] count_of_coef rsq_error = 0 mean_error = 0 for _ in range(ITERATION_FAKTOR): # create train set 80% and train set 20% train_set, test_set = train_test_split(data, test_size = 0.2) # training set Y_train = train_set[GROUP_OF_PREDICTION] X_train = train_set.drop(GROUP_OF_PREDICTION, axis = 1) # test set Y_test = test_set[GROUP_OF_PREDICTION] X_test = test_set.drop(GROUP_OF_PREDICTION, axis = 1) ridge = linear_model.Ridge() model = ridge.fit(X_train, Y_train) coef_array = map(operator.add,coef_array,model.coef_) rsq_error += get_rsq_error(model,X_test,Y_test) mean_error += get_mean_sq_error(model,X_test,Y_test) coef_array = map(operator.truediv,coef_array,[ITERATION_FAKTOR]count_of_coef) rsq_error = rsq_error / ITERATION_FAKTOR mean_error = mean_error / ITERATION_FAKTOR print_model(coef_array, list(X_train), rsq_error, mean_error) # - # ## Elastic Net - Global Sales # + data = data_for_global() count_of_coef = (len(data.columns)-1) # -1, one column(y) will be deleted coef_array = [0] count_of_coef rsq_error = 0 mean_error = 0 for _ in range(ITERATION_FAKTOR): # create train set 80% and train set 20% train_set, test_set = train_test_split(data, test_size = 0.2) # training set Y_train = train_set[GROUP_OF_PREDICTION] X_train = train_set.drop(GROUP_OF_PREDICTION, axis = 1) # test set Y_test = test_set[GROUP_OF_PREDICTION] X_test = test_set.drop(GROUP_OF_PREDICTION, axis = 1) elasticNet = linear_model.ElasticNet() model = elasticNet.fit(X_train, Y_train) coef_array = map(operator.add,coef_array,model.coef_) rsq_error += get_rsq_error(model,X_test,Y_test) mean_error += get_mean_sq_error(model,X_test,Y_test) coef_array = map(operator.truediv,coef_array,[ITERATION_FAKTOR]*count_of_coef) rsq_error = rsq_error / ITERATION_FAKTOR mean_error = mean_error / ITERATION_FAKTOR print_model(coef_array, list(X_train), rsq_error, mean_error)	10,866
/machine-learning/sdca/SDCA.ipynb	9f1a0b125fa65be457d2468e6bfcfbeaaf6fde8f	[]	no_license	MichaelKarpe/ponts-paristech-projects	https://github.com/MichaelKarpe/ponts-paristech-projects	0	0	null	2022-12-08T06:19:06	2020-03-02T06:36:20	Jupyter Notebook	Jupyter Notebook	false	false	.py	133,415	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Stochastic Dual Coordinate Ascent # # #### Guillaume DESFORGES, Michaël KARPE, Matthieu ROUX # ###### Libraries importation # + import numpy as np import matplotlib.pyplot as plt from scipy.optimize import minimize_scalar, minimize # import bigfloat # from lightning.classification import SDCAClassifier # from lightning.regression import SDCARegressor from copy import deepcopy from malaptools import * import warnings warnings.filterwarnings('ignore') # - # ###### Global variables # + n = 500 d = 1 T = 500 # 500 lamb = 1 # X = np.random.rand(n,d) # Y = np.random.rand(n) # X = np.random.normal(0, 1, (n,d)) # Y = np.random.normal(0, 1, n) def gen_linear(a, b, eps, nbex): X = np.array([np.random.uniform(-5, 5) for i in range(nbex)]) Y = np.array([a * x + b + np.random.normal(0, np.sqrt(eps)) for x in X]) return X.reshape(-1, 1), Y.reshape(-1, 1) # X,Y = gen_linear(1,0,1,1000) # plt.scatter(X,Y) ratio = 0.8 n_train = n n_test = n // 10 * 2 # car gen_arti doit avoir un nb d'échantillons pair... X_train, y_train = gen_arti(data_type=0, sigma=0.5, nbex=n_train, epsilon=0.1) X_test, y_test = gen_arti(data_type=0, sigma=0.5, nbex=n_test, epsilon=0.1) plot_data(X_train, y_train) plt.show() # print(X_train) # - # ###### Loss function def phi(y, a, loss): if loss == "square_loss": return np.array([(a - y[i]) ** 2 for i in range(len(y))]).reshape(-1, 1) elif loss == "hinge_loss": return np.array([max(0, 1 - y[i] * a) for i in range(len(y))]).reshape(-1, 1) elif loss == "logistic_loss": return np.array([np.log(1 + np.exp(-y[i] * a)) for i in range(len(y))]).reshape( -1, 1 ) elif loss == "absolute_loss": return np.array([np.abs(a - y[i]) for i in range(len(y))]).reshape(-1, 1) elif loss == "smoothed_hinge_loss": return np.array([max(0, 1 - a) for i in range(len(y))]).reshape(-1, 1) # ###### Convex conjugate in theory def phi_star_theoric(y, u, loss): phi_0 = [] for i in range(len(y)): def phi_i(z): return phi(y, z, loss)[i] def f(z): return -(z * u - phi_i(z)) phi_0.append(-minimize_scalar(f).fun) return np.array(phi_0).reshape(-1, 1) # ###### Convex conjugate for classic losses with closed forms def phi_star(y, a, loss): if loss == "square_loss": return np.array([a * y[i] + a ** 2 / 4 for i in range(len(y))]).reshape(-1, 1) elif loss == "hinge_loss": return np.array([a * y[i] for i in range(len(y))]).reshape(-1, 1) elif loss == "logistic_loss": return np.array( [ a * y[i] * np.log(-a * y[i]) + (1 + a * y[i]) * np.log(1 + a * y[i]) for i in range(len(y)) ] ).reshape(-1, 1) elif loss == "absolute_loss": return np.array([a * y[i] for i in range(len(y))]).reshape(-1, 1) elif loss == "smoothed_hinge_loss": return np.array([a * y[i] + gamma * a ** 2 / 2 for i in range(len(y))]).reshape( -1, 1 ) # ###### Illustration of 'minimize_scalar' function: How does it work? # def f(x): # return (x - 2) * x * (x + 2)*2 # # X = np.linspace(-2,2,100) # plt.plot(X,[f(x) for x in X]) # plt.show() # # res = minimize_scalar(f) # # print(res) # print(res.x) # print(res.fun) # ###### Computation of objective functions def primal(y, x, w, loss): n = len(y) return ( sum([phi(y, np.dot(x[i].T, w), loss)[i] for i in range(n)]) / n + lamb np.linalg.norm(w) ** 2 / 2 ) def w(alpha, x): n = len(alpha) d = len(x[0]) # return np.vdot(alpha,x)/(lambn) # print("alpha x", [alpha[i]x[i] for i in range(n)]) return np.array(sum([[alpha[i] * x[i] for i in range(n)][j] for j in range(d)])) / ( lamb * n ) def dual(y, x, alpha, loss): n = len(y) return ( -sum([phi_star(y, -alpha[i], loss)[i] for i in range(n)]) / n - lamb * np.linalg.norm(w(alpha, x)) ** 2 / 2 ) def nb_iter(L_lipschitz, eps_p): return ( max(0, np.ceil(n * np.log(lamb * n / (2 * L_lipschitz ** 2)))) + n + 20 * L_lipschitz ** 2 / (lamb * eps_p) ) def duality_gap(y, x, w_hat, alpha_hat, loss): return primal(y, x, w_hat, loss) - dual(y, x, alpha_hat, loss) # ###### Closed forms for $\Delta \alpha_i$ # + gamma = 1 def incr_solution(y, a, loss, x, w): if loss == "square_loss": # print("x", x) # print("w", w) # print(np.dot(x.T,w)) return (y - np.dot(x.T, w) - a / 2) / (0.5 + np.linalg.norm(x) ** 2 / (lamb * n)) elif loss == "hinge_loss": return y * max(0, min(1, (1 - np.dot(x.T, w) * y) / (np.linalg.norm(x) ** 2 / (lamb * n)) + a * y)) - a elif loss == "absolute_loss": return max(-1, min(1, (y - np.dot(x.T, w) * y) / (np.linalg.norm(x) ** 2 / (lamb * n)) + a)) - a elif loss == "logistic_loss": return (y / (1 + np.exp(np.dot(x.T, w) * y)) - a) / max(1, 0.25 + np.linalg.norm(x) ** 2 / (lamb * n)) elif loss == "smoothed_hinge_loss": return y * max( 0, min(1, (1 - np.dot(x.T, w) * y - gamma * a * y) / (np.linalg.norm(x) ** 2 / (lamb * n) + gamma)+ a * y) ) - a # - # ###### Computation of SDCA def SDCA(y, loss, x, T_0, output_type, verbose=False): n = len(x) alpha = np.zeros(n) # alpha = np.random.rand(n) alpha_t = [0] * (T + 1) alpha_t[0] = alpha alpha_coefs = [[alpha[i]] for i in range(len(alpha))] w_t = [0] * (T + 1) w_t[0] = w(alpha, x) for t in range(1, T + 1): i = np.random.randint(n) def f(z): return ( phi_star(y, -(alpha_t[t - 1][i] + z), loss)[i] + lamb * n / 2 * np.linalg.norm(w_t[t - 1] + z * x[i] / (lamb * n)) ** 2 ) delta_i = incr_solution( y[i], alpha_t[t - 1][i], loss, x[i], w_t[t - 1] ) # minimize(f, 0, method='nelder-mead').x #minimize_scalar(f).x w_t[t] = w_t[t - 1] + delta_i * x[i] / (lamb * n) alpha_t[t] = deepcopy(alpha_t[t - 1]) alpha_t[t][i] += delta_i if verbose: for i in range(len(alpha)): alpha_coefs[i].append(alpha_t[t][i]) sum_alpha_coefs = [ sum([alpha_coefs[i][j] for i in range(len(alpha_coefs))]) for j in range(len(alpha_coefs[0])) ] if verbose: for i in range(len(alpha[:20])): plt.subplot(1, 2, 1) plt.plot(range(1, len(alpha_coefs[i]) + 1), alpha_coefs[i]) plt.title("Evolution of alpha coefs at each iteration") plt.xlabel("t") plt.ylabel("alpha") plt.subplot(1, 2, 2) plt.plot( range(1, len(sum_alpha_coefs)), [ abs(sum_alpha_coefs[i + 1] - sum_alpha_coefs[i]) for i in range(len(sum_alpha_coefs) - 1) ], label="Evolution of alpha", ) plt.title("Evolution of alpha coefs variation at each iteration") plt.xlabel("t") plt.ylabel("Variation") plt.legend() plt.subplots_adjust(bottom=0.2, right=2, top=1, wspace=0.3, hspace=0.3) plt.show() if output_type == "averaging": # return sum([alpha_t[i] for i in range(T_0,T)])/(T-T_0), sum([w_t[i] for i in range(T_0,T)])/(T-T_0) # return alpha_t[-1], w_t[-1] return alpha_t, w_t elif output_type == "random": # return alpha_t[np.random.randint(T-T_0)+T_0+1], w_t[np.random.randint(T-T_0)+T_0+1] # return alpha_t[-1], w_t[-1] return alpha_t, w_t # ###### List of losses and outputs # + losses = [ "square_loss", "hinge_loss", "absolute_loss", "logistic_loss", "smoothed_hinge_loss", ] outputs = ["averaging", "random"] chosen_loss = losses[2] chosen_output = outputs[0] # - # ###### Prediction and error # + def predict_y(w, X): # ones=np.array([1 for i in range(len(X))]).reshape(-1,1) # return np.dot(np.concatenate((ones,X), axis=1),w) return np.dot(X, w) def mse(yhat, y): n = len(y) return sum([(yhat[i] - y[i]) ** 2 for i in range(n)]) / n # - # ###### Execution of SDCA on data # + alpha_hat, w_hat = SDCA(y_train, chosen_loss, X_train, T // 2, chosen_output, True) alpha_hat, w_hat = alpha_hat[-1], w_hat[-1] y_hat = predict_y(w_hat, X_train) print(mse(y_hat, y_train)) y_train_predict = predict_y(w_hat, X_train) y_test_predict = predict_y(w_hat, X_test) # - # ###### Display results for gen_arti # + def predict(X): Y_pred = np.dot(X, w_hat) Y_pred[Y_pred > 0] = 1 Y_pred[Y_pred <= 0] = -1 return Y_pred plot_frontiere(X_train, predict, step=200) plot_data(X_train, y_train) plt.show() # - # ###### Plot train and test error def plot_mse_data(y, loss, x, output_type, label, verbose=False): MSE = [] for i in range(1, T // 5): MSEbis = [] # Boucle permettant "d'annuler" l'effet de l'aléatoire for j in range(5): alpha_hat, w_hat = SDCA(y, loss, x, T // 2, output_type, verbose) w_hat = w_hat[5 * i] y_hat = predict_y(w_hat, x) MSEbis.append(mse(y_hat, y)) MSE.append(np.mean(MSEbis)) plt.plot([5 * i for i in range(1, T // 5)], MSE, label=label) # print(MSE) plt.xlabel("Nombre d'itérations") plt.ylabel("Erreur moyenne des moindres carrés") plt.legend() plot_mse_data( y_train, chosen_loss, X_train, chosen_output, label="Train error", verbose=False ) plot_mse_data( y_test, chosen_loss, X_test, chosen_output, label="Test error", verbose=False ) plt.show() # ###### Display optimal primal and dual value and duality gap print("Optimal primal value :", primal(y_train, X_train, w_hat, chosen_loss)) print("Optimal dual value :", dual(y_train, X_train, alpha_hat, chosen_loss)) print( "Duality gap for optimal values :", duality_gap(y_train, X_train, w_hat, alpha_hat, chosen_loss), ) print("Minimum number of iterations :", nb_iter(1, 0.1)) CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuAABK2QEBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYYKELDSUG4WhgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIEDACnkAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoKECBKw0lJuFIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBCwQh5AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaKgAASsN5WZhCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgSskAcQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEGipAwEpDuVkYAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAEr5AEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBhgoQsNJQbhaGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQMAKeQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgoQIErDSUm4UhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIELBCHkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBoqAABKw3lZmEIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBKyQBxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQaKkDASkO5WRgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAASvkAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGGChCw0lBuFoYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBAwAp5AAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKChAgSsNJSbhSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQsEIeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGioAAErDeVmYQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIErJAHEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBoqQMBKQ7lZGAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAaAgQQQAABBBBAAAEEEEBgJQLlGiMlagxjEAIIIIAAAggggAACCCCAAAIIIIAAAggggAACywkQsLKcEL8jgAACCCCAAAIIIICAC8SDU6Lgleg9/ls9rmjcer8zHIHtKLCSfWc7uqzXOlPurJck80Hg1QlQTr46e5aMAAIIbJQA52gbJct8GyHAuUkjlFkGAghsJwECVrbT1mZdEUAAAQQQQAABBBBYRwGvZIxqGldYYxONvo7JYFYIIIDAkgKUO0vy8CMCCCCAQFxguYPGCs9547PkMwINEVgu70aJaJI8vNLkRsnmHYFmEdAuRP7d+K2B8cYYN8khYGNWronmulny72ryw2rGXcumIGBlLWpMgwACCCCAAAIIIIDANhQo6YorvEKpXKiEH1Z6MbbS8bYhL6uMAAIbJEC5s0GwzBYBBBDYigKVk9ytuHKs05YW2GR5l/OzLZ0bWTkEXlqAMuKlCZnBKxTYLPlX6VzN6cNqxl0tPwErqxVjfAQQQAABBBBAAAEEtqGAX2wlzEpmViqZefBK6JDcyCuWbWjNKiOAAAIIIIAAAggggAACCCCAAAIIIIAAAg0RWKZuN1E2SyXMNqoOmICVhmxlFoIAAggggAACCCCAwOYXKJTMRsam7fmzISsUy2aJhCUSyRXH4y9z7bP5gVgDBBBA4KUEovuwovelZhaVqNH7UuPyGwIIIIDA8gJR2Ru915siKnej93rjMRyBRgpE+TZ6r7fsKN9G7/XGYzgCjRZQ3l0u/ypNyrvk30ZvHZa3lECUb6P3pcaN8m70vtS4/IZAIwTKQcmraJRlXsly0bo7W23vrn7r7GxbZuzV/0zAyurNmAIBBBBAAAEEEEAAgW0loMsW/WXnSnb11xv2l798ZcWi6omSPrzoX5YhCa99uCxfxomfX0pgqUvshXlvqTFrJWHh1LXGYBgC6yMQz5vR54SVPQvqn2hYuLSqr+uThtU0A7BvrJf5Zp/PclkxUZ1312mFl1vu0osh/y7ts31+nc8J8RwVfC4notw7/5vGTwQF8ytEmk/1K0wEi24aAeXP+TwaJCvhB/QwJ8dSmrDwxCI2bKmP1fNdatzlfwtybpC2BIEHy4NtizHUYFo2K+ummPkVDnKeBizMg5S/80Zb+9PC7d4c6xrLoJUERenU+/zvPjSefeOfK+OVK58qs9vwD/Np3PBFbesFRPmiuRGCfLp0WqO8nCoX7MSRg/a7zz+1UydPWjq9vnmJgJXmziukDgEEEEAAAQQQQACBphDwC5SkKuyTpp5WypayhAUBKyuqrw+vY5a+DGqKVSURm1ogyGjV1T7e1JRIVLouLRWLVtazrcwsmVQvQWE1aNBxkH+P/1M9v/hvL/VZ7QUvNQMmXk7Ac8SmQY5X+CjRQc4rqbE0DBD0fOvd8AZNPOWy8vE6r+CKZxelN3pfvDXq/7J4XIZsXoGls0zZEt4jW8JKpaKV1RgVVueHH61mg+UKy8ell70SU3LpSpS2+jhBLkjY/M2lJUWkeCms8jeRSlqxVLRSqeANqqlEylLJ5CtoZIpviRXuJPFJ+LylBRaEViWCXB2VkR4M4DEi4XCPDdA/y5FohPUtJytzW9FF5HLp4/fNLOB5Icyrnhk1oJJBojVLWKkcnD9EoyaSOnNYPHJwXrFspo5m/BLvUSKj95eYFZPWEIi2YfReY5RXMmh+ey88d12YzvlvwfglK/n5bzqV9nNhJV11afMF8PwUjVmtaD2i98YsdfssJdqe0Xvzr3l0/lCd0ugw7Wuic4iyymOzUrFkJa9PS1VPEnzXBGvIXgSs1OZkKAIIIIAAAggggAACCIQCutaI/vQkIAWsWCJluuDOtLRYS0vLslZ+gbPsWIyAwMsJeD7zO6HjV8dBo7+GlIoFy83NWamUM90xrWCVjs7OWNCKxtWjruLTB2nyudQY/jIpZr94Gb2VT7t4a6582oaOGWTgcJH6olxXtlIiacVE0vLFomVnZkxBKqqoT7ekrTXToqezretrvsF2udlGC16+0TYac7k58vvmE4jKsSDHRt+C9dA35WHPo+Wy599iseABg2pU6ujosFQ6HQS0hLWawR3WQSXnwrlV2YQZf8lxqiZZ/DXKmdH74jEYsj0EPAeUFbASNCAlEjrZLZkCBgsJs0K5bMXZGSsXdZZQtnQ6Y+2ZNksn61TUN4QtyrfRe0MWykKaVaAcnDMoedE5q8rH4K9ss7NZKxTyViyXveG0vaMjuIZb2KFFVQtTVMJG7+uz8kGODfNt1Bq2PrNmLptMQIdyXXdVcph/10oEQ9Qwms8XLDubtWIhZ8lkwusfVAeRTqtpU/moVhmo67kNxKjk241cyAamv+lnrYKpkiuaK7W+7ee3+/ynIJlRqoP3hEJVbDY7a7ncnJ9TqKPiTGurdbS3R6V1Jb97EEz1DDdi7cm/G6Eam6e2/qKDa+z3JvtYuZkgyrVB+sK1sEKpaLl83ubyOUsoeFBnyKXy0gEra1xFAlbWCMdkCCCAAAIIIIAAAghsRwHdGa1I+mSqbJmWlPX3ddu+vbtiFMEV9oZWEMWWxkcE6gnEL7e94t7MJqembXDwmU0V82blknW0Z+z40X2Wacl4AEAwr6CCszpoxedRqdypt9RVDvfdJUjpKqdk9FUJbJ670MMSNKy4DPKGAlZyxbKNT07awMCkqYeg1nTGdvR02IH9e8MK+1WBLD3yauvX1HvG0nPk120g4LlVFZ7+IWh8ipduquh89PixTU1PWz6Xs2SibEcP77XOzi5vgKqUud6YWqf9aYGjdxWwYMjqv0Q5N3pf/RyYYusIqG3MSzPl47ChTOXvbKFo41MzNjj4xGaLBWtJt1h/T7ft3b3LOtraXjEAefcVb4CmWXysyT8IWAnCBb1I1g0Hjx49trGxccvlcmZWtAO7+23Xzn5P/4J2Yb+Ii5fe8c/x1a03PD5O7c/BIqK8G73XHpehW1zAD+VhHoiyQiUPJq1QLNn4+JQNDg7a5GTO+3jt6+6y/v4d1tXV4TgJ9YIVMVXOSTc4YKWyxMqSoxTwvi4CUfkSva/LTNdhJrHtveh6aT6tlaDBctJ7o3j6dNCeP39hhWLRUlay3Tu67PDhg7GAlSBpDQtY8cXF1mUdZJhFtcB8fqj+pdm+R1dtniM82UHeUA+DChqcmpmx4dFRez405Nd50eWeglbW+0XAynqLMj8EEEAAAQQQQAABBLawgFcI6cKlVLbOjjZ7/dQR++I3HwVrHG878rujggsdLoW3cIZoslWL8preo8tnvSvOpFAq28NHI/btufM2l5u1YnHO+vu67A9/97l1dnZaKq0qojDPRjOKrV+NQbFfX+5jlNaXmwtTVwts5DarXta6fQ8zQzztxYTZxHTJbt25Y48f3LZysWDdHZ322vFD9sVvP7OO9uV7uVq39NWZUTy9dUZh8DYQ8EZPBZxUlcGlhNnYeM7++Oe/2IOHD21ubsbaMmn74rcf2cED+zxgUHlIf74LqBFgiUwV/bRuZee6zWgbbOQtvIpRHoyvou6Enpgp24NHg/bnP41YfnrGOjsydvzwAfv0w/dtz+4d8dH5jMArE4jKRSUgKtL0rh5V8gWzf/nnr+3mdNZKc3kvXz94+7S9++7rPrKmDW+w9vTHPy+5QvGFLjniwh+XKt8rY8YDGSoD+bClBaL8FL4rH87Nle3Bg2f23XfnLTs5aWlL2pED++yds6ft8OEDft5Qq5HfZxHNb53RotkG79G3dV4Is/OSbMVl0Svwqmz5BeeswaMElRyVv/pTm/635y7a1NioTU3nLZ0ye/3EYfu7v/3cU52szGh+JXzQigrK+Wn4hMBaBaIsGLwHNx94/k3oHMLs8dNxu3ztmo2OjlluLhs+3jUeJrvWJS+ejoCVxSYMQQABBBBAAAEEEEAAgToCum5WV7wldZueKFs6WbaO1ugSp/ZE8V/jn2uPzVAE1i5QK395RZEq7FMJy6Q1hroxDYYmrGgt6ZK1ZRLWkgrCVfRLvVet+dcbl+EIrEWgVh7TgylyLQnLpMzS3rF00dKJkmWSZWtLm/+tZVlMg8B6Cijvev5VdEr4UrCg8q/eZzNpS4XnEDqPSCSK1pIqW2tL2KtVOE00dfQezSt6rzc8+p13BNZTQOcEuRaVsylLJxOWLJctWSpbJpmg/F1PaOb18gI1CkflX93pX2oxyygPJxLmD7EqFcNziDBUu8bJb41BC9JYY3ELfl/yy0o6vnupBSy5dH5sIoH4Zo4+x/Oens7WGpa/yrt6aFvSytaaTlhn63zPKpo2Pt1Gr6KWF/1t9LK26/yjHlKbff09WNszb5ADo3TrW5QnM8q4paIl1FNxMriG6wzr0HzSGitZb3iNURmEwLoIBHku+Fd5N3g4pllbpsVa9Ri2ZNIKCWVmBavo/GH9cykBK+uyKZkJAggggAACCCCAAALbS0CXJkl1uVvpdnf59V//y5nll8kYCCjfeX6NGlN1q1a8Bqlc9uCr6kvuqIIpEiT/RhK8b5TAknnMM2RQTR9U10djV+fUjUod80VghQJR1owac2Lf9VjBsgJaKo9XU3msMnjlVZ7K8bFZrjBRjIbA2gS8hFWGC+rnrZwo+58yYRD8WmO+ZNAaKAx6VQJeZnoeLlkpUTL9Z4miWaI035NVjTxbY9C6rsJGz39dE8vMNlxA+aE6Tywsf8NHtQVduXneVbEcvaqnjYZv1Hujl7dR69HM85Wx54EmTqTngzAzBGeyC3OG0q+/qOwNSuCSnz/Ex4x/buLVJWnbSEB5Un9e+5Aom3oC8us17/knzLHL9QKkzL/KzE3AyjbKZKwqAggggAACCCCAAALrJaBrj2QyaanU/J1N6zVv5oPAegvMX3CXgzYnNZD6fSG6hg4DWMKFatx619arvN5e0WpoWbw2TmAjttnGpXaZOZcTVvaG/mSlwV89XW2pdVyGgJ83gUBVhoy+qqxVvKAeKVhSe6nanvRfOEI03krKxJWMswmkSOImEFBeC/4UnhKEqETDPPlRxt0E60ISt5dAlE+j96Lpv4IVEwVLlYNCmOy7vfJEM69tPC/qs/Ktv/xxK0GwSjlRsnIiaQn1ihmbIPoYvUeTbvR7o5e30evTjPPfLMbxdOpzJf9WziGUd8tWUtBrpd+KefFo/Ph85n/lEwKNEVA+jOfB6LPXl8VydTA8+nV90xYPRFzfOTM3BBBAAAEEEEAAAQQQ2JIC/ixh76Wi7A1OW3IlWaktJaDL6eCSOqgOCpr4w4tsbzSNfg9Wu/rye3769WepXtb6L2H7znGr2Ebr4WFWHrTi9zpFrajbdwOz5k0lEOXT6kQtGK4iWEFXHngVPQoo4YGE0XQaf8E00Q+8I/AKBaJwlShkRb0FkVFf4QZh0csKLCpHw8fw+HWc95IZNZEuOytGQKDhAovyrzf8JyodtEXnCrXGa0RiX9VyG7FuLOPlBarzh5/+VoJXNH/K35dXZg4bLTBfzpa8R0w/99VC4xGDtRLxEtmbHlZqgTIMAQQQQAABBBBAAAEEFgnUuy6pviBfNCEDEGgCgei6uZxQZed8/yp+h3/0YyydjczXjVxWbBX52MwCUZ6MZY6gWV8Dkt7TinpbCf5r5hUhbdtOIMq70YqrC2nVbVba94NMrbyb9P8Wt/trFrGsH82JdwRejYDf3J+whMrcKNhKPbVV5/VXkzqWikAgEM+PYQHqZW/UNBoGC1aCtv0cAjwEmlcgyr9e7paTYRkcvS8Mdm3etSBl20ogdgIb5V+t//zn6MpN77Vf9YbXHpuhCGysQJB3lbGDkO2NXZoZASsbLcz8EUAAAQQQQAABBBDYQgJ6bun8K6q4nx/CJwSaWcB7qAgfBhRV2HsDVN0qo2ZeG9K2ZQXijU5hxWdQ8ipQJRlWF6mXlfB26S0LwYptOoGgPnNRsnXqEOTh6Bwi6GXFq+2jQbGpagyK/cpHBBonoLzof97YHyw3ClQhnzZuO7CkFQrEzx8q5W68/A17aAsekLnCmTIaAg0UCM97tcSg7FU7aRCkovfoc1QONzBlLAqBpQXi5e+iMaOzCeVh9VaxaAQfwHlFbReGvlqBhPJsmGnrZN11SyABK+tGyYwQQAABBBBAAAEEENj6AgsvUPRt4ZCtL8Aabm6BqBooqDSKvm3udSL120EgKm2Dx1JojYMh5OHtsPW3xjoGQYIL16X+/aULx+MbAq9awM92PWhbPbRFr/lP0RDeEWhugeD8V6EAlL/NvaW2beqqTmyDs109STB4HFDYydW25WHFN6+AnwdHvbRRAm/eDbkNU67yV6/gXHhjAQhY2Vhf5o4AAggggAACCCCAwKYXiOqN/Ppaa6Mbo6mj3/TbdbutQFBFH/wbZuPtRsD6bhYBZdOojA07UfGb+xNlK/tzVcL3MGhls6wW6dwGAtEJQ3xV43lYw+eLYc4l4k58bnqBcjkMGQw6CKoU002fcBK4fQRqlcGVU4oo2CoaKXrfPjys6eYS8GCVhFkpUfa/4FxYwSvhucTmWh1Su80EPP+G6xz17FpOqKfMMANH13qRC0VyJMF7kwkozwb5eT7T6px4I06ECVhpso1PchBAAAEEEEAAAQQQ2EwCXFdvpq21TdOq6+ooo5b1GKv5rxtxkb1NlVnt9RaI8myloUkLCKqKzKJueecrjdZ78cwPgTULxPJuNI8gx8bujtYP0XjxMjqagHcEmkjAS9oo8CpeJkd5uInSSlK2sYDyY3RaEObN6KtUgs9qdCLiahvnkk216sqz+tM5hJ9HeNaNX8htqtUhsdtaIHocW8ISiWTQvwrnENs6R2yulQ/PHTzRG5txtafwQgABBBBAAAEEEEAAAQQQQAABBBDYFAJRRVH0vikSTSIRQACBLSMQDwTYMivFimx+AU4LNv82ZA0WC1TyNSXvYhyGbAYBPVElkVCwSpiZo/fNkHjSiEADBQhYaSA2i0IAAQQQQAABBBBAAAEEEEAAAQTWJFCp7IwqPdc0FyZCAAEEEFiFgJqXvJGp0miq2/7LPNJqFYaMigACCLycgB7JFpbFLzcjpkYAAQQQaFIBAlaadMOQLAQQQAABBBBAAAEEEEAAAQQQQMAFvKE0ai2N3rFBAAEEEGiYQDl8FAV3RjeMnAUhgMD2FlCQSrkqQDChx1pRDm/vjMHaI4DAlhQgYGVLblZWCgEEEEAAAQQQQAABBBBAAAEEtpIAdfNbaWtuv3WJd+RfTpjfKb39FFjjzSwQz8ObeT1IOwIIILCpBKoLX+K2N9XmI7EIIIDASgUIWFmpFOMhgAACCCCAAAIIIIAAAggggAACCCCAwJoE/E7pNU3JRAgggAACCCCAAAIIIIAAAltVgICVrbplWS8EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKBJBQhYadINQ7IQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGtKkDAylbdsqwXAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQpAIErDTphiFZCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAVhUgYGWrblnWCwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaFIBAlaadMOQLAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYKsKELCyVbcs64UAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCDSpAAErTbphSBYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILBVBQhY2apblvVCAAEEEEAAAQQQQAABBBBAAIEtLJDYwuvGqiGAAAIIIIAAAggggAACCCCAwHYQIGBlO2xl1hEBBBBAAAEEEEAAAQQQQAABBLaQAMEqW2hjsioIIIAAAggggAACCCCAAAIIbFsBAla27aZnxRFAAAEEEEAAAQQQQAABBBBAYHMJKFCFYJXNtc1ILQIIIIAAAggggAACCCCAAAII1BMgYKWeDMMRQAABBBBAAAEEEEAAAQQQQACBphQgaKUpNwuJQgABBBBAAAEEEEAAAQQQQACBVQkQsLIqLkZGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQeFkBAlZeVpDpEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFYlQMDKqrgYGQEEEEAAAQQQQAABBBBAYFMJVD05pWxm+lvwWjRgwa98QQABBBBAAAEEEECg+QVi572xj0G6owHRe/OvDSlEAAEEtoTAfHUDBfCW2KCsxIYIpDdkrswUAQQQQAABBBBAAAEEEEAAgSYS8EAV1Q+FdUReaZSIDWiitJIUBBBAAAEEEEAAAQTWTyCxOGB7/WbOnBDYEAG/UkskzC/Zymbl8nyz/4YskJkisFaBsI4hmjyqZYgPns+98aHRFLwjgAA9rJAHEEAAAQQQQAABBBBAAAEEtrTAfOVQsJrlsI4oGB5VJ21pAlYOAQQQQAABBBBAYJsJVJ/l6ty3+rx4m5GwuptMIOHRKpss0SQXgZgA4SkxDD4isIQAAStL4PATAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0UKAScRVr8ifiqoEbgEUhgAACjRMgYKVx1iwJAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSWEVAPK0EnK2UeCbSMFT8jgAACm1mAgJXNvPVIOwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJbUKBcVrDKFlwxVgkBBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAidzceAAACAASURBVAgggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoCJAwEqFgg8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACjRAgYKURyiwDAQQQQAABBBBAAAEEEGg6gXLTpYgEIYCAmbFrkg0QQAABBBBAAAEEEEAAAQQQQGCbCKS3yXqymggggAACCCCAAAIIIIAAAhWBUtgqnjCzevcxRK3mGocXAgg0VEC7H7teQ8lZGAIIIIAAAghsJYHoWiZaJ06sIgneEUAAAQQQaDYBAlaabYuQHgQQQAABBBBAAAEEEEBgwwUUpKKglXrBKkoAlbobvhlYAAIIIIAAAggggAACCGyAANcyG4DKLBFAAAEEENgQgaVqJzdkgcwUAQQQQAABBBBAAAEEEECgGQS4HGyGrUAaEEAAAQQQQAABBBBAAAEEEEAAAQQQ2K4C9LCyXbc8640AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIbCWB+NOA6GhlK21Z1gUBBBBAYIsKcEvdFt2wrBYCCCCAAAIIIIAAAgisg0C8snMdZscsEEAAAQQQaHoBjn1Nv4lI4DYRYF9svg3NNmm+bVIrRQpSif5q/c4wBBBAYKUC8XI//nml0zMeAgisSICAlRUxMRICCCCAAAIIIIAAAghsR4FSsWqtl6qgWOq3qtlsiq9an+ivZFYuBn8+bFOsQJMlsip/lEtNlj6SgwAC20ugqkzaXivP2jaNQDwfxj9HCaw1LPpti7/rPKFYLBrnCw3e0MpzUb7Tu87XonO2slk5+q3ByWJxoUB8+6wRJdqEZTbmGgWZbEsKrMO+1RAXlceVndiCY2T0vQEJ8GNyA5fXgFViEQg0jQCPBGqaTUFCEEAAAQQQQAABBBBA4FULKCgjm81ZoViwcqlklihbe3u7tbSkLaG79FQ5sU26lc7nyjaXy1uxULBkKmHJZMJaWlKWybS86s20aZdfKpYsny9YLpfzdUin09bS0mKpVNISyW2SsTbt1iPhCLykQL3jR73hL7m45SYvlcpertc8plEcLcfH7+slEM9r8c/RfhEftl7L3AzzKZvNzc1ZLjfnARKJRMJSqZR1dnSYcfvpxm7BhJkC1mdnc5bP561cKvt5WkdHu6VaEsH1wMamgLkvJbDGMkHXddls1goKAiuXLan9qbOz5iFwqcXzGwJbVmCN+1ajPXR6MJfV8TFnpVLJEkmz1tY2a2tr3Zh6Gi2wbDYznbdcLu/1Qzoet7VmrKU1dkCOzlsaDcLyENhCAgSsbKGNyaoggAACCCCAAAIIIIDA2gV0t8zU9Kz98svPNjEx4Xe1ppIJO3nyhO0/sN96e3uWbiRQBXdJt15aJfjAg1zWnqRXN2XJ7OnTIXv0+KGNjgxbyYrW09Nthw8dtCNHDlu6JfXq0raJlzw+PmFPHj+2e/fveWV5V1e37d27144fP27t7W0bU8m2ib1IOgJbSkANAaXwOKHYx2TQ8KkbvF/NsSJhxVI5aKxT2spWP4BlS20IVqapBMrz+4QCY6PWYzUoK0hju77Gxsbt0aNH9uzZoAeuKLh1x44d9sYbb1hPT0/Fabv6bOR6FwtlGx8bt7t379nwyIj7ZzIZO3r0iB06dNC6ezo3cvHMeyUC3tNN0DqsxuqVvMbHJ+278+e9kVvH3/a2Njtx4oTt27vPOjrbVzILxkFg8wtUBVUUC+pONrgxpXLMbeJDr24uGhkZs4GBARv04+OspZJJO3nqpL355pt+k826b6Sy2exc3q7+es2ePh300xQFMB44sM8O7N9nvX09wSKb2G3dTZghAhskQMDKBsEyWwQQQAABBBBAAAEEENhcAqqvmZicsG/PnbMnTx5boVCwTDpt+cLfmColPGClziopUGV2dtbGJyY8aKU102pdXZ3Wmsn4XT91JnvFg1Vjpdfi2pVcrmwPBx7bhR9+tLv3blmxlLODBw/YZ599YgcP7idgJZRbzZsa30ZGRuzK1av21Vd/NfW2smv3bjtz5ozt2rXLWtJpS2e4RF+NKeMisJkECvmSTU9N2+TUpCdbd3brTz14vYqXyqSZ7IxlZ2ZsLj9nrZk26+ro9OMdPTi8ii2yPZc5MzNj09PTNjs753dHa59ob+8IerTbniS+7qOjI3bt2lW7cuWKTU5OWltbmx0+fMT27NljHR0dln5F5cZ22CT5XN6Ghobsp59/snt379nk5JQHNHzyycceXNzd3Vnr1Hk70LzadawEt5VtamrKZmayfnOBAr47O7tMQUU1g1fCBvqx0Qn7l3/5V8vOZr2Bu3dHr+VyBWtv6yRg5dVuWZb+CgRyc3k/9k5PT1kqlfZ6Cx171XNIM8eKFotmL14M26VLV/wYqbobBbv+ofh7e/31173npPXm1Pmyejy7ePGi/XLxoj8Xrm/HDnvvvbPW2pqx3h0Eka63OfPbvgKv5qp4+3qz5ggggAACCCCAAAIIINCkAqVy2Stufvnlot28ecMKhbx3vX702FE7deqk94iRUHDH4vgOy2bn7MHAI7tz5453H76zv99ef+2U7dzZb5nWZn6ETo2VMbNCoWSPHj/yyvqLl34ys6Kvz9Ejh9xBvdHUrBRu0m3bDMmS2ejoqN28ecu+/vpb0x1tBw4e8Ox09uw73nDtASv144iaYTVIAwIIrFFgdGTUHgw8tAcDD7wL8yNHjtjxY8ds9+6da5zjy02mR108G3xuDwYG7NnzZ7Z75y47cviIHT1yxNo6My83c6ZGYAUCc7N5e/LkmT148MAGnz61ffv2eQ8WOjZ2dnaYlcLHr9Q+VVnBEjbpKAkFUE/Zrdt37Ntz503BK2pIfO21F/bZb35je/fuMz1S0F/bzaYBm1SPmVDPKpcvX7HLV67Y6Nio9fb0WF9fn506dcpKpSOWpKPBBmyJxYvQ4z8UcHL/wYA9evzYZmambc+evXbs6FHbvXuX95rivZbFe10J95GJqSk7f/6CTamBPpm0Xbt22v59B+y1114zsz2LF8YQBLaiQMKskC94710DDx/a4OCgP/742LFjdvjQIevu6W7qtVYZMDI6Zrdu3bEffvzZhoeGrGxFO3HiuJX0LDeLFc5Vvcm8zIrpRqbrN27YV19/bWXddLJzp5+nnDxxYmGALcfkl2FmWgSMgBUyAQIIIIAAAggggAACCCDgAmUrJ8I6h0TC9KAEPeFnuVd2Jmd37z6wf/5ff7RLly9ZNjvtvZH84z/+vZ19523bs2d35RFBy82rYb8v8wyKcqJk+jP9WVlPighe+vDKHl8RJWJzvovcykn/K+s96IA57OFGjz6oKG/OFSTVCCBQV0CPmLj263X79ttz3gA6l8/ZW2fO2Bef/9b+5ne/s9a2xlbPFYtlG3j4yL766mv7/vsLNvjsuR3Yt88++fhjy6Qzduz4EUs0Nkl17TbFD/Him8aKFW2yUsFsfGzSvj//o+dDPfpmz97d9sEH79kXX3xub7yhO6VXNKstN5KfLiSSVrKklW3+XZ/1bMrwQV5bbr2ba4USfk1Q0mmbrgnU5UBCAVTJpu59oLkM1zk1CbNcPm9Png7a//cv/2LXrl2zmWzWDuw/YL/78gv78IP37eiRw0tun7Ki7XWx5+HiFNbrvIU25+yi43d1dqg3fHOuZSXVpULRRkZG7ceffrLvL1ywmzdveg9en336qX3+28/t7DvvWKqJH/0bHP+CeppyWe86UVBdhT5Xvaq3adXPK/+q+Ze9XkjX876csBuaSi2JBq7b8laesi03phwjSzy33OZdyQpx+bkSJcZBAAEEEEAAAQQQQACBLS3g18Z+URxUSEeNAeoCNvpcD0BdUg8+fWE3btyxe3cHbGp6wqanZ+zMmQd+x596WUknl7/0KpeK/jghPT86mVKjxNqv0tWbh3rwSKXCinXNKt6/b/xzjRUrlxWwUvQ7lspW8q5vo9HWnqpoDtvwPazcCirWgqCVoLee1MLtsg1pmnuVFbDlO09zJ5PUNbdA2SyXy/tz73VH6I1btyxXKHi35YcOHbQvCgWzUlrt0A17+R2qI6N2794Du379lo2Ojtvk+JTt27PPxsaDR9ulXuIY1LAVacYF6YRCr21zsFQ5Weu1RNnpj/Ywm56etYEHj+3atZs2MTFmY2Pjpm721a3/qZMnLJXevj39BI1vCSt5AHUQuKKu7YJz0q2SuZpzZ/FrAjOd/XqgSikZNoR60EpyG+3btfbrVztMPYONjo3Z9Zu37Nebtyyfy9no+IQdPXrUTp08uWTiPHDcG7XD/cmSpnPxGs3cS86HH7emgK6dS8Wi32SSTDbwhKzBnKof0CO1Bh49tNt37tide3dN66vH0548cdLeeO0160h1NvScdBGBCuElDnNRGa0gTg/kVOCKrrWDnXzR7F56gOqDwvl70jxwMSw5NECvJdIbjrGF3qKVfrkV11yKxaKVtPOVE15fltxWjlsoS6zjqixfa7qOC2NWCCCAAAIIIIAAAggggECzCcxfcgd3z0Tf9R599rsq69RE5PM5m8lO23R2xkplBZ0UbXZ21iYmJmwuNxerPNHcYlfhsa+z2VkbHR2zQrFgHe0d1tvbE3T3Ho4epSM2dV1GPdt5fHzcJicmra0tY91dnabnuydSS9yqrAWoJkYNI2pIUsBKuP5e+eM9z0SpqLtofogLiCu2weQoVf3nuh6TpBH0IWkKZuHVbAJbt8K62aS3enpUIatyPpvNevBKoVS0mekZbzRQ8IgXv41E8CCanM3OZm0uN+ulUi43Z3Nzs/44vKDSnzJpzZtkW9GtoZwMffToRT3eYzabtWKxYDqfyob7SbFULxBmzVtlc03ojWNK8nxzugKao//8/GLTn5Y14Y4SuXsDpc7YwlelB8aF53bRz7w3QEDXJ8Wi5XN5m5mattzsnD9eT5+zMzNWyOXrJ6KyIatG8QD+JsyHVcnk6wYKqOeeubyfj01PT1tba5t1dXd5ryPL3N+xgYnauFnrEcj5Qt7rKlRPoXNQnaPq/FR/Cgorl0qWaOKgneBqOghQ0fmzXj4sDCxZ7+0WzVt2QXWJettSuRGWHVEREr1v3OZrkjmvz4pqvxubmPB9T/tdb3ePdXS0WxNnvSbx39rJIGBla29f1g4BBBBAAAEEEEAAAQRWIFCpxwyvvyvfNa03ENS4o1IjJczaO9qtd0ev9fZ2Wyqd8gqe1rZW27lzp7W36aI7aswJJ6hKz9xc0R4/GbQff/zRg0wOHT5k7737nk+fbgkStJJqAc1dfyOjo3bl8hW7fv269fZ02bvvnrXjx45aR2dn/d48tICodkcz8fgVdX0bVAnp5+iupaCCpmol+LqkgG8bNXYkgjt2S3r8T/B/2BgSBK0sORN+RACBzSmQMGtpabGenh7/a2tttXypaH39fbazf6dXeuspBY186bEW3d3dnp7Ozg6bnZu1ru5OP5Z1dXWGFfGNTNEmX9ZKDtKbfBXXO/k65WhtzVh3d6d1dnVYdnbKGwh7erqtt6fHWlriVdY6im4z5PCcTG8L1lxfFgxY7y3D/OICzh0Fq+gHvyaIjxGce7NJFpps1Ld0MmUdbe3Wv2OHdXV02NzcnO3o6bH+HX3W2dGx/GK9x6IgRp8daXmu7TBGqVC24eERu3b1qt2+fdv27d9vZ86csSNHjlhLOm3JVHUhvLlV0um0dXR0WG9Pr+8zqqfQMD/29vZYe3v7fJ1AU6zqwuP/gvs7woLXqy7C6+qNSnJQI6J/Q574sYADwOrZS2Yvhkbs8pWr9uv163bowAF7792z3jtxJqMeaFc/S6bYGgLxs/+tsUasBQIIIIAAAggggAACCCCwBoGyggjCoI/oPaiWqDOz8EK6ra3NDh7Yb59+9ont6OuxmZlp27t3j739zjveva6CWGq+FLxQMpuanrH7Dx7a9xd+sNGRUXt74m07euSY9fbusPSCBpuac6kMVOrzhZI/duLS5ct24fsLtm/fHtu9e5ft27fXOrq6KuPW/BCsvlfEhB+9ssA/R7cvRRNSiRBJrOpdlqpoc9MwgMUDhbz2DdRVYTIyAptIoDWTtpMnT9qnn45bZ0+XFYpFO/3mG3bmrTOmAMdGV8yqAWbPnj327nvv+rKfvXhuh/YfsHfeecsfyeINNJGvCiyKp0iD93USSKXNent77e233/K7up89e+bnKm+/fcYOHTpkKY3Aq2rn047IztiIbBF/9FJwChw8MrR62X4+F147sGWqddb5e9msNZOxvXt222effGr9ff1eduzfv99Ov/mmH7v8OU61dpPonHtRkthqi0i204BycB3+8OEj++mnn+3S5Uv+SLr+/p12+PCRIDJhi2WRVCrlwSqnT5+2XD5ve/bstdbWVnvv3Xft8KHD1pLJ+GORmj0bVMpebZ/oy0Ym2nvfCsJW1DNqdANPdEPPRi56y827ZDaXK9mjR0/sl18u24UffrA3XjtpB/bvs8OHDpol6tSdbTkIVqiWAGf/tVQYhgACCCCAAAIIIIAAAttQIHpG/XxQgSqs45XWtVB0F/DefXvs008/tkOHDvjz1NXbyonjx627Ox4ksvgW+lyhaKNjI3b7zi27c++OTU8FwS56TIMeLWS29CWb6mf0p7oaBb/MzeXt8eMndufufbt3/4GVyyWbmJyyfEHzMtMjgv2m3XjlWzQTjRAN94offYn+fECt1WfYigUiy+g9NmEiyGk+RD/zWl4gyvjLj7nyMTZinitf+tYe81XYRsVWfJ+K0hG9N0o9aXbkyCFLppK2/8B+L7cPHNhvhw8etHR68bGhbrJWk+5a6x/OWM+I7+vr82CB/p39NjY+Znt27rJ9e/b4I+nCzh2CA8xqllk34Q3+YYl1b3BKmm9xr3J7xpedMFPvPqdPv+m9/YyOjlpf3w7bv3+f7d692xb2OhTfiZuPdKNTVMnO1Tf6bzWWeP5YL9TVzrPaVE/L1J//l/R/a82yerL1Sj7ziQmot7JMxnsm++STj+zo0cM2Oztn/f39fne+eowIGq6DR5zGpqz5sToWv+ZIm3VgpdDYrCvQoHQnzCanpuzxkyd249ZNu3f/vvX09tjU1KTv+JVzoQYlZ9FiahU28ZHWsp2TZh0dnXbq1OvW2dltr5183TKZjN98s2/fPkuoR5nlXtFyNd4KRl9ydtG8VjWfYORo0iXnX+/HaOKVLHdB7y0rmaDeQhkugWLJ/PGPDx48tFu3b9uDgQHr7e6y6emslUvRhlmBlUat3hy1hq1gVozSPAJL1342TzpJCQIIIIAAAggggAACCCDQYIEwgMWXWn01HCRFASB64k9Pd7v1dB+z48eP+LOgU8mktaR1983SSZ6dy9rQyAu7efuGjU+MWTKRtLKVfLpE2ONLdN2uBsbql67JS6WyJZMJD1iZzc7aw0eP7dmz55bLFaxY0p1AqmYPGkRrzMIrd4PnMYfRL+GFvu4civ68W5DqhfP9pQTEHLzK5ts63N7R0Pn3OjUvdQbPT7eaT1Fq4jmkagFes1/dWraaZYTj1lqUfqo3vM4idEdbdHdbnVFWPXgj5rnqRGzGCaJtF6U9no00LLwrUSXRoorFaJr1fg+X6c2MNdKz7OKiddK08c/LTlh/hL6dPdbd02PHjx+zVLrF0umUpZLBowmWPFbElu9ldbSI+HrFxvGfy0EQo+Zbc956nF17ix09csT0GDrl/Uw6ZTrO6Ljm20nzjOar9/jyojQ06XvUEOlJjqd7NesRHzf+eU3rHJtB7GPFN57G1c5f89Orah4eoFodC+X7RThq1fjhXFb+Vme5S84gvu4ls3RagVwHTMFbuVzeH53V0pLg5lohhtsq6pYt4l7Sd6kfq2cQ3/7Rb/FhS82r1m8rmYfGqbcMPYIyDAypO06t5UbDouXre2wZvh+s8mbtsh4b4wVn2CVeOQhX0TlHtJilViVKUvx9vU6h4vNs2s8RUmw7rEtadTxLJ6y9q83ePP26nTp50gqFvD/ORI808UsdX3aNBWt4lK5KYhYNqPyy9IdwOs+wWlat5a0saGbp5YRprjH7ynRKSq3fo7I+nvdrrW6taSszf4kP8WVt1DJeInnxSScnp0w9fA0ODvrjEUulkl+Ly3Vh4GR8qnX8HNuGC47b2oa60SS+DVez2Ggb1PBPK3h67x7bs2uPFQpFU71FOr2KY6/OMcP5J6P0ReeO0fKi9VoiHfPHudWtZ+W8Npq37ypasOouVoCk6eSrKarHj+a5aLgmCI8JK1jEdhxlpdfRhWLZpqazdv/BgPcMrPoqbYikNob+fBuEG2LRBqqS9Y04Pyw4j6gqF6vGmR97gz6FSfe567POxaNh1flKIy312wYlsZlnS8BKM28d0oYAAggggAACCCCAAAINFNAV5HxldLDgWsPmkxSvyNK1ZqZFd19WtxDNjx99KhZKppbKXD5nE5OTNvDwoU3PzFh3V7dXPOoxQpqf/vzaPZqw6l2pU7CK3kvlsmWzczYw8MifxZ1IpiyZSnsDgOqQ9PLr4aiXlXBYpULOFxbcSarAGQ9W8WCXyERLqfMKp63zK4MrAkF+qq5dVgWP/mpXWIQVN9X81d8ry1jLh1oziw2LMmKtWa9228dmG2TwcKbx4bWWUzUsUSuCq2qc1X5dNE+tW63XKtNaaxZNM0zZLgr+WW69Io9a49UaFq2k1z8uNUI0Yuy93rLqDY8m1e9aVL1lVicjGj+aPnqPj6ddUOVmrd+iYSt8T7eYdabaFleQr3B6HXOUFvWopZcCJhfU5UbrkzDzRgRt3xplfrQ4tfGlLBk0PGha1RlHFauhYzTuZnqPH5sXpFvrFBkt+CH8Uus3DdOr1m/hT9Vv8VG9oTo8lvt4lYwU5tXqiVf7PVqn+ELjy4vPLxy30s4a/221n+PrscS0dZel6bWfmllLMmEtLZngywrnu8Qit+BPC1GCs77VraaX89FxM8rTmkX0eeEiVjdzjV1v+vDUxn+uN45Pv8KGxlopC8tDrUqU36IyYEFjb9U+UmtWQVLmA7aDUJXAacF5WjjxUqtUPf8obdXDt+T31cCsFKAqr6YzKQ/8rDQGrnD7zi8u2pNWm9hw/PjBd9HGXe0851O14NNKZlNrvXVuoBlVmVXmvZL5VkZew4do/tHy1zCLjZ5ESdP50cTEpL0YGrbhkVEPnPQkJ5JWLJb83MgDeWvEH0WrFq3qmtIbzSScOJ6l/PgYBYPUm/lyC6/3exgXoA7+UmHEyYKyst7yNFxpToZt8Cp7o3XQsuLL02f9Fh9WPd/wHGAFVSeVKX12uqmmMiQo9/V14dDYCLU+RsuO/abObXVevWDmlfkGaJWbJcL1rv5ePW1s9lv7Y3QtucRaRtcuxaJZdnbOA8VGRkc9ozinb9ew8FJwUHwj15pv9PsK8lll8nC71Z23XxevMlitevlKV7ScaMHxtGpY9L36czT+Nn4nYGUbb3xWHQEEEEAAAQQQQAABBOoLRDfR+PVm/KKy/iQLrj0ro1VfxOoaNZm0QrFk4xOT9uTpoA0Nj9rsXN66ulRLkvLGSL+oDyvIomve6mTou/5UX6RglcFnz+358xc2OTXjlfZeneS19sGUlYZIJU6DqtMWXqSrUr7yt+iKu7JmfFiFQCUfRTcOOX+0BdUKHfwQbRK9q+JMPeXkczkPTMq0tlimNT1fCRJs1lWkYnWjqiI3ly/YbDbrjzJpaWnxbqO9Mi+a1UukQXm8VsVgNOu1vC9qM1jFTOLTat0L+YI/371QKFR6Mmpra7WWdIsl411mv4TBKpK3MaN6/WtiYVBDlAnjS/QMHA6I/17Wo8jmLJ/PW7FY9EC39vYO06PSaheI8Zku8Vmm1cvU6HWstb30SLS5uVnfX9S9eaY1Y5lMaxC4UW9RNeaneeXzJcvlcpZIJE0BhOmUAgCDmXhIYo3p6i2ierjKYeU1vXw2+hw3rZ531XdNr+znk8R+0/6k+aqnipxvk4Lfed6SabHWTKultEli40efPSnRcH3RAUXr6j+EqY9+D79u6rc661IsmD9eQtu9XCpZKpWy1rY2y2TSlW1fb71FpT/NWnzFQtlyuWC/0LxaPT+q7PDcU282axse305ls0JB+Tdnc3M5a21rtUymxVJVjV5e1q1tactPpTIhV/RyYXZuLnDMZKy1NWPpZNIfr+JQworSrs91tsvyC9ziY0Q7qq/m2pHiAZna/sVCwbeRGmVVXre2ti08rq0Ta7SJq2en3gH1OBftb+rRQPtbW1ub97Kz1mjNxQAAIABJREFU4ByjesLoezjj4DwlyG9aFzUetra2+rlSdb5feR6bfxholDWD9zCQJUpD+K6kqPzQPqfHeablmWkJzm9i40bxQrFBW/NjVTZVfivkS36ekC/k/XGlXr62ti04ti6LUTVfHz8aFr0vOZMg06jvyeA/lTsrmnDJuerYq/XT+YfOFzXL9vZ2PwdZh9kvLCfjKdHq6FwgDExVeZqTcy7v6dANDcExIBUUuS+/qvGl1/xcKMhhzssXjeDnYi0ZS6bDhYdprjlxgwdqn52cmrUnT5/59fNMds4fVVI23TSiv6SfG5frHLarOde0atUzqf7+MiZLzSv2m/Ko8lDltcyKLIgj0Hxi84rPw/f7QnBOqv2+VCx5fmhty1hLSzCmL3YFcQmV+YYfosAU7cexOdVOS/XE+q40V03qg2utSzR+bFU16fyyw3lpYL3pa6WhmYeFgaYy8jIsYVYsln3f1v6ttU+ldN7Q6sc6rbd6JVtq9Z0nGQTcz2Sz9uDBgD0fGrLs7IxfZ6m8EqBvltVaxhYcpbeaV3UqKqdn52Y9SEvXiC2Z+QmVX31fiG3v6nnU+x5Nu+D3+VlXBhfzurksa3O5Ob++1Pl5RnUrrS1bJ+9U1nbtHwhYWbsdUyKAAAIIIIAAAggggMA2EdB1c71rZ1XATGezNjo6ajMzM1YqFi3T2mo7+/utq7PTK98rlSIJNSaVTN0PPx8etms3rtu1X2/YxNSU5Qsly+WLNjY+aXfv3vdABTX86uK5WCp5JUAymfQKAj1juqe3zYdNzeRtdHTcHwV08dJlG3z2whsBVBmcnZ31rlZv3rpt42MTQU2Kavf1oCCvIEpY344+27WrP7hQjio+4wErumNmm2znxqxmqBm+uW5ZFTRJy+fLNjMz689Tn5qasqnpGZuZnrbZ7Jyl1A16R8Y6OlXBn7b2tlbr7u62vr7+lw8OCFc8m80Hy56c9IYXNb6oYkXPclfea29vs66uTm9U6ujosM6uTuvq7LJUKrmoYWYpSzWoTk/P2PDwkDdWlcsly7RkbNeuXdbV2WGpljq1w0vMVPPTXVqTeu58uWytLYFPb2+vqXJ0uZfSlJ3N2sT4uM3MTFt2JuvrrgY1r3wvqkEtaZ2dWv9Wr6RTg0RXV5cbtHe0r3vwzXJpXs/fPZhNje2FkjuMjclhxssKPeu+t7fHG2BUrqhBZmJiwv8mJydtcmLSVPmo4conMpdTR0e7KZ90d2taNYaufLuqmJqZnvU7Xqdnpivz7e3dYe3tQeu7epDOzsza+Pi4b3ttu+kp7S9Zb6TRttm9e7edPHWykj9VqRhVLKoM1DxmprOebzStB3vklO9nbWYm6wW/KjXVkKoG95ZM2i36+nqtva1tVZtA5fTY2IRbqUeszvYO6+3psR29vZ4+LxJiha3SqbsQp6amfR21L8q/r6/P9/3W1sBT4+i57yMjI6btMT4xYVOTkzY7O+vbTOXEzl077cTxE5VGAiU8my3ZyOiwjU+OW7GUt0w6bT2d3dbfu8NaO9uCY4LXHK9qNZtnZM8fc/b8xXM/JqohW3lyz+49HoSiVcvni0FeHp9wY9kp35dLZT92d3R2eM9nXd1d1tnV5faRe7SiykMFbYOZGZuYnPBtMD09Zfqbm53z/NbR3mGdKjM72n3f6O/vt472dkulEkF+XPmu4Yv1vKEya2bGzyeUXz3vKlBldtb3Yd/+He2eB4LAm4xpPXq6uq2zsz1KfvBe7wRHv5bNJiaD8lU2alTY0dNrPT3d1t4R9Bak9VeZqXObsbExGx+Xw5SnoyWTse6uTtu9a5edOHHMMlHklweGlW1oeMTGJ8atVC5ZOp10p96eLk/rwkRuz29RuaB3lVmrfakxXcfy6akpm5wc97JNAYZqXNdxs1gseECb8ro3PrW2Wnt7p+3o7/M8uqLgkTDAVseBFy+Cc1AdCzo7u2zX7p3W2hr09pfPFW18bCzY5yYn/V3lmgJN1PjV2dXhx9Pu7i4/N+3q6rbW1tSihhzl//HxaZvwfDbhPRWq/FajaDKZ9uOOjs8dHW2+v+3Y0ev7fsuqzi3KlgjPf73vxPBxBWqS0/J1zqDyVr0zKK/rvEF/CtxUAJD2Ne3jfuxoa7Oenh7r0HFwlfv6ard3M4yvY5LOZVUWTE9PB+VRNiiXFEypQkUB0B2dnR4oJSOVkdru2m461i4KNopWTAFxs1l78fy5H6N1/qh57ejr9+NiS+vy53uaVXRoC97XsGOZ2djYlB83JiZU3k/7PqbzRe2nvTt6/XxZ66MyUMf6zs4OP/ZEq7Lce3D817nyiJ+HqhcqnV+p7G1tDRp2Z7O6ppz0fUHnClPTyoszfm4d5MNOL1PTLSnr6e62HTt2WHd355rKkur06sYL5X9dr+Tmcr7MKTnoGkIBv5bw6+C29jbfDxS8onNDnZvJIrURAZzViazxXWXizEzOzy/v3x+wa7/+6kErekxJOZGyyemsPXz0xK5fv22tLQo8006rnBLklnS6xcsTnV+qHB0b07XHlC9JeVnbun9nnz/yscbiaw4aHZs2nXfrPEKPRdEje3xevT2ej+ruD1VzU4P46NiYDQ0N+Y0num5pb2u3Xm33rs7K44VKebPR0Qk/f1cDvm5S2dHbYzt29Fh7dP5XNW99VbmnIAOVeQp6VN7WsV3bU3Hqsp2amvM8qXzpeXJq2rJZ5UndmKPt3+n7eUtLyrp7uv2curur6pykxrLjgyrHwmhHjv+4gs/at2azORsbH/PrBj1aLHgsUnCO3dPb49tgUc8v0YJ1EKj10uC1FSe15vZqhunaoxRcp+vcbHJiwgrFgp9Hq04iuDY0L3fb/by20+u9dP6g8+Surg6vo1Ae1iuSEtnUVM5GRsdM+90PP/7k5wuatwLadK336NEju379hrW16mYDVUqVrFwu+nEznc5YT0+v9fXvtHRLfWTlwVyuZDPTOj9Wfcas5fLBtbzqU/xaKp3yPKiySeWkyqUdfdpHuoLrsfqzX7BNpqdUb6BzENWxWVgv0+fp9x6LPDg46MVJ9YSjY6NhPUPWe7PUOUJff58dOnTIduzo8xsLFixgm34hYGWbbnhWGwEEEEAAAQQQQAABBKoEoitqv7heeKWqbwuHzE+roIJbt2/b119/bffu3fdKy107d9rvf/97O3v2bW8wVWV3NANV7l28dMm+u/C9Xbpy1e4+uO+9o6jRSxfWulCfGBv3C2k1sKkiXw0KqjBTZV9bW7v9p//jP9lHH31krW1Ju3P3vv3ww4/208+/2LVr172COF8omp4H/OzZC/uXP/7Rvv/+vE8X3PsS3IpXKuatq7Pbfvub39of/vAH29HXu+Du2hjHfDVdPYh6OPNMfIoLVLyCDx5C5I2C03b37l27dPmyXbt6zR8VpWAAf754KuF3DLe3BxXWx44etbPvnrVPP/nE+r3yZpWtIPENrIbbgtmDgUd27do1u3r1qj179jxobJiaDu8ESvhdQGpQ2Ldvjx05etTOnDljZ8+e9UpwNQRF9XjxVa31eXR0zG7evGnnz5+3hw8HvJFn185d9h/+wz/a6dOnra+/t9ZkC4cp/aGjHrF15849O3f+vKdfjfUH9u+zD95/3z755BM7sH+/JeI9oiyckwctZGdz9uTJoP31r3+169ev2+DTp74va/+Leg/RZKrYUqCCGg2OHT1ib54+7Wl+7dQp78nAK9gq27dqQS/7Vbc1+it6f9kZLp5ePSI8fvzE/vSnP9n9+/e9hfTkyZNeTmibq/yZnJy2787/YD/99KPduHHDg/UUrKK7DVXG6I5eBR8dPHjQjh87Zm+/846dPHHC+vt3LF5gnSHq2efW3bv2l7/8xZehoJHffPYb++jDj+zEySOmG/Gmp3Ne6fnzTz/b+fPf2eCzZ56XFDSowA5tI+0j//c//ZN1dQfBf8ozqjTV9EHAy5yX25cvX7ar1656D1VqfFGwioKVSkXdRZj0dVLe37N7l338ycf2+ee/8XyVTld1XVFnfTRY+/V3352327fvuJRMPvzgA/vit59bW1um0pAQzUIVxmr003Tnvztv9+7f84bY337+ub3//vt2/OgBL5v1HPhbt27ZuXPnTOuhBuOowUoVyP19fXbmrbfsn/7pn0yBANpPte4PBgbsu+/O2aUrF21iasJ2dPfYh++9b3/z5Zd2oO1A0MuKslpVWRGlr9nfFYyidfx//tt/s8HBp/7IGeXlf/iHv7f9+/Z7TwzDw8N24cIFu3jxkt2+fduGhoa9twcVLjr+BgFYO+zwkcP21ltv2d/8ze9s3/79lgmfXqOyOx8GFalM+/nnn+3ar9fs0aPHYaBbwf08yLC9zXb299mxY8fs7//9v7dTJ09aT0+n996UWmXXC8rD0zNZT/PFXy7Znbt37cXzFx6spECVoMwqeE8PasxVcOHefXvtnbfftvffe9/efvvMyjZf2Wx2rmA3bty0v371ld28fdsb3X/72WeeBxWAovykwIdbN2/bhQs/2JUrV3xfVAOuetFQD0V9fTvs7Dtv23/9r/+XZTJdvmw1GKlh7c9/+Yt9d/68ZbPT1tvbbadPv2mffPyRvf76a37Oo+3Aa/UC2seVrVQ+6DirsuHG9Rv25MljDyhSo03Uu4mMVVZEQX7Hjx+3v/v939nJU6esvS3j81kuBWpovXPnjv2P//k/Pf9rfqffPGP/4T/+ox08sMeDutQg+9VXX9tPP/5kd+7e8YZuHWMVwKcgMfVMomAuLf/DDz60t996y48j7R0Lmy+Ud5Qnf/ghyG9Pnjzx/U09tShDqmFfdy7rOKTzFJXXp06dsv6+bi/OVpSjvNzz28Z9nknvZSvl5UIuX/bGZZn+8stFP+8YHR3xYDE1vumlAJWd/TvtwMEDdurUa/b+++/ZkSNHrKuzTkBF7JxmOetm/316etbLpm+//dauXbtqL14M2Ux2xnut8/OERMIbh2WqgI7+nTvt6JEjdvads77fHzx4wDo7W2uupgJCHj8ZtP/+3/+73bt3z8+PlWc++eRje+edd9zYL7lWtJG1iBWPuCA92dmyXbp8zX766Sf79ZoCHp56A74CaFToq9xVYMnu3bu8MfKDDz+0N9543c+fV1rca5+6fOWK/fWvX/l5mfLUxx9/bB988IEdPbLPy96ng4N25fIVu3jxol8/KmBFQUE6J1PvagoCVOCDgrbefPMN+/TTT73s1rXE2tZ8nmFycsauXv3Vy5ZHDx/a0PCQDb0Y8mBZXbdqZ9M5s65d1dB84MABO3MmOGc+ceKEX3c2unjXbqbz/bv37tmFH360c+d0HfLQg571KF2Z3Lv3wCYnpuz78xd8ZaMH46p88cCOHTvs9ddft3/3b/9gAw8H7Ny5bz0v6nxBN5S8887b9g//8A/WVScPzwsGn1SeXbp02b759pzduHnLWtIpbzg/cviQ/eY3n9lbb52x7u4lAjrC3jBkOTwyYt9884398z//s5fvKodl/bd/+zd2+s3T1tndbgpqGRx8bue/v+DH68ePHpv2xfffO2uffPKRvfP2W5bK1Di31XnH9Iz965/+ZFeuXPVABgVD/9s//Ds7c/qM7ezvskKhHFzHXrpkV65etYcDD4OAppx6YdQ5iXr8y1hbR7sHqnz22We+7545/dqKjjMVO63sgswT5ebovTJmzQ86tx4YGPDrUF3zjY+OeY9ICtr54ssv7ZOPP65/LZpQjpg/MZ7/tObipGYaX9nAhOoEynbv/gP74Yfv/RxZQcgedDSX9+AVpU35XYGmba1ttqOvzw4eOuRl3JnTb9rRo4etv2/htbzqGW7cvOH73A8//mz3Hzy0iUkFKyc8mEmPtP5//8f/sG++/sbnrc2rPFMuFfx4rvJPZd+/+Tf/xoPwoxst4k7aFrO5kj9qSOc7Fy/+4vu3bmzwm1Gysx5Uqryj6zfdgKIgPu1jX375hZ8vdHQExx7Na7ncpF5idA3x008/e8+GZ8++69cJJ06ctNZE0GOyelNWHeH333/v12qq69O5sQpI3cxx4sRx+/t/+Hsvl3ft6ouvzrb9vPCMb9sysOIIIIAAAggggAACCCCwXQUWX4xqSFBJrcbXxb8vlAru0J6y+/cf2a2bd/0uq4l9U/b++2N+V6sqt6JamCuXr9m35875n+5QGp2c9LtdfXnqfaVY9OnVUJzUXcgapt5VvJvVhDf86C5t3QX+8PFj+/X6r/bHf/1XezjwyMbGJ2xqasZ7Y9GFuBoBdFFcLMzZ2OhoMD+/M7QYPGvGStbZ0WXHjh/3nlh6St2W0C1cWuFk0FgXVAbpc0gS9k6wUIBvaxFQtkil05ZuafXH7uhOIzV4Xrp0yRv7tM3U4JfPF7yCVNtADedq/Hv+fCjsOeeW/fLzRa/AUUPk4SOHKttqyTRFtWuloAeHF8Oj9tU333rFu/Ke7opVo4D+1PAZtPAE+aDlxZBXBF2/cdMban786Wf74vPPvfF1584dK7p7WAE4qvxSgNfdu3dscnLCK3lV0apuclfUohTbMRWcpfw/MPDEbty4Y1OTEzYzNWMH9h+w2Wxw52C9PTmXN7t8+Yr9+NNP9ssvv3hDm+4c1B2HWndVUEfTqkFNvQvorjE1bA8ODtrVq9f8rqxDhw7af/kv/8WOHTvqvXAs6b/cj7F1WzhquCNWgOqOuHCyVXxTfhsdG/fgJVWcq3cn3UX75um3LdM6ZC+Ghuyrr77ySreng0/9bnndvabxtOFU3ilfK0/dH3hkF69ctUvXrnlgxm8+/cyOHjnsDSiVVaiTNpV7I+Pjdm9gwK7fvOl3s+7dt89ee+1173XkybNhO3fue/vl51/s5q2b9sJ70Zj1itSS342XND3HRY8JyhfVyBAUw16ZOTtrd+/es19/vW6//vqrb8fhoSEvV9UTga9PUb1Qqdcjde+f8H1v8FnCKz4HHj70htK3zpyx3/3uS29cVU8Zy70UHKFGkus3rlcqK48ePjI/WVXtqNKsRiftK1qmtod6QTh+4qS9/kbW5gpmDx8+svPffWcK2lHg5Kj2IXXHXyyG22LGG6JVrqsMCQ8rfpesypfHT57Y9Ru3/C7TXX19tmfXXt+XFJThgZZK3fKrNr8OTfRJx9OpmWm7/+CBN0yoAVGP43v2/LkpMOvJk6f23bnvvNxVsIfukFavZEFFsryCyvjxyRkbGhm3e/cH7NbtW/bl7760Tz751Hp72mx6puANswrUUPnx+PFjLz8VNKJgJz0exPeJZNLvpnz+7Lk3PA48GPBgv48++tA+/PCDZdWUF/SnLtmvXfvVrl675oFcAw8fec8601PBHfVRcJ033PtOVvaGKAUC6Jzhwf0HPv3Zs+/YRx9+YIcPHfS7YX3UOtta5bXKZjUuKEhAjZ/79x+w4ydPeaPG8xcv3FGBszdu3rTxsHcm5V15a/3T6bQHJagxX4FYfuNt0ixXKNrTwec+36npSdu1s98DzSYmp+Ybo6r2i2WxtswIa9/x1HuAGtAVTKFgNt21rGOWzgkVOKByTmVbkE9UNiaDfDI0bk8Gh7zsVtmrhtePvKH9TWvNVEqEmsLKJ9qH7t9/4H/afxQs88WXX/j8r1//1f7yl//t+4seXanGIz/GRucjSkMiaePjU/bs2ZDdvXPfbt++60GnCj7t6Gjxu6YVnPXHP/7Ry1EFqoyNj3tPQ4oTiBoRle/Uo9fw6KifTymQRgEjH3/8kZ06eSLswWOFvl52BmWBGuieD72woZFhbxS+d/euDT5/7ndMz85mreDnDMGxY2Ji2l68GLWBh0/s8uVf7dKlK/bhRx94MNbhwwcW976wwuTUxG+SgSoPv//+QtAwd/Om9/Kg3idU3qo3Sm0fvyYK06syTecLz54P+bmXzsV27txpb791xv7zf/4/rae7a2EvHP7Im4I/9vTO/Yf26/XbfozUNIeOHLNTp2a9nF0thx5lUfcVK3/0cXhkxtRrpQJEFbA7+OSpl/kKdFUDqnrH0OO39K5zaR1v7t2/b3fv3w8CBt9/3wNH1NuWgkmWennZOzrq09+5e8/v3lfD8OHDR/ya7sqVy95YqmDLZ8+eeW+Ivl970IwO3UEkgxpmh4Y6fXvcuXPXb3L47W9+4+ft6lEjusZbKi3x37JzJbt86bKdP/+9/fzLzzYyMuy9QercSYFwegxesJ21LwSBnzpOPHz00APVhoaHra293XvXWM4gvtyX/azLGfXioIALHbNv3rxtL4aGPUBZQdI6Zuul46kctf20HfVSIJLWRWWmerbs7Or2G0NUzql3MZ1TamqVR+pl9YMPP7LWQ4eCXu2K4WMWfU6L/9Eibt0KAj6fvxj2XiXUkK3lq9eHg4cOLh2wojKqHPRuonPF6zdu2O07d3yfUwC5ArrUm6qOw8G6lC1fLNizoRd25/49DzDR8P5dffbmmTc9oHpRKrVyfk5a9MBuXb8NjwzbzuERO3Xydevp7rXhoTb78ccfPHjq/oN7fj6qHneC81GtVvDMFZXPCnRQel4Mjdily1ftrTNv2pdffOHnJJ0dmfAxpRp/6XPQShy/RgvrSippV5o1fZh2/5owm8nm/cYEBfYoiGB4aNjTqMDaM6dP+/Woeg6pPvcNZ+Ozi36LL7+y3E38YWIya3fv3bc//enPduPGdRscfOI9COlaSuWRnx+Hj85R3g/275Q9fzHkPf3q/PTPf/6TffDBe/b73/+dHT16zNpaW+zxkxd+/Je3elcZHhnz84O8bnYoJ6xYSpoeyfX4yVPPXzo2aN5lPcfHSv7ouJ07++3osWO+XVTHlVaBGxbdOq98Ojjs55IK3lNZ8/z5M7+hQkFWlXNjvzYK8oX2Oz+mv3hhz54/8+vB19943a/r3n77betsrx00Gd+86llFAeo699VjhHt7+2xoaMSOHT9pk9NzHsyn4LE7t+/Y8+fP/RxJ+4OOh8qY6m1I1x0KslXgD69AgIAVcgICCCCAAAIIIIAAAgggUKkPUfVeePVbedf1sKopar/UUDs3F1Sgjk9M+V1ZnR3d3lDqF/a6Ilb8iwV3E+turAf379tsoWA53VZlieDu0kqDVNHyZTWMF62cSFpRlWRhH+IpPeqkXDZd5uoxRI+ePLX7Dwb87kU1ZOml9OtCX3esqm5Iva3olUxpKv1W9MqwhOkRRLngThOfLqrq19jzafZnVUcVdr4e9S18QfwTCsgpyksLUXwbhdtKXeirIfrWzVtegaaAETV8qMJXDS66Q1IV+qok1XPpczNzNjebs8mp6SBo4ulz75Jb3XGrK17doa6uv5d7qT57djpnt+/ctQs//WzffPed96gxNjpmqkBSJY5Sr54qWlvbPACqUFBj/ow3NCm3vHgx7JVUqlTV3Wjvnj1rx44dXG7RXuGlrqFVwR88OmLce45QI68qxFb7Ul6fyxVsaipro2NT3h15T9ekP+qlqPlph6jxmpkpeiDAX/73//ZGFt1VOTuX84ZSrbfuBOzsaHMDbQ8Zq0J+di7vn5V+Ba2oK2wNV4VYVLldY3HrNEhbpXa+Wo8FqBJQ20aPGFGjsd47Op/ag4GH9ujxM+8d5LwqeIeHvZt/VSaqW/eWsKJNwRLT2Vn/G5+atuTwsI1MjNv4pB5HMWVf/va3dvjQIb/zOCxWFiVbW6tYLls2N2fjk5M2MjZqc7lZUyPHi+Fhe/ho0C58/5P99a/feoORHoWj7ry9xl7tP+pBRC0TybS1d3YFjeZhGawiV9tJDfcXvv/efrl4yRsbNUwNLCrvFCyou1L1WAyV4fmctvucP2pIj9XQ45DU4Pps8Jk/0iCTabMjhw8su1nUaKcGY93pFwUUKjBqwauq2FDezs7O+bbQI680j9HxcXdItbTYt9984wErKj+0H7qDCNT4m0xaMZf39Vd31ypPlHO0CF+vQt576Rgfn7TRsUlrSbX4dzXc1NllFiS12b/ITsc/lZUK8pSHeuG5ceuWNz7dVdn3/Q82Mjwa7LuplLVkWr2SXNtH5a3KFd2tOemPpxq20bERP7Yq8ke946i3HO0P574950Ex6tlGtulUS9CrmT8GMO+PsCiWiv54NzXqK3hAPU0pQE934B84uN9aMy1LkgbbreQNUVqm7nBWnlBgjlfuWzK4m7211dpaWjzISo8H8rw7p95OSt79v4JPVJGvXUU9u6i3rkxr/WVrv5hTeT2tRxZMWGur9sURb+i7P/DQfrhwwb7++hu/u1/HD09nGDClu7a1XNmrJwXtJAowVIOuEqDfVMboUYhTUxN+F616jlFjYbBDBWdfG1fiLUneBD+ubc1VTjx98tSPa3rchR4PoXLdb+qVbDJlmZZWa29ttYQl/U58lYF6jIfyuvaXkdERb+xRT1Nd3T12cP9+a9Pjeeq8dE6g7ab9bWJSPcOp162E99am85o///nP9vPPv3iDrn5Tr0N6tEaL8ogCEXR8LeZtZmbOg1ZGRvQ4mazlcwUvi/VIMzWgffPN1/btuW88EGBG50oKkkymrK1Vj+VL+PlLrpCzYj7nj19TA7KOEXq0YT6fs96ebtu7Z7elwsYopTsKSq21av6rAmCSSe/OXz0IqacYBSyoe38PTvM0BD1xBZ5Fm50tWDY7ZWPj05ZIDJnWR67qNe+TTz8y9Z7Q09NZOZpXFf21ktK8w8q6vpm2G7du2//6X//swQxqnNPd8drHg142Wn3/lqca7HScyeVylp/L2azy3dSUB2Lv3TNmu3fv9HJNZXh1jtP8coWSjU9lbWRi2hsJky1tNjM758Gp644U7oI6Zx4em7Hvf/zZvjn3nf144YKN6lE9s8Ed+zoX0qMhFSCg4HINn8nOhuXmmOd7PRJCZb96cHvj9VO2d/dOS+k5KtUvZQY/TusRjXM2NjHhjwXSvvjo8RO7dfuON5L+6c//6gFpQ0Mv/Fii8lznrepdKJ1SwO6cFXJ5m53NWy6f9/3y2fORmqk5AAAgAElEQVQXHqiqR2jpwPHWmdPWscLHsczly56OixcVrBL0TKbem3RtoOBMzU+BHQrq1v6ifUtBXHppP1H+VyO0zp09iMF/adw/yk8qGxVY+uDBAy9DlA91Ha9jqPJm8FJDuR6lpkeMhYcivXkDetlvJFGZlUyl/ZGXenSZzm9zhbwp+Gnw2XMv93b29duOHYsDH+JrrHPS4eFRe/jokffyOJPNWbGQ80fp6DpEgTA6ZzxwYN/iILf4jJT0snkgrgJZFXgeXY9oPn39/ZWAFdUGqG5hZm7Wxqem/BxdAJN6lJS2V8QQn79m70ExwTFbZfzoyKgPGxh4aOlk2vfnr7/+q92/rzSPmc55dATXNaSCeDTj7NycL6M4m/Np9bjBFy9G/Hxax4W//d3v7M03TgaBZ5UDVp0EhenT7hIEjujGijAILNyH/IRE44U+s3NlD4RUwJJ6+dSxSXlRj3dST1jqjVABua1V50Q+u6pkVC+iimtzfI1Wwntjy/n58R//+K927tx33jPh3FzWA9pSSfXWpKAKPa5Nx+ySX6/rmKp9Sudx6klEvbPqkWN79u4OrhHDi4mZqWn7/9l77+e2kizP94AEARCg90YS5b0plfdVPT1vunvmjYl4sW/3l4335+2LeDszu9O7M1PdPd1V1eW6vJFKKnlHb0RvAJJ48fnmTeACBEhQpeoqSfcqKFybN/PkSXPP+eb33L59S99ehDijDydhXr+Zr1MF8rlOaGzm0JynLvn2RlHWAThn1/XtpG9H6iJUH4RUHhsfE4Px2+++a/SJhPkVQwtttY75fcIam5o03q8TPpOFOfTTy5vqGxmzhkeG3Zy1vt6effqpQh2GXlU4xw55WlxeNr6jGM8Id8Qfc+WLFy9ogcd777+vsEHIirLRN5J55i7YW5iPEbazQW2kJPkn9qDCqPjEyiIqeCSBSAKRBCIJRBKIJBBJIJJAJIFIApEEKkvAf9BXuCoDjmLsOmMOn9nO8OUcg+FHABRgCIFKeG1jw+aXlrUClA98Noz3LU1N1tfTo3i6AioEq7pIlA9u4j2zEj6TSlpvV6cdP3zIeru65ESfx+A7Mma57JoMVU2ZtHV3YTBrsVSKkBiY5WUeEHAFAz9hO4jhS9oYAVxRgwLHnPmO57RtIwd3Q/T/ThLwIkSiGDeo+w8/+tiuXb1q09NTepwVdcSaJ5Yyq29wnuNMwsCMs+P+HCuTlmxZhpZxy21syCnNKrwTx49ac2vzTtkwcEvQ/f7uP35vv/ndf9jo+IRz/NTXGSs/ocjNNKWlh81NLcorzlicigsLc6KFxkiFoXJ0dDRwiOF87bRMY0rOyGqZkAy8rvlfGUO54iVU7elq551mYwDCKM2v/1fpCWxgACDe/+ADhQFCFmyEvKHs/LW2tVprS6sM/xhSV5dXZDheWV4SQGVpccGWlhYFtiH+NHVWXwNYqFJ+fjLnUExWxIptKSYHJnLCOY5BjpXqOF3QScLjNGWIR+/0hH6CkGc48aCyZ6Xt0vKi3Z++b98sfW3T4xMW29i0V155WeEZGqFdprrLLYGBGrACDeeUnFs4zBcWbWR42NZW1+x3v/utXb58Vefov6CebszgLEoIQLO6sqwQA0N791hDPC7nvDO2O5AgLAPOITsjAyKOJsJhwB6RaW4SfX1zc4v6Rdod4R5w+uL4hG6fcsJM0tX9sbW1dahfl9+pvCxbKhY9lZCDK9V1nrvQYRmGA6abtVzOFpaWtHrw3vCwmAbu3L4tJiEAAalUuyimif+O4woD6b59e40QC6xm9W9DroUtyHPRWfPgrbCQ5k9sh7LhmJqeuS+n+cLcnBgoMFA3ZZqsq7tLITza24khHxdYBQcUQArnOF+2NVYkT0wqjMBabt2SiaQcHjg+AL/glCX8UnNTs9IkHj0r2wGl0CZwOuHEIV0M3DgdY/V11t/fb3/xF29aT3fntlKT1sRiRt5npqZsfm5W4NPmTMbS6Yz6anSYPgsmHvIDuAqACgAznIU4DQkZBXCKEBGEAuxob1P5q78cJ5DTWbQG9iN0EP3DafXvb70lwCXjA/lg3KIdwWiDYwPwD8xTzDU4x4b6+flGeIUy+hltFSTgiBIqXKh8ij4TMKzqfXZWoDvCcmQybk6Bg5X+DV2lIqSTC4taMU8ftyygRVasTThUCGUDu19fT0flF/qzwVyReoR9ELAIgJl7d+7Ye398TyAW3sm7CZfC+IETCRYvwKvo6uz9Oc1vcNTDGsXGanccYv/xH7+zd975g8Zd2OY629utpbVVut/S1Cx9WwXoOD+ncFOkCZMUx7AOMC84dvSopRsbNYaFFmj7EhR/mZsEfSP6j/MV9pibN24q7fHxMek7jk7SY67W3tYq5oClxRUTgF1AzQWNF4RLWf5y0SanJmx5ZcnefON1O3LksKWSzjUTvKr4/kdhD73czAuEduv2LTFXwFqwAEAwRiiapNGntne0izkpnXagNXSK+kRHYbygjvjLrq3KsT4wOKgQlKSxdaskKTf/23pvrWcqpVn67NJyTuEsYA8gdAt9cH1dneq+s6PNWlqbxWSBTgMOXZifE4iUfn9hcUFALhiqALvC/AfLVCPy2SZUIv56dJb2ASMGc25A0l9++YXaNKECmSPQ3xKKtqm5yQhdmG5Ma+yhT15eWpb+TU5PaU7GGAC70fLSksCSPHegaajyXKxUBAKrfPrZZ/Y//se/2JUrV8WmBVAFgIzGvaa0QmYyJyOsDXmHPYA5E85h8tM/MCAGi+bm5gKgouw1P9ihY0isF4sPofnS6SYBgnCgAyCFpZRvbR/+kTqNxRyzor6Rgy+UttZ2O3hgyDKZRtVjX3+f1cXrbQNgcwDMAbSyd3CPtRKOshx1FSohIffo56hX2gSAN/pPMcQtLmqchcGGa5lMast0OZSU5tKAcpnXCBQYj1u6qUltqrW1RQBV3R+ACLQfqH6tYy/3aV7KIor1dYGvbt+5LZaSudlZu3TpW9kXCEPINxFza81J0hnNIwDC0zfCigngFkAi8yNCAmaza9bV1WmDA33W0poBZcBsoaSIOx8EJaFcvlDB/tpaXnO4jz/5REBjWGg21tclH8LR/uVf/lyhH2nPFbue8pfv3G2UP/GTPgY0RdhSQIfMFzc31sX6AbMJrELMGxob05ZoYMxel67Tb/Ntxi/tm1/CKjEXpi8EmIqYmH/09vQaYTk7u3tsdTVrUzP3tehlY9mBYtKppA329QTjaL3qX6w8eQd6bmttVt+BrcyBPoriBHS2urJic7P3bXRkxHLrWY0hsHQxx2cOQZ9DvhjP+T5lXgCbLd+09IeMR4T4ee/992QXI4RWqjFpEGiiSjtVN99VtH+Yh65cuWz//u//JiYnFhMwFhKmlu+0dJr+uUEgP87vGRzU90fDDoxbxdI+/nsRYOXxr+OohJEEIglEEogkEEkgkkAkgUgCkQQiCTxMCZR9tWKfFwzEO8m9o1yO802t3MLQxXbi5EnFkv4v/+U/29Lqml2AOvWdd+23v/2tsXKurbXVnj53zv7ub/7GDh06KMcPRvKC0VZhYVzMd1ZO7Rvot7988w1b38zb+NS0/emTT+2f/vGf7ObSoiUbGmzP4ID97f/5K8U87+nukROaFXgwEWxursswBmilsbGh8CXu7TtFSw/+DJ5xFp+wnethivXxTgupOvYGb/GANQdnHzTfGGQwIDc3ZeTAeOP110SJPtDvVt17Uzw0/jjN/+XX/9s++OhPYtfJxzblVL1y9bqxioeQChiJYtUMH3IwEOM5Z7/+3//L3nn3jzY8PGp5q5PRua+/106fPmU/+4s3bWhon3QSY7iMQasrMoRiLH///Q/ELoDxEecY4TAwSmP8fvqpp6y1vXlbC4/XM//rQRIyQu1kFaqiLE7K7qJ3NHGk5LgYSpcF/MSt/2//73+TsQr1xtl75MgRxXonBMHAwIBWd6H/tEGM3YAlMHDx7Oeff24Xv71o0Ej/7Gc/s6bm7ctcJds/qdNOhqwg3ZATCoAEgJUPPvzAYKthBX26MWWvvfaavfTSi3b61Gk5bARwYTXt+roc4l98+aXAQJ99/pkc9rm1nHT3w48+tOaWJgHv9g/tK6kTLwjpBHq6QTgmbnErsjEqQrvcEL+plfYY8Ht6uwxDM2wXR1mx3N9rjY1JOcxYmZfONFpjKuGTlhOH9nHgwAHV9ZWrV+W8OHXqlD3zzDMCE2JUxKDIilAM8rD+EDIGHceQe+3KVbXdjY2cffXVRevp6bfz585bV2e71VcnqggpYBloJRYyyId01K8YrQNwU18nIBaMIRj3CWc0Ozdr12/esI3cuvX29YrK/KlzT4nmn2OcgwATcChBL+8ZPJxMJeWCXPwOwI5SQI2/8uj+unAPMYHuAGvgQFzPZgVEo42/+sqrGiNp+9Q7fRGrIFkFf+nyd/Zv//qWffrZpwb1N6xkUJ+jCzj2Ce1Df5BMJNR3v/Lyy/bUuXM2ODAowzT9PGM4oBX6zLfffUchHEh7Nbtud+4M229+81s7ffqE+m76mEqbhmAxXtUJ/LF37x6FwQI4durUaXv66WfsqaeekkMBxwD6QgcMY8bY2Lh9c/GCvfWb34j6n/ziRIS6nZBA/AHYqbQJ5KXwEn7lMoxwmzY7P2fXrl9TH0/IGZiC+nrRwZN27qlzdvToEYG4cG5QVhgHmprTLvyQI3ATsNf3ydLGcNHD+5Uy9gSdw+nrt/C+P1fpF8Bab0+PQrABqsSJAugEJjT6S8BD6A69q1Yz5/Ny1n700Uf229/9zj7/4jOtwMexDhvBO+++qxXo3T0dAv9Vrh43U/HtTSv4l1fs//vv/902YMAys/6+PvvLv/w/7PnnnrcDB/bL0U578wAGwLt/fO99x8Qyy+r9TRseHbW3fvsb+/yLL7TiG0caDA7Hjx2z119/3V5++WUBBdAxAUvyeYWu++STT+yDDz40xiIcWTijAdh+9Kc/aV6DUxpnVMVBKOTrJN+Ma4C/AC5SHQCxAP+88vJL6juQKcAvQGKEusC7RaiRy5e/s4/+9LHaOH0HQE7CuL33/gfKM2PNvr39larw0ThHW66P2dLCon399TcaHxUaJxazjo52jUl/9Ytf2LFjR3XsgPFOTwAW0zcSmo1wmIRxoC85efKknTt7TrpRrT/clXBoPpUVtuZk1tY2tXr/3XfetUvfXrT52fsCMjDXOHnyuL366iv25htvCOggoAZA041NMcZQrt/87nd289ZNsWbAzoMOMtfu7uraFrBCBmEZ8H+A/27evGljo6MaqQGkHti/X2FBn3v+WQGB0WuFYCMc3Sbtekkh5P7pn/9ZbI6E4ESHyQfgExi+ABRu56BHhMurGwKf/eM//pPqiTAbPINDmv7k6afP24svvGCHDh4QGA1wIu0XBzZ9CGBnQjDuG9qnPqijs0Nz6por4SHcCKi4q7PV/v7v/t5++Ytf2sqqGx/feeddgZAuX7osRoZDhw7YL3/5V/bqKy8bTmT6Fua4Yr6sqxezCixqhCmbnY2rbwSUkYU5VaGE5sTmBjB6i/oFJ9Stx0xj9BdffqV24EAmLjQIzBCA9ph70m8NARrM7NlWCrncphZBwOIjtspYnfHtv3fPXquPh1YHbJvK9hfJvv+j3waMBRCLkERaLFMXs0OHjir8GfoAmAcnvYALsXp9V9wbGTHCyf7P//lrsdzQZ7AIgzB2sHCxaOe1V14MFj64ucz2uQquxoLv7Ao3Z9fydvfusH300cf29tvv2s2b19T/AJg8f/68/cXPfqZ2BJByO4BRhaQf7VNB30iEGsb/3//hbQEyQWsyxh09csj+4e//Tv0yIGy+0QFbMNYyH6EPmpmeUbhMFjZ8++23dvDgfjtx/LjCUHmWH1go/8v//Z/FQrSykrW7wyP2+7fftXfeeUff04ydhw4M2f/1D39nL77wvEJuoWdsZJG/uvqYJVOwFxa/6YJbLN1YH/Qtx2ULwdaFDePcuXOqX4Dh1C39EsAS/uibrt+4bv/+72/pex6gC/aMe3fvaTyi7yY0UEtzWq8Jmq5/ZcVfgHl37tyxzz/7zK5evWKEuuT7i+8L8nLmzBk7eOiQAMC0d9hDyRO2E5nZKqb65J2MACtPXp1HJY4kEEkgkkAkgUgCkQQiCUQSiCQQSeD7SKDM8OnAHFhwAlaHYEWcN+iEv0CTybglE02GTyq1um6tLS1ajeasP44mNJFMGEYBYk43NSWKliHyHF5siNG8LmFpYj1Dp76ek7HUGXjdijAMADgtWZXS2dHqPvqDlV7VPrxJi9VTbDISeAeagDicddd0Q/Tf7iQQiA5nhww92ZwtQX27mZfR+fz5c/bC88/b4UMHrKe725oypd5vWFFYLfSrX/3SWto67D/efsdu3bwhg+fM7KwAJNAbQ3nfiQOyTFeV2bzZ9MycnAuXvvtOjAPocEM8aSdOHDOM3i+99IIN7d9nLS1NlkjUSV9JatMy1s6K/M526+nttY8//lSrh6anJw3D7PUbN+x//+u/ynEJm1B9PU7O6iIqgkpwYACiCjnuqz+27RXKQrgJ/WLorygEc87mu3dEz47BjdWAGO3/4e//Voa5/r5ea8o0qm35JsCL880Ztafurk7Dafzqa6/I8cdqTbdafdvsPRIXUVNRpAe5xUmCsw5nPobIN15/Q055QB9y0sWReXBzHh1ttkaYalpbtf/NhW8EYsGQBysDqxoxviNjDHXVQEqqQ2SucFgbMmyvLK+I9QQQxunTJ2WIfOaZp7VCjdXt6UzK4nGztuYm9VUwXEAWw+Z7Loye1B3Ozt6+Pusf6Lf+vn7r6ekR64TCcAVO9U1L6jlWz6MjNCpijX/6yWcC7wDYun37rl248K298MKz1tSwU8zzoEEU2gW58n+Fk4Ew3RUcykB3uAu6dsJ4IUf0Ftr/s2fO2DPnn1ad7BlwK/VwpDU0xKy9rc02BE7EyVpsDfRBftPbS15dcuBve+R+y8E3hKLZXMsq9AgrQI8cPmyvvPqq5LZ33z7VfbwoFttoaxaDAkwqrW0t9i//69e2ka+Xcx0QF47AjVzOOjs67eTxE/azn71hx44dk17Td9AmfLvY2GySY50wToC+CEWFLoul6u5dAZA6O2Dq6asuZwCrdSYw6y9jfyVgYEtLi3V391hvb691dXULqMI9eq/8U2npbSrdqHXK77z9toAqrEJlFejIyIgLi7DGatRE6RyDnBC5h36U8A6E2mKCsJGXA5LV1FyDkQYjPEDF5559Tmw+zF9Yfc4C6Y2NNpUJ3C4rVf1WHvwNPeR9vEf7/sbot4IEtpcQINO9e/faz3/+cznhmBcyp+jr6xNwANCh5pOObV/pM294+aUXBPjb2MzZlWvX1O+zavrevXuq88OHD1lne4vqJ1SVW/KntheEj5ibnRMYF9DCX//yr+3smbPqrzPNqRLH4MZGs7388ksKyxWnj/30UzESAa4an5gQ0AwGDlZ7M08CHHbixHEb6O+zZGPRvYFkCNOH0x7GNPrM7y5fFiU/4Yq++vIrO3v6tMZ7B1jRpDcAZW8pSsExByMeDibGgqGhIXv++eft2WeesYMHD1hXR4dCSAhYhmAAwGbSmovBLkJIto8/+Vj9NsDj0dExgTRgv2L8od/ZTp5bc/XTOoOjmTHJASQ3rSmdlsPyP/2n/yRHHSwriWQw/gTyoZ7oTmAD6erqEqgFMCEr4AcH+y2RdCvzH0pJt28uodlB5bdNTE7a5198aZcuXxITIqMxc/E333zDXnrhRTtz5rQNDvRbIlHvSCFIJm9a0Q/rCSwXf3zvPYX9BDQJ0xfgDeZAXV0d6rv9WFGSAymUC68DaIUxLJ/NiqKqvbXVjpw/r3kMTlXmNLBmMe7zGLLll+/JFKv6M032L7/+tX399dcKlwETEf3/9es3jBAygwMDFk/4iVwxF150N27csAsXLwpUQJtkDgbgijEPZ/+pkycNkH1La4sl4uTZpdHe1i6WnT1799rZs2cF7AbkTZsgf3+uTd+2Cg1ilkkn9JfNEapoTeAo2HLIDmA4mG+o33bYSRsTOsc1hbMTgL2Y6waYUVtaxJRGSB3mBTDwTU5OiVVnfT1vifBHRFBm1ZEBWFmzazduCARNnwX4FZAHgOTZ2ftifQDwMzk1Zfv27anaUWSzmwoT5UJHZtVHM7fu6e2xvv4AFOcrs5j9B9jjW61oJYD9h8UXqYaE2u6ZM6fshReet5MnAUYOyAbBnLtQ1TGzxswRsRiSr3/6p38WAHh5aV1l5TsSABtjkRbY7CKHrngub3of/20yBzG7dfuewli99dZv7d7IsIBFMH2xQINx59Spk2JJCk2N1Yb5Vv9z6ukuivvQbgWscn92XuwzLAwA7ER/Q3v95S/+yp579mkB+z0bWPjFeUtrfIfJF6DXG2+8qsVWftyLB5M+5n9NzQBNEpZrziiEG6xtAppa3upjeYvXxay5KW2dHe2Wbk6FX1PYpx0rHFdBodwl6p5vzrNnz9j/81//q/rCnp5uzXsAZGMLC8/veaqtrVnjOe2EdvfWW28JxAJoBaAYcxDGeg9Y4RneU/bqQt7YmZicEPsMc2zmTvSryPHVV19VWox19JvxuPum21gHPONYqh53PSsR1A4HxRndDjdGlyMJRBKIJBBJIJJAJIFIApEEIglEEogk8HhLYLtP0O1KjiGm+rPhK9oPTvDjV8251INUAoNa4bmQ4bEkF570JHDw4NzFuMMncAwDC7/BPXpXycNFY6ZOh77AZfAJWGKclaaQk8iLVCbDWg9DEgxMHW5lEo7keL5Oq85xvrNaF+d7RgbSstQxPteZpdIAS07YRj5mM3NzWmWfnZmx1ZU1GUhZTX9w/34Zl0q8g0Fy2dymVvK98+47NoLRLrtmgKS6OrvsxRdfsNffeNWOHz9sRG7w+ZZOBHipTGODZfb2O4pdnEHZnH366Sei+4Z+//PPv7CXX3pZFMu9vZVX7aNIGJ2w/MiIrB32/ZvKyl7joYw9AFb0h7Xca74vSTEhWAbGRsdsnZjzUKonU9bb2yPnHr8Y3NlkZ/aPBw4AVnglG9uso7vNbPNw8TXF5B/pPXqPTWjJiR9OTyJe+k2tBj7/1FP287/4mRwTmXSytIsIxN3YWG979xJqLCljJMCKXDZrhES4PztnN2/etmv7bjhq6M4O6V9YYBK3HOUuQRkoN/O2MDdvxEEHDIMz9pVXXrIXnn9BzBbJJCw4RUtiMrnVA6h01e+adXR0WqoxbQcOHpAzJZHY6hzz8Ck0oTFRb3v39NmLL74oZyPsFPNzC7ae2xBDD+EQAJyZVQasOHVXrxwuag379BXOg09+MGxOTk4ozBFOWVbtvfbKq2JOOHhgvyUTrpTIjPaQ0HEp8I2X0kaKLdxLpobsPEK3uJHZyQ4UBHpMPeCQwnF/7vxT9sYbr2sfp1TYp0QxAT51d2Xs/NNP2eLykv3pk4/FMAXwKruWtbWVVetoaxfYkL4TJ01ne5vFGwK0k5eVmNHMBgc77emN83JmEYKMNDCMs8oTxibCEmwLWCG9mIlWvbWlTc/i9AOUJecgRfUoEAoaZIM+e8+eQRn5J8bHBdKbn5szHOcz92f13pWVNa2AFnjM5zvQEM0jOBeAcTc28gqfQHg62Ir2Dw3Zq6+8Yi8895zCrTQCHvAqxXxmq/qF3lC6qyHge44DpSk+nkc7jZQNccBqLXb2zGkBQQEGpmETCZzIkkqQiLoCMwOvtGdPn9XVPW2ED5m+P6OQJmvZnML1ANJijAewUmmj2op/AQgEtcnnxawCuwvtjZXElZiocBTuGeyw3Po5hb+4c/e2ra7BjLJgq6vrlsutic0LoOQvf/kLORjbCdkRLlOgs+lU3Ib27rX1bE6rpSfHJ2x5eVXglTt376rPZlxik0OyoLBbS4Yqcw+MGfSb3V3dWnX9szffVNtvackISKbORQm6TMQTMevpbbdk6qRAraurywIX43xeXFiwm7duiXXi6JEjAuGUO9O25uSne4bwSFOTkwL08P2RzmQ0Rj/99NNioRBjQVhpGZ8Qk4A9cWvKdNqege1Dou1ceqVY/bZAP7fe4L6Xtp53Z1ZWcwLjfP3NNwJuATQEtHT40EF7/bXXDDa+3p4OlcW3JT2JY74xbvv27hHDBKBu+nrCQwDsgV2RVf8HDxyw5uZWASO25KEgM/fNAGMJHTEhePoH+gTaArhF/86cJ9wWfF6Yy/f3dVp7+0su5NbsrI2Pj0uXAdwSlnR4ZEQsIfHE1vkLOPLllazBPgLIRgxl6zkBTgCw/c1f/0rzot6eTtcOygrBeNja2qS/fXsH3Zyx7J4/96GGJ80xmeMzcKIcxKlDJx2bIsB3Msux33TOYSDUlVI9ADYBPuzbN2R3h0c1LwTQAyAUWcE+lkg0avws6X/NbHVtU99tw/eG5dwGLA0DIeA+5pQzM1M6T33B6EZdlAAqfMZgwFleths3boo5B8ABbEaAq/v7B8WAE7rVj+zuVzomiQS3hPdLn9IRcyk/Rkt2JvbFdEuj+sPXX3/Nzj91zvr6eg2yqUpbprHOhob6xAwIYIpvsRs3r6v/YB4EoA/gD+CFumpsoaGEybGKESwc4hLHVGkul7ex0Ql9m37w0Yd2+btLYuog7CsA/Ndfe10gSuaElWVLSjvIJJSXR3EXffFhI2ERZa5cFzc7cOCgPf/8c9bX01UAoJWXD8mkxHrSZeXf/NKTCqKjaQkgRhOTLtEHs7nf8rl44Z2FPjz4wAkuuLp3IdYY99tbCenbYmnYg7fZwNK0tzUL9C8Wza++VB+PXQDWnxvXr9va6kqh9itqgu8+YmC58/rGpZ9fz+UEIoS582c/e9POAdZrSpf00WStHpBgHpT5Nhl9Ai8Ve90nsPBRkSMJRBKIJBBJIJJAJIFIApEEIglEEogk8P0lgBPMfTzLCTur0akAACAASURBVMhHJ0ac4OPTucjcoS4FL/TONP/rP8605sJ/ffvMhR/05/j1DwWOdd1WeNYZ4DDGaQUL38NBnsJJVN2XYcBbB6ploOrT0QXVQyAG7RdliNNZhpx8Xqtyh/YN2tkzp+zIkUPW3MRqvpD4eDacFk7oZEwGasKgsFIHCnqo1Qm9gCEcR2S1yl7ACHPzpqjyWYnHc8SbP378qL3++qt29sxhS4TAKuSkXG3ITldHs+htMcRg6EunM8rm1PSMER+cVXJbHgyJQw6gzU3JAaNiwQAaKvpud70NVc7pwkHlVDAoYUwm0zLsJqAZTglkkWiAgj0QIYUP10G5MGiD5ecqv/IROgtjSr1Mh2BVYFVg5SeOwueee1a6h/NT/Qll93++hAFQQuGhzp+3p546J4M56ayuZRUS4bsr39nw8D2xTfjHwr9KUgA8l3jM6rTqD0MqOv/C88/Z66+9YqdOHbHGxljBYV9Ig8e22ajjtpaM9fd2WzLhrOq+mn1zCxeLcxwf2D8gObBiHOc+52lvs3NzakvbvNJdEn5ih8yFE1EmAuCQMhELqN4JudBpb7zxhr380ot25PCBAliFx3fs630hHz/lLUhPwDXKVxjLHFsIfdWBgweNMFAwSQFWQV4FkQQpONegWxXK6vVTJ0/I8chlmEVgt+ns7FT4Gxw0rAqNJ4Ox3ydWVtX9/b324ksvaiUy+uNZpeiL6JNqIZmCeaClNWPtHS2WSBKeIJgL8GqwImV9EllIJesVIvDQoUMCxRAyCNaU5ZVVm5tjzFgr5KUgwGCHorh5jJsP0FevE1MtD8tAi0KivPbaqwIQlIBVeL6s/OVpbz12ggs/Ft7fev/je8bPHR+ohDhj682aW9LW2tZk6UwFsErgKFL6wVwS4BOsTK+9+qr19PSqIyEUHCwEiwuLCl1YLT9adR8wxqEj1CTzT8AyR48etVdeeVXtpcSJ6ZSrJMn+vi6tziekUTqdEmMa4YXYunu67dTpk2KBgzmwMAaVpOAOEgLttIlJCRYiWCZYSb64tCwnMmwp2shDhQ4zrHearwTj4N59e+3M6dNiOmppzlR2cvpy5WFaabQTJ44KjHxgaEiOulwuZyPDw3b1ylU5pjku5MXtPVL/A2QiZBoyRVaEpIKRDWd+IbwGAvV/lC4s4GqlRY5btnAioYsV7y1ed5er3VT5/PoG4aWmDYDh1evXBQbIb24o3BYgPcaE7i5C21RUIQ0qrKTv6+uy48eOa4U9oScZmwAhADC4e29Y4VA2NyrnQQxrAKY2NwVSRL6trS0K/QdQEhaLVCr80VAsc3gvlYwJ2Hr48GExZNTx3bCxqXcDhoCJaMsgKFa3TRseHlE4oOvXrsuRizNWY9mLL9jf//3f2EBfZbBK+P3sV2hm5bf8YMe827O+FHSP+b/0kP4K+QOm39Scl6O6gPqI3sdjQX0GXY8EsLXeWppb5dwHQIpiA24j5NLMDExsC07XQ9/rdL2kzzfYtWvXFHaT8H3JZMK6e7rs1OlTBZDJwsKiQKaz9+e0QKDSpw3nlpZXFPIPRit0hbAthOQjXNPAwKB7YWFuEG5Dft//+hJu9+vu9fYL5kJdXZ12/NhRAbhgtijp5yskRX2gk7ADHT5yRDJDt9Et5kJTk1PqTyo8WnKKUERs5Ej9NEx6EMFtwkiYV8jcjz/9zP7wzjticiGMUTKVtMOHDtvrr79hL7/ysu3Zu0eAr5KEg0SZJ/2oirslUw//BH0M+groaX1jQ22hvo6wdxmBsRAuc9NKurddbmQTq3CD0tGYHrS7YHxnvsDfln7CNU2lpOoIIVp0adOZwxrqHXtSf39ndbBKkBZ50FoMsYI26bv24MGDlkolpUvZbFbgQuY/220O7ujaA3KENRFbDIUg1NQvfvFX9srLL1pz81awSiFdHo+2EgmEusuS89FBJIFIApEEIglEEogkEEkgkkAkgUgCkQSeMAk82Bej+/DeXlQlKYc+vPUUx4FPvGgNcOGF3JWtthLeGTwmmv9wkvr8x1mA40BUto5xQmmVZCRkfCg779/LS1S+wolopyYJ+MqpcLOMJFhJWFlTXy8qXVZqQoFO7Hl33StEhQQCKykGN2i1CSOBgQUDqZxKi6ziDZwfFR6/deu2aL1n5+e02hfLDKFUXn/jNTGMVHhkyymvLuSXlYA4QVtb2xwbh5mxEvXq1Wu2thY4hMpTkFI5lhV0lD82jFvbiK48lQrH6H0AgpHuVk8tk84oJJfei1E+m9UKRuQzO7tQ6jj2BfY3h48r5OLRP+VXlbqVlNRLR0e74oETyodVa6y+CwPmtpRZoJU6a2tvU/gfjOawMQCumluYt/Exx/SAo7zqFlSfjOJBeCfo5VmR/vTT562nt9s5C6tXc9WkucBj4UeDZqliUcX+j3v9Pr/Nzc3W1U34FRfaa3V11Rk24R3fduNpv4X3/bkKv4qb5d/u2gjhPbq7uuz0qVP6I9SHQG7hwlRIauupGvOw9cFH4oyM5fQp6mPIsnOe4wgfGtqv1e1hX0QlaXCOVZhNzY4xACYmpzgOxIXsaRMAWrQ613uxilVWIquGuGl1Pg5HVsnDsALgiZBb6JHs8F4xd1Of4XvL3s0lTkHnDsAEwA7GdMIb8G5Wg+Os9P1wSYaDA7roQl+9CX17nbW1ttnhgwcN4GR/b59YufSiSgnsdI4MVtjkuKhyrcLt0amaJRAoWTAWFx6LmZymhLjAWZVMJgVsQk9XVlbEfFK4d8uOH9PdvNHrCyvZATvCxqA2Ul6f/jjQYUKawB4Fi4ULGefYszbzm0oDev2CXvhnt+TFAbfSjY0KUchcCWcwYOH1jXU5kZeXl5TRSk61cLIKgwUDTUODWCj27d2rkEYAUUqdsn7CHMg2GF0Ih5FKNtiBA0O2Z8+ANTamjD6cNj88Mmy3b91WeDAVIfziCmX6SZ4ScI3QCs2a05LHufl5GxsfFygju4pcygbbWstZ6b5ATx6eLMozV0yZehoZGRUbysTEpHSnMZ12gPFnnlHoVpi4CptPyv8GwGf6dcYddAdWlUTShRqERWJsfEJsEgUAlU9M5UQADhaA0qOrhHrtDsYdwOKEmt2232VMCsYlAJaEPmxtaVW6uZwDowFcVFdQQd4wyly4cEEsYAAJcCgnEgkBcGDcC/mOfc4fuV8n5UrZriCQstvor1rb2mz/0H7JFXA3/eXM/RmFBxEwnmdUn+5hwBRssFp8e+mSxn7e1NLSLMZAQoUROkRjtZnABKOjozY6NmaAqMo36o7++c6duw5QBBgvb1pM4MKPBJOA8gcLM9stF2o84b4VkqmU3oXDH8AjLHbh8lZKTDKPwTw3oD/6V/QbEBXAdtoG4YZq3rRuyAFxGR8QwejYuH3w4Uf21m9+o3BesN3AcnHs6FF74cUX7MUXXrCe7p7KYJWaX/zo3wjgiHA6zZkmIwQmYBWAPbCwXr9+3bLZdY25W4AkWzr2GmURqLBrd+6AfQekZq+0vWybasDWte094YvupSoPfRd/qCtzHsL30LfBqkQbBqSb28aeEk6WUvBNSGccb2iwoX1DYlU5cGC/m6+Eb472d5RABFjZUUTRDZEEIglEEogkEEkgkkAkgUgCkQQiCUQSeMgSKLE3lRzIyFNwHJVdCueisKqIe3Rf8BUeGKDycv67cyJZLUtrq+EhnLrfL3vIn45+H4oEMGxi5B8c2GNdHV2WaWyUc1SrAAObTcGeGK5eH2e9MSnK5ERD3Fj1ifN/ZXnVsVb458tyOjE1acOjI24VFQwvqaQc/4QkgrZbW43VDhMLxskjRw6L0h6zOiuMoHSemJy0+bn5yoCngvI5B1eJsb3Gd5cVy2U7DFKR9T1kpC2TR3tHmw0MDsgxBnAIZ/HI6JiYZy5+e8kmJqaNmPCbYBB2YTOtlK9H7Ryi0io36kIMNDE5SHDOY1Avqa8yuZaUNUaYiXrr6e21wT2D1kEoiPo6W1uF1WFe9PQY2SuuLKb++PPAO4VoiQsowgphnJ9NmbR73XZ5KMlQ8aBczVyZXdHC1wIRFMyyHEPbDvgGZ5bF8pbNYVxfrI1hxTfowktqyLzuDZxWebNEQ0JOWFaz7hkYsEy68YHlUJTI47tH83X1mJdBua29XQ7H3p5egZMwjPqFx9SGrxH/i2TSyZTCYBHnHr2kNqiHzq5O/aXScZdIkJDvfsqlSteXTDRYa0uzpVIYxukDN21paVmrpwvqUdCP8hSCDPqMhn8r3BpeFcu4glOIMAHxekJgxeSAxYmzHcOKki2Afih+3pBDX0+vHD8HhvYLXFDh9TWd8ivbqaWC3ApjRE1JRDftSgIoV0A3EEZsBWls4sBpiFsymZLzBup+dBQGAICd1bZC3XFDcMDqdJzqOMnTPoScT8Drrj8OfnEewdABEwUgE/Wz+bwcSwP9/QKIoR61qEiioUGOOAAw8QZYw9yAjuN9aXFJTAqVEnL9RZChoCNg7Ors7FB5Ors6KjCr+EwFD9DrxAjNCbitztrbW62vr8d6urvcOLjGODin0CFr2TUvsjJpPAKH+bz19vYK8Mx8EtmxwhxWknfe/aNduXbdpmeWbC0b6tRKBPxjljGUpwrZoP8kFNb4xITNLy4I5Nfc0mK9vX3W0wtYJFX6lNdp/xtKnudgRSN8DH0ojvml5WWbmpqy5ZUVzclLE3NHXs/pr2ED5J2wWRASqK292TFs0aK92pUn4vNCwMJk3Jqbmq2puVl3Ad4CsAiLh2f7Kn8cMOO9e/cE6GaezObCqRwwnLEl7b784Z/8sVdEB6ryonK/MZqv/vwcofyX+5gGMh52dLarfwBoR3/J98/01IxYLyUG/ypAsHVmkJQRjunKlauFxQOAoQFkAxCnr+nscGGyCL8HWIV6gIGkfMvlNm1hft4AtcCSoTE6ldC3GWmRz4e5ASzhHxv7HhhI+CvmN9pCuu9OlP0fAA0ASvCncIR5UztYWVlVuEIxuZU9ttMh+gh2HCDYF19+ZX94+w92/cZ1W1lZtpbmZjFjvfzyi/bMM+c1DySM5sOWz055/Kldb6iPCbgDc2Qq5b4n6A++u3LF3nn3Xbty7apNzcwI4FzaTzzAIpNALwoyD0D5eRg1oQdUR1a46kTFYdkpL8Mqp/3lmn8BETa1tKiPZYZP/7yyuuJYfsrzHEpVl5S/GOSuYoSjHbOogpCzzPWV94eV0dC7H+fdMA70cS5nVLZIApEEIglEEogkEEkgkkAkgUgCkQQiCfxgEnAOl9qTx6BSzZZTS1rh7159Jwevdt/9YaO533fvK3+umgGgaPnk+drLFd1ZuwS0UhLASjpjXRixM03G6vsdN18fgAHi9Vqtyy/MIqwEYmXa+rqLxV4pLUIG3Z+ddQ7cmAmw0dPbY/0DvZZK4ZhyDkkACztt3MFqT1ZPd3Z2yRlM7Gecn3OzszJwd3a2yTmzNS2exkDFFbdydOs9uz1TbFWFvYC1pbw0rDpkZd/AwICNjo/b6uqKTUxM2PsffKTVZEtLK3boAMamVks3phW6icVThXD2vADr9WO6yVAfGOPRBcKdtLW1ihUCHSmoB3IoF64HCATdDywrOCxJY2x01LJrq3LQiIIaR836uiXqt8YadyxRAQ91XZ2cp6wQhs0Ch028wYXkKX99oe7L6sbfV36d8/4cLCs0HxiLWI3PKjvlQz5YRzW+trZqrJqXJ8Ogj9+w3HpWToKyV25zyFt9jra5LbgUyzsHAbJPJZLW3dll+/bskaG/ZIX3zkk9OXcEIhbrGDUsH32dZZoycgQRUqR8c+OvqxdfO7SFJOwKnZ0KjcUzOFdwqsNO1dycKbaHIEGNxeWJBzWO45tVvoSiEubJTOxYOTxYfvMv98c7/Qb05jhqNjc2BJ7a2Fy3jQ3SJMRX3DbzMa3CRq8JBwGYYINxI5cTEAGwYbWtcCVw4KGDANj2Dw1ZR3ubfS8dVDgm/wb/Wy0n0fnvJQFCixDyQmETHBMfTDv0c25VcV7O0/nFJYUFIKxErM6BKeint7BAhDJTGBM4F7BBsHKbvr+ltVV9bMk9oWfZpea92tfH6y2dbhTAqr4uZusxF3oD5yt9v0C9/qHwg2VpAkYFrJJKAtIidcpJe1uRM4q+vTCOV0jHaaMbyAALtLS0ypmM47Ow6abwoMgVPz64FDiTSQM06LC+3m4bGR22bHZNQFnmZAA3mbc1NDyCk4q6OoFV9uxx4/L42IRl19cV6ub3f3hbgAiY+PYMDkp2rGBPJOJGqBz6v6pbhfqoeu/3vlCsJ58UqrGWyxpshPwxt+auTKZJgA/aDICTXA5mCDdvDibQAmzlmRtsrqsdxeoa1M8y76f/x+nLH2CRmZn7YtghveqbG78AjwHiYk7V0elCERWeqSbL0HnyCWsS4ZrIK++EZYtyeCahQnrBDm0eEDoAL8aoGAxv3d3WP9Cv+uQ2zQnZCb2rPJ2f5LFWfvi6Dz7MfRmcyAugFeXfXysrDIA0GKm6u7sUrokwQEtLiwr5Mzc7b/mgagv9X8xsYXFZLESwLCFjdANAOGBsABxdnV3W19drI8N3xWw1NTltY6NjLiRk2fsBiE1NTdv0zJQxP+U99J+EYuvo6Ci7+3seBn27r2ve1djYqPwSRq6AOdjmNYjRSx2wC3klzOJGfkNzb4CR9IuEp6l9I9U6AeBnl5fs4sWL9tFHH9kXX3yh7w1A5vv37bPnnn3Wnn/+OTt0sDSMZu3veczupCJgWm3NWH9fv+Z10zPTtplds9t37th777+nby0B5AcHrKOt3WCZApzI+O7g27uQCe8LKp/mp12UqKA4gGA43kWa4VsrPEf/5MDbxdBqzIN0nl9AlisrwbHrz5gXwSxDH8mzCr0Zfk9ov1AGNLC+Tn0ATHDMfWBS3LrxRIWMbr3xiT1TiznsiRVOVPBIApEEIglEEogkEEkgkkAkgUgCkQQiCdQkAX2thu/MO1+mnDulH6Xcqtv5Ug5vZYf6ci4/F75/y777yOdD333sOyM7liudK7enb3nenZDdIAAQYECL1h5VEdRDOI0zKJVKWaYxbawELmxQAqA2papTuOzVgvrheZhatGJzE8f5um0E8aALD4R2XHxlF6ca3YDhBQeQf79eixJU23g5+at3BiCcn329vTJcs7Iuu7oqxwsMGpOTk3b44H63nDCUnrSqYPSsk6FWFlbu2ebVoSS+9y4OE4zuT58/b3/6+GMbGR0RS8bly5dsfm7O7ty+bWdOn7ajR44qBnxXZ6dlMNIlWQEWMtB/75z8RBNQf+GAROgZq93TmbThSKTrcjpIz+JWoaoUQd1xTXoU9HWNjWnRLbOqV6soA2YHwgawktI5akL6T2K8A3YV/gScceFcCEMFdbNACFX6tJ1UqNJ13gGF+FpuXSsx11bXjFXvyzhpaFMCrzgn0/DwiBxNOH1V1lhMbRA57Wbj2do3f3dMDieM74DdAD08/M2/6+Gn/OdM0Y9dbkxEKV09wYwC0E7s3X48DjTarx5VVQZhqFDmhkTcUo2NhfEQR7j67iBsSnm5vCO2kiS5htNQbSl4fzabK3FE7VKVBLIiBBsr4FeWl/W7ll2xXC6rlaJEEcDofm942ObnF9SB4fykMdP+GD98qy4vS/GYBucaN05nwHwd7e0GoEBjAr723TWB4J1bpbTrZIqZfOz3nLQeTEJywuQ2BEpZXVkNftdscWlJlScHdjanY1h/2NTfK8zkpoCxnKv09pi5sVzzxhhgBNfecK6nGlNa9V5X1s2HK4tyka7TxE05enDuM2bADCTWlUxGAJTwc9r3D5ddgJ0FBz9tDYAWfQBtfH3dgRF31vngDspTX1do84RhZJrF7LqiMMrywSH9BW2GVewwMgBWwFG9vLSs8EAAWBoaArasCs//lE+1d3TYgf1DduzoEYVRWFxYVhgTwkMCBD52/KidOnnSjhw+bL19vXLst7W6EJi77esethwq6TLvQE9WV1ZsaXFR/SmADvrMhkRCwI1bt28LpED4E/WfPrxl8JsHpYCS1AFSabDs+oaNT4yr3hk/aGs45ucXFhzDynqpcx7QI2pNPgAW8kt7iDfEBTgBJLDbDcZA2hP9twAqm6a00UM3BgQphtoT4F1kQF7ZYOiCNQldhilEbRZcsXddVxPobjP7Y9wfyrt2qyFPy/IG+KqxMWk9Pd0GGP7O3Tu2md20ublZm5qeMhhS0pkmi9cRotCzq7iQWbR/5rkwQAFYOXhwv6WSjdbb020DA/128UJS/cTCwrwWATAXLd9YhDA8PCxQEXpFv4d+HDp4UGw4hTYWqtfyNB7oWGaGmMALzS3Nyr/eVQPuzs+RUsl6haERCJxIRoAqA52XsGrMGEUDVEjffu3adfvD22/bx3/62O7fvy9ZEO7l2WeftV/96pe2Z3DAkn6BRjDuFGRU4/seu9tiJhDPqVMnbGxiTAyYMJHBADQxOWFnT5+xkydPGAyX/X191tffJ12P11DX5bKijvWnC26fHkTfUFREqB2WPFvtfPimsI7DtrNJyN91g0mQsMn0Y7AQAdTWd13Qt8LIMzMzo3tlQ4MxNk/fy/wYo0f1gvJKbcx56uKWaWoywnrBtlS5LLUUxCf6ZP7+EF+2T6Yko1JHEogkEEkgkkAkgUgCkQQiCUQSiCTwhEtg6wfo1jPBtysX/EU+zotfu4EMi44hZw0MiZbnwh/kwbGMjTh5MQR4o79P2tMl+HeGktt2l/t3+8y2CUYXkadz6LBiOC6GlEZWl2H5rXFzVeIqp1DfHCpsOKuFMK6UbhhJ17IbiguuePWBjhB7HPpaZ5CBQqT0uS1HwXs4j8ER5y/04I2NKWds0vk6hSa6Pz2jldxb0tAJl//Sa15zS8/ufOQMXKwQxxjvgAOuHVR7Fmf1QP+A/fVf/0pAjM8//9yu37hhyytQtE9qBfa94Xv25Zdf2r59QwpDABtLf3+fAD4YlqFRZvMOPYUuqPbCR+S8rxW6JRyOyJQ/Vo9iAGdFnQy7KrqHBJQWzkmleA4dyzQ1a2Wyi/FtYgMiHAKrQTGwl28e1MSqO/aRMQAvdBWHHw4fb/Auf7bSMS2CNHx3y35ufVOhC6Ymp7TKeXZ21ubm5+Q8xKCJ859QPxg2cdpguFTIq5kZGxkZlcOH0Gsb+bwDG5DoA2/hTj2ciDOdckZ3xMxamlsEFGCVf7msw08+2L4vQ7X8PFiqP85Tviyu3uVsbIgbzmb63vDm5Biz+mAZJb0H/7yG+2PvQPeGdVaq44guGZNDCYfrp0SiuuCverBp6EG/qzG9yOzkS7SWNa1AxhmDQ3h2bk6O78WFRVtcWhToKru2ovYF4Ar2htwGDrQ5m5iYdEAw9LWav31rZoMccSEvAAHG+NbWFhdeyBfF5/v7/vqCft90nrDn1QUF/Rydw3p2Qw5xHDFjExO2sLSsPm1+YVHOTRw4gAZZXUw/DA0+v4QKuX79hgsvsYkjifmJ64erVbWrMq66iSd5wdHe3NSkEGplTW5rzZToHJdhg8F56RxayYRrtyJF8E9Xy4y/HvT7bmLDPIFyuDQpK2mzRrx84hOUQqn4to/vmlXljGfxBseqoUdD7yrtkEvndLFYvTU0JC2ZbHQseHkXQhHZO4aVEMNSOM1HYB8s0NGjR+zv/vZvLdOYsW+//c5GR8fEYkPIm5UvVuz27dtiWRkaGrK9+/bansE91t/fa+3tbQIDi2Gwhvr8IcRR6bWM+R78p9Ak6KKZ+tvPPv/c7t67q/mvn2+WONeDhojjXXOp+rjYIphjjI6Na75AKESAikvLsOvgFK1SsgDcrXmXEV4qbpl0RjKr8kT104WCBi5Zf8y7K2SAME4wXcDCQnsBWFFndQKraNwL3uTy5hOr/vqf2hUVm+6KrFfKfqVzFQpRHzeBNQgzCgsU/QLfZsgOwMrU/RkbCPoNHqcbunv3ro0Mj9gGYKHNTevq7BCQoq2tRR9znZ3t+t6Ix+tteWnV0B3A0sP3hi2ROGTpxnih5+La3Xv3VE/0V2I86erSt4pCRtZYjgpF23rK98lQEBEeke/ARINAiYQ53MavvzWtQOwC/insW8C6iS4KJFCtUXh99bYSQGH1Cq343XdX7de//l924ZsLkhlJIY8D+/fbqZMnrL+315IJ1zer2reRDW/f5nLF8jyqJ5ET4X1htQHc+cmnnwjgTB84OztvX31z0e4Oj9oXX14Qy+XQvn36HRzst+6uToVohe2VTfYoD0CpIEG1O24sCNd/5/grVeodtQjwLLrDVz+2D4HmHDB7dTWnUG4wQ83cv2+AvfiWA4TLNz4AMhYk0D+TV+Y8XAeYw5wIWQg0Rb8c3FOtXoOeVJf5XmCOwJwHUKD/nqj2bHS+ugRKv9Cq3xddiSQQSSCSQCSBSAKRBCIJRBKIJBBJIJJAJIGqEpDZI7jK56v/2A6fLz6MgafwnS7zOdfcGT3B9fCj4ZuLtxYTDPb4yC48GCQAfEG5KU9jy9M+kWoXovMPRQKBalBXOD9ZaQtdfSEEj1edHV5WdKSGjK0yvDr3SvnjGJqh/eZvLQu1udNSnC9aBSTlKX+qwrF0rHge+xSU+42plDXUE+ICy1FMFOeEfHFAmOL94T2S4rXuV/+HL+9i3yUilfdPkdwOW1NT0o4dParVi9BSwyIyPDoiIydOX8ALE5OTdnd42K5eu6ZVZThavKGut6fLmpqbHDtNre1rhzz9JC4jzgD4Rr/EP+KaO1YIACwul65n2b7gXCXMAc6NZKpRKyBJAKOoVu2uZRXCJFxunyLvRZ+8s1SOmkzaYFnZDViFtL16oxZr2U2bmZm28fEJGx0dtXv3hm1sbMzR3i8tyUlLWCtWHWfX1txKZC+PWEyrq8UMEygvaTqnlc95uDQPYV/+X+e05T0YQhtTjWIceKhvJDElWA3F8BDK8udMQn2ALxRhcWJiWsC5Uh9aFupk6P732Qv3xNtu3QAAIABJREFUr1zZNLfKEp3XnYETEeYH/koGdJ9I2a9/g+ua/CyhONi786GHghOF9qYQFXmbvj9r4+PjWlF97949429mesYAIq4T3mcta7nsmm1sEB4uK6e8fEuxOjm0uC6HgvQ3yI3mJFty4NoNp9WA/EpYs/qGuAG0BMgmjI8vXCj7O+1WekTntmZjp6SeuOuBFm4pN7pCVa2u5WxxYUHAOoCXODNv3x22OYAqSwAkVuUsB4xHmA8cNoA33GhMf4azdV7nXXVQM24s2PLS0AmvUjzjwCabYibCieP1OHT7NrtudNE4JN0DHNtQuwOIDKjfpO26tFwjdeUgb4W0VcDggSBHwnj7blDzKpzMjl0Dhg2Am9pUqOBGpbNNkWL1lkgkNZbCdEMe1gFFwHTDeOOBm+F0KjWSbV7xY14i5N+Z06cE7mxpbbfvLl+xkZEROe0B1DGfwoHIeNt9tUfsEbCywHYAELi3G3aKFquPl30g/TkKFZZ58D7YA2G/AEzEqnwcmMzT5+fnbW11VWUTS1UVxVa4CzndXZgIVuszp1hbdeAP0gOospbNWXad1f6lQHOfJf06tVXOADAw32Y+VkAs7FJGalJl+fbvCycF0xwgR8IhbcAAQwStujpryjSJFcN9XIaeeIT0NZRr7VL+SjLAQe7roFrxOJ/OOIYVGCsBxeL4BkA6NT0jlo/e3l6zWIP6Z/paQgGNjY0aYCieJ2QW7SGZcH1LW3ur9fX2CNAFUxphf5iv3r07bN09vZZubDN12WZyygOEgkGTfg0Wwn1796o9hck7a5mnlMul8rGTFCrEvIq5eUOiwbZj0Kqcju+Vgy/SgoCLc41qz4XHGu6J1dVrvPvTRx9roQEMn7Q39BWhC3iwsakvX57Vqwrv2/qWSrqw9a7H4AwyQB51Zm1tTUb4trp4nSVTCbt8+TuBVmbuzyqE1fziqo2OT9u94VG7du2GDfT32dDQXtu/f6/2e7q7rKOt1erj8WARThUBl5wOZjPBOE9m/Ii9nXSDbBcALLSFubklm5icFijQz40J+bu8tKS2wfccfXlhvBXgymWG8JksoKA9itmzFG+6XVaCfsONWyziIWSSQK3oXrQ9kAQiwMoDiS16KJJAJIFIApEEIglEEogkEEkgkkAkgUgCD1kChRApboWRc4DW/o4S44q+v51DzBvlvV19J4OVSydITUZ978SsPS/RnTtLACAHdYxRA4cnRj9t3gqzcxKqGOrXPYfeuDQqGXtwjsAagaFGKyWD53C+AEYos11v/3avbNhnWHWccE50jJbkgbLBTgGdcCXACq92fy6nhTJs/9aqV6XjwarpimkFoi1PgLwDWiH0T0tzk3V1d9l3V67YrVu3ZBiGvQBK+DsLd7Sq8WpTkyi/MQQfOnxQK4pZtdfb2yPHbXn6j+oxtaKVZ/Jy54sG6Xi8zGEYrLivIl9ffsiDinruGHBIH4rm9fVc4Cj1d4d+fZ8Y8FygX6lkSg7QnfqxUCra9fqNzwVD9sWL39qlS9/ajRs3bGxs3Gamp6Wv6C9tESM3q+MIJcFvXX1cTkoYYgDQ5LQimtV5zkgJQ9IOYnBZ8m2nPIM7HftGQ2gKhRioC+LHB4b3nZ6v4Tr590CNGm5/5G6hbDgYCakgYN2OJXDSoMroV9BZVt2zjz45Xakrgg13TK/oECv00VLMQHO0zDuUSJlCrazmbXJq0i5cvGDfffed3bx5U6u0p6bQ3WUlDuOL2IfqcCxSXkeTL2BNIqm+n9Xy9Muu33TvC2cjlAPtetXjV86EfF66h8MUx6lIK8ofqvlY0n14Slzzex/tGz3cqVIpcut5AQOg8v/666/s+vXrBmhlYnLKVgGrwlZV74FWgALrFeaEtkFIBc8Uhh5pTuEqPujfypTSZyAY0+UY5JxrNNIxAHb03Q+6+bYiJ9gDOICk5x5w6DPh86tjMuv6cX/Z/0rl6RWDsYhyABgQSCt4zCmv94D6J0t/wSKQhsbBRML1JwKsuLAwnsWLp8gvm5O9y1ppaj/No3i8TiwRL7zwvLW1dVh/f79dvXLFbt68JdY6VrADopqdvW+379wRI8WV/j6t0id0yfFjR+3ggQNiD8PhX+hXqqjcDy0FzVFWCSOx6ljWYBeR75txYMOywRze60al/IgNiPxDniMwmQv9AuiW9GF+CLMTVkqj5FwMoHu9JZKu/UpVvNO1Rjk57XL/+7RLj4o6B7h9cXFJTl6FxoD7o65O+W6IV4jvRUI15sO/+3H5BWfV3tZmnZ2dCpWSXZtUaDXYrQjZkwsAaUyrF5eW9Y0xOTUlNivqlG8J2oEXHywN3T3dNjg4IIc7IcS4n7B+h48esa7uNolueSWnUCbTU1MKAYheEaoPtgyA+FW3LZVe9c4tF7zO8wuIi8UXD8om4bPhf93LfAddejacET8u+HPMYfh2o3+ZGB/Xab4X+MZwALNhu37jusLeEJasMV0b0Nin/1j/BkpH/9bR0WxPt5wXi9PePXvt8ndX7OrVqzY5NR2wT67Y3YV5G7l3165dTVt3d6fC3xw+fNCOHTliR48eFrNPOp3R/LO63Hip//N3Va9vf0ehT+WEbyxGm1q1azdu2Ndff2NXr1yzkdFRAcKmpqfd2Buv1zyE/otx3M2T3XyHuUU8X2/5WN7y8w5sS9/qwgg6O4feT/ZC7/R54tfnXP0zoHi+CavcG34u2q8sgQefMVZOLzobSSCSQCSBSAKRBCIJRBKIJBBJIJJAJIHHVwIVPj516qF+lbrP3t0kqScKK0YDMABrVArnXJr6AKd2ghVjJcXBPiRjuTOYu2eDE9W+0B/fmv7hShYInRV2GPsUYgLHUFjG3FNSOVuz4wyG3FZ6o5wdrrpLHuIuDJm6FHqEXW98LHmgxgOflP/1j8mQWVeeO3/1B/j1DuWyVarko4I4lAHOk+90usEOHTpkPb29durUKbt+7boMmzdv3bLbt26LiQPwzeLigladEyv+ypXv7OLFi3bu3Fl75pln7NzZszLkl8vhByjpnyVJ+gpWBGMUphZZbYxuFZiAvIr+gAUO150AJPUASBxwZLevZZ1bbtNsZmbW3n//A8W2v3L1igz9OAtx1uLsZ3UsDoe2tlYx7sAg0dCQsPp6DJxxi9XH7fad2/b1NxdsbmFewBVWRjtnTjVN81VGrv2fP1fbr2tPdQUwTUODW9Fa29Pb36UcMeDsZtDZPsmfxFV0F51FdoWNUFc1Or1DT2lwJC2xRhSAnPJAFhzMhXdU2fHaQTq0L479uZ06/GzWbHRszD799BP743t/tGvXrglkRUgJjOrNTc3W09NrrOLGGYouZzKNouoHrBJvSOjvzp079u23l+zSt98G8wN41Iu5qJL1Lad5pwBdnmliyx27PYF3YPf52O1bHsf7/ZCHgz+3bgr9883X39jvf/97u3r1O5sUzf2qHDRt7e3W0dlpXZ1d1tbWLrAqTnM5l2BIq6+XXrAK+Y/vvWc3blyXs7qos7uvI/rWkja4i0qg6TLm5NE3AWh2r647q/cOfV8wX3bt3wG9dlEE3RrOgyQoYK+bazvZ0Nt42dJn1e3UJew2Cz/s/WRdYAqz1pZGO3f2tBgezp45ZTdu3JQj+dbNW3bj5k2NubCWTE5OOfDK7duaTwHCe+b8U3b27Fk7eGC/NaZTP2yed0pdPnOYGRw7g+snY9bX22t79gxaW2ub+l4AXtvqt/p6V7f+Ps3F84RSSWjO0dPdbYlkomqO1P4QMd8MMP3AaNEQD0A9JSNV1TS44O8kPbcFc3Qd+3PFJFy7D3S+cDkob3hcLT7y5O0hl0CwMAkSsrKrq8uxni0vS98BlcKk1NySEfvVrdu3bWx8XGFIeBaWyv37h2xo717XDSjMTkLsKvuGhgSYX1hctIWFRc0DYK/M5fYa7Cmz9+8bYbfm5h0jFgl0dHbYiRPHLZmoACrSZ71mR7vvTKndALwnXQ50gP7qx1AH6bFvn7E6A7Rz//60vnXFaGN5AdTX1lbVD8Foytj34osvWDLZbXWRV3xLe3Whfs1OHD8qsNTJEyfsuytXjbnjrVu3Dd0dHxuzleUlm5ubtcXFeRsbH7Obt24I2HLy1gl74fnnBT6EMcv3OSUvKvQlxVGv5Pp2B6EE2SWp3IbZhW8v2Ttvv2Mff/xJoc7RDxhsO/Vd12Htbe2aH2eamsT66cG0hJudvn/frly5YnNz7ruOjHsQVqG7rJCvAs48ACRqDYPyWPbtUeHZ6FR1CURNs7psoiuRBCIJRBKIJBBJIJJAJIFIApEEIglEEthRAiFb1Y736oayj+2KD4XuqXi9/GTw8e+NoVwmiYIBCQMTJ0PspFteEZzQDw96G/6WG8tfHh0/iAScwdppT9jwt9u0tMpsO2tKkCBOWlbEs7KI9/GHMSfH6jPRSNf45gr6QCkIKQD9MsZ9HPs4wQixE9bJ6m+okGj1mytekSN5cyMIYYDzcwcnQtHGrPQSDXXW2tJi6ca0HBH7hvbZibExu337jijsFTt+eFhOYlbcjk+41ZPz8ws2Ozdv2bWcnTlzylqa0xXz9yidpDZ8vVGf6Co6kvO0+NA21LIFnSMLS13oCReKBL1DHwlrw4pzv6K/JEnf/wRuffLDfQLMPIC6bGyaTYxP2TcXLthHH34khgpWvZLmQH+/VgiKLaev1zo7O6y5uVn5w5FQz0piDPJ0oHV19tnnX9jI6JhWiedIOHAA7Nb0WnsxHPCCeoAZxAMuPIio4C0pEeD3O5AjwI0a3y+hH/Vpx3qg4VFjmjS7oNsPkjUP2CowRxXSrS018uL/anvC3TUyOi6a+z/+8T0Z1e/fn5FTe++ePYZDa9/efdY/MChHGSuz0+lGSzWmrL6eMEhxF4rL6uzLr76yqckp+1bJSjJBjnaTGz+/CDTYJ0MStSt14YUPIo/Cw0/gjhNxWOhurkc/Oz09qxXGH374oX1z4aLNzd1XleC8I9zE/gP7bXBgwLq6uq25uUXU9axCB5Qnp2NdvYCKhA26fPmy3bxxQ356zRcCANhuRK5xJJhrME0pzEl3l4gDgxYepuw1KpqUi3sr3+/zt1126AsBb9L/Ei6Aec7mBkwXxad2yhH3ZtfdOLiWXRPQgRAWOMqYlxGmzPfrLtWdUiy++0ffq5DVZDJmXV1tAs11d3XJeTl6fNTu3LmrEGa3795RSLP5+TmxUGTvZW1+bsHmZmfllGc+SXghsayEqy68/wMX3IeAwqkJ6E/zlHxeTHwAlY8dOx6c33k+JD1TfkNjOQxu9fUK7dc/0C/2uEpFCqs96YiNUXIIUI87yYT6YSu7j0Oftm7x9wW384ODn3m8K78rJ6FVmAMTfu6J2iroucofkmsmnbHurm7r6+2z69dvWHZtVQw1MKPMLyxYa3u7LS2v2JWrV216ZkbfX4QX6+ntsa7OTsuk026CEDNriNcrpA/99oVvLtjExITkDtBrenpa7aa1JSOwCiwugDJ4GNBMV2eH5rXoV9WtAJqtUPFVH9Jk14Fdg/CI6KT6Lq9M2z1b5VrB6b/leki4W665E4LewH4n9qOYpdPNYr88dvSYwnl98cXndvfObYUgpf/5+OOPra29TSGMunscS02VpGs+raLvnNWa0/uxb6QoDXHAh4S83ScQFgxYhKRy38R3tZiDELqEVwWAODo6LqY/9Jpj/p46d07fUQWMeImq8Rb/t4sSh9ohuwtLWY0rb7/zrn322WcK1Ubf1NLaYnv3DNrhI4cVfq6bkHNtrWIdAtDtwSrobz5WZ7fv3FUYLwDdhNeUwazGOvX6S37oH2XfYYFQDbaZXZT8ibo1Aqw8UdUdFTaSQCSBSAKRBCIJRBKIJBBJIJJAJIGdJFDj9+lOydR4HRcojtBgCacMiM4p+yC2HxkzvaNXIBXntOd8LZu/i/woT4XfWp6O7qlJAiGDjTdmIHcv+5rSCN8USi98urAfXEcFZKRJJp3zMlg9Sox64jv7vJQ8V0OmSH51dcOgDl/fWBfTBKAY3tXW5lagFtIM7fA+2XLK8/8ABp78plkum7Ncbl2GosJC5dD7atmN15sAPanGLutob7OBgQHbv3+/GFbu3RtWaAVCyIyMjNj9mfs2e3/OVlfWZOTKb2wa8btTyb2WSDwmppbAoI2zjnBSGCBz6+uWrK++Ergg56Beqc5szjk4VlfXVD/0RzhAcITgrKtsVPetIqyEDkj3IPWLM3dkZNTef/9D+/bSZZuenrJ4fdy6urvt/FPn5Rw7dOigdXS0W1NTpmDMbEwl1TgVHYnCxczu3L2r/OPgBcDg+t1wPgtSKN3xbbFc50vvKhx5biBSdu3FObt8fw5L0/fbKsn4+6X4KDztR7fd5dXJSnVdNl67sXL3qZU/UdL1UbWqeLOVlQ0xFHz99df27bcXjVBlhCXp6+uz55591s6cOWP79g1Ze3uHQpMRToM+KB5HN91bMKiT/t179+TQKn93zccOA1RyO+nqPcG7Si7WcOAkW8ONT9gttRLOeLETCgin5oULF+2rr7+x8fFxOT0HB/q02v7ZZ5+xffv2GI6blpYWSyZTlkjQ/8bd/COoCMKmzc0vqm+W48UrJjrp93dRF67N7OKBsltd+wr6umBMKrul8mG4ewz01suq+MDWM8VrxT36X0AU2VzOYDVa39goGbdq6QMIJwZzDXMuNuQCYAV2G+ZLzJsY25Qjn/faslfM6I+9F8o3xWluTlpTpkcg0D2DA3b40CExRMC0QpiJu3fvKmwDfdr4xLitra5orINtZO/evQobVO/DZf6ZywaTCQAC79wELJrf3BDApKe7x06dPKlwPn7+oqJXbB+hflgOTTeOu5ARroJhAWD+uWXT5bASFPcrvmpLAtVOFD4UCzcI/F44cjuJZFLgXcYUhUOEqTGfV/hEdPnx2IJ5pR9w/XyrDKBH/RalX7nkAPUBnvT39Qnsgz4zbwZkMj83b2sra7a0uGQ3rt+w2dlZgd9SiYTC1MDuR7hCv6EPgE8JrQXjH+Fb6XsmJib1tzC/YC3NGfX5AFgAlbMBvIdFpLW1VSGsfHoVfymU/6t4w/YnpfNAuWVv2P7e3V1F0jtJu5giOlmXz1t7R7vGupdfetkOHz4kmdNmF+bnBfKZmblvFy9dsu7eHktnMpbOnLZMpjILzda3bz2zmzwWc/vo7DkdTFhzU6f1dnfaQF+vWIBg9Ll+44a+iWEiJeTg7NycQhEuLCzZxvqGJRsSBnPU3j37LJUKZByI0H/DlNgduMZEsjBp3VkF+ASanp4Rq8oXX3xhd+7es/Vs1poyGTt54ridf/opO3vmtBiHWgDoNqY0zgJWAcvla5RlB7QtGDU1HwryoPz5MbmGatN3mvqNGm6ObtlWAsWecNvboouRBCIJRBKIJBBJIJJAJIFIApEEIglEEngCJOCsL/qI9R+yP1SpMXC71fL1hRXfvJ4PebfSchc50De+W3kn50DgRCXv/ri8HHqX/iu/4o5lO6h8KTr7MCQQVG+JweZhpFueRkiNoFiHQQSwAAY+nE+rKyuikvaGGT3OM0FbKFh0ytMNjgGL4GxYWV4WuwpOGZLGCYMDFR0v32TUKYSrcj4w/37/u9N7labC1jh2FwAVGND1fPkLazkOyYndVCpuqVSrdXe2ynFy9OhRGb9Y6fjFF1/aN19fsLHRcVtZWbPbN2/b2sqqPX3+vHV2EHLh4azaqyXbP8Q9VL3qKLBkw7KyuLgoJ0Uum7XkNtT15flBF7JrawXACw5Q+iTSwKDemIIFopKnxqXkqsWxAemMHJZeOcvfVv0YcMfw8Kh98MEHMvZzJ/TQBw8csNdee1XGTcAqCVZS+xAQCtvl0kTPUWW0mVX2y0vLBssG+UPHoY6mXDtt/g791lAMgVa2MZjW4ijdKU/R9R9PAl4fCjlAJ8pO4ti6efOGXb92VU4X2Io6OvrtxPHj9pc//7kdPXrM2tszBTs/z5OEd2hi1CdsDOoppqP19VAHX+zqC3moslOWrSp3fY/TNbSH75H6o/VoIItaZU794rAkzMT1Gzft7t17ls2ta4X+kSNH7bVXX7WXX37R0hnYVGCLoM8NUndTAfVv6MzmRt6ya1nL5QChulBwEl6tmQlLOggjJ2f3gzyP0vLcbsB52+lR4VphJ5zb6vtqQ45pbG11Ve0omag+bpUklDcDBAQzyypj4cqyMR6JjS5eL+BmU1OzQJKF5x5EVoWHf8SdCvlm3EynATG3WU9Pmw0N7RMDwulTp+za9Wv2ycef2MWLF2xsbExsFJe/+046+vQzz9ih5AHLNIRCA+2y2r6PJGCmYo4iYG0iqfEdUIfqP7sm52dTJi0mAl9ssuezGD5XSz78/bXc6/r2bSYGtSRS6R4yH8oIzIOUn+8GQJIaRPJ5g1kQXS5sPOMLXpZG4Z6f4k7gH3ffyX7MdICi8m+JkFiqliSZiFlHe7vCRiGzpcUFfWNNTUwq1MjS0rJkB1Brfn5efUJja4udPnNKQMLCFDKQYboxJfB8Z4cDo+KcB5QIGJHnBwb7FI4FAHYW5iYzsax1dXcJcFhIr2qOuSCOkm3vqHgRJfR1rn66FglVTKn6SZ9+9TtcFoJXHzx40H7xi7+y5555Vm0XUA/AMMZDQjLBckNoxU8++0zsYq3tbXb40AG14W1eEVziJeEyutl3NRvLzuk9WncAQGxvbbKWpibbt3ePHTiw306cOGHXrl+3Tz75xC5dvqxFAcvLK3b16jVraW4Sc2VXZ7dCUxVMAQrL6boSScBPVAPpeglzuqL+er2rc5B+2Ivefe89u3tvWHOgpnRaYO5XX33FXnn5Rds3NLhF0P4d/gLfdYR1xY7AAg2nVG6MLqn18gd9Aq4gQSfoAVzR11mJeHZ5EAFWdimw6PZIApEEIglEEogkEEkgkkAkgUgCkQQeVwnUYBl5GEX3r/Ffwfl8wGbiTCHFFfu1v6zwDa0Pf3/kX1RqYtGHuL/F/1Z6VfFxXS07rPREdM5LALluIzAMXDi5Cw5uKGnDD2xXL/4dD/ALLS8riAh3wvt55+LikuJ+yzjr30vet+JMKr4R3+fY2LhAL4AR6uP1cjY0NzdVZVjBgMhq4gaFqnCxz1FdqPJFt0/ok1rCzgAs2ITBI2cYyXDYkQdtMmptUwkVS1P5ZCoZt3hHu1b7suKxr6/f+nr77a1/+61NTIxbNremOPWXLl2yoX17HnnACmqA/H34E2y0a2tZAYI2ghA4lSUVOhvoEuAP6gbjOsZA7wSA3YRV/qlUo1a0hZ78QXZn5xYU15z45IC10o2NRqiCY8eO2dDQkHV0dFiiIV4I86Dsh1Z246vZCPxDAnnNztrm+kaBg0p6p/63cvZJLxBJ5Rsqnq2uvwUj+e4TrfimwsmHnV4h4Ud952ELhnF/+3GCISGXy9vs/JwRAmh+YV4sVvSdewYHjbAUOGiamzOFldRSwXI/Eq8B0JDNixafFd/qHlUlRedcdW0r6i555j7asW/LSoYLD1tEj7rKfI/870aUOII2cw5UCBOYQk3k1sXe0dnZZQcOHLBDhw5ZBuYoVvDLC+TeoDqkXwv1dfRlKysAQHPF8VTVC1NELRODUk0itEphLHkAmZTo2QM8X3gklC3XTgKkjjS6usS9rvMLMHdpaUmhOZoyISBF4SUVdmIufBBgMcbB+/dnbRPKL4X9aNBKf5gZABK5NkReAqBOheR+cqeqi25rVmNmyWSddXV1iD1iYKDfhWJMN9p7771vi0uLcixPsIL/+g058TPIebt3bHdtaw5qPgOzCywMsKxQN4R6gHVlYQG2hinLZdcsnymGgFS/qL7RZRdWq6AmS94ZUsPC+Ur3FS5W3KmUSsUbd3ESYHAg6oCRi+auuXqiQf0J5cehS/kBMTPd1lQ9aEo8LyzcLt76o96ad2UGPEb7pmtUGeocCBkwNd9NtaoY3SiMPMwnAZnMz81Kn8fGRhVOdHxsXOFEYWgEFEj6zIOPHDksdj99d+EvB2Bab9ZQXycAzODgoHXduKXvjIUFN5eF5WJpcY9ArJobrOf0TcfcgDCXhAOsLeO1lq56TQmvUv3yrq947fahVsoT0HV14gxnjEvuG7q/v8/OnT0jRppEQ8zqWprtyOHD9uabb9rG5qZ98803tryyorA2XzR/ZS2trfpOhVEzkXgAgEEgOp/f8nw+dsd1rr2jt+h3YzqtPpowWCxQ+dOfPlaoVBhWpqemxSR0+tQZSzdmLJkqAjzBnyIz6o1NY3zAuuptIcGlKiLU07oGCOzmjZv6vmR+go2Db7pDBw9ad093ledLT5Ma8+LZOcd65PLg8ubzWPpE9SOe2u0z1VN7cq/UMtN8cqUTlTySQCSBSAKRBCIJRBKIJBBJIJJAJIEnVAKBFaLG0stYwcd2cD92FL9fkkRw0hvGBE6RowBDGc/ntaLVO+1Lnt3pABCEjGvOeMPtMADgfNCKEZ1wdnB2q5VQH9tc9daCgG1Ary/jqK9YRt0Y/Vci4IJRpigXb9CQLvjTP7BAqVvCAQwODAoswmsxOrPiDErftdUgJj35qCUvebOV1RW7fPmyYlvn1nNSG5xfTc1Nxio/xxbkC+h+4w0NWp2aTKUCYErMNqFtXliQI4hV3Xp/DXnAqUb89smpyQAQsal2VGsRSnNW+YgmSpSfdCpufT09duLYMXv6/NPW3z8g4zTtHaaVe3fv2ezsXOVEHpGzRbk54WPCRUehHIdOe2V1tcDcsGORgn4QBp7RkVGtCsVph+5DNd/a1mZQ/9eFnKWkubXaXd9Y6cqOeQjSm5qatImpSYGaSA3qdYzVe/fuE9MKoKSybFRMem3d5HScm501wkBhVJNhLdT/V3ywcLIo4cKpbXfwIPlnyiTjOuttn67tokufbqrauFBbOj/1u36CpdNQW6zIgoMmOAW4am5u1u7Pztri0pLaDg5EHGJ7BveIQQM/93ZgfbcxAAAgAElEQVQb9UofhTN4LgCOuftx8rgXOc0K6VdNotrt/dvlMrr2fSSAg4hQE4BHceBQ34y9hOUjdFRPT48lGhKOJirUyrnPt3rtxxwAdHhk1JZCAEP6bMb1MLBla369tzt0xbP9+flk6FLJboVHue60M1Dg4MSOqlnTDU73VfyQGpfkqeyAuTRsR1PTUzY3N1cYjnh8pyTU/haXBJqcGB/XnLwh7uZBhC8AEOHnhG4SVfbyx+gQEATO4eaWpPX2dotd4uy5s9bX32cNiaStb24KSH37zh3p4Jai71i/W554sBMxs3h9ndgCcYISPos2BXhwZGTYZu7P2HouW2hNTldhXHOfT8wL/LlwlsPn/P5uM6i2upPS+UTDL/fnqvxKlwPwCbfQbNFNz4bHNybfpzB8OJaPxWJKoeeKJ3/6ewDq1L8FfRX7zHlhXCj5PquxKLAHdnZ2Sp/Rm42NdY27fBuMjo6J7QOmj/VcTqFLAKP09fYJRK1X+M/vPEC3Oo3xe/YMWmdXh77nAeYzH4el5datWwo3BOMf/RNMf4QxHRzcs8N8tqg8bs9r4vbKonuDW6Qrvuf7PpPHwitdir43dfmqLvRK1x1YqF39C8Ap+hnGwLNnz9j580/Z/gMHVNfMpW7evGmfff65Xbp02YEIg7UO1d9YdkVjmp9DlV173A/r6Bdi1pxptN5ewqOdslMnT9vQvv2WSjSa5WNiFBoeGRb4le97bUEVM6+lbcH0Rp+qsU/9BzYrrhWmJZUlGcwnlhZZLHJf72IxBWkB/qS9dHR2Wiq1NXxtQd3KUnahuyZDTK0B27HAUGU3Vzn0+VZz0IsqaWmVh6PTJRKIACsl4ogOIglEEogkEEkgkkAkgUgCkQQiCUQSeHIlUGoxLxiPEUi1L1wvLDkrix+mOET1xS32lOJ53Y4xihVbsZiMUY5m2SXER302u2Zr2aytwyG+iw3wSzIwqGLwAayCwQ3jIs4MbTuVw79vy32c8OXwv/7m6HcnCTiJORlipPmxNnLQ39dv+4eGLJFwXk6MNBiev/76azlFS/K2Q1azubzCAV2+fElpoGtYuFtam62zq1OGwkpMLTjOGhvTLp50vWNY2djYkBOI1cuEXNmxzQUZZbUcgBsMYwuL8zKCOWvXDpkvKWjtB9Aid3W22L69+6y3p0flgK0GY/HCwqKtroZo0mtP9qd1p1Q16A/xFW7mDdplwk1gdAdQpBVyiLiamIMuMLuWt7HxCdE1T05Oqj8ifjirSrs6Oy2ZTBkOg503rMk7vLBCZniCvC4sLtji4oJt5F1fiOMllUrJsZAiD5R5m82Xd2J8yiZwCi8sKs4KWBL+kNHOWy33lKeiEpQKOgwiLL99V8ek7f+KD5aMfcXTj/ieq2AP0PhpFSZQvgo6yDhOCJGl5SUZ0sm3jPLpRmtpbbF43LUdhpVqQws1vL6RF7CPcAKwPIQ3rrNp3hLsb/8T6Iz0kBdvf3d09YeXAKvHCdExOzcnPVEbjsXkvIFRDcdp+ZiqapNnJchfzIWOgnUNECp6wj2kVfirVpQKusv7HqQvKU2KRMIqxtXSO6pl6WGfZz49c/++QEGE5qDLh2GiWrvz7+c6GNyZmWkbHxuz6ZlpI3RiYyopdhEcqji5vw8LjX/XT/63rK8AbAdQZWj/kHV2dwnAymCcXV+XEzlXZS4IsK8sqR+s6NRPV1eXwRoIuJZ2ce/ePbt27aoA3+F8eO3crYaWrQWooSy199ZKzGfI/+70hqBQ3M58DVAFAGPAkszzAYnfvn1bcvDp05XUAvrd6dV/zuv0T7D9xeMNAdOf6+v4Hsllc7a+nttVNDLynkwmrbOzQ0BBwikhSr5TAJ0Oj4zY8L17trK8ojlje3u7mCBaW4pjubq3oJ7IX3MmY729vZqriuknv6nvLuQP2GJqckpAbJz/ADYGYGPp6tqhm/Ray4toTMGfr8xqlRBudyShZAK2uFp1q2LaPj/BhJqEd90o8gYrUjxgxeE1sNQkknGFpjl79qydOXPa2jvaNYeamplWGJtPP/vM7ty9Z8vL2a0589kqvxKUNSh5+dXH7LhQ0aXlor3XmxEGq7u7VQBqmEcbU2kxuAKsWpifd8ycAWDFp8RCCOax2CEEWAnGdMZF2l4B4FL6RncUqCwHLJhh0Q0LVxhn1Z4bGqytvc2SKcK4VUpg67mllaxNTU/rO9eFFnaha8X0Gnrf1ieLZ7yq6HbNq3xpi/dEe7VLoBarQO2pRXdGEogkEEkgkkAkgUgCkQQiCUQSiCQQSeCxkYD//KRAzmhevWhVvmjDxp0gGZeGW6kab6gPDNWczQtYsrqaNf7Waw29EaQL8CWdbnRG7/ym6KtZwbW2uqaPeffe4v8qXbiI5MA7vQDgbG7K+V90xAZf/sFP8Npigo/QnreD+d8/V9Yl7pD8trx3u2tbbkZjWN3FFjxYVp8VHhFVNLGnMaqK3jtwwkDHDnBFG5YCktwhPxiKWOV3+/YdGaZ4FoP+iePHFYYgkaxscoDdArAAQAFRbsdiMlIBhiB8jBhWSGyH9+MsgpUFdhiMt/NzcwG7Sg2CcCV94P8xtDU3t7hV6wZNcp0MZBj0f9jthy9bef5xzM3NztnI6Kj+1ta8cXCbOoqZgV+6f3/O7t29a2Ojo8YqUKqU1br9/f02MDAoA3s5w4p7P+XEAikMVCFLBQaKwpnwTmWFgRkdVh/qBoOmmAIUR73OGgBuhR+rJt6YczoC7Lp69apjagk6zN2B0HyrDZu6yUA4E75M/pwz3scM0E5xGWi1rPqnt/t1zwYy1tuL+98n3e3e+We/FhTJS7H0/eWDc+nVSkfFdFzCrvctyq3SM+FzPF9Mwzt7wneU7svRzSpvnlMIN9rUuoz5gOQY8wurUUsf1ZF36pLOysqq3RsesRs3b9nExGRwt8+RH0c4zdvKN19G/+uvB/eWFMpfq/1Xa5QF7i00+eDh75lw7Vn4id7py+9/XTYruap1RzBvK1ZhTE5RehrpT1nV8oxngyBljgFWEGriw48+ErBC52v1+ujF/iX8hkIs+NM1Spq8qEzcz7Ph53c5cSukoxTL233Jmwq5K38l799c37T7MzM2Njpmk1PTBbBKcd5ceHzLDmPEyMiI5ljMyTfWc9bUlLGe7m77/9l7r++4suzMc9/wBh4gQAAE6D3TMysrS6XKqpLU0yqNul9arYeep1Gv+adG89hrHtosjaq6pV6qrOpK74pJJpnJpGfSggbeBsLcWd8+90TcCNxwQACIAL7gAq879nf2PfeYffY5ODKsCkUN7ba0IeTOv4GtO+LxpCTiSVUcQN0G5Z2obtEXKpX/hkLxi4VpKzRGIyCgkpBsCALtFFitGBockpATllw2r0pLH/zhI61LoVPTyE+jsFHbI+Qc59V+mq2SUqB5o4JltloQZfdNU8K8CcV4S++pvz6w/tBWv3DhvIyOjmh/FX1DTOrevHlTrl69Zl5UhIWFGKVdP6z3tj6iaoMSCLamhGIOOsFoI0JpHsr8mHTPZUttrpqZ8XgmE2GBIgqsrGDLFADClppQ2IaiExTssR0QJsOh9H761GlVLCxVeF4sqFiwsCWGbYO6ZXCwX7cNwrarpu/1WL797rpamIDCIvr/sKbV092t7w62FdrcrygYDXiHNRp/+6EBL9voBAsHoFjo/0GJBduKHTt2VF5/43U5d+6sKl/BIsezZy/k008/k2+uXlWFfL+/mueKyFYaW3gfa0bSLg9tPqunB2sO8P4kkwkzthAOSyQclUg0XrQaDGSqx2EVXeIx/Qbqoi1vkVU2l1NFsWw2r9tibYixyN2kCcpmsCoExW6bSpWBfN5TfNkQQtkNvGL43b17T27dvKUW1GAFFN97k9b6gyGO64j9M6HZlJgr/r85AsGjR5sLi75IgARIgARIgARIgARIgARIoGMJ2ElHDN1hwMr8dLTQN3Jeyh76uV5f13MKP5WD4abTW/JVOouEHcHqq/7+Xh1cgm8Mas3Mzsnjp89lYXG5PPyS1w1n8BuNRKSvp1u60ik9z2ezsrCwqFu1YAKi0gCA5lD/8wXnXaMTj9VajoSMUoQ3XmrzXJZvn/eOPa3ksA0Z0UkmjcfSw4UdKLZkG4+4NN1tE2/CM8Jq75WHh7u9vWk5dvSovH3xovT19ulKKKz2u/799/LZ51/IzVv3yj1VuVpZycrNW7fk/ffflydPn+jqbgw8YTX3ez9/Ty68cqGKT6xAjOhq1e4eDKwaU/irK2ty9ZtrcufOXZmentWJM/8LoANLFp0X8uzcoty6c0e++uorNZG9nsUKOVctGJnRK1CHp408suuurK3m6q6O3pCJgkgu6+r2Q7DqMr+4oAOkyPv42Jjunb7BT8tuWDmxx5YF7AvIsLJ1ILhjSzEoEd26dVs+/vgTefT4qSwtZyRfUR6+QARjxljtf+WbK3L58mVVWsFzTEbBOs358xfk0MRE8EC9lqKdWXHF1e3SKupbf2R6vrGMcRt3kcz+/j4dpEa+UNdjIgIm/e/euyeLy8smL3Zs0gvbn73FpXW5dfuOfPzxx3Lnzh2dUDOMjHIfBkn97jckTxNjXOhAqLrGNVJYEXHRs1cW6hazD3jrC+LYUdYAuS56beIEseikmS8DpfqliYDazqnht9VkmVAAB1TM0YYJbs3G0qh7KJzA+lB3V7cqx8EqFWKDBTZMfj148EiWsVK7OLhuB9mNWOE+/hYWM3L37l354A8fyr27982WMUXLEL72TlDC9J4VDHNUBRNw0Ev7zBJp7mibWqae3htS1xyBWq7RnvQVShnqsgvBJCa2loECBFbZaz1XcGV2bl6ePXsu09NzdVcbI8Tr12/Kv/zL+3Lnzj1ZWFhSy2FIIRSXUW+Wx1ot7daVPXqiUs15wH07MW9bSJoG/Od9CwK81LwFiubPyru9U8Wb7zHecShQYCV4LlcQbFVz+fIV+f77m7K0tKKZUzmGn4oflGqXllbl9p078tmnn+kkf3Z9Xdv842OjcubsaTl9+pTE43X29aoIt90u19cKklktSAHKG6Vi35hMywhuPHfLizmZeTkrM9OzankCq+sx+X78+DFJJpKqGOQVnrf/HoK1smgDsseNUZbfMXW41tv6HbU1uk1YuWtMxEK59uyZs3L+/HkZ6B+QaDQuC/NLcuXKVXn//d/L9e++F7u+AEGi7YNyD/rpt99TwoVCgSoV+GTN78fett8Y+06gTtBnwUn2B7HxXJnDvxd6EZu9Z14U00Yx3hOJsJbF2bNn5ciRw5JMJdXR/bv35eOPPpEPP/hc5udWy+NCuDZ/5U9Mm1vTUfFgFy6RT2whAoUQWKHC9xYWSdFnfvDDA1UGKW6pWy99XnkgzEQirnVxX1+vWj6FIuDzF9Py4MFDtbICZRjUqbAyePLkSYlV7utXUcBIH6ysjBwcUSX/RWzl+uyZ3H/wSGbm5vS9gdL85OSE9KJfpcrZ9RJc/hzfjJJIBRSQ9zCk0oP/bbsVSmVoV5eHt/krzbxXYzcZCl6Wih8UAcNhkaGhQTl79rT87L0/FSza6OrqVku22P7uy6/+KH/84yV58RIWQTy9cARl/xBmwLolM27ki9MmvSINbX/pmn5tNottqTem1o6P2SeVlsWwOGFhYV774bAIiPGmnt4+mZg4LD092KIpbr3qEeWRSiVkbAwWWeLatoB1FVivevjosUxNPTf9eBSBvx6xWgwe8u6utAz090kSC3DCjhTcvKysrcjDx49leWXN1nJlR39CkO6Z6SX59JPP5auvLqmVVDzHOwyBxrdI8+4rYvvd8odj6mhHQlo3mzaG97r4nfG8CQLbvfSniaTQKQmQAAmQAAmQAAmQAAmQAAnsDgHTF7U9Ul83U0dg7P3gtJkpFuOn5BN+yidfcKf03Jxjf12YmU6nUrpnOyZRX7ycke9v3JbR0THp7euV3m5MUnk/OzmGgPyBwcJDOCzpZEJ6u7skmUjI0uKiLC8uyg/37svtW3dk8tCkDo5FMC7uTXAVB5i8gZhisBqPHdg3YzY2CZ14NKVRYoizCnzbny2N0E7Lmeh0gNA/MtxkKkoSZnNjVvrUCiYWETk0Pip//stfytTUU1lbXZH1zJq8eG5WmmFv6ZXVn8jx48clmYSZ+hIsDBxlswW1ZPLtd9/JZ599JjCnPDc3p6uZ+vsHBGaXz507JweGeqsmIxIW6e5Oy6HxcRno75e52VlNB7aOufT1Fenu7lXz3INDQxJXk8EmKAyCQWZhLh4m+aEM8fnnn8u1b7/VLTPAE3u+u7rCDqy9vbADUoIBsXv378vL6ZdycGRERkcPyoEDQ5JKJ8zglhUai9YLAwPFjx49kUuXrupe9CsrKzq4BfPDh48c0QmNgOhac6uYFiQOP3v0Llt1UM52H3EzkQHukJGvv74sXd198qMfvSvHTxyTwYFuiXqrapEaoMf2I3D73Xffy+9+93vdXgIyAlPdsK5y9MhRVZrq7urSgeQNybYyp4PPJo/6v5f/zeQa5uxhCjwWj6nVqcx6RrA9CiymHDp0SKKxmCpzeTusqJzZSagnT6fkxo2bmnesxJyfn1f37npGZQVyBoWVasWB9Jqkm8ky4xB3vUl//6R0IAzjzyirwEFpNeEG503dKKXBIlfvHuemgmo3x9630mbFHjebTDtpCP+Glb8sfa9inYjs44B5lbKkWRnH1lU9vb26vZpOqEHhquDqSu1Ll/6oFrOw+r+7G5OIZrtBvKuoK3M5VxVbvr9xU7786mu5dOlreTk9oyuiI5GYuFkjmTau0vekLCme8Lqq1ITpXTtJU1RcUcG3OavwW+fS+ELYNhXWA55sLkwbQscfXTMhaFfvFmkEoLHbRI2MDIupV0O66hgTrzdv3lZz/emuNyWVNBM7/rAwabO4tKL19ceffKqKqwvzi6q0HI06OmmDSdvKSavafD1p0pkfFKVRDCjGC8+2yMtulkItVf/Gb8mLkduSy4ozG6532+Ay3zEzJeufmK3w610iCBsMZNMpGMsLWMkNC0WXv74i0XBE3n33x3LixAkZGByQSAjtD09sXRFY3phfWJC7d+7IBx98IFevXtWJcCi/YAsQTJoeP3ZUrSZ02lYqldRu376r7aLsek5GDg7J+KERVRKFlRT/lg/WH76XUILNZApy/bubcuXrq/Js6plk1tbUGhBk+OgRTCqnpXLHQCtSgTPkVWTJxqt1ltY1tnTxxNRk1o3/CKfJZEQOTx6Wt958Ux7+8INk1tbVouCL5y/lk48/0+vp6Wm5cP6cLkKIRb2ptoLpWGkT3xHJrOVV0Rnth6mpKZ3gxbZuaGvDIkckUjEjXkyid6Ims6zs1smoPxMV58X3wevnQdDN960ifs9fOIT2elzOnz8rT58+kcePH8vczKwuiLjx/U359T/+d5mbXVArLOPjozpxrNveoqWCpEPJO1fQNjq2xYElxHQ6pdZFenu6fEpIFQndgUu8d+hjQNEef7ASkSnAktKsKs/f+P57rTNHDg4LFpngZz9VRTkMSCcU8QeHBmRgYFBS6S5ZXsmoVR7IPaxC5vI5OdA/KCMjB3X7HvRbNvx8RYwtNGE9Bdtn3bx9W1bXMvJyZlaWVtd17ABbZyX6e+XUiZOqNA/lFf2Bvy+cDXHYG2Vu4MmTOfvcOxpnpi7VgPGNcvGtwZOyQCp8NnFZjLr5ME3KiwFopBoKFJMSITl4cETefPMNeTr1TJaWVwX1Fqx6on0PyzToB16ABRZst2jrci9bGqotdL0w8Zj/W5T3JjC10mk+J/L06TO5d/+eWu+BotWhQ+P6XYPVFHz7IVLIJc4xTgAlpUIeyv9ZtbL6zdVv5Pad27K8vKTPoagyPj6pikGwcAm/qA/UihO2zopFVdkEi6xisUjRyuqt23dlfHxCRsfG1WILFFHgF6+fKvgZ4OJERGCdq6e7S/vu2LYWFlpgfej23btapn3dvTI2Mqh9dL8FMyiQzc4aC7HY/vDzz76UJ4+fqLUWjKVZjUNV1PWUdavxNm+DXazmvRveWEU1P7xfnwAVVuozogsSIAESIAESIAESIAESIIE9TsAONZhh8dK0jaoX6KpSr4fsccAV/qy/Eh7cMSZy9anPge+06DwajUlPT68cHB2VhcUlXd2BgbKr165Kb1+PhCMhmTg0pqb/4R+rPbDNDyb5013YT90EhWfRcEhSyYRgwmKgr1eVVbCK6969+xpHb0+fnDxxQtJdKTOA7IhuFYTVZX29vRKLeSnEQU/NxJR/oq6Y8L1yghVldgBqh/JkhsZVshQztifZ7M/v05STkUyd6KkSKPwMDfTIz372p3Lt2jVVbHr86KGsra2pWWQMKD97/kL+7Jd/JgcOHJBYPK5bqWCgFSsPMRgFJaj3f/e+YIDq5csXaiYdKwqPHJ6UX/3lv5bRgyNG0aVaGhyRrnSXnDl9Sr6/fl1mZ6blqSrOZFUpIJfLS77gyokTJ3XgEMoEGETCnvJQNMBKLli5+MMHfxAozszNz0k61aWDzdjLOrNqVnva97T0RpcS9OjJY/nd738vH330oZqIfvW11+Tc2bMmz5hkcbCCUHTlOoagkP9Mdl1N+mMl3kcffipTU48ll83o4DtWS2PyqX+grxRJy8+Qo535Ib8YmETedQuneEzcfFYePHgg//W//TeZX1ySuYW35djRw5JMmPJBxYEtSxYXl+TG9zfkiy++kA8/+kgHhJFqTFJgEurkyRMyNjqmg4gY9/S/grbMTC4xgWIHAj3lDk2RX/Jr87DEsBIPK1RHD47qCtd1VQ6clq8vX5GhoQOCibaJyQld6Yo6IZ/Pqdn7tcyqXLnyjXz22edy6dIlfQe6e3pUKfDRo8clCwSNWh8oVqheyvRltaksz4veLb7MuDKSbHIf7Kc8hPpXCEu/e2XBmbi8D0H9QNrVhZcnowzhZ7eVBFs25lhWFg2KZcmZDQNpM0ob9pk9RiMh6e/r1e86VmPfj8ZU5p48eSqffvqFDA4ekJXVNZVtTJJhchgm8bFlA+r0b7+9pgoIly9/o20MWCxIJxKSza7LUn7dixevGWK0f5V8vDxbWfUdW0UVMZo8Iy787NG73JcHKKZ57Is8jOJQ8dJDBSUItOWgBHpgaEhSyZQsLS+rYicUOjERi20Ascpct44Mw4y+K1jVDCVMTEL/+te/kW+/xfd0Xnp7uyWeSOi2GPNzc6okpTO1drY2sDxMWjXNngAX6/ag4vTcBAVlZQFHiBu+Q+and4K81LhnIkda4DsoKdU8a6xqcdDR7f/wLcR2Iffu3ZVnU09leXlZZmfn5JhaA0loe8lYYsmpcgK2APnjpT/Kb379a3UL3lA8w2Qg2j+TE4fMxGi1BHTCfVfk2+++lT/84SN5/PiJnDlzSt548zXdggMT7dg+0Wz9GDKWf1ys4s9pvfXyxYy2pb786it59vyZZHNZVXQZGxtTRVIorBSLHgWnBQLFbHOqykSe5auN37FgeFrXQgqcgveHgIOlAnqokJuhoX55/bVXBQoMSwsL2saERb/vv7+hyifYJvBXv/pLOTw5IcgzLO6hTtV5XW+rroXFRXn54qX88OAHVZRF2/nUyZPy13/9v0s6lZZIpNwKAVJvpN1+G1AflEy3BKc4OM9ldz3FlxJYE4teFxlb1sYnXKDdBste3333nWRW17Rdh0ni3/72t3of5Xfx4ltqXQTbCEEJI5/P6yTy8sqK9hXu37+vvCYnJuSdd36kCknYsmW3fihbKAqhD4zvKxTJ5t2Cvqt4xz/55FPNx/GVY6rQAMHDO4ztZCAyUCCJRjcqm0A5G1sCweIVwnz+7IW8eP5ct/BDPx6/Q+OHZHxsXLdGLdaTVUBAwX94ZFitOGKBy+rKrPbdVpZXtV0OhD3dPXLq1CnpheVMDA74y7JKuLit71KZ2zrl4X3/i++R9/5Ve4dqRF18ZGO0R/MAicJf8E/dOqbtZNyZ1kixvrDefIGmU7GihdG52Xl9H2dmpuX582dy7dpVGRrsl8F+WASJSnc6GahMVapzfAFXT6ZNRVsfs7m8PHz0UP7pn/5Z7t27p+3Jt3/0tpw4eULbE6jLsKUqtrHStqIqRGPbrDXBmMEXn38hn3/+pdy+fVMtBRuLQMMyOTmpFt+sspdfJo1F1i45MDysCxmgXAsFT2zNA1nGAhhYt0K7BvIM5ZFCPqf1C96peBhWgEV6e3rl/LnzunhmehbvxbLcu3tP311oyLz6yqva/lFFFIElmaxahIXVwa+//loXvbycmZFYIiGHh4fl0cMHnuKkGR/SLa/8iwp8xY5C1XcX3dRi28jIrS6YqSG/bS0QbZA4Kqy0QSEwCSRAAiRAAiRAAiRAAiRAArtPQAc7kQyvM6qDMcUOaCl9zY1LWLO5Jf8402AdmETt0g79O++8I4uLy7K6elcn5G/fuauTBpe+/lomJsbVAgvSs4pBvxfP5W///d/Ij370tnbc7UBXIhaWwf4+efP112TqyVPdCgiDhVgNij2a7965J4cPH5aBQQzGxNSqBlaiwBrGz997TyYmxsoUYMyENba5wCAp/jWX8/Ic88oMchnxMooAZqCjYuyjaVCV/s117bLCYr6udET+zb/5a1WK+ud/+meVKyiKPHj4UPfy/uTTT9VaSF9/vw7UQhEBkzNzs3O6ShirJNezGZ2IgLnfE8dPyE/efVd+8u6PJY2Bvho/jHf2dCXllQsX1Oz27OyszM3N6wTr3NyCfPHlV3Lj1m0ZHh6WwYFBXfmIlboY8Mfk2dzcjCwsLmh6MHgGJQTItt0Ca33NKKwYua0kZBKGdxB7Vc/MzspnX3wpl698oxNJGCRLJZM6kI4tkzBYhvcI7woG1GCVA36WlpZ1AA1KZSeOH1WWR6G8kdw46VADRds9su95wVtVhgHKVCotJ08ck8zasjybmpLZ+QX5zf/47/LBhx/q6lOKvcIAACAASURBVHAo0GFFYjgcUU5wMz39UleSQskIExfgOHrwoKCuu3D+gg6+2zF1VC22HrNAdFAUlXGZKFdeW9fBR3i13rHoGatP/+qv/kr+y3/5r/Ls+XNZW8vIk8dP5R9//Rv54osv5eDIQV1RiMlfmLXGQDYsw+AIU9VQhpo4NCFvX3xbDhwYlv/n7/9eViFrAd+J4BTZuzZlNnX2ftARcPzu7TncNuI/KEz7nbNheQoTduLBmwys4rOjbhfrXS+rinLTOSjx0iAUv/5XbDc0FLTKi+evGJD/2oSCmgurSjGJcvb0aZl6MiUP7j/QCYL1zLpODv+n//T/ygcffCgHD47qKn28a0vLSyq3s7MzOpGIetsJhVWhEPV0PpeTmzduytrqUnHi1yjoBdeVdjJIl8qrzG1Ma0P5rnBkXhuEZWS8VAe0JvyK6Dru0sGEur6TVrw8ZZWAMkBd2pVKyckTJ7VNN/Xsma4wxrcLk8T4bl379pp+U/v6+gQTylBawrcX1iFmZmb0+4aJWHx33333XVnLrOtWUovz87q02X4TqoJU8fHK0yvTUh1Vp0wrjDvoZE9Fvarfc8/kftU0BD7wpSmAXaAXr4rUdzDkSDe2Wxoe1vdsfn5Onj97ppa2sCUitlIEU0woQ1kB3y5YUYBS2ezcjCwtLsjK8rJGA2WVsfExtdYBixxQ7u34H8ouFBIod2LS89nzKfn088+0PdXX36dMYFkNW3DEYeEsA6XjRZU79E9mYCliaVly2XXdGum1116Vv/jzPxdY2MPq+sqfaRvgnfDU5VS0jEKrbb9U+sE16hcTGjzg3cIVjtBKCfJh7uHdSiYcGR0dlj/75S8kv74u2cy6PJ2akvVsVjDxfenSZX3fsB0X2kLYghBbvkJpcHlpSVZWV7VttLa2qhO6OGqbaGxMMFm8oQFUll6bV58Cm1aWGxUlqufCe6KskFnvr+za57uCBy77+7rkzJnT8otf/FyVth788FBWllck7xbk6rVrcvfeXfmHf/gHzdfAQL9EY1Eta7SlYInQ/mFhxU9+8hN55cJ5owhXC74vSdt1iv4Q2n5Y0HHr+C25fv26LC8ZRbT33/+d3LlzWw5NHFIrDvi+rq2uablGIlH5u7/7P2VycmxD0mCkAcoqsNiB9jEUSrKZjFqYAEu0q2G1B0ordiJ9QyC+GyhubPUDBRdsS4XxguXlVbVcAyWmZCqh339M8GNruJC1DqLl6wuoeIoHnoKHd89IBGpZ/+R70YM5wcOS9JhxARcKOKYNaZ5u7v/Su2vShjCrJt+rx01ySu1XJN7kwJcGL8023egLnDt3VutvfPtgpbNQyKrSykcffSSHxsfU4kdsckLiCd9Wbfge+dpuqIc0aE1k9ZT6UtK2p3ndw8zRugnfrbt378mlry+rdT/U3ZCvgYEB3ZoSdTgs+uC7BmVNWCl9+Rz9vSXtU0ejYTl75rRcfOtNrS9g/Q91KH62zwdukQjqlF559ZULMvX0qX4HMM7w+MkTWVpZkVu3b8v4+LgqpEBZBlvpLS4uyIXz5+VXv/qVDER7JRwRmZw4LH/zN3+jY11Y+IU6d35uXrevheWc0ZGDcmD4gOnL5/IyvzCvbZ2ZafTv5rVe6u2H1dAfydtvvy1///f/tzx7+tRsA6gLV5BanxDZovZuFRVT0LnQP+vcOmzbYm/rhFFhpa2Lh4kjARIgARIgARIgARIgARLYCQKm34nOpR10MefFQfOKgfONafJ3TG3n1tfBhQc48W7ZiZl4PKSr+1+58Io8evhYO86wboGJhBcvXurK14ePn0g8GhHHcXVAd21lWX75y1/oQCmsWujPG0dJxKJq5eGV8+d0YhkWAFYzGR0MXlszE13wEwo5ks1mdKUYzCBn1teNqVYMKGDAxxuIsQPCZpmgL9fgYTPhu81TH4GK4scTMyBnZMyIlF9ufH4bOFUp87zbMRIzuofwG/i5IhOHxuXPfvlL6U53qbURrJDEZGcms66DP1BOgXUTDBZByQYWVqCAgAEh5AUrsIeGBuS1116TN15/QweeuruSxb2na6UCg8TJRFQHDjGxCmtA93/4QVeFZ9az8lzlf1kePX6i+7FD3jCYhUmNXG5dt/1JpdMyMTkpr7zyiq7GxcrP+blZmVE43vtWRU6htJVMJXVFNEzTLy2v6ADw7OyCwJw2TBjjXYmEI2rRABYLMPCO/GOFJJQaEumEnDh+TH7yk3flnXfeFqwqawx+LTLt8cyB/WTdlx6moENy9uwZteCE1WeXrlxRCyvTszOqWIctkrB6H4PwsEyysrykq9hQb2BFOlavwuz9W2++Je/++McyMTEhibgZjqq+uLZciq2M6xCxf7VbA7jsWwaLQRiQhNWpa9e+1UlcyDomnLBS9cmTqaISYC6X1VV2WPFdyOdV1sfGxuVf/cW/UlnPrGV05SDqTtSXmHSwE2nVk4SU2D/ryhvktJeBR5MDI9bWv7ln3vlAT3Vuev6LAZTCtfVUnQA65nFpRaypG40s2fxvLhvm22jCMLwaD6fk3rY3jF9844N+qCsnJg7Jm2+8odsKXL9+Q2UXdSaU+O7d/0GeP38p2JYMW5agroIFIcgu3kdMkk9MTKqy2JHDh3XrjWdPn8jUFBTAsFIc8dpKMygFeAw35s+8mV5a63irEprvtgmtNBHkhetzsS9PFUuJeXnbtOIzo202bAspOqmECX8oTCzBCtb8rMC6wfzigqzfz8nUsxe6LRpWN+Obji0BM5k1reMwGXvs2FF5662LamHt9p07srgwLzdQ/I6j37xadVxJrlFiNu0+ycItr7jrl6knB17buyh+Nuj6AXguTDiaNj1trH1Uwu8KLEBAiXVsdFR++tOfalvl5s2bcvnyZWULZR8osT6ZmtLvIKKBUgbeT7RXoBCByXusGj996pRg5fqPLl6Uw5OTOjHacFba2CG22kukkhKNx7Rvgi1L5heW5eXMnMDyE/7QpoLlCSjDQ9ED7UhsAQQ5jMWj0tffI+fOnlHG586fl1g0XNaWtNtJKAbIhf1Dvw2f0eK3rBFQtu41FktQZvirFE/d4skLLh535NTJExJxHLV08elnn2lbAhaJYG1lfn5R5hcW5eX0tCqEwUqDtdQG5Wi0X5FX9L/AI5lKqTIwFL43xlzKg8mXSa9uC9hUPkvhbPUMbKB0/N577wksdn7+2efajsKkP/IG6wj4HqG9+OTpU1VURlsZZY0yx7Zi2paKRU0fE7PXlcC3mshN+oeC0fHjx+TVV1+Rhfk53d4Kip5QLPrhhwc6wY02LroTUALEVn2wZAWl5zK59OJH1tIpWC/t0e1PUbei7KF/FQqF1ZIflJ+xJRCeVemmlOUGSoawkAVLgdiOanlpRd8BKHVBSQpbBul2cKqtUua1xoWpD1EM+ucvj6AXwlP6Mu7NW4P2VakdUSOqBh7ZdJScIo6NP/tOlL4z5j021xvd2zsIHyH2dHXJ8WPH5OJbb8jM9AtdqAHFsmfPngmUVlB2UBAaGTlQJqOlcjJ5Nyo1wWm0cXbCEZZeUSehrQhFuuWVVVlcWpallVW1uBqORPQbCIureL9Rr8FSCSyerq2uilsoqFWp/v4hVcL62c9+qu8SGKL9ip8S8w0doe+HLQrPnsFWY1OCrVdfvnih305sm4Y6E+NgatnF2woT31Jsn7aWwXfDbDtsrOYcUyVbWGbFtkT49kJBDluvIay79+9pHw3vIMJdXVtTxW3ka/jgiPz4xz9WJTooyIyOjMjczLSmo/iJqSxE/3tSfIZtA0tDY012U4uh8MQQoMIKJYEESIAESIAESIAESIAESIAEMFgDk6fhkGDriL6+Hslm4hKNhLWTjo68/dkBD9tfxeQ2Vpz09KSlr7dbV5Cgk56Ix+uunMIiqG5Mep84rhPe4UhYrl//Xl5Oz2iHHJOmGDzD4AwmyDEwhTRiMtlM3JbGOTU9YZGxg6Py+uuv6yDhN99c04FDdNgxsYoBuJWVZQ2v4ObVFDEGfSsnjTGgin2L1Sxrb5/2wLFyFNsJ6ES2zbyFwmNNAsClg9SRiFrLgSUcrFbu7e5W8/wx3cqhOagoM1gdwWo6KEpg4gl7QuM6iqVLGB0KCtKOrbkYUI2qWXoMciIsrGjCCtnnL17oqlAMQmMya3WloINUmGzAXtPpVL9aIBkePqCyCyWEU6dOyvCB4bIJhqpp8NKFQV1MxF68eFEVY765ek0ePnqkFi0w4IT0Y8B4xdueBvFjoi2d7pL+/l5VfDh//ry88cYbkkgm1QLR40ePVM5hMhiTTPq+BJTO4NCgnDlzRq3JwMw5LGmsLK3owLoOxuWysrayoj7tJB0Gu/BegDFWmx06NKZWYl5//VXNR2kwMyDCDrllxAOmkENitquCWXtXLYrAygombLp6elS5aOrZc5mfW9DVdhgERP4x+A4lH6yiTSYSah3n2JEjqtT01ltvyeHJI5JImu2WMBgY9IN4ICwMVII1FF7ww/YAiXhMJxB1AjNIvgMCtM660nE5fvSo/OLn70l/X5/cuHFDoNSHiTNs87C6siSry4sqd4gfA7PIA9xihS3MTr/3p3+qk/9PnjzRbR0gjxjg7e7uUkWngOj1FrKKCQ+8a309vSpneG9NneqbBfAFoDzxfYnF1cpRf1+/cunu6lbFGmNKHyHbHPo81zmFD3zfsE0T9qtHhYF3BubmUX80N/lXJ7LdeOwNHCOPvb1d0t/bo4Pr2O8eFqAgW838IAuYgOzt6dJVofjmgxsmkTB51cwPYZkJkR5dyY36H/IDJbpqv/7ebq2rIat4H27cvCmQQQzKYwJteXlRlpYW9F1FWUKBKpmIy+AArE9Nytmz51RZC5Yg8G7eGB+Vl9PPi2Uetm2KigRATtDGScQT0tPVre8C6gOkQfNuVxlX+Gv0EmGjnsYqXnDAEZMmYI24Nyfdjcbexu5c0cn9dCqhE2dQ0sRWPnhHzfu58Z03E0BhVaJE2xHfb0zePJ16oiuJs7mcrK6t6B/IqhJKyNG2Ktp3hw4dktdfe02/x1iBjjbLvbt39FsLyxF4b1BG1X6o+/UdgYUJWHGJx1UhFO8KwsKvZnn6sgS5QHyoI/HuYuIbk7Jo45SFggDtz/rHsSwiV5VTsc0RVnRjcg31nra7oJhp/dlwVHEbWwBFtD8A5ng3sX3Pq69eEFhWGB8f0+8RLNLB0gZWmqMNppN3Go5rvoOJbn3fUFdgewQonb399kUNK+Ypbfqi7chTfMexxREs5mG7FEz6YuU62nCqnJJZUzaoN4zMhfTbaupBYxkC1mugQP36G68LLM9ga1O/PkOxraCKKegHhb36qFcnHvt6wDkpUSy5r/HD9xpKBGjbwCoM5B5yGgmZ7SVqeBXMu/b1JeSVV87J4OCA9PX3ypXLV1RhEFYGsD0GFFdgwQqKStn1jCeIpl0Uj6fMpHAqpVtPQgHq/LmzcmBoUNu1QXGDQRz1It6Dvl71j3ZQPIZ3qvFtX+z7AC/IL94r1OHoVyJstGv1HQ14F/zpwuTw8WOT2h7p6e5WC3vffXddJ4WXl6H8ltHJbCy6yGY9nw4UkmL6DqH9Njk5ofKCfNi2tT+O3ThPpcKaposX39Ty+/bbb+XBg4e6/SjkFhYWoQSIigXfe7TjUCcoM+hreE04m3a03aKxsFragSwfODCoCwEQFrYCRp0yNg6raL1qacL6CzpCKQL9fdT/Bw+O6LZT6LOgn4J+JOrd8bExVYBDOeLdqPcDd23/JZOCNlF/X49ug4r2tn57kYEAWcAtxJdKJUzdvNyj+UFZ+sdJ6sXvf44wESfkEZY78B5jHEZlHOkI+OE2/HSloRTUrWUG2U4lkirTfi+VIeAan7Gx0YMC5c4XL54rs6dPn6gVDxzv37+nFumgHIG+serv63chJN3plOEVCkkfxkWSybpjPf70tOM5ZKZ/oF9Onz6lyhw/PHig1lSh2IHt26AIjfEjbB+I7yf4o/6B/OObDGukGA/Au33u7DnlOnHokJQ1abXuLhcrWLuZnDyk1ljQ14d1o6dPn+rCFfRB0K7FNwTjVOFQWL+5aErgmX760U+MGutPsO6Kd3JgoE+tyiGt6JeiLlpdXdG2MeQebWPIKsZhoOR16vRpeffdH8vp06dV5I8eOSyzs9OyMDcnPT1dqkxZbetmpAH1s44b9vRKMh7X/h3eI/2mVJHfdpSBdktT7S95u6WW6SEBEiABEiABEiABEiABEiCBbSKAzjcmYI4dPSKwVIKOLia7MaiBCRT/zw6AoLOKji8G548fO6IDrDD9PTA4oCuhMDCo/dWywXN/SCJRVTIZkJ+/9zPBvu2jY6Ny4+YteflyWgd8MUCGgakoJqCSCent7lLT4+iYF4NFgvDninT3pnSvdd0mZfKwfH35sq4Qg+UMTFhoxx+TypjI00mKcUkkEyadXtLQocfqsePHjmrcCByDCkNDByQcxkSSJeB5KCakPG+dcLV9A6Y+RpjIDzk62DI+NqqWSAq5vAwODOiq3a7uLp3caIYXlJcw6YK94BcXFlQGY5GwblkCU+Sw0uxU6/F7k7mIL5kIy7GjEzI29rdy+/ZtuXrtqlz55qpa6MFkKGQGA0aQcwwgppJmgntkeEROnzopr73+qu4zHYsFDJL6EGzIm5eGvr6UvPb6K3Lk6BE5cfKkrlq+c/euTM+YVZt4DzHxhqAwcY/JL0xcYXDpwoXzcurUaRkdHZKZmUU5cuSIKp5gwHMAg1EjI6okEDRmNDkxKj09v1DlgytXrsitW7fUzD+syCA+nXTI5XWQC3nHJBXCAXOY+cbAHkwIj46OSFdXsnwUbkNmO+eGXRWG9wIrQVEvYrIQPygXYZLl9NkzWq98++13cu/uD7KysqaDghiMx2A2JiXACWUwNjYqb7z+upw8eVKGhwc3KMdVkkE5ozrBREp3d1omD0+o0hIG5rG6FytMa02a+sOrFL9ISKSvNym//MXPtW67evWafPnlV/Li+QsdjMUkb96zngPlQcg7FJNOHD8ur776qly8+LYkk1FdMYi6860335QHDx7ooC2sxmAiotYP7/vRI0c9M/gix44e00Fe1LfefG6Zd9zTAdj+fjl+9JgUIJf5ghw9ekSGBg2HINkuC6TKBcoXZXRofFxXtUOpEZOqmIjFfa0X7SzhZiOpEvdO3bbv66kTxwUT/9hGp7evV02+YwK+mR+UBFDmUC7FdzgWjWnZHTgw5Cl+eqFVCl1AJAhrbHRMt6jCQD+UZ7A1ANoR1X5YND1ysF/+t+H3ZHJiUr7++mu5evWqTD1/pop6a5mMrnjVwfhYtKgsdu7sWZ1kR305ODiodfna2rK8cuGc4COByRbEjVX+2BZoww8Tb5GIDPb3y9GjR9VyC95zTL5jywMop9m2h/ptIP82DpQPJp3wXp89fUbbO1CogeIElGW379tsU9DeRyg1aX17+qTMzg6p3GFLJ8hukQ0qS/vDJJIjcmCoR9LpN1Wh86uvvpTvrl/Xempufk6wAhkTTihDTMIgrMGBfq0HsA0QlPKGBrvVagDqbmwBAqtasLSGSVFM1Fb7ob6EMgC21sDkKrbOgGUR1CuqIKYTTdV8l9+HXCDvJ04cM1sMFgo6yY9JTRUxf77LvZorTw5htQj5xNY7J08cV8UwtIGhYNGj29gFyLxuVxBRt0eOHNbJOvQNTp06IWi/DQz2Crb/O3X6pL6H3353XSfIsM0NtlXEDyu3wQMTufhmwbIRtj7Alk1DB/rUIEGpAR+Ugc66B2UVKJ1AoRXtx3v37sn09IxOAGO7CbQh8A3HD/UJvmtYrY9yPnbsmLbl8I0dGR4WfEbx6fF/dnBur9Eugdyif4J2NVb6o02IegQKl8oVEdm6CEdPXpLJlJw+c9pMiDqO9PT2yYGhA6rE0SjxaNyRw0dH5fCRUVUixHY4N2/e0i2OVlZWjVLY6pq2IxEx2lCQ/1QaSl8pGRoc0jScOnlS69EDQ1gUUBG7l2adSO7rk5PHj2u/FOHBwga2mQHHSk4VoZRfuuiHwApTv37H0J7GhDPCw1+j7SrUMSPD/fKLX7yniuqfffa5yv+zZ89160QoreAP7WikF3UA3h/0c/AuwIrO2TNnvC12KjNenuSdvBoY6NKJc7SBDh+ekD9e+lpUAX5lWS0r2vatmRQfkGPHj0sylVZlhqBcQIECW5GcPnPK28Z0Sa1UQgbR14fCCrZasXJdLa+2vw0FVNRbZ8+eVr74JqN/hvKALKFPAgXrEBq6nvxUCxMyiTra1osIG213KOWhnYPn1X6QO+PvmCoHgMeRw5Oq/FXNT/B9vJRQjofCQZ9acYTCCsZNoPgAqzclxeJywkbRqksmDx3SbWKhMAhZhnWMego7CAl//b0pOXPqlCwvLkoqkTBKDosL+h5AfmGdA98ypMFa+MK4CdJ24dw5bUsNDQ3J2MERZRmcx864G406ogom7/1M2w1oM2BramyfA6uiUIpGGxMLn9BHVgW0aETrICisQCkabU30yU+ePKFKzpVyXXmtZBwoAabkrYtvqmxje6Zvrn7jLSSAhTIz/oC2qSqZoK1yaEzbQsXwtB3pyGuvntF2zPlzZ+TjTz6Re/fvqSIplAgRDn6wAhqNmDYJrA6eO39OrX6irxqLObK8tC4Xzp8V180LtgxCfd3b16NKMcWSLBdFfV+OTE7K9OnT2g9GGwjWk7AYAd8q/jZHwHFR4/JHAiRAAiRAAiRAAiRAAiRAAlUIoMOAIdilrCtffHlV/uW3/0sKhbAcOjggb104JT/76VtVfHbWbeQzmyvIixfTOsiKFSX4YYWNrpaImF6qjrl6g6noMGNMbmUV+5Qvyzo69oW8DphgNWcqnZQoBo/8P9sDq+j0ZvMiMKONfYBhVhomxmdnZ2VxYVEH3NPJpA78Dw0O6GARVrcVF1J5Vv21A+9Z+c9mC8Zc+eysTuLDXDP+sOoPk1rwr+aFD47owGFlOpeWM7Iwv6iTSOh0Y5URVpv09NiJPl9GfKf+rLbLOUoSf/cfzsnvPvhY7j16KtnVJTk6Piz/4d/9W2OyuZpyRwszsZLJCVYhwvQwJo2wGlQHcrvMqjYdI6yQi2rRoycP2VtYWNJV9hgchqRhpQ8mSWIJbyKxMgCUVZU4llewRzTMmi/oysKFhQWVa5j9TaWMpQtMKMLCAyYaMLCoFhniWxuUQZLykP/VdZ1IgHIV0jE3O6tpgdl4TND19vTqJBMmJzDAme6C+eKUxCIiuYLoti5YBQarMBi0RNqgvIM9tYPyjHd5ZSWrqycRJ95hlA0GKzHZtroCiyuuKr0g/7CCgEku8EXYGPCNxiKqqBYUfiX6zV6j/p1fdeXG7Yfyj//fb1Rp4cBgn5w/dVh+8dMfSTLlbQ222Qh8/l7OLsi1b2/If/7P/yDXvr1mTKEnYvJ//ce/kz/75Xuq3ISyAi/IHlZSv3g+reUFhQ8dzB/s19XAWLkJOUEZYGI8UiEmtSZb8ijP1TVZXFgSbL+DAUsokUC+8afjgFXk2Jedqqdr6wUtb9S1sK4zPzeneYCpabUoocqBPTrJj4Hznp5eSaVK1gXw7j1/bsxGo+yxfZRZKV0epVaN3vdicWlVlctgJQMTaLC4Alnq7es2ngLeTcgo6oylJZinhz9X4+rC5FcqbSYn4Fsjqv5ul6fKOF/z3jfIOQakMRgPuYacY5KplT/I8PRCQX7zz/8i9x4+keWlRemOOfIf/4+/lYlDw4Hv55bjd0XWMzn9lqIOwfAn8oVt8KB4F4kGW1cIihcKgDD/j+8yJkgxCQ5lDUygY2uxoq5HAzKJpsXc3ILKN1avYkIK6cI3OZ2u/y5nM662E6CAgAkFTNagroaCoVrd6upWSyVIG+pKKBkgjdGYWZG/traudR7kCqtWsTWFWTEeFv2IBABYWVrTuDAhqzKYNNaT8G7ru9ik/NkosjnXvHv4JmILMSjmar2dlkQitp3Vqk1C3SPaDgsrrvzw+KX80//8n7rlB+q2C6eOyJ/86A0ZGR6oG0bTDiC7a1ld0QzLDboNRSisysWYEMHq/Q1wvDJAvYo/1KEw66/f0vl5mZ4220xiyyg4RZ2M7yjqN0xS4s+s0DepxcLmhYVlmZub1e8vLJuhLZxIBjeW8rmCrGbWdVsQbQvnYZnN0cl1TPxU1v+1mCDt8/MLsrqyWlRYhWzr+5ZOlOo7fyBB7x445F19P5ZXV3S7LLfgSle6S+s5WArYwBFhuiLZbF7bIMgL2h/YcgsKZuEorI6h/VXQ9gnSiW1hoPiI98PUpRFVsAFfvCOJZFwtB6DNFG2i3vFnbyvntg38D//jY7l2/ZaWa8xZl3//b/9S3nzjzFaCVr/gkc26+o2CvELm8M1CnbS8jDbqqqyvZ5Qb5A71TTcseqXRRkvrogAooWCbVPwQXnFCsjJ1BZG1TF5mZma1POEO1iIgm6ae8xoalfLgog7HdpfPpKDWXszWj1Bwhl99pyrjCrq2dZ3g+wLrViuaT0y2wxIHZGF2blZZhLWNH9d2PqyaQBbQbsX3B8oFUMBWozAoIKTXn2ZXBLrCy8ursrS0aPhhK85Y3HzD0ilT9yowMDPKWUFJ1nteHCvLBbWIgu0QMfGPbw8UJ3p7kuJUtNGqhoU+VQFbX6H9vKL9RLSbUfbot0IBFu0VbbfjG6TfobQq7eh3LpU0FjSwR06dH5K9mnXl/qNZ+eDDj+TO3Xu61eRbr5yRi6+fk6OTo3VCaPwxihZtBpQpvqn4tmLLI5yjHsCkOayAad95ZESGh4dK/fCAaDLrOW1n4n2A4j++9aEwFLaixrpPAlZaAjxWuaX1ome9CPKmFlZ0sY3pi+F90rZIPayov9COX8Q7infTfBO6u0z7L47+Y5Ufyh1bp+LdRr8TTPD+4Z1OdSWq+Kq8bV8ik9DFxVXta2B7VlimwHcI31j0sQLbvO/s5QAAIABJREFUJK6VvWUd54AsQ+Gmv7dX+gd6y9+jyqh918jL0tKqyi/qJ+QHbbt4NKZxQ1Zt2w5u8XvxcloV3FFBoQ2I+gv1hy7aCOCOnMIr/v7XR5flw0++ksXldUmEcvLeT96Sf/0Xf+JL0e6emjo8o1ZWYKEE/Wn9W1qSlVUo44FRXi0Eal2WThkLYni/UV7JpPZPyuqxRrKE7wfagviWzpmxL7x7+HaojEVgZapH37vhkWGBopBamLS8cdRvvanjdbxrft4by1iQxaVFXSiA/ikWoGF7WLzH6Aug/NA2wXcEZYw0WNnGlrZQzA5ckOPlK5MpaB2Id1zHdaIRtUCEMcBw5fhfIyza2M3DqXm5dO26fH7pimRXV+To+EH5k3cuysnjRyWVCqgz7GuOPNmyajB/AaE16JPOSIAESIAESIAESIAESIAESGCPEYDSxvjoAc2V7Wf5+1gYRMUPHVs7mIqdBbrSCV3BbRVIdGDDW+1qfHj/20DLbpoLzKmH03FJpeIyNDQgY6sjxUFADPph9RtM+WNAAAMzNn71bVeueonFoGMM5tkTUDBJy/roiK6OwQCiKjaoGf6UUajBhLs/k17aMHGGLTTsb+MAMjzVyJD1yGORQDIekUS8R4YGekp9d29yqVieQBpQHsVAcILBfM+s8eBgl+BPf40UR42wU8mYJOIDMjTYrwOYWFGlg3jZnK6wxoohTDYUlZtqhFWW3joXCAYDRt1dMf0rFLByMF9UHoESGAZ6McGPVctmdVe0OJCI4DEu1NMN89ZJI5YNpA1y35WOSle6Xwpuv+SyBbMyNGv254Z1F8g9JvJhgQCTC1BMCNmRFD/vRsqtDod2euyGHHEdRwdZsSofg+2Y6MP8AiZYBrDNSk+XjB88KEvjK2oFBYPNUIbDVg5QxMIEudaVVTJWlPmA5xjET6ex/VKi9DpYxTzr3l8h23sNHhOxkCQGsN1Et2SGh1QZAYpZULrBVgGob5EHTCwF7TKAev/gwUGNTfNh6+CK+FUMPVns7sJEXdK4gLzgzz9ZESCzkFGdROiyioIVEdjLAL/2UdARzuMJyHNMnKH+ICedfw8rLxMROeh907eSIXxTk+m4jKcPBgfTxPuPCZD+gR7pl57gsOrcxQr/gVi39PZ1yXp2WNsJ9tsOBZR4IqGyCyswUNaz8qnBQrkK9XxyQIYODJR/wmvIECY0y1aDV7qtvK6TB/s4GnFkaKhf/5pAaL3v3SNkNxmVwaT3bjYCB2VglalDWE0sMtCf1r+x3IgsLo7pdx0KFTCor1YfkilVgkE5VBYh6uC+PmyRWW5hsBp0TM50RUydDTdIsv1Vhm3vVzuqJYj+HnH7vLZSZQD22h9JUGD6vXJ0ErNfzNZyaJ/rd8mGUcUfFBgOjAyZp4jHFxf8Q/FkcMAo+8Dq2sT4uFpmg+IALBRAoRDWC3dDQSUoS9t5DzywQj0WwzZOSX2fs+t53SIHk+FmlX5OwWOFO5Qk0J5L2gl7lIWfb63EYlubZFjGRr2y8X9D4c+Gg6O/jFWx1GxJU3a/VlzVniHsEL4vYYkluqW/v1vjzWTzRmlHtxnM6op+vGf4s21XbT/701WZTl+ceIe7u5PS3eu1G3zPivnEPW1/+AP1O/TO8dgRSXWFJJXuL3Gq4y0gJL2F+iGZiEoy2SviYptDs+gDk734HsESA140tJlR1tF4VBUS0U/eZJTVktKy+0hXPB6ReLxH+vt71Erj8sqq5gdWGlCOaBdCMQrW9vTbWiP2eCwi8YFeGYASRY1yrhFE2SNjIcfw3gAxKPygewhRt9wU6etP619ZJHUuUO6Dg1Dm3lz7xQRfLgEq490T5Ypq1dLupT+eiEo80SeDYixWFcvCHzTC8Nx7Z2UH5AX1Ff7q/VQxV0RGRtDuN23/snERG1e9gNr4OSytRKNQRk4ILD+tZ3PaZoAymrF2aq2OxnTLPNTj6Cuhb1zkv5n86fcUbcFefe9guRRbyiFeKIFA8Q8W57D1L7bqKw5aVTBH3ZJOQyllSLfhWsfik7U17d9pm0e3NY6roiTCUQU9aBJ5MgP/AwM9+tdoNqBkGY93yeBAVzGcRv3SXXUCdpilugs+IQESIAESIAESIAESIAESIIF9QsAOPqAPbM/Lsg4llLIb5gITUG4evrxeLw5BAej9gAC8W3iMCcpwxJGoN/mOe/ir+avhCGElIzFJprBdhbeSv2Zg5iEGHzE4Zn+aBl8W7X0eGydQLEc/R5/ykw5o4hl+RcfedSMH+IF/f/iN+LNu3IK3agkmzBO6Ssg+KjvaNJbdbN0FBhHjsbDE+3pKA1M2eM2fp7Gz2XzasHxHvHexWEgnXPR2C8P2RdP2pzB97UL4QiFVWIHSiiLH/zAN4eOCAUrsBd8/ADP8nil+yKCVXbjd5M8GYY8avRcWBh7NfTvSWHTVeGxe2nTSJRmTZNKrH+sE5ct+/UnPitTAWgqC14FdPalwsJlLf4Ka9B/0iWoyiPZ2buWvTpmWZSLITz3G9Z6XRVB+odF5SgblT+pfhcOu+bYnoiIOJo98H+z63o0LsGkw/UW59YetGfC98/5nPG8dgUZluIo7KKQMYFLd92vq/d9EOVdJii8F9U9V5pBQ+7PnVhg3FQm+H0149EWv74pNiyf2UEDAdnCp+ED5K7iFz5Mvio48xcRfOBGWeDwpjuNNBvuRgyn+/FWW/3kjuYZ7f9lYP/77eO4P1//Mum/6CI0nePIlHtcOlB2gMJiWXglQ8rJp9afHxm3vWTf2fjNHG0Y1P/7n9hzxaZvPh8k+qxaO/751iyZjWCSOhRLRLnH6PCV6+9zvp0POUcUkklH9k0KPdumLsgRLphWitaPZquTqlWMxfUjMVmRpBzPj56jVuo3b1vXIVGV+rRvvaK2gVM1zJYs64VUEX/XSfJ9sIqo664wH/oKAbKM+g8JVLFJStPcXhC62cUv9mRbl0mx3nNSFD2o1KrD8vQK1AmPL1x5D6Jsa5RtsB7rhZ8u/mAd7Y4PL+jcQJ/62EET9SPafCyqs7L8yZ45JgARIgARIgARIgARIgAQCCKCvib4vFDXsQL7tC1vn1fqjuB8KO6ITklYBQT0H9GKrBeL1d+1je7Rx1zwGRBPo3ufOzj3DnSa1YkwI8YMFJvK1Ix6YoMCbgVHzpiGgxIKwoWzw5xsDr8osyL91XOuZdRNwLHhWTIwweA7sgGExTO+keB0QUCtvqfBVBIi4IbA+Wa5wsfWBI9XN8Myr+/KKd6ayTtgQ9164ofoq2C7F7C+Pfb9h5QnMlYE1G2/rSntdmXcfu8pHjVyXebf1qpY70uIW6+lGwipz45Uv7jl+k/R2gtFzjLyqG987iTShXsQPyi72Z78Z9rryaP3oa25X+Pv8V7qveq0BVKmTa70TQQFWusc1fmXgvXudeKjMR2V+g/JU6aeSh5UR684yCwqrgXsIBrqufjEs81YWvpcBeNL4bSJKPlzU45i9aXFFtWFCCFFX1gelZDR1plncpNJOUxF1kmOvjqne9mouM1pvV9Q3G6Wnepi6nYV+D5rxVT28xp5Yebdx4tq711gAga7wLcOWMGiw44Bti7ANhP6Cgscjex8McY4f7tuk4drytc+Nq/L/CxptaSuX8qd768r2KfyMbA4r2dn7tn6110FH8EXd4ymAqhm4yvCC4vSHZcuonju/H3terAztjQo5sLcRhz98/3kzbuDPXx9Yv0Hh2WeNHOHfCwOKylv5FdtKpmlmPj9bTd9WEtQSv664BdMGBidkB318/BSdfd8bjcvPo16DsdEw4Q5p8suaLUt/fM2Eh+DQRm0mf1uI0ybTsi1Gax/YYyN5gFubFrhvxm9A+MoBYfjDseHbe/Zo77cg3oCkbO8tb+s8LHCq/qt4pn2yinvVPdd/suGd8BYm2CjA157riR+496ziVtVIbTj4huDbb6+reqj+AMm23yIrL029O9WD3tdPqLCyr4ufmScBEiABEiABEiABEiABEqgk4J+ErHxW6xr93Y1zRFvoBdeKrPKZjcZ21u11NXdIK+a0Kp/7rpviUCsgX5g8rUEADFvFcRPhhKy9Y38SNwq0/+nunm8ijw0nGONXATPI+2cQyhVsw+S6BW8roIKYbQ4cY0LZgvTXIa2UXxt+0NGWu06eYmjb3ghyXOUeytdOUPqdFEfKzc1q5a1eK6Kt96qU1adlA6/+BDRwXsm5Ih0NhFB0glXwZb8thFUWTrtetCJ/reBfUf5+S2Zl6Oz3vHizcmDdy5BP+Jygerzov8pJM1z8brWerBJmE7c1SH+4Tfjds04tD3vcYkYD67ImwsYWN5upareW7MoEtkbg8LoYBUwvfH80/nN/4v33/ed+N/bcPrdHex9HbNW04b32O9hj50EMamWx4hsc6BRhogwtx2bjaNZ9YCIauNlIPI24QVSNcGkgSVWdNJqOKgH46xdt62wxvCrR7PBtKLL5ooQi92ZlzheMnvq+2ZWPmr4G6xbzLst3IwlqQfwbm+SbDHST3oKyqRxsmVsH1cLH/Uq31k+7H2HZN6DPW0p2tUyXXGz5bMM7gUreF6r/XG/7bthT//tqvdpn9tp/rPXM767GOWREv0X2m9SCMGtEt28eUWFl3xQ1M0oCJEACJEACJEACJEACJLDnCbCjvOeLmBkkge0lgOViWDEGZRVPacWFwopd4ru9sTcX+hYqvC14LRtEbS7BxvVW4q4V33aFWyvO/fasFYwbDaNRd/utDPZDftut7NstPa2Qgd3K027F2wpm7RQGObZTaZTSspfLpRPy1glpLElL+581w7MZt+2W805Oey2WO5EvG4c91koPnzVEIEj3qCGPdEQCJEACJEACJEACJEACJEACJEACJEACJLDXCMAUutnvB4oq9lztHnfqCsK9VkTMDwmQAAmQAAmQAAmQAAmQAAmQAAnsEQJUWNkjBclskAAJkAAJkAAJkAAJkAAJkAAJkAAJkMBWCWCRmCNm/3CzvbdrtlCwAVNpxZLgkQRIgARIgARIgARIgARIgARIgARIYIsEqLCyRYD0TgIkQAIkQAIkQAIkQAIkQAIkQAIkQALbT2Cn7A27gsEio6wigq3FizFTWWX7i5kxkAAJkAAJkAAJkAAJkAAJkAAJkMA+IkCFlX1U2MwqCZAACZAACZAACZAACZAACZAACZBAhxLYTmWRYtiu6NY/Yo5QVAk5oXILKx2Kj8kmARIgARIgARIgARIgARIgARIgARJoPwJUWGm/MmGKSIAESIAESIAESIAESIAESIAESIAESKCCQNHOScX9Fl66rriFgoiLv7w4rithKKz4oyi78D/gOQmQAAmQAAmQAAmQAAmQAAmQAAmQAAk0R4AKK83xomsSIAESIAESIAESIAESIAESIIE9RoDz73usQPd0drZfWh2BkorotkCILYS9gVSBpWiGZU8TZuZIgARIgARIgARIgARIgARIgARIgAR2jkBk56JiTCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAu1GwHVFHEckHo/L8PABeeON12RgoE8WFhYlGo3K8ePHpLunS8pNrbRbLpgeEiABEiABEiABEiABEiABEiABEiCBTiNAhZVOKzGmlwRIgARIgARIgARIgARIgARIgARIgARaSgDWUxxJxOMyOjoiP3n3HXnlwjnJZDKCJ8eOHJG+3p6WxsjASIAESIAESIAESIAESIAESIAESIAESIAKK5QBEiABEiABEiABEiABEiABEiCB/UPAt6OK73T/5J853QMEWiy5al3FhBmJhKW3u0f6enul4BakUMBDkWg4bLhxV6A9ID/MAgmQAAmQAAmQAAmQAAmQAAmQAAm0DwEqrLRPWTAlJEACJEACJEACJEACJEACJEACLSbgn1/3n9tonKCb9iGPJNC2BKBg0iLFFX8wjkgobG6EnZCEQxUA/G4rHvGSBEiABEiABEiABEiABEiABEiABEiABJolUDn00Kx/uicBEiABEiABEiABEiABEiABEiCBtiYAnZRAvZTAm22dFSZu3xOAxgi1Rva9GBAACZAACZAACZAACZAACZAACZAACewRAlRY2SMFyWyQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQKcQoMJKp5QU00kCJEACJEACJEACJEACJEACJLAtBLgt0LZgZaAkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkUJMAFVZq4uFDEiABEiABEiABEiABEiABEiCBfUGA2wPti2JmJkmABEiABEiABEiABEiABEiABEiABEiABNqHABVW2qcsmBISIAESIAESIAESIAESIAESIAESIAESqELAqXKft0mABEiABEiABEiABEiABEiABEiABEigMwlQYaUzy42pJgESIAESIAESIAESIAESIAESIAES2FcEHKEhoH1V4MwsCZAACZAACZAACZAACZAACZAACex5AlRY2fNFzAySAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQHsRoMJKe5UHU0MCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACe54AFVb2fBEzgyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTQXgSosNJe5cHUkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkMCeJ0CFlT1fxMwgCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACbQXASqstFd5MDUkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIksOcJUGFlzxcxM0gCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEAC7UWACivtVR5MDQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnseQJUWNnzRcwMkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEB7EaDCSnuVB1NDAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAnueABVW9nwRM4MkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIksMME3OrxOY4jIvjjjwRIgARIgARIgARIgARIgARIYD8ToMLKfi595p0ESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEdoEAFVZ2ATqjJAESIAESIAESIAESIAESIAESIAESIIHmCLi0R9EcMLomARIgARIgARIgARIgARIgARIgARJocwJUWGnzAmLySIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESGCvEaDCyl4rUeaHBEiABEiABEiABEiABEiABEigeQJO817ogwR2loArIvjjjwQ6jwCqWFaznVduTDEJkAAJkAAJkAAJkMBWCLAFvBV69NseBIqjEFac7bEyedXuV7oLuKbCSgAU3iIBEiABEiABEiABEiABEiABEtg/BNwtdKr3DyXmdFcJuH5VFTNc5DgQXArvrpYLI2+IgF9K/VLrQgGrOPrZUFB0RAIkQAIkQAIkQAIkQAIkQAIksCMETO/NkZB221z03XQcohQ5brWiS0eFlRJTnpEACZAACZAACZAACZAACZAACZAACZBA2xHQASAX0/vmX0kBoHTWdolmgkjAR8CvqILbKrkq0z5HPCUBEiABEiABEiABEiCBPUTA5eqYPVSa+zUrTklJBcoqTgMKKpsYpqDCyn6VL+abBEiABEiABEiABEiABEiABEiABEigowi4uqSpo5LMxJJAiYDjlGwCWeMqrViOV4qBZyRAAiRAAiRAAiRAAiRAAiRAAi0nsAktlCbSQIWVJmDRKQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQwNYJUGFl6wwZAgmQAAmQAAmQAAmQAAmQAAmQQMcQ2N5VIR2DgQklARIgge0kUNNyijElLdwSaDtLgGGTAAmQAAmQAAmQAAmQAAmQQEcQoMJKRxQTE0kCJEACJEACJEACJEACu0+A0/y7XwZMQasIQJop0a2iyXB2ikBNDYCdSgTjIYEtEzA1MOvgLYNkACRAAiRAAiRAAiRAAiRAAiSwBwhQYWUPFCKzQAIkQAIkQAIkQAIkQAI7QsDOlbqc6t8R3oxkewlYed7eWBg6CZAACZAACZAACZAACZAACZAACZAACZAACZBAFQJUWKkChrdJgARIgARIgARIgARIgAQ2EuB66I1MeKfTCJi1/SbVlOhOK739mF7VrXJEXKGW1X4sf+aZBEiABEiABEiABEiABEiABEiABPYyASqs7OXSZd5IgARIgARIgARIgARIgARIgAR8BKig4oPB004j4BNf13feadlgekmgjAD0sKiLVYaEFyRAAiRAAiRAAiRAAiRAAiSwnwhQYWU/lTbzSgIkQAIkQAIkQAIkQAIkQAIkQAIk0LEEoKhi5/btsWMzw4STAHVVKAMkQAIkQAIkQAIkQAIkQAIksO8JUGFl34sAAZAACZAACZAACZAACZBA8wQ4Udo8M/rYfQKuOMXJfqGFit0vEKagKQLFeldl117ZY1NB0TEJ7AIBK6umHvYrX+1CYhglCTROoKzObdwbXZLAbhNArWv/xNfwZRN4t0uG8ZMACew/Ao5Ww7SSuf9KvtNzbHtwxXZE6UbLs0aFlZYjZYAkQAIkQAIkQAIkQAIksHcJaN8Eo5zeSOc29lX2LkTmbEcJ2EH5oux6seNaB4ysgx1NFSMjgcYI2DrWHq2vymt7n0cS2HUCWrluTIXrmza1jQhVWvFZDdroi3dIoJ0IsOZtp9JgWuoTsNWxOfo6cNYrRdqS4LHtCdgOmz1SeNu+yJjADQRKUls6K3eE+9WelbvkFQnsBIGSNKLDFtCOaHEiIi0Oj8GRAAmQAAmQAAmQAAmQAAmQAAmQwO4SKPWs7byopkeHgComR/Xe7qaWsZNATQJ+cYZDew07FT6bQTXD4EMS2BECtkK180neUWXWsZJrUgLpLf58p8V7e+mkPOubz9le57R5Mjvgs1WFuANJ3Y4otpJ9ym39EtkKXxt6BWcE6f/zV7nwUnQOR8ULGxiPJLDTBPwvQTWBtBOmO502xkcCWyXgl2mjwr0xRP87wIp5Ix/e2U4CVvr8kroxPjy1fxuftuIOFVZaQZFhkAAJkAAJkAAJkAAJkMA+I4AOje3U7LOsM7udQCCgp11NXre3y90JsJjGdidgZdSINZVU2r289nX6/HWvd27rXiO5Rs1q382O+rnsawHpvMxb+e28lLc4xZThFgOtCG6b+WrwFcJcvNzmuCtyyksSqEKgGUFsxm2V6HibBHaQgOu4Yv5qjaFBrm3NTBnfweJhVHX1ViGP9q8kpdsBjlsCbQdVhkkCJEACJEACJEACJEACJEACJNAZBOy4UGeklqncZwTKhytLyiq0rLLPBKGTslsazyym2lSz9gGGIu150QlPSKDtCJQ3DyizbVdATFBDBCC5VX81H1b1xQck0AYEKLxtUAhMQg0CaEPYPzR7dRtM3KkpunhY00GNGPmIBLZKoLzli9CMNBq5NNYxt1c+qbCy1TKkfxIgARIgARIgARIgARLYtwQccZ3t7bDsW7TM+BYJbOxsFwMsPnKkYoeKohOekEA7EijVtlaI7bEdU8s0kYAhUJLS0mCnGfDESL4n1SVHxEYCbUHAL5I4L8psW6SOiSCB2gT88guXtva1vszzUqvC3ueRBNqeAMW27YuICSwRMO0HtCHMv9LgAwW5RIln7U7AEaiRhMTRsV/HO25Pqrkl0PZwZagkQAIkQAIkQAIkQAIksKcImIFOs0zEdV0JOY7Rtndds3SEfe49Vd6dn5mNAql3IK4F0bEiSDAmoAqeCHd+npmDvUjATjppVVtxYQZBN8r6XuTAPHU+ASOpqHVdHfaEwiDaEtbeSufnkDnoZAK2ekUeKs9R/9opf7s42jR/Wf92cpnv1bT75bc8jz55tW1fOPbdLnfPKxLYHQJWhnHEX8F1pYAJf2yrIt757iSNsZJAbQIVdaqtXjHu4P+nFW+F29oB8ykJ7B4BrXndghQKOaPCbQV7G5JEhZVtgMogSYAESIAESIAESIAESGAvEtB+iU74uxKOhUw/u9ps/zZ2YvYiW+ZpZwi4BVfcfEEHjELeetNcHkOg5lc5bmTvB6au5sNAHxtuFl+T4skGJ7zRCgItKKtWJKORMDSpnjzYZONoJkdds6IJA/c6aIRjxexqtUgoY9XI8H6rCVjBhWjCBLoXvq7KKyq84oErYSckISvveRFv4V6rU1Q9vCbeC5uP6oGVP2ki6HKPta6aSYSPfa0g7bNG09tkEmzwrTk2ELlRLimPzhryKb+78coGb49wUfCqWLOqFHcMWG3+ok3sd7wxyO3XBWi04ALSxluNEahXxo2FUnK1XUWmoljlvUecmCzFDwsPtG72K62Ukhd4RoOagVh4s8UEIMO2SsUR9S/q2ryr6ir6DEoruIISi3Xb4mQ0HNx2vcuagO3I3HYmuNPS23ApN+EQDDwOevDqY1v/hmChQtDutf25BsPeznJrMAl0tl8IQHLLBQ53CoWC5PNZyWXXdfVXqU3cei5UWGk9U4ZIAiRAAiRAAiRAAiRAAnuSQMEbtUcHBRP/OvQZCsM6ZMM/7bw37Lq+w/LuVH33zbpgepsl1pz7VvNHFHq+AAAgAElEQVStFTtkJRyJSDgclpADk6YhcUIhkVC4OKlq0wO39hxHe47wizJXPKkVa+1nCHcr2xJpEvyJqx3dvn3aSYjsxKo/zTjXqjYckULBFSfkSAiy64TE9eR33xYuM942BGyVWJRdb6Ae1/ZeKBxWpSu0I+C+UHB0wh/nOiHaRHtipzNu89fqeC2bRsJtdtJ4O9K8HWE2knd100DkQYwa8KbBW1mtdB9GUwGVsNcGQPshjGuntsA2U7YaeBv8V5n37UiSn8tOxLeVPKhMtDCRaPP587+VtFX6teHaI55r+j2HaD+o8g36cZqOUHlaWpjPyrTxmgQaIeCXV3uOdrETioiLNi/aDqGwXqNhjKGJej8r1vZYzb3/vanmZifu23S2PD024BZnwgbbKeltcfaLwSkHn8KglV/rwHVNvQuhdVVT2yz+Kg0sWJfBx5bzDY6Gd/cxASNjdkmXAWHlGGNmdhwtl3NVgWW7UFFhZbvIMlwSIAESIAESIAESIAES2IsEMGikA51mZVO+III//dkRC9/kFO7jtnaAfJ14z8eWDzbKrUz6ByWC6TVUOo2vvyw17abnLXlHJC8i2ULeDBKFHSngnitScEJ6tHJq82wG830TC15YNg7rzl5v6VgRdrNhtTQtzUbeAe63iHdnc1iRWFxCVnOoa3GhE6SOuA4UrSC7jsA4L38ksNsEtB7yT8Z6sgz5RTMhky9IruBqXawyLSFZL4is56EMYOpaqASU6QB4Yehhmyq6VltPaKQcoFShefLzquPR8tVjgyw8fHVCNo+tslwtxzYNtdxUPmswqZXeal7bfNljTcfNPvTKxrYrIbu5PP7yajJIN7XyJvsLbkjludkoWuoeEFoN2S0FqYw3E74vDJvfqmEFuLV+qh3LwtqE/2rh+u9rHLYP43+whfNqKG1cWwhavdr6zMaDcCHDxkoFnEDhNSzihqXgOF67wou1VYnYaibof98TUFH02g7aVxMoV0FJMGL+oKziho381qFl34Vy7awAT57DZl6DYtgBwW35VjMJ2XJkLQig09LbgiwHBWHrYDwDEv3TdgWkxW6CCWUrr/1Qyc0KlXffPrbHoDh5jwS2SkCtrnnyChHUP18fJVfANtqO6bCVddS2GvNG/1RY2ciEd0iABEiABEiABEiABEiABKoQ0FXRDjosrmQyWZmem5e7PzwvjWwHLG2FH/y2q6Nt+/VVkrzp20yvQddpfEvCWCp6KKVkCyJTz1/ISmZNTUvDnPRKJiOPnkzJ7EJSwhGs/EfP3PzBZLrVxdIwsTLK233ahLx1CUF4CNu+I6UUt+Zsu8quNanbmVDMwOHWy2qHUqs1JSTN1pg4KzgRWc7k5OXLGZVJyEs2l5e5xWV58PipJOLxBpJnQm3AIZ2QwCYJmPpMTVZBhnXGH1tPGKXA+aVlWVhekWw+L3lxJFtw5cmzl6rEYhQ4ChIKoT70R2+1B/z3WnVutUZ29t3Q7OE/HQhurm4yShR2Y4/qHDRUj2PDMVgc1YM1ehENBmipbtemDc2EqyjqpVvhFkQcLH8GBPgyk6Qrazl59mJWcjmjHpjL5WVxaVmePH0ma2uZQGLF6LxyMGEGOt38zYYytvng4bOYj00EY+TV82g5VIbjRVDtcaVze11Ml99j8aZ1tYWjhusPfAthBXlFWht454K8Vr9n3zqvjkAd44REFasKIgtLy5J3YaUNGoIheTkzJ/cfTpu2r6/FWxm+UdLaRhaVEfJ63xIoft2wOEYcndRfy7ky9XxGVjM5VdZG+2FxaU2mnr2svyebV5M3CrT5KmS73ovmU9JYHpnexjg178qUmOWLK/x5CwwkIrNzC2ol07QrROYXl+Xeg1njDu2O4tfWlj3CMuFpaPZ280mjDxKoS6DgeFsFYrsqNySO6+ifUbIKyfOX0zI3v6hjwDpuVd5Zqxt+Mw4cF6Nw/JEACZAACZAACZAACZAACZBAFQLoMKAbvbjmypd/vCbv//4DyeQKEgk5EguLRNDBgV/bR68RTpVHW7pdJ9pNhm0HfTfpvYa37UlvaZijRtSbetRp6S0JolEHARkorGDrlLyEJJtzJZsrSNhxJSquhPLrEolhqyBsEYTxIphMt0orhqvKty4/bz0Nm8pNFU4dT61PbZ0I2/Xxdox6bBdcTCahRrWT/Z7CioSiak1ldXVVIuGI7n+OtXmxSHF4v0jfnzSTdXOHoz9FRDzZFgKQM8zAwhQbJM/8YRW/E8b2gWHBxH9Bwvo4XMhJPCwSVsttSJD1g2B0ptVLpV+iW5/w3XovNjscize+ESL6yWoQl6knGnPcSNylkLzvaelGS86QXh0rb2DAvExpombsqHfVFoXnygzWu05ElQaxmj+Xy6oZdNTRkRD+ROvioGCVaXOwgoJp4N72RtKMbPgT23CqvAgadu9FUkxXsx79iax7XoylrsvNOmhtDIBhgFi5d1XpCluphGU9l0er16yQzq1JLOxKRHe6QipKKtomL36wRnnAhr3ZvNIfCTREAOLo1e2Q17xEJIdjoSAF18hp1MlL1CnoGET9MBt/yxp3iVjtO2KP9VPSmAubCntszFd9Vzad9ljfR2MubDrtsTFf9V3ZdNpjfR+76aIy96g1TR/fKL7CwiCstcGySlhyEg25EtMGMHzaP+TAhmTybccJdqutuptMGffOEUCfQaUQYqdW66zCCvocISmIIzlXZC2Xk4jk5fjEQfnpO2/IiWNHJJ00NlHKJdcT5U28vrSwsnPlzphIgARIgARIgARIgARIoGMJaAfE68hgsCgUwuAnTPnnJZMvbUhR7Kg0MInQ3jA20btq7wztk9TpVL/m1ZyZYR5MmBacfHGlKQaL0P3Ou3lj6j9bECeHgSWjqFJW+laWK0aKytxskq55X8yQ1iaDqO3NvpC1Xe3pp1pOmDBvYS4t1s1OONdKiqbUyhwcOth+rSAFJyeuC40qmEE3uYF0r8F0kCebNo92ogreS5PW3t7pLSVRKyd8tj8JoD6DFOMtMYpX+r6ECuI6eV3pb57AlasTqCEorMA15NgKcbG+tbLumxtqJVgvmZ7abStDDgzLvN/eIwUT6KzOzeY8Nue6TtSberxdKbDC0lii6rlWGdAVpia9uroUk/0O7LFBflGHhnQiFbKbcwuSV4sr25W/xvJVemkadd+cu2Lu6gH0B+t5asaL33v7nBdzv21JKn2jWxAFVkT7gjF6f5DfsLfVGuQ5JJjzxxZBbi4vWd3XFfm0eTUh+NsipiXtC5inJNByAj7J1ZfCSB0UVrCFoISjKqIhtOfRp8vnZd019XD9pFjZbsClLxn1XdMFCfgJmJYvvskqRp7YoQ2hMottiOE8hH4dtinGNplWUdAvozinIPrJ8nwnCEB+IXdW9iCbZvAX9a4qDzohcZywqsGqlZVtShYVVrYJLIMlARIgARIgARIgARIggb1AAF1mfxfa67eIE8Ie6CEJh6ISiWzsVtiuTucyMDnYjkHa7WRTVlYtgt9p6UW2MdbpWEsVUENRhZWQ5PIFWVvP6Ui9sgrHJJVMSTgcVmWVQsHrqvsUHIodcqsF4JtTbQViT02m4kVrRcgMAwQgv8UybCESyI9d6dmKYM17hv+9/c014YgFEhKSQsGRbDYvK6sr6gZ1cDgclXgsYheiqlEKDceTUZVxvVEaRG1FWhkGCQQRsN9Lc8T/GOyE/Dq6BQXq4ZXVjGTzrrgFs+VaOpWSqC7xd9RUeuldVen1orHya2NV1QJ7saWjfV+2FEiTnkt5bNKj53w7FOU2l5LGfHlVUGOOd9GV1rZ2hSm+Gy7WlJotKbClClZHr2cyqlgFncJQRCQeTek2VruXbKXrfel2LxV7N2bzDe6U/Ok0qVd1GsnABerPsIgTlrX1dVUS1LaLE5JYPCnRKEys2HyW5MnUU7i29zqFAtPZuQTstx6SjG0pTPvXbCsYkvX1dSkUctrmjcTiEgljHAJt5no/K9/13PE5CWyFgF9+vXoT2wnrmIIjmey6ZHIZKcDeqyMSjsQlHovWjdAOP9R1SAcksCUC2AbIjEGg5nVV8EyPDu0B6LZiO+L1HDRe83atzJZirOZ548hyNZe8TwIkQAIkQAIkQAIkQAIkQAIYuvQG9DHJ1NeTluGhwY1csHKvXNVlo5u2v8OB2rYvoioJtEP0dugIFlXcUEiWVsye55g0RWcbJkwPHzookWhEO96FQl6VGzBUao1clE8uImSzW0WVqJu+zWHUppE15cG8xa2fcEH91lqFFbsvilfvaJKN5gkmm7I5kYWFZXn4cElVWmKRqHR3JWT4wKBEwuHilJLPm3IyEmvmnMywU1P46JgEmiQAKxRW6kztBrkLhSOSL7jy4PFTWVxalZybU3P+4wcHBUorqHNVYdBWvBtiNXur29ulOOydzR3Vioa+NPrf5gKhr5oETJF2Bl8ou8IeBX6QDUzWQ1kFA/RLKxl5/vyZZLM5iUXC0p1OyIHBPkkkYjXzv70PLVd73N7Y9lfo/nqsU3IOE/6ltEIqoPCqSq8SksdPpmR+cUkKLiZMCzI8OCj9fd0NKqz4Ai5FwTMSaCEBW48ZhRVUxZDdXMGRpdWMzMzMyPJKRkIF0fGH3p4uSSUSDcQP2W2B/NrkNRAjnexHAp6AqKhZYbFKr468nJmW6dmstifCIVcGetMyciBgDK0Cnb9Or3jESxJoIQGjsGLGCqzCCoI3Vq1WM+syv7As07Pz4kJ7xduerYUJKAZFhZUiCp6QAAmQAAmQAAmQAAmQAAnUIqCTDq4rBZgvDYeluyspp48flZ/++GKwN9tXL3vaggGjsvC268Im3h63Kx6G23oCRk1FS04nTrG6CXugizx8PCUff/K5PJl6Ibn1jAz29sufv/euJFNJtRpUgFUW6AgEWVjxJbS1UmFC65Q3w4eh7U+NDGxDMr2ATZm1quT8UuWpWnmjlAWJyNLSuty990CeP30k+XxeupNdcvLIhLz7zluSTCaLCivIbeDgZuDNbWDDIPctAasQpdWu77VQC1euI3MLS/LBR5/Ig+wTWc6tSVciKu+9+7aMjh7UAVFYXfErCJYppVTIb2sUYu3krv/d27fFx4yrMrZfBnHDbH+5sJxVZauPPp6TudkVScWjcuzwmLz52nk5MDiwu+x87ZXdTcjejL2sHuqALJqqFxP+Zo7eKKtgdbQrv/3dh3Lj9l1Zz2Ql7OTkjVfOyisXzqhDq+Bt6nEzQWVCQUi2QrfHDgDBJHYYAfs99icbUhmS1fWCPHz8TL746ktZXZqXWCwqk+Mjcv7MKRkfHfF7qH6ubYjNy6++C1WaCpt9VpbYFi30qZUWja9OPHX9lyXad7Fd4fqiaNtTL+9GyVUvTI2pWwBBYSUsMErx2Zd/lKXLCzrPH4sU5PSxw/KnP/1xMVtF8QoUU9vCLjrnCQm0loBnXUWtsnnCaEURQ78YO/vuxi2Zn5+TvGCbq9ZG7w+NCit+GjwnARIgARIgARIgARIgARKoSgCdlXyhYBRWpCCRkCNdyYgMdNc3Z1o1UD4ggRYTsJ1rBGv70tiYAgor88mEJKJhiTqu5PM5Cbs56U6FpasrJuGwN1yvCislv/5w/Em1Yfvv8Xx/EfDLWqtzbsPGEX/RUEx60gmJhlxUxHpMxsLSl05IKgWT/uZn5dIe7X0eSWCnCBQNrHgRQn51J/RCSuJhR8JuQcKFvEQkJN2pqPSlzbZWaGPYAVB79KfZvhP+ey0537aAq6cuKH/VXZc/qeRb/rT9rraS13bIDdoOoVBU5ruSahVI3Kw4kpNExJXedIxt4HYoJKZBCVRWZWgH2Po37zqSjEUkFnK0DRxxCtKdjMlgd30LQWxPUMB2k8DKekhmk1Ft9zpuTq1fxSOO9KRiMtRbX35bkvYaL0GNRy2JmoF0EAFUuPjzhAKnaEPgL52ISggNuDwUY3OSiDoykN44NV9Lnmo96yBKTGqbEoC84hd0XFlOSVcyLpEQ5JkWVjxUPJAACZAACZAACZAACZAACew+AdNVtlthdPpExO7zZApaTQAS6hsrKg/e8VZPqxgXxHUL4mKWFHul29El7MxSGmsq91/j/gaHvLHnCZjasLXZtINEVo5t6E5IxAnZvaWhXGUl1vqwLovjpKUbPCOBHSRQ1i7AYlNbp4aMUiBkV6XWdU39axakasWtdXeVF6vK7a3nbNsC3nrSgkIo4xvkgPdaTgDMw2FsrSK65RWsbmDeyayobnl0DJAENkWgVlUGGVYLVrDIEwppC6KRuqRWmJtKJD2RQLMEYOzK244YdXABbQZTAReVXJsNku5JYFsI2ArTO+KAP+29qRwXpIDpfjcPIbYjD/8/e+/BHjmOpel+NOGNvJS2qrp6uqd35+597v7/n7E7M23KZKWRD+/JIHmfDyRCVGQopcxUyH7MDIEGBA5eHIIgcAB8URQb5Bc96aII3CIB6pz5Hstc6i8Hdrle+kHHWS7XOfvc52Zct5g4BSUCIiACIiACIiACIiACIvB0CHgeP1Y8OI6bTdu/3o+Vp0NOKblrAlc27vCCXVrCdqLmTvGD3H6gXxnGXSdG8T0rAlbvqIf5jW3zNBSMaGSVXUgbi27W4JkPS/sicGcEsnKWOmt+VnkXPaXpCXNoOlRlFXhneaOIbkTAVBuMT2OhkmqyLahvFII8icD9EbCqypI2Nc9OwOUvEzNCWvWH+8sZxXwTAqbeQI9ZQWwMVxwus7K4cpNg5EcE7oZAWqVdxJU/TJwE6Y/am+qvLZ8XN2hHBO6RAPXR6qzdX+ioMdSmwVWqu+sUUwYr66SrsEVABERABERABERABETgKRGwXzC5NGXjpHNntCsCD5eAbSBig5ExXFmsaX7RaM8P88XH+cNNiiR74gTyOmibhuKYswKlhoJ2ZJP09YkrwlNL3rLCsijO0riwYXlqaVZ6ng4BUw/Ohvo/nVQpJc+OwIoPumfHQAl+DAQuNPXLe48hLZLxGRBghfZCVS8l2Bpmc7oV7qsN7RIeHTxAAvb7jCptZiU2M6vEmdHrFYp+C+mQwcotQFQQIiACIiACIiACIiACIvBsCXCokzYReCQEHDO6iWNMs2WAuEQQ4uznPZJUSMxnS4DFrfmlBldpg6fK4GerD48t4UZVzUTSpkXfGhAuksHrplVUVoMLJtq5BwJUwtXlqj2bLshmlfUeRFSUIvAVBLKiF+AMbYmdZ2V9nU1fIZq8isC1BBaaulDkVIevvVEeROA+CNiKQi5unkp/tt2B7kKzcz61KwL3S2CF+i4E4mAZlr7r3mSwsm7CCl8EREAEREAEREAEREAEnioBfWc/1Zx93Ola1stsSQqTKDujihMD/GWGK5d7SZcDsDi+9Alv/cgVgdsjQI2z2pg2dHJUXjolhTFWyev27UWrkERgLQSMwSA1moaDdNlxyrWutInAgyFwtT6a6gMcOGZNCtv5dBdN9w8GjgR5DASWVfhS1TWt99rlgB5DciTj8yWwSpWNOmdGV47qD89XOR5pymmk4iQxHNMGsazhjzRREvvpEqCK2jpE4qSfbGZJwfUmmUt0axMBERABERABERABERABERABERCBZ0DAfnUzqZzWP/v4vpTyvB97YdU5e02uCKyPwGXNSxs3U2MVXrl8dX1SKGQRuAUCpqP/Qodtl/8thKwgROCWCNy0TL2pv1sSS8GIwK0QWOogXTq8lSgUiAjcIgGrok46peDFNGx30Gl6i8lQUCKwIHBRe7jYW1zUjgg8FAK28M3kWTpcq5SaYWWteBW4CIiACIiACIiACIiACIiACIjAnRJg+89VX9VmCSsXSeIB/MGDg9S93PmvRqQ7zTNFJgIi8AwImLGllyZGT+BeXV4/AyJK4kMksPr9n7DDNJvUn1JfVc14iCmSTM+QQE6Nja7SRlt6+wwVQUkWARF4GATydQjOIaF5JB5GvkiKlQRydYj0uq312gvWXXn3d52Uwcp34dPNIiACIiACIiACIiACIiACIiACD47Ald/Q7BxlAxGNVKxL4xU1Gj24PJRAnxEwnU1mCvRsSRV1mX7GSCceOgEWzvaXlsfU60tF9qWDh54eyfd8CNDgKjNUyYxXksua+3xQKKWPjoDpasqKXuqtdPfRZaEEFgEReOwEsoEzTIYx2H7s6ZH8T57ARb2XSU0rEWZpzDWmXK1ya4SroEVABERABERABERABERABERABB4aAdsbuuw+NDkljwhcEEjtVEyX08Ua0uYwPXfhU3siIAIiIALrIZD1+K8ncIUqAt9PwFZtvz8khSACIiACInDLBPTVdstAFdyTIyCDlSeXpUqQCIiACIiACIiACIiACIiACIjA1xAwa6N/zQ3yKwIiIAIiIAIiIAIiIAIiIAIiIAIiIAI3IiDD1xthkqdnS0AGK88265VwERABERABERABERABERABERABDUaVDoiACIiACIiACIiACIiACIiACIiACKyHgANwWSBNs7IevAp1rQQWplZmWcz1RSWDlfWxVcgiIAIiIAIiIAIiIAIiIAIiIAIPncCaP7ofevIlnwiIgAiIgAiIgAiIwHMgsOhyeg6JVRpFQARE4N4IqLS9N/SKeF0EHAdsOlvn7MQyWFlX5ilcERABERABERABERABERABERABERABERABERABERABERABEbhvAouR/Zpf8L6zQvGLgAiIgAiIwEMnYAyvjKHK3dQbZLDy0DVC8omACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIjANxPIj/m/m86nbxZVN4qACIiACIiACDwrAjJYeVbZrcSKgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAisl4AMBNfLV6E/FQIyWHkqOal0iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI3BsBrsK2WInt3qRQxCLwbQTuw8xKBivflle6SwREQAREQAREQAREQASeHYFk+Ytl+fjZEVGCRUAEROAuCTiXWz1VBt8lfMW1DgLLOrx8vI44FaYIiIAIPEsCKmCfZbYr0SIgAg+DAIvgRTG82HkYskkKEXggBGSw8kAyQmKIgAiIgAiIgAiIgAiIwEMmkB8Z8pnhykMWXLKJgEY2SQeeLAGN23uyWfssEpbVLNRm/yxyW4kUARG4ZwL5j7l7FkXRi4AIiMBzIpB+saUWKwkcOCqPn1P2K61fQUAGK18BS15FQAREQAREQAREQARE4LkRWO5H4gc2NxmtPDdNeNrpvaTnakB62pn9qFOXNnReSsIl5b10RQci8AgIqMB9BJkkEUVABJ4EAVUYnkQ2KhEiIAKPloBtS3u0CZDgIrBmAjJYWTNgBS8CIiACIiACIiACIiACT4qA2jqfVHYqMSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiJwXwRksHJf5BWvCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACDxTAjJYeaYZr2SLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwH0RkMHKfZFXvCIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKwdgIOkGh917VjVgQiIAIicCUBlcFXotGFZ09ABivPXgUEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARE4OkTUIfp089jpVAEROBeCCQA+Ms2s2uKXP5R2Wu5yBWBVQRksLKKis6JgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwJMhoA7TJ5OVSogIiMCjIJDASW1YcoYsj0JwCSkCd0xABit3DFzRiYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMBDIrCYLChJwH+ga5z1WV7JYOUhaYBkEQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIF7ImAMV2iowvhpsbLGTQYra4SroEVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABD4nIIOVz5nojAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwBoJyGBljXAVtAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwOcEZLDyOROdEQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERWCMBGaysEa6CFgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER+JyADFY+Z6IzIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACayQgg5U1wlXQIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACnxOQwcrnTHRGBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERgjQT8NYatoEVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABB44ASeTz3EcOA7g8B931rhphpU1wlXQIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIvCYCNBYhf+Ns0ajFRmsPCatkKwiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi8AQIyGDlCWSikiACIiACIiACItxtoMoAACAASURBVCACIiACd0YgSWNyMvfO4lVEIiACIiACIiACIiACIiACIiACIiACIiACIvBICLDtbL0LqTwSEBJTBK4hIIOVawDpsgiIgAiIgAiIgAiIgAg8dwL6uH7uGqD0i4AI3CcBlcH3SV9xi4AIiIAIiIAIiIAI3BeBhANlcoNluDSF6sb3lRuK91oCOeXk7uIwM1pZHF8bkDyIwPMjIIOV55fnSrEIiIAIiIAIiIAIiIAIfDWB/If1pdlV8he+OlTdIAIiIAIi8CUCKmK/REfXREAEREAEREAEREAEnjaBnLUKE6rK8dPO7ieaOqntE81YJetWCchg5VZxKjAREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIHbI6Bu/9tjqZDuioC09q5IK57HTkAGK489ByW/CIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACDwxAgn4D3DU8//EclbJEQEREIELAjJYuWChPREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgXsmkNBSZbEqkAOHViuyXLnnXFH0IiACInD7BGSwcvtMFaIIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMA3E7hksfLNoehGERABERCBh01ABisPO38knQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAg8OQIyWHlyWaoEiYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiMDDJiCDlYedP5JOBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABNZKYLEYW5KA/0DXOLyynk0GK+vhqlBFQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARE4FERMIYrNFSh1LRYWeMmg5U1wlXQIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACnxPwPz/1AM5YIx3nlmRheLcV1i2J9OCCscwp2HNiJd14cKoogUTg0RGw5edy2XnV+UeXwLsW2IKzBfQy2GvksbfT21feek3IunzbBGxeKZ9um6zCEwEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIFHQeDeDVZsd5ShZTsueJDf/56ODBvOpYi+Nm+uuvmq818b/j37vxVG95yGa6JfmVPfmG572/VqaX1SuOt9X5MEXRYBEXiIBOxjbl096t+RSxYiXf5iAJwIzgUS52bFqA3CSsHjJ5onK5OWO5nbTXEuc6CH/LZ8PX/t1vZzkTJP7XZJWHvynt1rZMqlxAiaS809C67oRUAEROBuCKwsJm3hqELxbjJBsdw+AavDNmTpsiUh91EQ4PeT3azyWjev3Pac9StXBO6LAPXS6ib1Urp5XzmheL+eQF5zv/5u3SEC900gX/5SFpXB950jiv/mBFT+3pzVY/N57wYrV1ZF7QXrfi/Z2wrnkhxP5NGwbKx7KY06WCbwdZioI193x3J8OhYBEXjABOzjnbm2uq/19r4lzyxM3mvfrxlRJ3/tW8J+evdcR+TS9UsHGYtV59aOiZFm70W7u/Y4vzGCa/jkL1tt/caYdJsIiIAIPEoC+XJw8dp+lCmR0CKQI3BJsXPntSsCj4JAXoHz+xSex6q1PopsfHZCLuvqswOgBD9SAtLcR5pxEjsjsKzBy8cCJQIPl4C09eHmzfdKdu8GK5cSYDUt68u4dO1rD/LfYTbcrw1j4f+7A1iE9FB3OA6DP6Y0/3uo8n63XLeqH0vS2LAXanMbCr0Uhw5FQAQeFAGWn3zS7eNP4dxFGfCgRH0EwqwClye76voVyfoKr1eE8PhOW1QPNu0PVrBvzuunl6JvRqEbb5sAn2cp2G1TVXgiIAIiIAIi8HQIXKr7X1dpuO7608GilDwWAtfopNVvJucar48lxZLziRLQd9sTzdinnqxrClaVwU9dAZ5G+lT+Po18zFLxsAxWLNprykrr7UqXSnqbiroyrMsnLx9dKdmDvBAB4G+e1f89APwxG+zvQQr+rUIxs25p+yzfeWIRPpewWBzcUowKRgRE4KERsGWoffzpPvly9LYzgdAuvfvzb5+rytFLN+TK3ky4pcu3LfKDD89iuwGHr/B6u8mmbDZyujeQ9XYFWF9oTyw56wOlkK8mkH826OsJPR9XJ1pXHh0Bq6ePTnAJLAIiIAJPjIAtj1VfeGIZ+/SS89XfSfYGq+NEIj1/eorx2FKU10cru9VVeyxXBB4ggWXVXVmcLntiOqTfDzA3n6lI0s8nnfH3YrBiV1W9bskE6p4tC1cVnva6zSH6Mf6sZ+taD9/q5sKxsidwFrLZdFhZvzWau76PBioBgEkCDJFgksRwoxhlADXPQ9V1Ucw6Xu9attuOL5eFKz9svirvvuSZEeUiS5BqR+7UbSdN4YmACNwjAb4TaLASAhgkwBgJggTw50ApjlHzXTR8B3zZqhz4QkY56fvezlTjwIWTEeP7dnnLn7Hv4BXelm97+sc5MLaORKb80YiKrHJeFjxWnVtcvKUdymOfFwb5JXluKcpvDsayIxfuc7OMrMtz1p91ec1et256t/6KwDcQkBJ9AzTdcucEqKe2oLSRS3ctCbkiIAIisH4CV5S5tmimSy9XeFu/fIpBBJYIXKeLVmfNbcvKaxV7KUwdisCdE/iCIi+r6Re83rnYilAEqI/UUaun1r1U3K5SWnvTqmvCKgJ3SeALOmj12YrzBa/Wi9wHRuDODVZsZwWVhT8qkd0nG6tUtgykf15nxwYvOjzI/MUJEGfHmWM6Y0znlT2Rer+Vv5TF/kwHZQIUHaCQyWdlv5XI1hwIZWUH6ygBToMEf7ROcTruww0DbPkF/LC1hdfbu/BcJ2W/ZnnuKnimm3nHzeYXVcV2eN5YbXgzJ1DJwso71BFetq4N/6qOwiyofBDaFwEReCQE+Jwb478EOJyM8WEwQHc8QWE2Qz0BftzZwV/3d43ByiNJ0p2Lactiy9KW0V6uhF0uJ/NlNq/ZMCg8J7ZiXcH6ySdoOZz8taeyzzRaltwnz3kClFhfiQGPL6VVL681ArDvQz4rrHskNOrK6k/m3Zir361RjBsHbRlSNvKj/KyHOhHguql+2TRZl/7o36TH1ltvHKM8isAFAeqf3e74UbXRyhWBryNARaXiLiksy0eeWjptwuZ7wH7Xf11k8i0C6yOQL38ZyyrdXV/sClkEvo2A1Vvr2lCWj3l+pU7T48oLNiS5IrAeAnkdze/b2FappfGnOoRFJPeBEKBe2p8VaVl/l4+tP7kicF8ErM5alzpq27R4jkqdX+o+jhNEcYRC4c67k+8LkeJ9BASs/mYqu5DYlrm8Tr3W9ngI3HkJw46KKQ1NkMBDYmb0KLge/JxhBBWJDf+c/WOWaR1n+iiyg8O76AygsQr9sCPGdii4NCJJElQ9js++nY0isMGNsrOzhUYevfEU4+kUm+UStspFMyOJLdjtA3E7sa8vFJMWJDgbT/B7u4V3rWPEoxEOKhVUCj62Gk3USqX1CXDHITMfbR6GSao3FIEGRyU3da1IV+YhA/nCRj2xHXKcZYF6ybC8GKh4FyPKb0s3vyCKLomACNwBAfPMszwBcDYZ458nh/h4egZ/MsVuoWg65f+0t2uMG43h5R3I9JiiID9bbrJuYN7pWbm5XA7z2P6YRu6zLCVXGmFw31x30nMsrp9yWcv0Mb35jefsO4j1p1GYYDidIQjn2CmXsVXyUb3jmp/NX75/hwnQDyIEYYDiPMF2uYKK76DopgYs+bTc1z7lpR7adzh5knMpM1KmoQ0364/1CfrnRn0j3nKmg9lpOSJwIwJ8fvM/e5Mp12z5Zk/KFYGHRiD3QqIes4zkj5t9V3Of17jJWCUDIefBEKC+LpfB1F3+cur9YOSVIM+cQAIkzkVZm9dfkqEu5+sPVpd5bfFNagvk/A3PHKuSfzcErL7Stfs2ZquW1F9b/ub1l4qtOoSlJfc+COR11PZFWT3mNXvd6m1ej1WfuI8cU5x5AlZHqbv82eNlveU9NFbheVtvcD0HjnfHDYp54bUvArk6LstdWwbn9djqbL7spV8ea/t6Ava95TiOqX+Z2fDXXBG701KGSsQOlBOE6PUHiEdjVB0fu81NbNXKpgBkxwAb/rlMTSeKcNLvIwkCbLkeXjebqLlpByBhGT9I0J7N0et3kCA2I4g33AJeNzZQK/rGGOHrs+LyHVR0ysX4+kjw23kH74+P0J+MsFcq4d9fHODf9g9Qdm7PSOayBLd/xDRxhhp2IE2SEP1ggu5khHg8RIXLAwUB5kmyqGjdvgR3HyL1isZGXRrp9PoYzabmQaNuvazVsVkuo3DFrCkLaal4hLdi4+mUJzBAhLPBEMN5hGgeoTSf483mFjYrZVSWRv7bB39FkDolAiLwgAmYcjSrIPHZH4UhepMpuqMxvPEYXqGIYTAz7w/65U/P++UMte/8MesGswDtYIpJFCJOYiROks6WwtZYLhBEgOajyYHruij4Hoq+j7L5Fcz73hi3wkGVRq5OajxgK6m89ynxX5UWVsKtYQhrRb+dHuK43cE0CPCyVMH/8+o1ftpp3umMP7b+NADwe6eP9+0zDIYD1OYJ/v3lK7zZ3sF2xb+Q6Z4fFNYVWpwxaTTCaDJBGMdwkwS1KMZP29toVkpGj1in7SbA2WyG0+EQXjSHjwR1z8dBtY69chnlxRfRPScqfXT09wET4LNrP7ipWzTG57PDHz/WWJ7RhJyNRQu1esDpkWjPjwBLOdtgZHXYGvMZHU6AgpvqL2dXMbN9PT9MSvEDJMCy1+qv0V3qclYGs7zlDHWVpRGmDzAZEukZETBlrZMZqWeG06z/h0jMdwD1mT/qL7+NCnCMa7+NeM00BK/6mHhGHJXU+yHAui31lXVdujNT301MHYK6Tf3kRv01gwvhmDow22qptzzPX9pCkHmWIwJ3RCD/vcZ9DnKhDge40GGe55Yvf/kdx4Ev/Jazepx5kyMCd0bA1nNZ9lrdnSMxbQ68xo1la1p/SOsO1F3Wham3ttxlOa0qRAZMzp0RYNmaL4Pt4MF8PYLXqZ/58tfsZzrMMtgaYN2Z4E8ootRYJTUcpgHLujbznbKuwPPhUllMpwWA94M+PhweYtbqYrdSB7wiapW0YZ+FpjVE+bXTwi/Hh4jGE7wpVbBR+hmlUnFROZ1wRHsc49fuOT58fGcMVhqej1flGmqFIgp+3czccoHPVn0vzuRlXLXPO6zsYwCnCfBfpyf4+7vfMJhNsG2MVGLsb26iUEqNbm4e+qoY7+Yc02Qf8rkPhG6CuQckvovIdTBP4sdvtp7Lbu5StwZIcDif4b+O3qM96MHzPLzyy3APXqLi7aJQ9G/+0mWgWWZzlz/q+Ih6Mh3in+fHOBlNMJvMUJ9HSH4EfH8PpeKdPXZ3o0yKRQSeKQFbWaKbNhA6mDseYs+H4/uYex4ix11YrT9TTF9MNstMGrIaY9B+D+97bXSnY8yjOWKXBisOHL6OaLRipv6lRS+NVVwUCz5KBc4YUkCtWETF81HzCtgsV7FbKaMJBxVE8B3PVEgfw7v5i7Cuupi9i+x7iJX2IRJ8mg7xf44+4P3xMWbhHHvw0CyWsL/RQN3PDGyze68K+jbOM49ZfzpPEvz9/AT/+PQenU4H9TAyhkfVSgWNStN8UJg8vni13kb0Xx0GP9zP4jn+z9kxWp02ZmEIL0nQDCNUS/+BYrlk9InpOgvn+Pv5Gf51fIRCHKGIBLulCqL9F2j4+yhz6hhtIvAlAtkzyPcIdYp1Vc42NUaMUTjFdB6iEAF1t4DdSnUxI+CTLc++xErXHh6B3Psnr8N8r/M9NJzNkIQhSlGMquNiq95AOWshuoPXz8PjJYkeHAHqIXXXNuBTd9nGNI7mmIQBnPncGNi+qNbMDLSOXusPLg+fm0DUWerrRX0hQZ+zKsZzjGj4HwYIwznYnOc7Diquh5pfQKNQRKNYQN3zzEyArEeowf65ac/9p5f6y/ou67osa0dIMIhjDIMA0/kcszhCPJ+b9WPZasrBfhvFIpqlIpqFIqrwzCBD6i6LY/5UJ77/fH1OEtjyl20G1GHWdwdRgMFshkkUIZzPMY9jM9iq4jiouS4ahQI2KlXTVsWZWLmxA1WbCNw1Aeov67psnzO6myQYhgGmYWjK4DiK4CQJfMdF1XHQLPjYLJXQLJRMeUwjQpa7qj/cdc4pPhJI+14Atnkv6hDJHP0pB75GCFj+RpFZgp6TStT4831ssvylDmezYavu8PD16U56zm0DFkdQs8Pi17NzvDs8RNTpI9rcwavdA8yiBKHrmIKTldZWkuAfR4d4d3yEeDIBqnX8f29/MLOCcCkgVnSNAUIc4XQ8xO8nJwjnARq+j6i5jZ+29oxCJq733RVYWwGm1WF3FqI/GWMaR0h8H5PpFN1+H51eD5v75ZU5zvRz44uBPx6zcM9XsjMvN3ZsWHRtRd26Vt7rAjP5Qqv2JMac04k6TtpgkySI+ctkpcuwH+WWwbBpZYHWmk3x++kxTjstFAoFjL0SDkplHNQbqBb9i7RSyW4K0xqsJMAUCTqzAB/aHXzs9jAZjlALQrzc2MR+s4lNGaw8SlWS0E+TgC1L+bhz4yNvy9Lryj1bSWf5YoqLQsGsW8eylDOAuAUfLs9l17+ySDH3PfU/ZGIbrY56Xfx+eoLOaIAomiNhS2uczbISk7ED13HhuF66jmoSIYkjOFxDlY2vXgHNSh0vd/bw4/Y23mw0cFCrou4AfDvb/LqS6WPNoOw9RfG50eWo9vNBH/3pBJM4MgZV3dEInWEfw/EUlWYFXELxLjY+HzMk6E1Zf5qY+tPcBWZxjO6gj96wj9lmA1WX+ftVr91bFZ84+KN1fjsM8fv5OU5OjhHOQ7Mk5cY8xv96+xP2dnbMaD/6Ox+P8L7dwh9nx/CCOUpxjGmtgZfVBuYbMcxaR7cqpQJ7cgSc9HvGjMzLRujxG+hk0MVpu4X+eIRCMMdBuYb/ePUWO406PPcG5dmTA6UEPUgC2XIU/Cbnjw2g/I7vxMCns1OcdjuIxmNU4xgHlSr+9tOfUKxVNLvKg8zM5yWUrQJZ3WXDJ8thzk533O/htN3GYDwBZjNsc3aVN29xsLWFir7jn5eiPKDU2noqdZYD/DhIqsfZpoMYnzpnOGu30Rn0MJyMzICpJEngOS7KnodmpYrdRgMvt7bwdncPu5UKmm46IyU/I76iyesBEZEoj4kAvwf5o/6yX4BlbYeDBAZ9HLVaaPW66A9HGM9mmIczo5MFz0XV97HXbOLF1hZebW3j5eYWmvBMx6nt8L/2G/8xgbpNWfVg3xpNW/6y7dCWvxxw1Q5CnPQ6OOm0cd7pYMC+otkMEWdopcEgO0rLZew2m3i9s4+X2zvYqZSxYdq10u85ZdOtZZMCuoIA9ZflL/WX5S9XHWiz/B0OcNRto9XtojcYmP7NMAyNsVXB91Er+Nip1XGwuYlXW5vYazSwUSwbIwDN/HoFbJ1eCwFbh5hmK2fQ2KodJTjtd3DSbeO01TLfbZPZxBgNshLBAa3NYhnb9Tpe7+7hxfYO9hsNbHIwrJPO4HZdn89aEqNAb0TgTgxWbGcUC8WPkxEO+0N0JjOUObVfpYpiuQzXT40l2MnSS4Dj6RTvz86MpXXF9VEoVVAuV00jrU0ZF6yJXddMYzWNOYWVg0LiImAfFxzTuWUbI9J7vq0qYB8M3l3yPBTYEcmAI04Tz/6IAsp+cWGAYuWja++lSytyUzEfBwj7PWxVytiuVtEsZlPhM8xrRLQvGnLiSIpzWqH3+2aKxM1yBVvVihkxcd1Dx3AufimrdCHQbH9JkBuIlk/26n0GYrdr0mm9fZebxcFoyZ8vZzZGha4LfgIFcBGD++n0fTRIor9L25cSviINvN802Lo+JnBMPIzLT9KphRlcHsOluG5ysHzzChluEsyT8JNncRMO9H8TfwbO1wb+CIiuSr9N5he4rLrt+tSuvuvS2UsHF8/FQpSl69fH+fU+TLlMy3JW2oMAnVYLpUIBLzY3sVXwzaiHz8rSK+TiaTMJCBPAmzLDSr6neM1uV9xuL1/t5gNZQLra+82u5APlvj3OEnCzQG7FV1p2OpgmjnmHJ1E6q4rvuvBczqpCH655r1NMNgKwIdZsZhabBEE8x3gyRuf4BJ9aLbxsNvDz7i7+8uIl9ooF1LIpABfJXMXRImDAq65/R2pt0NcGaz1+gwz2VjbcVUpl+F4BiF0k8wSe58P3PHhcd9aGbW/4jnR96VYbPDWKyzfx+TIz3iSuqUvViiWUCwXTeckcplzX8vlShN94jXGbOgKX+aHR9HRmlvQLEgfzCCjwY2ejhlqtZup6fM+nxqkzdMdjTMM5/PkcRcdFqVjC5sYmfN8zqpam5z5S9Y0wvnDb00jFFxJ4h5f4bFi9yzd8nsUwhnufzk7Q6nYQRnMUZwEmtQZ+2N1HM6ml5eAdyvqUoqIOL+uxLaeeUjrvKi1kx29SO1KP37nvRzO8Oz7G+8OPGAczuMEMzSTBqFbDy9evsYXK9Qakd5UAxfMsCbDs5Y9tVHZGFRpancYRfj87xR8np+h0e5jNI/izAPuI8dP+Hja5/PRjHsjzLHP78Sc6X1+w7ao0VPkYAx/O28Y48Kzfw2QyxiyYgp1NccTWUE6YnBqDnwczHI6GeN/t4WNvaL6Pftho4mWlYpavZKPwZ9+8jx+dUvCACPDbiTNS0NDKDGIdDfD+7AQnZ+cYjMcIgjnm8xhRnJgBKfwyc5wEXcdFKwjwvtfH5vEZ/vTyJX7e3cfbRh27S0utP6DkPlhR0jpwWhs2M/mrEnxtXtnvNZa/1GGWv4cJzKCV90fHaA36ZhnhIAjMIOo4js1AawY8CAJ0gymOJ2O87w6w3+rgx60d/InGg7UKKm62PBDzYfkD5VrJ5EEELhO46nHmtxp1l+4ZErwbzfDb2QmOzk4wnLHuMMM8CBHN56b+YNQxcNF2gNPJBH/0e6gffsSfXr7Az/sH+HFj26w2YY0GVX+4nA86+jYCK4vBhJMrpP2sth/8JEnwrj/EH0efcN6nofYYwWyW1n85SxstApwE43CO3izE6XiKT4MRdjtdvNnexs9bO3hbq6HppUteSX+/Lb/WfddaDVaobPzxxc6KKS34PvT6aE9nGMfsACigUqsbgxUmlJ0FXPevDeDToI/2eGLWD66Xy8YfOwGsItmwzXILrofI9c3SC3PPN0sywPWQODS++L4trcqldQfO1VLzXexsbqE9GGAwm2I7TrC9uYV6Je3ESGNjrGltg3tMF39DpitM8NvZKQaHn/Dn3V1g/wCVYsMYNKyW9HJYPGKFicYvLST4704HvcNPaLoeftzdQ7lcQdHLPjjpeXlbVQliTdWwujDy4Uh23m5/q25bDjp/bKNe3GdPWE88Xly0J3P9pTyVu36V99ydV+6SF+9nHlAX59QVzzfLdsRctsP1MHfTpZDo77O4lk/k5FqOlHHRKCbwCgjdNOzI84xhFWddoE5+82aFWw7gO4JcDurK42UGV3q87gIDstvVgud90fdnPlexWHiyd1uXlgPZRZ76fNcKlLk28Myz6RRPn5Elj4vDXLCLc/eyQ0HstuCRe5B5zZ7P+7X3LLn0Yn/2Nutl+diev7iDd9oS++KsuW9F3DYe3nHJz9URXUT5jXu2LGWjyW/tFt79+iuqpTL+91/+DfXNTXCdb8p18SbJhElPmmuM2ibHpsGUp6ZMvTCCs9e+OTk2kusCsP4o2Gd+rRR5YPacde1NubFKvGRP52+9hX0GzY1uxHLYlJe+UZ0ijQTqVdSrZbicbYUGK4lrGgBosBLFEVgdjZwYQTRDGESYBglG87kxIuhOJhgEIeD6cHZ2UaimSwpeaKWNOctoe2gk+oY0f4ETL9nfQsdtPOZKBpieuLHccewBT3wuNUPMTE/MLTaL6DL3Nhqb2N7awXAWYxpE2HcTbG42USoXL2eniYZ/vjS9iZXFxmKivPIPfds76In1p6oPbDU3sDWZwIWDZjDH7uYmmlV2XvIpizND43yqLqKw4a2WwF617sV96d5lfuYcvWaBmXd31nFFY+AuDQT4ceT5gJeYJR/3Dl6iVqubW7gEGOth/SAwhtU0nub7vVgso1lvYHtzGwUutK7tWRLIqdZF+s0zzcNUL+gn/e5Jvw84q8rhPMEv7OhvnaHd62IymSBxEhSDAI0Cp5kOEdniamUkF9Fp78sE7NNJjNq+ngC5WR1mhz9HmbYS4N1wgn8en+LD2Qna/QHf0PDCEJMkRtn3ME54RpsI3AEB+3Dbhz2LMq+7bLznlOg0tDqNQ/zz6Bi/n53htNvFdMK54Vz4ZqRphFEyR+Ck+nupRsEAl+K4g9QpimdEgCrGeirbsWx5e5QA/906M8aBZ602xlMOwUrgchnUcgG+55vR/THL3JjLrIQYhQF6/Tl6YYLhZIbZeAxvbw/7zbqZrcKqsXWfEWIldR0ElspGGqywPZztLr+MRvjP40Mcnp2g3+2b2b1d+PC8AvxiCb6fIHEixPEcYRSZTqfOaIzz8Qy9KMFoNkey9wKNnaYZc5lrtVhHSp5emHzIzYO++pv76SX421O0XP5Sh1n+/v381HyznZyeYzILzUAqDrLyimV4fgEcdMVZ66MowDgOzTKDnFn2fBqiN5oimMxQOjjAXoPLBKXZobL32/NJd6bfZdTX/GaPWX9gfZffa79PAvzn6Ql+Pz5Gv99GjLmpL3i+B5+DyzyWqI4ZIBhwmSDOijwZw5uH4JfdcD5HNI9R3No1Hf6X6sT5yLUvAl9BLOwqbQAAIABJREFUgLpq9XW5LGQdmMYqnB2Ixir/7A3xX58+4vjsFOPZxPQLePxmK5Th+27aDuvGmJv+AS6zHaE7GKAVBuhMJ5hOpvD3D+A1N+D5zlJL+1cILa9rJbBWgxVKToWbZ+tYt8LQNABMZjPT4eRxPVUaWBRTuzz6oxK254GpvM451T9nMOGaq9U6/MyC2iqxVWhjBGAMAdJuRVYMOJqAv9vYWABzUQcuKbDtAH85eIGy46I96GO/UMRPe3toVtKOsDS+i3jtxyWNGAZIcDQY4NfjY7Tf/Y6yk5ipvXebDbMm/eqC/iIswzL7WOXSNp04xu9nxzh7/wd2SyXUigW83T+gLTpMxl7c+hmGBbtcofCZp1zryxeC+vy2pTDNvTYAm3kr78oqzvRj/Wf+lg6vuvvK8zZak25jv5Aaj1gDEnM+k3tF9FeGu3xhEQ6XV2Jnl/FAvfxSR+ByKJkgy4lePuZtq86tCO7hnLqZwNf6WvZw6dge0GWOrNYr6+sym0UOXpxe7XFxPYthcXyvO1fJas9bl0Jy/xrhrXfr2rRdus0eLDzZgBcn7G1fdOnb/ozHr7v9i2Gvushn04z0oZX5PDQjKv/x/h0qfgFvDvbx0ybHUaYbO9cX5XNOLibd+uF++rt41s1zvzi/SorVWZCLIr2JAfMk3es26++zQHgjT6bvyTTIi4BT7zaCRWrT2FaGdZ0gN7/OWG3MZi9J4MYxGpUiftw/wOu9XRSND8pFoxWWr+n6wEEUmk7czrCH7mCEfjLDNIkwd0KzjuW7s1P4cYyy56JSPMC275hZyHIRXqTR4ri56Jd9foGTvUTX7tubL0W7uLjKp73DhrHwvLjAz0uPSyAlDg4c19RX6n4J02COV6UC3uzuoea5V49uvyTMItgVUuevrd630lEmrhW65QA/bm7D5Tq51RrqYYi3u7vYqtVQcmjUwiftQhOWQ7XhLZ9PRbZXrbvsa8VxzivD4LPMj/lpDIzNCCkepcY/1WIRr/b2UCmlxj7W0GA0C8yIFIczAcFBvVQyU65vFhxj8JaLYoUAOvVUCazMdy5rxvqnm5Z37Fqi0RNH9Z/ToH8yxa/HJ/jl4wcMplOEXHrUGOrxvtRolrOtXP2EPFWaStdDI2AHANjR0mz8PE2A31tt/OvTIT6et8yU6I7vImbB6mRLzS4GRzy0FEmeJ0lgRUFM3aVKUnfZ8Z8OqEpwFE7xy8kxfvnwAe3hAEHEWfwcOGb9NRcOO52SxNgSP0lWStSDJsD3PnWX7YmcAp11hnfdNn758AfOztuYTaemclEuFsCBfo1qFZVyxXxvsN4wDqYYziaYYor5LERn3Mds2EM47sN3gUK1Cr9AcwFtInBLBHKVVZa5/LHOy1ksP4Vz/Oe73/C+dYLhaAjMY5S8IkoFH5VixbT7FysFIBuQMpoG6A2GmMUB5nGMw/NThKMR3OkMO7X/ib1yulSw9PeW8k7BXCJg9Zd1BhqrcIDBb+en+Of7P3B4doZgGhpDQc4iWy0U0aiWUWGZ6hdMh/9oNjZlMJcJCqYB+qMR5pMJ4skIZR9ICi/xslo2S1hfilgHIvANBFj1tdVf275FHaaBNvsjjxPgn58+4t3hITq9HpBEKPhsJy2gViqjUS6jWCqD63RPwxCDyQSj6dS0d3HmoON2G9PRBPPhFI1/r8Bp1rFh2vG+QVjdIgJLBPL6y0vUYbafWWMVDjD4oz/APz78gXcfP2Ae8osuQcH1jA5vVDkjdgXFkm/qEONpgOF4iiGNBIOJmYklnE0xHw7NcvR+wYdfr6OZPTT22VkSS4f3RGCt9TpbQLJRlsv8nAx6Zm3VIAjhui7KhRIalQqKvmcqsVTCEf21z3F6fmI6pLzYMS/+bU6vTiOUDJRR3JwCmws8uYaNcbLDhR0qzcRBoVrG3g9vMZqG2C4WsOk7xqDFymRlpCh8OfDjkgYmHLXbmU7NiFxOJT9DjJAjdZz0I5T35e814WXGNzYsfqzakRWtyQjd2RgTRAjsFElmxLlv4rVhWdeisXLa45Rq6uvy4hXWR+bm+S4HuuSVhyu93+C+SxBWhPsoTiVM/wWB7zGgYigLbIudzynY2L7g5fOb7uHMjeS8lOgrhFxKKJ81nrp8OneU210dIiNNpbs8b8Fl31b+y2cf5pGV1ST9S+mnxyuurzq9OMf7bCREsLiQM3jIrl9nP7i4NY9y5cm8h2/bp66YDhezXn2Ck+EArckYcbGIMEkQOo7puKa/1L7883h4jb88Ag6gT5NLwfO/C3+8bpN16d4sCnvNup/xXVz4XKbFmSv82PisS0nolX+tXFfcugj6tnesLHRJ1EliOPEcXhSh7nl409zAX5tbqOam/E1lTpkyH9kA1otDHPX6+NTu41Org24SYzaLMYwj/NE6Q7NCo84iqjvb4MQXNOpIt8XORcbYS9e4VnZ6szJ96ZZcTKnw2QmWN5/xv+Q5CzWFdKWc9hZW7GhgS4//vrWJH7c2EYQJtj0HW15m0MrLNrzMr3HseRvY4uT1OzYN9Jm/nfJUuBxT4sCrlbFbfYUwjFEMA+xXK6hmU+HyWbt85xVxWrlzkeTjvuKuL57m/VGSjV6NQkwDTikZIIkiU7+rF3xsN+soc3mqrPxgR9dwMsFsOoPjJmYk1Valgr1aXUsGfJH2M72YLcVFMygzyilruD9LgF/OT/Hb8QkOz1tmlHTie/AK/sI4j435HCVttM/q/zPFqGTfLYHlspXvXNZ92PDJhnsaq3yczfDryakZqdfu9TCZBXA5xW61jCSYpPP42pkO71Z8xSYClwhQd9m0aWZIQwKWv3/022YwEZexmsymZtrpQrlojFO4PEWczM0o6aVK06VwdSACayGQFcB0qLtT01kKvOv18E8aV/V7CIIpvCRBs1LG24MDvNrexk6thmolnb0wjGMM5gHOR0MctVs4brfQGw3NjABH4wFKJ0eo1xqo7uygytGB2kTgewnYemqmv6bOALaHJzhhZ+nxIT6eHZtR0TQGrHg+XjQ38Gr3APub29io1lGqFAE3xnQeoheGxhD28PQUp2dn4NxXreEAv5weY7NRx//+008o5b9vv1d+3S8COQJUY/5Yd2Cd93A6wW/HR+gMBphHkSl/G8UCXu/v4+3+HpqlIjbqDZSKRVN3aE85M9AIZ70ejs/baPf6pq5xPOzi/x65KJRo6PIKjWKu/TQXv3ZF4KYELr3BOVlzdoLfbpxdhXXe/z49wu9HHzEYDuA6CQpwsF/fxMutLbzY3MRWuYJSuWQMtln+tmcBTgYDU384PTtG6ACDkIM9z7BdP0Sx8BMq1aJp+8qK/JuKK38icIkA1ZU6ZDfus/5g2/vNssNhhH8df8JJ5xzzJEQSBWiUKnixtY0fXrzCdqWKZq2GCr/j4gi92Qyt8QSn/T4+nR6jM+yDxoNnwz7+fnqEYrmIWqWMiu8bo+1Lz5AVRO69Ebh9gxWrYVlOU8FYOe0lEVrjkRlxRcs8WqDWKhXUSiX4XqqYbMBtJzHOe21MpuPF9H7VYglbjabpYFqlxJaeidrGf6m7xPr4dtd2APE7jh1BTddBwMpFZqzC6oVVbisCXT5cTBcr1p15jPZkjGEYYO65mLtcAsExfuiP96fjx3NymtFo6TFZ0viFBkBDGr/MxhhFIQInwdxzMHcd8wLhTDVc44sbnfyPMuV/lzx9duEiDLPH69yysLOjz5x8MPlb8nJ8dtN9naCA9k2eT5oV/DvkSkzvtaWRBXQNO+NrRdzMe7vlg7BerUs/vH6pumsv5m+0gdG11+3N+Wu3sG+Dz7tWFOsuosl74r71kLk8ZVlYr/Ze690+i/ZWe/06NzUwsmZGDrvOF9FfCHIZF8NkPIu4rBAZ1sX56yK/5esUw/7yQVtZF3LZnVWebeLyAazaz6XZRGrDpF8bbnaOh9xyj1x2JnOsB+Pp8qXbPDJlqSmXU2PCs/EI7ckEoevAZac1l6bJym6Knk8S922yjJuT2WoPZb04fTGjyaX7lsPJ6ZLV4c/SnBfks4tXnGCk2X1MNw+ty9PLQdp30PL5K0K/ldOWC6VLDVZiY7BSmkdoJjDrUzez59FdMlxhWoyxq1fE5tYONmubqNaa+NV1TF1iFkwwmAX40G5hp9HAm51t8w6/kvENU2Q5Mn5u5GXDNOxy3DMvFw6vZRvvt+9/3sefvWzDXJywN1kP9thEmB5w1xqIcGa4WpLWMWilQ6MRzme3WGnIhsObcmHYYL/GXdapfHCUh2xYf+I+F9WJix58P1232RiqRGm6HQvxqsitzHSzpVEYdz6+/D69ZV4v+VkOnn4YDuev4Mf5NAgQzSO4cYRaqWhmw9vkaKksPNbraLAym8/NFMCMs+x5ZraYnVrVpDU1wMliskIsR6zjp0eAeW23vDJm5/i80/C8jQTvgxC/tE/xx/Exzrs9M7OPVyqi1tyA63sIgwnGwx5irsUbR8bYf6HIK8K20coVgdsgQFVeLrpYTrLRnuUfp/V/Nxrit9NTfGy10Br0MZ8HqNSrqDbrKNSqOD09xnw2vbredxuCKgwRWEFguSjmsdFfGjlzlP98jl9ap/jj/Awn3a5pnzHlb7WCQrmM2WyGyXCMJJiaqaaNEqvcXUFap9ZNwNRPzQC4BCfzGH+02zhqnWMa0mDaQbNcxp9fvMDfXr/Bq2YTTdc1s/xx3sIICYYo40WjiY16DYVaBfOTQ0y6XWPI8mnYw975GXb8MhqbdRS8dLDGutOk8J8oAVvwWtd+X5nR/cD7bhe/nR5hxBnXkaBSKuGg2sT/fPEaP+zsYadWR92sDMz2Ew+B76Nfqhgjlmq5Ai6xzsGt0XSGzmyC//z0AXtbm2hsb11a1uqJ0lWy1k7AKm76sucRf/x244Bjzq7yrnVu6gzjYGa+yyolHz8fHOCvr6nDO6i7aZsA22F4b6/YxH6jiaPmJgrlKub+MQbdNjjzyodBGzvdDexVqtjZTgdVsa1Bmwh8FwGruCx/nbQ/krOrHI6H+OfRJ3QnQ0RJhGKhiN1yDf/+4hX+tLuPl/U6GhxI5rMvJG0X6ybA/vYOmhsbgOvitNdGOJlgGMX49ewMO80mtksHqGcDc75Lbt387AnkP7Ns2ct2V7PCSAL81jrDca+LYTBG4iZmZrY329v491dv8OcXL9F0fVR8rpDCvrwCBqUSuo0m9ja3jHGKc36KbreNcDzG4bCHRusM29Uqtnb2zSzsLLfZHK3tYRBY2/uQykXFMlP3AOhOJugMRwiCwEzpUyyWUK+UUC0VzHppZrQLl8zptNDtD82aaByN68Mxo6KbtSJnpVpsF7tONj3rxRkaClzZIboI4et2mJ600E5H4bLSknhpQc7zjN0qdv7B4gieMRIzuwpH8PfGYzO1FlwPgedj6heMQU+a1rQj13Zy2H4bhscfjVVoGcmpQDvJHO3xBOMgxNzxEPpFBIUipp5n/NBWgjKRA8NhmDa8C1KZn0Xrd8qEcZkIM0TmmPv5G7NrdJh+bvTHfbJhfnK0sj3mNd5OhTOj2zN5KJP9XRF8FvptOozp4kfZTBrNn1uKJ0uMCfs7wuWtfI7I0bLkUkM8z2Pr5vPWdgqSNfP92meBsjKgXOcf4+SPlxgGZyPIx3FTSlZ+6gR/dmNYRhfsCevmZLHy2Huta5daskzocuOtNs1WXnvMa/aXef/M4aIvNDjgbAf5sLlPXWb8TA9/djMvtGwKPBPXUme69bdulzJZme0+5SUr42b8bTlg3Ezu/P4lOW1iCc5sPJEe2EscMBtnBofWl9UTHhvDuWwGKeqT5ZjPM6tf5EfdtffTve3NMmKFh2Uzy1JWwjvjMYazGSLHhcvpDz3fLNNgy2XLiKnnfl6X+HzRAMDIbeBfPKDpjCupPjHtZjmILFG2fLQyMUyGwfDJgS7162ImkK+jQVHMxjzKnmebB/aYLjebrnz8jNum07qZ97U4jCMlYEimcXBpoITPtWM4mHzIGNn00WXdwEvIyoFf8lB4sYPRdIDJfAouGTTHDJ3h0IxsmSaJaezifWmcF+WofVbIhddMXmSp5TneQz+Wo3Gz8/RLYxC+38iOeWg33md/JpzsmWBYVg/sdbq81eaFnQ3Gpj0XrA0+DXzpAsPh808ZbXpMfAzbjriw91g3Sx/92x8j4bNJeezGsG1Y9pnmMc/T34JBdu8l/9m9PMelUXgfN9dN5VpkSnb+M8e+p7KPb5sXVl4TbpZ3ZGbfBZaf1S6bZOufctj66nA2NdOrx/M5/CjGZqmCl80NM1Uvw2H9hobBvdkcsyAEK6E+XDQqZezWG9iq1ozuXJLdRnjppA6eJIEv5DV1h2vvniMxI6TZWfrryTH6vQHCcI5KoYzNjU1s7O6acqrdbWE6HIIrA5nVgb4Q9pNkqUQ9CAIsJ6l6tpwcJMBpNMPvvT7+fnSC0/MW+qOhmZGqWangxe4uNra3MfNddFotzM3sKpnyZo4N80EkUEI8SQK2uMy/51n3Z/n7cTTCL6cn+PXkCJwViO/ycqmEZq2B5vYOCuUSzlotBJOZqUexPp8+BTbUJ4lMiXpoBHLfC6w/cLbmk0EXp90WRoM+YidBo1TG681N/M9Xr/CnrU1sw0lnWcy+qeMkPa5xJsDGBpxiAeMwxMlkhkkwQG86w4fWGV5W6+AsgZtVLtCZavtDwyF5HgEBFpH2BZ+1Q3GwJduw2wmMoVW71UYchih6HnYrNfz1xUv87eVL7FXKqJoupot0cqn7sgNUPBeFgxcIo8gYd4/jPiZBgE/tNt6fneGgXMVWrWS+WS/u1p4IfB8BqjLbGtLl2IDT4RCf2i0MRwOjw5ViCa+aG/jb65f4eX8PO65jlkG2fTCMvZBw0FCCEme9evka0zBCEgQYBDP0JgGOOl0cVRt4yfpHuWDK33y7y/elQHc/WwJsA86WUmHdl23ex50Ozs/PMZ8FKLgutkol/JnGVi9e4k2zgg04ZlUJMkvrzo6ZOaXiAeXtbXDigSCO0QljzKMQJ4M+/mi1cFCrY3erbtq/2FamTQRuiwDbeNnuyj71TjDFx/MzDAcDxNPAtDkf1Dfwl4MX+NuLF9gvFkz9l+3wrIrwl5a/QLVchPPqDSLXRRLF6ExmGM5CHHd7+Fhp4YfGJtxicdF2fFvyK5zvI8B+iNvdskoqCzg2bBkjiwRoDUfo8MMqa3UtemU0ub5fwTcFW4DELAd03GljNJ7AcX04SYiS66FeLKDK5YAY9qrN9AJedXHVDV9/zlZW+LDwg5EPDjemjx0i7KDiZtPNtNMvp+5kw16bI9FGIwymE3BqTm5Tx8XUTQ1M2FXOTjl2fDIl/DFMdvyYl0U2swrXuufySueTGVqDEUbjKZzEQegVEfgl8zFAP3ZJE4ZnOtKzjhvb8UT5TLhGEh7YWBmh3bcXM5enlzamxIZl99mBxXRzVhmympBZPDdpq/k+qoljOvYoCwsTk857/TD+QpqX0nvTQ9rzpf9uekfmL58pWUez1TfqEzvUTKUZiXHJnj+yJEdWbktZw4R5LLJrK7LusmCZnjEsxsfRv8xD3sdwiw5MZYXxUMRrw8v8UScoL8NjuHZjmHye6X62ZYHbe+1HAu/njyPgKSdZ8PnjPmWiLnH2ozIc+Hwuc7JbNsvSf56Oi9kwTByZ3IyL+kxZKBc3hm8+qpNMh7PZC3j+Lht6KA9/lJc/wy1XTrFspfz8WT0pZozIxT6HpSxddCg/O7vNx9ICEimTYMqI8TA/OC28VVt6pa6Qgbme3pLmVZZ3HJVr72HcnPWhnJUJlIdlHs8zDP6+fcsiz7SVbJh/dFkmsdLDsvSUFXgu6xFFZurDeVYms1HQc1guU4505hXyWJaRaeZ5/pw4NV5JIq5zn5gORs7UQ0kYN3esHGGmTzzPH9PKsEtsqMl0ih8Z6dIuF++Fm/Age8ZJlxvj5DNo42K+UZ/ZgMTymu8wxsP4qRulzLiGMjFdSQQzExr317olfBOSaFYmZ+9DK4fVi8zHQhSmj2lghwIbtvgGfLWzjfZkhD6XenI9TMIQfa7BGoSISsWF8RDDsrrM8oRseMzzjI+NZNxnHMZf9i4mP1MuZHnqRhE2fM/MaMKy18pIlxvzg/6ZB5ST7LnPkY8XOpHGwXhtXpSzd7g1AuH5lRsjyCKjw0OGy/TQ5cb6ADmRJ39mswIaOZysjE31gvJxIwMbLxnwx2fYpD97z3OfusSyt+YCFaQzujAeysKf5UuZeMzNPjvGIHkhS3ZxhcO4U24XMvCYo57oMux5FKDiFY1O831oDYnIlRujMc9rJgfDTOstqXFxbzzCZDxGEkZwoxg71SpebWyYNJEf08H0nrTaGI9HQBSjWPCxt7GJ/c1NNEtpvTaLzjhM7w2Sl79F+0+BgFX0rD5nZkhEgn+cHeO/PrzHh/Mzs0wolz+tFcrY3dzCzz//G2obTXRGIwy7Xb59zNronrtkHSqlegoa8qDTwDKLasafLXtZlzibD/H3o2P896djfGj3MecoUySoc5T/69f4+e2PKNeq+KPfp/ZmA0vSpJpHQrr7oPP9sQu3Sr2s/vLb9t2gg//7/g/8fniEzniC2BgLFrFfqeGHVz8Yg8FpnGDQG5h6E+tsruOmhoOmjeSxE5L8D5uAKSUXNeUY7qL87SQxjnttdEccK53ORrlXr+Nvr1/hL9vb2IRjllDNf9Nz6XHWedloTwOXsFTF6NUbTPsDY5w9nc9x1O3gqLmN11vbaFZrpsxXnfVha8mDli6nPPw2Y72B7dMf2Vna7RoDbS9KwHbhHza28f/+8CNe+4V0+dj8N2pW9+CgFLZPcMRF8Po1TvoDHE5nZvlBtpW8OznFj40N/Fg7MFjs996DZiThHgUB00aQtaX0kgTH/S5awz7m0RyIQjRLNfzHTz/gry/2sZN19rP85SNg69DGeCVhX1YCt+Bh+OKVaWdgpysNsE56fRzXuhgeHKC+aHF5FHgk5EMlkJXBbANkmx1XvDjnsijdLmbh3LRx1vyCmZHtf/34I95WSuBs1uxPYflp9de4puhN9TfZ3UVrNMVkHGA062KUxPjU7eJjrY6/bNXTmZyzdraHikZyPS4C1GH+Ruy36ffRMoMMAjhhjGqxiP/x5gf8x6s3eFMsmn4otlnbPgvqr5md3fRrOUg4ucP+SwxHU7RPThG5HrqTKY46HfRfByY82/fyuCg9XWnXU5+zI7qzTplBHKM7onHFxEypys+uouuiXi6hUuRkPWnneAcwawHOgsAYTbDzr1Ero1Eur3x18750Szuo1vV1xc9GPiTspOC6hbTkbtGqy3VRCUO8bTTxdmcHZY8Tb6YNe3wx9NmhkST4OJngQ6+L3z8doj8cIHEdOIUCzgZD/Nf79zhvDlCOOYKcJg4xkjhEIQaaxRJ2mpvY26ybD1VOX3s0i8CpO9+dn+G810PieEhcH73JFL8dHyOYh6iZj1KOTI/BdUH9ODYdN41CEX9+8wbNArtfLzpsLMXLrjXiSM9e1fhjCxDTUZQZORg5uwOc93vojQdmev15GIJ0yoUiGsUKGn4ZzWIZO40qDjaqZuQyCxZ2LK1zS3WGf/O/dcb4lWFbsTKdY8MadYmd/D3OqjMaojtKO10nQYhZMEMURSh4vlknk0tt1b0Ctgpl7FRr2K8XzccfC+5FB2UmUsriQj5WyNnZRwOr4/EIh4M+gukU/jxC1XHx17dvsVssrHwWL0K5vEe94XPzaTrDcaeLyYRzWzhoFny8rNbw886m6UC9fFfWMZx94KajMhJ04znOen10hyMM+ZEaBJgnXFs8AW3AfNdD0fdR8TzzstmuVAyDnVrFLOHFTtdU89Pct3GSA+Vk+u2PVshk3o0SnA8naPV76A4HmEVzRFHa3ep5QL1QQqNYwiancazV8aJRRy2r6LlxOmuADZ/xLTO3MnyPa8omdtJmHecso9rjEOeDgRlBNQkDTOYBZlEAJ47hew7Kvo+aX8JGpYadagO71RI2PU6alpYLDJMzHlxsJHR5Y750kgSfOgP0uh2zhiuXeXv74gU2PHaxpVO3deME5/0+2sM++tMxRpy9gMsbmIleXVT8Iqp+ERuFEl5sbGG/WTf305CF+UG9vSTKZTFudMRwLCcaq7Q4JWJWLr/rdNAdDEwFhoowh4Pfj48RhjFqcOEjMcuC0CChlESoeR62qxUcbGxgq5yWWMxX+zPKRJ2i0QcteH0fNE6ZwkmNZMIA58MeujQ4nM4wC6N0/VvPR9EvoOr5qHF6xlod+/UG9squ+XhgTPaF/SU9yueUTXOqzwlajLs3MHH3R1PMwpB97XA9DxUucVcqGAPRjWIBrzY3sVlMG4+KXpoXizTeiPrNPV2kJ41hkQbOlsZZVjI9uEoX7Pk5DetoMOEAG+UyKuUyXM81eRtHc2OUNByPERUvG6wYgwcaLkURPp6eYhiE8F0Xu5UyXm9toOgVjP7QH9fOPOY68J0ORpNJtnTMHMV5jKrr46e9ffx5/wX2S+kzwLQwH4xxR/bBOOQa9MMJOqM+hlMaS4WYzUKEYYQ4SsCypVjwjEFv3fWx12iaZ2Mnyw/ysD9D+QKgOaS+21HEJ+0OeqMp5pQvmOFv+wd4u11fkTmOMfjgx0BrOsZZp2vKWXKgHr7Y2ETJT5fK4nupaxptRjju9DGajjALZ+CMJMUoNmuHvt7cwluuhVsrmkeCjZUDNrR3ezgfjMzaoVUAr7a2sL/RxGbRT98tBLaUHgq7zJHlD+sardEI5wOWzxxpN0MYzRHTgIQNn34R9UIZzVLVzHyyV69hs5AasNgoGK4NmyX7CBHG0wnmQWB0YLtO9hvYrdWM0Sbvs/nZ7nXNrIF8r3A5oNe7e9iupU1N+fetjYPpsPGuyACdegYEqAumnhVM0B8NMB6PwQd+u7GBH3YP8KeXb7C12TQGbcM4QhJybV5Or8J3Cu/WJgJ3S4BlltXtU3bsAAAgAElEQVQ8uiz/pnGMURBgNJkZ42aWt7vNOv706iV+fvMjdkul1Og9DI3xvin5bCA58XlKZWIOiHbXSoB1I9bjhsEUvdEAo9HIGANuNDfwZnsXf33zFtsH+8bP2TQ0BqtUfoe9TWZgB2f1pUm0NhG4OwLUW9vh1JkM0Z9NMJsHZgnVRqVivtfebm5hK2esQgN1u7GxnuUsXQ4a23ASM5L/dHvbLNXeG7D+HKEzm5rfflLLlhOyIcgVgW8kkM2Gadry2L7Y7WAwHsNxPXjJHLvVGt40N7DvF0xbMNs67PeTrRtQlTlYgG0gQeJgCwnevGSH0zCdDdNxTJsXO7JGLw/MchaqW3xjfum2z2qlpg0xG6jCGZnbwyEms5mpC7CzlG0LL5sNbMA1xoJsS7XtdRanLX+TxEHgJDiolXC+tYNOv4dZPzGDqs6HI5z0euAshewnW3efiJVN7tMmwDov2yDZr3E26KPT76ejhuMYzWIRr5sbeJEZq7CvxLa5UmdtGWwMsMzAwLQt+9XuPrqDIUbDAcIkQmcyMoYEgzAx7Wx8BrSJwG0QsOWvafcNp6Z/mX1bbOutFIp4ubGJn3b3TB+l7YNL6xAXtQBb/nKG8ZkDbBd87G5s4aixgcFkhNEsMBNBcIKNjUoFNd/7qv7O20inwriawPL79GqfX3nFdpRxFPKAo5rHYwSzWdphytHcnodaqWI6FaiI7MxoTfoYj0aIghBOxE7CBM1K1Ux1ScWzhaZ1F99idmiuvfCVsn7JO+OgfGxg5lSGXK/7X5x68PgIQZKgGc6RvH6NnY0N+F7RVLL5UmCn1vvJBL+1zvGx28XZeGQK9oANzyzwCz567FxptXDaG4BW5mbqIidGHIUoRcB2uYa3+3MUNmsYxsDHdhe/t05xMh6iOx6b5YASx0fixRhHc2PxO5xN4HOd+yiGG8fGaMWfx6i5LvZqdewd7KNWqCw+Br6U9i9dIxebxzRIGJhpHtmRNzCGCcftjplRhx0/7JiOowhJnMBzXHDqPE57Xi+VsVst49/mL/BmewtbWYMQlZJZeZvZeZthfYkLr5m4viMB5EqdY8c6ubJj8HA0wFGnjdNeF4PxFLNgjmAegYZA5Oq47PNw4RdosOFjs1zBfnMDP27v4M1GE1uea6Z3I1tWRCwP6zI/WaFhXo5ohEBjquMjjAZDFIIQlRjY2t7BRnFjYXV7HQf77LBz83Aywn+fHqHX6ZrbXlRrcPb28OPO5iV5mG7eZ563bI3xdhzjeDAwhmLtXgeD4ch0TEYRjVVgjFVotOI4LjzXRcFzUS54psOaRivsbH270cAPzSa2Cmn1n3Hks8g+4yb94KwbCT6NZjhuneO83zWGZuPJBOGclHg3fzAfFHxZspw6aDTw0/4LMy3kdsE165fSz6LsspGaO2/nD+XmC5ydzBxrxVlBPnQ6ODxvodXtYRbFCMIA4ZyGCRGSJDLW/QWfhiIl1Es06qnjVXMLf9o7wG45/diinvB38cK38qazhfCIBjK9CHjX6+Dj4SdMZ4FZ2zLZ2jRTulK283CO9ydHOGu30RsOzAdeGEcI48QYGrHo9jwPJdc3xj/7/SFebW7gNX/NOtitTsMV6uzXb6l229wynYScSjmY4NdOG3+Q06BvrGrJicu0cZm3eZyka9lPJmY5EJN/LE+jCOU4NkYcXJ+2RGOlpZk6UhnttCAunFIBYdEzU+ByBojTXgsnrXO0+h1j2TudhZhHCSLqLxtvXA8l8vAL2KJObTTxZnsHL2sV7JeKaGaNjl9SJXLnj5rKMqRr1tuFWevxQ+sc7W7PPEPMrzjmTDCU2oHvuShyGj3fQ6NQwJudHbOW9OuNBnY9Ls+XPjOpTnx9blx1hy2DUjeVniNQ7HRj9jnNl1urwrJMKCdnqakXiiiXimYmJ5aPiZNgHkeYBmkHG8OwukFW5v0+m+Ffp2c4G47g0wBhq4lio2qMiFiOdWh81z43Ri1cO948W5yZZx6jMI9RpqGE6+PV1g5i6kb2nmT9hnWCs2mE8/EY58O+MVriEgo04pvHMeYhjeHYMU0T8ASe76RlGTzs1tmZMsSPWzt43WiiUUinm11+LkxWZh+mLA+Oghn+cXaG0zZHs0VojMfYKZfxerv+2TPFe8nB3DeZ4F8nx2YZpaJfxE97EYq1Bqo+P1SB43CCT2dtHJ53cNbpIwhnZlYTRBGK8whbpTIQR9iu1xChaPSR/Hpxgt+7Pbw7OzM62IgT3mLqBM0icy73ckqPFn+twY8xJESCk9kMh61znHY6aA8GGE1GxmCXU5XyXUC9KfB5KpRQpXFstQYa0fxp/wUOKj4a2YxYlhn9c59GXNPxFBGnSoVrZkzZb26hWSqbxn76Mx221IdeB+F0iqLjYLtYMfnerJQ+q1/ZsBeJ0c7zIcDMz20sP1k+7dcaOCoUMXY8FCt1/NsBp5J+hZe7nIw3nTY9LWtjU3bZOoeMVnIwtXsnBKwK06VOsiGy4Vew5VexXSiZafV26jX8sL+Ln1+9wEa5ZPywPp3ELC3TuoN0906yS5GsqEZYHWadiV+BOyXWqasYsE2i3sCrvX38sP8SP+7vmTojv6U4uMroL29m+wTn7jOW6EuIbSVi6bQOReDbCViNvZihk9+wHPLTH40xHo3NdP5OtmQlO/03S2UzG+LiG80GkQnBQ16j/tNYfAvAdqOO42o1HZDDKf6nE7Q44+iilePbU6A7RYAEzLelGcCamDZNdtBPpxMzF6qfJNir1bBfq6WDLfNtZjl8Vnd5iiOf2RbyZnMDh9Uqzjm4yYUZANQaDtEazVBppMsCLT0CuRC1KwI3I0D9tW1qaZ/WGKPxCGEQwJ1HaFB/q03sVutGN21b2XLoVoc5CzVnld5Ggt16DYfVGjrDAYJoZmZ7Pu4P8HJ7F41sQNxyODoWga8lQP3lwEkOSOby6EMOnueAdsfFBtuamw004CxmSGc9ebl9kfrLc/z+Y7v8Qa2I42YDHzkFtwsziLgzGqI1nKK6WZHB69dmkvyvJMDyl60Ipk+D/VyTKfqclSqYwYlC1Co1vNzcNIMSK45j6riLOvCitzOt+zIs9rUXabRtyt86GrW6GcDAdvjhbIaTbg97jQY26lVTnq8U6pmfJEf+2F9hhm/QXfOguqyH4PbJW+XiR89wMsVkMkUUztkPA991UPULqJXKpkCjH3ZmnHVaZtRtPI9M7cCjwUq5YqYYZiGZr3jafQPMzLPGRV7t2dtND+Ngwxun0mKnEzvf33U7CKLIGKzwQeFoM34AcuPI5tY8xL9OPuFfx0c45ZSbcEynJEfc07rAKXiYxDFm0wl6yQSYx/B4idPKxHMUQhb+cxRqTbxIgMPhCP84PsTvJ4cYJ5FZD5yzq8AtAK6PeRJhGAYYB1MzPb0TR+CHrBMn8Odz1F3PdMSO43ixBMRC4TK5b+rY+5jH6YiPtIP/l+4Z/nX0CSctWs9PEdCYJ2HHW6pmtIRL4jlc6oE7MaOX622gF44xDF/hL/sH2HO9xXSQt52bNjyOlLL7N03zTf3ZcE0cpoHLnrlJCHzs0yUZ7Aw9Z0mC3wc9/Hr4yXTOcaai+TxdamSx7JPRrQhxPDedvByFQCOKj8MuTgdd/P/svQeX47iSLvjRyUsppc/Kct11/btz35s5u2/O7v//AXvezJ1r21VlpTfyhnbPF0BIlFLpynT3dJF5lCRBIBAIBIIgIhAxeXaIN9s7aNNIzCq8iQ0x4ySGh07G2acTB7gIZ3g36GNw04M3DVGJUvwblevsfNlltjweLZj5idl48MwJ/kUS4d1ogKvulXhMoBtFWqOzPhVCig/TZDc+Fa4p8M35Jb49PcHx9RVms6l4OBE3uF4JDkOIZA6SNEVKaHS1SCpO6TI3RdV1sVmpYrzLHRc+Op2Fvfq8Z2j0wjAPVml9mmX462CEf5yc4PT8BOPpGHEcI6MbF4bU8H3ZbSdGWFQwjycIBgOcdLu4Gk/R29nFr7Z3UKqVZPHIkuKznEhf8so15VIa4YfLS3x3fIqLmxuMJ1ORDQnlKceh6yBzUmRZjGyWwncmcLMuam6A97Ur2S379d4eDhoVbOhuLOlu7c05xaQtouR3gfNwKrxCOb9Bxe0sxKxaEUPF747f493pMYbDIaKIFHbg+AEyxxFjI3JeFnMHLuCPxzgZjvD+5goXm5sID5/hdactyuCPsdQm9jJZF69XCX64ucJfjt7haNDHMI6QzuWoJ7zEsHXDMMQkoixlKDNX5DPiBOU4xoiewSpVPI8TkaWLSb2hj4TiIv0o0MsBZp6DUwDvLt/j6PgIV9fXGNGQj0hllOG+uLNJY/ZNBNd6FXnfu8bbmzKO+jf4w84O/O0tNGv0n/Xw2NMxRA8U7zPgm8tzfHt8jLObG0ymEyTkZ9LcYyA6464/mcZwZrSVyBCI94w+uJNkFh3A294SL0UaTuazMLM0zBqqCHbE0PxIWc4dRKTeU7l8TMkOALOoReMf18mQSqwm8gHjrZr3klKRNVCuSVzMOMbxaIwTuoF3HcQecJDEqFrDn3+enuH4jOPrCoPhQEBQDtO41g9jlKIEQ3ph8jzhOcLm3IFGHiejEH87P8cPVxfiEWQcz2RMLAwsbBg5jo2Ehog0MEvhxsDZcCy7bq5ubhAdHuLV9i6CgN5/7KTRnoWlrPykEdtlmuLdcCiyKY4SNIaMVT8WviUtdVKvlFZcr9MER8M+rvp9lIIySrUm9hJjAHWFFP958g5HJxe47g4xnUZiBEeZ7CScu8TyIXEQbiH1POlB0pfvgZHj4HQ6xdvREP1+H/VZjP3ODp6HibwLzEhdjCjtahm/Vj5fZRnezUb4y/Ex3p+dodsfYhbHSFUx6rlivEkeRxbBmc7geiO8u7nGcbeL3izEHw5f4HktQIuGnrkPc9KBRmQzeg0IY1Q8H3vtTfFwwzBHenD0jGcz+einJxZ689lvtLDTbMjuKmYlrLzE5HX+XmEV5y+LAuSNKoBX7W2MdvdRS100N9r4zeELHLQaEmaQsohemOhxkZwkck+M7njHr8Ivi2ZFa396CqjsIv8yPF3bDfC8vY14kojRL41VXu1syg4nzvQYipKHzJt5LQAKxv3pe/LLxYAsSP7lV+Bho41o7wD+cCKbMZ7vHWCv00bDcWSth2tSTsaNNpxNK/8a+Ssy+MslY9HyH5EClJicBTBkJWXqmN+x44mslXKOGyQZ2uUqOqUKao7ZEco5LcupzCa6KnlVfletl5V2rYpqrQLH85A4DvrcZToeg+GwEut19UdsblHVL4wCyr9cL5JNC9wUMR6b9SDxmJJhu1bDNj1K5ELPzsmgjEvZbdelyuIlCNim4Wy1Kh5i+U0W0pv7eCKeVnYb5UJhOidicfFkCqwI0PwaxHA6xYRr0dRnMBR0qYy9ehMbji8bQyl/87JX62Ya12wIi94qaHTVrlRAz9SO5yN1XAyjGOf0WhFFiEul21YDCqw4FxS4jwIqN62+hjzHTV+M/NAfjzCbzcRlVcnz0KlWsVNrzHVv9/Evn3EdmJ6sN2n02qjJ+h/nD3EcoT+d4bzXx3arghozFkdBgQ+kAFlYf/wKE50T+Xc6w2gyRppG8Oj1vuRjf6OFBjd5rm5wVgDEwa73UgbzG7DhAJ1aRQwOzwc9xK6LaZzi9KaHl1vbiGq1Qv4+0HdCXl2SNLufHyjx4Y9VV/zhENaUZAP4UcQfxVXU6wGDIbzJFF4SoVYK0HQcNHxfmIsfYohnmFxew6PydzITrxHcqdv2fTQ9XwQk4eUPwpafncSuisbV+3zZx14TNXoIoVJK0IQrk+5ZliJyHNmFQKvFRCLJGqhSbxwjmU6RzULxdkIvAo7ryoATwwJu6KcyChxgrnjIoKKdYYG8oAQ/S0VZUvIY/RsCK51OAbrcL/kSR5meAIwSneF/aDXGHdk+HJfGKtSBukaR5jioOi5KrrE84+I322KXYeYfspaad0y1TNsMPUx5Kre4qH6dZfjbxSn+dnKE46sLTCczJNaygWFaygzVUipJ+8NZKLug6VEgSmP04hh/PXqP65suri6u8O+/+S32K8ZTzWP76OPzUZHPH3vAtI0GDMJbjwTOcjy0vL1dAHkUMxr1BIUyPavQcOIfF+f4P999IyFLplEoRk9cuXAkhBTpGiDwA4QM+UKlX2Y8+EzjGWa9KYZUUI9G6I9GoqTbK5fmIWvmONoL4i4vBSoXswzTLBMlI0OiUO2YMLzJaqF77pUWbE/oOuKCKyRdM8jYEXg54xfmz38UXGfA38/O8Y+jdzg5PwPbT+Wz77soBSWUgwp8lx4UXIn/yXiMUUyvEVwu5wZ/3meYCu2p9Fl0Aq/mbbHuSjVUzH9dnOM/j45xen2FmGEu0hgZvShkmShvJcyI68kH93Q6FQX4LI5wSQXmeIwbxsDr9VD+ze+wXbYeS1jZovp7qLbyaI7kojyT2E/8saWMCfz9dIj/79tv8fb4BAwVFdM4TGKuZ/KBRB4h3jTqCeOZeIagZx4O0yHvpzPQJfBVv4s/vniJ32x3hE8oe8xLgsibBrB+9pPwCvvW983P8zBygMvZFNcnI1xdnuP9+/cIRfluZBRlEr2HeAGNViDeLthHDGsVpglm4RizyVgM+Tj5rRDvVkM+Aj+EfEpNlpX3EelCN55RKKF+GEJqlmSCizEPTUVJKLLUdUSuiq8GxwSTovcqhn4rUyluZYTWQY6S0PY0iJNQQB4Sz0c/nOF43MV//NefMez3RbFO2ruuj1K5jHK5CvgBZmGEkCF6EoZLckQJfxWPMJxNMeleo5b9Gvuv6qJgZ59IexaVz2UPeYIGX5TNpxnw53dv8e3JCa5urjEVoyETYocWM2WOI/Ec4ZodIzGNJDLxHnYxGIhXtEGvi153D79//hzPajXjCSxXr1T8MZ1jOYsgOMaMG+s84+cru/9a0WBp+dG1CuW6vG8hHpjoOSUvC5SfKaciz0PouZiJ9Sgwch10HYbIGeLt+SX+/t0PhjeTUOhAbEgvJ0ng8d2cATWG2yobYxL2hcKny8HTizOcnp9JiK6YRjQSLMGEAyyX2Rdl+H6AcTjCNJpIGB+OM/Ggxnid47GE3amUq2h0NmRc5CnCunRcsj0x2+PQ/SyQ0O7KceQ58/HgmTTjWcvyvR5xTLOs7yEhDbJYjHXD4RjfnR3hn0ffYjig1zzKGVfCjDmcz6QxPHpZsWHZapWyyAqhLenLORONAz3X0NqJRYZFaYowyVBFIoZCaplECvEgHWkITAOso+kA//nDD/jH+yPM6CWIQkzsnDKUyiWZa7jEO6Y8Cc3CqOtiEic4pivq0RiT8QTRi5d4s9Wcvw9V1mXDMTAYoRyGaG5UcdBsoF0uywcOactfRNjXnK+OUY5CbDUaeN3pzEMbEm/Cyx/Km/m04vrLogB5gHzBhfnNzMH/fP0Gv9k/REnCZJq5AvNMMxPmNGN8dGFuGusxdnTBRV8Wx/z8Wkv+pQExF3tebjTQqdVlDtUqO6LsJ2/zHc45DHlX5jM0EhXPVwv+1XfQz6+FBUa/VAroO5482s4c/GbvAIeNloTTrZXKJuQfvwczMeM23u7IxWbrmJknrYYDKhj5l8ouP6t2cU5J7yq9OJGdzPQuy7UPbjDYKFewUamCu0vJ4ypl86zJa6bzJ3MQynCGZ65UUCtXZCNO5nmYJjH604koBXaqZWMl8LOiRIHMfycKkO/4E/7NgAt6kuCGGYas51zCddGp19CuVuxa0z2tE543/MutNg1kaNcbqNfrCCdjCcE8ikLQy0qIbanzHmjFo4ICj6IAeZff/TQY7NELOcPRcx0elL/ARrkqYa24EUHX5tYBVrlMpb94qRBPwAHqlSr8IEDCdRcaXTEqAT1jZ4RIaV0cBQWeSAFlNpW/NpwVN9WOw1B2/6n8bZbLaFerwrt3Gato7SxDHg/goO5kaFbKqFWr6IWMpgDZyH/e7eKrg10kBFYcBQU+kgI6h+BqmEQTmM4wDSMxVKXOvF4q2ZDtxhP8XGKyYP7IzR/ImvTUxk387WYD/lUg695JkuF6NMJgNhNepuf94vh5UMDoIj8xLirQ6DJKFmUPDnCw0RIGkLAKnovNWh37TTqgMi/u/aCM//fNb/DH/eeYcad5kspu9pebm9htGjdrnBiwAIUiy/EnH2f8chPLHsOdTJcZ8idql/rjmPO+GDdwVY5eAhjawtSn+DD+236lin97/gqv9p7hJknRTVN8e3KMix49H4zhRhEY/+359jZ2NzZQ4g5tZGJBzgHoRvQO4aNZrWOTHjM2N7FdKePX4Sv0XeDd1TWOr67Q6w/hRBFa9Rqeb23ixe4Oaq4DBiPgrm9+zPpJJtaQdcYYr9VE4altEVrNqZknGJ+Yp3lyspxO3mgtT2OVv/b7+PvxMU6vrjGdzuBECXbqTex2trC/tSUfxIFYCrvidWWYJjjr3uD4/AyDXl8W58/GM6THZ9hotuE8e4aDsjf3WLPAIo/fh1+b/qSlhP25nnhZiOHKpFQHRZ5GSg3b1VK54sU0TmZJF/6ERpYlFUYe27vSWFY8EjHGa5bhL2fn+MsP30kYoHg2BT/QuLiwu7uNrUYLG7U6KnS97RsPO5yEULl9enOFs5sr3PS7mMLF0bCH8MTBdBbj3158hYNGSXaB5ycm2hbiKfgLaYyRFu8ZToM/fZ7Pn2+bXksZhSV0ccV6XFTlAssYCSm9mJ8wRRlow9u86w/wDT0U3VwhDqeouA72tjZxuLWFvXYblaCMwPGlH6mzpNIzylJM4hmu+zcYXF0inowlvMnh5pbsxMjjzTpZP+scWpr/YzTEX969wyU9GUwnAHfPVyvY29nFwc4uWtW6GAgx/BLL96OZGLacXFzg6vISEyQ4HQ/hXpxhY2MDv9nbx7OKJ4v7SptHn1nBysEk4suxRwUud2L/ZTzEf7x9i7dnFxiNJmLsV/V9dJoNHBLnWg2NShX1SgXchTJLIgzDGW7GUxydXYingyieoZvN8Pfz92KA4zi/wputDTHK8MSsboGI0k1plziuWKYmgY9pGuP747fIwhlmgwHS6QSbdDfI2IKdDlr1OnzfE2M6GoVNkkTcu132BxLa46bXRxiG4pIYl5fYY7/5Piq1ynznwgKTx12xz2WBzgG2vQD/Y+8Zmo0mbpBKuJyjbg9HF5cYD4biXo7vqN++/hXeHOyjKh5WqO1P5efHCRq+h06lhp1GA4Fr+oPMoLxMozF6/HFdD+MoxvHZBa6vrhAOJ6g7PjobbRzu7qLVaKFWroqBAo3lYjiyq4LxE48uznHT7WLGsHGuJzsu/nF+hp12Cy9bm3MvDnkKsH7+jHemDGcZ8JejY/xweoZur4s0DGWCtt1u42BzEzsbG2KwEgSB9Mc4Zl9McNHt4fTyEr3RCMM4xvvhELHno9JoigFRpRwsPsrX8Ggep4eubxe3I1Rk80OlF8/Zbh4cG2w/dxLQq8k0ZJgdB0mUwY0SBJmLjVoDPu1YbBmetO8Q+Iitszby5yBN8F33Wgy6Ls6vMBkMxMiWBgq7nY5YZldo9EODUC5gTKYSFqxl3RISH/Ie3ykMH0XeoSc23rfam9jqbGKzuYFGqYJSEEh4LHp2mTkpBuFUZP+7k1P0BiPEaYR+nOKbqxtsnZ+jGfhoNuvztzfbo2Nz0baFVZV5Rm8zlL2Lxuf7gHQgztxpGYnXqRQxEvSzCN90LzAZDXFycoTRTQ9lh95H2tjb2sEG3TnTqINGXjQGS1Mcbm6i6bsy39DqBAfWT6ytsSJ9q6R8t9ADFBbGRMwruFhZx1BFb2ngenyK74+PMZlO4cYZWn4ZO6029na30eJckWGgPF+MNydRLB8f705O0GWItFmIXhKKca3n0vjxFX690RQ5RzeRrczB77Z3sPUvHqZhKAYwz1tttEqmHTLfBMRb2W83N1H+3/83nDBCp2RiqLLfuRCl+RZULq4KChiZo/NLcUvqekhqdSMjrMcjjkHyD3887LRrPs5tcnEqKPCTUICyXHmYO54rgYM0cMSAk+l8rvJP5H5OwW/kvr4NfhL0i0q/cAqQN/mOpu1fyXHRajRl3UV5VudD5mwmKSKDudiZo53myyUVlwUFPjkFVJ6S32SDCr18zkyYXxoBkm8b5TIaJa6HLGSvIrLKp7xXmJTXZZchlM3Gp9idiTEB3aJznSCScJ7GCEbhFeeCAh9CAeXf3niCmDtIuEZN/gs8+XYsO1xpWjlWmTcngLl+ScNDboDlpptsPJaNh/zmo8EVvx1Xi69AL24LCtxNActr5CH+uLFa1ufpVDtKZNNKlpiNePUSw6uX12xiuxu8hEPPgLIH1KoVcB2Om4PoiXowm4Kbohk0QL8H74ZUPCkocDcFyLvkIf6oJ6FsNAaDnCs4soGUa3c1buR7QGbyuf44d5DQgnQq0Khj2A+phJGNp4PpFJF899lBdDd6xZOCAg9SQLmI+iuGhB/FkWxSFy/1SYaK46JTqQg/al4ByhsOAE20Z57IvzS6qiAT3Rg3jE5dV7yDD2YTzDKuSz+IWpHhR6QA++yzHAQsFkwOUG/U8KpeE+8irIyTUj7jDnVe8yNrCw46jRayRmuOj8eXuWsXx3TWYBcamIm8xPLclW12xy8Y81PymcIiCnJwpUN2blvPHDZZcLFWWzQ6aTZbYg3WRYZzvij6ffSHNNBI4UUR9itl/GF7G687m6DaiTQjDP5Yl16TVtu+IzHmemjgigsnKTAYDNHLenDjGE3HxYt6E3/a3UWHMZnF9ncBQ723sA62h5Zq2i5zZZVIxkWAbdHtE/Hii48fAwyRdAngv05OcHLTxWg2g+f6EhP3d3sH+NXuviitWlZpxTL0RkML5fedNurVCr55d4zueIrRNEQ6muDvJ2ei1N7Y2Zx7VVA8SY9PcrD77E5VwzqOGFPQHR9f6ORH1qksx3p5zx/brnzAeyzdIgcAACAASURBVD5TeqhyTfPQy0J+Q6zCW9cGhUHPCOSX76cx/nl5iffX1whhQhNs1qp41dnEr/cP8azZRqfKBQrjNUd2oNONfJbi+3YLf2/U8N15IDwy4O71QR/JlIZEHdRKOyiVjWJN22V6n3e23dZghQ2YG6swnMw65O9J0zaLkpShRzhuaORFPxaEzaFrqhXYpKG4DSWPdLs4H/YlzFXZd8Xo7ffPDiR01GFjAyVYj0G5PiB/8YV2Md5Ab6OFcDySBRkaczWoyMzhSppzHPBHZegxefnsFCfda0xnDFnloN2oCR//6uAZnu/sokGFrPQ7IWXoZsBRu4VavYrEycQd6XgW4nQywp9Pj9Go19EutcWKM88LOTQed0lC2o8X0oh80kcmsuDP79/ju/ML9CYzOJmDZrmCw3Ybv9rblR/DVdBYjS4o2VbuEqD3jbMoQ63SwtvLU1x2LzAOh7iYjpBenCLwArRbv0ctCGRRgEgq7eZ9auHRU05CjxRU9rvAVfcaWTQVr077W5t4s7mLl3t72KfBCo1VxKsEx01m4nEz5Nl4jH82moi99+jfdDGlW+JZiO+vrrBdqWKzUobnmf5+HMEWuYg3f2w/x3azUkWrXJU+v6QsdT1cdm8wyejeM0QQx3jVquNfN7bQsbgaH1tmcZDTenrF0necjnehi303iIcVeklJUsSDIYI4Qqtcw7ONDXy1tY1f7VDJ30DFNYpwhTFEhtPtbbTqTfzz/ZEYQ4UM95ZM8MP1FdrHNWy3Nm/FVVT5wf7lLjx6J3o3ivHt6TkuxAgoQtXzJM7jm719vNndxYtWa94XxJ0hWyibjze3UGs08M+TEwz6fdnRFw6H2Op2sVWtYau8IWNAeELot6D1U65YZ/6QfjJEXAhZyWBzKgPmC+Wumcu8Y0xb+rMpGKZKQ6j58FHzS2K8RWWxHlKlXZCgoQYNVlLXkfHMkFHfX14iGk8wG4zEQPCg1cLrrU283t5Cu1qXMUKDFSpB0mmIelASAzV6OfM8M88JMmCrUcfrvV1UAl9CTe1s0vCug516Ay3PyBXiwr6UOMkZcLa1h0athT9//x36vT5mUYLLKMbbmxs8b9bxulmfKw/ZHiObcmNV5DcnWtZAROS6lfFKAHtWOggvsf00xpIwJCn69Nh1dYpwMMR0OEA1KOPF1i6+3tnDKxqsVKtocuHQZTijVLxSlX1fXIwSJx7aNvFWZ/FitC2Gacpc42RfvZwJL2h/2pBK5Ol/Xl3j+8srdOkFxYOEjHy9sYk/7B/icG8HrWpJ3guExnbwXcD5UqfWwN+PjnB6c41JOMPlbITvrs/RalSxv9GQMvz4bsoCqIfD3R0pTzwoM9i3nIfxYHtaroNqtYJnlWeSzuf8qctqm7U4FRS4RQHyD3lNjjVyiHM55uEj8e7Fax2cT56BaUXFuaDAp6OA8rDw8QoPU+4q/7JG8QxkLqyfUrn5dMgUkAoKPJECnLvzJ0eOf/UbXpOMStVa1lIGrzuY+a5n6/IXaQUFnkgBshjlKo3xZbNKbDxxMp3fovUgQNXjF85i7q9VaFm955lplNGc05bgGm/Ovg/ZfJIk4gFymiaIxSermYWwTHEUFHgqBcg3/FFESkiKyUQ8ADPFdRxww0eVBlN23XUt/BzzEQ5vDe8CVT+QTQosxzXFaRzL5lgqt+Rbc82YWFtHkVhQYA0FyG+UvbJ2Ss/FaYZJnImHFXKi73gie2uMBPBIXiP/qvzlukGlVIIf0Ds+dRDcdBliliaIZHWEOU3+NegVSQUF7qUA+Vd5mGsLwymNUjnTJfdS/vp2DdPIVaarjF0FnOdbzjX4/Vf1XNHleSNXDKyiJMGQHohEz3M3rFXYxX1BgVUK5F77wpOUwaMkw5R6FQpl7nMkD7se6p63PgRgHoitQGQvNyzQxoBrvuWSGAw6niPyd0T5S4PBe8bCKq7F/eenwPyb/XNURT7hC5xudzgZJX/pwWd5PhKBaies+oyL/+ZVbUvpAwXyU53ziK+gpijqxIUvCAp1o/Dg68ERpTIXoMW6ix+bcNDkAoptP8uSHjxWqwozB1VkohiSXeJ0w58lYKiKEsMIiZtEhgAytCP9DIy5XxGBvUi3FelpqcKlG8lBvCg0RGFOhVp/iHfv3yEcj+FlmXhz+N3hC/zrq9d4Xq2ADu0oEAiJ/Z9kjgiVSrmM1svXsqP6L++O0OOODifDD9eX2O9u4vlWG20qwe2xuNKUDztri9gOGkpwZ/fUyTDwXFx5NNvIUBEhaPqAAotl8vWv6xsrO62BEhVlKRLpoFxJrXwFdaUpP+YY3oWKue/PTnBxZXb2u76DzWYLv9rbwx8Pn4uiuUXL2NwHHvukktFFm4dyewvNjU00Wi38x9/+hh7jEUchziYhvr08RbtZw0apAXoCIkqmPQY5oYvgt7g36GqrF3d3NGfeugUsLZPNXSszRdwsz3ObC3ormiITK+DrQQ9hNJN81XIZXz9/gd+8fIlDvwKatVE+6AIN62If0EtFlS4aa02gZjw4sQc4HmiwsGiv5je8PCAvd7t4f3IiYTecOBbDqd+9eIH/682vsV8qoWyNynRs0+iG9gl+qYrK86+Et//67h2GcU8+mr8/P8WLrW0c1ptolzwEnql/pcl33yqB2Th7rb3A/havMGmIo9NT8VRE459yCrxqb+FPX73G7w92xOCCcoU04I/lEzioMW8A1J7vodWs4c/fJ3h3Ppax3R2P8Zejd/jq+QE6O9ti/KZtziNLWPJTB1dc12Wor+kUQZpge6uDP716gz89ewbyq7wHbL8RTiqyzLhkKzPUzMuXGDCM03SKIeO3OhlOry5x1Wgg7LSRefRBNSdFHpVHXZOEbAfpUBNcTax67rKk9y5HPFIBPmmTw43ljOQ2Fui8z9PTPF+g4HqehDxKoghpEkq4GD/NxGjnT69e46vNjtCDvMv3ovYL+TfIHPhehureJjLXkQ/Xs961MPtFr4dv33v499/90brbX9RJupAnuKBJGXIF4OjmChfXl5jNJlIPvcL8r6/eiLHKDq3i7To8oZB3Qo4d7phu1FCtvwZDZNGjBcNbccfH8fU1Dmt1PG81JSyXvNPY+I88iDsPPZs7Q1Wz4OSad4d5IPSyl4b/bNspq9XrEA1vLgcDDGmoF87gpYxzWRLDCoZ0Is1ZQ56feJ2kiXhNo3BhKIPxcITJsAfMQtSdAK/3n+OPr1/hq62OGNmyD8lTNL7l2a+V5zxGHCWNcwBrlPvHw2f47eEzmQ/wXc25AX+U5TyIA/mA73nK54oHVJ49w82E4XdmGMUjxvrD9bCPm+FAFpTZGtbDw1Atd86HWLJ12Kx3npjNLFHTwNBDkqQYj8YYXHfhzEI0XBdf7+7iX3/9OwmDw7FNOlDOEA/HZ2ljHEhYy/3Ke74HTPXGcJSDz+C8ihT7lLuaaFB4maU4OjtBr3sDJIl4eHm+tYV/ef4a/7K/awzSbF8QGquoZ5QvGbb3dlCpVJF89y1+OH6PzPWEpm+vLvDV3h6adRN2jPgLLXO00rGuuPGefcY26ztY+UnzFOeCAk+lQI7lpOjyveFnHTdPhV3kLyjwWSlAYZuT4cq7PMuXJ8PeSgaT8llxKYAXFHgqBVb496nFi/wFBT43Bcii/DagF9mYIVVShjl2ZaGdIX/p9VXl7n24qASWvRUMJQ5PYDAkBQHEVJTGsQlPyy2snNwWR0GBD6QA+VZ/XKMYjYZIJNQlQ/R6qHFntL9G2c9CdxzkYbIlV2QYNroclGU8sCaGmh9HM/FGxPFSHAUFPpYCKle5HhEmKSIJcUn5a2QnveNKqGm7BvNQfSqDDQ9zr58nntLpXZcBwcMkRkijxJSbeXJrKDomFKGHKiqeFxSwFCDrUB5OQm7ii4WpZKXOC1DKGVs9hrUW/Gs2b1KfJ2t5XP/PEozCKWZpjDR7qtKj6K6CAndTQNbG01i8q1DXxPmvT0Nt6lwYMvsD5C/Xcaul8tzzPzescg5BGUx9gF0Rvhup4smPRgH21Wc/yERUAqwu6qtg1MmsMlv+PEdOM88TzIW8v/M+s1eef8pbxXMO0yasVs8PSSq8tB3ML+hz8pFkkG/AJJOwR1SSkjZzZc+a4cFByvKiFLJKzsB6enAYB5RGK0glpFDJuqSX3b62nOLBs07gFaY+m7dJlPBUxPPJ7YPlIloZI8NZnOHd+QnGk5EYzLRKFbxqU0n9Gs+qFTRsu6jcYd+zLF3ccdGdL0rXyfCHly9w0uuj1+uJh4YRPYRcnGG3HOD5qxdGAab0u43Oh6WkojKTzygarUyyBN9eX2IQx6BrP4be8VxX+lA/rLSPTYXCdbZuXht6uY6HsOxjkEa4HA5w3esiimiGcv9BCPyQo7KVitaz8QQnV5cYjoZw0xSVzMer7V38/tkLvGm1JKQEFWU6eNlTykOkdZaZkA/u7nNZdPjL27cSkoQ7Zt71rrB908RWo4J6iS21vHk/ik96aofFk8poZuEvTqriCNNoKt5duBDDj9JOtY6GZ+KMU0FM3MlX/LFOveYzNU6Z0yY3rpjG/KyLCn4qRC/iWIwjJpMpsjhGNU3xrF7Hv//u99h3TBgPga9KWVueClF+NCfI8PXBIS5HRrE87ffFeOPd5QUOKzU8P9yT/mLdTz4UYYs3X6H0JHOJDP/nm28wmJgwQGXXw3a1gn99+Rq/29vBhjVqUtnLM9vMtlPe0GDEdzL4G00Ef/gDbsZ9DKIeojRBL03EI8F24KHT2RTaEm+W1YNoLdqTIuUiBP1mRjE2qjW83trD7w+eidGMGi2SP4m/HLZdpGuLCv0M2N/cwvXlJcajkSjjh5MxxtMp4oQUVo5XAMRmgYGmPubMOkkP8orPePWpgyxOkYYxXJeeXKiEX3h00bZqbZQgRswv5Lw+S9NUFmTECVeawMsSCUv1b1+/wcuNFmhGpQaF2jfsFzm4YJg52HIyPO90cDHcxtW4h8xzEUfAKIpFTm1yV5E1OGFbeJAac0OmwRDH1xcSUghxjI1mE795/hxf06uH74vHH74j5ocdT/RqRkMsevj4av8Ag0FfFpgI97x7g6NKWdpQ67SNkYY2eg7I4LEmOZfj9iVxF95iQblgHt4wlJgnPKN8o7TSMrznO4nPGSJLDP6mU7w9OUavew0kM7hpjE69BYbyIReRZoqjVsd7j24xKft5SAUpkmiGpl/GV1u7+N9vfoVnGw20afRlZYH2IYvM4bIsXyH2vc1nHHM0hCK+rCv/zudzwmG6tI8XHEo2TvfLg310+10MBj15Pk0jjONQDOM8n3skF+1hUf54mDORyafaptk8POVpQFic/HMxMUtpuJMhjhIZHxuVKr7e3MT/8+tfQ0Ll0DRlDR0UtJBQbx445zHkHIHvZno8Y5/Sk9W781P0hj1x2UjPRAcbHfzh8AV+tb+DjZxRHuGQhqYdgJsZb2JftRq42n8Ghgei0Q/DkZ0PB/jm6D32v3qDWtk3ZeZ0W9BFCGSIOW/FKs3nD4qLggKfiAJmHk7hzPfMCgN+ojoKMAUFfgwK6Dvmx6irqKOgwOeigMjkO9ZHPledBdwvmwI6j+Y3TpyqwQrDztJgxZVwoly4f2iGwOf8ce6qP353MByF5/vykIoAGq2EUQh+yxZy+8vmvU/R+gX/ZpjOpsJXZDaus1LZKeutj6xIYZF/+S1fosLVN6sZfMaVInql5cqrrhk8EnSRraDALQqozOSZ/EVjFSr8KRvpIagUlFAqlWTtSHmTeR86VP4KD9PgxfdlAyRhyEbaKJR1z1vLng8BLp4XFFhDASMbgVlEQygjGbmmEHgefG7atHMD5nsM/7IK5uOso0z5yxBAWSrr4owKwJCCMcrrls7WYFckFRRYTwGVuypbOf8l/5LXhH/9QOa/rkutzeOPJflbsgaHqvdNMwmbRW9BTxkPj6+9yPkhFFjVAH4IjEeVISPdxUz5Z/nruwCTgfRQJhaf2fkHmuFHOiveIvTtYoYqpvRFQGsw/bmyq9+Rnf38YNQf4fA6fzBND60HaQKHLx0xWEngMswQMtm5TUWYKuW0nJ6JCw/FSW7yFZjHa3uL5OVrji5J6ZHiffcKJ5fnYgHsxUC7XMar1gb2q2XxgEGFqmkXS5qQB1TtV2mVzHZmDraRYXdnG1f9HgbdGzFouRyPcNGnYu6F4DlH6RNd8INcWMV1BHfGKjsZ9HA9GcNjH2UOPN8oLsUSValh+UuNVzSUDdFiGievaeAiclJ5WU+mE6TcBXMP3gRJPqHClYq5qyQR7yqD8VgmxPwQ2643cNhs46DSQMf27SqPsD+ZJvCs9wiG8nm5uy+hdXr9nngMuJ4OcNy/xvN+C8+3tz5IGLMOtknP9zTvyY+EFlyUYfghYXbjviOLE6TTCE6cwKXrRPuxSj7P05c48cdDitvrdSfWxY9ahmA6urzA+fWVvAjdNMPBRhu/3T8QYxV6olCjK9PPi/arlxe+Ovf9AHtb2+gPB5iORwjTVPj4vNfDbH8Xnr8Iw7QOn8ekEWf+JKTPoI/3lxeY0Vo6TVH2Arzc3MLLegO7nqmLPJGng45/MWijBwcS2ckwDCrY391DHKUYDcaIXR/vLi9x3GrgZauFwHo3URyXaZ73nJPBjVPsNTbwotXBpuNIeBTSz8gCI5u0n2gwwvbQyKqBTAwKWo0G+oMeJrOZfBzyAzHNzMLZbZ67naI43nUm7jpeRE6mxrsKZ9rSvyllk8Ezz195OoryMOehZPHMKBdJ9SyJ4GSxuFwkPx3WG9i0Cn4aVPHQcrpznq8Odgk9rWz6GdrNBrzARew5SF1PvKD0aBS1sTFXyhMOaUjlPg1Legwr1LvGZe8acZagWi5hd2MDr7d3sVsqiWcVkb8Ghfl/0kQNChsOsFMrY7vTwfWgj954hPF0gqvhABeDPvY77VthiQhIx94c6GMuOMRtPj0L/ShPnWWDFX3Os+Eb807iOKZnJoZ4ejsZ469Hx7jodhGGE3hOikaljBe7u3i5u4u6GCQZ2q+il+m7m+4s+X5N+W4FNhtNfLW9izetBuqOMTgiTxNx8vD8UATnCYbvddxpTEwW0TRmZVv0nmc+l7HB94IDbNYaqFer4A6czHfEVew0jjCJQlT8krzv82gQprlXhIwXE71bzcv8S89SSjQXrhsIgdM4lTGx1drArw+e4XVrA026cxavQPPKFq0mMFsJL7VNiwyrFVoPEotiYtjK+YaEeAtHeHt2jNFkyGByYlj6urOFZ42mGA/Rkxur05+OJ44lzoeI5xjA7kYH27v76F6eIYomGIQhTm9uMDgIsVX2Rc7PaUMLfiGMNmb+xDYj/263z1azLDW4uCko8KEUIGPp70NhFOUKCvxEFDCT+Zx0/4nwKKotKPCRFBBW/kgYRfGCAk+lAGeh8p3HUD12hz83fomynyE25zN4C1mnrbmKdHrK+bj+5Lvc8+Faj8bkb35tc4c/v7vvPNbAvzNv8eDLo0COP3jJH/mKyiYa/XG9lAZX3BjBdTTlTSEUM99z6EyYvMvNm8ZYy0Dguie98864buQFsp6yBPseuMWjggLrKKD8Q7YUgxXyllX6U1FKXl5m4HVQFmmEp/KXvC/X9JBl4XCdKKRhQVKYXC2oVlx9LAX4No/SxXud692e48kGTeXxh+pgPuFf2ewJlFwH5SCwKthMNhkzlCC9VCj3ctw8Fv5D9RfPv1wKkI8ShuqhJ3QaU5N/53MIs6XrsXymPMz1fdmcLJtVzTp0Sk9BaSJ1kdoF//48eI76uJ/NQQZ6DLORefS3QP4xJRe5H3ulUBU3uWflxEASKaCpXDefi/LcTkCYS+5tZVQ28SeTc85v5FoNOUwmgs4rr2zReXv5jD/ZgWyc60stnOjIZMfWma9XYehZ0Lc0NGmam2f9ae7FmfVSIRoyZAu9UnS76Ha74ITN8yBu9bc3NyXUEUsJnnI2bWS9ETxJp3cC3kuohM4WWq0r9K6ukHmOKKoH44l4v+D8jZO5T3WwThqsSOW2d+IoRjINMaHims+osKbw8ty560pjPWHK8SXPI79wJWksGjjI+KOCOaFC63FuWkkrGgJ1J1OcnhvPCBSdtSAQ44ndZhPNsgn9cIseFh+dM/MjjsrUGoDNoIrNZhuX1RtMZjEmYYSbwRDd/gDp5tbc3aDQ5VMReS0cq4knzZXd1uQj7lTm06ipSs8BmYcwBcZJgvfDATanYwRBE20bMoN4s4zSxJLiHi42XS+8nAETGgnRq83NDfr9Ae2/xGBpd2sXLw+eC2zy6lzRbI0UtAmEQ6ciPEjzVqeD6tUVHPJyFGE4nqA/HCNMgMrHSFvHKOY5/tQTz2W/h/5gCBrz0GCtXgrw+tkBWvXq3GDt7tFs8CWhaLTSQobXB4fodweYjCbCF73hAJf0fjSaoNlqCo3XwpPxZDiIzwPHwUG7g/3WhoQEU+MI7SOFwRK8ZjpJw/A4jENYr1QQBCVMwpksmImLYk5Q1vDLvcy0Nv8ikXXrYbqQvLlQnPMZcVN8NW/+nIfBdNMeTpwcUMlPQ6JmrYYXeweo+7RkvwOmDItMpAVpwbZy/DZqNZGvcDxknofIcdAbDRHW6+KRSnHhhwHDz9FoozcdodvvYTzqw00TNBoNbLXb2Oy04bvGwwf5aJWevDe9aNpBI4B2q4lGo47eeCCTt3EYojseiXeKhs+Rt3yw/YaWy+kP3S3RkWOMeHoupr6DnpPiMstEPip8tpdtoJEOvQ1RdvbCCc56A7y/usG70zNMRwO4SYya7+Nlp403O7s4bLfFu00ec8VXzsrLnAzHiYRzapTKIoOfb22i4dIFpulD8gazy7HUAMsI+mzlVpPz9GZa/uOOz3ivaZSJ5UoVDDeV0JAnyxAmCaZc1KiaviQKt9EgZK1REV7Ntegd5mTd9NlGaOadksEJIzRLZRxutMXbVwOuGJ1ysn+r0gW4taRZebzec4SVs5StDE11Mxzi4vIS4SwS47lGrYHdrW3Uq3VpHQ2WdKxqpYu2LOjTqHhiIDvsXiKKuKiZoDeeYDQNETZrYBS/pUM7eB1xc2TVOpfKFjcFBQoKFBQoKFBQoKDAL4QC+h270hzOD1bnAytZituCAh9KAbKWzmdjKoK485Ob1egJkYtvsrs5Z7LyAC/mvwA45eWiPzfRSTGZe2fWi4DWnC/xoa0oyn3JFFBO4rcrvxllzVo8bZv1EtJmzrb3yFM+0h/LiMLfekVlecKnMku8YHhcfSqOggIfToG85OPaCNcjRVlqlRhirEKOnDPvA3Uxn10Gl/UVDgdrvLVYxHDEKJH1rD0sjLXPisSCAjkK5GUlkzl3EL6ya1t899Or9FNYijA5b9CfRw8rUpGZg/C/hLPK4VFcFhT4KApYtS2NqMm/RjY6siZOw1eVqU+tY87LajBoHRoQPnm4OH4+FPgYFeonbwUZ56GDQjX/M/kfU/IhyHc/V+g8m58aq9BowVgUitHKGhDE1SiAWJizFIry1HhGwWKHrtah+QlKdSd8pjCk7WpvoSBpCCPxJ7TEGkRsUp52Wqd5ZFq2fL3IwXI8OEdjCJXrJEN3NMIsjJCVAlEwt5pNbLU7onxmXq2LuC9dG5uQ+QuyUfJRr9VNKAbPQZICkyhCbzhBh8p32yz5zlmgZBD6kP/z3X5G4UjB5HE3NS31qBiOzUKAUISecPRjSCaoC+KzO+cHWYKPZN5qjIeWns8z3r4gbaiYpJeVaZqKkYPsQqDhhu+LknWnXpcwHlS0zqtlQUHSwNR0DmoaAjH8BL1W7G5u47I7wPtuX+JjUuksBkFRBr9srBIJys6/byP4KVJYgT0Uz4W2V58YrwK1zEG7UkO70YJ/eSMhtIbTEP88O0WlWobvufBrdRP6yLaVbSabKGw9LyAvrsiPpDcV3hMaXk1H6E+nCGm4FKfwU0jdW7WK8KiOPYWQhy1Kc4GVCTzXC+CXynD9kvDDbByKkVB+DOTLK8z1ZyWaKTHnESrnZxP0xmMk5Nsshe842KxX8ebFARoSzmYhP5ZgEyR/smBgDEWoDKcXmTe1Fo5aLZxfXIhnjijLMJxM0R2Nsd9qioJaYRGjfDvIRVliXGTWSxXst9rYrpUkbA37Jm8gsAqD6JjQPLAeooxRH/NRWnKnlzFeaSxXuoSBQn3cWXHXsyGILbtIlISV2/srkFeDBDyTMENuAnRqTXx9+EIMsVRCkx9u86sx7COtaBRBgxEa74jRm0tPIy6iNENvOEKybd4dpJ3yJ3mRXiQolyczmmKZJwxz1qrWULGuQGgEQDnF+pUdtFF6z2fEo+R7KAU+MnrccF0wvuM4jjEKQ3S8qhhy5dtBOE+ilxZgxXIwnBmRc5B4LsZIcDYdoZF20LRymOGhZmGISTTDeDbBKJxhGIU4OT9Htz/EdBIiSR34cYS652KvUsP/ev01Xm9uo+mu58V59YoG381ZBj+DeAt62dnCHseA9RqmbeZrd+lYajyhLhJ4lR8HKhO0/whH6U9DHKZzzDONy26lcgWu54sM5zO6RpzGsRjMqfGklp/jpJaUangxf/DAhcvYs8SYsZYSeEmCw3YHr9pb2CqZcc220BORIMisi6beAn7Po1t5CVIPtp8yuj+bYTgcI2Os51IZjVpdZHTJN9UztJvWwfJ6TTh5+pKOxgiMHzj0WuQgylKMowizOEOVL0492P77XuL3PVMYxbmgQEGBggIFBZZkckGOggL/fSnAOUJunpBvyOrkI/+suC4o8AEU0Ck2z/JNIMYqhtG46U02V33AXFS4mP84d6bClJu0uH6VGWVTZMNesKbiKCjwKShADyviLdcaWJH36NU6fxjOzqcsX5Nl5ftbeNWIYvKvEcnWWCWJTdiA+cfpMozirqDAgxS4ixFljZ+LaEYXsOC9ByHOM+jsQXmZYcj5M7C4qSyTtV0aXy3JXyuv54CKi4ICT6AAeUm8U1iPJRDzAAAAIABJREFU5WQnj97VHLMyeRfLr6tiiYdzLrIIg1xr/taVLNIKCjydAioHxSBVZK9MXEUvoTrap0Al/+qP5Wh4qPKXD9Tw9Slj4in1F3mfToGflcHKU9BX5s2XWZeWf/4x18rYKqQFFpVBnLzIBGY99Hw58bBiPyxlGqKTdluUk/C72qD18rn5KWRTWNIE3t1rKasY3qpLErSm1dymXn4wcyd/fzoRZVlG60y+8Bh/0Q/k2QCZGLUwtIqhDVtLzwXWe4GtOHKAPndQhxnSJBGXpHT3FKWphDmhN5BmjWpbc3zAN7kWNd8yKqDUAEXknYtyqYRWrYFWrY5KqQJHwiEYpalgTkFGurM9dpIqgEUZaHHLqGR3EQcupk6CcThDv9tDOAuRRVS73X+QrtwlPkkSTGZTicNaYugJ30fL91Gl28vHdK0NUcG89PhRdoCaX0I5KNOljyx4hGmGaRiJgUalFMgOhVu8cD+6H/A0x6/Skbf5jCmcNjF8w4bn4aCzhaObPiLiOhmhN53gr2/fYtjv42pnG4edbXTKFTQD33jyyCmTH6IV28teoaJzEMeY8gOXfeu68EsVMQwYxEDoZAjE7RgfkX/tRwoBMNaz4xglvg3TQwVyTMsfsVo23oToCWESpmiU8+rqp5NQFqsE70wMmwhXxp4bo+I6aJRKaNg+X14CWFMX8bf0MsZNxkCi4vtioJWKO+AM4ygGjYVY90M8YnrYeKAoZ0ApBeiE46FWa78zH3mWZ+UOThq4o4FGCsSBvwfbtqa59yVJV0oGU6vt2vuK3P9MP2TTDG6UIkgd1DzrkSKHv7ZxAcyEYVF68WwmUWyxi4zj13VFNlBOan8QDnlZPI3Q6C1JxB1jliVG4U7jD2QYpRno9Zljg54xWM603UAyUs7I95GEKANm0Ux4nvHNHS8S44gZdy9xF5NFnKVvt2XRqqdczQ0v6OnGBQbTMf76/Td4e/QOPscVsWHdXFRFhpjuAbNUjMWmcWi82rgeAsfFRqWCFxst/HZ3D6+3trFVLolsmYtti7RpvcFS5HvubcU6m16ADRpJMJyT7T/tIymlAFaJsPLCYjb2E+mmZxqm6DXP/MXSLnNN41CG/6JHs/GEsb/NO5WW3xz/fFcSnqHMmn5QA9k5t9zfGwpH39XMTYM4GqxslivYKldQtwY0agtzZ+cT2CpN1ifdQopFaYBFb240vaJc5VyD7zDQmMb1hQeHEZD4RkZLXayPhS1zsgtkDmLnGuKJhbxF5CnPHUfoSCNOesEqjoICPyUFyLrFUVDgl0QB882k304yg37kW+CXRIWiLQUFCgoUFPgwCui8gGfKU7mXufVikZ2L7fPpts6DV6pjuXWPmCY/CVvOK9ZDl+ta8wqg4ragwEMUMGwkuXKXZg1Ny3LDVZ5v7eeb5Oe/O9iPj/Iw9YabarguJ+tGNOwSAEs5tebiXFDgYQqowLSsyFtZTzDLwEbBadfSHgZmc9iyutbFVLNUZMYC7w3bG32JPLdFl0453JbSi5uCAndQwPAVZaedK4gOzGzM4jOuND8oLaXMSgVznY4pzWkDZbDh5AchrgArbgsKLFNA+VbOS3w152S1H1wueM+dcqfC1rmGuXcMPJ1r3wPnS32ko1rmb2Iv/MAGz09AKOqu/lseSqw88uvS8s8/5ppMTB2IKIdUEZU5Ej7Eyc08mI948Kw/1iu4McFeU1+iC3kmdfFM82h6Ho6mGUswk1Oe20k6XxJLyiYtsOZ8P72Wa9U7nqk3HIdTifFFxTyVPrMsw+lkhP+6PEXJ9SSUgrjGkHrNGy7LTDgKUb7CeAyYlkq4mk4xnI7N21LiktFoJcEoikT5z/A6H3OsltYPJCcF3BSoegFetjfxcmcX7VoDnoTyoULLdJYE9RFjILvoqm3iY1EIsgb+XMzKAbpphLNBDz+EMbpxIoq3h/AnX9EQaBbFiKIImWc8u5QDH7VygIDuqh4Cws7JKVSpVOWO8kq5jHK5JJaIrCdMuaM8xCSOUc2MwYq24ME6HsLhEc91WUV5Kl+EOFccSMifl60mrnd3gTjEeRJhlsxwMxogiiMMZjMcdfvYbjTlt9NqoVUOUIMjSmka6lC4idI/X4GOP5tGHSWVlaL4pZUwHCSuh6NxH97Je6Efw+3QGIkhoji2wBi8dMdLK3jHlV/k+ZiUyjgbjTEcjYxiWezkycszjGYjbDZa8rGzgs49t8u9IWPPyqBpGGI2ozobYmBV8n00SgHK4o/jAZDLYIWvPDgoI0Ml8MSrxiQMRT7Rg9J4FsqYZ1ikJUW9VqMLDnYiWwoC8YLjabrme+DM4Wb6y/QDs1NGinEC3cBZmfoAmA97bKvMiXKdvzwK3oKXeZUAWWS8aGWZyEPyI8UY20fZL3Rc6Qf7lpiPczFTEVeh6u1CKCK8JbuULC8zlTJZ5AfDAoV0GU3h5iL1MvTiCG/7PYS+a8LhSCN1oid7ngxSxIoy2nURug5GSYiL/gCD8Vj6gfJbDCXoiSgxBjNsLZuhZyHW0s0jyMf8+YPvV9sfEUO2DAYYUCbzJ+POyF35yrd5uVDluy4CN0C9WsZGpYGDVhOvNjt4vbmJ7WpJvE4JyXN0v1W14MG4YPSylcB1UpG/9VJpbqzyqI862x7C1x+NJWggx1HL0E0MYzRlqKM4wSxOwbbSm9BM3ns0fIslBNTMD9BPElz3u2Kkw34w7/jFgkaefLzWOrV9el7Nt+7e5OVoW4w4vn1alSrq5bJ4mZE8+uFq++oWrByd78pyq8wK7pTP0zgSA0sarGSuJ57IrsIJ/nZ9jtqwJCHIXH7EqEtzmZcZliZ7ZBK3KMMsCNDPMlyORtZdqSOeg6IsxjSaIkxpKlS4kF7XJ0Xa56dAfozqtZ4/f+1FDQUFPj0FlH/VTEXvP31NBcSCAgUFCgr88iiQl5kyj+Z3dS50bf75Uutz8++ldHuj5ebZNMF+0+k6zcrX3TpQRVpBgadTQBX3UnLOhbfhrHmUY1VdKhBDFbO2awqIXus2tCKloMCTKZDnN7O4YlJk+WkNf95bgW6i4VqHHQOrKzbUKRHsU0HfW2/x8IungOGphZGrfJdZRlNes7d308pu0M+PCdGPcDCQY/mAPwaSuBtK8aSgwKMokOch4VGJKqDcmhOS9oU/z8+LXLbVyvhY85prc2ckr2j6xKyrMHpdpdzte6GZtVcR24TbWT5Jys/WYIWscxevMX1ZaaRs90locicQw9T62M46rK1i3mhFc9zCigNKdolT4WaHxR2z6nnbqTPizTzBQteZkk1X4xfFkefVIiy5Lk3xNWeFsEjVdvBMdKhEokKbRiWiRKJSKYnxvnuFwXRkJmFs57xtBoIo2eTSKtzoMcDz5DcLzU5+GoBQo8sd/Aw9QRWSER0LfD70at52ob19saYZqp6LF/UW/mV7G9uu2UWveYluntd4b1qz6BbeK12GyHCeBaABwfXFlRgvcKf4QwdhUKE5jaK5ZapHXAIP1XIAn+4Cbd2K21qYVolInH0H4h2EynIarIgrFbhCW/HkksSi5KbXAMKU+YY2bi3wj0nMAdYG5JIImclqZNJygH04+N3ePkBFYhrhajJANIvEI8rw5gbvrq6xUatjZ2MDh50OduoNMHTSZqWKpueKdxm2Xdun2LMe0oeHGqyQz4Q+9JiSAe+617ga9UGji4yeTPjneSZcCZXmEtsuFa8X9HyROB5iN0CYOQjDmbjdQ2YU33EWYRwOkaA15x1b/aNPSirls+ksxGw6A43CsjhGuRSg5vvGIOLRUE1GpQf5RQxWAvNaYJ1hnEh4LtKJ9/rLV2E40wx3XhOXwGdM7MfImwUklvWQCc2VRfj0J1F25BFYoHjvlfSRQ0MOw69OmsAnPVx/bkhEvhPeY+bVOiQtA40D+Ig/5iVVzJ0xUJCdb+x3iw3P5Iu5wQoNStIMju8L7XqzKcLLC5wN+qDPH4FujVYMj7J3SWjXhNmyr7YECaYRlfmxGMmw9jhJxTOTeLyxbbAni409rU1czrJ6xyJzohAHukmlARaN9wIXXsY7YyhGPqEbQMZf5c93XJSDAK1SBTvVJl60t7C/0cJuo4y2DQNEKubRMvUZLBZdYUxBaLBCqroOw8T4qAS+8UyzwHCB/qLwPI2w+SMUnuWdmdFTSoZ+kqKfhLiajNCfTDGaxpjMYvGKE8aheLWROPVpJO+/1PURpsbDioRLspwhc4g17pQVCVO/jh5NfdyZ/nPIZ+QP9jv5uO6XUKU3NQuC8HnIe8Nez0+rNOG9FphneviC9KMBD0MEUs7Ss0qYpbgcDTA7jsRISaoSg1IzWmjQI1ecfzjctWTONESMafCSZmIcxNoZ6ipOGQ6I85kIaWGw8nCnFDk+KwV0mMhZxxHPev1Zay+AFxT4tBRY4udPC7qAVlCgoEBBgV88BVSGcgqQVw5xds+Q1o89CIcwFJ6W4/oh58KyjihzeteGCComHUqj4vxxFCAnce3BrG6QB80GQOG9VYbUqu5gP2a/XYTfeqYOMjk9mhdHQYEPokCOuZTXNGnOVUxQPcwHfJzl4ZqxYDX8XJvjksXSSsu81g9qTlHoy6ZAnnd1/kDW5WZQ8YRidXWP5TKFJ0PAzhfMai0hmJ/m+bIpX7T+U1BAeYmcpd7hxTBCDLeNXuQp9Sg8ltFrMw4o0umIIjPe7TXDYwfGU5Ao8j6ZAj9LgxVloLtao7yjZ8NyC8UMy8s739ok3AXnQ9IFNgvyYkkha5WJVlyvwmZ2flhq/E4pLFa0Zvq+aMtKSdaRe8hLudV/orCymezOixUIt27nMG49YYIAlieLq+WM/DymhwfGuOXHBgc42zYejjDrM3iBURyxFJVehjJWqMwtio07fjaOiij2npPRc0UMN07h0HAjiUULfhcey1jdf7cKQ4QdiyRUfKYIogiVMEGjSuXyggrsN20NYcz73+bRNBqlUGGcZMZThYSvoEtKa6DEqkRvtgZNhcny3GHP/lZlLL0GlDxvPnVdasfSzRrAFkfuGQ+CQHBxfE88rbDOME1EEU3LAm3jKpR1VWianteVyT/T9i1G6OLp4spAIR48iHMNwDMfcJ4doloJ8N3VOa6urzEcjhGGkXhFuRr00BsO8P7kGO1qDQedDp5v72B/o41OpYZOyUPLvW20YqsRhTIVolTwSxgfGlFRQToZIxynojBPY6vQF17leCUTk6/p9YOuwywPuwHgBYbnU/ZmDC+J4MQh4pgeSxiiRXxraPWPPiudSEtR4rL9Ieswoag8xwO9pJAP1bhEyrCAFr6jNj7WX+D6CPzA7uByEMWJeDdgnQTFg3nz9ybNyDGGsKGxFg0KSCddnDAl7/6vsE0OawGudc4/Chc43A3paU+kXitgDQ5snbnif/0x9bGHWYAx/OF7xmMNX7RK4zlQAlfApspcAmtb4GLqNsLTwDejSXOwOPtE5JAarDge3MBBEtJb1QizyUSMm4xMYs+wNEvxx0OsREzrKawola18Zpxpn7Iyi5HRs09Co61MFoW0CRbIo0+KOwto883ZtJPwfd9Ds9ZEs1pDkLn0y2UM8TwX9ORTCRjuLABDWTUrNWxWatiqVNDxjOFhlcZ6FiPWtw5XTdNzvgHkYcrf/Ki9nY9YL1J5p1TlCJ0x7B2A6zTDab+Hs14Xl4Meji/PMRxNEMcZHMoNie8dIyWN7cuCnrbgl5E6PuKEkF2R3xCZxHFCIx5zLGOhrTCuDfVukXM5JX9HOOQxGpwqTK79+dbwh/WJnFZhnS/8gdfahnxx5WeGr5vNGPDK5OLCepTE6NErm0HWnO1cSHAWGxV5aOZPNCTjPMMRUyeRxeI5i3I8yZBGMzPfyCNQXBcU+JEpQI69dawMjpXbW9mLhIICPyUFhD/5b4mZl7k2/2j5yU+JeVF3QQEzk1vLk7d4eg211hZck69IKijwBAqQrfjjlJurQfpdzWn6fKF9/iVgAVPIWn5kubzM1arn7DqHYwpRqeV7vqwDaN7iXFDgYylgVh2UmwmNRiuGN8l5/H3IZ6UsEfEjNTGGV9wYGrj5kfKxmP+Cy+cEQ+7yF9zgRzSNLLqGGMK5ki4WJXP5yiTZA7a+2HKFc6Fr1omkrJRbhM3kBmgJB34fvByc5QqKu4ICCwrk2ZgswyVFikqu7smfhJfP51qUvetqwbOLYWKWDM0qHTf6mXgKzFkw6l10LNIfpoBypp7zcwiVuYbHDJ/Jf838EHibjyfqRcy6t9nIqFy7WAV/CFjx/HNT4GdpsKKM8qTG01iDUpiFXSMqqfAQRlwB9EHwrdhlWWPZZYAa8UzpvwxV0MiJasHDGtAoThx4VMEtl7wt3qXOFVhsmH01zFXDWnI1/0rzl26X616+EwsLWy9xVri8pocHCTtBhWaaiFv+ZrWKps+PXFeiAfEFximZ1QcbcJJGchkjG1GI2smaCbWSoJ45OKwzxAvDD1ic2JmKwFILPvRG20pDGSpiU/OzH0yLp+YDSu/1vFqrWUhY5CVM8aZjiLCUXWFYUsgzXtPQIEkYToSdS4U3w4i4cDLGZc0xwBK03I0CziXxkriJVaJ4vTHa1DhOrMGReU6FrH4oKl6is1ZYVoE9v9WLR5yJ1hwm+3N+Y9rE51q3gmMavaNscOLuAfXtPey1Gjhrd3DZ7eK638dgMsFwMsVsFmJAI5MsRi+e4f1ggE69iRedTXy1tY3X7QbosYWKa9aj1bMuXidxiowfuiQ9PXxkQL1URl0ML1zpRyGtHeayY9+OOo5BwqBfjJRmGkSWz7JYfvVqFQfNDTCkCA1KBI428gPOSituqoqjBEmSmQUs14fjB3PFOocL2yEHz3dVbJ/xMV8GrhfAISyPhk0JGAZI2UbBESbzL0CaPuU96UuvNBTFvF7tV4vR0om4mp+ZGhDwXK7Oa1rUtlT4A2/YlsXPGs/NW2SNkCxey229u0LFUIwIGBaNhle+i8Bf9LuQWwFqgSWQpIE+MPQQQ6clrmUBk0f7RNtCm4Y4jpHyIjXGVzTmqJUqqJUrYozFDpXSUth6oBAcmKp188riYmV1UAmx4fvYa7VQgQNfQxvl8VeE8mn3XEttfBXYcmY08T6Fm6Ro1+r47eFzvNk/QDXlKOPPGk9wUdV6V/FpKOW64smmYsOKcSTypy3S8z3oSG5Tg+FelpEJMtcn7PRiubxSnqlWtnHMWOOhUQbcIMNZlOL7s1McXZ7jut+TcF7j0VDaXfFLqJdLKAU+PM98TIrxnBirBEhKVcReCRfXXXS713BLntBnbpW7jND6O+2XxxHhNgwxoCEQkSy3nz+QIq+0B/LkHyu6TOM8gzwtgog84DioeIHwxhwtO55F8LAnaEhrxwybbEaSCekmNqR8v5JnkGEjy7DTaKBeMvKTLTT9nseouC4o8DOhgF0rvRMbDp4PHed3Ai0eFBT4CArk+FEWhW7NZz4CdlG0oMCPSgEK2PwM5UetvKjsC6OAzkV55vcMfT16jjEmofF2mqSybiRrR7Qqv+PIieCl6QE52Xzjc8Ge63lcgnIQeIFZf1rKnQOeB5hLLi4LCtxFAX5Le1xfsiKU34Xk26dKVB0T8p2dJogTbg0xxipumqHkeKjIethi7eUunL7UdKWhtL94na1ng5yMU3rJmhM9zUqIeCDmxk/hYF1xMLODXNFbsJXf9ZymGZI4kc2OZkNaioDrz3Yd8T5Yt4AXCQUF7qAA+YhrjI71IM0w2gkjJdCb9CPXvVRU6JoeN0lOplOzUZeGKrL2FqPkc6Pf3fORO1AskgsK3EkBw7+ebOjntFQ8ZCdm473x8GOXvsik9wjNOQ9bvWvEKA7Ctyxk9CMeN6pSD3YfoDsxLR58Dgr8LA1WntpQYT5r2GB2hAOpQxG8vKyQZ1Ky5RI/60NWvvTAYKP59ZzPZMIBLRfinb4A5u2xhaUq6wJGYxXO86xWb8EuQ9d2GYB8cehhq1jXBM0iZ80nN1I+n8Lr5YMp+uphdu5U5svOkD1D2Q+w12nj1eYWXO5ilkzE0xiscCrHdyQNJ7Q6MVrRPtLwA2mKGhzslKro1GqoMOTDMiqf/I64GUUoTQ6W+4243qbGMgpKm3xZM3WVlprMuct8aSabn1VOi2rNGqyQiFw8oPJZCPoQJnnIK9e0cGBFXJlgJ9hLxZ3366ErhsxgyjHf+ryLOvPPBYKEbOCVTupFDSxw5nnNYwFCXqOBiYSpgYMmgO1yAwd7FVw1G7gYDnA2GuNsMMLNaITJdIJpOMU0mqEbxTgdT3A9HmM44c75A7zsNLHpOaAyW/BZoAqHRiZiGGRaVvYCHGxu4lm7LRMul4KEh9WOmhATTCAkHsLdwjl81ckdrV/SGLU0wW65io16Yx7WyRb64JPBn7XQkMmc08wBfzy0feYFTO2WjtyVKi36ptW2TzNXWMTAoYae5tgLo7pFi1dgSQHDp2KwYg0L5n27JrupwxqrGFY3uKssX+aOOyB8eDLbIu8Iqc8Y0C04m3Q1z9mGOyg4r3xOQ/GaRbloStDmgD89CJPddDddTL2Sb96XsoKoIG6NPs0rGQQwLSYYygriPWKj1cD+1ja2NzpwIvIluTSVDwuWEdwtzbUSO9QtSCO0gzRC3XOxVa2iXamhtFJGy87PROzuhs6zLV/Y1mQpvIRBtFy8rtfxh3IZLTt1JDlZteCdk9eSnqtSnz+Ewu3nnAlTmnti1CDvaJnM3tUc4qyHgUbjw9CGADpOgX9c3+Cbs1Nc9W7EWIUTr2qDRpkVbNZq2KrVUCvRY4yHMr8oxXLFQ+KXEJZqGMJHEn+L3nVXGMh4CDMGGFrz6lnab724CbHmaM4vVovk7smHljJzfjUhznKZHry0vSnj7KHMgm9OqjK/YMCxJAvp5mO45AfCg7/a3UPJNSGKyK+c82VimWk/mpeaaYyJ2JdqSS8GKw7QyBI867TRKJWWyGRb/xDaxfOCAj8qBZQv9by2cvL+vRnWlioSCwp8PgpYe05+6SyJ5s9XYwG5oMDjKGBlJU+rPwEwl6VqLVhw8OMIW+T6YArYd7jyI79v+N1AL6ji/YSrVmmMhG79ZYPCGp7MJ8152GCkt8xCo3ANLURjFX7x0tOqb71UfHAbioJfNAXIW8qChquAUqk8Xx8hceipW9fqmVf58k7C2UwKm2F0w2hmasq4gctB2WMY30DWD++E86U+WCHyg/T+UulkCUNy6cFVocDzxGM5lZn09BpxQw0NBx9aD7NAlG/1zCV5yt8kYSjxRW0+DVbWKUyLDtPuKM5PpABZh5soXVmT5noew7xHSFPrRf6R8MillNn8xRkwm84s73JDWQbfSVHyP7/u7pHoFtn+G1NA5aQ2gXNSykaXa+QJN22bH9eIRTTm1KUsyzQjMvUun2bmJ4xWQMNv2Uwj6o4MHjfDUv4+Uq4rfsX581HgF2CwotxJIilrcmWMnikWKaskNAxsU8nHH3UshoOCWR1kghm9kVCdLfXJlUyyc1hr8VvnVRQNfJMqta9muAVhkWDKLhTbiyd6pTn0fnFmXZy0VStl+B5pzLAQrnwcHG7v4E/PX0k4F896WFBILKcKRU3jOX/o8xLhZ/zxw8Mqi+UFm8/96a4pkFQBSqhCzyeCZxktJ+e8kFtt6BrYFLVUP1LZz/IpvdTIZCDFjALZ6Izl40/rWQNmnqQ0FhhckGACh4XsLPdl0k1LcXVKRMDW3kHqWEJZK7QvBL2dV3bPhdYvVug0SmLhufGKKTiHZ3FkKtPY5coTJKfwBXy06x0c1Nu4yiKcDoY46/Vx0evi7PISo8lEPiBG0zHeDgYY9/qYTsaIXh7C294Uq/U8uqynxJ315GXXhNioBD6+OniG/7G/JwtEIiQFb6MoN+N3+UNccLYL8oo7l+WDJBZepuchqkP57GMP0pReBvzAgxu7yMIMYRpjGkcSEmbed+L3TxdY19RqkSE8TldpKR0m3K0SG7MpGg0wJBUnByvFWXTelnmFJs08mz9dKbl8myu6/GBl/JDmS3Uu537yHeud1y2MfxtfrW/edhZYyaYwFJ7eM+Mi61z1fzeezLwonMtn3dTderiAzszEkXKZY4ShcoyXqwxl18PmxgbePDvEy1Zj7mWIpRVCXvYR1jw9hwUvOQ4In95V6g7PFmfirURiYW2HAlqBs+5WixijOPNOd9IUXprAS1IEcFDKee0haP0Rnl7zrLB4fd+hz5lfr/P5zXthYay1Ls+iZpY0lh0cTzRWYRigbgYc3XTxz/fvcH5+JmOr5PnYqNTwencf+xsb2GnUsV2roxy4qIicW3iIC5FhkAEXAI5bTbgu5xCpxLGnDJAJdh7pW9fEWn+WMusbcqvkUsJKmbtotlRm5Ub7ZSX51q1iS5biNcNC+YEvXsfYu1yI3G138MevvkbTcxBYgxrCJ+0VN2VJrUCbwLPm4ZgpJxkanoMyPUPlWFnLFeeCAj8GBXR88KzXD9arjPxgxiJDQYEfhwIqv+e1qeCVBDOfefi9NS9dXBQU+JlR4NHS+WeGd4HOf2cKUIzKNx4VpuLJ2EGSpgiTzBityPpiroVLcjeXbucX8t2fmVDWYRKDXnd5uFlmvvV8GsYYT6H3gFoGXNwVFMhRgHyjvMOzhPouV+wOaa5xOhJymiG583lzINZeknepKOVKFdeswoghYw0MKrQqHtfbFssSa4EUiWspoOvAax9+wYnkT64P0BiKm2Y8zxeDFXp/pdGKhGbLrS3cRyrOIMjDwsey7hqLLDefcw7om4IhrWSN/j5AxbOCAg9QQOUqeVd0d+Wy6Dt0EYwGgxG9VDwkL3NrDUv8S4OVODIbXW0oKxkjK6HUH0CzeFxQYC0FlH9V/pY8GlP7YrTC9fEkihHFxmjQmKsaMMzPQ8+LVV+TRh5WvVeUJoiiWLxcUT/ppil8xzV6QQunOP30FPgFGKwoS+bYWoxVHPnwWqib8oybIzy59iMOUUsuRoRDENjfAAAgAElEQVS8Awgy/1uAX67MmK/wNbGMG3PlQC7BUrjLMI3xi4ShWa5ikc1eaXnWen9WzWkKEh+m8MwXX1VCGPBjNpVZlxNHKCUpmtyxDEc+TJhPobCctknTFDmFy3uWIVMGrlGO8v5TH2Z/H2tdHFSIy2+R9AB9chnvuFRrvTseLyWTNvzAKpcCCcdCE9goTTGOQoyjGWLuHp97J1nQcgkIb6zhCVvHPqZA5t6Dqe5i4A4Ehs4olVAplcVARmDMdz9aiLyXn+05Gqs81mDF4sD6aTAeZcuGEIoz6aM8oWnKZLZWSSY/8J68QBuMIDNK85pTwlarg5fNDq53dnG6s4v319c4vrrGRZeBOIB+EuKby3OUvAwbJQ+tzS3hL62XMCulkhhfkWLcZTQbTlGKU2yxrjwvq6eBVVpZ/BTnxdmB75ekPraBdWm98/bed5EbGPnxSjilgEpcD5jSbRHAF+4sDuUDnlRVHIwLJAMoB25eK9PII/zRqepMJq+xWJWSTyqlALWK8TyguOtZgZh+tDWSRtYCYjWf5v/5nQXpXC8aviSdOcFfascaIjKJh5yXMtsHuZOlUi7l4y61Op7JY/RKVAlKsgOE3ZBFCbJZCC8M0WCILYbyybWUta/ypcLkM22b5iM9aIxIWaX5xJgoTxd98MSmmbr430g6eT/acG1Sr8qAFfxXq1u9fxoa7tJ7QOym7gI4bzMpuDh0LI2R4WIywdHVOU4vz5FEofFEttHGbw+e49c7e9iplVB3HdRscVZFGScbJqWdDiZ2IU7etY5xPpvSCe2K/LwLzQVmT7iShlOQP6HMJ8wqdLD9Xa2WUatVhS8yfljHMdyY3quADR+oWp42nGOQYI8s98oCuXyTWI/nO/JOURm9yFlcFRT4cSigPKln1qr8rGl6/nEwKmopKPCZKKCM/ZnAF2ALChQUKCjwS6IA56kyV+U3Hje/BIEJSZFlstAuG024y/+RjaYIZl5uUpnRuH42w3Q2FRfr9Aha9rhRxZONMay3OAoKfCgFlHd5psHKRrOBU88X7738ziXvhSENTnLfbLy5g/G4Lkneza9bUeFq1lu5vumgTC9EsgnwQ7EuyhUUWKYA2ZEev8UTrmxyNJ7kozgR/g2jGBk31txzkK314No470UGR6F4GiIPezacVYkh2WxIIC1TnAsKPJUCKn+5HsZ11EatjlJQEvlKOTqLIszCSOQpn689cozLS/6MDM4wSxOMGRJIBLbxqlLzS8b78R0yfG0dRWJBgXsowCVpLzPz33JA71MusigVuTklD4uO0xhY69xhwX7KtaYC3nH+IJu0qZ+k3iuKkMaJrBvLHJsbJQv5e0+P/PiP7n+7/vj4fFSNKjApiY0bfzvnpWRVqZ2vgVz7scdiRAgkHRZ6VvBL2TjycuFF8s9Yjvd6Znle3z7YRqYuP83DWi3DnPnf6vOH7gmbP774mrUKyr4H7oIXwwZ+cEymshu+7nmoUrjk6lPY2jbFY35vm6GeWe5SNimcjzrbulj3/8/eezbJkSPbgiciUuusLMmqItns7tFXvGdrz963/fe7+2Ftbe3tvTNzZ1qwKUuL1Jmh1o4DnhmZlaVYxSbZRJRlIQLC4ThwIBCAwyElyqxXGz9DXXl7UF53SMx8+OPHnExE+D4iz0OUpKJoMgznljMUt+zAgn5ZvjWOmBrkgCTmoCQyx7t4CXzuSs/RZGZOFkevZTFLVCJlrUao5LHVLUXMKM3o4i1NJ8pZiRmFhmvzXQqgLPDHzopNWeSNAy954XBBF2iVylgrltGqN1FttoCDd+ienSMcTXCZRDjo93DU62K7UUMzVxR65Jo4looFo3HMPRppgjQMEUwjWQilFYmilWU2Hi4m8yIfy90Kg5Z/S0W5/6OtXDq8FIdiPo9CMQ/0uYAdYxoDI370kK8M/pZdYzHF1tPczw46dadKSsWm0Ly4aQ0HKQr5HMoFSqYpG/NXXsTzyj/TmIwin+Z0JdJn4THnjnf6U9aybxP1u+oqFupKjDnhqwkezWfe6pjdTC5EYSVvd8ZxZidBQnONo7FYRGmKEta8tFpqdbPsaZnUZZgWjfnpJboN+vBgl3uueASSOQaJSpjc8cf8+GN7zeb94OxuJaAlviYiwVmKogNhKgqejwY4G/Yxiafw0wTVYhHbjSa+7azjWamAJi172L5fT20jvZg/trUUYkGEbTqKJoILJzX4E6UVy9YSC+JL1pj+g69MWrE288GEriac8buCwVmY9s+5gvTRKmcpP0xGE/jjMcqFiihiUYEqe1FGsnQ0zPRq8zDGkXjZyMqTZqiJnesQ+IgIZJrbR8zFkXYIfAIEMv2r2TDwCXhwWToEHAIOgS8UAY5puWBKS4y0Ckuz6BPfR4wUOmEfy9fE7QXkWEO+KbhRJU1l0Ypm0dMkNnkEHgpigXf+nXk7VRfDIZBBgN9R1mI0fTkE4ExSvVSSo044w0KFFc5NctGU37tyUTgz4wX1VldkN7PJajCZYjgayd4sfhfzmNgKN+PdTEbJOdchcCcEKJKUKc4H54OcWAmiiPNICva/Yy76l0szS+W3EVU5NgorEabhFElC+Q1QyhVkc1PAo+qdHN8GpQu/BQGVIa6h1MrlmaV5yiAX66dRLIv3ZqZ/iRgj8aJr+2Xe8kfZHYdTsWzPuTUKP08JKAcF5D3/V56rNWy6/78tBFR2Of7lr+R54PoXFVYohSKH6fwkCpFReq68GOAZZRVZ91IZDuVYNo4feJ5AOZ9DSY7EZB7u+lwQ+G3Vhiw0cBicIqVFCmsJQaTcdrQLwGtLWHYXIt394bo2QpD546efWB+Qft2TRalIF6bsC4A0lumQPaVh6JijScSTsS3/Ul7e33Bli3pDtJVBpqmbBbRWqYZyrgQkvhy/RJNMvckU56OxWPSQxTaWmYv82d9SWVgeXYRkvf3al+gOWQBXfZrfAueD2dX64ECCe8hr+QIqpbIc+UBrH+MwwsVkgmGSiKUSGRQsycgCj77VGuRAhIOJFOiNxhgMB/B4xEuSiHWEcuDLTh0qCOnFW9YFeckHtC1ipC31PCSBj0kaC81FGc0QUEK+WWzVYzH6dlCTyJFAVGigYKxIp+lXuCoZktTKEPnkj8do0HLElufhWbmC73Z28Xx/D9VqRYSPn8KDyQiX/T76Q2oCGxZYVi50NvJ5FDk5w4XghMcvpehOxjiPUlEAmcmy1fBUmdX8SWcuw4Y2i6A8ryjO3by0sLa8zIMDSh7JQgWdWpHtz1hamKYxulGIyzTF2A4kjawYItL32HJr5gxn2eSDiYvrAPqTMaIoFLqUl1IuQLVQsJKgKeduhkWpVobcs2rnxH7FO+V71p8K4xxve/ZnjuQiRrN61ES38TlLcFvEu4QzU1NxxiJXOjs1x3oL7lSkYout2B0gPKeUtcv34CCcojsdYxgba0daJi0O3SwO6k8//S37Gabsfw1c8Lz/A8lkr+XnbNhnca/1rG5GkY0Y02Rxd9jHaDQUIfJTD9VCEeu1OjYaZVRyxtoNMeYl7yIW2tLT8rN99rkTbTo24xo7SJdEqlxhSFz5n2HtStiyh+an7jzc+NyH1jytuSOFGd1M+fiBsHzRR3+Uab4Xq3IeuRk/UfFxMB3jbDTAWBR3ZpCZtkCCSsC6+ih5zYdMc56yTMwqIuvp7h0CDgGHgENgJQKzzn1lqPN0CDgEHAIOgQcgwC6W3//VAKgVi8hzw5i1NtGdTtHloqmdhOf3x00Xx8Oy2IQUo9goDFBhhRoEpSCPWqFoFFZuIuLCHAJ3RICyy/krfst1alWRLfpxewqPPKf89uNUZHL1R5n5pKNc88d5K27OGvEbezTCYDCQxVJucOFGvEalNLMke0cWXTSHwCIC7CQzc2A6/1qlHOd5XJovxzNTFnthiPPpVDZPUi7pd9NF0kaG05mFq2gSgptxOOdRLxRQCXLIfQmTqTcV1IV9Fgho/8tj1dvFiihEcUaOy/3sf3ucIw7N+s4V2WViXtal7Mr8Jtd1ODfJUwCGQ6Qxe3OjcNUsV1DIKKwoCUvJOQ6BeyFA+eE8Oa2msf+tFfIoBIHsyKQ8jpIY59OJyCP739mCxkIupCLbYWdjCB4pyI2lw/EEURwJPR4x3yqXUKGFFSv0Tn4XgPxkD7pW8skYeLSMaVWAix+yKM7BrdHu/hD6SmbFWsqt5OwYZxZPGxo/NHnEhkeNWd8XZRoqq7DB8AXBdKt+JGSa2bwN8pnaZT4HTLa88FiVRhlgmQdlRhud0tNnDb/JVZrMhTvC2z4Xkcrw0wBp4mMaxrgYDHHc70t59IOZ4y2mMUorZrGVzxz8ZX/sJHRwpnndxM9Dwygrv0Y+N/Gp+NOVjzkPWKtUsbWxKSbbqG09nIY46HZxPhhJZ0xcbU1nSJuS8D9/MhCmmSt25ABOLs9xcX6GNIkQpDHKPIKCR70UAjneI8sHaXNQU6/W4Xvkisz5SAMPg8lYjqcgD/xdhx/9+dLgi4DHWRwNh+hzQEMJ5cq6LAjyQXPOFCXjxfjLeTE4W37es21xoZ4vMlqQ2ADwTWcH9RoVVqhTRbN1kTnuJuJ+JHMxLbXl28WyKArlfE8UVqIkxsHpKV4fn83iMl9lTZSwbJ2RxowfJWzjMr70JRn/TEnvdUtarA22E7a/zWoZ662WWIPxAlrkAS4mQ/xy8B4Dnqe6gvqyQpjiy4krfvi/m05EqYeKUtSSZnukRYh6mQpUhiDTXClOxkMV8iT2h3SgK/j+GF4sjtad6ZdtLlRy9H0kVNKyZc0U706sSPxHLLuxbmEVvSwz5D/7o2xQ+UraQKUskzZIjcJKbzzEab8rSljsE/R9o4WRqiVd/pYbXCYfE98ysJBYHx7uitWwTBZ8zipbCa8Pz+bhFDI8ZokpfOxl2P/1BwOMxxN55/M8+Gq+gGaxhDqPGrOWmyiHvLRbtI9Sv8yGin9HF2foj/qisGLizpVfNf5DXMVV3dW0TKFvjrOYknHvGj8Lqd5TrnlcUr1YFPOMPq2PcYJz2Mf781P0uatJxn5GfjnOEBnSxkt2VEfStnkqaV7hixlqpotFcE8OAYeAQ8Ah4BBwCDgEHAIOgU+CAL/Bi1zM5PxbvSq7TDnBwG+Ns+EA5yPOEZlF/+zwdxWzDOe34FA2qUwwGk8Qh7F8/9VprbZaQ8kPQAPrdx2/r8rH+TkEiABliN9yVQ/YyRfM/IQ94pwWfkR+uWhk4aJ8xjQBZC+90880BvEQoQGAy+EIw8EQSRzLPDPnqzqNhuSn6Z3rELgXAlmBy8wb8HB0Wvaul0soFLhlMQFyPi7HY1n74Hz3lQV/m7GS1P7UKAxyk+AE/f4AYuEqjGXz5Fqlgmohj3zGQtG9+HeRHQIZBChzZvwAdLyCbHblHD+nqXmUyml/IOtMPB6Q8sv+d2EMYYV21v+KwmuKYWrklwor3DxLi0DVUgnbax2xUMFkKu8ZdtytQ+DOCIjs2j6Ya30NKpRUKiiXivB9rtMk6I6HeHdxjkFqlV6vUDeSqPKrwVyn5Ji5Oxia/jeJRRFmo1Y3a4Ku/1WoPgtX10o+C2YezASPP0iMIoJYG+Fi9dI6XLYjpvDqs8ajnywcWWRkMeSejJEGr+WOmudhyeKozXcchxjSHNfSWsnK9NaTNLnQVcjlxCRokMsZLckoxDCcYhLRQsS83JaVR3Gk47A7+RsA1usNNBtNeF6A1A9k0Pb69BQnUSw7PRRTk3lmW3OGGy0r3cX4S6Bk0ny8W+VmnsNVn3nYY95x6MuFuVaxgE6jiXyOXbOPaZLgZDDEYbeLyzASjHRAQd6UP7rEj2Ehj3ah1jeVRcIQZ5fnmIyHCJCiks9jo9HA9lobhcBYKFE5pUs+ZOG7VDbHinBQI5MbCS7HQ/Sm5tiZbN5ZHMgDB+LkYYJULAMcnp+JhZcFCysZa0Oz9JYRKYOlQ3r6y+apPLOZ8kOYfFP5hBM6VOig0k2xUEAub9oHG7Uoi4lil8mRAzge+bMOYKPRRK1SETOPXi7A+/Mz/P31K5zCDMr4YmP+qy4O+nichzZ4xmMZpJ6oAKGWnlYlvoeflpPtv+5xp0oDjXpD+oHE99CfTvHD2zcyccWBJ3lmXZA1cylqpixST/aFfQng5ds36A9oD4eLvilqpTLWG02sNWqz3SqkQMyzWBhFwayPze4zdlgO/ohpqZCXgQ9XumlhJUoSOV6JAxnWIUu2qnSr/D5qkSXDxVy1PmayQes7lQrWmg3UajV5mfGYp9N+H29PT3ARJ7O+WV/+QpGEeCkw9plhlBO+o4wcaUQbX9NkHj/kdk7VMsBG84VdxIo/TqZJ2yf/vidKeoJxwuONeA5nKnJH/LOllDhWn49YU1GF/Tgnlal0OKDSH/sxKrwyr0dUiroNaqOINudWeL0t0QPC+dbhjxhxV16rXMbm+rr0dXGcYDCd4vXZCQ4uL9BLUrFWl5Vd0bXMACx4Zd4lytrHLofm41yHwG0IzFvXbTFduEPg80VA+9qbOXQ97834uFCHgEPAITBHgMNZzs3wuG0qlZQ5v5HLwQtyOB+OcMqNQWE8swo7/+6f02CvS3+zocjMz5wP+nKkim93RNOiaqdWQ6taATfxuMsh8FAEKEWco1CFq/VmC+VKRb7yaIn0sHuJ435Pvnk530A5lc1tmYxVdnXeipvxDsWC9EiOO6f8cpNVo8x5q/p8I1mGhrt1CNwZAQqc/an8sv/l5shWrYp6tYp8IS+b285HQxz2+7JBlPJMGeUc0PLFvpf++uumwGmvi16vZyxUJKlYk92s12VzE4+Ac5dD4DEQMOMHD1y7W6s1UK3W4QWBHAl4MR7hZDCYzfNmRF+agORvrblJ/2vXF05lbvJyFifn+aIMs91eQ9EzNvofg3dH42tHwMwFc52Pa3a1YgGNShX5fF7W1y6HQ7w6PcFlGIsSFce3V8e/i9ZVuKbRS4GTXh/nlxcIwynSOJYN8J1aRay4MD93fT4IsA/7wi/7RqfDxWjujvc9RL4nnS93OfNH4cz+sv7ZcAr6bKFXaN4fHk1Gl9973KVAwc/5OTnqhS8D8tmfhOhOJ5jCHD3CBiZh2SytB5VoWFlm0A8U/QB5nyZBjULBiMeBTEYYJkYBhi8VHTTp4EjpZ19G2axuutcyyQKv1XLbbTews9EBl89olaA7DfHy/AL/dXiMg0kMDsa44Bby2CNqwlmLGuRD+WOdMA4/PrROFP+b+HncMEXkcanelRqx5cC0DA9NADvttVlnPE1TnI5HeH1xhve9rmBKnLRuiWWsZ7JZDIlnFykOUuCnt69xcXmGJJ5KHp1yBU9aLWw2GtIxZzsAkS8qQ3EnRM4XpSg/CJAGPpJcgMOLSxx2e2LCkHWkckVX6zRbnxcp8D4FDs7PMBqNjLkRKwNcXFRFDpVL+pEWabCMNG+rbVNlYjlPfZbwzJl0whOV1+IUXkIrLAGKQU5+WmbKMjFf84DdtTVsdtZRKBSBfA69eIpXvXP85/sjHCVmYkdw56Ato4Ai+NNSkm/6DZVlyjMXmlkW8vIYF+WEPItFGXjYrDewt7WDQo415ouy2rvzc/x8forD2Ly4mb/KCnnI1hO1o+WDifU0HOLw+Bij4RBelKAQA/ubW9hZ74hFHubNa7ml2O5pafGcsak4qKE28WfkkEP2zeyXyyVzHqKIJo+z8j30xmN0J+HC5J+WXd1scUhPMPqViqz5SZ5S+0Y2KB9Nz8NWs421RtMs8HueKDH9cHCAl+fnOEtTsahDuZi1PRbGEmW7pMyyTak8U1Z4T3maFXEVEFlQ7nivZTCuZULeazyeidbIzDuOvC7we0f6HyWaZVNoawHmEAqUrAtRLA04AiCmCcZJiEESilY3lfq0bWq7JObsZwYpwM/AozTFL6MRji/OMZ5OZdxABVHZ90jLIo9QuAz7V6lZBZpswGPkmaW3fK/80GVfR+W8zXINz5/so1yqAn4gRwG9G/Tx49kZXncH0o/pGIIYSv9v5YX3Ks/Elv0yd+bxp2nYp5sGvMyNe3YIfEEIfOzG+QVB4Vh1CDgEHAIOAYfAF4mADoTt0JTzFjph36xU0aAVlFIZCHLoTSeyw58bE/RYoFVDAf3OoDI8t6YcJxG4oajf78LzEuT8FPVSAe1qWRTF+Q3jLofAByGgc31WfmnZUhf8tzsdNOu0M8rNbTFO+n0zv2ktk2a/81WO6fI7zhw1bubkXh8do9sfiKUAPwGqxRI69SrWqhXZ2JlpQh9UBJfoK0ZA5wSsEM37Xw/tcgXteg2lYhG0cM0NNMeDPo77Q5lTkPnoJehm8mstWg+Q4jCe4rB7jt6gDy/1kPd9NIpFbDbqqBV4JIW7HAKPgwDlV9YOZLNrE+3mGnw/QJSkuBiNcdDr4SI1Gxp1/owyyx/7Yx07ULY5D8w5yjenZzg5v5ANu36copLLY61Sw3q9JhuIda3lcUrgqHzNCGj/SytX9XwJ7UYDpWIJXKMcRlMcdC9wSOW/1Fg3z65vqPxSrunPMQT73xOuk3YvcNnrIo5COXGCp09wfZSK2xyzuGs1Ato3cPAla3109aSb1Uke7PsFvA8JCy8jOfyvPjbAOtx5HMiCTux5mKQpeAiJz0HALMY8xTIN0s3++KFGJYL7frApDebEPPgsDY2D9SAnJrM4MPGDPHqjMc76AwxiYELWrVIK0zKdXMqo5YX88IzDgh8IPS5cUVmEu+jPBn2xgrHJ8w9FjcS8ZJSnGU2lfQ9X0zJ/WrKopR52KiXs72zhp8MDRDzHLopx0Ovjb2/eIZ94SDfWsVEKxAwk0xEH/lgkdiDsPPjjwp0O8KJ4imLqoerlUP81ewsFKYOJQp/xuvb2PnGXiTBrmYjwgCY87NSr2FrryLmsl5OJnE38+uwMzUIerWIeW9U6amLpZi6zxFMGEjIRkeI4BX48PsDP716jP+zLZEQp8LHTbGK31UKrWBTZ1nolT1o/lC/ufaDJLVrwiWMKZg4H5+d4XT3Bt2sd1HwPJbvgz7Skoy8CDmZEYQbAP96/x8nFJSbTqSg1ZPNjOungMgogWgYuJI7CKYIgJ+fd8kOXVlMoR6ShdIi7vISsVQOaCWXaXjIV5Yt4GkmbK/o5lPNFlPLzcrO8tMbCNrRbqeB4cxvvLi4xSC4QIcZZNML/evkj8pTD9Q1jLcGWmWnJg74I1eVkkVESSREmqSh7VHnu3gf0JQtywoJmyk8tUyrafL/7DKcnpxiHEcIkwcVoiH8cvEMxn0N+ewdr1D7IHIFBfMmrvrDPUuD1eICf3r7BZbeLcDxFPklRL5bw3e4+ttZa8uEvdWUZsqwssJd9MPMUjMXf53mx7rQvLSNFLufLWSKiHOEB5/0eji4usL+1uXBylcpdtlT0409lYlWcbPz73F+lZU00p8bVcOZNMc2lxmQe2/lJex2XZxcYTCdiGemXoyNUSiVRmgxYr3yPWNmgTPDSWmNb1vbMRX5amwmmMUqJh2rgo5RXPkzZM3NTs/eeJXlPhyXSH1UhjZ0NbV8sp/KoZVf3nhk9TnTJXOV8zgn5lD69WEA+z7sUCU0YT8c4H/ZxGacoB0aRlYxQFok3y8n3Ia1jsW3+Ek3w119+xHn3EjyqzPNJy1iLEsFMiZC56ConC4UT61KrQzXtQvzMgwm/LVYmwQNvNSe6xJDWpDjW2PBSfLO7i1fnPYzGY4TjPs7CMX46PUHRL6Lkl7DZyImVMqbR9HT5Iy5ZeeZ7gh/qtHjDvrkW+DKZyjwfKMAPRMAl/xoRUBm9d9k1IV13OQQ+AwRWvoM+A74cCw6BaxGg0Lo+9Fp4XMCnR4DfCJyT5PxYMxeg3Wzi+PIS08EIoyjGUbeLN6en2OFifaEw+27Pci7zbVZR+zIFXp2c4Egs4PbFGme5EKBdKaFdLrkF/yxw7v7BCJj5FmNl5cn6On656OHo9AxRmuKcJv0vL/DucoByrQo/Z+btdSxBlz/5huMxVtzZHwNvj47kuHF+OPN4gPVWUyxIc5OD+VJ+MNuOwNeIgH5XzcqeIAAt+AB5zrEBYgm9Vq1h2h9iMo1wOhji58NDdAr7KNJqc2Y8oXLM/ndh7vX8BEeX5xhPxrJWVSmUsFatYqNWFUsCMh8x48HdOAQ+HAGZTxMrbR7W6y2sr63jzdkpEni4HE/w9uICb06OUVlvI+8VZv2nyi5zZv9LC0I8+uoEwJujY5xddmVtx08SUULcbDZlkxn7+0wT+HDGXcqvHAEjgZRfrnGw/+WxbJ1mC9VKFSEtA4UhzscjvDw6xFqhhFq9Iv0v5S8rg7rezM233FD/rneJ9xenGIxHcjoLT6BYq1ex2ayjWsjJvPxXDv6txWftyDouY35kq+8c031xl3agApQMY40Pl25i+BjSKkUSIxfF6AY+EIU87cGemMmBL3dus9hzUeYRGBInSZGLE5R9X0yyVQv+gsKLNAB7uo0Ax3v9ZZCUeHYhigozXBgvi4WHADw7jhZWuuMxXp9d4KfTU1Q2O4hSTxqIvFisQoKso2Y41UXJcuDLGYdCKwgwjSIxqfiPgwO0ajUEfmGmgEB6rGim5T1/XBhTHAUFfVDXQqPlsI/CH19ELE/bA/bbbXyz+wSv37yRI0nGSYJXx4eIxmNcDAf43dM9bJbzKFkrM8ybWegCZIQUPQBnvQGO379D9+IM7WIJL9a38Oe9nUwNZcB94C2XQE19m8JKdViMtZzMInu/kOUSRhqW9Z7TN0uuJk8jKxo/mwcx5VVlR+wBL7a20O91Mez1ME1iHF+e46/TESbjAf7l+99ju1IFFSHIIzEVpQ1RWknxJknw//3yEn/9+SeMwzHSKJSzMFulEl7QakarLWZll8tHOvpRyWMY2qUKTnJ5TIa0oOOhOxqBVhoq+RJ+/80ztANz/I5lXQYzXAjkAvdPwxH+89Vr/PD2rWg/JjxpmfJHkGSRNV1QBBBvWw7KAyPW4NIAACAASURBVM+j++Hnl2Itqd1qYXtjAzzXk3JEHlWOmI6yxIEU8+ZL6DiN8de//x3nJ2fANJTjlaq5HGqFIkq5nEmbGgUxLm6SVhvAXrOJ950OXtJEbhTK2Y5vT48xGQ5xvLuHPz//Bnul4mxhk/gxb2JPl9cFeegPcHp6gsPDA2xVa3ixuYFvN7ckXOvZxL7nf/YzVmmFfUrLA57XqnjTWceIik39LiZxjJ8P3iGcTsTvX589Rz31ZjyTV/6ogMAJq7+fHePvr17h9cEhwiiGHyeo5wt41l7DbrMF7oEhz4o5OVY5V1d6VHaCVr1gFoMal3bx+C4lVXnUtiOu0LyhLd6F8Io4Kuuy44f9I89Ri2KRzShNcHB2jp9KVexvbKDum0kW9qFMZ6xmGUyIiyk5M/HkXWDe3sb8hYVlBQcrvLJaH4aavLOIw6zNzEYG83yJG3/khfJcTT1sIsWz9XUcHx1iehFhRKWVMMJff/4Z3W4PF7v7+Pfneyinpg2zbFoObU+0/EULPCfdCxyfnSI672KnUsd320+w0ayiSB0fKps9gqUPrfsVwrUCqI/jJThaRSCV4Vm/vSpLYXreCrQMZlDtoezRvGtBrDqlcSKV1B0NZVK5XT9EYWcbsX3ns+7kY9C2zRO2zaP3+Mfb13h3ciLKb1TIJd48Eoc9P00ga8vQvNUlu7zXtiTykyamsmxZluNqETWdYCADUKMUQyz0UlnJ0tCwm1wT37wTSV/wXUpg4rCEqVH6oQl0q7TybHMTo0EPR8MeIi/FwdkZwv4UlxcD/P7FU+yv19EwgztTfkubNIkvlZjP4ymODg5xfnoOP4rxtN3CH3f30C6yN3CXQ+DzRGBlW1vp+Xny77j6ihCYvypMoWfPFFj9Ze++ImxcUT8/BGw/quMaFVe1EqnPNzLu+uIb4XGBD0OAo31+p3GUym//zVoDh+UKzlMP0zTG0fk5/hbHaJfLSHd20PE9OSpTe1t+9/OoYDliNE3xlhYK37xGr9dFGkXIBT4222vYajZllz/zciL9sDpzqecI8BvXKFwBGzyGu17DL/kcxvkcRnGEt+dn+OubV6h+8x22goLMUWbnVzhFw4WmPlK8D1P8588/4uT0GOF4hCCJkU+A/fV12R3NdPw5+Z3j7+7uicCS8PCRfSJ/YvW1VhVZG5ydYxSnuBwN8P/+13+hxg1Ku3vYDGgdeJ4n59XY/3Kx/zQF3o77ePnuLS4uL+UcaS+Ksd5ew3a7hUbR7O7PJJ8TcncOgQ9AQMYPovAKdAKInBVzAaZBIBveuebyH7/8gnLeh9/akLUozmXy0jleY6HYWO//v//2N7w/OsKUylZxhFwMbNfreNJuyzw458md/H5ARbkkKxGgLPGdzk22VBjcKFOxpIlpr4c4DDFNUvzHjz+Aa32VynfYCObrXkqQm8k5huCG0LfhBL8cHuD49FTSc61mrV6XNR+OoUt+IPlpWud+egT47v2iLp04UJeLGjSsYiYWAoRxipPBCP/nP/+Jcj4vShqeLNTYxUR2vj4XUkmBP2PeQRZOEh4fkqAcRditN/Dt5ja+2dmeacqyw+dCsW/T0EniGGliFoLmPFlIU7OASIskldTDbruNg0EPF/0+eheXiL0cDgdD/F8//Ih355dYbzRRLRZll3w66iMIx2JV4/vdPTk3kVTZYFlpTc+XF856u4XT4xNMqZwThvjPN28xCCNs8ozQYkmssPhhBH8yRTAN8f3+PtaKtPQyX2QnFDM4pBBUJvBMuewirQoJOw3mz4UzLoxueSn+bW8P3miE10mE3nCIURLh1eUZzqdj/HR5ilqpiGqpiHKxKJYfElpUiWNMwhDjaYjRdIrxcIgpzTKNhnjR2cB2a2O2PPYYL71s3XhJbLXvUlYg0ti3dWjUDqxUaJHv5SqvaRILbebFc9HSKJajoQTnFRR16ZGy0kw9PC8XMdzclI+x1ydHiKMUp5MRJseHOJxM0arWUS9XUCqVkAsCUZiQY6H6ffA8zdPuJXqjIXyeyZYmWK9U8cfdfTxb30AzyEkdrmBDBhiUcVpw+Ze9Z4imU/xzPJY6DZMY74cDjF+9xMtBF2v1Gqrlspzjyd3/k4h1ORFtxbPLHs66PYxHE1TbDXj5PEKGd7uCC3FnW5MMM4yo9u4F85pMxFQoTs9Qff8ejVoVlVIZlWIJ1EQvlArS7qlgMEmNdZGL4RBnvT6Oz84RTaYoBwE65TK+f7KLnXYb5ZxR8lFFCv2orcHDXgBEe7uI+z28PTvDYDzGJI1xMOxj+PY13vS6qBVKwkO5UEQhn0eCBFEaY0oFl+kUw/EEk9FItD4n/R68nR1sR23ESSKWYjJFvf+tFS7KJxUTuIjLfuxfnz+V40Z+fBNh2OtinMR4y/p/+RN+PjtHo1pFjWdec0E28KW99cYjUZg7ubxEt99HFIYI4hS1IIfnax38j+++w1YpL/0O5WEm15l7lVmPtlhTKnvwcCruQzAX+dR2d1thlT5d9tfSZ1M+uNCfmGeNI7SU8ILnbbnMw5lM+/OGKIjtiEWjw8NDJH4OvTDEfx0eoh+G2N5YR71SQtH34IWh/Br5AnYaTayXSlJGshNTGtNElLJo2Utw4EifHZ7FbcYuvWYPc754x15Ii+fzSXCIgSgSK2Kky3DTW80nZtgvaxgnNJ9Wyujv7yHxUhwmsQzouGBPOe6/+hkvL85QygegCbxSsSBWv2h1gn0z2zq1jkfjESaTMSaTCWrTGLl1D3txJFr2VFqUy1pI0uKoq8F3dZkupQxRoU3OP2VbNRoxLBfbKl3+Hvti3nynsX8OeJY7339UDvPNveVknu3K+qPFjhQ8x5UKf1Qr2Vtbx+FgjJNuH5N+H5MkwUHvEuMf/on3F5dYazTkvRh4KaZxjNE0tJbXujjtXaI/HElF7z3Zk51oPOt4wGO7KFNxgtTKFhljGVQmpDz0i2MgjoCE6hpWakQJZV6U7B3TySXvL7bredub0dQ4d3Q1HaMb2MilHmRHuzOG61netizEXMYacnSbUZD9vrOGcNBFMh7gctRHGCbyXhydHeHdtId6tYR6pYxyuYhCIS8ZRlGMMIwwCaditWs8GWHc7SPhe7RYEgVftTxFHrI7o+5YRBfNIfBgBLLthMTYVlKZKeLRhuxNKJue7AQxRjizLWYp+xuClmK6R4fAoyFAmdVrJs98R+mMpyiEBmL+nO/bj/Eu1/yd6xC4DwJ2dCSjEel7beJENlhwcMgFKB1h3Yeyi+sQeDgC/P6hXPLbn1YHd8tl9NY3cHl2jpNeD5NpiINBH//HTz+j5/n4bmcL63bDCftibg4zyiqQY4P/+e4tzroXiLihKU1R83zsNVvYbjTA71v93no4546CQ8C86zmXxB6UVi33Ww2c7mzhH/+8ROR5uJhO8I+jAyRBgO/3nmK7VpJNecSO411+wXKT1eswxD/fvMYvB28wnQ4QJKFYK90oVfDt+jo2qjUnu07gHhkBWfURuaJlcWP1NYdv1jo4PzpGFMeYhhEuwgn+n1e/YOh5+MPeHtbtvBWZodzT+jct51JZ8B+vfwHnyafjCbw4QS2Xw3azge1WE41Swa41PXIxHLmvEwGZ1zTrlzz+ug5gp1HHd3u7+PnnlxgMxujHMV6enSP3poQBAuy12qgwnWwdS2Wjay8F3scx/vn2DX45PsRoMgLSEAXE2KnX8bzTwlajLoqJ/LabfQN+nai7Uj8KApQiM7PAMSnHAUV4WEeK321tY3TZlfUJriN3J1P8x5vXcsrBn7/9Dmuy2djIISmoZZXDKMRfX73CAU8nGE0Q0GpLroDdRhNPO+toFsti4Yr5MXd3fR4IcD3gi7t0MsGIMLUtEjMflnpIPB+DKMbg4sJMkqnAcfWJP15+bH5cnRJ/mRmWhTIuDhYnE4zDCVqNOvZmTcUIrggwJ+D44wIm6VmFD+2gJQ+SToAgMDsimM1GDtjrrOO038NoOMYUHnpJiuHZOU5HYzSqF6gWSgiSFMnwAsVojCfNOrZ3d+X4Bg72mT81zGgSiQPz3c0NOfokTM0RJCejEYbvD/HLRVfO98rzGKJJiGA0RnEaor2+gVqxhiKLMMPGTCjKTn7x4+SMUY9cbqx8ptCQjwrPwuPCbLWCeH8PuUKA16cnuOz2MIy54NnHcTiE7/soFAoo5s3xCJy7jOIIIbXiplMksigdI4hC5KIIG2mKqR9IF7Wcv2B7j38qK0xCWlJHs0W4dL4AmnLxj4tn9lpOeAMjmoZRZj/JgyCr4goX/syC6DIpTcOcedQLT+DZhodwfV0UESZegsvuBSYh5XKK05ND5M8vpH6LGYUVKk1wQTNOYiS2LLUgj51qFd9ubOIvu0+xSYWPzNEyWly65IP4qJWG5wUfg61ddPtDvDk/RZhO0U8SDEd9HB2MUbkooVQqIlfMS55USJlOxxiNhkhDKugEqNaq2NvfR+L7uLw4x/TiAgkXpe2CaBYL4siXEU0mDoNArCMdcxGXC7SDHvKXeRQLJZTyZZQLZRRKeWn7ojCSUlmqj/FojHASIeGRG36A9VoV33Y6+HZ7S+7ZdrSs6rLMxGST1ibKZUyePkO+UpcdHxfnpxgnU0wmQ5ydTqXRiCxbhRX5kE4oy1OR5ZgLxFQQC0Pk41isPfFjXLsZk/sH/M8ARZzYF/Cjv5B6iMpVTJ7uI/WBX96+wWA6Qi+O0O/3cTAcyzmrPN6pWCiIpRwq1ozHY0zDKaIwAuIUPDKpXa3gaaOJP29t4Q8bHaFPeaQ+CgVD5Zyu/qQkXgzPs9YSRDnQLK5lWL69wFZZgGnY8rWfpQKAKDbJpLGRUSFGBhhZ3dtzWIjBOuelsv79xoaYlqX1EfZdVCw4Hg9xfjDB28lQjsfi7iB/MkEuCvGk3hQFj2aRNn+M1RqyQrpiRUiUH81XKsM1P5utcVbwTqjpzYvpsoqUVOLxLb5cyGRcjUf6xCygjgEf2C/TGsfWtvQnNBHNHXiDaSgfJoNBD+/6XeSohZzPSf/s59iOU0RxgiiJMBmPkVCxzNZBx8+JhYqYsrBUIOV5ydtyeDfH0FCrGxkrHHzV8r3D/pOgPOKl5LSOWHdz5RRa4qJmJ3mZX7KILHWT9TXh9BEFLyvPGxwEr2/hqD/EQRQhjKaiDNWbnON0MkX1vIJyiadyGkUhHu01Hk9ABU6+j3hG59ZGB7979g36oxFesk8Z9JByu5kocjGlkZU5h+aZmEn7EZmx9ch7K2FXuZ+XgXXOA5lkYMXyM51VzGUspr0ufZYPvZ/HtwOjNEaaRlYhZo5vtizkn+noshqoyPk0nyLd3hGF259PjmTCZzydYpRMcHoxhN8F8oUcymIWPS8L/tE0lP45nIZg/8x3I5V4ibpfKGDs+eDbinkbGVSunesQ+MQIWKEUxTQrnAnHqlZW6S60w4WHT8y7y/7rQSAjiFZMZ323gCDfPubNamz2OUH9eoTj8y8pZVZHRrzXn9nJw07WymtG2df4MKaT5c+/hr9wDu23D+feKHFUiN/0gPFaB6fbOxh5nsz90IrmiJvh3r7F+XSCLVGIL8smu2mS4DKe4HjUw/uTYxydnmA0HSNIEjQKRTxttvBNZwOb1frMusUXjppj/3NBwFo0lgUHu9nxSbGIwc4mTk+PcDoaYRRGsjlscvgWl2mCnbZZOMoVc6IoOE4inIVTOcbq+OQYvWFX5idKXiq7rf9tdxfPWi1wAxIXoLTL/lwgcHx82QjwLU/55fxOxFPiadW71cLJ5iYmPEac1tCnCd50LxHSWnyaihUszr3mctxUmuByGuGkP5S5uMPDE4wmU5l/rfgB9tY6eLbRwVajJv27zn982ag57j85ArPBrLFwxccKPHQA/H7vCQb9Pt755xhNQpxOQ6THp+gnHg7HU3B+u1gsyvEMPMWB/S838747PkZ3MkKUhOBmu2Ypjz8+2cLzdgutnH/thuhPjoVj4MtEgBPAdt2S6xxcl+Km3G/KVZxx7SYMMbo4RxhFeN+/xOQdMPQ9bFQbqJbKyNPqFU/zSBOcjkc4vLwU60D94dCsFQY57LdaeNFZxxMajnBfdZ+lnMj48bPk7A5M5cHjDBLkJhFyYYRYJnL5mqflBrucZ3coygobhZ4XFVYk3E420LFHTzC1H0UI4mSmmEE/WiTJpTxXM0UupGJFKMeVUPGjIFqIZiDDuHrpgJkNjIOcJjzs1yrob2xieNHF2ZC71ydIohCDaIJR7xJ+kspif246RN1PUUtjxNwhncvPFj1ZCvPCCfD9+jaGZ11gGqM/pv5YivGQu7l7ZuGNvE0jFMMYDc8XqxNMT964AMgF23wYIT8NpVxBHKPIXeJJjIJndjhredS1KBqlFVko9fCXTgctaga/eY2///ATeuMJQipNRDwKJhbrE2MufOqEjxDRCUwzECx5AUp5H7V8AaXAaDRrnuYzXXOe+97njnXD8hbjGDkpbyyLW1xy5rloXNxjnFkuVjyu5DGLsBhCb6lr5pGkKCYJ8mGMOIxRSFIEtLKSmDwWU86fmD9NvlIDdi8XIL+1jXw+j//6+UccU3lClAwShOEY0WiCPi7mH2bClyfloLwWghx2ajX8t2+/w59297DueaB2OHnMyuk8d7swbF8IrJ0XrRrGe/sYDftiwYeTHonnIRwNcTkcoss85WeO+PFFCSxF0aMCRBW720/w+6ffoB9FeDme4iJKEEcxCmxfGSUEYd1+ENDKTDVNUfU8OZqLCjy04BKOQ0TjCUYsdcrlVDPFKNaSfCoNJDIxw8Ow/MTDZrWC7zY28K/PnuJJoyFKKZqPsi1N3+ZbliOLPPxbZw3ttTX8/d0R/levixF5ttY+KL7haITpcGSK7XMdNwYXkWiNgVYPPJrXjWLUggBV3xcrL7S68FgXeSc1dt5USmilHv5QbaDxuz/Kh86Pb1+jR8sMxC0KxdzfZMBTf1NZsOWCO8/7zecKolSTS4BWsSjHYvzl6VM8q5TEwo7QV4AyEwDMn97kgf1aMUqkPrnLIB/6UvYclU48szRxU7m1HkwfafrzUpKixLYTRZa/VCYgmB/jL1xXPBZCb3xgUpaRg589DxhubePs+FiU7iZUoqOEJQnOjo6Qsg+OQwRhiKofoLIZYdLqwGuvSR0YLDwU0lT4DqZTwSHv+9LPsnwrWV3qYxiHP4MHBNdibNoMd9kX00SU+jjJSOUK1v8CLvbsZrYhNkXuyC9tbqNTa+JvL3/B6+MjDKMRYpHpGOE0QTgaY8D43EEqqhPm/UBrMTny7xnrZDU/QDUXoBQEokGv5VGe9flG0FcEkn/+WBfFKDb1bt9HhTgWTBmm79MVJB7sRbzZ7zL/AvOehnKcWJB4tt9m36IXS6oNY7HUtAxDH/LL4QaPp3lWq2K6v4e035UdjWO+8+MUI1pD6nWNXNhFaOZAGgV5Fxaw2VzDH7/9XnajnZTL6J8e42QyMeOTJELeNwpSy9iQV77rSinbUooCrYywfSbsd42MLXJuyqY4lDgOiNkGY8QcD0Xs12ithZZRTH0pGndxdbxRkHbN9+/UWH0LI+T4brTvReoxsqvU8iiP0k49YI3Hm1WLaH/zHK1aHX/7+Wcc8r0Yc/9oIpYoonGEQTi1SszWCo1UF8cfpm0VUw/VIIeGn0PZ86W9MS/N7y5lcnEcAo+JQFb2eM+2KH2vKIzRohIt9UUzBU6K9Ope6DG5crQcAndAICO8cqvjD5HjRNQBObrgbj26+i7lKyVHQXeXQ+ATIkCZXf5RLAN+K/E7ld8D9icK3LOe9xMy7bL+ehCw/SsdGaPbb4udfIA/7e9hMBnh7WSCQRjK5qGXr1/hzeuXoMn/RqOFQq4gVmDP+peIOFMi3xvcDJKC6iy7rQZ4fPC329to0qqkGwt/PbL1a5TUyi9ll7cVQOYjo2YHvW9e4D9evcLJ+ZlY+j29uMDF+SX+7udQyhVQrpYRpzGGkxEGkzFibhQVpZQEhSRGM1cwVoH/8Ee0PLNYarP7NUrm8viKEFC54rwnj0tP/QB/frqPMa0SjyfoT6ayGffdwXscvn8nfXW93kCpzFlGD2cXl+DcIq22cU7RQyLH3G/UqvjXp0/x/dYW1uyx9ZrXVwSvK+rHQICCZCcKeMufHiv4rFjHcP8p4sTD24NDWWs8617KMVX/+PEHsf5crpTh0TL7eIThdApZA0r53eYhF0eo5wLst9v492+/xU6pIWsCH6MYjuZXjIDtDOlwLphz2zy1ZMMDfr/9BMMwRI8nOniezFefnJ7Kke95zqOXubG+BC7PXQ4GEpfHY3Kene2i6PlYL5bxhyd7+HZjC+08NQvc9TkiwLr/oi4KEj+mZEGGC0zTGLUYaAQBhlGCCBwE2F28LJkorKjL1GbHtNk5Y3txWcAxO5Z5fAsX5hq5/Gwhg2SYkoMUDpDrvodmLsAoTlDhLmikErYg5DYrpiWvBJq7Ira5oLi5gVapjL+/eo13R0foD7rGSgwnpLmL3k9RzPlyjmGrXAONc+pAXyZRrEWIDSrsFMuo//EvoHnPn968wenZiZj+lEV82YHNxp2iVsxhg7S4wKUfvXaxtpomopVeI2xyHAwXcwLZgcz8eMnGbCa0F+MxjB0Hi8pF8yDIof70G+xUGnh1eISji3P0x0MMp2NZ8GdV0JqKWdSjBQBPLAIUgzya5Qq2Wg3sdtbwvLOO/TqHg4a2yZI5LiCsrNzZJb98UXMxrpKkqMQpclGCWtFHJQiQz54FwKxYXisit2WivIqiSGoUiuqeLyYtqe1P02pFKjtZpZib6FHOuNjJ+iBW5U4HO5Uq3p2d4t3ZCY6OTzEcTTANQzmSivIuC9dcvPY8OSqIR01sd9bx3c4Odut1tMBFUFN+4nATkoKTnXDe5OLp9jq2Wv8TP71/i9eHBzg6PwMt+sQ8kkKUTqyySkpFlQCteg2765vY39jCk86G7Na5yOUwzBVx6AUIEaPq+ShRsWRJEYLyRFl63moi/7vfYf34GAdnpzjj8RijIcKQR3DZBU1pysYyDhVU2GbKuTxapQr2Oxt4sbOFvXYLnVxerIUQV72Wq5bPbKN8h3GR+bmXov1kE9+s/e/4+88/SpkHoxEGo6FYoTDCSVMT1ipPahSSSrm8HMGzVavjxeYmnq9vYKdWN7Q180dwVTxJSrRNqXTl+Wj+7s94vrmNV4eHeH98jIt+T47eSqxFKE6+0lJCPqVimI92qynHh+2vdfBiewcdHhFj23SmuV/hWNt/OU3RQA61xEMaJSh7ifSfeZp+p/JdQFm7ue0yH+lLWJY0QSVNUGX7TBJUPB+1XB5FWoqyfAkzNzF3hdvVHsSQMkHlDh6v9KJcROnf/x3/+ctLvHr3HuddTu6xiinntJRE3FLU8nnU83kUPSowGqURMXjBPl5MK3uo+b4oMJH3kr+kfEc4tF8hExl4+KhelMdK4qEZ8Gg7T44c4kRPNfBlkKXWghZKZzFSWWZYDh6q1TK2fv8H/NBs4+DkBGeX58YiURgiohIa5YMZy2WsaQS+h1ohj7VaHVuttuxeet5oYq1WlfpStiVptm6UzB1d1j35lXqAhzqtvFBhMknAPpT9ph1bzlm8I+27RGOxaT2nRCUG7jbkGZe0AgYPDb7TaB3Mnx91RV4yYK3MQvsTWkLbIe1mC5v/7b/hp7dv8Pr9exydnIjin7GcQDUhThJTyQso5nLoVBv4w/Pv8M3uHupFg0/qedgulfA+8NEKcmgWC3KkU7Yp6D3fB1TCLEaRWGOjuW+ec1+FD7ZNxptVd6YErAcqNVZTiILrIKXc+WiXiqjRqpQ3xyGT7NZb5keLJlTg41itjkCsmlS8VKy98b3IstMi3TJffJZ3km1rARWHPaC82UGnUMSr00O8Oz9Fb9AXK0k0zcvyG2sqVknWKqRyAr9eKmOz0ZTjmp52OqJZ3+C5vfaVv9A2by2Zi+AQeAQEbGdKWeePskiZ9xOz2G/efcbyGJVfGc5L+2D76ByHwGeBgMqxyDKVa0WZnEq2fLGGxrIWOeUQOvMN8Fkw75j4+hCwyt/an+pYgAorVL6XfpjK6LJQmtl48/Uh5Ur8iRFgn6rj9Hbq4ZtSEeHzZ/Kd+fLte9lIhJwvVgAiJBgNqBRPC4Lmj4ulVL7it2y1UMCzdgd/3NnF77e25ahx+ab9xGV02f92EWDfauZcOM/p4c9P9sX9IQhwwM1BfiAbhcZJikkSojeQw5ZlDpm2e8X6J4/YThJs1Gr48/4z/GV3X2SXcyIyNmYjcZdD4CMhoP1vSquvhRKip89kjujvP/8km2W4eTr2PDnGOZ6OxOI2Z3m48ZEWxtn/BkmEaj6P/fYa/vBkF3/c3BRlFdls9pH4dmS/UgTsB5l8j9l1u2pKy+8pfr+2AcoxLZm/P3iPSRSJ7Ca5HOI0lTk1HgjEjbkcOnDOknP7fhijU63i+yc7+O8vXmC71JC1Hp2b+EqRdsX+yAjoXDDn4bm2uhv4iPafgjPTP7z8GZe9LmgNKPYDmcMfTydAOBE7+MY6utlU6ocJKrmCWCH84/Yuvt/YwgaVW5R/WqvnuoYbSygin9zle/eLuyg/KrQbxRL+tPsM6402RlxE5xZa8PgVuuYSeWOHPDvHgEMHa4FFllMpwOyJzQ7iegpsl8rYoWas3bFOjSx+yDWDAH/55hm21tcQwkNtEuFFvYU6tRAXJpANHxw+kzqB5kCEZu1lcaxZQ/2773Cys4XuiFpfI0ymtLYSI/B8tLwAW8US9utNVAK744Ek+eNijSgnmEW2Qi5AZXcfm/U6TnuXGMahKDNEITn0wBfTWr6IJ9UGNlpNUVwgPxzcc6F2v92C573A3s62DKxauQC7jTqqBatpxoYre+9Ny1Vk+WKSXen2BcjjSbjY3VingkUdF+MRutEEfVqP4ZEpYSi7vKmkEPiBLEJX/BzqQRHNYhGtShmtchHNfCAKReuGoAAAIABJREFUBiY31qGxdjArvFbsLa7CpdG4iFXm2b+NFv7nX/4F/dEUvh9gLfCwVy+jWizNF2JVPDTxNa5iobwS1yo8bNcb+Pdvv8fz3X2xalOLYvxucxstu9i7mhwz5WIiLU2YRVS6PC+zUS5h48ku9todnG0P0R1PMByPMZmMZDI4H/go53MoF/JoFkpoFohpCevVqljLYD3xYrtRXq3X3LGAsV5LvpEPyjwXcmulPFp7+9jrrOFkOATPi6MJOR5DlNDyRM4Xqzj1II9OpYKNah2dchWNnDlqgQuTpe1drOfyssOnkc9jt1KdWWogT8yefJoXUYASz1Iul3C20UF3MkaPRyJNI0zCBGGcWkUCY8mjlM+jXKAs5bGWy2OrXEGnVkE15wtNocuSMhMLgHQV9l47QgZzcYitmQvXrXIezRff4YLKKtEU/amRZQ7qQrHCkYgyAZWdWFc8+7lVKGCtWMRWtYpGoYSKv9pS0Rz4+91l6491xb6AL1nKHXmutTvYqtRwvLmJy8kY/Ql5DsXikR/HKMsifBGtYgmtQhFrxRLWSmW0C4H0U6TJPOguXHZhQfNn/9HxPfxp9ymeVOsYT0co+B6eUFabTdC6iGm7SkVTmudMVYhcEr+mn8P3Ozto1Gq4HE9QiWLsVGto17jEPas6JfgoLmWDdU4LWiJHz55jf20Np8OhHN8yHQ2AJJKJa1qr6JQq8m7YabZlAptMEAu+cnbba2IN69nOrlG+gYcnrdZMse8KwwqCuPzHMpq3E8vbKeTwP/7wJ0TjPtJojLLv4Vmjg1ZlNqxakGmT3i52UguemshyhJyHRg6ob27hvNFCbzrGMI7Qm0zQn05Fcz5JIlGyyVFe2QbyVIoooU05KRbRoYzkc9I3q4xofos1K8W48z/S0nfj73d3sbbWEstjpTjFWqGMJ2ttefcqVHcmfI+Ipt8GntRK+N++/x2+C8eg8irHAk/rTdSpoX0PeozK+mPb5LFCHEdQUaL5/Dl2KVty9FQk5x7T4pTvBWK9rZoPBPO1QgV7jQ7Wika5hG8GmkbOPdlBu1ISC1Q83q1ZK4uVlWX8JW/wXdeG//3v8O3unijalUcT7G9ur2xLpMF2wDHBs7UO8r//Ay4mE7Hs0IKPvfYaKsX746BYsJ01PQ9/3H+Kzc6GHH1UmoaiMLLebErfs1wOhZz+fBfxx7bGvoLjmkqrhk6lgGdbHXPc1WSC4WSKSRhhQis5bAM+lVEDFKmAlTOKPu1iGWulCtrlIuo5q3immX1MQdM8nOsQUARMt69Ps3cv2zBH4KVcTpTYcoUCyoUC8kEgClma4Lo2o+HOdQj8WghQFrM/vk+4SYJ9b+RT+RSoFAuzzQFuMujXqhmXz7UIsP+173yVXY71OAbmWKMkcxUBglxOxtLslzlf5PrdaxF1AR8RAcomx8H8ZuGcFmfVX1TryD/dR7VaxavTc3RHPYxohTeJkCSenOZJGee8G9OWA1+UvZ+22vh2fR3Pmm1sFvIzuh+RfUf6a0bAjnXZr3JsQFnmt22ys4dqpYpms42jbhe94RCTcCoLpgynTTbZmBf5Ms/XKhawVSnj2846vl3fxG61IfOc2n9rf/41Q+3K/pEQYD9qN3/K+JUbHMtl5Pf25ejw1xfnOBv0xCLQNA4Rp778aOXfL3DjaAEl2fScx9M2+98NvFhbw5NiXuaq2T87+f1Idfe1ks0MVjl+1XUWznnTKn7UWUc+F6DZbODg8kL63+mU6q4eEh7dzVlpL5AN5ty0RkWr9WJJLNjTiv3TWgt19un2++9rhdmV++MjQFFmH0lZ4zdawk2VtPr+9BlqxZJYyD++vJQTR6b29AOev0ClLK49c2M3jSS08gU8abTwzdo6XnQ2sF0qouEb2tL/Oln++JV5zxx0nfaeyX7N6Jme1mZLH/losybiq5WyHGMR2sGvpsi6vNfn67jXcC78cqFJF7mZFy8uqtEkfa2xhheNNRqgl0Ximl1A4cCal5n70ByVquFZFgLFLL2HWjmPp+U1TLCGYQpMoqkorHCBpZnPo+X78hLgJB95UB0cpSyVZy1CFAIPG+02xvyRVhIhFIUV7sjPoZXLo02LALZcbOy8OPDKBx7qnRYm4KKRWXhnh8Dyar48QoQXy6Z4iIf9R15EG006BA+tahHTahETpKBu20R4MkeSMEkQmAUkLqYSby6/Sidk81wUTMVQ3WzO198rTvYbST7weTZZ3vOwvrUJKiHxYt4sK+tby0uwOSV124TqMkfEVZSJUmLaEWs3/NSidR05W9XmudoxJqoYJkobKkuZRc+1ShlhpQQe/sR6phIQlbPy+UAm1piPHHGUaSPEdVWdrebB+LJcLAvTCi+pJwvYW/kmRo0Gppp/FMmxM1RYoXUTWsRgfRJLpqW80/oE5apWzmFv/4l8oDKMOLGemZdpM+ae+bI+OJgq54vYzhcxtXIUsswApjzSJDY1yAWcMhVmrKIJrVAwPemQ9kLZtcKsUGTzFtztRBD9tdzVUh4hf5aHUQrBfxLT9kCCnFgjotULY+qUyi4qUywf+XjsS4shfFr8WCRisEEFp2IJu0W2P4D8SjuMIjlyrFIoiNIB64l9CycPpK4sk6RJzDSP2Y31YFiQmIUIsdxTKQCVrVnfQBmsWvyl07qtEdm82DdRQaBQqYnCDQ/5IJYqS1qfj4aldgzWOghxYP48gmtrbQ3jtTX0iZ0sfMeyyMK2Vc8XUPc8KSOPVyEZ+XnAdrGAVnFN+hbyTp7Jv/YtAuEM2AzgcmsC+J8/4sH3zb9tUF2gLX6kx/cN+7HZtUyPAbRCRNeGMR372u2Ch3XuCAF/KUZWPoZhijgKkcv5oPIblY0oz9qfMD1li+XgffZalX02/LZ7ypPg4wEv6k3s1Xl4numXiQHbEH8L7fg2ovcMFx48oO17YrmDYwnywPJr+/iQ/JmGZeC7m31gJShgu93BpN2RdskBdcR26fkSr5LPo2bfFzKplykHLYtUqjXsVWl1zWDGOpI+mvEohLYymC95p4WsdmcDcccssBTZD2vbzNBmMv6IM9tukRLX2QBxUFqsf+a1XP8ZMtfeMg159VMP39YbeFZvSH76HrgPXfJJesSH1rgqhSK2C0WRZ/LLsYb0edOpySPIoTB7P5j3I/Fbli0L3QzDawvjAhwCj4kABc++i3jL9sa2QuXTnXwR4+YaWvkiCmGIXSoPlitiYYttgHHd5RD4nBCgDPM9IjKcetgr1zBc28CoWBElw+1qTTYo3KfP/5zK53j5jSEwe/GbV7+OVWhpczOXx3fNFhpBHml9gnUq6Veq4PHFjOcGC78xWfhCiqMiy36W8yibnHOo1dGp1rFVa+D44gwXQ7NoGsnxrzKbhYLvo1SgFdoSaAX22fo6tis1NPUbzI0pvhAJ+ELZpODasS7HrvwO49c+v8VarbZsTvrl8Ainl5fojUZmtz83htEatxxRn6JZqWCTVpxbTTxbW0MDvsxtkJ62C35v32Ha6QsF0bH9SRGwQkZHx7lyVHG5hLW9p9ipt3BwcSZWwbvjAdj/ilVw+GKhtszNpKUSOtUKnm9tYLtSlrUZXeeRsqkgf9KCusx/iwhQtNhX8vuLy3pcyN/1UtSbbTyp1/H6+BjH5+foDgam/03MMeC5HJVdfVQLJbSrVew06vhmYx0dOVbbzMll++DfInauTJ8PApQ17X9pabDkp2hvb8mJBu9PT3F8eS4bzKM4EqtWtCxoNoAV0KCV7Vod+zy9olGX/lfXI2clZENx/fAMjs/hhvX9RV6UI3a4FCguGoomYKYky3KmskfXjpczsee3DOdCiCygzb2lYfCRCh5cjBat8NQcs6IgsgEpbbpmb/SciJngMC8JpqFFElWaoLIH8kV4UihTNi72M95sYpoZ2IvRlR4bGpPxTC/mK7Q4wcKfbdT8IJAXlH1ZaXq6fGmRFhdYmQXv9cdw8ZSbFe3XFpi48BIFGDG+BDH1z0Vx0ZDnojADAx58ML9YPpaT+TFv/VlyNuLi0zz13e6YmnSl/BYH8kRFCvKi2JAHXvKhE9Aij/W4h6P5kEaBllfFXoKRKS2FulfJ2pVzBtgPLvIs2Fi+RfbSOaYo8KCF+cV7gTmDJXmiP90brywhG1Hlj5PPXAzkjzvb5eJgJ5/nCr/gSPrMm2kYQ38JtSBtmyGksnhr25hmSZdhdFUWmI7lZxuJta3YBGk+gMefls0qOmlauhqmZVb6yrv6W5L6OMufNCgbzJ/8R5aHWXkyZ43O8rWyLHJteRDCKkvLmc1yvf+NkqJL/rSuyDPlhEeaRJRxieghzbNXMxe9lvsXxUzjiMuIC8CZ0MA3+BJklSuNttB33nHWQGSHWVl6bDvs07Vtqkwt8PbQB8FlLid8ZH7khZaYiCknqFE0uPFW6jWjREE/9Sc7VAagUgtlXNuhygbjza6Fh5mv3GTpET4e4aIX+2q2CblukimbRFNq3YoCmciy6Zt4ZI0UQF56VGew9Wqz0HcDMVH5skGLjlb+anFZjLv0pLwxb6MsYSII1rY/0HIsJX20R5U/AkCFNz3miFiTD4Z/CA8sGy/KAvsRSpK+p9mPIMiZXyYPxVzlRquZNJiecskw/ZE3uSyDQtbKMtsuJ0LIPf1ZzSyTjWrS2f/0Y1klvY3HvPicxWEh0R0fyCt51zIQC81P82SboRIwj7VbeVkgtA2w3LwnXdLjhzf5ZXORJlPSktsy2fwVV0m/Cotrsl/Jk/N0CDwiAhQ9yicld8Pz0GzU8V2jLkq6bIN8r3O8f63sPiIvjpRD4EMQ0H6dctzxgH9f38Af1nmArXm/UI75PcF+310Ogc8CAfvOp8Mf+98WFcNp5fbFiwXFXSp+65jws+DdMfGbR0C/AbSgKqfq8ruOlnAps7SCG3XaGAK4mI4wnkzN/EBgLLVV80UZQ3C+j0rt/OZVeZ59S2hGznUIfCQEKLscC7DDpcvv4vV8Ad/t7WG4t4tBCgwmEULZeJAin8uhWiiikjNz1eyjuYmCssuxhrYRaRNOkD9SrTmyigBFjLLG+QczD+GhDmC71cS01cQYKS7jEMPRCFEYw/N8lPwCmqUyagWOMexmPCv/lGHSc5dD4NEQYKe4QqhUdqWblD7Uk6PDN4I8vtnewWR7RzZ9XU4GmE5DObC8QAuv+SIqgRk3cJ6bfTDHD5RdJ7+PVmuO0B0RoPxyTkzmhGkkwAO26lX8qV7FEPsYxDFGk7EcEcSGQMsqtMJSy1GGzcZyzonrGGJFU7kjJy7ar4EA6+mLuLKDUWVYO0h+rOmVuV3op+mfDVN6mi7rkq6MfjWSTUyw+KO3/OzEcZYu7zUZ3WxY9iXBPNjRaxybxYwNPmv8LI1ZBBvOOEKL+Vp9B8YX/qzLOEprnt4YWaQ/Ly1Xlo/r8rVJFgtKPjIvLuYvC3KWB6WrvHFhiZfyf2teC0jaxPdwSJ84MT9+HDF/8kc+6ae/BZLK7ILnzQ+kQyzpysKZja55317OTOUxrS7gZ7yzdG2UlfVNHj74UsGk4gzv7WInO/fYFkLLYoOlhujHn/rR1R9piL8tEzERDyVk0+ojcdRLlUT4rOF09afxbnKVD0nPByWUTSQMmiDyx59+VCsPsihqZUjlhqR4v8jPTFvkSjmzWT7o3pZjVV0zSNugFlXk3maovK9Ku8CTJs562jPf+bGmfYcGE7NVSTR8lcv4Ig9WJLRv1Dq4L71VeVznp+VnHsSM9a1r5upnxWLWTwg/9LQBHDARh6y8KL7X5Xubv/Yj+n5THrLt4i5Ak9csL5QB4kuX/gzP/shXpmhXwmZ8K0MzD3Oj3oLRUth1j8of8aNM8aLfr3UxL8Vb8rTvUsXnQ/kgBqRLTNS9qc6ymF13fxsvTKdtie9kXnQU4+tw1XSKhdYj0z8WDooBaTM/Yc9mRIUVUTQxLF//3ypzat8w49O+z5lw5mepaPln+cnN9Vm4EIfAp0KAbU37QC7s0yAvf9ouKfdOfD9V7bh8VyKgHboNnL1/+C6yCtQaJSvHCx21E+qV0DrPXx8BfgNIP2xlVxVs6c8xzExUVagzLK7wyoS6W4fA4yFAOaQ88se5GbNRhd92HhopwKNF40J5dqC3xuU3pY6fKef6m3HmhHgGhbt5ZARmnafpRznW5Y9zVRzncl6Cx9KGfC7lkZZkBk76XOl/M3MtOhYmyQzZR2bYkXMIGATYLfJSWaPLPpVySBnm/AXll+OFMPXQDgqIaznZAMVU3KSsSlYkwvTa9ypNm4VzHAIPQ0CFdQUVyprKm8ou+18m4diB/TAt2a+XqkhKtM7mz8YLIu+Z8QPTU4aV3orsnJdD4OMgYDcGU/Gac2VGfs04ghYyp0EOcaUqfTKlmLLLMYQqWfFZZdfJ7/2qSPHiVliul8mWWF04ux+pO8dmfX32V7bf1XuCpT92mKsuBZRhGndVvEU/XcZb9JWWYAku012KKY/ZOMvhDNNGomH00zTZew1fcAmCjUxueUt62UtxUj+lrc8zAtYjm+eVuFc8MomUYCYOeVEW6eq95qHPTLqMg5L7GC7z1Yu8KJ90+XusS8tJV8t6b/pMvHSpl9LVYPVnXh+c3zIxfaarGdqMNL9l97okGm8Z/2x8zWbZTwuk+JGW0rsSN+uhQGT8FtItPKyIlGXWBmd5oJdmoTxdIZldfb0SmMnzIbc30GWQ8qxZ8Jn+5F3vNexers2Xjv7ulf6aelym9SAeb2XIVDJftMxH+1Ims8WbYbWSVDZSBoesXAjQWYIrCV3vySzIG68FutbvWkd5sxGyj4qpuhqmruZFl376u5JXNkEm8Nr4mTg33V5D9qYkjxKmeBipMCQXeNGABc+7ZZ3FRMksptTaXfTl05Xsro86S8w0Wh5Nn+VhFvGGG023kocb0l0bZAtuBreZWMzIlsnYgcmEZW+zDM2TzNoHo9osZq4mXyj7Eh2N41yHwCdDYJVMWqVQfqRpsLbpT8any9ghsAoBFdClMHrz+5z9su3iV48nrkm/RM49OgR+NQTY1/JSGaZ7pf9dIbcrvCwl5zgEHobATbKl8snxAvtaKqVwBzQXoLTvpfyyP2Zc/amczyKRxZsyelgRXGqHwFUE7FhX5/LVEqiOG+jyUvnVflhdG+ycD0BAsf2ApF9lEsVLu0h1CQbvKZMil1axSmYXrXIK5VtllnQYX38LYGrggqd7cAjcAwEKlgrrqmQatmLjPccQXNRXm9skxUtlVV2VZRs8d5z8zrFwdx+GQEY+ryWggmn7VRU7enOjDJVTdPzLMMprtg9WOVZ3IZ8M7QV/97CIwExZxWz09j6i0spnr7CiMptFiH4qmBTA6677y5ulrBkoYc2MLi87+Fikr4EMXgyxqTSphM5jm5dANo7c3xaB4VajbJabprH8XaF5g8eMxg1xrg1akZhe6q0Lwfqs7rX0PkKAQqOks/zdyA8DmXiZgBKiu4LAavqpJWMSrEiWpbryXtOom41EP7I5C1t4yMa8x/0Vouz0byasoTM+mB13xdNd8LyOD1Lgz7Zu3vJJ09rnW2kxXjZNNp36X8fCsr+ldVN/I0k0j+X0fL5vnqto3OpHMwUKtmbJjOeZz++yvrcSvjYCMbmp2NcmvCZAuVX3mmiP4D3nWpVWlOhNGM1TEVY+mdiaRl0BJRvZBqjXLJ5muuCaXpN8aTyVPX2eBUhGM98FKqseGNMeHjdvYwsRqYus1zx/9bnNnae9LeY8nJgoktem1wjzZI9+x+pcOd7SSmOOD+TDlI9ElKi6rOF56WfZaLC6ygPdefSFe3oHjK9p6JGNewNy2WhMnn02yZTwPYhmeV7m2z4Te2XX5LP6fzbOjDfLqD6ru5qCyUfpaLu6Lq7zdwj86ghQOO3P1+P3fnUmXIYOgQcgYDtYjts5ScRH7ZfVnXk8IBuX1CHwYASsrM7oZBaYxC8rvLNI7sYh8OshMOszZ1mq0JoQ/udYlj8VV7r6YzL2wzM6mpwB2ftZhFlG7sYh8OgIcIaDV/b7KztOEDG0VjdVhlXG6S6IaTbCQoDNxDkrEZg1e4fZSnyynipidFfBRT/1136Yllb0XsNIkzSycp/NZ9YXX5fRQmT34BC4AYGs0GWjqTDTz8rZLKoeZ28X/JeT8XkWd+le4pIeLye/FgjnfDACKktZgZsRuxrIaPyxb2WoGU+YdVcjjmZz8jK55ecFAZ/l524+NQKfvcIKBUnFUsHK+l0RNI10nbtM7Eo8S1EzWZWBkfwrKe/jsYrsLP2tPC6+JYQW0yzzlaVzY4aznG+/Wc7j9hTXD8zukPaxorD4HwzHcuIbmFKYl11NkuVB/R7T1Xwfk+aDO+9sofVeGVX3LgxrWsbV+4X09FzwuAvV1XFIRvNYHePuvh/KkuZ/3/RX4s8JXQm6eylujPkYdElDP+J4/xg0b2RacpjLzF3zu2u86/Jmeq2R6+Jk/Rn/+jyV0rwc2bQfen99fh9K8fZ0N5fzRhBuJ36XGDdBSeY0/C607hVnNdqrfa8hvCqy6q6tCruGzLI3kz4g+SI5EvpoGFraj8bsIuvuySHwyRC4TonukzHkMnYI3B0Bdsk6rtNUrptWJJz72SGwLJwcs+i4ZTnss2PeMfQ1I6DiuewuYKKyrJ7ZZ00oAj970JjOdQh8HAS0j/UzSlX0SxIgWB49fBwWvhqqy816+fmrAeJ+BVWYst2lUtAwfaarUsswCc8kXLkpKpuY96uILsdxzw6BD0WA8qUyqa7Sss8ighk5zNxqTOc6BD4RAlkBXmJB5r4TeD433863/aa0o5/aZyvMTqaXsPuMHz97hRVit0qgVvndCecbEzLQRmCHrXHVvTGDO0W6kcIsUEktv0Q0goZnBkUrec3E06QPdj8GzQczdTcCD2L9Homvj3pHawXXE7hbQTXWY9FRejP3ZsJXQq94zAhdc8MENhGdbFtkCn2+QjfjkbmVTLLP2ftrOLiS5qZ42bC70s6mue7+g2gx0QclvI6LX81fuVb342f8GFjdwG02KHt/pxrSz93banOJ8J1BY7rr0hr/60LvnMU9Iv6aeV3L1m1MMFz7nmuJ3DWAxG7L0NK6Ld/ryNAyg44hrotzDbs3R2fozTGuIXtzsjuS1GjqzvK64jELuXLDqPeIfiW983AIfFQEssKZvf+omTriDoFHRCAjtxzNPNqr8xFZdKQcAoJARlavIHJT2JXIzsMh8PgIrBbB1b635s5k+l3AyCvJrPS8lbSL4BC4KwLzGY5FGZxJHm9oXvAulyZS9y5pvvI42S6AUMgJ5sueXzlGy8UXkVwU1+UoC88L0ntX2bxrvIWc3IND4B4IUMbu8kH2IbJ4V9r3YNdF/QoRUNlTdyUE1wTKMVcLva+klhXYa5KsJO88PysEvgiFlU+C2Ocg1HfgYSHKwsMnQc1l6hB4fASW5Xr5+a45fmi6u9J38R6MwG+uin5zBXpwFX/ZBD5VfX5ovh+a7mPU0h14uUOU1Zx9cMLV5JyvQ+CTI+Bk+pNXgWPg8RBw4vx4WDpKvzICTnh/ZcBddh8VASfPHxVeR/yeCFwnj9f5ryJ/n7ir0n/FfqqnIkorXzEOdym6E7O7oOTifPYIfExB/pi0P3tgHYOPhsBD5OghaR+tAI7QYyJwVQXpMak7Wg4Bh4BDwCHgEHAIOAQcAg4Bh4BDwCHgEHAIOAQcAg4Bh4BDwCHgEHAIOAQcAp8EAVVW+SSZu0wdAg4Bh4BDwCHgEHAI3IKAU1i5BSAX7BBwCDgEHAIOAYeAQ8Ah4BBwCDgEHAIOAYeAQ8Ah4BBwCDwyAp4Hz+2OfGRQHTmHgEPAIbCIgFNWWcTDPTkEHAIOAYeAQ8Ah8Pkh4BRWPr86cRw5BBwCDgGHgEPAIeAQcAg4BBwCDgGHgEPAIeAQcAg4BH7TCDhdld909brCOQQcAp8YASqqOGWVT1wJLnuHgEPAIeAQcAg4BO6EgFNYuRNMLpJDwCHgEHAIOAQcAg4Bh4BDwCHgEHAIOAQcAg4Bh4BDwCHgEHAIOAQcAg4Bh4BDwCHgEHAIOAQcAg4Bh8BjIeAUVh4LSUfHIeAQcAg4BBwCDgGHgEPAIeAQcAg4BBwCDgGHgEPAIeAQcAg4BBwCDgGHgEPAIeAQcAg4BBwCDgGHgEPgTgg4hZU7wfT/s3en340jZ57vfyApapdSyr0ya3XZ5a09vYz79tyZ+2Lm3Df3b759Tt/pbne33bbL5XLtVbnvmUqtFFcA9zwBBAlSkEhKpERSX1UpAQKBQOCDhyEsgQCJEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBEYlQIOVUUmSDwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMIUCsST7VRzL/nNDN3BTx7JFNFgZCyuZIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC0yXgGq5YQxUrdjy+xiqWPQ1Wpis2KC0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDD1AjRYmfpdyAYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALTJUCDlenaX5QWAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYegEarEz9LmQDEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB6RKgwcp07S9KiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIITL0ADVamfheyAQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwHQJ0GBluvYXpUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKZegAYrU78L2QDQvrV1AAAgAElEQVQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGC6BErTVVxKiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBKgSDNLFCgIJD9KzcyypX05EUPKz0gfEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBC6lQLuxStJeJbDWK2P6ocHKmGDJFgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBfgAYr+S5MRQABBBBAAAEEEEAAgYxApw197KZ2PmcSMYoAAggggAACCCCAAAIIIIAAAggggAACCCCAwIACNFgZEIpkCCCAAAIIIIAAAghcdoEgtveWpj82bm1XkvYrfipDBBBAAAEEEEAAAQQQQACBCxaw87b2uVtm3J3DXXDZWD0CCCCAAAIIIJAVoMFKVoNxBBBAAAEEEEAAAQQQ6CuQvfDZNzEJEEAAAQQQQAABBBBAAAEELkQg23CFxioXsgtYKQIIIIAAAgj0EaDBSh8gZiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiMVoAGK6P1JDcEEEAAAQQQQAABBBBAAAEEEEAAAQQQ6CMQxzGvFuxjxGwEEEAAAQQQQAABBBBAYNYFaLAy63uY7UMAAQQQQAABBBBAAAEEEEAAAQQQQGACBeIJLBNFQgABBBBAAAEEEEAAAQQQOD8BGqycnzVrQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJBEgxXCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQOBcBWiwcq7crAwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECABivEAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggcIkFYkn2a//E7p9YsY3bP2P6ocHKmGDJFgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmCYBa6ziGqpYocfYWMWyp8HKNEUGZUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGZAgAYrM7AT2QQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGCaBGiwMk17i7IigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIzIECDlRnYiWwCAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwTQI0WJmmvUVZEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBGRCgwcoM7EQ2AQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmCYBGqxM096irAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwAwI0GBlBnYim4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCEyTQGmaCktZEUAAAQQQQAABBBBAAAEEEEAAAQQQQGBKBeIpLTfFRgABBBBAAAEEEEAAAQQugUCQbmOgQEEg+1duZIzbTg8rY8QlawQQQAABBBBAAAEEEEAAAQQQQAABBBAwAbv06X8RQQABBBBAAAEE+glw3NBPiPkIIIDA2ATajVWS9iqBtV4Z0w8NVsYES7YIIIAAAggggAACCCCAAAIIIIAAAgggkCcwvoudeWtjGgIIIIAAAghMn8AY741OHwYlRgABBGZYgAYrM7xz2TQEEEAAAQQQQAABBMYrwNNO4/Ul99EL2Lso/Pso4vZY13q4h9rFwYcJF/DhPOHFpHgIZAVi18tKpzbOzmMcgYkXoN6d+F1EAXME2ofASQBzuJtjxKSJEuiN0d7PE1VYCoPAMQJJ3CYVcOCuPnAQcQwVkydcIIgDyf2Or6A0WBmfLTkjgAACCCCAAAIIIDDzAna+wg8CkyeQfyHIXSqymA2OaawyeRtCiRDICPi4dkGcXDBKb/xnEjGKwAQLpLHbvmDvYzpbZFdTZycwjsD5C6Rh2D7Mjd2hQ3eNG+TF7/kXlTUicJyAj1A/TJoJpsGd32z7uKyYjsC5Cvi61x01WP2brt19dp/8lHMtFitDYDCBNDxtYDfgC3GkQPYbpsNosHxIhcAFC7g61x0DB0r+G2+BaLAyXl9yRwABBBBAAAEEEEBgZgWSy53JcyIzu5Fs2OQJ+OvsJ5asO1H7k7u5ZI1V0jtPuXlYaruIZEN+EJhUAS7UT+qeufTlOqbqjNsVcSrkroCm7a7aaNlEx2TUTssIAmMQyIagtW9N2rimN0uzMZkdH0M5yBKBEQm4SHX/pMcNsfVxZROI4RERk82YBFzEWpgGgQL79c1W7B1BvCdoTOpkO0oBi2G7AR/EUabRil1noMHKKJ3Ja0QC/tDAD9vZpscLvocVXxe3549upDS6rMgJAQQQQAABBBBAAAEELpuAu5B/2Taa7Z06gXacpufa7c/Hboml4AeByRJIbjHZbaaCbDy5cE+sTtZeojTHXsPsrXjdxdDYOrzqPDrd5sud2J7LCALnJZAeNiS39oNsI+3egD6vErEeBIYT6BwluEq301DFB/dJ2VEVn6TDvHMQSGpaX9+mwziQezXFOayfVSBwFoF2NeuOH+wYInscMUTO7YyGWIakCIxAIAm95NqDxW/OSdsI1tLJggYrHQvGEEAAAQQQQAABBBBAYAgBO10Z6uE8TrSH0J3xpC54TrmNp1zWn17bzdGkoxX/nF5vhvbZgrV3+inLy2IIjEzAYrIgua4q3PN6aSyPbAVkhMDwAgNWlUkyq1uTy522Ips24OLDl4slEBiRQPJAaXqjyW46uSf7CwriC+64nOPqEe3hE7KZoQrK9S8YpL1auV5WBjyPI85OCBBmjU2g67tndW3ya422hzp6IH7HtovIOCPQFa+d6T78InvYILCb/p1YHuo6WidLxhA4VwGL4chit/1rwX5MwI+gZDRYGQEiWSCAAAIIIIAAAgggcDkF/BP+A17wvJxIbHWegL96M75z3a61+tNq11il6zJnMscXp7PQORWss0LGEOgIWEDmhqCPZKtzk7tOuck6OQ02dvQLkCw3kswHKwKpZlcgN7xsot0wdcPMtttn4i4Dwui5CmSDNY1DH6LuqWirdl1H/oFi3y16dplzLWy6sote/0Vs82nXeZq6Jc/3NPn0ljkv3940Z/mclrFdVBtJK1z3Wkz/8SzrYFkERimQ/U5YvNoDBtkAjq3RYOdmv+thJbvMSWUZNN1JeTAPgX4C7XhNElrYdX4LskYr7sa/xbEdQwzyQ+wOokSaswr4cLRhJuay8et7eD11L0EDlpEGKwNCkQwBBBBAAAEEEEAAAQSOCrTPZ3pObo6mZAoCPQL+xLhn8mg+5mfu+lRxZ97+EVO76TSaNZILAiMTyIRvd3hmZqQr6/RVcYa1W7bdK6LRwBk4J2rRoyFzfPF6Y+D4lJ05A+TfDq+0d6vOItZTRaE71tzdqU6KzooYQ+CcBez7kAnFpH+VdJI/cLB4zaQ55xKyumEFJmlfjassPXHriWx17jGDIFDB4tZulnZaA/hkJw9HWebT/L05uXTMnXaBbHxlx227oijpXdD3DCRrdhUNvsW9+Q2+JCkRGImAVbdJ/yrJ7f6Bq19ro0V9OZJ9QCYnCGTrSBs/EnNWB4fJ7+lfbHVCATqzaLDSsWAMAQQQQAABBBBAAAEEThBw1+fd00723uj0Qn4ghTbNljtyYtOT2Riu62fPrXrWxsdJF8iLl5N2aG/6Y9NmEqajNnAXhmIpjpKn+pMXAgXuGuiJN5x8dseub9KhKd+0C7gQTO8tBUGgoP2UtFW7fQIzb7aP6SxMXrrs/Lxxn89pls3L7zynTUuZvbEfDmJk29Zv+4bJ76R1HpdPz/rbH12jFd87m8VyyR06tOdn15WXd27C7EKnGM9bzymyYZEZErA4s7hwx7zpdtmNUqt/3X/ptHHE47CMk1CGYctM+vEJ5MSDv99ZLBZUKhRUtIaC7o9ET4PB8ZXqaM455TyaiCkIyDWwCsNQURS6StkaXMVxpMg3HAQJgUkRSI8n3SDtkc3Grbqz6xD2W7SGK+5cbogLY9SXk7KHL0850pjzoRdHoaKwpbDVTBqt+BljEKHByhhQyRIBBBBAAAEEEEAAgVkUcDf87eQ7tqeakrMUN0xPyPveIBugTcuwbv5cyd2/HXbhi0zvC36RZZi2dQ9sliTMTW7v3o3sglFBQVBUoKIUFLt6WYnSi01HeI6bfiQhExA4m4DVtT7cbOieIbWADgqKLIAlFQpFFYrFoz1UDLDqQXuh7peV+5vQLxHzzyTg9pU1tMut0I5m7fdJ3+Q5CXzMHc31dFP8Kixfn3ehYHWvzUn6q0i6l+5uX+PTDnJMcbqS9SzlC9ozuV3o3unDfD4u72Hy8GnbMH7CGIanLe95lC1vc0dc3nZ2me2xGtfui9oka/BqP4Wg4OpfNy+vXExD4AIE2vGbWbdFrI/TKIwVtiKFob2YIj22yMR6e7GcjPKStdOfcsStZhwZn7I8LDYBAnmxF8eam5tzda7NdsfBdhxRsF7aJqDMFAEBE8jWZZnGKm0cO46IY0VhqDAI1Tr2gkN7ia6RbPZdM/iAwIgEuqrTNOB8HesabBcCFextVhbsY2wwSIOVEe1QskEAAQQQQAABBBBAYNYF3HlLevJiT4ZEClzvKtbDykX+dJ1cXWRBhln3BZsNU9SJS+vtTtrx9tCSe5I/Kb0tYr+tyC5uzkn2W2gqCopqqaBGGCsuJE86jfH8e+IoKdAECmTi2oe63WxqRUlvVtZoJQ5ixa4OLsieNz2pDvYXmsa1pdPWWNCZZozH5TLKfH0cDJLnpG2alcfitx3DChTZ09HpMUQrlpqxVEh7suja1q4Pg2z9BKaZtm2gvF3tpIzD7ilZHRtab+g2190otZ4pAoUK1JrAsKNIIxCYtu/CMZtsxwAujt2xQkHWVCVWUZEiteKCq3+7FrVKO2fbcyZ1LXaaD+7v1TgyPk1hZnCZSTseGIbYh4XFb8Nit1BUZI22rf51x8F2/FuQHUPwg8DECaSNXK1c/jwsjK2hdkE2DIOiwrjgzu0GKbsL82n+Qg+ykaSZHIHsNTQ7hijYcbAFYKBisajIWq24hw/GU2QarIzHlVwRQAABBBBAAAEEEJg9gfTJENuwoFBQqxVqv1LVy63DgbZ1PDc2/dm7Hw5UlAtM5K+s+eEFFmVCVj3cnktTG58b7XW07lOS95oX4uSlP3alKFLJXdh8u73vLg5F7mJ9SfVWrNdvd3VYW1Bprpg8NeJarPTm60vphxOCRzFmQMDHml0d8jdE7UJQMt3+jVVS5bCpvf1K0iOQQjVaLR1Uq3r9dkeLC/NHHHyuSZ5HZo9kQrZOn4ZvRmIyDU/kJnvd7yR7InPQn6T3kkFTjy+dXZZPfqw+LimKi9rZq6haryuM7FZp4G40vdnZ09zCvAqF5FVXybYm3wX7QiS1eCa6MqPHlz69Q5v8kTg+WaaExz8paCscaKU96+lsf8+MM3z05fDDM2R1ZNGzlteXyQ+PrGDEE8ZYXtcmxWIo7QUoKOjgsK7tXTt+sNi0G06RDut1vd3Zc8fDg23caG0SAZ+nHw5WElINKuCfIvbxNuhylu7kfeJ7qhwmx35prba1Bq3ttbubpkkDFWukUq03FdmdJ7v5H0farRzq1dtqu7bul//o549DYfSlnNoc07A9ORInZ+tcY9a097WkVLYBBdWakd7u7qvaaLpjB3vAoFpvuDp5cWmp/wY4h2lR6L85pJgeAavh4qDo4vjgsOYaqVgdbI22K7WmXr6p9d8Yd13DkqVf6P5LkAKB4QX8qZMdvdg1tLjgzsFcD22FQG+3d1Wp1lxDlaSn4vHVqUE8zJnv8JvKEggggAACCCCAAAIIIDADAvYU/0E91m//8IX+33/8J/e0ablU1Hy5qPKcnYj3//G3nnov4p799NtOmEZ90pRXqmHXkZeHOR03vb/hLKYYStU/puQhsnfM3bSkwYo9z2+z7ITbYsMu1BdKZTVCab9Sdw1VgjjSfCnQymJZxVJBResf3Z7ii1quy95kP2VLlx1P0vIvAqMRyNQJLmSTWPM3nuyGv4Kymq1Yu7u77pUqc8VAC3PSYrlovaLn/rRzPWZ+7kLDTPTtCoZZZgLStl0moCy5RXD7q1PK01y2u/CGKy42bBuSBitBsezq4d2DipqtSFEUqRC3tLq04I4h/MN6saJ2/WvtdLrb6lijFvvNVctM9Fdd+yZMy5d0057JIDNqeQyST2YRN+r3nx/2zj/NZ18OPzxNHsct48vph8elO266L5MfHpduVNN9Of1w2Hx9Of2ws3zy3fHTk+MHe7K0GUY6PKwoiloqFgLNlwtanC+q5A8eOlkcM+bzTGaftuRHM+/O9+j8yzml43s6n2yju9EL+jpqhDmnWUb+HCNKGoPEdhwclNwxcOWwplq95V4JZP0Lri6XNV+244vBzkyOk+xYD7c94zUeriyzmPrIKdKEb6Td2LfGrJF74MCiyn6tgWBRzTBWrV5Tq9VUIYjd+dv8XEHlUnrydsy2udh0/xwXvccsyGQEhhbIiTHrUdAq2KCoWr2Z1L8W31HTnb8tL5T7r8Vl65oj9k9LCgROK5A5DnAPfVnjVjsPCwIVSkU1wlCHtboq1brmglAfvXtb/+Mf/k4ff/SBlheTPlFcVZs9a7IJOV+LfkWkh5V+QsxHAAEEEEAAAQQQQAABJ+B64bWb/+4OUqBmq6Ww1dSheylFFik94zlygpL2eJHk5v71JzbZpYcftxV1VjaaPPNy6axjsDLm5XHCHd7exhiDrWQqU+VJ5k3L27hE1WLJxrqN7XKOmxZYgxV7OsQiw3pYSZ8oVVGh/bqLoXIn33YD1d3wd1d2Y8WxXe7vvlOa3DQdtIR5pWYaAicIpLFnKZIoS+LaVwm+wYo1vHJP6ym5eRqFLdUOWwpOuCvhchpX6J5QnZ2wtRMxy7kkzBNRnnYhcvZVd6ONdspjR1yDjn6tOsYUGL74Po5taN3428X6OLCeVpLXWyVVd+B6abP7/e74IojdU/9WB1tLlWQf+ZySL4dr+Npv2zIyg+zndq8umeWSUb816V+ageNl4IRH1jj4hE7ZBl/muJSjLO8oy3WO5bX7SenqXIy5TzbReqew16hYjxT++LeQHCfUmmrUa+mxSH5ZfR3u5vqYzk96yqnT0GPUKTftrIu5sPZ79fSZdRpV5OWVCZzTr2JkS9rfiqSZijXYTo5jkwYrBRWKpeR1QHYTyl7JFsU6qFRVOUy+/12xekyJjjvUGGTZ/CxHWffkr+EyT23vr2EPIi4Ezeoyq2vtGoJ919IGV2n9m/RSYQVLeriq1luq11ruDM+mJudtxxTcfXU73+RjUjEZgTMK+Dv+9nchrVctx8Dq36K9TMUdT1ij2DCKdVirqV7v38OKi20Xw9SXZ9xBLN5XIKl3k2toFnTJa1ytC2J3/hbbeZo9FmbXy8YXjzRY6bujSIAAAggggAACCCCAAAK9Au4pZ7tsVChoobyQme3OqN0JTvs0xk9qp0pO6N0p0ZF57URDjKRPr6RLtNc7RA5Hk8bpU9yWm92ksBTDFTa5iTxoaU63jqPlno4pWcns+HClN9teX/85vWSf3lC3G042pRVJUZikcSfaxYIW5svJDf/AUiR5Zi98utQ+2+EKSGoEBhNwX4Ik9uwmkzWxsp/kJlDySqsoLqkVBgobjSTqC9YrUFHzxfn0Zn/3NykbstnxwQo0WCq/Rj8cbKnJSDXOC21n3cK09hr6b057vX13iE/gh+0lBx45aUmb537tn/TpPGs0aFVvvdF0jVHsdRTW7mR+flGlUsE9MW31rmswaA1W3O2n7MX/pLZ363X/nFTUbALL86S0tipX4/dJlKTrn4gUUylgPfekBbdO0F0EW8NW12AlaeRqT/i3mg3Fkd1StRtQUnnOhn7J/C3vRFdSx/cLx/xcTpp68vpPWnL2543SJo2LXrQhGtAli/oI8MPeDE//2TVOsQYpFsF2/OsqP4tAu4EaqNEMXT3sXncVFFQqlzVXssaE6dH0MVzthg99inb6hit9Mmb28ALtBsWuFdPwy1/AEnauZg1SLF598xJ3RBwX1YoC17uK9XBlaYrFguZKZZXSTl6Tv/PHfad8bhewUazyEgn4vxEWb/4LaMPANVhpNSO1mmFyTFoIVCotJD1c9RNy9bPF9nHx3S8D5iPQX8BFr12DSMPMGq1Y/Ws9rNjr2lpRpLgVKmq15J4r6J/lqVPQYOXUdCyIAAIIIIAAAggggMDlErAW9XaTzxqrWEMV65ViZWle1zY3ciDSq57+ImhXCn9F1A+7Zp7ywzguRmUvDJy2rD0XGPKycauxGXkzT8kxJYudfYuz+8g2utvb5e+SFBQUizqsNfTq7Y7stROKIi2W53T7+qZ7JVCngVFvnmcv5ZTsDop5IQI+3txlIVeC9FKnG09uJM2pGRZUOazr5avXrheKuWJJa0sLurq+4i7c5xXd5Xwe4es3Ia8QR6YNlfjI0iOZ0GsyAUXq3i5fQD/snjsJn7pfm3K0RK7HlHSyazAYFNRohXr24rWiqOkaCVpP/jeurmtxcd4dTySvA0oaq9iinYYm4/j7frTMTEHAfeNcD2zJhXo7LrPeVayxSrXe1M72tprNhqtzlxfL2lhf0fz8XC5cV7Xi7gAkU9y/k/vVzt2WqZzYbj1xWuyuPTiGY/Te/EehbL3/WOXpexi0D0kT2DCOtbW9q8NqXWEYu4ZWVzdWtbaynBw592HyN7HySum2pM/yecsxbcwCyb1y1/xjzGsaSfZ2Y9QaXdnRsAWyhZQdP7SigirVhnZ2dlSv+/p3Qeuri1penB9o3e3qYKDUJELgNAIWt2lF2G5kkvaCFhS0vb2rnZ19RXEse622HT9c3VjvvyL3dUh7f+2fmhQInErARW5a7ybj9q81VikoVKBqvaG9g4r29g+si5XsSdqp1nfSQjRYOUmHeQgggAACCCCAAAIIINAlYA1WXHf9KmptdUU/+dF7+odf/21XmuRGlk1KTtrzLsm6Z1mT2V3LnupD+nqXvPWcKr920dMc3cWH0xTWrhRmS5Ud7y1ZclI4mQ/PnGbbe7cv73NyMTJvztFpeWXwnn6YhFzyNHOat5uVXvwMinry/IX+/Xd/0PNXb9xNp6ura/pf/9d/09LSgopFS+fjO31FhYvh7Loz6zpaSKYgcEqBzJN46ahFmlU9Fsn2SqCDalMPHj7V1usXasWh1pYW9aP37+jv//ZXWlzI9nLVKUI7Wq31QPtDZ/5AYy788xa23gdOk21eXgOVZPhE7sHi3vX1frZsL/IJZNu/nZ9ObdMZ68wd9Vh2zSfn3fm7fnI6V/Omb8nwfVZYV/5hXNDO3oH+97/8xjVaqVcbWpwr6v/8+7/RrZvXrbdptx+66m9f/w7dg8HxZRxkO5LVnYf/8eVkzgUIuEbZduSaHjPYhfo4uVhvN0ufvXytf/u332hnt6KFhSV98N47+uu/+rmubV7JLaz7drVjN6nYXVSdqt7MXQUTTxTwNdCJifrMtGPC3nryrHVDb359ijDU7CS43Ja7kLPGKnazSWqGkf75X/5N9x8+VqVZk+KG/voXn+jnP/ukaw1J3HZNGvwP/bA0/tCnZ3V8HLHAsPtlxKsfNDs7fnD9C6YHl1ZsexVmrRHpybNX+vTTT/X06b6KKur9O3f1i08+1t07twbKPjeuB1qSRAgMJmA9UiQ/SeRazLlGWO61VoF+9/s/6rPPPlc9DF2t/NF7d/Tf/9uvT8g8/RvmqnWXm3to7IQFmIXAGQRcLelq3eQYwuLYjoGLasWBXrx6rW++v6cvv/5GYWS9Cya9uZ1hhccuSoOVY2mYgQACCCCAAAIIIIAAAl0C/jzGTmXs6ZC5otZXl3X7+lo7WfvafHtK/og/pc+fO9zUUeY13JpJPYkCmTBNm0x1rrU3aqtani+qFIQKo7rKhVDXryxrbW1Bc/aQdCy1wrSxStKD+iRuImWaNYHeSsxunKY3NZNLlNLuwYK2txZUVEtR3NJcMdbK4pxubq5reSn/0o7/LvjhKNl8Xe/y7i3/KFd0xryy9xp9mfOyHIdR3npyp/Ws3HP6Ye4y5z1xwMLYptivJfeL2Ge7YVoqrWlxvqS5otQKIpUC6dqVZd2+tuper2K9tnVRdH1IMvR5nvfms77LJ2DhZzdP7Xf/sKRafUXlOYvAlgqFUMuLc7p+ZVU3ry7n42S/BJnvQ35ipo5L4Kx1Rm81NK5yjiLfvG21+LX6txVLS643q1hxZK9ma+jKyoLeubbUterz3t68MncViA9nEjjv/XmWwlqsWnl9mS02bLzajFWrrWixXFBBTRXiWMvzJV27sqo714+pfzMF8fn5YWYWowiMTMDiNfuckp3H+WMIq4PXlxbdcW89aqlQjLS2VNbtayfHb/a8hbpyZLuKjHIErH70vy6W0zQWw/YitmbTrjckPWJGYw7G/KsaOYVmEgIIIIAAAggggAACCCBgAvaEchRF7mZUMZDryj8rM+ZzmOyqGEfgiMBJ8RcEBfc6K/+UfRxFUmxPOqU/aTzbBaL2RaKTMvTLMURglAJpDxX+wpFdLLIK170yJYjSK6L+Mqibm7t2C13LYywhbBm7vweZuwu5pbjYib3b3vv5YkuXrn0iC3U6GduUNDRcBt2fLXYjBS6Gre6Vq3utoYr92k8XRffCYwzmZN38i0BWwJr7q3gAACAASURBVMLPjg1cPLtjAutVKonQOEr6vgqUOX7ILmzj/ovQFdS9ifg86QLTvvus/PZrday9+tJ6yXQ9ZcZ2Hnf0+GHat3fS4+m8yzdN+9PqWx+R2XLb9KL1N2jHDy5uQxUCe1mQT32yqs/LD09OzVwEziCQCTIb9dcX7HDAxa+rg+0FK0l/QsV+q3IL9kvEfATOLmDx6mtUH7s+/Oyz73vQ1uR6nss+EXL21XflQIOVLg4+IIAAAggggAACCCCAwOAC6QV7f1HeFrQzmln7yW7fsNs2ix7DGkxAetsNbje6/dE55bZ7T+5Gqc20X/tsV5fYbxOw1yhCr0DsHt3zwZpcvD82XtMY7hvK7ovRu6acz30zylmGSZdSwELFh1UnWm1a6BoIuqEbT6paF1qWMC/GstN8ZqNSzeY9qjzJZ/oFfPCmDQctTJLfQO76fDrfjiSKViefFEcnzZt+KbZgCgR8/Lqi2s1S+3U19Kgr1CnAoIgTLWCxag1Tkio2qTxt3I1FYdJYxTV6LcjeRlGwxq9pfXzihiVZnZiEmQiMQ8BCzzXEskOF2OLVGm7b/30aXBGz49gd5NlHwMVqpqGVhaGP4YJrZJVkYMfCg1S9fVZ37GwarBxLwwwEEEAAAQQQQAABBBDoL5A5Xel7cp1J2z/jIVL0vkdgiEXHnXRcmzzucvv83T4d10b0DRhfipEOk1dMJ09KFwr2lt6k4VV6SbQ9GOlKyQyBvgL+e9b5XnTGbGGb7654uhtOSb/TyRN6J94w7bdeW8lAT0l1l6ZftuO9lNV/7aS4KIEkTny0+KhOSpPcLLXY6Lph2p0ov+CDpMlf8viplqcv6PGpTjlnHAW2ooytwJlmRqfc5NzFpr+8tgWdrUj2a/LZHzvkbngy0YdB9j0BJyRn1iQK+J04iWXrLZOP1Mx5kZ/kkma2JTgpfn26roV7Vza6z351o8uRnLzAVNU9FggWl/Zj/ya/yedkXjLfxq2HoAEDZ8BknowhAqcScIHqgy2JWssniWIbS87h3NDFr+/PImdtPhs/a6q+x77QDKdRoBO5Vvqg3dOg9dDW7n3YYnmgawenE6DByuncWAoBBBBAAAEEEEAAAQQyp+ButK9I9ylQ3+QkuHgBu2DSOTu9+PIMXAJX8HZqf93HX553n2273LZ1LiW1F2AEgXMX6Fc/JnHqL+VbDPt4PnNRu1bd9eEMWY8qnzMUgUUvXKA7CuzZPesA3X4zz+p1J8ovs6XxFXl+igmbOshGTViRBzuQm6BCj8n4mGxd41Z3vymphX1z174gLr9jMu27MAkQGEYgjbOeujI5XrB80vk2iJPm2vm5n3O8nvPq8rd5VqdOE+7xZfW1rjt2SM/dYnceN6v7je2aToGjMeyapdjkIGliZQ/OJOdvaTduA23o0XwHWoxECAwp0Im0zoFEEr521JseRXRmDZn7YMlpsDKYE6kQQAABBBBAAAEEEEAgV8BuQA3xM+oTHH9W5YdDFGXgpGcp8zjLNcgGnKXsPv9R5GF5nZvF0QL7KXZx03VjakN3wTOJXz/fb3LX8MSZXSn5gMDpBLLfDR9v6bTsrCRzf5PJz/HD063a53mWpVkWgZMEOhFaVOA6/C8pjq3Rim80eNLSmXmdjDITGUVg1AJWCXeCzcbs144Wgjju/Po0naSjLgj5TYTAlO1gfwyR2mU/JuN20ymJ6uQ4eCKQKQQCxwr4uG3f5G83eJ2y7+axW8iMWRGwWLXfbGT6aa6hijVWCax/oKSPoFnZbrZjBgWSLonbwezaB2YDe4ybTIOVMeKSNQIIIIAAAggggAACsySQXDBKtsiP++HA23lOJzoDl2eQhNNYZr9d01x2vw1DD22jj4/MzgVPj+OHA6xoiKQD5EYSBIYU8AHohydF+pBZkxyBcQu4i58Wu/brG7sO84TpuAtI/giYQKd+9R5+inuZlTu88FP80KdkiMAkCyRNVdolPP5QuZ2EEQQuUsCHqHvgwDe0cmd5SW8VF1k21o1Ar4DFq4/Z3qOD2PVqZYcY/ri3N0VvbnxGYDIELFLtrVRu6IoUKx7klUCnDHEarEzGfqcUCCCAAAIIIIAAAghMhUAcuMv17ozFxjqn5VNRfAp5aQROOkP2p9t+2AflpKz6LMpsBIYW6Ik3f+HT5ZM+npe8yjxJ2JN86NWxAALjFrAYteYpxYKNub7Q02MHonfc9uQ/GoHO0W7SK5B7PUX7ptNo1kEuCIxEwKrVrgOHTq6BazRYUCD/Sx3c0WFsUgWy4Zwdn9TyUq7LK5Ae5TqAo1Vx2r1K5rb/5ZViyydeoPfwILZGKp3Di57mr+2YH8V20WBlFIrkgQACCCCAAAIIIIDAjAu4C0TuxMWa19vZil20t6lcOprxXT9jm5eefbef9E8iOPNxxraXzZkVgU5NazGcveg5K1vIdsyygEVtIWi/iIJDh1ne2TO6bf6IN7bmV/ZaQY6AZ3RPz8BmpYe62S1JJmWPHXISZRdgHIEJEnD1rYWsb/NK+E7Q3qEoXqA3LNOQTWf7T72p/NIMEZg8gfb1B9daxX/yvQSNp7y+D87x5E6uCCCAAAIIIIAAAgggMOMC/sRlxjeTzZshgeRCUTtyuW40Q/t29jeFcJ39fTyrW5i8/9wimCie1X0829tlDbYDBRbICgbrDn22Qdi6KRRIOgbKezZ6CjeGIiOAAAIIIIDATAnQYGWmdicbgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMAMCLhXUqSvpbCn/flBAAEEEJg5ARqszNwuZYMQQAABBBBAAAEEEEAAAQQQQAABBBCYbAHXWcVkF5HSIYAAAggggMAECMSuoUrSWCXmtcQTsEcoAgIIIDBaARqsjNaT3BBAAAEEEEAAAQQQQAABBBBAAAEEEECgrwCvpuhLRAIEEEAAAQQQQAABBBBAYMYFaLAy4zuYzUMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgmgSsZxVeAjRNe4yyIoAAAqcToMHK6dxYCgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQOCUAjRYOSUciyGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgicToAGK6dzYykEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBUwrQYOWUcCyGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAvkCQTvbD/FRMReAyC9Bg5TLvfbYdAQQQQAABBBBAAIFTCQQS59mnkmMhBBBAAAEEEEAAAQQQQAABBBBAAIHLIOAvnvnhZdhmthGB4QVKwy/CEggggAACCCCAAAIIIHBZBYIgkPtVMlTASfdljQW2GwEEEEAAAQQQQAABBBBAAAEEEECgn0Agxf3SMB+ByytADyuXd9+z5QgggAACCCCAAAIIDCeQ0zYlZ9JweZIaAQQQQAABBBBAAAEEEEAAAQQQQACBmRTwV878cCY3ko1C4EwCNFg5Ex8LI4AAAggggAACCCBw2QV4PdBljwC2HwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQOI0ADVZOo8YyCCCAAAIIIIAAAghcMgF7DoRnQS7ZTmdzEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBMQrQYGWMuGSNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggcFSABitHTZiCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMEYBGqyMEZesEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBI4K0GDlqAlTEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMYoQIOVMeKSNQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMBRARqsHDVhCgIIIIAAAggggAACCCCAwKURiC/NlrKh0y4QKInWYNo3hPIjgAACCCCAwAUJcCxxQfCsFgEELrFA9poD53KXOBDY9BMEaLByAg6zEEAAAQQQQAABBBBAAAEEZkPALhHF2etEsRSnt/9nYwvZisshwAXOy7Gf2UoEEEAAAQRGJxDHyVFvHNhxBMcSo5MlJwQQQKCfQNpMMHstot8izEfgggWOHClkLqZlRkdaShqsjJSTzBBAAAEEEEAAAQQQQAABBCZfwE6/XROWzHDyS00JL7uAv2xkQz9+2U3YfgQQQAABBBAYRMDfK40DKWm4MshSpEEAAQQQGFbAX2nIX47zuHwXpk6yQHIMEcsawHY/CTa6UtNgZXSW5IQAAggggAACCCCAAAIIIDDRAlwcmujdQ+EGECCGB0AiCQIIIIAAAggggAACCCAwAQK+uSCPHEzAzqAIEyxAg5UJ3jkUDQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmEUBGqzM4l5lmxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQkWoMHKBO8cioYAAggggAACCCCAAAIIIDAGAfdWFV6tMgZZskQAAQQQQAABBBBAAAEEEEAAAQQQQGBgARqsDExFQgQQQAABBBBAAAEEEEAAgekXCER7lenfi2wBAggggAACCCCAAAIIIIAAAggggMD0C9BgZfr3IVuAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghMlQANVqZqd1FYBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHJFvCvIg4UJ329TnZxKR0CFyRAg5ULgme1CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIzKhD7Riszun1sFgIjEKDByggQyQIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFuARqtdHvwCYFuARqsdHvwCQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGDMAjRYGTMw2SOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh0C9BgpduDTwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJjFqDBypiByR4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgW4AGK90efEIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAYswANVsYMTPYIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC3QI0WOn24BMCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAmAVosDJmYLJHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ6BagwUq3B58QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEExixAg5UxA5M9AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQLcADVa6PfiEAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMyMQBDPzKawIQgggAACCCAwYwI0WDlph9pB3HEHcifNOylP5iGAAALTKECdN417jTIjgAACCCCAAAKTJZB3fs1x5mTtI0qDAAIIIIAAAjMhEEiy394fGq70ivAZAQSOF8g7gTs+NXMQmA4B4noS9xMNVtxeyVwh86PZeO2d5uf56e08kl3sZw+3w0+3VP468vPKn5qfA1MnR6B7v0WTU7BJK4lBZX/b5esWtMmRYvdfOwkjJ7TO65nljTEbQMC+r5fgO0tMDBALk5fkaM14EWUcJniGSXsR28I6EUAAAQSyArl/Z3xVnp2ZnZadns2McQQuQmCo2CR4L2IXsc4TBAjJE3CYhcDlE8g2XKGxyij2f/YgYRT5kQcCkyzg432Sy0jZLpeAj8meA96ej5fLZDa2tjQbmzGKrchrb5yT7wBBP2BOOZmPalJ+CfKnjmqd5DMuge791v1pXOuc9XwLuc8XzPpWs33nJtD+O1HIf5Tl3AoyhhXZtlENjQH2/LNs78Z2vF7Evm2XYgCATFricAAvkiCAAAITKmDVeaZKb5cyb1p7JiMITLoAATzpe+jSlY+QvHS7nA1GAIHzFKCSPU9t1nXRAsT7Re8B1t8rYDGZc3GYUO2FmrrPNFjJ7jKLcX/jJi+4/Ty/TFearg8+xRDDsy4/xKpIOh0CPt5s2A6PdKT9eTo25VxKeaxJ0Plee1MrUDZ9dvxcCjuJK+kgdIWct8raTWLxJ6VM3imW4nQ8MNpZ6M/Mb1uvdSd0eufweVIFbF9mYtSK6eLUjYy30D6Mzhw2ltGZMxnvtpI7AmcXINDPbkgOFyGQ1PUD1vjZujw7fhEFZ52XUOCEOCUeL2E8sMkIIHCxAr5OzpaCyjirwfjkC2SjmOid/P1FCXsFLIJ7I7f3c+8yfEbgIgTOGpdnXf4itnn210mDlew+Tm/etG/a9Myzj3YD8sj8vHo8u+yw46POr71+ezXFLNw1bW/QbI6kR7axf5tIlAad1aHWYYOvS/1wNhVGv1X+5qz5pt+xwL4OOHZZex6b2EVjH9LY7FqADx0B72Ox5r+/xlaUXJerDtQLz2ZdbFvXFTcdHcYuUKBrv/TEpyuWTbO/L+OsEzOFyIyeqGLp7KcrpvzEdB4DBGZLwAe4Df24fQP872xtLVszewI+apP4tU8Wu/ZHJq3J04Hfcp+iu6L3cxkicF4CPnJ7AvS8Vs96EEAAgUsvYPWwr4v90FB8veyHlx4KgEkRyAvTnLJZMqI3B4ZJFyTQL3DtYralGefFwQvadFY78wL9ort9mEGlPNGxQIMVt3syUdp7JOFn2dDaDWR3p/8WdE1ME/h5Pr3V99l0Np797L4x6YSu6T6DIYbZdftxl2d6gzQ77azrGqJYJB1AIOrc6I5qsaJKrOZBrMhuJJYDzS0FKi1JhfmgJ34GyPsyJfExnm5z3JJi86zGatQS46AolRYDlZalYC5IbtTOZhuCofa8VQmuWugxdBNHXV/YOuweRjq0groGWVN6XByHUtyKFdek+k4s2WfblrI0vx6oWA4UlAKpMGrIoXbx6RMfV2z7+5b+0KDOS0zo0L5vFpf1WNFerLAhhZFk9WHR/31ZOm5Hn3GbMtm6qjbzve86HvJ1T/bPnJ+WV4RMvnmzmYbA1Ai4OE8Dul2ZTk3pKSgCTqBTJdtY51PXTIt1H+rezdfzPYv42QwRGJ+A9caZCTw/2huTvZ/HVyByRuD0Apn69dhMiOVjaZhxkQJW+foK+CLLwboRGFLA6tSeepVTuSENSX6OApl61setrd3G3awZfLX9OeqyqnMUyMZvutrcuten4zDjHHfO2VZFg5Vev3YFLYVx576iq7OzgW3psgHfm4//bMukN4janZvYzT3fU0b7xuwgmflMTx66J/sz23FsOdObVJwTnOx5LnPT3e9u3Iex4qa0/zLS0+8jPfymqai4q5XNSDffW9Z7P17T0magoHwuJZu+lfivki+5ff9aseqvYz29H+qH73fVakjzy6Gu3inqw59vaG2jIC0ESWMJ92X3C1/CofmZmW+EYB5WV8yN3sJunCf1Y9K4w63BGnSUkhvoo1/jmHO0hgBVqfY80m/+eUthrSSVmiquVfTTv72lG3cWNb86G3HW/hNjsWIffLwY8bh76hjzbpzF7F3vcOm+ipuxwr1Y3/4h1ItnNVXrFc0vlXXzgwXd/nhB19/LthQZnYYLE6tL/Hmwff1tov9s94vSOGqfZOTFVyYDV127f0ZXTnJC4HwF0to0/S64v4m+N8dMrJ9vmVgbAgMIuJhN49cnt4b3Ppatbvfnudl6OpaaYai5UtEvxRCBixPw5zxpKLvjj2y8+pKl6dzxlD/O9fMYIjApAhbHvlr2dXE2nv28SSkv5UDgOIFsLGdj+Lj0TEfgogT89Yq0zm0f+2avj1kM2y918EXtJdabJ5A9Bs7Ot8uBdm0ue00ije+ui3nZZRhH4IIE/DVkv/r2tWRf56bXnbum+8QMJ1bg8jZY8QcPmQMH1wtDI1azJjWtMi5K5YVAhWKmIyyr0O2mZCNWVJfCglRckErW44X/8b1kWDp72r4lyX59Eqv8S1Lsnrb3N2Z9QXwmNsz+RchOP2HcL2LDULJeOvxNYffl9PNtmxbtSf9MuU7IllnjF3CVrMWMxU9D2nsd64e/tPTbf/pOjeC1rt8t6ud/d1ubN1Y1vxqrVPYBNf6yTc0afHz7AvuTh1A6eBPp/heR/u2fv1LtMNTqZqCPfrWq6+8sa2VlQcV5v9AlG2bNzKuZ1htpnWU9L0RFac4akuSFnC2fN/04Rkvv90uUrCvcj7W7G6vZlJbWAy1vBJpbmbL6yf1tSHpF2noU6T//v+9V3bPXAdW1ePNQa1fntXZlQfO2XVP848PFb4X7e1hN/875G1Vzct+ngJ6gJmZP+/3lDiusx6mq9M2ndX31xQ/aq2xraaWsn/76usqrt3X9vdWxXUyxV2PZr/s7V08aZ9qJcGRfd4sbaxiX1jXu2KUVK2pKzUZy0mydE9kxmTXYDIqBO06bGGQKgsBZBfzfxvQ70v7bml40yn4+66pYHoEzC7g/LHY1M21saAcIdn6dOVBwjVcsXTaGXT3e01jFL3PmQpEBAgMKpDHnzr99/Nl5uMWrv07ks2ofREkFrp14FYaTLmBxa7Ht4zutiye92JTvsghkAzPdZj/JD22yjWc/+/rYDy8LF9s5OQI+HrPHv5lYdccVvrR2zODT27EwxxBehuFFCvh7lv68zR8f+Fi1sE3rWBsStxe5s1h3roDFqq+DLY4tXtP4dbHrjxEsfm22+2fIe1e5K2bieQlc3gYrPnhTabtJa08c776M9Gq7pZaaWlwt68Y7JS2vF1zvAu7mSZimexXp5ZNIYflAm+/M6cbdZZUW0kztYkc9VrgT682LSPs7sQK7+Ws/6U2ZYE3avFvUymagkt2BaTf1StP5xNmPA4y7gyO//r1YT36IVKvE7uaQHRvZq2UKcy0trQW6+UFZ5SsFeuoYwHXsSdLK1lWwaaXbqsTaf93Qi8f7qkZvFcVF3flgVU17tU3vhbSxF3CKV5B6NirSzqu6nj58q1o11Hol0PqtWLV6Q2G8ILv36f6STfGmnqrott2+oUFLCrcjvXwQae8gViuKXMODpSstvfPhksprPRXnqVaYPn1r9VQzVuNFpPvfR/r+u6eqVBq6+f6K3v/pht798YLmFu0VOhe9XzJH7f0CxGKtKR3uxXrxeE+V3VhxoarVZlWVvZq78d4ms2xHw9nO8jxG2kW2bbV9WI21bfGyHatRTXrKKV1pavPdOW3cHUO3POexkTO4DtdIJP074xqLNGPtvDzUi0fb2jl4o+WVed14b17VytXkQL+9o0eH4bK0xjKtWM3dWC+eRKrtxWq2IkXFquYWW3r3w00tXbUGw4FrGNzairX1KtLWVuwOk4pzsRZWGrp+e0GLG0UF1vCWHwSmWiC54d/+O2zHd1HS2Lzd4N0aac1J1gjQGpHyg8BECKSHR+5YoBkrricPdNhxULvhilXRdprrGhomvUNaLBfmMg0Os4dZE7FhFGLmBdJzQ1fvtmL3YJF/cMnVu+l5kbvgab2pWMyWAxWsd1N7jayvhzkEmflQmfgN9PWnxXR6nO/qXzuOaKaNCe0ioD2sZrHrT82I3YnftbNfwPT4N43bdvxab9N2vmg98drDTRa75TR+idvZD4tp2EJf39qxgh1DWF1rMWvHwo3OtVV3i8euZVrv0aWk/i3YA132mYYr07CnZ7OMFrd2HdcerrdzNotfe72Em5Y2AvCNVEpp3WsPi9kD03Y+Z/Ww1ds2pE6ezRiZ5K3KHjOkda473k3fVNF1DcLq2fS6g3vg0Y6DrU626fxMvAANVvxNt3qsw9eRvvss1FfffaMwqOv23U0tFD/Q0nysOAiSg5FQqryI9N2fQv3pt98pWnyjn/36hlZWfqy1a3Y0HbjK3no22XsS6evfN/Tohy3FjUVFrpuVSMF8U/O3Kvrr//mu3l9eUclOHEdR0dsX1/3xiWU9Fmzdi/T7f9zX9puKIjuQsgP+0p7mV5u6+d6i/m7xY11djFWmp44J+qIm+8luVhRCqdia11y8oWa0p6J9juZUiAP3hPoEFXoyi2LfB/9jr6EIreFYWYVwSaVYKrrH+ucU2oGZfQVH8R3065uaodvydmntoPXgRazP/72mx8+eqlo/VHkl0DsflbSy/omuLs8lTmf5A+/3i11Ia0ivHkT69F9e6l9/80ft79f10c839XeH72n16se6eqOkovW2cJ4/vnxutfYhO8HGTyiPzY7se2q/q24YByUXbyWVu4+LTsjmPDf3VOuyvzP224wVHca6/3moB9+90d7WvuJmoLX3G/r5/7iujbs3Oyczp1oRC41EwIewDf1vKJVai+63GC6qFC0oCMvJVcGRrDQ/EzsZjiqxtp9E+uxf3+jN04oOa3uKy3taWm/p//5//ruW1qy7q1hRVXr7INLXn4X65pt7ihWovFzX1TuR/urXt/Xuwk2VF/PXw1QEpkoge+HT/jbajf/DWIf7sepVKSxKpdVAS5vS0nL6mMhUbSCFnTkBi1nXuCp5SMPq9XA31sFOrOqBVK/GatjDGna8aPFblpYshq8EWlwPVF5LbvzbfHf87Y+J/HDmwNigiROw41i7WH8ohQexa0Bb2Yvdgz6Nf1dJnAAAIABJREFUutSym6VFqViW5pYD1wPkqsXvWtJL7cU3qJ84UQp0UQL+GMKuaaQX7Fv7sSye63bz1GJ4KdDKupJX01o5faOriyoz60UgFXANVfzxhF1bqEn1/VjVw6T3XTs9Xb0eaHnNjiU4BiZwLl7AN65y9W1N7vi3ZjGb1rt2DGEPmLoe2cpyPSzbA8Pu94oULGQav3Lce/E79JKVIHnIPbkWb+dvjZ3YPXBZrSTXHez4Ny7I3acsLQZaWJWL3ZUrBRWW0vM336ClHb92kbH94ZKJsrnnKpC5BmHXiyM71t1L6t/aQaxGQ+4en4VjPCeVlgMtrEmrVwpaWpOKS2njKwtXQvZcd92wK7u8DVYyUvZ6H+td5dXDSF/+55Y+/+aBykuhiiqo9Vdpq8Ny+vRNU3ryXahP//W+/vyHB4qXn2vjVkmBdV1iP1ZP2wWQulR5E+vJd/v66k8vVTsoKbRmt0GkYK6u+Morrd2a1+b1JS0tFJOnHc76ZbF120lqQ6q+iPXVH0J9/ttn2t6yBiuR4ihWq/BWi1ea+uDgin78q3e1/s6incPyMwkC/kailcXGrZV2QyrECwoiu5EYKbBHDNwR8iQUeALL0PsdMsf0pxjLNRwoxPMqKFAQ2Tuzigrcp8v1x8pYEqp0zAZ2YNqUKm9j3f9yV9/de679w30trgU6rM/rl3/3nq7ennMtVL3paYd2c8JWGVVj7W3F2nnd0MFOqDAsa3+noa1Xu9rd2dfG1Y2Tr6elDSesznU//p3ytnG9sdCvsNm80uUDK6j7tfxsJTajo3ckS5vlV22Ny1RQbM3Xw2onztI0R5adlgn+oqjbgVJck57da+rrT1/q9YstqRXo2k5T1z+el+Kbw++HaXGYpnJm95kvt72aJyzJ1YdxWUFUUlH2O4Yr2NmYtzaZe0lDta8/faGn93d1cPhGWnyrjdux/uv/8Te6G867m5xxJdbL+5G+/uMbff7FQ8WKtbje0LuVkt77yapuNa9rLrY6nB8EplQg/W64p0jt+N3dbJIOn0V68jDSkwcH2t0+VFja1do70id/d1t37l7RPI1WpnSHz0CxLWbTC0V2/myvl6u+jvT6caSn9yK9eLqj1y92tLN9oEqtqqBQUHEu0uJyrM3rC3rn/Wu6+6MbuvPhgtavBiraaxLTG1DuadMZILqYTeAv4UDuPn6trrXecPdi7TyN9PxhpKcPWnrx7I22t/ZUOaiq3qiqOBeovFjU0npJN+5s6s77m3r3wzXd/bCguU3rcSVz85RdMNAuINEZBbLH1JaV1cdpTxTWACs6lGr2GuTvIz1+sKft3W1pfk/X7pb1yV+9p/d/suZuRNl5P0/4n3FfsPjpBfzxr11ecb2pJNfareHrs/uRHt6ru+tB1XpFWtnVz379jj785KauXLXH+0+/WpZE4EwCFrcWs9awyh4o2I218yLSo3uRnj060Kvn29p+c6BqtaqoFapQLKgwH+rK9WXdvLuhdz+8rnc/mNfmrYLKV6TCUtCph4nrM+0aFu4j4I9/7ZjB4ncv1uGrWK+eRnr8Q6Tnj3f0+tWW9nYPVLcWV4o1v1B2jbSv3lrW7fc29N5H13Xng5JWrhVUWA7cK4OsF2fq5D72zB6NgI/hlmQdRET7sfaeRXr2INLThwd69viN9nfqqh7W1Wg2VbD3yJdbWlqf09Xba7r7wabe/3hDt+4WtLiZPnxgl76t7qX+Hc0+GnEul7fBSnqxzU7W7CC5+ibWi/uRHn93oN3XTV19p6T5+bLm0+63rY2ANR4I30Z6fs8OpLfdk4/luaIWygtatgtu2craetRqSfXDgqqVQLVqyTrNUKzQvZanWW/p5Q8VbX0Y6tpGQcXl4159kc305L3vymjdeR3Gevs00jefvdXO66ZqVStb0TVYadqN+lKg+mGgqFVy3Y6P/ds5+CZ0EZ68tSOaO0TZjqzRlvU/PbvfTx546CtJd3M8m7HlkMy0p8tju7JgNxotyZGy+4ln6f5i4BJPRkJPlfcHxptm07h0BQWxNVkxx0yDlUnYomxZjymPJcnb3GOSDzY5a9WI1Tooqb4XqF4tqBgU1LCnD8MRrdW+K9Y4xOI4qZoUF5qKgrrrySooBO7mxly5kJzA5W2BLe8a2MRqbNvrdyI1VdfyRklXrtuBdVqf2tfEetfx25eXl02z/JpyPT/sPIl0UI1dT1gbN+e1smFd2FsG1hrG9HN2UnZyZv/Yd9b2lt1o7yyXFsImDUvqV+23Y9jl/XJnGaZteNrvNA2l5mHRNcq0p6rtSZPqQaRWcp4z/DaepWy5y54GOjejo1WuS+Z3SnZn2DS/3uz0/HzHPtX2mTv+SNfki+eKntSFFqNWUhet9t30hWqP+AlnGNoFnjh5am73daz9N/ZEc1HVujRXCrR586oWV1aSJ/KtvE1p902sN89rOrRXG6ql8oo0t7Cg1Y11zc0Xku/2GYrEogicu4CvGtLvYdJIJelVxS4e7T+L9PWfI3375Z4e33+q+mFDYXlLNz8p6va7V3Tz6rrKC+krKUb5/Tx3CFY4VQI+Xq0et/be9VjRbux6wfr+y1D3v6no2cMt7e7uqVo5VK1WU73ZcA1WgmKsUinSy8WKnnxb0b2/7OjdD2/q47+6rnc+Lmj9VkGFRfvrww3U4WMi+cs9/HKXYQmLKPtJK0o7rnDXfaynN6n5OnI9BH77RVMP773U1suKKpWK6vW6mq2mmlFL7pykVNBcuahn37X04Nqu7ry/rp/88q5+9Ms5XX23oOKVJP/2a4LStTJAYOQCvh7259D22dXJsSJ3nTLW24eRfvgi1Gd/fKI3Lys6bBwonH+ld3+6pOvXruv9j1fdazdpIDjyvUOGgwikMWxJ3TN4duPJjifsqeiXsR5+F+rzTxt6/OCx9rb31IoOFV95rRt3VvXeBzeT60d+PZYXx8Feg+G4BdJjCHezfz/W4aNIj76N9PVfqnrycEtvt3Z1UDlUo95Qs9lUFNnrjO26ZqCXTxp68l1F9z7f0oc/uqkPf3Zd7/20pGsfJMe/7lVtxPK49+DlzT8Tu+6e5ptYz74Lde+LSPe+e6tXz3e0u3Ogw6o11q4rtAelVVCpFKo0F+nFD1U9/GJP399+rZ/88o4+/uWm7v6koJId/9orVuz2UyG9IWbKxPLljbVxbbnFsPXcam8NOYhVfxrpiat/D/XkwWu9frmj3d0DtZqxWmHUjmHr8r5YDvTk+wM9/PKtHv/opn70s5v66BfzuvFeUaXVTG9X4yo7+Z5a4HI2WPEHynbDs2GtY2Ptvoj08kGs7ZdSWJvX4tyirqyta3lZsoZZdhXNWnHtP4j04kFVu1stFbWiKyvSlfUNFaxb+t6K2Z1ELkrhsorBkuZcW4NIzXhejfquXt2r6tWDA3340RUtLCTvNezek8ndndie0k8zt1Ukl/QsZbpCW4/9pH+Iwp1Ybx5FenJvV2F9XguFVfekfzNuKI4bKsY1FeIlBVH6R2XAg32/mrzN7J2WlqgzyF1HMtHnm26CW8bll7tMJ8v8MZ9bb4n8dL9U5g/q0OsxaMsnzSNdlV9D75r9GvsNXVsU2+U9P538AsXpXeKj981t7bawzyBp2OJDpCfL9ONZS5zJ1WeVmXTsun3azoa5pWxyz6RsbgOMJxm7hj1pXpZftrGCuSU9qhTdDVw3bqa2S7M3aQdYW26SQTbCb7/PwG+0n+6Hfnry1Xap/Sz7kMy2KZmEPs+Bhz3LWteVLXsV1aoKrRWVokBzUaCiGVnf1z3JB15NT8L2PikFWl0PdP3mFd26vekOMG69s6TrNze0urakYl5nD7avLMzt1SKH0ttHkf7wTy9UC7Z09+N1/eRXd7W4ZH14p0LZ/ZpX/jQ/O/m0uvPb/2jp+ZunKm809MnfvKOPFlZVmEvjqL0dJ7vb3GRf2Qr9SpMp7Y/tvAYcSRd3qW3csvXDk7LILufT+SL5z2cZ+pgJ11SID+1FLkmPY/FcUrxByniW9R+7rK3Y/3b+hvrkHYK0gDawn86MdEIyw9cr3Ul8/mnS9k7xdbFvsu3nDzJMy9OVNK9wfpolPFLorqXdbDuRzBTXXSR0qWyGLZ9soUklO647i76fssXxibPFSrO17651mXuwFatVKasYFTUXHGppMdKPPvlY6xvF5KTX0rdiVbaTV0wUokUFpZaWFkvavLapqzeuqpR33OXXzRCBiRDwX4zsl6HzXXSNN61nFetOeivW9oNIX/0p1Bef2Q3UPW2/bagQSWGppdJCy3V3ahea3KFeem7S7+s/EQwUYroF0jB2fzdcA99Y0U6st/ciffkfob7486EePXyj3d2qO5ieKy5qvriiRWtUWAgURaGajaaq1ab2t2p683hbr35oavdtXZXaLX2ksq6+U1Awn/ZW0fN1mW68cZU+RfJVzLhWM7X5Goz/TY9z7KNd6KxZT7SRHv051Kf/HOn7r5/r1ZsdNZpSqVjWXGlBSyXr/dZ6x5W76BlWIm3vNLTzoq6tx6/19mlLB/s39NNwTXc/KSYN2/POWabWj4JPpEB6zuoO1X09aefD9qT/Vqxn34b64bNIX/5hV999v6N6PVCoghqlppZWGqrtJufQ7jUVfvmJ3FAKNRsCVun2BJrFsE22HzuesGvxO7EOHkXu5uk3f4701V+2tb1dVathTx1Fam0fqr4bKbCHp+wU2x//ptkwQGA8At3x687Z0tcIVp/G+uEPkb74Q6ivvnyund2amqH1qFLWfHlJZTuGSO/hhHGs+n5Dr7Zrev14W3tPQu29quqwclWfFNfd8W/JHrazQxV+EBiXgN37tOsN27Gefhnqy9+G+vJPe3ry9IXq9aarq0ulVS0Ur2iuXFYhKLm6ulmv6+DgUDsvq3rx8I12XrS0t91QM7ypu78ouh5YrKdMV9P7r4wfjmtbyPcSCPQEkXtgOZaaUv15pPufhfr8PyJ9/dULbW3tqd5oKgjmVS7Pa6FUVqFk8RsojFpqHNbdNYrtF4faffFKOy+bOjzY1M+1oZvvFjS3HvCazAmNqMvZYMXvDDtgbsrdpHzzNNbrZy1VDxvuuHp+YU4rawvJu+LtoNieJqvE+ubzSE8evdZhre4eA7t+65rWN+1lxpmLbP643Bq52IWOOFIrCrWysOzeY1gIW6rW5/X80Z6e3dtV7e/XNb/pF/KFs2EyLTvn6JT0eowlt+90JFVexHr2JNbO3oFaUVlrC6sqBwvaae0qUHLw5Prv8mvoqQuyJRjvePfWWDGO/JxUNr9AFqj3pKidYVei9lQ3csKs7oT+U88CaRltas8cv8DgQ8vA8kt/k4+x4jjzaydqftvbOVtKC1R/83GYwmQzO8UWZBdvlyct40nZ2XKZ+ZnRbC79x/36XSuI/smTldraMo2W/GI9ZfKThxoOm4dPb0Xy4z0rzM6yJGP9sRW4xmzWE4017LHGT8kTAtn9deoypPTWGruwHOvm+wX97HBTWvxEzXqoWx8s6YOfXdGVDXvhoCXO+UkvdsTVWM/vR/rXf/xC9eJL/Zfqu7p+Z10371xRYO86K6TvjvVZ9PpaPva0pR3A2xM+e7F+/6+Pdf/pF1p/J9LCaqA7765ocdXHitXzx5Qpuw5Xvj57qk82Pruu73o2y5xtccv4fH1aP/QZ+vn+82mHmXxcAzqV3N8X+15lG4KNJGaGLaOzsQJ2CunHejnaXzpLcHRmbh4nF8cysno4c4PEL+AL4T+fNPRlccvkLWjT3IaelEtnXjYLW6z9d8Rm+N9OcteAL7tMZ1b+mE/ry22p/Hh2GEn1SqyDXaleb7kT4UIh0NLyvD76+K5W1pKMbP12Y97S7R9YQyhpbk5auTKvqzfWtXY17fnIrze/VExF4GIELGBdbOYHqH9CxL373J6Ktiedvgr159+H+vQ/H+rVy7pq1ViFaF4l99jdngq2UJheELqYrWKtl1nAt8OMYnfDf/tppC//GOo3//uNnr14qcPqoXuSdHE+0JWNJW1urmptbU3W60St2tD+XkU7O/va3w9VqzX19Om+tqtbqjT3FQcfaWV5SfNX7VQmcD2fWhtpfvoJWP3if/ulvWzzzcUffCQ3OX0DQesZ6Mk3kX7zT9v68o/PVDmsucbWRWtEv7ao9SuLWl1f0sLispqtUNXDlg726tp+e6Bqvantt4d6+3ZHe9WXioofaX3jHa3eKrgebN2h32WjZnvHKJAeTGRC2a3MHcPbtSG517K1Xkf64S+RPv9dQ9989lhPH79VFJYVaMHVEMV4XkE053pcdl+L9jHKGItO1gj0CLhjX4s9i1u7SJ5eh99/FOmzfw/1xadv9ej+tnZ27B1BBZUCu3ZeVCh7zXAxOXe1PP33oSd/PiIwWoHOOVzSO1tyw7/51urbUP/5m4q+/uKhdg92XUjOz5e0ujav9c2y1teuaL68oDCMtbdX1d7eofb2Yh0cVPXq5b4Oa/vaqbxWXPiRfvFfr2lzrqCi3U/KXMof7baQ26UR6P37bjf605v9dj9z/2mkv/w+1J9+91QPH71SGNU1Xy5oaWlBq+tzWruyoqWlFRWDshr1lvZ2K9rZlvb27cGDmr7+6rm2dre1X9/R/1z7qd790Bp49+h2vjo9M/iIwJAC7WOG5M0o4X6sB1+H+v2/1/XZb+/poLqnVljT3FxR6+urWt9Y0uramhYXlhTFsSr7Nfeaq53dWHuVhra2KqpUD/Rm55FU+pmC4JZulwPXC0ty7W7I8pF8rAKX83JQO+hj16q7+SZ27y5++ey1orCl+cWillfntLxWUjCXXOSwXlhab2Pd/y7W27fWEMS6IYh17dZtrV5ZTnaSPYZjF9rSa0d2XzOOQ9nLKuzu6drqkkqlgiq1pvYaZe3v1fTiyb6ePY60eKOghbLchb3OF8XX9H0uUNt60j9CNrT8nj6pqdGqK1JLN69/rJW5QNs/7CXlTO+5+jY2bmLvH7Yhws6Xsr2I5ZX9OZIgO7MznrJ1Jgw6lu7PtptfX285fH5+vh/66WcZ+l4czpCne9rAb4vPzw2tsUB678PfA3E3xHPuh/j3dAxUDp/Ir/Q4AJ/uuPlnmG6rPin73n2Ypm0vlnWwYvQ65RTNeijq/OYkOO0kz3jS9mTz9tuWTZ8dz6ZNmfx2d5J1xnqSn/jxyFJp2V0MHrNkkDb7tzRHlj9mmZMmu66zS4Hmbxb04/lA199/312AsEYiSxuBCunr2I6szMrqWgJKtbextp7HOtguaO7KYnIxzhqgZJ9ytMJ6ayuQH/cbYTFkT6ftW6PFSG9etHS4X9Rqo+Tya2eVSd9Vpqydz/ukDe+d55fx+Wfnp3lnJx0Z98vbjOz4kYSZCZYub32ZJEOPWqM6+2/QMgy9gnSBbP6922Dz/K/N87+ZdfUukpl1okn+cvlTfYPQrv1hSa1sxyzSKUcmgd+WzsxkzJK4ZJm0vWlO+txe3hL5D3Yg0dMTja2/309vEbKfM8u7uEhveNYqUmU/VqPZUCNqqbwoXb+9rjvvrmthOcnAGqtU3tqFndh1zd+Ka1pakJJ36F5TcT49XupXPuYjMEkC7u9N0mDMdyldfRbrmz+H+vLTir778qnevNlXoCUtlxdUVEmhvWe6UHI9jhVLJdc+vn89MkkbTVmmWiD9O+TqcDtWqkuNZ5HufxHpiz8d6NWrA9UaNZXnQ129tqif/Oyu7rx3RddvlXT1evLEktX326+uaetVU49+eKvH93f18s2e9nZruvfVGy0slbVx/WN9uFJUecH/fZtqNQo/EQJ258cq3aRxutW5dmzx8utQn/8u1NdfPNFBNZQKoVZWC7p1d10f/vimbt5d1ca1QCvLgZpNuVdA772J9eR+rHvfP9OzZ69UqRb07MG+vvzdY62tLujv/9dVBcVAwfxEbDiFmBkBq4DTixs2mjY2t5v97lW2e7G2H0b6/i+h/vTbXT158FI72wfuobqNlU01GoGqTeuWLecnzTpnDpMQGJFA9qQw/dtu16ytl4qa1Hwa6eE3ob7+U6SvPnuu16/2Va/FWl5YVhRGiqJIoQW6u7DTk9eISkg2CPQV8PVuK3Y9WT38MtKffnuoJw/fqFaz+y2H2thc0Acfb+qjT27r6u0lbWwErgf9sCVtv1nT1stYTx9VdO+bZ3r7+kAH+wd6+sOeFheean1tRUtri1pOr4H0LQ8JEBhUwI5/ffxWpYo1VvldqM//+EQvX2zJ+mBbWo516+6S3v3Rdfe61vVrBa2sBO62ZrVqr/Be1vNH1/Xohzf64f5jVaoL2n59qD//7r6uXJvXfOkj3VxKG20PWi7SIZAjYIel9pP8tU//5vuJYaxwL9bzr0N99vuWfvj2hSq1luwa8cpapHfeW9fPfvUjbd5c1sZm4OpTu2VU2VnW1stNPX1Y0/ffPtWrV/s6PDzQi8cH+svvn2rRNdTa0NpqsfNmBg430j1x8YPL2WDFu1uvKYfS7stYr5+G2t3eczeyFxfKWl0vu0YrrpWr9ZSyL718HOvVs30dVppqKVRQbOjaOxta2ywnrWGtsYBdG0l/XJwHkWuw0lJdSytFLdpNltKS5g4W1WjE2npZ14Pvt/TOJze0sCTFQdodXPtL0h5JcvVfWL8SG6Y3gaxdjPUQ8PRRrJcvtl33RwqaunEj0OZSoM9+SO6X2b9B8ki8ey2KuwHds5ps9rnjeeXwCe0PYzr/xK7tsnkMuH5bpJ3UL59ci3Lb4mb66ZbQj/uy+WntTPyMUw59ftn1nCZvv3w6dH5pw4DkNTa23ZmXUlg6+/XrSj937ct0XpplO2l7S9sz0oXbM7IjfgP9irLzTjnus/SL2+qHyN6X1i2SfujNot0Jho9FS9D105nQXnWa1zBlaWfZya4Tc+2Mu3eVWyab3ibY57QTDxvNVCPtVdhIJsuu6Wf94IrTU6bcdeVOPP3a7Qnawmqg+UXp5rVisoGF5PVox9UdFuP2lINdv9iyV7k9bqhVX9Jcsy61ylKUvA7LfRdS1xNLaPVnKNXsicsHkSp7cwobS26aPY1mbRNdHdtuuZJTr6Qr6CFsrzZhO25uO1n3iCX3v8e552Vp07Lp0+v13Zl3Yu7I9DNMcPXWeTVayW6jlTm1cmVIXVySrEfPMmmyri3uSdI1L/9D/hLu5DCzgEvVldQCz35yvu292+ILmi7v6jdbrCu/NLvjBj4PPz+9/p00h7WMfGOVnkx7l/PL27AnaXZWezxbB9sF9kas6kGsykFL9WZdYdzS5pVFvfvBVa1tWK8pyb60dG9eRTqsWAs0u/be0sqVBV2/s6obdxYU242h9koYQWCCBPx3xg/TQLWP7tDbvhN20dMaXN6P9O2fQn352aGe3H+j3e1Qc8Gqrm1c10J5WfVqqLfbO2rE9krICdpGinJJBHzQJUFsx172dN6rB5Eefh3pyYMdVWuBSsU5Xb81r09+dV1//etruvleQatXA5Wtd7qC3OsqqjuxDneKunb9ppbmryv6y5Ze78XafV3X4+92de/D11q7fUM3Fgs85XRJomtcm+nq2nbmQedivfWkuBvr3ueRfvhiR/u7keK4rJWVou58tKj/8g939eEni9q8XdDiWqCSHY+0pFY1Vn0n1p13Yy0vv6NCvKBHj96qcdjU4293tbr6WD/56aY25mNit+3OyNkFLJL9b3LQ7mpkO4awntm2Yj3/JnQ9q3z7+aEefLur/cOm5surunHjhjaWVvX8+aGqOzvt42WOm8++V8ihn4CL0iMniX6qNbaKKlLtcaTvPw319Wf2WrYXevOm4Rprb6ys6PrGunb2atrdP1ClYT1gHXPSa5kS1P12CPOHEfCB6uPKX8ewhlY1acdeRfGXSI++29LeTt31LryyXNJPfnlNv/z1Hf3oF0tavhpocTGQ7E5bJN3ai3W4HevOgxWtLX6srz/b0cs3z3S4+1ZPvjnQo/ffauP2TS1em3OvxnQP9fn1D1N20iLgBdI4dtcj0wfb7Wb/yweR/vKHUC+fHajZjLU8P6/N2wv6+a9v6ce/uqnbHxRUXtX/z957P8lxJOmCX6QoLbq6q1prKAoA5FDsyHtv5/b2ntmZ3d3v948+s7Oz92bFzO5ockgOSKgGWovq6tIiVZx9npmF7IYkCJIAWdVWnaIyQ3h4eES4f+GOVFrJBhnPBdymxuJqgJmZGnxtY/tBG63+CZpHJ7j15x0sLVZRnSvLZlPRv7MME/6NW2Jy/JoUeEwE8wbt8SNgVNe4/WmAB3eaaJw6CAIb2ayNjWsFXP94Ge9+WEJ2SiGbV7K5kVlTb9E71VhaJShwE19+1sLO/iE6PeofOpip1lFbKKFQNWBkVOgtaMK/X7PVvr3Hf5SAldigJSEguhr1/QCnJ210B106nUc2b6E4lUW+mJHOQQWdc6bxcIvxiwcYeS40HJjWAHOLDF0RgkwuKpPJ50pGCbo1dJDNAuUyYKTTyDVSCAYeuo0RHtzex81f1lCcUrCe1SJx703yAydR0SBELzB08XW4G6DZbCOQPA3UFhRmsoQ7+OLyNtwHn0wkWg+f65jMLHEjAk9IXhFKU9bQ4b8wrEZ8Xyoe2eHocYZ26Dip+JjMPr4X1y8xMRw/xnU6DdnRs/Hgy0kgw+XIkeeRDVCM3cxXbIF8KaoPT/mN03qCrXCc55NOkmVkXqEtLXwyTpdpft10480zTCPxiS95VFpJeBYjanMG1taM5crakQYsC2Nzs614Wwz/PEZKDh6SCY7zYWFZsbhy/OEJD0aPCJ/HbS2hiqJX43t8PaKBYoZxWWKaxEmP809knfwtWZyYJ6J3aPQR0AK9GsXtznozD7Z7HEqG6TGdZNlgSNASJhVVKVmSlztPlpspXLgeG6lYDimLHvPquUJEfKkT7o/Gi5YoTZb5QvIvV+aLb0UC7FHaNAgnryKCPbp1MYWvfS1ZRvxKT6+P9RvSivnxG/G97MoZAUFX44QvVeUpAAAgAElEQVTgvB2GSsjA8HIwvCJMJycLSlr3GM1IeD7Bh5JWnC55h7GTR0D3WOPhHQ1nFEAFKZi+BcPNAyPuAgrba1x1phfxeLLSkhdvJHmX1/zh4r3ki8nz+LmY59mv5Tzk9TEvx4xgPV5HKUfc3yT/ZAZR+eL3L/z0Ki7HdHoViT0tDZY/plGyX/F5XvPn6Bi3lcijqN1II6FTOEiELzztf5wX0+N5/GEaCTqPZaPIJC2GjnEZ+CxlE/mc46JhRPnHiUXHiM/HciKS65JOlHeYBtOK5GvUR6QsX4P4QgMSJ3rRgAmDLsOjcY1zhcdoHBdX6hL1gzj/6CiPJM55StqwDgQJdzoa3V4fI78PZXqYnp/D2uUF2FTusKtwyjQCDg81OnSzqxwYtoupuTJml0uo1IyxE5u4OJPjhAKvDQWE4R8fqBXlAsGWjpYQQLu3fXz51wB/+/0edvd2MRqOkMvksTC7hI3VMp04Yn/XRKOl4BPgogNxoR4N1a9Ndd/8grDB+ImP0eXkMKaJjD8yXwKCpsbe/QB7D5toNI/gBgrVqoGNt6bx/s/nce2GCatqQEWeUjjuGkWN4rRCwQXyecAyAceZwfDWIXoDF2cHQ2x9cYCFjSwKxRIKDPn2rPXwpG0mFHgGBc71ZM6bRPZq6JGWuOd3b3vY2z+AjwEseyihRK+9P48PfplHeUnBKJD/OBkJvQaavkKqCuRqGoYF+N60GKoa3TTOTge4+0Udd/8+xI3pLHLFaOWUBLk/o6yTnyYUeDoFyMlcZCTWkJxHDzT6xwEObgf44vce7vx9F7t7h+h0h8jnslhencE710owXc63M9hv0ttzcvHy9Bwnv0wo8M0pQL4lv8U8F0pkuUvQa0ujuU2wdoDP/niC+7f3UW+cIW1nMV9bwgo9bs4p3L2bwcAZoOOQe5+gvY6T/+YFnqQwocAjCoTs+uiafBbNIRiKor4b4MHtEU5O6xi6I2SzJhY3y7jx83W8+2EOxZVo/ht3AQ0JMZ6eg4QzzqUV3OEUHL+Dw5MWGnUHW7frmF6zMbM2i/w01TKRfudRKSZnEwq8OAViEcw3qH+jzcgB+vUAO1sad+48lDAqthWgWM1h8/osbvzDHDbeNWFWEro9AClOP+itYlqhWFbw/CmM+gqjvRZGro29u13xlLmwVsTytDFxhvXirTR58hkUGIvhSP7S7ine8PcC3P27i5PjE/ScFkxTYW4li+s/XcOHv6ygfCkKTxUnQFN0WaE8DRRqBmzLR+CW4Yy6GBxYaNVd7NxpYG4lh8XNZRSrAKykBewZhZz89J1Q4MenDoonD1QajzRceiR5qNFu9uEHPkylkc2ZKJYzyBcjS5QPdBsB7t/tietYL6DCIkCuoFGbM2GXwkkFbT5MXj40wvIrmHAfpuHBsoHKjEJpWuHwqIDANTDqNrG7dYzmyQgzsxlYmQiNG6fD4zjR5M3onEWkcYm7hjrAvVsBGkcDuCMfSvlYWK5gblkhTQNQBFgZb/GMk2P6caeO78noxryjCVPCiEaDcWhMGz+MoB+6dqQynjukVYxOM7Wg1Og5RvJIGPfEWBhfx+3CLGkUZPsQeREbHFk+Cg8qgHjOiaObACvQu8xAwx9oOB5gZxTsXFQOK0onbBBZ+3MiSAO5VJv/5ORRfV7oLDI80Hgg9j6mF+2CleTiur1QYtFDibJIGsJDYbpi8QsUJEQglWgen4hoIJioqA0cvqREWSxKt5QO6UZu5CsspySeKJhW4nZTDKpPIUassBbWIA9IG0XGYfKHE06G+DtdIquUgraicEYpljRSuDyyjz7O20lej/kyqqbwOc9jxSM93PKcgIMu4Hoahh21ez4yDMf1ZJlYRqFn3DDxjwnaJ0jyUqfJJOO6xH2H1ywv+dqN+FzqEsoi/iZtQ0RzWo8BEWKU5rtMR2RMSJhkVi9V1osvMcH4K7+FmZ6HrVx4iY+87Ifvss6kAfmXHl/5YfPYWpTFYpgLqxvyCp8n7UZaFB4nOwFOD3uwVR62DmC6RRhOyBMC1Bo3dUhPMYDQyM+Je5w3n+/rcMfE7QY8R8MyM7B0Coabg0HZ4gBibIyIrogaYz9iyLiIZuw7AroJddyRhT6q09c8sGxxH43lrRg6Wfehhj8CfI5DpoJdDPuaonUzlkFxvdmkLF/i+msW5fV6POY31inuO/E4wfaM+viYp0YhaAQck2wipkNZKLIpbrcnzoKSGUXpkkeTY19MVxo1eM5P3L8pGyOlnMf24rWChLsxcpTNUf+OAJ3ybpwlL2L+5Htsc7qxHxBZLhASmHkFg+AOOyEn4jGNbR2XJz4yzeQ584q+DPVlyMDKow0Tpixs5XepT1QvgrZYFo7xlOGsRzy2EDTFJKlgScr3i/n6QL+pcXam0Rv04WOEbA6YXSli9VIBVuxKn/UeaOzt9nHWbkCrEVK5ALMreVQXc0gVwr7GKiSrJXSc/JtQ4HWgwEXGjGRTHH7usz96+OvvdnH7b4c42usjUD2UptJY3ijiZx/Oi7Ke7qMPj8OZ+9jcRHFzMe3Xob5vdBlI0AlRn9mEMo/VstZrHmjsb2mcnjThuB0JPbtyaQPX/2EZV963YM0oGASrJBTuonzn+s3WyC0buELvACPgtDGNvZ0Ag9YAD748wvrbU5hfKKIw/czSTH6cUOD5FIjnVDxycwPnZG3gb3/xsffwBP1+H7CGSBcUrt68jA9+UcLUphHqMDivk3kzN4pEc2hDwzQUlt81ZC62t1XD6GET7Z6PTkPjt7/5BLWNj7A5n4aO5kTPL+TkiQkFnkMBUdxEz5AXNdA5CvDlX3188m/b2P2yh/rpGfpuC1ZG49qNDXz80TyuXTZx/3MfZopgV19CmWsuUiafCQW+bQqQT2WiyhPyHBUk0VrS0di/HeAP/3KKz//8EMf7ffQHQ1i2h7mVEt67uYgr6wZyKeCo7kPt+wi0P94cEq6CowpMpm3fdktO0qfIjXiXLD2sBzjeZjjyLgaDAVzdx9xsBT/7p+t4++M0ioshWEX0g9Sh8GXqnqmfo75pRmHxmoHr1IP0FtBut9DtOQIAr961sHFjBvmpyQRiwniviAKxCKZezQEOtgPcv9NDu9MDDA+5soHFKwX89NeLWH3HhCmbBR7ptsel0BrGlEL5koFfpYHmYQntZgmHjTO4vRQefnWGhY0Clm7Oh5tdJ7J5TLrJydenwDn2iecOGuifcnNxgMPtJjqdLtygg9yUjQ9+9TZu/HQK5Q0ztEHGGzqZNd/nvyxkDbd41UDvTKPbmsVZ8wS9vovT/SEefHWI936xhMJUtO77+sWevPEtUeCJpppvKa/XJ9lI8Ua3bu2Gxu62Rrc7FHCJVi6y+RwKpQyyudDA7hMJfqzRqHcxcDx4gQ87A8zM5ZDNh/Nw6QzsXewU0jHi6tLdrA8dOPD8LnLFIvIFhZl7JQzaGqNhG+3TIba+OsD03DqyZS2gh/jtZx7j3szdFkOgdxpg646HZqMLz/eQSht4970NVOcVznY4xQ8ttEmM+rmiXswsdkHO1zjQeXThqNGrMzRSgNaZRr/rwhlpDHo9eCMHOghgpiykcmlkijYK0xZqS2nMzBrIlxnb+dEgyLxFERTlOzZOU6nU12gcBrh3r45Bb4RcPoeF1TJmlyyki2pclsGRxtFugMOdEdqdIfoDD57vw0gPkC36WFqZx9VLBdy+7aHbb8OwPKQyCovrFVQXbKQr0S73pJHvIh2S17HRYajhnWnc/cJHs9WHF/ShLA8ziznMrZYxs/Q1u1bclsm8IhW6qUwxKNIviKEt0FYuireuxqCucbgX4PRYo9UYYNQP4LlDKBVI+9sFhUzZRnEmg+pcGosrJpTwbOTuimlFTKAMapOji6TyPn5GjKdaeK1zGKB+EKBVZ9u7YDSW0ciF6zrwPB+pjIF01kQ6awhPL29OYXbZgMG8yVes20UjutyMi8CdxPRcFLlyFtbVoVvxk0DavMPJftNBrzNCrz+CT28zpgc7G6A8ncbMfBlziylMzykxsgrgRegb5h+tIaQsca2T1b7QFC91SeNu2O1CgATd954cBjg51Oi2STsHo6EDZ+QiiAAIqZQFO+OhUDYwPZvH/FoaxRkDYuimSPrWrFWEpkSUED4fU0XYQlg0NpbFbfVSVIleiuRl5yDA7kMXe7uHIByLE4XaUgarl6YEwCdAEOZHfXMrQHdH4+Cuj4M7Gl9+2ka/G8BEGt5oiIOHbfzptzZODiqAFSBQDgLlw9MeVMpBuZrC2uUZlOmmnlivZoD9ewGOtgKJn3x60oNomoMUumcevvpsF46ziGzZQGCMQl5RCoExgJXtYfVSGbWFCrJlKrcJhAnL+ZTu/HxqydgUKXW40I1iPJ/uBKjvEIw4QrvdRX/oCMDSsBQy2TQyeUPC2M3U8piZN1GqGjBZx1gMvYr2en7pzz1xjgavOn+hzSP6eG0t8ujsVKPV9tDrjeAMPQFu+l4A04bIJDujkc77mF0oYnmtgPycdCixVYo8GsukxwscDDUGRwH2todoNnrQ2sDsUgHzaylkuBuBzdbRaBxwPNI42e+j3ephNHTh+xqmpZDOpZAvpYQP51eyqM4byEoYnJAf4928XFSOjgMc72sc7jpoN1z0ewM47kgm2wRkpnMW8sUUpmazmFtKozLLNif7hmOsiIlzjXCuecainuAoyh7KQ0ulie0RABk9GPVPInl1RFDvUDxABL4Py7SQygPpIt3mmyhULMwvZzA1S0VNKLMla6EnZZ8aA4yaZwFaLWAwHCBQI9QWp7GwPiVeUwg4FHAOy+MBpycN9IZtGVvzZQurl6uYWUgJeJUtxM+zqhg9MjlMKPA9UiCSJTxEX+HtIx8HO2donLZhqjSqtQquXJ/DzQ83cP2KIcDMVssH1yQydycIecLs3147xgLl28vhzU6Z9OEaYBiGYqwfuuh3hzDNAKWyicX1PObWTKQIVslG3lEu8ivT4Lw+B2SrCvNrBpaWZ9Gp+zhtBmjXW6jv9mQDR205K2PmRMC/2WzzvZU+7s/RnJrzHAJu6db8qy/aaLeGUjSGdq4tF7FytYTZ9XCd9Rjolk8yPYNrUg1dVphbNnDjuoV6oxzqHUY+du4f4/igjbVuFSY3Mr2oXuF7I9Ik4zeGAhE/i97C1xj1NJonAxzunqF55oHA88XlGVy5uYCf/GQFlzcN0XVpmyKXu3vCzUNvTH0nBX3zKUCeFSVvLIzDBRvnv9TfHe50sLd/CmdkoVjKY3WzhA9+dglvXTZRzio0jjR8hhPUjujRx/qpN58ykxq8aRTgPIIbiTzg7Iie+TV6LRdaByiULMytFbB81UZhLvTOJpvHLO4ktmTOK3pg1pl7jGg0rQBzawoLDy3sbs+g3++g2xqgftCXTXgMTZFKRXr6N41Wk/K+fhQQ/g03cx7tahzsNmCotACoyrM2lq4WsXTFgE19JO10FNU8nFvDhdEk+Js5a+DKVYXDvUWcNk8Az0bz2MXhThN+e050keLReuJl8PXjhTegRCHbyQRCSivrN25O9YDuGfX9BJu48FwXmZzGwloO62+XMb1oiHdM8u3YQ2s8d+b6jbZEpWFUFBbWDKwfpLC3XcXBcIBBp47jvTaOdpsoz1SQK5xj/jeAaj/sIsbmrB92LS/WjswbhDGJT0+5g7GO/qgHrTxxT58rTaFQyouXDr4anGmcHGu0W114rgcdDJFKA9X5sjwjQjnKgwJeNkPQPhN9tQrBIq4ewM6VxIA+O19C88RBZ2hh1A1w/9YRNq8uYWE1DXCX8ZP6SfIe6xB3wshF0il3ve0eCzBDKQelso0r7xRRnDZw9ICaRvZUfphQZLSPDdDJtOWZyHJHJc9QY1jXaB0GOKUxbk9j/+Epzs46AvQZDhx4zgiB50FxNW2asFNpZAopFGdSWNqoYWWzjMUNEwurpigrBXnMfFiHZN7RpNBjqKadAJ/8+0Oc1puYrlRw/cNLKGXLsA0DBBEd36dLPh8P7hxj7+GplMXxAsiffYbCjI/RTYXVSh63fn+C/cM9BNzRnTdx/R9WYJiLWCgbYdgQqfPz/wkJCdzpajR2Avz1306wf7AHDy2k8j7e+WgFhUoaM6rw/MSST0RtOb6VpIlgVW2JkWnokuy0Z9zX04bGg7s0oLZwdHCKZqMNxwnb2YBmM8hOsWwpjalaHnMrRVx5dwHzq4YYFunybQwaCW2cFxoj7CecoNNw3j/QODsMcHYU4HjPl1AsjZNOaEgdjuC6LjzPE08thmUilbKRztnITwFrV/rYeGsOi5smKguhUlB2oI0NxImsWXe6EuGB1eEigSFgOhonD9jmBGY10Gp00T7roNcdwHWJFGCtfRh2gGIpjbnFaaxszGFzM4+lWQWnH7KaaZCWfD5iPfUtKHKi/ineX9oavRONDsFFOwH2twc4ODhFtzNErzfEcDiE77OjUR+qYJqmAKuK5TRqNKxfnsHqtYoo9wuz3Lk6biypw6v4JySPeY59MOqa53ayxBnxufjZ+N7LHJmPq9HaC3Dnzw18+skdAazMrqZx5f0qZuZKKFmhxlfAcl2Nh5/6uPeZh+1bLRw9GKFRZ9srmMqG71k42R9hODjE3r0mtBoiAAErHnw4QLqPhY0i0qkbyCwU0GP/+SrA/S8GONwa4mR/gEGfNbehgzQ6bR/3v2yjfjiClfbFuC673JWGb3aRLnXguptIZ7LIFLKhBxYhHP+9xIf0oIgWbzOhjCGS+Iho+FsB9rZOcHrYRLfXg+O6AlhhO1i2hWzWEq9g1dmS1HHlahlLl0xkZ+jWI/JMJRO1C+ViUV9FW15I9lu7jGhEvgnaGu1DjcY+wXMEdgxQP2nirNlBl+AOgktdyiN6zFGwLQN2CkjnNOaXKji+NouNtyoyaaWrQKHTmLeTgikEcNALT+tA4/Zf6ti+fwjX0bh8fRFZaxE2DDAu7P2vAmzd9bCzdYDD3RMMBo7IJrarYRogGC2bt1GaSWF1s4aNq1WsUjnHHTlWBBShu9mdQBDk2/cb2N+po31GOTGCH7gwTUN26lgpE9lCGtWFHNauzGLtShlLmyay01SIJMBKz2gMVjdmWXpZMXQBfg/wdgPsbwXYuhdg90EDx4eUVwOMBJSqYRimAIAYPjFXslGctrF+bR6bbxWxuElgohIfosJaBHIxkwiEQqBrp93FyOnCsBwsb1awtFZBlgArkp1tPAKctsZZs4mB20Yqa6K2WMTi6gzKlbBtxk31jPpNfppQ4PungPSCUM5G8216IazMmchUBrBLTZTzs7h2Yx7vvL+Ka2+bSE8p+MeBeCwKxK2RB4Ug2rD6kuPL90+I17gEkQs+EVSvcTG/z6KJV0fA6WnUTzQanHuP+jAsHzNzBVlfVDg/5diTHD4T7MrlIT8EIRNQO7OgsLBsY+duFkeNAF7fQeOgh+bxEE4vgywVRlH3+T6rPsn7DaQA+SbmPZ7Lxh6NTiPA9oNDAZkQCGimfcxtFFFdTcPkrrpnacWYDoGDKQgw68p1E3/6NIuTugnX89E+G+Bk9wzt+jQqxYmm/g3kmtezyAkZKGzN6XVWITflwy51kJn2UK7ksfnuPG7+dBWXNwxY3NjV06E+UnuiHUk4vHo96zkp1Q+QAonJgMjPcPzPlBRKs4DKn6E8U8L6ehU3f7KO9z/KolRR0F0NdUwBTsUF/TNzIwQXh7FQ/wGSalKl15MCZDl+qY/uh/Pfet1Dj7pb7aBUSWNhpYjaggGLG53Hc4jISwr5PtJtyHzWguhISgsGaksBpqYLODrqYzjycFbv43i3i7XLRdhFeqhPCP/XkzqTUr0JFKDcJH6qFW5Cqzea4gqf3tem5jJY2JxCejr0LkhePwdWiVkw4mPOkbn5ePWKgYW7Fj65pUETRqc9wulRByf7WsBYmp6t3wTaTMr4mlIg4p5Y/kbOGVoNencdgLbnIHAxVbSwtF7G7IKCSZ1BPOVIThXi80gHxw3ghXmF+VWFynQJp8cu+gMfrdM+9h80sLQ+hezMRP6+TowxHlZfp0J962WJJh6DnsYxPVN0OnB8BzCpvAiQL6WRL2ZhpMJQEGf1QAArdFlIDyJQLtJZhdpCeezCnmPBePcj+1gEXCFYhecGUeIYwUhD4nivriqc7JRx1j1F27Gw/dUJTvf60MMUdD4RNytK64k04dydEygXGDY0DrcDNOoELfSRTSvMMQbougFGHyCOIZxxPeq1BNbI1cURhTfjdUEA+D2Nrb/7uPWHAR5+1UD9uI1uJzTEeUEA1/Ng0dCuLIHBMGQSlA9lDmDYbeze6ePBWgpXbk7hZ//rMmYWQuOcgCWYVzJ/XrNOg9DIv/1VV5CgtaqJuRkPziYR9xon9338/jcu7t3aw9F+UwZK1/clABMMH67Rx7Sn4PRSEg6pe5jB3lcOOoMzpMX7RwYzCyXMb5YhgKOL5XgSwflMtEvLa2rxynD7L2fYOTgG7I6AQC69tQoV2CFhk/V6UnoX7zH9ix96gdAEVNAkmgH8AvoNjfuHAb68FeDuV1vot4fo9QcYOtxFo2Cbpmxq0NqDp32Ytgc708ed4j6279Zx/aMlvPtRFbNZ45ExO5lvohw0tIqXhwFw51MPn//xCLt3u+J9YtAN4I4C+L4jXx2Q/hqGMsT1LNvVZDgmq4cHX53hzt8Pcf2nS/joH+cwvWQ8is+ZpFN8HumpxVYzBPxG2OZ/+p2PW5/RGHwGx6F3H3pW8SVP0zCgtYKvA9TNIQ7vH2Hnyx521xbw0U9mxhs9bCuDkRokPBklKpykwzc5j5Jk+Vu7AT77Twd3Pz/E8e4ArTMHvcEInh+INyBxdaqU1EE6pPZAI5VpuiD/f/n5Aa69v4ibP1vDOx+lkKk+ZffqNykv6R7TPk6HQu0iaZ70XPz81zyK7BrSvaZGfUtj6/M2tLYwHGRRmc1j0A9QKhgQwF8f6Oxp/OFfGvjij/toHXvwhxnowIZSKRiGBS9Ii7eVAV27HTItB1pFXzjwrSY8Z4i33+6ja+dx+zMf//GbLZzsDOB00/DcFIIgJaCZACkMhy6Gx0OcnXahZJe7A59/yodvnCFdbmJtcxqbV4gw+YZ6FNKZX5HnoSer7l6Ae58F+PN/DLG7dYB2oy8eQ1yfhktTPHz4QRCBnDxYVg8P7T6mFo5wea+KwWAVl96xkKuF4VukeZgH2/Biu37NtvteHyeNhkD/WOPz33n48pO6eCoYdHwBiAxdF6QRKxkDruh0BIEg7wSUun9nhPt/Z/iBMn7+v13DpXdysKuh96LxQk3eCdtVAFNDYNTQON5y8eCLDgY9D8rLY3N1HjkA9/4e4He/aWHn4aEAMoZDxppiImwrJTtxDBXANIYwU13s3+niaKuL3ukqPvi5PVYwH90P8B//NsD9L/dlrCU/+17UZlwpEjClA/hcfRouHtxp4uHdFi4/rOH9Xyzj0vUU8lQESqWf31LcmUl+Mk0byrPRPtK43/Pxh9+PsHVnD/WTloDqZOwTkUDFoS8xQy1zBMvUMLIutm+3cLS9hF/80wJmNwzY5TDwbQyKFD2jS08tQLfTguf3JRzQ+pUZzK8YMjdiaUXsDDTq9OrS6cDFAFPlEtYuz6M2n0GKxI7GB1J38plQ4I2iQBSu8cYHFg7r81CZLpYX5/HhzzaxvG7ApqGJC3ObEBXA80fQAWNxRTJNOtIbVePXuLDxwBs88i73Gpf2uyxaPFWQPCn3uRYYafTbGo1TjW5viJE7QipNz5JZzCzkkI8M/vJsuPQNX78w35BhLKeQrhqYXQxkvU30vOso8a7SPB5g0C0jw3Hsu6z0JK8fLgXIwz2gdaJxWidg1oRWAWzuzrtcRmmWQJRHoNmnEoJzDxMwigrVSwaKlYx4nCNAmmL6eKeB+sE8KhulpyYx+WFCgRenQDRGcSIdyVF6ni3WDGxcLeHwcAadZReLK7N46yeLWLpswsqE8lpCx8rrBKwEAlp58XwnT04o8JIUuDDeP7IgRetYC1hcN/D+z9dw0LmD6doMrr29ihs3CijPGzLXYGhmWecJ2IopGHDFE+hjib9kISevTSjwAhQQ+Rnp/mnw7wLiybfZx8ilR/chSpU8Zpej+a99wdhPdUVsa4mykzDvKQ2zpjA9b2BqpoCU1cTAMdA9c2QzaK81j3wt9MbyAqWcPDKhwNMpEOmnuYGWXpMbJ310+4wqYctcgYCV2kpWwqiI0CXP0+h/YfE15mPOgS2F/JqB2ZVANke7tLcNAzTrA+xu1TEzW0OKm9AmnwkFXpYC8VAfy2BG3+hqNE/57Uk0B24+L5ZtLK6VwygeEaoh1jtL1lE6wr/C22E4e4a+qswrlCsFpK02DJgY9QLsPzhD5yzAjGtICKGXLf7kvVdLgR8nYIWTBxdodoHj1imGOIFjNGGmXKTLFrIVIBWhZJUPnPaAeu8IA30i3t00zmAXbMws5mFxNxk9WTxBLod9JPyB58oMZGdOpqywccXA1i0Le0cpwLHRPCGqq4X6QRlz0+G69ElpJptf9NZM2AeaxwEe3tUSLmLo9TFfLeHqO2vIlZTsRPdChxLRCMQyRQWOT6PLcfpRuvRu4QyBh/c6uPP3A+w9bIshVwcGAmolFZBO2yjl8sik6LlCYTD00Os7GNELAN36H3potegZoIHiVArv/GQW1dUoZEUCCSfChG3DZD1AjQAMC9CjPoJeAV4rj/YBd7n7YsT94q9bYkxjyAUERujmifUwCJzwkC8UUSqXBFS0tlDA0e4iOq0Rhg7jTw5kUnipUUJ+hjbFiwQYU+LRCQd9erMZAp0Tja1bAboNBX+UQdr2US7mMFubQ6loP3rnlZyxbCkoncOoZ+CTP/nY2u5hZ3sX3XYbvktrJpDN2MjnC7AMU+5xRz4R4J5jwHUD9AYuBv1DDPo9jJw2fpnfRHHOgCKvx5vB2O788sNseR6BVra36jbB7xYAACAASURBVPjy84eo73vwXQOB+OqkVxAfhWwKuWwRqVRadkEQqTgYBRi5PtyBgzPHRb/fQL1xgmLlF7iZLqGweIHmFy5F6e1qeCcBHnwR4D//p4u7X+1Gbc54zGQQDynbQqU0hWwqJR5euv0+2r0Ogh7DI3XQa/noN7OYrWbgjjTSdg4DgwxGg3JU11d8kL7JPsdY002Nzz65g4d36ug2Ad+1xAsQgRi2baGQySGXzYrR2HMCDAZDDIYKvqvQa/vo9Bj2Zktcn2UL13DtQxWGB2KbXaDZ16/GN07g62cZ0zziK8NlP88jGGbFuwlGGWgvJSGeRBZEk+1eX6Pd7cJxHaRSWdhmGYOhD3pVcqiZU/QqZIjHEZuKZ6rnOGtR9Lyj4Jo20hkbdsoGcYd9hlEb9OS5XDoHFxZ6I0+8RzDcC7XSdjoNy2Ts5DCNwKCnFQ+emUY6n4FpW/K8ECFq76cRhNWOq/7YM6wj6RHJl952gL/+zscnvz/Gg7tnGHEnh+cLyMq2LORzOWTSaQYJw2BIfhnCcRxZMPSdLnqDvnga6XU2cO2DIioEiKUjj0pPLcRjpXrxG1GasgsqYkrS/FV/YlstwU7tM417t49w64sH6Lbp4cukEw9pK8qhdCb8pixLPPn0uwzPQy8lNLr5GA576PZ2cdY6xf9p/SMu5WwYtDHExR7XKQIjUcw6GnqYB4YlBEMXzQNg97bGya6Pf/3NPezvtqT/sjGZbz5LRURaQBj9viMgO4as8wcKZ6MATv8Yo66HUuYy1pcNHOwH+Pd/a+LunYdotwYYDT3xvpRL5VDI5qVOBLi1uwP0GfLIDTAKFI53RnBHBxg6baQy17CZyiJthzuBz7VBXDfejOrHSbpJ0FeQwqCjcf92gNPmAHfvPkC73ZMyB4GHQjaLTDorwNTBcISRM4LnBfA9hvFR2Bv10O3cx0n9ED/7p0u4fGMKpcUIOMO+QaCRD7T6x2gOd+GZp5hZsjG9aCM3pejYSBbJNAaNBsCDgwB9/wRGdojKYgVvvbeC0jSfSwB6Y15O1utchScXEwq8PhQQmxN3J2nArir87B838Nb7SyjkU5iZVTDpmYhzYmKPuTobry3I6BwkEnWZ8HyCGN/wdELL5xOQ4HkCh88YftSD4wayBjRsjeoCPXlaofe/56UktKbXNwIdIbupS1M55DMFOIM0em1PlJ6dlsYU14Lx2uR56f4of4/kgtQ9KRx+lMR4ZqU5v6ZXvrNjuvUP5/MM70NPFfOrUyhMv4h3lXCOQrksni6ngcp0FvlsDqOhD88zcXrYQuOoA+0XQ9DwRLY8s10mP74IBaK+HfMS+S8H8Q75v/zv1+E7GrmiQrEaelaRhZAbhl32iNMPfFnWcPNT+IkTepG8J89MKPA1KUD2ilnt4qs0dtoK6Rng6g0Tmeqvkc7bKJYUijRwcrznuB+xKPXMQRBIcvHKb8zGF9OeXE8o8G1RgB4GndBDG+em3JDkyyZNF+UZhqDPh4DXp+Qf87Os74g95H6jjEKhojA9rZDNZDAIMnCGPdQP2ui2NKYdDVpVJp8JBb4JBUQUU33gACe7Afodzge4US2AlVEoVcNw3iJ7yW7xN5Ep0xAeFtW4gra0gLbLNPpXptA8CaADE/22g92tI1y9XkWKe2wmn6dSYDxETrr44zSKiRPZf0T95QO90wDthsaw74r3dG7uzhYzWFgtI03vKtSb8R2a+GIbWUxfYeKIv3luKaQKCtNVA4V8AZ1RDjroo37QRasxhDPII8vQrpPPa0GBHx9ghbxHgZsGKksKN/9rDdWVHEajoYQSyRUtLG6UMLNkIkbB1i4b+MBcxMq7WXgjwDO6KNUULr87D7o1HAv3RAdjh6FSWtHwKburFTztwjdc2HkL0xsKs0sK+Z0s6v00vAGwe7eJrTvTmHurnHApF038k32GnZEDAY1AgRbvE0fbGgc7PbR7XQToozxXw9XrJXGP5LU1fD90qfiosBf4L+7I0W2pCqtmAIYF5MsmfLMFV50gUzaxsDiPufkqKtUycoU0clkTKZteIsJQSwwbtPOgge2HdbTbLvodYP9BF3/61/uoTE+hOJ1Glp5kWK9k3eJzhlDwCVxJAW4W/iCL/qmBnVsB2j2Nzz69haP6MTJZG6urU5hbmMHUVBG5fBqWDQxxhmLVwvpqEUZJYeMtA/cflrG9b0B7Njp1Fye7PZzsB8gWGYIlGozj/JPkITFIc9KbG137Gp1DjftfHmE0CGDCQjaXwtxyGdW5EvKvKu4Z85Qm5jItjUFXYeduCw+MIU6aexi6LZRn01hfn0dtbgalShHprCnzYQprAo06DY2t28fY3jlEoz1At6Wwe7+LdPYIC0szuJaeQjYOGZKsc0wHHtlfbIVc2YCdG2GkTzFVLaNWq2F2bga12RKKpZSEJTFNBd/T8Bjn/kjjwb0WHtzfCUNHdQIcPWzjL/9+D8XiTbw7rcQt4mM8ENGaiwN6Vtn5MsAn/+ngq7/t46RRx9DpShuvrFewtD6N+aUpTBMhmVYIGLqIu+iaA5zst3C4y3AWLWzd38HZyQJMZDDoM6wX2Y6WIRPQ1iv0MsqOyYlg6FyBO0ntvEK+mkLvzgmCrIXqQhXzSzXU5isoVyKeTSmZK3JXSa8NnO572Ll7iv39E3SHIwzOgO1bTZSm72F25Qqm6aWIQKNX9ZEOfzGxV5j+xaRDxhYGV0QZEHCmDShtQsk58w7dGRuGgpHWoPvMn/7zKt6+vgLV0TC6wO9+O8TtrYcwVQ5WaoCF1Tw2rlWxsJINZSjjzRMRoxz4Rh+FGQtLlysoZIDL75vIlK5Dt4GMA9z5PMDv/rgFP1DQRh+lSoCNd2rY2GS4kijEm+EJYCUw+zAzPaxcKmOqlg2NjE8g17lb5y4SBIl2X4RhjwDvMMCnv/Xxtz8eY+veLjrdIZQxwux8EeubC1hZW0SxrJCiu0UNjPpAs66xv9PGvds7aPXpUt+F9jpQ+j5S2ctIZwrIz18AprEITytToniPnV7klbH2KJlY8vyxFF76BlOl0xLpVzn2LQdGroPAaWG+No/Z+VnU5qqYmimLJ61UhqGAAH+gMWgBx/sO7t0+xO5uA4PREN4pPZ+4+Psne8jNrGL5JuXB+eIlL8XHQcCQUVkEvo3OGfDZH4/hGQM82LkLldJYW5/DxsYSZmfLyGUJeAICHxh2gf1dLuZOsL9bhzsy0O942Ls/xJ//pYftWhb1Mwe3vryLRvsI5ek8NpfnsLq2iEIhg1w2BdsGfE7am8DB9gD37xziqF6Hx50Nxz62jDpWN2ZFgbJQjnYMJ6uTrEx0n5LHCGz4QwsH22c42B/htLuPodvBzFIe84vzWFisIl9MIZPhbuJwbBm0fTSO+9jfruP+wz0Mh4B37KE/OoKVMmCYV3GjWBBPdRRu5FWjoHDtoyoK0yn0e0PkZzQ236kix7Ka0XzAVuJFav09E/93/qegF6GZWhHrb+dkcXFuvPh22CxJscn5UyhAVpIvFXlPeWZyO0GBiFdlbmCF4EHG2y3XMjDYP9JcYIeDgXhmJH1lDsh+Ea4jxsJpwvcJwr6CU+HhCRc/kZLkQZIm8rAy7DB29EDm2vSmaKdNFCpZCd+jYqw+x+iYR5PnzCDalCDTbwtIZ4BCXiGXyeJsYKLfD9DtehgOQgAvlUovNU95YmV+ODdj8oaNk+BdDrSTz3kKkCQa4h3o7ESH+hNNb6AaqbSBmZkiMtx89JyPyG4+Ey0dGRqoPKVQyObQPCPDWmi3HHRaTgjQTXgZek7Sk58nFHgKBciX8c6u8BGRrdS3lYBZhgimPoM6LG7UiAz+Mnfg0ppyWzZgMA2m9Xw+f0pBJrcnFHhxCjyDzTidZQje1AywVkiL/tUc62CVeHWOMwrnwPHVU47PyOspb0xuR+s3EoLkm6zins0SwoceMGxrcJ8bvXtTdwTTR7ZkozCdlg3JsvmVBI15Mj7GhI7WdZKbEc1/iwq5dAbtoSlelOk9YDTQCc+6zy7b5NcJBZ5LAa7jHI2zQy2b5Dgf1nCRThvIF9Nit5JoA7T3nJ9uSNIxG8scmEsyPpdSKJQVatVp9BodDANDgARn9S58bkSdfJ5Lgclq7SkkIsMliUMdjc9Q8aEMdkaMRMCJL224NipzeSjaMuO4l3w/ZlpmEZ2PkyUPmwBtBLSnZDJpGMpC39Fo1PsY9ALR24/LkEzrKUWe3P52KfDjA6yQb7mgyyhMLRvifvDax2UEQVkY2uKkWb7hwo8KZLp9rawaCIJaqBtSNZlgk9HFEBOvA891rhiwQi6PQ5W48NQAyGTFM8vsskKlWsJ+PY/Az+LgYRtbX+3jp/+tFIapeVLrxHlwEUpDrwd0DukxJEDjZAjH85ArA9PLeQkHpOgaVIdGLhlhJEQByxT2vsf6YJw++Y71ChTsNLCynsfmW7MolE2UZjLYuLKI1Y2KxAzLFGhsip6nMnIEMJzFnb9VkfvPEu7eaqLe8tFrurj7eQPvvNfG/HoVWYZg4KDHdy98KIcIWDGDDEydQ+BmcXYQwOsBzd4pTo4byBVtbFybx7UbS1i/Wka1plAsKFgpBVeXBM+TpiEAwNKmgfnFAopfFtHvKgxabdR3+zje7mJlrST8IAt91v8xoiQUti7gtzTO9gMx7vmuLTvoS2UDi2vTqFQVjEwkaJ+QzoVqnr9MPh8puuSWIgglLQrcw/0ORvoEVp7G+SKuXJ/DjQ/XsLhqolhRMKgwFt4I0bTuqcZnv59D9g853LploNP10D1rC2jlzhcnEhZpmW68k3Eyk+WIJibc/b68MYO331tBNpfG4loVyxuzWF4vYXFZIS0eiaIXPS0ejLr7AW5/OgXTUNi+20Sz3cKwP8LtTw+xtrGAa9fnZXdbrIs5R3pRjgO9vQBbXzCdAzROHIw8D5msibnlAj741Tqu/WQKSxsG8gQ/sQ6Rh4pRz8Lu7Ty++mQOX/x5DycHA5yctKH0ECPPlT7BMBj8hhqe803x8lc0aZNo/IZJF6sKV9/bRKN3LLyyurKEtcuzWFyzMDWtYKYfDcwEGrk9jdZDA3/77SysP2axvWsICKix38ZXn+7i+s9WkC9nkaWCNdlWL1/ocExO9v1vkNYLvSqT5bDerAKBKoa2YMAKQSsip8YRVWQiUpgHblYtBAONoBOGibq7ncKtrb5MNIx0H7MbBdz8L3lce8+MFo9Mg5ZAyqfp0IhCmQNgfRpYu2rIrmG6ujdMD7/9cx+BZ8BQPeSnLWy+l8M//NJCke7CiWVQNrShoFVJErEIGqGMoaExHgciAnytpiHt6SjqLMDR3QC3/uRi5+4Zet0hTCPA4moJb3+wgPc+XsbmVSOM00jW5e45FxgdB9j+cgq5bAZ3b++gcdZH98TDnb8do7pQwPTsJtYr3EEalZNlfIXtHdaV/+PvC3HB13+IybP9bKBYMbD+Vg1O0Ee7k8fGpRWsbS5hYdlCpRqFneMYyvYahmGWTh8aqMysYPA/bByeAN7IRq+hcOfzE8xfKWD5Zu3pZYqARfR2ZSAHBuwYDXw8uHcK3+yIa83N92p4/+NNvHt9GnNR6DuShBNtPQD27xu49dclWL/N4njHQX84RL8d4O5nHTzItNFzm2gNuqjMFvD2B4u4/uEG3n43jUw+ku0sHd0itjT2b+dRmVrHJ382UWcIhUEfjcMeHtw+w9xKBdXFbIg4f1KNEm0vfQ9paDeF0+OW1MW3+lhYK0oosmvvLWP9MsGKSgAzsmClnGpbqO/auPt5EerfU9jf6aPT66NVH+LO3xqYrp5gfjWHhTLBv4Cmgt0CNt9LY/XtmhiNOF7Z8TwqYh22rVU1sF5SWLm5IizFkIp8l95Vvm0WexK5JveeQAHyECeXFCSRvH7CU5NbFylA+UVe57hB4EpEQvK19C0CoyNZIwA5GVgM8V54ManJ9augQKSqT8jEV5HqDyoNdvFoTu72gUHXkXWGZZritS5TtESOy/wiOS0dTwdIXF6EH5l2c0OCwfWlQjZLT52cCJsYDgMJ7TgacqMFYJ5/NU5iciRFx3OSmByxMImvf8THiBQyRJEMAUHD9A5EXiYv0iMjec9GoWiG68eY18KfHydeJKNlTskdSQZQKkHAVvRUp7SNQddFv+vJnC8p2x9PbHJnQoEXpcAj2RnPf9n3Zd0Z6Q+EdaPHeC6fuA9IMKAw9GdSDsePTY4TCnynFIjmwJwvpOlYlx/OMSJ+FYOpUpGK4oJiJXp8cng1FIip/Cr1Qa+mZK9PKjFfykbQHjDqOfA8B0r5snkyXTCQEh10ZOxPiGupRfI64vFYjtNekcvRQ3oKZsfCyAnQa4/gcv4r3rGebCN5fagzKclrTYF4MkD56gKdUw2nH4jeQRkB0hkT2ZwFk3Yr6iRicZvk2biCvMcv05T5B5DKAuVyAaYaQvthWKBeaxgCVuK84/cnxydSIBYJT/xxclMoIDLY13C7Gm4P8B0qygDTNpAuWMhy0yN1xDEP88eLPJzkXe4BMyH2t3yRegjGcjPhOBqtpjsGDAomJk4nPkrOk3/fNQWeBIn4rsvw3edHQcuaR8piFWqFH5UjVkrwDjEpWQUznlSTYZ/EtEnBHJ8L6CB2Ysj3AmjlC0iDbotqywRR5HBvy4bnaZycnGD7vobTvoaUhD+OOuDF/GLpxniKA+4aD3B0pNHr92BZGnOLZcwvl5HKsfOOp6Oi8GZp4uTi46OKPzrjoCUeRWjvywKr1wxMlTegsIHCLEEZoeslMdYmd8DJoKhRypm4Sc8Sdgpebwajex3UO0102y72H55i7TCH2mpBQps8yjU6Y3sQOOdrmLBhqrRM3B7u7gDmEJ5qwco4+ODnb+OjX67j8rsmbBpj4919bFqWKULkEUBD9+vzKwrz8zVs32+j33dRP+jgcLsFt1eCUdTn3ffGbUgiRfxBgJCESDrRONzVaHbaQJBHNqMxVc1gaWNa3LKKEuGxSj3nBvOJ8+SjLH7EhxLixwdc38EoGMA327h0ZQ4//ccr+OiXGaRmGO4jmtTSoCf0C0Ej3NH+fspCuVCC66zj9lfHaPZ7aDUG2PrqGMuXayjMTWE6jjWYLEeUPw30dBd05bqJpaVLGHU2MbVowMhGNCfAiwb7mKHoocLTKOVNvD+l4A/LCPp5DAYeHCeNZr2Fg+0G6sezWJzl7CjxiRXjnhZgwsH9AA/udHB4eArXt2GaHhZWS/j5P72Ff/gvOZSXDRiFKH++SzeLecCaUbgyo7C6pLCysIz/8d/PsL/dR384AkNqKGoSVbRzeVzwRDle+jQmQpgAFfL5WQMf/8rGxlu/RLmokGV7pSLDe4wGlT6tZcFuTimkSgofsR3dIs6aedm11+26ONwj7U6xuLaM7PRLF/JrvBjKC6lVkj+T518jtfhReZ3/In4L5VKkfY/lprRPYgJNWtkAjdfSt0eAYRM24MLXHrRyYKRHSBUBM5YHMX2TbMZ8o6/OAioLBL0wDJwyXDAmIni0fajMEGY2L54hKF8Evcu0ovSi4ofVkkrFNbzQnxO3L56KnCUIwQF6Jxq3vwhwsNdAfzCAYbrIl4Bf/x8f4INf2CisRX2dsi7id6ZnlAxcLivMzWbxr//fJXz2lwPsHh2hc9wVT1CzyyUsLM8iw7BAsZyMCy+Ne7FUL3Ed7VLXYwvBS6TxAq/IgioduhT+6FdF3PjwXfFQQNd9grBm/ThpFY1umKDOAWZZYXZK4dezBg525tAf9NFsuXBGAfa26zg6mIJ2quG8IEmTRLuKgUhQlpbIkJHXl7Bj+SkfV66v4v/6f64LCMqIlBdjoUjaeMBy2UKl7COjp/G7/3cE9/gUjueg2R1h2K7DRQOFSoBf/7df4uNfl1DdDMEesnNS+D6sT1DUWMopVMoK2l/BX/9wjN2jvgBotu8dYnEjj+Ura5ibfcSrY9Im6hM3Vdz/Bm4XqfQQixtl/Oqfb+Ddj1OYinmOYyrZzue2TcpZhcWKwtyagbnqMv7nf3fw+Rd34Lsp8ez18PYp5tbymF2blTlUaKCHGIm4OJZisK+zXkl6cyhhXyPAJVHW8TPJZ8eVmpxMKPAGUSDmYfI6efwCn4dyhvfDHyjL5LHkc29QdV/vosaN8XqX8nsvHXmP65ChxnAwBMd5hifMZNNIpS1wh7TIaJKT38d4lTciWsszIfPTUxyxKgztSa+Hw9EI/d4IwwHD4YXeD6WPRK9+73R47QrwGKFfuxK+LgWi19FeV0tYSDKobZsoFPNI0TNFNKeXsj6NpAkeFBmtIGEsspk0GG5Fa4Xh0Mew74cecJ+WzutCkEk53lwKxLzIo6yZnyRzw+qF04hwlj+WwXHN43Ti68lxQoHvggJP4Dth5YnM/C6ofyGPCdEvEESmrzGLCl/yAXrK7WuMRi5835fNmNksQ0/b0fw31IOTmpS5VD8+9olIzfkDp8OmDWRyCpl0Cqbi5sQgzIPhoD37CfPox1Kc3JhQ4PkUoL7YB/pdCP9y/ZaxLeSyBlJp+5ENJ+LLZyUoc18+EOnv0mkbBnUU3EPvBhj0XbgMnx7EkR2eldqP97dzUvdJsuLHS5on1zwAnIHGiN68fV/kq50J5a/okeO3SMtzxI1/iI78jaI62vyaKwB2yhT7ThBQv+GIpyDy8LmB4EIyk8vvlgLJJfp3m/PrklvMtDRwRcissQElFtzx8aJh5Wl1eKLgiXtPuLJkHtVVA0vrStCNKdOGAVt2J//lPxsYNcOOIrvaCCSLX4/zJHgiCk9zuK9xfNxEb9iCF/SxtFrB2uYUFI1mcQvL+/EFE4k6YnQaJyvHqL7yLl8hXbIKpWUDpVUDNKgzfi5BK2MXpLGjCj5LAEOGu6QVNq4ZWF5RKBTSIWAmSKPd6KNz1oM3oOuXqG4sTgQMkbpqYm0ULIMRHA3xHNMenKE9PEV+2sAvfv0OfvXP67h03YTN2NMEbESeccZeBBhigOWhkbuoJN7vyvo86G6dO6wIniFgZX8nEMTemAZJWrNcvI5BKy6w8yDAw4cOvMBHoIbIFjRmF/PYvFJCphx63QmtEOMUX/wk4p2YhSRrTY8gDgLN0CAOFldKePsn87h8M41UlYAN1p/5hiEVZPciwVip0NuLWVNYvmzgxs0iysUiLCOFwLPQbXnot114o6jCPPAbtX/MC0LXNN3PKuTnDVQ2TAEEiFE25oG4b/BdDgLMPxMCB25+ZGJl1UQ2nQHDT2jPQq/l4KzOmIeP+JCvCg/QG8EQ8E809h9o1I8GGDoj+OhjdqmAtz9cwo2fZ1GaD+s3dgHGtk6FtCA9GDIntWDg6nsm3v+4grm5CgxD0Dxhe4iCMWa6F2+iZz/JjhC1HmnJWNNZBXtGYX7DRG7ekL4jxliWkR46+OUuKTsMiyEDqa1QWDZw6W0DtbkKLLryIMotsNA5G6HX0Qjidnt2gb7Rr1FNHqUR88ijO+EZ73+DT5hsIhGtH+tC47LwhPwmcEuNIPCgORNXBJowZA8ZKBKZlO+UCzzGsp7XNIjzyBUl+TWKuEAXieK2RHlQpgfTCmCSp+T5BEiP77Cpo3dl8cn6R1UYl/UFaUJXjUFT42QnwNbdDlrtLoZeD4UphQ9/tYErNy3kl1TY18Xtc9jfSYO4fxKkU7xi4PK7BhaWqkjbaZhIY/9hE3f/foijvQD+MAQbSLEe2537goX9Ph+L2p51ptxLVRUKiwbyBNCVI3nAfkRwBfkjbiMC+UirtBLZ9eFHJlZX5gT8pJCCO2IoriE6HHfjMSmuZ6IxH53Syw7D/A0RGD3UlnL4r//8DuZXDQFySL7SkSNejfiM4M9szRCPJYuLtnjECeDDF3Sih8qMhbfeq+HGxyXMLBoiQwnCCXk9wV8c00oK2SWFzWsG5uamkU2nEfgB+u2RxJF1Ob6+wIf+BQj48hjg1hyhWDOwcWMab39go7xkiMcwoScrzz4k42oku3IhPS+9b+L9922sLc2LVzZnYOBot4f7tw/RYGxbVz+ai8RtGPWdWFxKUflbsthRnueeeYE6TR6ZUOCHQAHOPWX++UOozOtcB/EQRGEz+cQUGFMjIY+pwBkMgaEAvz3QwwqN9Tbj7o1fiFNIHmNBHt2L0zQgoQ1zWSrs0wIyNZQp6zPX9SScxbnxIJnk5HxCgWdRIMly5DcNuK7GcBRI7HPOe6ikLBRyMDlP/DqfRNrcHc1wQpZBz4wWPBcSdtbvJ+baXyftybMTCrxCCkzmD6+QmJOkJhSYUODHR4F4bhtAjKWu48EPuPERyGYz8hXQa/ScyNxYB3mRWvHcIdYJ2UAqDfHUYjA2LEwEnsKgN5L5ysXXJ9cTCnxtCsTrrQDo97UY+wlf1QgkFEoqRQX3C6Ya6yQi2yA9vZeKSgArTCQIFByHa8TQo8sLpjp5bEKBxynwBJ4kbzmOL4BBKuszaVvkrzgpeMLzjycam+iozA4dV3D9xo0z1GdIv/AJWhnBGT3x7cnN74kCFDmTD5k2/iYN7/G9+PgsSr3IM9SY8Dl+6IpoRqG2pMSQnkmlYak0uk0Xf/vTPdmhTC8V40+kcImVd5wQBUONPsMBHWq0Ox1oeMjnbSwuVzC3GHlBifOTKj6hkIkszuXFCz4uhlklRnezFIIWBKgiQIWE4ZdggdiIS7ACd0fnlIBWZmYJWEnBNm0onRFjWr89hO8QCBJ6lYgneFK/qK502oGADUJXpgZo2MsUTCysl/HOR8tYumIiXQvLJkAFGiplZ3YEPpK5X2iQZlmm55WErynmCjAMG8NegMZhH4e7LQx6ESGS9IjPo/IQtEKQEAEue/unYrC0Uj7K1ZQAKUo1Gvei8EgJuo/p+ryT+J34GNnuNL1HELQCB6l0gJVLFaxcK2J6JTSOCuAhYZxlu4kBnfWnYZfAjRmFhRWFmUoFuVQBCGwMO3Tt7YO7zoTuzDeRtxSX12xXppNRAvwRwBK9VCzV8QAAIABJREFUqxBoQT11DFKKzsVYK/dCHkgtGpiuKeSyOZjKFiP6sOuj2eiGQKAkXYTGGn5b4/hhgN37Ac4aI9kRp0xHwiCtvz2F2roBsxiBUyRfGkT9cVmkzKnQqJtZMrB+VaE2l0FGJmbkuUB2iIb1jRs6WZBveM42sEIjrQCm6KWJBvVc2DdIO5YxpjdLIAAI0o0G4RRgTisxWleqBaRTVOan4Hsm2o2RuLYO6GHpWyj6xZqLM5pHRb348yu7pgyQ6kR1EoBJgh/PVTX2viLusSIQEr1XKReBGgldhY7xrt9QjIzpLYXmvYSiOrRZ0VOLByge3dDTStwmsYE90W5x+10kwpO60sVnxtfkeXpX2Q2wfUdjb6eBwciFnVKYWyng/V+uorpmCEBB+lwMtiHfE5hBfuE5wQPTBubXKOdsTJUqMI0Mek2N+t5AwmIxLq6AwqTLR56GxgV5A04S/CAyKReCeNivDAGARbRgm0VyUORT3IakUwrYuGKgWsuLHFDaQuCZGPV99NqhF60xJRL5yb2ICclxvvbhY4RcSWFxo4Sr101k6NlHvNhEwEXJN5KTHJ8IIiwq1BYVZuboCpZClJlYwm+VORvvfDSHGoEvlG/xmMZ0yMsRH7PdGXqOoJWZeYXqrIVynm45TbgDX2LUelwwepFsH1fo8ZNQFgbiecpKB6gs2lh5p4SppVDG0hOdyNOoDCyHOJnhGJ8N62PNKlx5y8CVSxUBmcLPotvUApa6f6eOfifu3FH+cQeJ+1J8nezk8b2Lx8erMLkzocAPjgLj8TCe9P/gavg6VIjChZ/4GF1ODucpEI97AcTbpedTYc/BJYBhKFgWPY7FC5ZwJ+qz2FbWe3xeQr0pUdZblglDMfSVCZ+uf50QsJIcEs4XanI1ocCLU4A8RzDJiIAVYaoAlmUgneHa6sXTOfekUhID3aZXP5lwMnS0IYYmhxsKhNHPvTG5mFDg+6PAy/L591fiSc4TCkwoMKHA90eBWGZyzhBouC7gesHYYGpbJkzLCj0/R/oMmU/wvadMXuX3WJdDWxA3IFE3ZdBgakL7JkZDV+YrkynE99f0b3bOT2A+DYwcwPfDjbq0LdHLINdeL/yJ+wNfoNNzG8hl6fmcP3AXpAnfM8D5L0NaTT4TCrwqCsRrOEYkIWAw0NxUbAj/ioOFWOaSvZN8erEAcdegjcCkdxWAe8INIwq9HSh4jivrOD46kcEXCfj9XHPInHxIAeHKZ5Dieb8/49VHP4WJyGZqGhkFRGFgfbOGXMaGpSwM2gHuf3aMs8MAQT8qVzQOxOnEXiiCjsb+VoDjg1P0Bk1YtovZhRJml8soTRsSPuNip5U+nMDNxGk+dkx2eL4UG5ZoXA3Hukc04z06NYi+AuzwI8OooVAqKBSyeaSsLBTSEhqGBllGFqCSR/Q8F4VLlCeNgjSocSxMpQLMLuSwcbWK9Us2UjToRePseIIYTQKl3kwj+tJImZ1WmFtWqM6VYNu27Kpv110c7bTQO/NALwfnBFNcJqmfFqNycKZBjzanZ6dQykUq52FmIYu5lYrshhc6CTHjlx+j7NNvkB7xJwoJxEuiYAM4MEwPuaKJlc1Z1BYysPMJN8ZCy/Bltk9cD6EtDe1phWJJYapsSrxtRRTsgHHaRqIYjo3YcfbPPUZ8wDYX7zN8IeIDufYYAiPkAQI2Cjkgn83DNCwYKiWT8Xa7My7neGJPHR+9Kfc0DvYC7O910Om2YRgeMlmGAypjYSUFm15lCIwatzfp/Uicsf7S9JEhvzpPTyUKxUJOQr5ocaMQXOwez632Mx8Iu/ejR4Qpo/7CdozoIzSL25pH9ifGKqVzD/HuEP1oKWQYH7KkkE6nYRoGAs9Hp9mFM/SFTmO6Pcr1DT9LEpFY18Qn+dP4diChWTjN4Ie7JoV6MTucS2D80vmTSK/M9wiMC9OJxFuM1uHNF0nrfMovdkW+8DQO9wJsP3BQP22CBqFSsYSVlRVcuWLBYl9nOelVK5a15Bt+KUil0iFokLJ/fl6hNl2FqVLwHAO9louzehfOIHpeSsa6fluVerGqv9RTrGtUd+lTcb/iMWpL/j6mE+nF/sUxiecBkCKIskj9g4+APBNojAYuep2B9MFYfj61fDIuOYAaoTaXxepmBdlZA0Yc6ovgjkfiaDwOMT0CPuwpymKFLF3JKg1Fjz6Gh0qtgEvvrCBF+cY0ks3D87jJeB5NtjN5hXIJKBTKMI00XEdhNAgkFmcsg8f1iN6Lr4Ve0mfoochDNm+jtlDGyua0gFESIvURq8SeeeIycHC2FMor9BxDYCLrZMId+jg77mDrqz0Br4zHmIhX4zJMjhMKTCgwocCEAq85BaKxlcb+IKB3RIK+GbsVojSSsYqync9FY3E8LzlXs4T8l3fohZBfg7v0TImF6ns0DBCw8mhucy6NycWEAi9BgYAhrVxuAOEEUouRybIjJUKCL184aU59bPJ/+Aa9wRLNS0W96zxah79wepMHJxT4FikgK2rO2yefCQUmFJhQYEKBp1JgLCYT8wKeBr4Wgz91R2KbMLmhNkwm1vlEqpHwZuJ9uRH/GB3p5Zke3kyCt2V/fziHcBwPNMxefP2pBZ78MKHA8yggXgYhG4BDztIwTEMM9c97Nfm7sDsZk2GvxEsm7Xjhl0GAAoZucQjqmnBvkm6T829OAZ/2QZ9eMql/8EO9AWVwnHRsA4ivn3SMH+bRACyL3lUog0MFO/XinuuHgKsJCz+Jgt/LvaRJ5XspwGuTKRk3ZuKLhUoybPKcz/E6/sbvXbxm0uO0I0s6jVFpoDiv8NY7BvK5tBh5vKGJxr6H3Xt9dOvczkZjVpQH04/Oabhk2KCHdwIcHdbR6TXw/7P33k92HEma4JfiaVFaowAQJECy2XJ7tm9u53b27Mzu7z27X9Zuz85sbuZmeoZsRbJBEiB0aV1Pi1Rx9nlEvMr3UIUqKDZAZpa9ShXh4eHhEeEZ7uHu54b48M4K5pcrOuyIUW6JgQyVWwY/e7boPndmAua1oRF4z/Udnrm2Q4UVFyRTSkGj/9fp2NuNQpEWyXkfEg6m4JfgOgXEsQMaeHKX9thB+Kmf1sMmspM9QQgvF2FprYzrH86gztAqRe3OSXAydZ1sCwqQ4oWAwmDNweI1Fx/crokb4Ch00TqJsLvRwNFuByF3gduDePCQxVcdJiLpK2zei3F80EMYhoiTPvziAIvXqrh2s6w9Y0zWyYB55RNtUkRBGCFfUJhdLGFhpYpqXe/WtwY7pJvlMVF0st6sAzObnfE5iRMP+K4n3kWCQYRwEELFVPqneOwiZNN8betJ+lAZHAAJDX6GDFWlFcMjHgBQYpx6MgKRUR7CIMGgP5DmGhWXgs/Nm90W440PEARD5PwYi8tFLK1VMDVnvClwV5vFg0BYCcs/5FMJXaE9KpRnHSyvOliYr4lVJp3hsXEllMw4FiN0XuoixTppcKL4Fh4yfYhADa9qJbruR+w/WqGe9jhEaRAoFYGc78Pjqj5jpPeGiENtxCXz60sh+h4mNm0qH4OGdmc0toQ37Wk7AvNcdtisqb4jxguaM+Q/WUqSXQXeueWdYwSXhkXeoCFFCAlH0zwZoj8IkCQxpupTWFhYEGWObAk1PAJ63pIfecr0NdaB/UEMtCAuGqslDx5y4k0p6DnonA4QDkx6GWrTiJyL/Lv5UOYe3TfEy4qZkwTZVJvKWMB7mUjMC544ppYdlEqAx1UCKIRRhF53gE6rfyVDMNdR8Bz2wYGMSWs36+K5heOtjKVEJsVXI37lM2NER2O0HD2oGGu1csnH1FwJtVmOb2ZuS49vBGkNYdh0fOc5sjOHQwN35kDRJb0jxph0f3+1gyaRMamAar2AxbVprFzzNA4yfxrvT1IJDZM7gmTuIf+acD8MybSw6mJtbRH0GJdESjy99E5DhHSPT569DCXWy/6uhnyWKqNARoGMAhkFfgAKyKddog1WOE57nifhgGRnkh23zdzEkxznjfl8Zp9zPhaDFdcYsDMkED25cFEqU/pbMmbn16CA4TcarAhfMRwsP1AdJZ/IrwSZslzK2ErzO+U5V5Raw2HGu69E1yxTRoGMAhkFMgpkFHjXKEBVQEyjFW2s7ToO6GGF8u+Fx0gQnkhhNw6bNX4uRekNc9pgJZKwFxSAJ/JltxkFXoUCRgYm//Igz/qeK2ugNDa56mF1fzY9wWrViwOltLFKFCViGJ55WLFUys5vhAIy/tJgkMaC+ttNePhF4+9FBVuWN2eqSsXoSvqJgyiKQT4WHd5FMLLnPygFzL6QH7TM968wMnRaaEhfp2tz7nNmTnsKoJRiPIP4kDAhqzdcLC5M46RRwbDTB4Z5PL2/j7XbZdTXXW1wQKUc9T0sI1ZIOgon+wp7mwrddg9wQtSm87h5exnT86541BDFTxo/owuSR+fiahLzHRW1k2nMREdENB56Fzu4659pE6UXGOkGjGkHCsmJQr9NbxvchF0Ql89J3EMiVjQphDSZNAJmABGbF0cvLHFxiXEea7Ou/NySVuiJEtukFwLRI4LgbQRIvqMyjzQvApU5BzdvO7j/oI5Ws4ckcHG43cH+Vgurt6YwP2VwIpiR0Y02ykgaCt/9NUHjpKXjVeddzC3Tu0oN0wuUOlPENjRMPXnpS6mG1I00COH4MfLlBDm6X2MkCZY3qjvBM4cp2AogfM904naQrrtNrMGEFoQJKBRT+B6HM4GqASv0sNdMQlmaFuDGM4gYqpAXSDemIw+QV7tAr0shn/YzHHJ8qMTV6dJFCX/TBBhg6JJBj8p1VoBWwDGm5nxMz+VRorcJHjyZyzEwY88ZVkPBqTgoVem+mSnJoDTaicUwQCOrIVi2TMN7nWs2g/QN0sMepBuND2jgY/oKlbtiwELa0dlBqJAMgNaxkjh6hCGjiHIMfa0lhQX6YzsbxX+6fXlt6Wif0zuFeWhfXZkShk+kjcy1tJUBYC3QRzxm0lwZ/lUTku8HwLDHXZlU2tA4yUepUEHBB/otwI+U8LFinDSLt613oru4DD9UJrUYf1GPxb5XgB9HSEIP7eZA+hWNwdJD1VXRfCfSsZFZf/7jdWqcppcUMZKTccQY0bEfcRxin+MwxzB0nJcGwKBPKC48xxdjUYd9i2knGInFpR9p8pPvYsRJT7xsFWuJjLFCWNM+Y5kMyvKMTcjFCRm/WWAowjd36ubLDnJlbtvV7zkcSHXB8gwQXpj8hKFdyTKhy5Fy5JKTBisc2i8T8sjn9DhFYy0v76PAUH4VbQwjRilSLv9ZKpizFG5CFXF8KgLFOjA/P4WD7SG6QwfREAg49pt5QddhBPAMpKla6k12mVHgJ00BvQik5Qd+nD/Xd37S1Mkq/zejAKdZIyhxwZ6eUawMZnGynx/2fuw8yciyUMT5y5qRU8blwqf5zmT6yTxjAH/qNySOnZt/6rSYqP8E31AG1F579EYg8erDuOUT6SagnN2SzDYtz0aW41leMaQVhSYu3NMQPWuWM9plVxkFMgpkFMgokFHgfaWAyL7Gw6BelL3QQ8WlMoWVI7ToK4azWqTQ/zPZ4X1lkncb74gGV9wJYPmOYVAuZdazOjEp9SQiB4s6U3sIGsnFXG41cnYm/p7RLbt6MxTguKjHRs1dwrtWHWb4kie71v5cqZZ3+UIS6hSiI5YHOgHX3OwYLN0jlfY5mNmDH4QCl+kyfhAk3otCLmNWOzLbs6mUvjVGKjSmsAtLVmlVdFCYc3DtAw87B2Wc9jwkiY/NZ4fY357BR7057W7WKirpzSICBqcK+9sJ9nf76A+GKBQczC1XsLpeRXWK4VLOEBh1OlO27o5jffX5JtDzmUZXlIL0ngHtQYPeNAaQGHU0LOh36f5LIQh0jOgoUIhpxDAA0FLY+l6hdRIiiV2IUpCw0/BZeoo0Fhk9h2p1muMoFEs+6lMlVGuert9YmxjC020LL1Pv7ARLDwT0srJ808XC8hRODrvodoc4OWzhYLuL08MQc2uu9shi8xMWJ9++wvBI4dHDHTTabcBNRLHHEDULK3W4VTaoxfzNnS1ZSAXXV8iVXOSKjva6YOk2Ktde2PM4HWiEKIaIjiMOGyhV0GWbKHotzXjmYUFYhfDIu4NRBFPpS9sq0qWvRDFJJfmQivdAiYJSG14Abl/h2SOFbpdM4UGJYjW1xG3LZLlULJOXesCgp90vctZg3WvTRZSrPvy8nZ1SeGqsx/6P2p22RFSmlhlWiuwRi6KaVdSCmliDSJXTqIwBu+zG0ovpCMT8RAEuNDaeaEwfivoKvQ6V5grBQCEcAOEQsH2HinV6q2keAXtbCoMB4/WRZvzpvxHC6bIvw/MdfK/7uSWcQfC8Op3TOJJMAGijwFE2e2Hz2Pt0/W078dlY99WJR1ksjHTe9DUTMg3PNpM9p9Odc80FdI7nUYfjqBI34g5ycFFA1MvhdFPhwR/E2o+OieRHMGnwMr5rFtYodBR2Hym0W+y0dJXnIooidDsdBMMhVFw+w/ccnN75R6bdJDwSvTmZvhL2oPvSkH2J/cqOT5yXTDzVAHCHgBso7G8oeAktH3PwaCSkXLjptjbXo0ek8eiGbUCmUciXcsiX6d5m4rCNxDbmq1ReSSkGKzQT4S+Gn3eRL3J8E92dJCEIDcYCS5ehyxfvKuJlRiERa0G9AyihdTjtYSYPi4c9swzZaawEh1xez4FiUDOW15o5nWVkGjGioic4D8gXHczM1JD3m8KsSeSIV5+IhkKc85mVVbHnMfjZTUaBjALPU4DzvT5eZmHpeTjZk4spYCl8cYrsjZ2LzpnL0sSxU9WlJNUJ7HRgv1P1KpF+N3qWhp9dn0MBUtH+znn9U310Lg/SoyhlJ02v1x1TyaPyk1GaBRre/anSPKt3RoGMAhkFMgpkFPixUYAigxx6jtcqndFDkQPO7mzac85MlJJN9GZEm04vNHFt5UqwbLbsnFFgjAIp7uGluZU1Z6aj3Gq/GlJJx0Ccd0PRmeuN1EUyP/Urwqt8oTNo8VqvyZ8HInuWUeDKFCBP2bGS1/YnAOjVx7y3afjc8OFFZZA/xeiKCazuRM5p4OPe0i+ClT3/4ShgzRp+uBJ/rCWRz9Md5rnbVK+yfUI8rSgxdvjwExePn8xg++AQQeji6KCBg51TdE9nUas4ejc2YVL5HQDNwwS7mwpHh20EQYDZxTxW16cwt2R2aFO3ZI1CSHODm+7HFoHJzm8axxopMJkoVLX3B+7c7x0rNI8TNI8VOk2GbVFoNxW6nT76/R6Gg0AMV4IhN44X4Adl9Bsx2v0egjDRYw1x4W53UWDRii01MFj+MOtJek1JSf2L5Tzq0yWUaRxC/02jwwCUkAhndZXd7rbe2suehEqaXnGxul7DwcYsuu0jdFoRDne6ON7r49agAKd0tgNeFqJosNJWONxJsLt7gu4wguslKFZcrN+ax9wiDYTOqcMIvytcGDxtOzGHfSS5GX4iBxQqnigFqaSUwyYakWN08fygzVep17ZuIsBwsE69GxVO+MZYRfWBpKsQNRQOdxM0j5SEMel2IMr2Xi9ArzdAt8fQI7HeVR95cMI6Ok2FZqePOHGR0GWyeM7RyPM/i7YTDxXRAQ2hevQQoNO4nkK5npO6i5KUldevDCHGqnZGGqmzI+G3imV6WOEDanG1G0eGBBnVO13/EdRXuCAcY4hgw70oGvh0gO5JgsahwsmRQvNUodNS6Hf6GHTp/SLCcBAjHCZQUQ4qzCEYOGi2AnT7CaJE77JW9CpidcdvCudXqOYbyWLxFxfdzzWpLmKinUflynMKxRYIz9qkZyyNfT16+IILI7RomBOwXpBNM7BJwHH9RWntO+JPPgkhfEAvRCFtukCrqhxOjwa491WCpw8SJAiQOCGUQ5c85hCJS8f7kniONGpjPNyInrpctFv9UQzcKI7Q7/UQDhluqGwhvH9noxyg5yYaUHI8ClsKrVOOSQrNEyVz0rBHQ7oQvVYfnXYfwTDRRiuhBzUswFdVHB83gKSgjXoSR4wpZVphu9hyLO/ZM8cqzldG2nU8V4xVcmWJyTNBT51Jeq2BORqzyCAcwz0zEToc13MoFH1tjGgGpRfyEV9yHBA4Djgu8E9M2hQ97NAYcQKliVuCYFHcKe/5QL7AEA9mpeTCwide2FuGiMgxHBWQ8wpwxZOWJ4Z4NMoTT1zp8lM0TT/OrjMKZBTIKJBR4N2kgExN+p94BlOyW+9sN9JojrsUfU4cehIgiHhisuKcJK+zeeJSSmYkupREkkAi5LquxCqnlyDuotOx0K+Wf5TKyjzccS28q2FZT1gsR28OGeXILjIKZBTIKJBRIKNARoH3kQJ2zre6CbPaqD0BXl0CY0oLSta8U/cUeO32BIbbdESIeB+JleH8rlKAn1U00uaPm5WtHHwuvmlmTSewDHxmo2K4WMNluCHRrVg9RTpvdp1R4CUpYJcCmE10HPTqyo3vEiVBr3VfGaTl3XQG6n1EIapfsk+41A269FqeHe8KBTKDlTfSEmZUJ69b7j6vU6TLsu/p9aIErN12MfvnGvx7HoLAE48Le5uneHK/jV8sTemd0lRu0rtJW+F0X+FgL0S720GiIkzP1bB6cxa1GRdOPiUBcc3PWFJa5GzRaXTk2uJuX9CCcqgQdxSau4nsSN96mmDjyT52to7QPG1j0GciD0kcyeChrQ5yIBI5p4Ii6nDCAgK+F4W7NlqhyCbCmlXu2rInz0JQ7ohSWplWNB5QdGaLaarC5pGtpD1zQOIgVwDys8DKuovN+Wns7jahEh/H+wPsPGvi5KCO2ZILj4YNKVx6+wr37ibo9+npIhaPNrNLNazfWkR9jqtTE+WmMHvlyzEUOIAq+AUHubwetEkCs278CkVQqU6kddxBKs5HdTD1FhKb0DVJT+H4CXkgwcGWwtOHu9gjDzS6ohSnpxYal8RcAJTB34UHHz7yyLtDqKiEMGIIjUQEJO0yLoU2y7TlMiTQEBgMqFzXXgMkJFTR096GTJtK3c+ypYDpDwJLG2tJyZAbpJ3iwrj0i0RCAnGR8bWOdHbiljL4Yn8NjxT2n8U42FHYfNzDxpM97O4cYDiIRiGZtJcbeiDy4SIPzynCQxEOCohjHxH7vgkLkCQ6ft+ovV4L+cszk37pKl6e45IUE8BIfj4i6Wx3HV1PpD0fskltAIm5iAV0foazp+fAl/wCS0wNztK+6StbNqNg0SPIEIgjjo+uKG8avTa6wxBwBjpkixOJN45R1cTjDvswf8ZYwVVwkpz82MdC1UWEHoJkgF7gIUoC8edxZqX1pit1Bk/wNIYftq+evX2FK8LieESvKn0gOEpwsKkNN7c323jyZB9H+ycSIk9CbEl4H1LTh+P6cBgcJ8mLV5W8M8RgEMKBB8/NyY/tHtNfJtsl/TsPVdMInAtyBQ+FojZyHE/KQVW7m5chZtRwmtFJE45rnL9ptJnPe/ByLojChUeKZ2wavVuYMPlSrEClH4lyJl2mzZAeMwUHjrGxfMDmcjRYMS7yJ/IS+sQjeaDrYerkwYSd8wy9XRnjBv0YUeCRGiksssuMAhkFMgpkFHhfKMCxnj/6beMcyYWd0YK9nTOvXBkzmTkSTVaHJmVelcClUfabljuvjNf7mJDzaja3XtZyHr3A5Xz4ro+Q36Ixve+ljcAvgzDx3nzr8ZNSS17y8StrDZ4nXxITGbLbjAIZBTIKZBTIKJBR4H2kANdVRA4GlZlW/n0DNTHisEByFHyfmyr1um8m270B+v4UQaR5ytTf5xqd68N1XFl7jONYhwia/HyweXk+5528FoNt7b1a6yf0dxvlbM/w70+R7Fmd3xIFuCmSe0MlDLGOOMDQq1yDSB9cbxeWneRbm4jPJ95p9ZF9qOC5ni7L5snOf3MKZAYrb6QJDJOP95nnIMtruwhnOww3Mxcc+CsuVm46WPpuDlsbbajAwclOF9sPT/DZb+twqBCjRWQIREcKR9sKx+KNpY1i1cHCSgUrN6bg1XQ4IIK3ivqzjqnxPBdN+9Aoj4k8Q7NE+wl2Hyb49qsYW4+Psbd7jMPTQ/QYr8VVyOd9lCsl5HI+cj5/LrxcAa7rI+fUUIwX0D3xcHraQtTrw4modI21FbEh2wg/rvhIwUajZnHiUpATwfEUFH9iHJLyaMKKUoLkwZOFa2HpN6LXlbBAVQfzaw7mV3IoPcqj03HRPA5wsNnD8V6C6oKDUs4YoZAegcLRXoJHD0IMhiFiBChWili5Po+ZpRzckilQ8LKFveSZIFjfc3BnKBi+ZBV9z4Hvn1X30lJSFrAWPJfR5E/ibVuFtymfJyYUZa9CQiX6scLW3Rh3vxpi4/E+Tg7baDZPMRj0xU1OsVRAsZaDn/fhUdnpc4d9DjmXvxKKySIae8DRcQcDiZ/oGlMZxjgxNZiodxgrhHFIda6mCfsJFwDpTSBNZ2u0kyJEGtTosUnn0UCMlpPGAEQbq1gkRqlf7cKAkY2inEQHQPtRgod/jXH/qy529w5wfHSKVqeFQdBFvuCCXoOKxTz8nAPfy8H38sh5ReRQQcGtIQkKODkKcHwyQGDa5CyA5KuhefVcFAh0E8mZCnES91wCXx3qlVJepUkED4uQPqe9X4z6E2FdGeezgrU4ZIxWzh5fCf0rJyJeSgv89K4SxQEUfS1CoVj0Ua3n4RVjOC5DaflIxN0RoZuBmt4+rMGKaR9HeXAZP4hOrpw8IidBYcrF7DUPfsUFGF7OtCvhEMLrHmIrYYCQVEK10QT0AujntY2ldRotVpe2JDSgbCoMdhN892WCJ/ca2N46xMHJMU7bTURJKAYTlWoR+UIOuXwOuVwOvk+jFB++KiEXT6GEaWxvnmLvqAfXKQirUOZVk/PIefhJamkBMX7zfFf6r1nFSPGa8VRixh6pVhqeqZ/eUcM2Ni1xXv1JQvvcktPcE4wGxQf6N8a7Nv0557O8us1cCukSY8gfCkw8AAAgAElEQVTYvqQS6DJeAMTVoer0/ETTFAo7ClEYSCiqMMyjcE720SNbvwsLGqXMLt4ZCqQY5J3BKUMko0BGgTdCATsmG2BcrOeCJ5X+Q3Z9KuojKv7Nx5sdDuy3nL0/Fxl+nGgvYDTSpDG7A/5CuE6CXM5Bjt86aXn/XDjZw4wC51GAzEsGPDvEYMXXZrOOcsVjbRRyA4BOM8oxnu1M9rLPeaa8mABBCNDmRYy3+JjhFX29scSKk2cYZFcZBTIKZBTIKJBRIKPAe0MBCgZGOKBMmvN8cK2Esi/lhzCMYEXgdJ2MWDEhhaRTMCS4QhBQ6QrEsd5EyRQ0WPF8rqlYoWM8X3aXUeDKFKCsavi3kKPBiuYprh1HcYyYi6v8HOOyMdPZ7zcWMMl+Ru61acj3lIEVrb+18kP0e77vgEbb2ZFR4JUpQF60P8OK5FF68OF6Ozecyqb2ONGsZ9OS7S5gPSYRmEzCNArgvuBhxDUIvnXFmItrHDkbOeMCWK9cryzjK1EgM1h5JbK9ZibbmWSCcICcgjftYOUDF+vXV7C/0UMQuWgfDbH/rI3+oUKl7AB5hkFQaO8kONoO0Dw5BRBgZraIpWt1zK/4EvKG4Wl0h5SuOervVCLq39gYMOq8rJVMaqbTx6cK+98n+O7zGF/9aRuHe010hz0o38HU9CxmFiuYXahgeqGMctlHseiiUHCRL9IFCOCrAryewrNvFR48qKAbnCKMOLOZLeQpOtgBZIQskZFJk8gwfQTl0lglQWJtLOwgoqupG8U+k8roR6P/ZkKmR5uZJReL1xLMzNdx3PPRbcc42R3gaLuPpZsVFMsKTl5v/UvawP62wtZWAzSkcP0E9dk8bt5eQXXG0enS5Y4KfAMXFA6kXbSxAHWJZqx+eeCGTlotyv/cJynmiuOwRCBRQvbkVOHofoy//FOMb77dwf7+EYIgQrHkYWZmHrMLZcwsVFGbKaJUyyFX8uAVIEpiCvX0a1DsA/e+SCTcTSfwkDAcj9BrfCWakwd13DRI4jkxXiNEoetol+G2qSWtUQSPI5+6GyXWz4SOtNCk8j9JNVjqMpX7lS6NvIakD4S7Ce7/OcbX/97Dg7v7OOkcSAiQcqWM1eUpzCwUMLNQwdRMBYWyh3w+L3TL5/LIJ9Ld0T1W+ObLgg65FBr3ZFKRs0n3lRC9QiZdjBkzJNzIRYZShtDWQOGq9EynM3XiI/u7AoraMGKUR/O0ZqIr5DZoS8o0LmYQ0jVPtOOKK4B7qSQsz9SZ+c4WtvVYx0Jn56dx404d9WV6V6IcJfs59CAteZXgJrudZchXUnXdr4m9QugMEHsB/HKA+hJQn69I/9TCHAfYsYpfrQpp3NN1MLktGwhs+55nHuni7DOe089NUqGPKUv6VaQkBNBgK8HjrxJ8+a9H2Hh0iNNWC0N0UagXMb8wg7nFKuaX6ijXiyhUcsgVfVGw0UVlLskhPwRKEfD7f57BztGuNp7kTgHOj9SOpXHhEGXxtHgxh3UnT+WE58A1ktRZO6YqS3haNyftkwJjKs6WYrunCkvjMMoweaEnBwNe0LYzvCj+hGjn4Z+CI5l1YTLOCkbm3rCHKAz1o1TG5y9lTOa0zXwyXRlOFEug54g4TmeCOyfJ86VkTzIKZBTIKJBR4G9BAX57cMG+kM+j60SyM48eKuKQXsLO5vHUpUHTyhpmIuHJXMaxDoXIhVM9C8fwfYVCwRejFZaZHRkFXpkCKbmCxrRciBTbbuUgDhMM+8HIYOXCMsirKThyzXsudg6ULNiTw+XgN6bPtRCGcjZMbt9l54wCGQUyCmQUyCiQUeC9owDXOAo5B3k/r720qQBBEGqDldgs1ptaGfHAirlGgHheHqCenx7FqfSn0bZ4qnAc+PR26+v1/veOUBnC7wwFrDG2RahcoMEKw6lQw8JvrxAMGT++dslvMU/kYuFYy7aWqc0aH2HGkZaBz5SH3HwH0KM9DcSzI6PA61KAbGcWuCXsPL+vqMejRoxe6bkGoaMUGN23ZHhBqeRny9iJwiDQRitisOI48BxX6zCzDTMvIOIP/yozWHnbNOcsIDPBeA+S/kLFPMs3HjNWrjlYv57D52AoFAf9noPj3QCbjxPcmXHg1hwkTYX9LYWD3QZa3SY8X2FpdRZL16a08QQnCAInYBb9gvoJDhYtK2vR80SkJPTQ4eMEX38R48t/38X2/i7CMEC5ksPS9Xl89NkKrt2awuKag+lFF+USkCvqkDv0GMOC6ZkkPlLIhREOD/LY3ydeukCqloUsFyFIfAxOVKQxdIJksOQU5FOVM/VNPRm/tOm5gKQUirMOltYdrFybwtOdHPqDBKeHAfY2G7h+Uka9TvWhEtua1l6C3W2Fk2YTcGIUygkW1sq481lFvNtYm4/LUBhH6Gp3hKm9gJCk2svKaAHX0Ee8G1xER1vM2Hvxf2AYRFpinFFMWtoVnTxL8Id/i/GHz5+i0TpGmPRRKLpY/3ARd362hhsfVbCw7GBqzkWh6sApMcSVYTziFyokHSA+DbG1UcJRy0VErxCOqIctdmdnOrbxaPDiiNceviAN2B9C8pP13kwchTh24jkDkb4SdmM6JidviyGxqz0pSBxHTdd0npe+TuNi6hw3FL7/Msaf//0E97/eRqsZIkQfSws1fPjJMj78dAVL1zzMLbmYnnU07QqAGJvR8QztuvoK4eNEwn/tbZVw2nMR08uSVN1U6qWRfdkMlsj2PJnfPj9r88kUz91bej33YvyBYcPxh5feWXxSfPEiQGyvVBbdEZhBq/6f4w6mnYQ3lv9SBJ9P4DooFB0U8nr3suPEUvryyjR+87s8bv7cg8soa2K0orNrDMWG5QyefWieEK3Em0LMIY87NQpAhQZ29B4l1XjzmiCW+dLHJD0nAfDjiMb7tIBuKDy9l+CLf23hm7uP0O8F8HIO5haquPPrdax/uIDltTwWlx1Upx0UKg4czkuUdEiHEGB4M9VWePYgwX/8eQjP4Y5udbaT2+Bjp20xwEnjaDzZCJoM00XPIiSlWGenE05WxPCOSWKmQsNPhomey87nzz0cA8y3ImaMnupW0COFGSdH7865kOQ6NBwXTyi0j/oEgZ+HAp/pYkYAKTcMB0Cno0A3o0SbtPELHkqVoihwRomzix8JBV7Mmz+SSmbVyCiQUYCyOT1HFIBCgcJqgiimIXqIaEjXiWdTlcydpNhoeJBZakRDvpcphGELGQ5xoBAGdE3N0HQxcnkXxWJOFj3PYIyyZxcZBa5OAct69A5UcFAoaS8o/MKPogjdTk/zLiFauWbEt6li0s9SC/Z0NBpwex6FJsruHsNEAvnyhDfQFKjsMqNARoGMAhkFMgpkFHgPKGBlCM9BsQTx3ksv8gznTPmXhq/02p2WH5hlfIlECxDyny+YnmvS9KwSAUHA9Hpzmusr+HlXPKyMeRR/D0iVofhuUkD4zgcqdUf4Kk5iuImHfn+IcBhq8ZU8ybVM8wEnPK3ZdlSpsbVBbiQOgU6XWeiZguvU5F32EbPuOsqZXWQUeE0K0L+D8BaN+fJw3RhhEAn/ykZJGVPNTnaOsRO8y9LtI/Ixdatc8xbdIpcwZJNnDOWE2jCGehdmuADWa9Ymy/6SFMgMVl6SYC+T/Gyn+ZnRwVh+9itODr4jrmkLsy7WbyZYWVrG9tEAg+EAx4cxvv+2gaUbM5hyXITHCnvbCY5POhiGfQkpsnZzDkvXZpCrckXGdEfbK40e+axc9jzrZ0V3XpmAbAIqqwIg6Sg8+S7Gk+/bOD7uYxgOUKm7uPXJLH73j7fx0S9cVGcd+CUHLr2/cGO6THR2wmMxfK69ANBQgEos7rm2Qp0YWmh0bOljZ7uLXc4yh7JStmJjSa90I4ukBoRbcTC75GLlhsLco1kcH/Qw7ChsPT3Gx0ezWFkswxUPK8DG4wTbW33EcYQIPVSnc1i+WcHSDRduUQ9mHOdEoXwlTK6eSNA1wgNHTnt5dQhCOFGA2zzaAIiE19SkoMFD1ttYIAd98sCpwsNvY/zlD09w2ggQOzGmZn1c/2gK/9M/3sEHn7iYXdY0oJGSKIXpsIUGFwRPI13inFeg8XkQhgjjIeIkMLpdJjATQrpZfYgBVKVcgieaYAdJ7GDQjWU3G5XO5CGhRTqfQEv9s7zFiSlSiALGK2fYFXrNePOmv1JnukjvA93dBHf/kGD7aQP9QSghQ+YWavjNP6zjN/9lGdduuijWqUzXRioMVSWszfrYfkTjHBcYEOcogKL1vTGksCZHqdq+hct0aSQmGeOS40XtcUnWV3otg8hZTvK23j5pnp2DD2sy9tjwibZDIPH1T8zkaCCUTpy+PitWX434jRfPH9bwbPSGsETABypVB9UqUMwznI9u+DihN6s8SvM0BKOhSQpx5rXF2HMat/QzMRzQhpGj+YHv0+lHSL25C0HB1HHE22nw6TpMPk/ds1+JEWUEnOwoPPouwcP7++gPQ+RLDhZWa7jz62X8+h+WsbTu6g8y9qucoZmxkhZZlNV2IUKqjHcMssS4Zwk/2KiESxHF0miClinUxmmYzjuW6Oo3Qq4UCi/MSbwsbhMJ9dh4NUB6/KfXKa00pOJQj90XjLGpMu2YZxdekgDoU/8TR7KYkzgRPHpeK3tiMDWBZnabUSCjQEaBjALvAwX4OVcAShUHxXIRjhMgFCPFCINeiCgQN2XcmDc+L0rdxucizsWyI4qLRAypMgCigF5aFPJi4JhDoZTXO/TGs74PlMpwfBcoQMHdyipGBi5UgTqNtmVdxEUSKfS6ffmurMZKws7Kt7z9Bpush4U3Oiu0WwqD/sB8tsUolByUar4YSo++5SbhZPcZBTIKZBTIKJBRIKPAe0MBrnGXag5KlQJyuTwUAgyHIYa9EOFQr1MxdL1dW7tQdKX8YH5U+NNL2zCIxdMF3AT5ko9yNS/K2feGOBmi7xYFjIxKHpRL6uhywNS8g3zRFeV8HHvodwbiZRChguKaqWVMWgAY3ZCuGKEYjualdogpyv5WK0CYRLJ0nst7KNcLKHKzoNVHvluUybB5Hyhg+HcMVQcoVR2Uq/TymkOXqsogFKMr6vbk8Jyxzb1j+dM3hE99XahkzXrANYg4AFw6JXBFt+Bzcy+/BbPjnaBAZrDylpvhvD4nzzjuW2nGXDNUzdyygw8/msdp7xSNjotOJ4eNx6c4OZhG2VM43NHePjpdKsE9zMwXRWFXn9XeTcaqk4Y/9kLfiIKWyKSQlIXEoUJ8onCwqXCyF4AdmXGZZ5YK+OAX0/j0dx5q12moYAxVrIGChWP02srjApCO70yXTYm4fNYePSxq56A1emTBjR68zoUtkLKkMaKgwc3SNQdLi4voNxsYDFo42GrhZL+H/rUSKkV6iQG2niU4PmjAlVWuEHMrVSzdqMClRxFO8OcNaETelvk6eAsYAuLv9YBK7pEsbak7jqS0P0NvDIEhPcs8VdjbbSOMq3BzCtMLOdz5T3O481sP09dc0PBHULORhSya9IhABjO8QHeHw6CPJAmRqFBC8tA6fUQjiwbB+UCx4KBYMLHq6Osm8TDoxRgOlYRlYqghOWy+NI1ZNSq5WbZcK/HOEIUK/NGDAOUocqIGY2mRBvIK18Y7UdJWON1V2H8aod1MZFE/lwNu3pnHx7+dwwe/8pBnf7WToTFcE3xZLOtEL0cOECYQd2VRRLppYo4ZULwCmlfNMhJcpREv8IhzVWAT6VjFN0T1EWQxVhGoFLTHbQ9solGZYl2bQoJtIIn0SrWY6jj0sWTCMJ3HZxboOedRORPvRMGffka4HuAXgWLRQc5ngB/tYWUwaKAzrEIVfHgVQIzCJhfSWZAtzOI4WTdLC763aYmDvbb50ni9iesJY6JzQV6lbOIpfQs42kmwtxmi2UrYRVCreFj5oIbPfreM6z/zUJozRipcNGA+wjfjs6BDLygewD7aH/B1DM9VcGlYaYcDg6hkn6RZqhIyjxiukXZl2qvWh20ksPR/udRWJqkSXu/yKqhYHqAhKQ3iwpC/WJSJsvBCFC4DZKuQUOmo0OvQQ1wsuy24I98rubKzWQwaX69KWe6MAhkFMgpkFPihKWBkCKcAFCtAsVSE6/WRBI7MF4NecLZgbzy4XYoi5w3uLg2AkCE06aUCCoWij0I5h1zBzUKqXErELMG5FLAyCcUXa7fiQjyi1mb1pgonosxDY6kI/S63OOeBkoHGTy0rz50n/xCm4d9uGxj2h/KlQHmyVHJRqnjyHSsi3bkIZg8zCmQUyCiQUSCjQEaB94IClAM8rTAtll34dKOGASIabfcCDHsxELkAH6eOMfHByCWUO2QjLrMMrcI0EIMVehgvVnMiA3uZhi5FyezylShgGZDfcL4Dyr+5kqfjFiQOgmGIQXeAaAAwZOboYD6bVx6SecceyPfboAucnjYR0k2Qo+AXuGmwIF4GZfPCCGB2kVHgFSlgv+dcGgs6KJXJqz4UIgRhhH5vgF53ABWXNYeSTc9hVymdz+1Pxl+g3aR38CHiOIDjJijVcjIG01NQdrw7FDhP1f7uYPdTwoTWj3kH1TkXH//CRX2qJDES48DD8e4QR88SHDxLsLelsLvXQ3/YQ6nqYvlGBbPLRbH6FQWT7dgTtLvSwomZi+jBon2i0DiN0O0NENFNsxtjca2Gm7cXUVtzddgX7lw3u+nGdG300kJtIo0WYoAT2nBAd8/c2UTrFj2aTEx9ExjbQeWCCj2f+uInqYLEuIS0ZtiZOnfnO1heq6FSLiCKYpwed3G03UfnSCE+VQh2SfMeGq22uPv18gmW1mewtD4tc7eFZ5WiFyPxGm9S+BPKc0rvq4K+CimZhot1kcLRvsLJYYBBEEn1qNSdma/gzmcrmFp14dDlsT2Yz8K3MGiBG9HjCAVyxvnmrnuaiWimEfsLm8fAIR9RqPK5MF5meCAFl0ROfPSawLANxDSgIm+ly7R4mPMItjFcUX1g2AWCPvGklx8xVxGjlTQKE6SegHrBrSlD3MbQvVhH4eQgQbfVRRgMkCRDuG6A259cx80PC9pYhRYz6dHXFkxkTL9RNBrqKQz7DLHB2KYvrvMF2L32Y00pDWbEeyOiEfFJK4pUkaN0qWdv6lIGHTIrCyGWxIPjC3+mEKJnaTt+qZ1p2HdcgJZrbfYi+WUXsFnttvDs+bI6vChd6p3wO429Sg4KZbq705ohBz7azT6OD5oIerofSb9M5RUUbP1sPSxeTGd/fGbT2fc/4HkStZcq2jYvMyUKR4cKJydNRElC3ygo1jwsrk/hw09cCfMm4eh8Y2FtC7Z0ICz+GDP4SEnoGmoVfC8P1/NeSTlGo5VLB2SLx2TFjdGKfm0SPZf2uQeTUM7uL0o6yTMT/UCPhRwTPUR9hWEnljBu4tXG0syWkoI1Gn+V0uHWGAe0w4/XDqIkFMGf4zjDMnl0XpMe7yy87JxRIKNARoGMAu8+BSiaFOk9wpEFST/nivF/HLroNRIMOgqUWUey14tqxLmK8lWgMOgqdDtKPIpS4U+5n9+24lL6ojntRbCzdxkFLN/YMylCXVLNwfQ8XaJrC/VEueKKv3E8RNgzAtllLCzfe0p/2w6AdoPGz0NRANCVdKnuozzlj3/fZS2SUSCjQEaBjAIZBTIKvF8UoAyhl+XEQ0Wp7qBcBwpFLtzRm6yDXitG9zSWDa6jNVJTy9SSiTyRtWmzrkJdC9f3Oi2Fbr+HMBnCzyeozvhiGO5lHireL155l7BNyb5ySR7OA1OLLooVfrtRkHURDRz0Wwq9htHbyUK43YGcrlBqAY8AuR95qNBtKhwfN8R7PhdXC0UX07PlceOXNJjsOqPAVSiQ4l8ZfzkMe0CZ42+NoYm5pTpBEiv02xGahwMkA7NJnqxN/eNzg69+pvWDChx/+d3HbzhtsBLJt+HUbAGVWubh6irN9EOmSY1AP2SxWVmTFKCxCd11uXMOPvqFh6mZIgp+AZ4qYdD0sPVA4cm3CXY3FI5PWhgEPRSrwPU7U1hYy0l4kTEF+GQBF9yP+nNqcKBXkdMTusodSPgUWgIHURfzS2UsrhlPLja9VfQaIxBtwKKV8XSgkbShXeYOIsQU0kDfGPQiMCr5AszewGPiaPFkebw2btFoFEEPKTPLLq7dcDAzXYXv+bLTan+jjdOdBNGBwsZ3CY73uugN+ohVgFwRWL05h9UbrhgYncGfwHdU7sTzN3lrhOgLcXiVsog37Uk84ORQoduO4CGnTQHcBLWZAtZu6PAOYiBFi9y0bCNtfDZRcGJIGkpiHNISncYqnsvYczkRlkZkGrGDI/ByJQdTM47EsfdcD2EA7G+1cbrfl9BNwj6mLGYdZTd1FlsGM7qRn+OmNsBpNBQ8J6e9q6TpM0Ik/fCK1yzc4qKAXkcr1qNhhDhmqJEB/HyIWx95qC9Sma7rSAWu/bENpe8QZ3qB4LzbU2idsh/SIw1Zl/3GHFJBe/P2zuJFQspytF6eSgbjCnBkQGEbgDSwP2N0k26nEZasxKgio6cvdTFqX3rGMDldeHCUK4Zxtj3OAyrpbSaeUz8K8Rzv4iRBFCWIAjKwgTLJZOcBf9lnbHcZi4DaLBVBDIPlI0ocHB01sL2xLx8EFKxIS/LBc4KYrYstexJPk09e27ratJN57fM3cBZDJ1veq5Rj8RZLLY3QyXGEdrsnNKLCrFhzUZvLobjg6pAzZiySRYN0/2J2GlKSLwPgcJ/9KoHHGAf0pMOyJun2BmgwAmHpMHqgL6zZ3MTjl77V4F9AZFs3ezYlsM46rwtH+ei0BjjcbuBgK9HKR2sYOImRhWP4UQ0VkpbC6VGCre099IOeGKxwJ1J9poACwwZy3MuOjAIZBTIKZBR49ylgx3hiyqHbhYR/pUvpxRUXhWJONiBEgYPD3S4aRzHCrjFepLMUKzedV1POK/w+7EEbxh9H6A26sjGiNpXD9DwXjCgcnZc5e5ZR4OUpwG8Gd9rB0pqLYtkXA2Xuqxn0E+xsnMguO8rYYyxnbkZdgXIieTuELI4mTW7q6KPT7UNRuHRjzC1VMbNQ0Qa6Y8BeHucsR0aBjAIZBTIKZBR4GxSQ6ekHWkt8G/j/oDBdhrd3kJ9zMDPnoFwpiB4jSVycHvVwsNuSDT/cpDl5jOQHvpB1LW3wSoNtKvyPjhJ0Bl3EKkSp6mN+paJDWzMMeHZkFHhVCrCDm5/oGorA0i0X0/MleB7X9B0EA6B5FOB0ny6SjWf6i8qzjEweZjjXjkLzWKHRbIr863kK5UoOK6tz8Ln50qa/CF72PKPAVSnAeYre6GcdzCw6qE9VQN2gUi46jSF2n52Kp9bRRsv0txf5MM2LZE3RBSgEHRqsAL1eX0ICFQoultdmUJ0qQEICXRW/LN1bp0BmsPKWSXyhLJjuQOxYbAmPC4JAedXBtRsLmJmagaNyGPR8PL3fx/2vE2w8U+j0+/ByDqbmi1i+XkF1xgVdNY8U3qxTurNeUMd0/x3LQucoDElCjyhwZVCgdw0vH8PjJERcrbHExDhgJ0cuVnIxZ+dJjJ0dhW5vCHoN0F4QWNqZovkC9N7g48mait2M0KwwxcVXBwuLJdQrVSD2cbTdw8mOQmNX4em9BK3GAHESi4uz67dWsLg+A+aTHeNXoPMbrMj5oM6p3vkJJ55yZ3xaS0s4rA/PMRCHCgldHNK8Q9pbyQDul3QsRPKBfq7hitLXKjB5w+u+wsN7CXb3FIZhaFqd8Fy49H04qSg2BkW5ioO5RQeLSzOolitQsYduI8buRgOHu4l4bZEF8VTdR5dGmJKYjIzLGCg0jhMc7CmcnrZMZ6OCmt40dOidCcpc/dbSLM0HXKgX607L7QxRE8LLk27GWMXSmSWRxIRjflKPGOgfKmw9Uzg9aSOIInmt20K3x6iPS4aro3z1lERS/1yHhka0qNO5pUiDr8XbwhU+MO9G16TJm8STaLlAPu9LLFnCZqgnenIa9vUXI8uexM3iKGfCYA1NNf2cdnXvuS5YXxW7GtaLFC8axKv9Z7mCI8PUOJhb4mJ6HlP1KeT9EobDBPtbJ/jmLzvoHSfCx0z/3JwiH8BGAKNRhlRq1HRnuKXrYep89vItXNFS3/bvi9retpHtM5ZP7HNbNz5n+AC68Itj8frFkGLKjQA/FmNP0jA9JkvzEgXCMoYXHAvikwT37yXYP2wgTlzjreUN1f+ier4h8OeBeY4fyFZXxcPQmYaEnJ/7nRCHuy3sPD0RgzlF4zkeJt3omhfptoqBxnaCpw8VeoMhIhUgX3Qwu1jF+q0lVOvjbSNwsn8ZBTIKZBTIKPB+UICynw/kyg7mlxxMTVVQyOclhvn+VgvNwyGCPifsVHXS13zMezNv0MiRcs3edoJOh64PKdtEqM3kMLtQRn3ayLkpcNllRoErU0AEQJ1aZCQaXBWB+pyD5dU5FAtFJAnQ7ybYeHwkC+8q5jfxxSXIOyO3c7F+716MdmOIJGFISQXHjbB0bRaLq/TAmkLgYpDZm4wCGQUyCmQUyCiQUeBdpQCnctlcBjgVB1NzDqZnKigVish5BZweDbC32cKQBtuh2eUvdZkQJqz8awy24xOF4/0EzdOODvnuxCjXc1i9PiteBLKQQO8qQ7xHeBneZYxuGlx5s1yXK2K6Xofv5BENHZzs9bC/2ZbNfFp3cU79LCvLmiy9sQCtjQQ720pCiTM8C9fQp2bKuP7BCoqlbMPBOVTMHr0MBcwnlJzITtQPlh3UZhyJ9lApllDwS+i1Imw+OpLN7DRESa8znLsewTUI6jhPFQ53FI4P2ggjvcuGnrPWbsyjPu1qvcLL4JulfasUMOrHt1pGBlwoQAMNPXOYPjjWjyQJO2PBkV1ANz9yMD8/i1gBYajEenfz2Sn29g8xiLooVXwsrkxjcW0GpZpRhLNDc4e5LeASymtsTKL0OiNd5xYd+Nz5T5zEzKSAYcdDv0kDAKMApNDFTUU0TKBlJk22TbsAACAASURBVH8hILutmwqNrQQP6BVmL0BvwEyEZUcTG4LDKFYvwfX1Xo/VdATKYUijsoPFVRdLq7TYK4uiunHYxzEHse0Em48V+t0uEtWFX+zj45+vY3El/2LvKqMS3uLF68oCqfYWoxUrjBBlwz+FkgPGcBPFsBi3uAiHLoYt7UpLlMDWQIXNKkK4FtjpGjxuKHSfJbj/1wQHBzrGIYvRRTlwz2NUw8NuBVhYc7G27mJmukKzKQQDDztPm9h82EbnIJEdbuQ5h/Yg/BGwxYfGNgGQdBXiYxpOKeztKrS73RTjaaMVi9Ert1aKdiSdl4O4K6NBhZIOmYdKSmieKsTG7bR4yhDBzxgamA8YfuyoARAdJdh5nGDjYYJWc4jQhASyjWOa6LVRH6vzCKh+Kkps5cOJc/CSAtyEoZnOlNfW28fobOogXZz8wLRWOWHzETTrfcYIYyhc6YZ4kk8coFj0USqUoOAijlx0OzFajUiPUTS6I04pPEaDri3fwKIw7xeBWq0C16XluYtw4KB9nMiuCWu1+xwsA+eiRW7NGun/Z/1rVFd+SOSAmWsurn/kYmV1GuV8ASr2cbIX4+7vj7B1PxE+Fv7gOJui7wgnqaejjaWkP3IXh/0Z7yzpdhgh8OYv6K3DSQo6NBzbwc4PF53ZTpM/4moPTUIU8h7yPo0rEvEIEg9yiLo5MWCzbSRtnOpbMi6ZfhUfKmx+m+DxoxjNVh/KySFS9LBCt1LniEOmXIvGpeeJPvSi9C8L+kWw7JjNNM/xIgu6qDCZR/QMz/7OkGknO0Ns3muhta+gGI7K9iEDh/CF56jcYdvSg9WpwrPvEzx+2MIwHCJx+qhMuVi6Po31W4soVn4Qn2ovJFH2MqPA+0wB24116Lr3uSYZ7u88BSbmCxGTHB1qj99MNFiZm8+jXCwiCl0c74XYZ+jUQ8qvJhyslTVELjGyOeVz/iift4CTbRplD9DqtAAngkcZbKGI2kIZuerrfuS881TOEHybFLA8bGUys75SnHZw6/YyqpWSdok+9LH7uIvDrVA22Yy+WZjf8LCASPNxyG80hXt/5fdZD1EyhOMOUKy6WFqbxuxCxrtvs2kz2BdQwPL6Ba/tY9s17H12ziiQUSCjQEaBF1CAYyv1K3mIh5X5BQe1Sgm+W0TnNMb+Rg+HOwkCMVrR61lcWWE2GZbtuomRK6gn4SYf8Zp/TA8VEfJ5GsKUsLg6g3LVEe/LL8Aoe5VR4GoUIANS/jWb4qnzWl5ahOtQp+PgZJ8eKroIThnWVXvJHK0ZWn5N6Vao31E9haffJ9h+1pM1R4UQ9Kg8s1jB0npONt/LBsKrYZilyihwPgXsAMrleTP+0hv94hJQrZSR98vod1z5hjvYTMTrj9Y9mc0H5F/zk7VroxtJ6FloT+nx94ghrQbwcgkq0zks0WBwys3G3/Nb5G/29BwNzd8Mlx9dwaI4YkcxSiSz9/3Ceor1GCeUgoObH3lYWtYul5PEQ6+foNHso9luIVEBpmbLWL42h4WlHFxaMqZbMn19YWn6hSj5Ux2aHZuGHPUpB8VSTowKaNBATy+HO10JFcBQJWJFTGsaswBJxahWtivEbYX+VoJn92Lc/2aIg2MqsSIR3RyViBs9mu8IaUSSuwTJV31taP9cdvvccWTHVWXFxeKai5nZqlhLd9sxjnYU9p4p7G91EAyHgDtAoRbg018tYmbhst3iLMCu1j5X+g/7wNb1iqXSCECaxAGm6fawmoNyEiRQiGIHjZM+nj1OpI2prNQTA4VzE/+QBks0FCEPPEtw708xHn57iEajLRbkMmHIP0rxuvHTLCACDhX4tKJcc7F+i4ZbdeT9ApykKFbsD+/u49n9BEMrXBmFqiyE89rgRcGLxip7DxN8/22Cvd1T9INA3JhTUJMj3UmvSKPJZBaEnB2gUGBoF8DNe2KwolBAFOXx+NEAhweJxs+2C8807LT9pw/Q6v7g+wQP7ibYeNJDbxAjppbKYdxJ/QkkNGNeC2cSqZe8T7eBzWoNVpDkgSiHhEIqhVkq/+0vMAIunw9S17znj++N0YQYWRD4a+KdxrVccVCt1KRNw9BBqxHgcK+DoKkEXzGe4Rhly7RnW0meKcjTWwvDUE3T+g9g+Kp+J8HRbl/CMiluADZ5BZbt3mnjG76fPCSxeTjp0YiPWTYN5/KAv+rixscuPrw9g6lqFTm3jF7Tx4O/tPDNF21sPohlQT3hLmbyS9qQwJZLfNgXGaKlp0NhsZ9Ebd0W0l+Z9jxcLYw3cKbBCqK8GF9RMBTemOSPyXvyiuUncz05jNbrDurVMvIuwz/5GLSBxmGAwZE29BKXlhRIebCNrFEFDejYrx4k+Pz3MXb3ThFEgOvQ2CkHRUlYj3wm8w9wYlud0xBvuWnGKuZyWJFxhUZAHqLQw+lBiKffNfD0+xhtYxhIWsr4Spqan/Af6UqXtg8S3PsmwcbGLqI4hJsLMbNUwo2PlrB0LSeKSMk3Vnp2k1Ego0BGgYwC7ywF7GREoYs/X3sBXVh2sLzioF6rAEkOnSaw8fAEG48G6FMuD8y3oZGPKAaJ/GfmZsoEg4MEWw8TbDw9QKvbhuMlqEzlMbtaRW2uCDf/w0/J72w7ZIi9HgXk80kbXLl1Bz/7zMX0VA0uhe+ogMOtITa+P8Hepvk+o4xjeFdENCv/GHmSMs8uv2/vRmg1u4hiGqwEWFqZxsLqNArTdOfyeihnuTMKXJkC6Y9iO1Zflvmq6S6Dk73PKPBOUYBCixVc3inEMmTeZwpYGcIHZhdckX9nZurIeUUMeh72Nwd48E0XreMz+deuldj1E2FLrgMyHGZHYftJgs2nPRydNJAgRK1WwtxiHbPzVeQljPL7TLAM93eJAqJuocLfd7B6zcH19TnZih5FLhrHEbYft7H9LEbUMeGq+K1mhtLR+jk9LnPTYV8hOqT3fIXt7WP9oeZEqM+WsHhtGqV56tYm9JLvEjEyXN4fClg5NTX+1mcdrFxzMTVdRs4vIewzMkaIe1/v43g30frpFO+e8a9ev5bxt6Ww/yzB1pMEp402omSAYtnD7HIN82t1FKvaQOb9IdSPH1P/x1/Fv20NlXH7oCix8Frux3Fif5Rdk5xRPIaHAKYkPIQjiyqHx8eIuFCCELHTh1+IMDVXxOxSFUWGprGtyA5KYDzYuVNiu+67NDtIaZxS78fycZfbooPKlA8vT2MFatTzeHx/H1MzVdy8fgPTt1w4NJRhkBd6kmCZNqbdswRf/H8x7v5pC1tP2xiGBUHJdWKxItY4SI4XfldIRAmH9TZ/xpXG2A5TA0YKmPxnaTH5XLaHm9Ukzqk1B3PLDhZWiph+NoVuO8Du1gm6p7PodAcIowEK5QTzawWs3nBR/KFiqxN/IbFxISL1SFX4ovpN1tfca3qS1xgGh3DMqtxkeobWyAPT8+QBF47LgN1KlPhbT47xxT8/wtzMR5inorNimMiAoyFD0gV27rL9Y9z98zZ2dk+RRAUxKqDbOO0OxdEuECfLZp2MFaVbA5ZvuFi5XsbWdgWHh320mgN899Umuv02gv6v8emvPJRWqPU3tOLCIhX5IRBuKdz/Osaf/r2Nh9/uoXEaS0gi8Rokljm0wKSnBt0tOakJWV6SrmL7wjws2wUYzmhm3kGupOCyP8NFGLj48g/fY2btUwk/lZvVFRdWtIujQ4VgT+H+3Rjf/KGHp/caOGoMkCglC6uOk+g46ZMf4yziJXGeJPuIfgyhoiO5CFDHoaeMHKKei9ZugnZNG2vFDPnFBV3WmSkN7XjmvRyEw27mAYkP5KsO6vMTuwaYT0piLt3PNYQRFAtNEjKtMuMkw6BVp4BqvYSDgxaGQYyTgxBbD1t4eDfG7c885Oe1UYgUQlyooefZjI8WT+KYKxFeAY7bFiU+Y3vuPGnhmz+2US7VMHeH1oQ6L+tF46GLFqVJw0QMvfR4yzGPtRM8JqsmRivscwqVJQef/MrF9uMqmoMCht0ceh0Hn//LfezsV/Hpb1fxX/5xGoU5R0KaST00AaUqwvuBwpDhpLYSPN7YQbNzjLnlGn7121uYX6X7rMsM7s5IfqWrFO9pcyofblJA/ySH5sNYeEDRaw2BGd4SlCfpYHnPnMXrzJwLl9MHaZR3MMsxqZaXHa2hitFuRth80MBf/n0Wv/2vQGHBuPGj/Qk/rMifQ4VwV+HuH2N89XkL3369gWGfwYHz8D0fEUPfpepg63zeM77TXKrnUmlXm9eeLYDJ80R99TxmBx2+dEc0msx67r0QUdOU7+28qsd33YvOzWcfjvJzXAmRgIZxCp12hGcPTvBP//1LnDQ+xC/+bgoLtzztHpF57HjFXcaHCba+SfB//Z97eHL/BN1BB4kzQLGex+qtKdz6ZA7Fiv5IFt5Pl22v7ZmwJ2hkX2XnjAI/agqQ99PHWD/gjX3A0SfzVpQmVXb9Filg+ZLsZ2QmegH1F12s3nKxsFnH3lEZ/X6Cxw8OUJhOUJ//GJ/83IM/BYDTrJmiRNalbM6wfrvaIPuL35/i5LSBKAlRqXrijWv9w0XU5/gRoPNm/y+hgAgSdnyw1L4kz0/xtfkWcasO1j/zcONWBbsHZRy1Wug1Xdz946Z8s/1vMyuozrtwinpPhVCWMqn5ruRi/bOvYnz+bwm2tw/RD3qI0UOt7uF3//WXWFwt6u+CdJP8FOmd1fmdoIBmQ/6XL1CNk/244eNsnH0n2ilD4jIK6G/lFw+rL357WQk/zfepceGnSYAr1ZpDpqxnJoA378ja9OJqHttHRUSdBCcHA/zz//1nVOd+h0KuhNoyF4Ot8Gu+4IwMIZun7ie4+5cEmxv7GIZ9WdudX5nC+q0FzCwYg9eMna/UNlmiyyigZO3b8bijVmHxuotbtxN8+UUVqtdGr9PD0/vH+I9/KsD1ruP6p4A35cr6qSw9yGZLAFQFBcBgT+Gf/nuI7787RrvTFt2kX0hw884aPvp0VYxiKC8zL726ZEdGgdeiANcezPhLvYs/42BFdIMJDptlDIIKgl6Iz//5W1RmPPx9bRHlNSqmGapVj8GybiYbDoyx4Lcx7n6Z4NHDLXT6HSRJiPn5Cm7eWcH0ggevkAnGr9VmbyGzNXV4C6B/oiBN5+BHoKgqVSSKICqb+YQKcrH2sh+MJJM1LqFSjotPvoI35WDxuoOV9RIOGxRmckgQQLld1KYdLF6rYGGlAqdEhfA5HYuPZPu06bBcrCFGYvRBxX0s5cpal8FZoNBYoQB4M8D6Rx52N6bQbA8QDbmLLsG9Px8DQRk3b8+K8lBc/TvAoKfQayu0GsDezikeP9pA87SPUnkK19cWMewFaJ420Gx1oRzOZMnZZDjJKgYf4k/vHmB6J4ZDSwNX4y0f2VaY41mQnwR0wX2a3vRukAPmVh2sXHdQ/66KYbePRruHbi9BLxgiQoD5hSJu/3wJtOyj4lSOC8u0Fbig/PMe27rYd/ZeFORsr0i7XUuikerixTikdBwmB0FqLiRP0uNNZOhrV6INHcmHOaC46OL6bRc37y9h60kfKvbQPorw4I8NeIMjXPtgRlwfl4qOeHUIhxAeODlW2Hx6iK3NfZyetDE7u4Kp6gKGwxiPN5sIowCxSP6xNtSydU6dHXpZKQBzN13c+pnC0dECer1tdPou2icRntxtwRk8xOOv5rGwPIVazUGxCCQxeREYdBQOdwfY3jjG9uYx4tDD/Mw0fLeA/qCHTr8tsazoacWDJ91CiPOyTZfmATGGUHDrwNK6iw/uTKPdOUEQBlCJh6OtIf7wP57idHMF19arosjN+QzhotDrQDx5HO71sbVxhL2dAzjwMb80g+nKMg732zhshehjAKCov36MwYiMHy/TByxvpXEn7U3drZEJxwZO8gFDkm0E+H//jx1UZ7jt1Rq+ncV7d5lZOTJ+MQ+BUYGeuAHghkgKDSx9WMDv/tePsbRalNBnlpEZGsp1aJSTSBx4Wr+MPqENTgLS4ihW4lSGOFi75eLWnQU8e9oAQh+9ZoyN7/r4l2gL29+vYHnVFdeaHHF60RBOqYvZlQJW1qeQr2iDEzE48iHGHL/6uxk8/b6N4YmHaOijdQB8+S9b6B0t4ebtaVSnHPkIHSYJhskJphbyWF6bQY0GJLQoFxw5btF+LwR7GhDINWPcS51J/3R7mWsaHjK+6LVPPHzyuwK6qor+ozbavRjHx0MMv47R3E+wdz9GdTqPSq2AStlFseAgjoDhUGE4AHr9CM1GF0enxzhqbsAp9HHn56v45GPtlUWMsjSmr/7fVHUEQO7pPcsDlI/WMfD1v+5j70lNx4yTuccY7NhMcquJwRA/HOs5NyT8eR141Rb+l//9Z/jgw1n4NBL0gfUPGTapjsePigg6PQz7Lnae9PD7//EEh1srWFguSBtxXgr7CsMe0KUl9c4Azx7vYXfnCN1+Dz//5JdQUR5Hh13sHzfFLZSLPEcCi92ZVx77RIyQgMRNEDtsXbYvx2POURNzEKtljzOQ+gnvJVwYexLzx2L8xHOK823us3MajpTHcVtmcj2OOhYXTWcx7LJ8ls57BhGJ1GkAeAOJxzw9tYwYAzQ6h9j4voVB5ym27i9iZW0RtbqDUgnIeUAYAN2mwvGewvbTU3z/YBPdboDYCZAr9nHjk3nc/tUi1m66Ml+mxZ0UiVOYmMsL8Hw+YfYko8CPhAKW59NjBocTdm96VOM0xWuRL7RR21hSm39sUvmR0Carxt+GAuSpNJPxnt8ilPHy+vt09Y6L9b0ydvensLPVQbc1xMO7DQDPgN513LrjoUgD5byWh0Th31OI9hW+/VOEP38+wMbGHvrDNnwvQX22glsfL+HajWnUpy3T/22q//6UOur8BuXJ+/enJm8NU0sSftd6DtyCgjfn4PZvXOwczeLwLztQUUm8rHzz+T7K+Tp+/dsyZtdc0LjFyis0fGaY253vEvzl9zG+/24HzW4XgeqgUBli6dYMfvm7OUzPsSA9ftu8b61uGeCMApYC5PPRmH12I7K39AG9QYcbYOT72vYL5rHXFlZ2vjoFZEE3o+HVCXb1lFw94VqKloVduC430GW0vjoFX5xShgtNXL2GMBo/XpzvJ/mWY6T5DuNmyvmbLj76tYvtozrCZwqdfg/7G118/v88RdK7g1/9J6CyYvQFzMtNfoFCvK+wfT/Gl18kePL9HhqtBmKni0IlweqHa1i/PYepBVc8DAqv/ySJnVX6zVLATPAcO+klc9bB2m0XP//1Ev745SEaLaB9GOP+Hyn/VpH0ZnD9Y4DeCGVDPJeu6RWIOr4nNLSK8fUXezg8aiGIu3DzPcysFnDjkxlcu6HlX+IvssebrUgG7adKATP+Cl+VHNRWHXzyn13snlbQ7ubR6zk42h3iT/+8AxVU8ff/UEZxyXj6kbw6CgCjLhw+TvDnf4vx4N4BjhoNDJMOqtwgf2sBNz9ekG+4zMPru8domcHKm2wT6mwpYZtDGweEYmwgSlh6GBEFplYomSlk9LFI4US8PPgMiQIsrFM5l+Cb+0OE3CmuAjj+ALPLdazcqGOewlDOKF1tZzYCJycKMUYhLnzncsc/PaVQdSsxSOTbVtKkF1eoDM4BTsXBh5+5ONmr4/hogHB3iCgs4mgvwX8cPcHjb08xPz+DSpXbpxXarT46rT5anQ4anQMoZ4i5uRl8fPs6bnMn05bCvW+7OG6FopQUOqSF4xExDPFkcYl1iJGI0Q8XPRlihUYW2uOLpfPFH9usKw9DALk+U5rS8pPKdcdTYpyxvK4wNz+F5oGLXj/GIBzIjvPECTG/Usenv1qDx7iSbyt6BOlhfuQDXpKHYhXIDnixEFT0UpNal9AVvOQ/G5heVYDYKNK5m54eb+gFYrR4YdpDhOQc4E07+PBnHk72bqJx8BidToSEXix2XPzxaBOPv25gZraOSrmGJFEYDEJ0Oj00Gk10Bg0kaoh6vYxf/vIm5uYc7B8pbBxECOKhhODQhkumeWwNDA7yYeAD/pKLWz9XGPanEfRcbG7k0Gh20G/EuPfnE2x810WtVsH01DRK5RKSKEav10On00aXi4lhAN/1sX5tDaurs3A94AnX03eb0i/pQcSlhwWGt6L1Ow1liMMkP1r8XnS2C6IloLzi4pd/56Jxuoh+N0anDYQ94NFfW9h7NMDS0iIqtRLy+RwQJ2i3WmicttHu9DAIQnj5oYTTuH3nGlamXfzpizIaw0iMLsS6Js0ExPeqONu0rIfUcSIjxyCOFa7mF74NYkhsy5M/nMj4Jfu7JTQRYZzFaD0D50HRW4T0W7b1AKG/i49+XcZHt9ewMFOAy50HBG70IDRW0dyuDRdYR4YiExzFcOasSaT/0dAnr7B4w8WdzxS+/XoOu/sRhkGIxp6Lb05PsP84xOxcDbWpAqKkh1a0heJ8H5/+52XU63XkuIOSxn6sg6+QX3Lxi994+PrzFQyHAVqtDsKeh83ve2jv7eDpVy3U56pAPkYvPsbA2cXtX8zDcwqo1iqyI9Pim7gcZflHY4YQsXgWYn0MY6XJbuhAV41uFfDXgE//3kPgrgC5PJ48OMVgMES3CfSbCTYfPEMh76JcLqJWqaBYLCIMIkkzHIboDfvoh0MESR+h08bMAhBcd4mGJjFJL6RNI6Hb4vL/bCfWwdRjLAMnKQ+OU0Cvm+DJgx6ePKC3GqbVldTXY5ngqBg0WGEHpBlI7EQInWP49X3curOE1dUZ+DQUAlBbd3H7Mxcbj69jeC9CECi0Th20m23sb0SYmipKe5erLoK+wqCboNcZ4qh5hH7QR77g4tr6Iv7n/zaNXhO4+xeF/aN9HcdMRdrHiWkPQTl9TfLJGMrWDBEpGmhwPtJexkYkSec5j0ysiJmfZT6T+dg1koExMLUwxkk1uhOKyuKJnudJPTFqFONOpfswjVG4sWKUy1wws/lJfdAH/AHqcxX8/GerMv49eDSNrd0dbNwfYufhDqrlU0zVq6hUqsjlCiCfyXjV6qA76MsOeddXqE/lsPjBEn71D9fx0c8ryM25er609bmIHpM4ZvcZBX6KFGA/sX2F9Zd+qh/KJcfSc1cxbcasg/0U2eat1HmCleT7lCxJHnQVpj9wcePQxcHOGton+2j1geO9AXrtQ3jBDI63qlhbp9teB9xrEPcVeid0w6vw5ZdDPHqyjU53CDj/P3tv+uTIkd5p/iICR95nVWVW1n2wWCySTTbZ6qZao2NsNNJo1mzNZsx2bXe/7Nf9+/bTmu2aRrYaaVajow+xu6Um2d28ySpWZeWdSAARa69HOBBAApnISiATx5MkKiL8fP3xFw4P99fda1pcinTz/qwevbmhqzbINJPtAjeQgo1PovmmolGqxot/w4Ubp7fOltodTRzOSHffjvTtsxV9/vlV7b4o6GhvT199XNHfVT5T9eVD3bkfumNxS6XA7XB7uCttfR3rlz+N9fFHL/T02a6q9bqCYlXX7szoyY82tP4wVDSXdfOPdbyoBghcAgH3ipv2D2JbMJeO+KSCtLXxlyDd2GSZjoSMTXGGoiDpe23aD3bDCNamuo8p9VCIOHpCWFPQwq7Zi2jejV6xBitxOnbn+8C262/peqAH70T66ssbquyUdPT1C3d8+7/+ZFM6+FKVzQ3duhdqYUEqFQO3mNKOKv/2d7E++ZdEH/7yS71wxwlWNTWXaOPhrO69uarrd6PUUMDqqKWeBltCUh9zAo2hA3u/klbuhHr3x4m+framWiXU4WFNzz4/1If/7VtVXha09fWcVtYCzdi7mKTKQaLdZ4l+88tYv/qwoq+/2Nbh0ZGisKbZZenx+1d17415la7QNo+5Jl1a8Vz7a1MqJdvlKtT996RPP1/VzouKvvpsR4eH0m9/caDq7pfS/j3duBu4DR6KpcDNfFdfJnrxRazPfp3ow198q2+f2wkgNYWluq7bjuBvrurWg3K6QNbm82h/L62uO2WMwUonKudxS98LXae6Fkm1cEvVYFNHcV31cEmKqumxP74z4r8QdrW42SCHdYiWNgLdfj1U4a92tV/ZUi05VHlqT6s3r+j6vXnNr+WszX16WY/TJiRdejbBZiuiC4EO46eKw5cqRFXVw4KS0F4H2vbrst+ayIxDEq28EenJTqJvn8/p5d7n2tuqqV5LVKnX9PnXu/rs688aBjjp8SBmhlJTHOxpbWNBj965pj/6w3nduBnoJ/+9ro8/33MTkbXQJiXtqJO2DpmV3dxMK4tSXJaq4UsdJs+luKSjxCaeD1SPcwYrnl9W7s5V5wOZb3ZvF5+/bdwyH7iB0o0bC/rmk7oODu1Ms6O0fFFVVzfm9eitufRcvnxynTM8u6s3djKZrKG0Ce9AquiF9uvfqJK8UByVpeKRmzRtKe5J8pifcXUT/NJRuK9q8EL18KXb+cAsu+uqSEmxycOkdzvtJLr6ONSPg4I++vW0Pv1kU7vbhzqqB7LdVHa/eqnffWlGWNkgdjYUEprRR1TR1bV5vf3OLf3JXxRUKks//Xld9b/dVFyoKC6UVA8Wm0ZVnljW1/Fjrma4NftapO8vBlpYWNR//atY//KLXT1/fqA4DrRzUNXe4Z6+efZU9dh2KjCznlixnQkU1DUzXdLq+hW986NV3bsfaHdf+mJzX9Wnm05Pa+GC+17VkmmHyaH0euFl6uVqclsdOtapsc9r/6ag7zbntbn1XDu/fqaaQavVdbQtbe68UM0ZH9lkvTPvcEZFxq5cLurmgyv6wR/e11tvF20/FX38WaLg213V97cVh3NuFwQlZnGR6XEvMraHSSuu6WrvZNYcFO17t6lasKlqUlVgumF/bmVW9g3Kfskt+7Q76/dVcQ2IK5FZR7hdocwIIa66Se1aPd0pyCmNpWntU1GqFbacrgcqqxbOqh7MOUMtybaQyvJ0d9k/9h2ZDhSthrr1KNEP/mBFf/1XX+vbp7uq10NVqqF+98WmPvn8UElScQZ0p4w4WQAAIABJREFUcXFT1x+WtfFoxhm2KCmlHRIzXEusE5+ovBHqh39U0vb+tLZ+8Y1q1UhxLdbTF1t6+uIr1T8+Ui2wz7aKC7tauPZY+/u3VKvPqGSrjzODv1rB2t1t1Uwfg4ozCAiiujOYaiparkDZ99QZrcxKq29F+uProe7eu6r/6/8M9fG/fqbtlweq1UOn49XDxB2/8uzFd+m3zjX4tlNNpFqSGn3EQUVxuKekYOcdVRRHsZwAma7mcu/5tlHLacWnbUsUqBrV3e9cTdvydeasYizlXGPVeVDPdmax3yIzo7OjaaqqhRVXb1aW2NqTQrolu7XT974X6Q8qU3q2U9DTr7d1eBgrjmN993JPzzYrzlAuCMzIL1SoosLQLN+qsi0rbz64qT/9szf09g8jPbPzg7+R6r94rjgwfY/cDiEOhsls32Urp4mW1U8tCnQUbuoo+E5H2nU7wSiqpNtt+vBtZW7AtTSydE1P4pJUi7Z1pE2HqhpMux2JzKjO5d2I2OEmk8f9fJfS75B9Z6vBSwVhXbXI2vVD1TXrmn6XQq4cFs8MU6vRSx0G36pW2NHctXm9/UGo5cVAKx+u6uj/3dSXX+657RL3Ki/17IW1caZ/kWNqzK081ubG0Y6m50Pde/O6/vw/fU8P34lUsBdXY2Jlto/JzB8EIHA6gcygzb6yZrBc15FqqigOAkW2w599r+yv8Z3yjUvmzgUC/SZg/Qavd7aX2rJ053Gkg60pPf2yrMqXsQ53j7T58kD/5S//Vv/1r2MtLczo5s3rznh5e3Nfm98duJ3ranHd7dNn6ZWmD3Xj4bre/uEtPfp+0e0u2jjmtt9lGLf07HeVv84EGm1j5m36a10R6/uUpNk7oesHbm8+1t/8P/+qw52K9var+uSjXX36u89Unko0M1vS0tKyW4yxs13Rzsua243FJv7dLqVhornlqt7+0UP9yX+8ne7I4ozgO4uEKwQulEA2JhGHtnTCFk7Y+NFUtrNu833kQmUiMwicRsAaar+jaZD1f5OK6nFq4GoGs42/9na+4cHNMQI5bC1+nqG/tnjy0CCQ9YET26ltXs5o+/1/E2lve1Uvdza1u1nT/r70k5/8q37285+rVKpr4/p1zc/Nqlat69nXO6rsJqrVJOsDm44Xpiu6slHSB3/yut54d0kL69m8jmVKfTTQc9MHAqZPpsO2IPlqoAc/iPSD59dUr9X08UdfqFKt6De/+Uq/+/Rz/dVfJppfLGl+ftGN9+1s72v7ZU31SlFJUlAtOVASHGlmrqaN+7P69//jW7r2wI4OT3eFdYv30d8+VBpJOAJel0x/3eLeRNM3Qn3vR5EOtq/r+XcvdbBb1WFF+vjjL/TRxx+pPBVrZXVJi8tLbjbq6ZdbOtiuK6kVVYltM4lAUbmm+SuJvvfBHT35wZqW7SihbA6W9ne4dA+DlT7XhxvQs8n+YuKO6yktHmhmuarCdF0zy7FKM7ECN3HY1hnJOpKNSW8bk54OtLge6OHbS/rdR091UKloYa2g9Qcz7oxlM2pptzfxXzCTwyVZt8msQMnsjgrzOwqKh5pekqaXEoWlmgK371ETgs/fTSwWE914EunP5uZ159H7+sU/fK7Pf/etdrYPVa8nbpKwsWNMWFehKM3Ol3T9zhW99d5dPX5z3a2ws6NdZtZjzW8cafbpgaZXpfJcku6ykP2AmgRuDtw1RlIyFSielqZWDjW7VVN5KtDUQknlmUhFO5PARUgvx+4bzn501bd0DY/WG/MOpemFQPfuhfril0vaOzhU5WDHveTfunVFN+5eVdGOEBngn68zdwqSnRM/G6i8dKi51YpKQayZ1USludhZA/qJ557E8Z3sklRe3NfM6pFm45rCsK7yfFXFqVpqqJBPzBrsYqBwVlp8EOp/+z9u6cN/2tCvfvpb/ebXX+qoUnXWGTYpXKsdOWML0+vClDS3OKW7D+7ojbfv6s135tKzPBNp6UWiuWs1aftIU8uRynbMh68in3emD+57lBlFmCZHK6EefBCotLSijQdz+td//lTPnm25YyiqtgVIEiuq1xQEVpa6okKstevLuvfaDb3x9h09ehQqKgb67LNYM9cONb1ZUTRVU2m+rqBUV1hKdytyBhtelrNejZnJb9Y2ZTviJtB7f1zQ6vp9/eqnG/rwZ7/U82ebOqqYnJGCqu0AlCiKYnccztRsqNW1eSfzW9+/q3uPIs0uB4oPEs2tH2n2y6rbMaK8UFdUNsO33ARCr6rZKZx/ic3YJ6VAxYU9Fed30wl0FdNmxe+qcioXO7bAKtZ2S0l3cypPJ+5swWgqdkY9jXamGKhmFjnzO5paOXLGO+XlkoqzdVdfDRXI55nJaYYE1gYu3A71B39e0NyVt/ThP36m337ylfYPDmRvhaF7KYzdREsyFWpmoejaKNMFbxTlOic2M2j9lNlATz6IVJi9qRv3FvThT37jdr+pH9WVxLGOEmszra0LNbM0pbmlKRWnI9eBMt1xBktFuYHrmeWjtI4KNU0vxAqLNfc9cTCtDJ57ru0zJ3cMRDFd0XHn3Uj/8+oV/fQfl/Xxr77QN19san//0OmQvQDbzkbOWNAlZrwTZ5A4P1fQ8rVF3bx/U/dfX9W9x1e0drugsJytXO6kB3nGXe+bEV3dmM6btfPCoaaWDzVd2VdSs61c0r9GEZ1BjXdtv2ahglhRYAaPVYXRoQqLsYKpmupRXfZjkX43ExXWAj36INL/tPRIH/7Tnj761ef6+qunqh3FqtetLbB3ssjVZ1SMNTWTaHV9Xm+8c1dvvH1DD14L3Y4ts5VA1x4UNfWPewrnjlRerCks24rZ9HfVl8+J7tpDuZUJ1oZOL+/rKNhXaTpWcda4Z/XZxJMONuSfG/WcHm9kBlfFxX1NLR8pSEJNLccqzwZO7/3veDup/LP/Dpk9WTR3pNKyyWXHBtZVmK84dpHtlpb9XDZ2fzM+Vh77jVna0fTqnoo61PLtI83dDLV4I9T766GWbr2mf/nFmn770Rf65qtvdXRYU1y3Lr6tmDdlT78H07NlLa+v6PG7t/Xkveu69zhyk47uHBMT2ADacSb8QQACvRHwBiu2M0VYUVWHqgWHqgehM8hzLaZvcyxF9/Vq70z1lhWhINAzAdMzUzP7DSkHmlkP9fg9O2l2XX///wX66KOvtPniQHE9VBQnOtop6IuPt2XHUNTq9dQAOI5VTyoqlGItrkzp7usbeu8P7urR+4tu4YDb9c4bV/Kz0XPVEPAkAml/2kK4d0t7t51K3Lb+v//vItWq9/Srf/6tvvl2U5VKqLgWqbKXqHoQau+lLZIx/ZXq9UiBLfQJKipPVbV4tawf/7t39e4fXNeM7SZn75F+VM115k+SCT8IDICA6V2SLi2w1G23Tzuatx5WlRTqCovpGKRrWmlfz1kBTYAZ9nRn2HOmOvHRDavpbWT9X+v7Hqgu+xwqDtPFaI0X24mHBYDBE8i+53axj3UBCoGiBen6G5F+aJvUTj/Q3//dvnZ36qoeFRTHZcWVQE+/rOt5uOPGS2zMzhb92u7TSXiohaWybj5c1uP3r+utH1zV0jUbh85Kk2U5+LKRw8QQyOaCZP1fWwt6VXr7921H9A0VpxJ98skXOqokbjeguFLX3neBDjfNSPDI7SZow6t24oE9h6U9LV4t6cn3b+vHf/q6rtrOQHaEpo03+nzQ4YlRrYsqaGOe1BYGTCXaeD3S7x3ZWtzH+uk//kovXxyochioXp9W7UB68U2sredbzkjb5grtPS5JqqoFu5qdi3Tj3pLe/OFDPfm9G1q1Y2BtjiQbM7+oMpFPbwT8q3VvoQl1OgFroG0iuRxo7Xao3/vDR7p1f0W2KcjVWyXdeG1OM/PF3Eq1XJIWNxtXSeycublAa3dD/dFfPNHjd26qUjt0Ri+3H61qaT2dYG+Oidid/7Nfi2zyvBhodiXQg+8t6n/4X35fQXSQTuCtTevaxpQ7FsPHcnOe2YCOTcDZxGnhWqD1hUDTy4GuXL+jbz+/qa3nB9rfqenw4Ej1uhk+BCrO1DW7GGnxSknXbk3pxv15ra6FCm0yOpBuvBHpg+iR7r6+rJmVRFaGaTMAcT9oJrtNSqbqaIYSxQXpxmuR/vgv3tHL5w9VLBW0fF26dW9JU9PZzgjNwjeK0HrT7dfSZgOaIf3EpBnW2PZ9U1NSFFmAisLgSLfu3dDG7VXXkLnB2mbU/t5ldWbW23ZO/JWNQG//6JYWl+dUi3Y0txro+p1FLV0pqmDGSvbXrYje24pqOlWSFq6EevtHa1q7PaXdvQcKw1h33ljV1fWFY+mkBiP20hgoWpHWvh9pajnQ+p0Hev03N7X9oqLaoe20Euuwsic7DqI0k2h6OXSdmOt3lrRxu6Sl62FjdwQ78/7P//MPdbhnW8hF2ng4p8h2fmkrQ6NqCun8vgUIbJvlGen2dKT5a2XdevBQm88Tbb+U9nZq2t/dUf2ooqhQV3lWml0o6OrNOa3fWdD126HKS4HNr2p9Svq95IEevLUuFSpa2Qi0srbgDG1cmd12Cmef+GmoohXH1NjtMpNoxo6sWTTDs2mt339XL77d0/7WkQ4P6zo8MIOVuoqlROXpUPOrkVZvTGntzoJu3Co4mS2t8DDQWz9a0cLG+9qr3tXK+qzWby3L9LWdXVbtJ188bxPaf3yMUFpeD/TBv72r155cVb0SKzQrDG+s4r4sPrC/2guYl8UST/eMscTdzhl2vE95S8s3Q12/dU2FKTNUsqOXErfaceVmqHf/8L6Wb5fcnt2L1wq6fm9WC4tT6UR3h8kLN1lvrG2yfiXQwmyk92YCXbt+V199ekObm3YslFnb2rFehyqXzfjrSFfvlPTw8brmF4oKrcPj/6zK3VFsiYo3Q71WCjS3uqj1O+9o+/mhDrYOdbhf0WF1z32XSvOxFq4levj2NV1dX0q/j4bJVl7MSuv3Qv2H//T7qh3YAHdNpYV93bm/rqkZb0noM/bc0mfXUXId/iC1SStJV5Yj/WAh0K3Xbmvz2S03kL63nWjPVoTuHeroyAYhAxVKgTMCLEzXNLdS0HLWxl/dKLgzGc0ALijYm7avrFz5c+KcdutjO92z8k5LT36woJW172tv60BBzSx/Uj04LS3zt/RMv9xW1XbETmhGKrvS7JZuv7au0lxqfOK+n6XAGUNMzyR6fSnQ/NV53XrtsZ59fVu7L6s62q/KjIvM6C+aTlReiDS3WtTyWqQ7jxa1fjNUwV6qQmkuDvX6703rP+oDaXpbV++UtXZzOZ10yInv8g3TbTTX7oRu56N7r11zBhzV0lM9enJL5Zl0gK1R3k5ozc0Ka3iK0r3H1jl+W6+/YyvQpYU12y1o0Rl+pm1RI7XjN1n6pm/TS4Huvzmt0sybev0HV93K3/U7i9q4cyXdjcHL4vO3qxlVzUvv/Pi21u6XlEQHWr+1ouW7oQqrgdsd5fFyoOWbS7r3xoKefXVHuy8PdLBrRwPWVCoUNTVb0OxCWXPLJc1ciXX9wbzWbkUqLGSGiO73zKzKMmuwgf54HkeECwRGmYC1EWYAXJoNnaG2HWo+Zf0ga8PP3kUZZRTIPkwErF3PBiVtx7OZW6HetH7Y9JqWV1f06a83tflsW7UDG5wPVK+GzoC0oLoKbpFEVVOzM1pZL+nOa0t6/P6a7jwpa+lmmB4F5EclXMdgmAqOLKNLIOu1Wt/H9NcKYn3JFenao0gfHE1r+cpjffKrLX352Z4O9ivOANo2kTMjXVvQXwpjhdP2vhZqbiXW1dtF3XtrSe/8aE3X70XO2D0/0JnlOLrIkHz0CNhx0dbdtr/MyN6OvrV3qKm5oorlskrTUTrUZi/R7ouQhedyZgJuHCKLlaLkR+vMEH0EA+gbTVNN2xjV9X8DTc1GmjqKFM8X3fux7Z7ccSjKp8X1DARoBM4AywW18Rk3RxLZWIl093s2llPS1Oy7+uyjXT39ale720dSvaxavaKkXlUQxCrbLsvFukrlmmaWZ3Xn9SU9/N6aHnxvSdfuRm6Rs9sSlyo5a5UQvlcCplvZgrWoIC3cD/VWIk3P3tDK6qq++fRAW27Sv+LOQE9snD5JVAhtJ+WKCsXELe5euL6o+++s6vH31/XaW5GipaaxlWvG0eFea4RwvRLIdMr1u1z/NlB0RbrxZiRbnDm/9JY+/ZdtffPFtrZeHqpuO+Tbot5a3RmshIVY5bItHo41uzStm/dm9fCtK24M4tq9UNOLtuigV2EId9EE/NDQRec7nvn5L5PNr00FWrgX6r1bs0rqs64jbl8Et11WnrrFyb/jZM82EWUT9FNTod5ZLympXWm8gLoVaDZg2DJonWXuyWZpWqd/+kqgR3MFPXp3LX1BtS+6GQMUU2tI1/Fvi+7EsPRtcfR0oKU7oRY3Qj05iBTvlrT1ItbebqKqbfRQkGbmAy0s2Q4YNoGZrSD3aYaBrjyMtHp3QUllwU20t3Bo/3ULpciO6Hk91J/cvyolV1vL7stt8Vzc1klfj+DEa8bHwtgLvnvJr0mVinRk2/Ulh0rCfRWnjnTzwbLWbtjb04kpnt/Ty5Q1xHM3Qj25FurJB1dt/zY3Od+oM2Pg+XbLOedvK7/Ka9IbSwW9kaxK4ap74TN3Vy7PMZ+WxbcXR5t8npIWH4ZauBPqjR9Hqm9Oa38n0eGedHC4qFJRmp4P3I4gheW00feD2m7ytSAt3wn17+/ckDPQddsy51aj5fK1bFvEMQdjYsPeK9KVxUirjyLZqT/JQaLqVkEvnpedZbCdVTe3kBppmcGXTaabrvtF/gtzod67Oa0kmU6NxkwOM5rxOnUq1JygudscaufqflDNyMOdtSdtLEa6/nqkeK+o6naira1EBwepoYedkT43H2juim0zGSiwk35MHuNu/0fS/R8VdO+HGwrCjdQgxtqHfluBmmGTTYDbDgtX7Y1sKa0Ir5e58na8zfSlo1+45FhY22CGQ66CYytDosW7od69W9Y7yYNs5UKznXSGMJ0S9HkZB7svJJq7H+r1W6FeO4wUb5W1vZVo11ZJ1qSZ2UDzq4FmbDJ+0RqYlLHTs6x8qZ5aYolK1wLdXiro1luJ4t2iDjZntbOd6OBIzjhlxoyQroQqLWeddNMh+7O2fybQ4t1I/+F/vdUYPLS6Mj1sGWVJs2pV9iwZl5ar+3S3nuWHoZbu25KjdHVHfSdRZauora1Zp0em91MzUnk6UMmupvtmEJjpd5q3pXqywYzL94R/8uI1ZJyRHvx+QQ9+aItISmaCnxokWeBjEU5JPIsTRKvpShOrGzNusmiuLcpwmTHlinTLLKQfR0oOFrT3ItH+bqKjw8TZP5Zm0/ZoZiX9TXK/v76erNmz+PORbr65rqBgHzPkyPSpXW5fr3dCvXd9TornMh26qWCqczvWsaT2vbbvWVlafzvS+pNpJfGtVO9tNxSTIfve98LOwheWQt15J9DtN+cVRPMp8+z33bfBDl+Wt5PLbakovfXHZb2V3EnbEsvf512USmvSrSuRbj6JFB8s6vDFgna2Eh0eJiqXAi2vBCra7322qsJkabSlVleuLl1DlqJwX7aOVHCEAASMgH1nsv65GdYtbgS6aTsWzZRUjxa0di/S/KIN2mftlI8DPQhcJIHs99HafGv/y3dDvTUX6MqNUGt3rurzTwJtfbej/Z0jHVXtGEiz0kwUlerOOPvKxrJuP7iih08WtGH6PZ8ZDOf7tO2/wRdZPvIaMwIdlMm9Z6fHt958P9KV66E27i/pV/88pefPXmp321bqVVWrVVMT/EKg0lSouYUpXb+7qPtPVvT6O5HK9g5gfUDfv/LfjQ5ZjhlUijNsBPw7UvZebIsnzBDw5pNQoS0AmVrWlZtlzS+n78DDJv5IyuMHKfi+n7/6TH8tFdPfaWnmSqDrD0PVVNTq9rSCxStavjbtFud0zsxiZwM6nQPgmiPgWKO3OSKvcFuQimuh7s0GWrsZ6l9+vqDffhS53ZArezUdHlbcwt7ITuYu2jhdWQvLC7p6c1qP31nT7YeRStkCZPfuR328QiUQpScC+ebR7m2nlQVp+fVI379qC6Jn9Ot/ntIXvwv08nnV7eRdt13KbWjCxk4juV3Fr24s6NajZT15f0oL1+1c13Tcwk/coMI91QaB+kDA9LJwVdqYibS2EerXdxf1m38p6usvXrjF7EdHVdVt5UFop3oEKk4VNbNQ0sbtOT18ck23H4aa3wgVzAxgTq0P5SOJJoFBT8E3c5qwu3SiKJswjNOJaWdoYoMa/s+36v5HxD1bZ7uuICykbb/N+rgB7GzrlWz+J50M8gl1uPq0LZrtHDBtRhn2YBNJ2QS9pWvh8jLlknJ+jb5/+mVOSunK7NVrkVbML/N35bV03ERwc4V1Izknvk3sZZOZ2Qo9598mhA38uHOmbcLfdjqxPJycGQu7t09DtkYur3ZjWZixystEv/ttrOcvt3RQ3VFYPtLSug1iLWrZturLD6a+Wk4nx7Iy+aIZRyt/IXET/G4wzHaE8HV2ckotvo5nVu9mDJFuh+GZ+onFlijZW2NmOOFZmx5Z3djq/NlAC7Hcx02iW3ST2U2QH590dUXL9MXVqU2Mmr5kZW7LvemcDcCYg9vow8zajUGUyHRR04Gz7L1+K53It3Cunpwepi++zi3LoLG5RJykum8vxtl3Ks20i0DtAp72bHIUU0Mo932zZAuJonKgcCnQNTM88Pprfu47fnzC142FmJ9tNW15ZsyczP0Q1ctgVZHZMrh2qpzzOK2s3t/K7IvVFt1NhGe/Nk5287fAtttKtiLM1Y2lZe6+nCeU0Tcbeb1wjG2nkblAK9eVtlEZNzehbhN9JkendL1MmZ7bsW5Jku6YMncl0pz12y2e17HcBL2TxbyyHY1cGNtazlVgpuv+u+vzbmOUYmw6GjPbacuxs7YnPfvHBSvYcS4rgWYsuJfb5LK0jZ3T/+y7krF0EX3eaWav9G8+CVeXxtR0x7ahdnqdhcgHPEtOVg5rH/x3OJ+OZ2/5mcGTFdGOT5oJtLAcaN5/r4yJxc8+zggkr1OWps8jM7Bx30Fzz+eXsW18N9zOSbk21J4t3TP8ubS8oVWYpBveWHzfvufrq1u6eRlNr0zXjIOVyf7MrT0dH8fribUpbiexNv20+BbW4NqGPO54w0Czi4FmPF/ztvRNL11ebfXl80qlSf/t5Jb35x4Ck0wg+3649sFewlekhz8o6MFb95QcZf2fcqBoJjM25/s0ydoyHGU3HbTf2CBd6bQxb4bZoZKj69r9bk0vvku0s5O+w9kxjLb7of1Ozy0HmprPDEkZKBqOuhxrKdoaS9fHtA0dsz5kIE3fDfTGRqDXfzing51ZvXyRaOtlki6EMN2dkmbMGH0lcCtMbVGNMwr3/Sx/HWuOFG6oCfg+hJ3Yad36mUBP/qCgN967raR2242r2aC9W8hj+sofBIaJgOmvtc0FO2Y40NqjSH92+7qSyrp7F7Uxv8gW45QtUDfBu3p0i4A7BM5GwMY8LIaND7m7ROFCoLm5QO/fDPV9W+CzvaBn38Ta2ZFqVTuqO9D0rNziRBtLmV4InC67BYpmDJD1Sc4mCKEhcEYC7XpmfeC5RMF0qBtXQl1/kqi6c0V726t68TzRwWF67HypHLgdXhdtF/3VwC0qt/6FGwPMpie7t8lnlJHgEDiJgLW/1gBnOwraHLcZZwc3A71xJdDjDxaUHM3r6VeJXm4lqhwlMoPB0lSg2WxzBVtgX7QF4tl4mhvPdu151qSflD9+l0IAg5W+Y89abj9RZl8Ae3P0DbrlZ1+09j514zk7ssENAmbHuTi/LIAP568nye+jZJNKTg7L2tx9fH/N+l6NR3+TXd2X2aJZWdr//Jfcu7cHsWcL4ww+ur1o5CJZEC9zQ9As8Vywhlfezctw2tXLlE3AxduJnn2T6MsvD7S1u62j+r6mFuq683jVnSsZ2cT6Rf1Z+S07G1Aw3jZpbc/+cxY5sjjOGMClZ5HNMffX9pjzacnT1Um7DtgPhtWtcfR5Zddj6ZiD7WaTNHdMaITpQYZGEKdvWRrtutdIsClP3snpoZM31cNX5ppP1Mtg17ayN/TY/Hw74OM2CpQ6NGXxCWU11ai34+n7pF7pavl72bME3GSZoTFZvR62ydk1ryycu/h0/dUc02awqcvmZP6uXeiaamcPL5OX0UJZG1sI3K4mDRnaw2VyZMFddTV0wuTxg3hZW+XTcTruy2KRfb659Hwj30jDhfMCmHBZRLv4v3yaeUFcYew4oTQv1xakCbiYTld8GpZuSzreI4vrRfDOPmy7u/c/6erjWhiLbxxMRvu9sgffHpyUxil+Tqws7VyR01heZs/fOq6Zrh5LNgvrvNvkdo+N32hLrC22D59z9+2fC2mJ2p9dcmFSx1P+tew6/b69SlqWvU/LczhJHu/XkCFzyOVtRbePM/Cyolh7nTnmsfh7h8Hr6SlFxxsCEOhEwH3jUg//3YwCRXZKnu2U1a1/5b/PnZLEDQKDJmC66g2Rre9lx1TGqdXuwlKkuZvpLpy2g6X9Tpk+F203CjOWLGbvOIOWkfQh0E4g6+803rnMgNiFCRTVEmcMPr0mrVYS1etSIZQKNolqxvDldDGCO0LU+l75jlB7PjxD4KIJeN22PnliOyAHaR8iJ0dD73Nu3ELg8gj4RjTr0Np7vdtxNF1s6foUJpyfqMqCnfnd+/IKSM7jSMD3f63/4CdPp+WMV2ZXbafmUNVKItukwtYSFc3gKju62/WBre9s7bT95V4BMxcuEDgXga4q5dvPbJzPjfHZTvRlM3KVopVA5SNp7jBRrZaKEObe3+wEica4o3l3zehc4hMZAt0JZDrc0ENrg21MwW3OkI5DrC8mWqnKvcNZn9d02Ha58u9w7qQPe4fLfR9chm3dke5C4HORBDBY6TvtnOZ7pbfvQ8755CxbZ34a8XqO3yV161gcqusWAAAgAElEQVSZl0/HX3PBOzjlfLPb9kAn/VDlw9p9jsfxhLu45NNoD3KSX3vY/LOXw67WyaxLle9iffV5rK+/fa6dw23Vw4oWVop6/Xs3tHQlOzYnn8ag7vNlytg6J/sn73fW/E+Ke5Jfp3wsvGeY6ZXb5cLCmp9Pz199Gvny+LDe7yzX9nQ7xe0WJnNvMSroFP8sbpZmjsexqOZvnCyM/3SSLwvXAOjTtAR9eH89lskrOnRIzzl5d7v6+7Nk4ePY1cqRPXvnRlLev+GQ3fiAeQa5dFwoH8bHzZ5dByYfL++fi9O49Tc+jn+2eFmezinvbn72nHfzRgz5/Fy4dsGzAPm4Llx7gq3ptwTPP+TvO5XBy+Ov+fDerddrt7g598ZvlqXp5Tlv+p3i+zzbrz7PzN1d7B/v3oo1Tdn7tafl822L31rvnRL0Ebtc8+l1y7NL1K7OPp2uAbp4tMWzxzanNGJbH6IlTMtDl3xwhgAEuhBo+wLZo7VJ/jtnL9X21xYsc+UCgeEgYPrpBuDTFU+2A4Vtqul1uaG/Fg5dHo46m3QpnNFzE0KS7cJYnJKK+T6919dMd10/1xsSNqNzB4HhImB9CLcDZk4s2t8cDG4vn4BvXNskyfq/Nr5of67N9UH9tS0KjxC4cAJZe+pV0ha42WRoaVEq+T6E9YNz0zuNxWheWB/ZP3OFwDkJ9KRSFsjmwXxeNvFvi/nK0pQd993wyN7ZrL+c02MXraeMfAZcIdAnApnuunGyMFFiHQRbYGn6acbaS4HZrzT/vC5bvE563AzJ3RASwGBlEJXivxQ+7Xxjbn72ybv5cO5qHpmnXfJh/bOFy9yzkC0pHHvwgfz1WICzOOQFaorqUjAv++uWj7n7MFnQrhefhr/6gD5+u7v3P+3q4/sfaLtWEj37KtEXnyZ6+t2m9o4O3NZnV28u6NH31jW/Gsodk2KDU9mL02nZ9MXf87Lrq5a3XZBj6XggFjDzzDt1i9+WTsskdS6p9ujuuS1uxzAtinJChBO8fLpWnJZg9mCfk8rpI5/l2pJJh4je3+efD9Iui3/2wvu4+Th27/3b3Xt97pauj3+avw930vW0NE7yNz/P4qQ8vJ9Py1+9ey9Xi9Mezz/btSPrzLGbjM5K6YTMffonBOkqUz5OL+nkw7vC+AbtzJFbUmp5aE+q/bklcA8P7Vwb6WUe7jl19EGPtUU9ZNMI0kik4dK8sWy8f9M1vfPuDfna9cUCBJ1VyFLIx2tP+yzPvaZzqrxZpp3C9ZrHWeQmLAQg0ErAf8/8tdU3fUqblU4+uEHgYgl44yrTV//pIIEdv+p+CL3u2lGL7QOgHeLhBIG+E/Btq12tO2yq640DO2Xm+0PmZ2MHXTZO7BQVNwhcOAHT66yd7fxe5BXafxEuXEIyhEDzvTqvhifpbj4c/CAwLASy+QE7Gtv1DezZ922tqfUGrl5//XVY5EeOCSCQdQiaXYNmma0/6/XVt79ZP9cHcv0I8ztBd5s5+FhcITAgAl4P/fiDKZ997C/n555Nl/17nvll7XUauO1fH7fNmcfLI4DBykWzP+uXwIfPX/vya9ApkfZveTsc8z8hTGNy1gvbHj/XgHTwOubUKZlObscitjnkxHaNVdZgKU4UH0rVbxJ9/kmsLz59ru3dLSmMtbYxr3uP1jW3ErrtoxqdzrakB/5o5fXI+55Zh4Q7OJ2abXudnDWN9vinZnhagLwAJyTuvfLBT0u6m7+l1ZZO22P62+nzbE+nk7tPoJNfe/x+Pr9yfpclcC+FN9lOKdgp3idGt7i++L2Ic9Ywx2TzmbV5tD12zsbiWiNoV/v0FKlzUjlXL5E5nS/FXmXyOdr1pJ5nTsheBDspTLufFyGXxUm3LdH9g7+eFHFY/DJZmzWUBzBKBRkWoMgBgV4I5L9nFj77rvGV6wUeYS6KgNdHf83ybf5eZEcIZQP6MuMVG2jKB7goWckHAp6A6d+pXUhvnWKR0tHRxpCHT4crBIaNQFtbPGziIQ8EuhJo6K4ftDWHhmPXaHhA4OIJmI5mnQh/dJUJ4dXVrubtn9tf6RoDiD7AxZeAHCeUgOmrFd0m9O2aTf67zYHMw450NY+TjLknFB3FHjICpq/dmlDT6zC/0qDLS1+3+ENW1EkTB4OVQdS4V3Zr4P29z+dYJ8V7dLha3E5ptKfZIerpTp0S6eSWT8n8Twpzkl8+nQu8t4FR60faKj77SxLZCr+kKsV7ifafJvrNz+r6zS+r+u6bZ6rVdjQ9F2rj7jXdfXhVUzPpj3TD6jRL5kIvA8PaIeEOTmcua7c0url3zOBMgdtSaI3b+tQW1B5PDdAhTg9O50q218i9hutB3vMHGZAwfUn2lERO8e7OJhfRbvPttfPK+XdP5BV8zpOuxbWOmgnbpcN2wRK1ZpcrW+72eBjzbALvGtRH7Bagm7uPd9L1pLgtfi0PzRS7ODcD9Hh31nR6Dd8lXNO5edejpASDAATOTIDv2ZmREeFiCZygose8sgHRRt/7WICLFZ3cIHA6gQ5K2sHp9HQIAYFhIYACD0tNTLQcp6ph/8YpJpozhR8ggTYd7aTTndwaEp3o2QjFDQTOR6CpZ827LEX/XtZ8TO8s4LHA3aU4Q9DuieADgTMR6FHrGlsEnSlxAg8BAQxWBlkJnb4/ndxOkuGs4U9Ka5L8bB7TpjPrUnKYaOd5oqCeKAoCJbVEtT1p52msr38b61c/ifXZx0+1u72rUqGutRtl3X1tURt3ZlSYsvP82n7FJ4kjZe2dQL+/q/1Or/eSEPJVCYxMnZmgIyPsCbUxwmUYYdFPqBC8IAABCEAAAt0JjEv3o3sJ8RklAvTFRqm2kBUCEIAABCAwugToc4xu3U2a5OjqpNX4eJcXfR7J+sVgZSSrDaFPJZDt+pRUEu1/l+if/npLh7uxpgozKgShdjel776q6qvPNvXtV99od3tbsfY1fzXQG+/e0MM317WyFigo9nUTglPFJgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgEghgsDIJtTyBZUxsh5W6FB9K+89iffi33+r513sqRdMqBWXt79e0v1fV3t6B9isvFBQPtXK1qNfeva13f/xQt14rK1wIFBQCXepxQBNYdxQZAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATGnwAGK+Nfx5NZQjNYSRIlR4nqW4lefl7Ssy8PVFCsYpCoUquqFtcURKEWFma0sjGnO48X9Mb7G7r9+rTKq4HCYiDZsZRsHzWZOkSpIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgYAQwWBkYWhK+VAJmZBJmR/rMBJpb29PMwZbiqqQ4UlF1TRVDzcyXde3Wgu6/cUX33riqWw8Lmr4aKChjpXKp9UfmEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAw1gQwWBnr6p3cwgVmbxJJ4Zx05V6o//y/v6Gt57H2tu1zoEI51MzclOYWQ00tBFpYDTS7HKg0FygoZccAGT7sViZXiSg5BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgMjAAGKwNDS8KXSiAzNDHjk8KKdONJpGsHoaqHiaqHRQVFqVAOVJqWO/qnUJYzVFFBCqIuktsxQxiwdIGDMwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCAw1ARvj5g8CEIAABCAAAQgMEQEMVoaoMhCljwTMsCSRglBSKVC0mO62Uo4DKbbjgtJPEAXu3oWzON0MUujI97FySAoCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAT6TqDL+HaQjW+7IXDGuvuOnQQhAAEIQAACEHh1AhisvDo7Yg47Ad85tw54ZDunBM6IxYkdZLYprod+hoJYWj7dM0QjKAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDgwgl4YxUMVS4cPRlCAAIQgAAEIHA6AQxWTmdEiDEg0DjmJ5YS23nFjE5slxX+IAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJjRADblDGqTIoCAQhAAAIQGHMCTNmPeQVTvLYdUWxnlbPuqmIQ/a4q/gpYCEAAAhCAAAQgMIkE/Khn42o3/mESgVDm0SQQKHill4LRLC1SQwACEIAABCAAAQhAAAIQgAAEIAABCEDgTASCQPaf+xvw8C87rJypZgg8kgTyRiZ2n38+S4FeNd5Z8iAsBCAAAQhAAAIQGHICdvZ5o0uFvcqQ1xbinUiA/v2JePCEAAQgAAEIQAACEIAABCAAAQhAAAIQmGACthHEBSxXZIeVCdYxig4BCEAAAhCAAAQgAIGzEGB+/yy0CAsBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAInEcBg5SQ6+EEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI9J0ABit9R0qCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAInEcBg5SQ6+EEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI9J0ABit9R0qCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAInEcBg5SQ6+EEAAhCAAAQgAAEIQAACEIAABCAAgWEgEAyDEMgAAQhAAAIQgAAEIAABCEAAAhCAAAT6RwCDlf6xJCUIQAACEIAABCAAAQhAAAIQgAAEIDA4AhitDI4tKUMAAhCAAAQgAAEIQAACEIAABCBw4QQwWLlw5GQIAQhAAAIQgAAEIAABCEAAAhCAAAROJpBk3nZNMFQ5GRa+EIAABCAAAQhAAAIQgAAEIAABCIwkAQxWRrLaEBoCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQiMLgEMVka37pAcAhCAAAQgAAEIQAACEIAABCAAgTEl4DdVcVe/3cqYlpViQQACEIAABCAAAQhAAAIQgAAEIDCZBDBYmcx6p9QQgAAEIAABCEAAAhCAAAQgAAEIjAgBb7wiDFdGpMYQEwIQgAAEIAABCEAAAhCAAAQgAIFeCGCw0gslwkAAAhCAAAQgAAEIQAACEIAABCAAgUskEGTGKo0dVzBeucTaIGsIQAACEIAABCAAAQhAAAIQgAAE+kEAg5V+UCQNCEAAAhCAAAQgAAEIQAACEIAABCAwYALeWKWx48qA8yN5CEAAAhCAAAQgAAEIQAACEIAABCAwSAIYrAySLmlDAAIQgAAEIAABCEAAAhCAAAQgAIFXJGCGKRinvCI8okEAAhCAAAQgAAEIQAACEIAABCAw9AQwWBn6KkJACEAAAhCAAAQgAAEIDCEBdxQF06hDWDOIBAEIjBEBa2o5+WeMKpSiQAACEIAABC6FgO9R0Ku4FPxkCgEIQAACEIDAiQQwWDkRD54QgAAEIAABCEAAAhCAwHEC+TX/GK0c54MLBCAAgcEQcNNMAUYsg6FLqhCAAAQgAIFxJ+ANV8a9nJQPAhCAAAQgAIFRIoDByijVFrJCAAIQgAAEIAABCEBgqAjkDVeGSjCEgQAEIDB2BJLMPtAbrdhZQayTHrtqpkAQgAAEIACB/hNwfYiEcwb7T5YUIQABCEAAAhDoAwEMVvoAkSQgAAEIQAACEIAABCAAAQhAAAIQgMDACbRvatX+PHAByAACEIAABCAAAQhAAAIQgAAEIAABCPSPAAYr/WNJShCAAAQgAAEIQAACEIAABCAAAQhAoC8E/O4pbvP+DoYp3r8vmZEIBCAAAQhAAAIQgAAEIAABCEAAAhC4BAIYrFwCdLKEAAQgAAEIQAACEIDA+BBgynR86pKSQAACEIAABCAAAQhAAALjTsAfMzju5aR8Y0Igf5oVww9jUqkUAwIQGCUCF9H0YrAyShqBrBCAAAQgAAEIQAACEIAABCAAAQhMBAG/qYpd03sbJmp+vP9EwKCQEIAABCAAAQhAAAITQ8D1fzNDFd/nbb9ODAwKCgEIQGACCGCwMgGVTBEhAAEIQAACEIAABCAwGAIXYWM/GMlJFQIQgMCoEgic0YpJn6T2K6NaEOSGAAQgAAEIQAACEICAJ3Da8MJp/j4drhCAAAQgcC4CjWUygZTflW2QzTAGK+eqMiJDAAIQgAAEIAABCEBg0gkM8nVl0tlSfghAAAIZgcZW6K1trl9pCicIQAACEIAABCAAAQiMPIHWrm7DOJs+78jXLAWAAARGmIBrmgfcEGOwMsIKgugQgAAEIAABCEAAAhCAAAQgAAEITA6BIBvE99fJKTklhQAEIAABCEAAAhCAAAQgAAEIQGAcCWCwMo61SpkgAAEIQAACEIAABCAAAQhAAAIQGHkCtojp2EImZ7TCcUAjX7kUAAIQgAAEIAABCEAAAhCAAAQgAAFhsIISQAACEIAABCAAAQhAAAIQgAAEIACBISRgtinZpipN6ZwFyzEzlqY/dxCAAAQgAAEIQAACEIAABCAAAQhAYEQIYLAyIhWFmBCAAAQgAAEIQAACEIAABCAAAQhMNoEks1NxV2xWJlsZKD0EIAABCEAAAhCAAAQgAAEIQGAMCGCwMgaVSBEgAAEIQAACEIAABCAAAQhAAAIQGGMCXYxTju2+MsYIKBoEIACB4SDQpUEeDuGQAgIQgMBoE2hvYrNn+ryjXa1IDwEIQOA0AhisnEYIfwhAAAIQgAAEIAABCECghYA7oqJ9IKklBA8QgAAEINBvAn6g3l9d+rTF/cZMehCAAAS6EMg3uHaff+4SBWcIQAACEOidQK5Z9WMO7pql4HcYbOkL9546ISEAAQhAYIgJYLAyxJWDaBCAAAQgAAEIQAACEBhmAn7AaJhlRDYIQAACo0rAD8bnB+rzZfH+eTfuIQABCEBgkAQCyZ/NNshsSBsCEIDAhBPw/V8/5mBX95lwLhQfAhCAwLgSwGBlXGuWckEAAhCAAAQgAAEIQKDPBDpOjnZ07HPGJAeBvhPILd/za6TR5b5TJsHzEWjVUksLJT0fUWJDAAIQ6BMBmuM+gSSZCyOAzl4YajI6P4GGumadYXtO3K5WqUN6f/58SAECgybQ8j7nFLvFZdDZkz4Ezk3Aa6y7NhrncyfbMQEMVjpiwRECEIAABCAAAQhAAAIQ6ETALyod8HtKp6xxg0D/CSRSkCTu0//ESRECr0qgtYW1wSE/UJRP0bm3Bs17cw+BISRgCovSDmHFIFIXAg1tzZb4B9ZvyML6a5eoOEPgEgic1Ma6XsMlyESWEDgPgXxL63U4aLTD50mZuBC4SAKmtQ3NZaugi0RPXucg4FpgN17WTCTfKjdd+3NX6E8ypAIBCEAAAhCAAAQgAAEITByBxij+GUpucQb5hnMGUQg6iQTSgc4kSK/ZU2eVfBX97oQUfe9EBbeuBEzx4qyhzA1suvCZUqJTXenhMcwETH/71bAOczmRbVwJmLGK/eWNVsa1rJRrVAn4NpaOwqjW4OTKne8jdFpjn761pUeyod+TqyejWXKnsfQhRrPykDoj4PsXgwXSqfUfbI6kDgEIQAACEIAABCAAAQiMHAH3epKNDfldVpRN+o9cYRAYAm7SNP/Snb8HDwQum4AfiE+vrU+5Of9GY3zZ8pI/BHohkG9n8/e9xCUMBIaDgG+Ph0MapIDA6QSstXUtLsp7OixCDA2BhromaX/BnlOjwQCFHppaQpDeCTgNdgsSOM6qd2qEvGACba9nvh1Or9b2ej0enFzssDI4tqQMAQhAAAIQgAAEIAABCLQT8G897e48Q2BABBoD9S799Clwp6A3fezumGp6B/N81T+fxqvGJ94EEvBK468ZAqeH5pZtJt0wVsmF66jIE4iQIg8xAVNS36j66xCLi2gQaCHg9TfVXTS4BQ4PQ0Mg1y9okykJktRkOwuCDrcBGqPH7lowrIU8LrG5uI/1eZO0/+t3uhrWUiAXBDoRMCMVp8am0UE6EtEpHG4QuDQCXToErmVOQsk+CrORiOPtdb/kxmClXyRJBwIQgAAEIAABCEAAAhNEIH3htrGjwb2sTBBOijogAsffu82l+fF6fNxaJScQKp6Dwe3FEDiudKlLkK4uTbyBlbkeD3sxMpILBF6VgG+Dz6m7xxv4VxWof/HOWaT+CdKHlODbCtHNknooNulvDbF/bg3KEwQuh4DpY2sjZC7NT6u+tj5djsTkOjgCVr+t2jC4vPqXcqvE9mTNbGDGKv7qyuV8+pctKUFggAR8W9s0WmEMbYC4Sfo8BLyytjbF2a9JarBiRiuD/HXBYOU8FUhcCEAAAhCAAAQgAAEITCiBbI3/hJaeYo8KAXvX9gO27v3bWaiECuIoXSViz0H28n3sxXxUSomcpxHwYy+nheu3v1epfP7ezfLy7g23Yw6pRObf/vFTUOla6X5LTnoQ6B8BU+v0kw5wpvc285StMk0v/cuQlCDQJwIN3c1OoDAjbdeN8Edi2gN/EBgaAsf1saXvkCl06mZvcrzNDU3VIcjJBJy1inWGEwWu/U2aneiTY+ILgUsj4FtkP73vrr77mx11dWnCkTEETiJg/YX8nzMaTBTE1vYO9lArDFby4LmHAAQgAAEIQAACEIAABE4lkNgLtk0wnRqSABC4fAKmp/bObe/X9WqiJLaZp1BJbJ9AYRgpLIyONrePH1w+YSTolUC7lnndbMRvD9DwkMJQKkShwjBQEMdSEiuJY7n2OK8U/r5DWt4rl+y5bjtkca70iDyeBEzv7BPb1R9/HgYKoyg1GOxFkTopr3fz17Pg6yXPXsLk82wPn8nlbRr6PTdxlg3u2kXLi924b+fon/21EbCHm14y7CVMPqsu4V9FvHyy7fc+G5+uXU1360kshU3rKtf20hNux8fzsBDIFNjZBWZGr4l1hON0oskZq/jGaVhkRg4IdCJgza71fd1svz0kin3/t1N43CBwmQR858FkCNK9KOw2DEK3L0WUHariRPRhfcfDHM0te27c+nAuEv9AYEAEvJ75ay4bG3OIa3XVq1UMVnJcuIUABCAAAQhAAAIQgAAELpFAuqA0W2ZqL9JnmS25RLnJGgJGwNQ1DAsKFSkMigoUKU4CJW4GtTk41BOtDi/yXeNlX5mu/mfwOEu2Z0h2IoLmxwIvqsC+vvzV52uy5N3svkW+vKfXH3OzRU1x3Q0UmULbKtPARvBbIvtcjl97DHY8Ii4QeAUCXo0z1U3H4KOCm/Q3t3qSKFbgjAl9d8Lmozr+dVLeTm4dI1+SYyafXay8voyXIY2vixORtXs2BO+TxO3p9ynZfibTLqLXXWtmo0LB1WOcHgYkhem005ny9xVxpkgEhsArEsj0zfV/3YRpwfUZmsZzvoPR3gl5xfyIBoE+E0jbYDNWsb5utlqm0fdtb7H7nDnJQeC8BEyBvdq6I62yI67MraHPbZl0UutObm3ReITAQAhkOhzZAq8wTMfT3DvN4JSSHVYGUpMkCgEIQAACEIAABCAAgfEmYC/ZbseKJHArT3subTZ46sIP7j2nZ3EIOF4EvHr5qy+dPTt9VWybqyiIErfkyWxVjuJE1ViKwtQOwA3kZ4NLPr67tifa4tn5wS1e7fOu1SyI7cz6JNfLbmq86ng57Ord7Jp3t13PG57Zra3ur9kOQQoVBwXFQV2JGVwFRdWTgtPtjuVvm4vyeXYMew5HL/85kiDqmBFwupa1fbapVd0ZqFg7bEeqhM5QpRYnqtXlPm5A3w3gZ4YdmbJ2M/IYlM6dJ91u369u7hdZ5XkZei5jPlI/hE0353vllDr99vVcljPmakW3j7W96e4qdk11N7F22AytTKeza8/JD0rgngUg4FgTyH/Hsu9vqqeZLoe59tf6FGd9hxtreBTu0gnk9de3wYH1GxLFSeT6vdb3tTZYYVGJTuj/XlRhaNMvivTw59Olz2TjD9Z/yTa4crsDxTaO5voSLa98roymUj4pu3oV89fhB4GEo0jA6VymZF7XbEzCdNee0/5CoMAGzMyAcIB/GKwMEC5JQwACEIAABCAAAQhAYJwIuIl8O7M02xK9Hsc6qFS1s+9fq31p/bO/mru96rTNnvrgXCHQJwKmcf7jX7YtaXvZrtaknf091ZKam/BXlE42be8dKiwWVShG7oiVet2mVtOVT273ivSpeWa6e+7tH/8N8NfeYp0QKvcVOiEUXkNIwHQgr5MmotcL7+5ayU4D9gq0cxBrv1JTEBWlOFA9CXVUl3YPqopVdCX26eTTznu4/HymzqOHf/KJdgh+ineHGDiNLYE23fKPZrBSk7SzV9HhUVV1O9EqCJyhyu7+obb36unC6SDp0M42NcyOsEj/b1oUNn3PTzXddcBLnabnnlqdOmd0oiC2E1LnaK/s6oyBehEsNcRstja95NiUt59ie77ptRc5OoTJCWTb6zuueUOnDlF6dfJJ28k/boKpYegq7RzUtbN7kBoHhpEzwLL+7+7BoXb2Z0/NIj2E5dRgBIDAuQiYDjujV5+KnQBk7a99EungqOra4jgMXfu7f3SUvsM55fftiV39vU/IpewfuI4MAd+qjYbAprt5/TUtNP09qCY6ODhyOuuMtZPQLTTYP6p1GIO4wLKOFt4LBDOZWblWM9d0mnrYo/skUuWoJuv/1OqxokA6PKpr28bQWvQo7S2kyWR93SzNlmCTiZhSD5CA09NsnMl0zbfFdrV+8c7+gSp2HJBtOZg7HnMQIgVJeujmINImTQhAAAIQgAAEIAABCEBgTAjYZNPuUaL//o+/1P/9l/9F1XqiqWJB89NlLcxOt5XSvfK0ublXn9RyoM2HRwicm0A2imPDPLbyzq5u/XNga0uTdDVeELrBogSysHYAACAASURBVJc7e6q47SqqKkeBVhfnVCzYNqdeCtNfPyzUerWU3F+PM25ZaJ/wua4uLS/OuVKarMiOW4/1NQgyDR2wussecreNLH3VugH7bHbbtNl0OVZB9aCog0pNL168UKBQpUKo6XKkxdlpRTZw1NBan2MaN5vhd9+IVxv+aZtBaEic3fjs2t3bn52IvQZuj9zn505bNfQ5i4lKzitvQ7+97llbHDrjFFtNWo0TbW5v67BaU1yvqRDXtba8qKlSwRnCBpnBisW2s9JNX+2rm359TQ+tXffGFDnjlVeA3ct3wRXnnG2H2/Ldy+fk9w+vfk25NL9LvZTl9Nw81zRkQ+5XtbTpwq2TrJ3cTpc3206/oQ/WADqTpl6idg+TVrrTOes92F89MAMVmyANtLm5qXqtrkIUaroUanG2rFLBjgY6+S+tLf9FOTksvhA4D4H0pzZtK+333/TYDFSSKNTT715o/7DqegthXNXVpXnNz0z5KdVmJ8V3VhqCeN3114YHN0NPYHTqLEiCbJLUZE5bzTiIVFNBlbr0cmtbh5VD1/7OThU1N110YxGXVgVuRrf5W3xpcpDxUBBwO//4MQjXBzLdMF22XYkT7ewdaHe/4owHw6SmhdkpLc7PZpYBFjb9NLW/9bvb+tSPImcp8k7UD5ijm4ZTA3vzsmHaxH1SY5W0PQ5sa+IkUKVa185hRZs7uyoGdT24dV0//uH39drDe5qd7u+eKP1NbXSrBskhAAEIQAACEIAABCAAgVMI2Lt3fqKkVq9re3dP2y+3usS0V5/839letdtj51PiHgItBPyYi20TnRmtuA3QGwYrNrkZKklCt6WpgoKb+Dw8qunrp8/SM3kbCXrNa9NXt6K9EcjPouYcjt8OYgzIS3c8N1yGmYCvt7xWdXKzAct8GCuTGawoLCkJCm53CrNPOarVVa8daW9n2w1ytsZJU07/zbYfcHDalfgUYo1EvaQdwjuvE/xbovQariXSAB4aBRtA2pOXZHut2lRpqsXGOb03gxXbVcWOA7KPnc0WJzU9ff5dOrwfBgoz44i0n2EGKylL9+xuvZGKZzz4evQy+BzPem219+iXvKkhT1OWdKC5+dx6l+baW979lbddM1rlsqfz8rU0WmU+nkevLqm0KSd37/XPdDa0Nrjg+sCWnvV/d/cqOtjbU9BLIdgdrddqIFxfCGQTTW6HiqztdbuqxEoCM9C2vnKoF5s7ermZ9iHcZGk6S9VBgt7ajw4RcbpEAn0w4bsE6b2uWQNsv212/GVBSVSS2wHT2tJA2t3f157tzmZL/9v+fAptzv1/bGSU/Vj0PwdSHCECqcFK2ue1f1MDFLtkT0Hk2l9rg62PvLW7r52dHWcgkBYz1SOvVnmt8m79xZGlms+ovxmQ2rATaChWqgSZ2UpqZ2Wyu4Es60+kiw/MeNt7WoyGcXufy4nBSp+BkhwEIAABCEAAAhCAAATGmUDTYMVW40aKwlBRVO5c5MbAp38TbrwVdQ6fufrQJwbCEwI5At4wJJ0kTVf1txqspC/d9ThRXLXX8XQXljCSClFBUZQasJh+2wxaqw62622rb04MbiFwfgK2uimb5k8Ts+eiM1qJ7SigeuKOpbDjMMIoUMF2p2jk6nUzvbrvg/NshmgE7fnGp9klgpusPaMhTJekcB5NAr79TaU3XTODFF8Wmyy1MU9bYRqoWveGKHa8YKhiVFQYWn/CjndJ9dS3wz49S6qRnE+2L1f/vfDXviRKIiNKINWzVF+tCNaFtWkm6y/E7mPtr23pn2pjGBVVCEOnv70UeTA63EvOhJkUAvm2uKm/qU7Hps1BLT3WygxZEqlYLKsQRk7PfSubT8O4udaxX1Zhk1IRQ1LOxi/biDQ+vq9guuim+O19LLD2N3LHYMZKFMf1tFWOIkVBwZ1M0Yrb+hONkrd6DeRpROAOpOwk2krAtDbVvcxEJfV2BquBarXY7VBcT0yjA0WFogpuDA0dauXI08USSPUv1VnXU0hbYFtk4PoAqZWg9RniOFYQ2y6Y5td+nFX/pMZgpX8sSQkCEIAABCAAAQhAAAJjTcCN/9jLSzahbwv0ZmamtDw/36Xc/gXcX3sfQGofMO2SAc4QaBDwr9s2WZpO1JuLrSa1q71YBzo8rOrly11VjtIX8ulyUStL8yoWi27HlV4WSqeTVV6nG9mf76bPyZ1PGGJfGgHXRNoQUKoQfqjIdlipxaEODqva3DxybXAUBZqeKml5cc4ZDjZkdvruFSpNIZ1y8m6NkH258Tn0JTESGVkCTe0yJfYfK06qITbpVKvHev7ipQ6Pqk7H7SiV1aU5lUs2NNk4k60Dg7Tv4CcCOgQ4h1Oa9jkSIOoYEXC7AGXlSSf8beo0VC1OZDuybW1vO6OVQhRpulRyR2IWCz0MrbsJqzECRVGGkoBrh7MmzXb+sVv71/agqCfSi82XOqwcqR7HUlzXwtyi5mdnXFn8b7l//7rYSf+hxDnyQjlVyCvFkJcoPY7CC5m43atc3yEOtV+put0oKpUjU13NzkxrbmZaU6Wij5Be7Wg4V/Dsi9DqyxMEBk7AvnK+PbXMrA22vsXW9q5e7uwqrtZUKISam5nR0sLcwOUhAwicTsBpadZbsFGIVGft6ndZqRxVtbt3oJ39A9d/6MsWiV0E66FX3SUmzhCAAAQgAAEIQAACEIDARBGw8U2brLdBzKSeaKpc0v27N/Sj977fhYO3vLdBJwtig0e9DSA1gndJGWcItBNwOuP1y0bcs3N4bajIVkfbYP3X3zzTT376ob59uql69UhL88v60z/+fU1NTSm07VbSRNLVee70iaa++sF7b7DVnv+rPLvUszxfJT5xxo2AtZmmEE2lsLtYRe3uV/Xp51/qb/7mb1WL65qbnteDOzf13jtvOv1tksjHzw+Z9tz8NpM69S7dWaX5LTk1AgHGkICf4HRFS/whBKYVqWb49tcG6v/u7/9BX3/7nSoHhyoWQ/34h+/p2tUrqcGgC9/U/RRVPj1zGYS2tec5hpVEkU4lkGpBuhtF2mdNtc3WQu8dVvXN0+f6+3/4e23vVDRdKuvOjTW9+fg1rSwvnpq2BWj5nvQUo7dA/hvSW2hCnZWANyA9a7zLC59pctaspTtUpMdh/s1/+zt9+vkXOjioSEFd7771SI8fPfRTqk7kRmtoE/8DaW8vj8xk5jyI38xBkWxoX5ZBojiIdFhN9NU33+lnP/+Zvv56T4VCpFsb1/T44X1dX792TJhRKvEx4XEYaQItGpwpovWB7Vjif/rZz/XzD3+lxO2wUtODuzf0we+9f2nldeK1CHxpopDxpRFoKkDgjtE281bbyyo9vtWu1nm1k9eePd/UJ7/7VL/41a/d8cSD3MgKg5VLUwgyhgAEIAABCEAAAhCAwGgR8C8m3mjFVkWvLM7p3q3VngpylgGk5utTT0kTCAINAl7PTIf8x16/axYijvXrmbLCIFY9rmqmFOj2xhXNzswoah7L69Ly6fiE/XO/ddOn6/PhCoE8AdM309+Xu4n293ZUCOqKVZNtSrE0P627N9Y0M32yFvVbZ/PycQ+BPIFOmujb3+82p/XLmbK+CxNV4qrCJNLGtRXdunmtMS16bJcrZzg4GDOVvNzcQ6AbAdPfnf1EURDonwuhdup1FRRrYbas29dXtX5tqVtU5+7bX389MTCeEDgHgfb2155Nf+vZ58MPZ1SMQlWca03XVuZ1/9aKy9HHzeupdzuHSKdGvYg8ThViTAPk63KUimg64WU3/d2vpse1fjJdUhTEKgShFmendGNtRQ9udx6D8PFHqdzIOj4EvP7Z1XTYrr/93awKYazIPnFNy/NTenBz+dILTRt86VUwZAJkO6zk2mHT33Ih0ovvvlMxU5iT9sU8b4EwWDkvQeJDAAIQgAAEIAABCEBgQgjYEUC2y4Q7vzQsuBVOxSg6cSP/V0WTH6x61TSIN3kE8oMuXof8oJG9WEdh6D7NY31sELQut3QkCN2kqY/XTs+n0+7OMwT6RSCvv+1pml8YBArD0LXDaVjb8ao95PHnHoIcj4QLBPpEwOlu1v6GoW3Xb4OhdgZ63W2b7nazyJS0kz6bFzrcp8ogmTMTcPobSlGUtr22OtqOVLH9q0yfT/vzIfz1tPD4Q6CfBKzva/1X+6Tta9r+BrZkWqbH6Z9dXZh+Zn5KWj7vU4Lh/YoEPN9ReX/x8lpx/b1dbezB+r/2DmdX9xdINi7R7c/H7+aPOwQGSaBd/1ra1uwLaX0IC3eZ3892OQfJhLRHh4DXC381ye3exoCtIxHYmFlwQgN8zqJisHJOgESHAAQgAAEIQAACEIDAJBGwlxUbOEpfvP050YMhkH9JGkwOpDruBEyH/ECQf62O40She9EO3BFXNvmUDtqnBivG5CJ17yLzGvf6HsfymX6Y7toYvdug3w0Upe2w02+nv1HXonv97xrgnB7o7zkBjmP0vNLlFMQMVfyZ506f7Wj0xCZPMx0fRxaUaeQJpO2sDdTXnaGVdRDcBKrNlh7bEmjki0sBxoyAta++SbYDMu2jIM76FK2FzTXXrR48jTSBUa5Xkz3tOiSuuU2bXNcqu6NVRrpiEH4iCKTamhU16wY7u6tR/mJORM1NaiGtx3D8QEDXFmeKm/YrfM+i/5z8mF3/UyZFCEAAAhCAAAQgAAEIQGAsCbhXGDd5mrhBpLEsJIUaKwJ+TMgNGsWJArcszz3lNusdqyJTmFEmkI4EtZQgr8OpR5KO4jemolqC8wCB4SCQH890A/U2YZruDGST/m53qyRdZTocAiMFBDoTiG2yPzH9TQ0IO4fCFQLDSaDRFLstrTIjWI5cG87KQqo2AmmnODVvTb3SPnFDq9vC8wiB4SMQJOmCr+ZrW16jh09eJJpUAn7EoVP5032BzHBwkPba7LDSiT1uEIAABCAAAQhAAAIQgEBnAvYO03iPCZQ4S/vOQXGFwDARcGrrDa3chKlNkqZv3F6tG6o9TIIjy2QSaFNGNyzvJpoyvc10tznw2RlTWzKdA+EKgUERyCtgYBP+2TFArith7W9mdzWo/EkXAuckkE6VpttUpDuy+UnShs85cyA6BAZPwDXFSSCbNLV7MxjMN8+Dl4AcIHAOAmbY6oxb03Y3nTY9R3pEhcAFEnBtrY1BuFn+TJcx1r7AGiCr3gl06RnYGoPcp/f0zh4Sg5WzMyMGBCAAAQhAAAIQgAAEIGAEzFgFgxV0YYQIuAHOIJ3wd4NG2WSpG7wfoXIg6pgTyI0VtU6J+tn9TIf9pP+Y46B4I0ogp8eujbV/kliBYoWuHc4mTpk2HdEKHl+xvUmKldDd57q7jUlT02V3JNv4cqBk40Gg0RRnHYrUaMW1yuNRQEoxIQRSBbY+RKq9+ZZ6QhBQzJEi0FlDbZdB6/82WuaRKhPCTigBWzRjKuvOKba+cWft7gcdjgTqB0XSgAAEIAABCEAAAhCAwAQQyBaFuJfsZnF52W6y4G4YCbRoqL1rOyOr9CXb/LxeD6PsyAQBI+C0taHI9pR+so15/WMWEGYQuGQCpqsNfW2qpx/b9AP1fri+EdSrtld6p/iXXBayh0Cmzs5G29FIlCTpBzgQGHYCvn1Nm2W/PNqkZrp02OsO+ZrdWnccm72wNV7a6CCgH6NFwLfFirPdBUdLfKSdUAK+pXXX7EhBM1bx7oPAwg4rg6BKmhCAAAQgAAEIQAACEJgYAoN8XZkYiBR0gATSQfrWDGx/isYyEdvf1C0XaQ3DEwSGhYAb5PRNbbbAybllhivDIidyQOBkArZmLpSS7OoOZXPntB2Plir4cXdcIHABBPLqZ/duYWnW9lqzm+6y0pxMvQCRyAICZydg/Ya8Mrv9/FOb12ze6expEgMCl04gt+XVpcuCABDoTsCaX//61jhPxY05tDfO3dPABwKXRcDrbnrNDF5NGO8xIMHYYWVAYEkWAhCAAAQgAAEIQAACEIAABIaDgJtw8pNONnrvXrQbrm0j+sMhM1JAwAj4uSZ3dYtLzdzK702RMcqrMtggMKQEkhZjFRuO9IqbE9grfM6JWwhcJoGGSrrV/V5rve42fC9TRPKGwIkEvLZaoHRflVRvMVo5ERueQ0zAXuPSxQdDLCSiQSD3HmcGK4HC7JNvlcEEgeEl0M02ZZC9XwxWhlcfkAwCEIAABCAAAQhAAAIjQKDba8wIiI6IE0Wg8WLtV0k3St/wabhwA4FhIuA11K7+fpjkQxYI9E4gr8WpNtOL6J0eIS+HgG93nfZmCuvdLkcicoXA2Qmkxipnj0cMCEAAAhA4HwF3mpVPwvoRdH49Da5DSCCvnvn7ixAVg5WLoEweEIAABCAAAQhAAAIQgAAEIAABCEDgVQm4mdJ85ERJy+hn3o97CEAAAhDoNwHf5mKs0m+ypHc5BC56GupySkmuEIAABC6bQBAE8h8F9CIuuz7If3gJYLAyvHWDZBCAAAQgAAEIQAACEIAABCAAAQhAoEEAG5UGCm4gAAEIXAgB1+66uf1sgt8mm9znQrInEwgMhEDCnOlAuJIoBCAAgTyBpmngsdUH+WDcQwACkh0gyx8EIAABCEAAAhCAAAQgAIGzEeDc87PxIjQEIACB/hDww57MNPWHJ6lAAAIQOJ1A0rZ/vx2vwhErp3MjBAQgAAEIQAAC/v0NEhCAwEkEMFg5iQ5+EIAABCAAAQhAAAIQgAAEIDBGBPwkv7+OUdEoymQRyFSY4c/JqnZKCwEIQAACEOgPAfrC/eFIKhCAAAS6E0hbWjsSqHsYfCAAgZQABitoAgQgAAEIQAACEIAABCBwJgKN3VUSiffuM6Ej8FAQQGuHohoQAgIQmEwCdr4KllaTWfeUGgIQuEQCbQ1v2+MlCkbWEIAABMaaQIC1yljXL4XrHwEMVvrHkpQgAAEIQAACEIAABCAwWQQaliuTVWxKCwEIQAACEIDAqxGwOVLmSV+NHbEgAAEIQAACEIAABEaHQHOpTPNudKRHUghcLAEMVi6WN7lBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMAYE7AdVthkZYwrmKL1jQAGK31DSUIQgAAEIAABCEAAAhCYRAKsk57EWh/1MqO1o16Dkys/uju5dU/JIQCByyXQ3v62P1+udOQOgdMIoLGnEcIfAhCAwKAJJEnCToODhkz6I0sAg5WRrToEhwAEIAABCEAAAhCAAAQgAIFeCfhh+sR2423syBsoUfrpNR3CQeDyCHjlDaTE9JY/CIwOAdfsNpaX+sH6IN8cj05hkHSyCFiTm5XY9SHsWKtGP2KyUFDaESHgFTYnrjk1nX1/IheAWwgMIYGmzg6hcIgEgVMINNtdu/MdB9rfU7DhPcEEMFiZ4Mqn6BCAAAQgAAEIQAACEOiFAANFvVAizDAT8Dqcn2BKgoAJp2GuNGRrJeBGPFNDldYBz9ZgPEFgmAm4IXpvtOIaZj94P8xSI9skE3BNbxuAfF+izYtHCAwXgaTZYxguwZAGAicTyLe9+XuLxXT/yezwHS4CXn/tatrrjgdqGK8Ml6xIA4HLJoDBymXXAPlDAAIQgAAEIAABCEAAAhCAwMAJ+EEiyyi991kyYepJcIUABCBwsQRofy+WN7m9MoHcLis+DQxXPAmuQ0WgtZN7TDTzzveJjwXAAQJDRSBoWF3R5g5VxSDMqxJAkV+VHPEmgAAGKxNQyRQRAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBMSaQtwPM37sin2LNMsZYKBoEIACByyOQmgra7ipsrnJ5tUDOw08Ag5XhryMkhAAEIAABCEAAAhCAAAQgAIF+EGBFUz8okgYEIAABCEAAAhCAwEgRwFhlpKoLYSEAgbEh4Fpfd0RbgL3K2NQqBRkEAQxWBkGVNCEAAQhAAAIQgAAEIDDmBGzBXsC455jXMsWDAAQgAAEI9JsAnYd+EyU9CEAAAhCAAAQgAIEhJZDv+rLLypBWEmINAwEMVoahFpABAhCAAAQgAAEIQAACEIAABCAAAQhAAAJjTiBJJPvwBwEIQAACEIAABCAAAQhAAAIQMAIYrKAHEIAABCAAAQhAAAIQgMDZCTDZdHZmxIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEGAQxWGii4gQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKjT8DWGLh1BnaeK38QgAAEIAABCEBgSAkUhlQuxIIABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEOiVQDfjlG7uvaZLOAhAAAIQgAAEIDAgAuywMiCwJAsBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAELoOAP8XVXy9DBvKEAAQgAAEIQGAcCDT2bRtIYTBYGQhWEoUABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALjQGAwZrAYrIyDblAGCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMDAC/TdawWBlYJVFwhCAAAQgAAEIQAACEBhfAhyBPr51S8kgAAEIQAACEIAABCAAgfEg0P8ppfHgQikgAAEIXBiBQKItvjDaZDRQAoPT5MJA5SZxCEAAAhCAAAQgAAEIQGDMCNjLSSI5i5XBvaiMGTSKAwEIQAACEIAABCAAAQhA4EIJ5N/W8vcXKgSZQQACEJhwAq79ZdXXhGvBOBV/MD0KdlgZJx2hLBCAAAQgAAEIQAACEBgkgcYL9mBeTgYpOmlDAAIQgAAEIAABCEAAAhCYFAL+jc1fJ6XclBMCEIDAMBHwbbBd/f0wyYcsEBgWAhisDEtNIAcEIAABCEAAAhCAAARGggCv2CNRTQgJAQhAAAIQgAAEIAABCEw8gcaag4knAQAIQAACEIAABIaVAAYrw1ozyAUBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEXoFA3lglf/8KSREFAhCAAAQgAAEIDIwABisDQ0vCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQOByCGCocjncyRUCEICAEQhohFEECPREAIOVnjARCAIQgAAEIAABCEAAAhCAAAQgAAEIXDKBbMAzYeDzkiuC7CEAAQhAAAIQgAAEIAABCEAAAhDoBwEMVvpBkTT+f/beszuOJMkWvCFSSyQ0QIKyqrqquqe6p3tmzpt9+7f3y357b3bfzM68qi5JsiihRSK1zlB7rkd4IpBIkAAJkgBpyQNGhIcL8+vmFu5u5uaCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCHwABIIPUIYUIQgIAoKAICAICAKCgCAgCAgCgoAg8G4IBEEA/slPEBAEXo+AGKy8Hh95KwgIAoKAICAICAKCgCAgCAgCMxCQ6fYMUCTohiIg3HxDG+7zI1u8qnx+bS41FgQEgeuLgAwfrm/bCGWCgCDwiSAggvYTaUiphiAgCAgCb0RADFbeCJFEEAQEAUFAEBAEBAFBQBAQBASByVIRFaZaaaqvAo8gcIMQ4OamkHUDYPIw4fAbVBMh9bNCwACESz+rFpfKCgKCwDVA4HVD3de9uwakCwmCwBkE1DhCGPcMLhJw/RAgm55i1diDjIevX3sJRRdEQLysXBAoiXadEKDM1XI3FMV6YUKHXh21YrBydVhKToKAICAICAKCgCAgCAgCgsAnjYCeqFz9tOSThk0qd60QiLg4WvQM1NR7YsFyrSgVYgSBWQiI/J2FioQJAoKAICAICAKCwEUQkHHERVCSONcVAc2/+npd6RS6BIFZCEzWHma9lDBB4DojoI2tjHAhLeTlqydYDFauHlPJURAQBAQBQUAQEAQEAUFAEPjkEZBFok++iaWCgoAgIAgIAoKAICAICAIaAb2hVD/LVRC4yQjEPFbc5GoI7YKAICAI3AgElHPXAEHAvxtBsRApCHxwBMRg5YNDLgUKAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCNw0BETPdNNaTOgVBAQBQUAQEAQ+LgIcO9BYZXK2ysclR0oXBK4lAmKwci2bRYgSBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAELg2CIhXimvTFEKIICAICAKCgCBwoxAQe5Ub1VxC7IdHQAxWPjzmUqIgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAm+HgChM3w43SSUICAKCwBUhIF5WrghIyea9IiDDhfcKr2QuCAgCgsAFERBpfEGgJNpnjoAYrHzmDCDVFwQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEgZuDgHIpTXJl7fPmNJpQKggIAoKAICAIfCQEOFyQIcNHAl+KFQQEgc8aASV/RQB/1jwglb84AmKwcnGsJKYgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAtcCAdnhfy2aQYgQBAQBQUAQEASuLQKiJ722TSOECQKCwGeFgMzcPqvmlsq+FQJisPJWsEkiQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUHgLAJBEABir3IWGAm5IQh8OOYVg5UbwhJCpiAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAoKAICAICAKCgCAgCAgCgoAgIAgIAu8dAePDGK2Iwcp7b0kpQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQUAQEAQEAUFAEBAEBAFBQBAQBAQBQeBzQkCp++WMts+pyaWub4GA/RZpPmwSbbgjnfnD4i6lXQwB4c+L4SSxPlEEpAN8og375mqd1/QMl+/1m/G7RjHOa8prROKHJ0WDEi9Z+DqOhtwLAoKAIHA1CGh5KzL2avCUXAQBQUAQiBDQ4pWPImKFLQQBQUAQpLcJdgAAIABJREFUEAQEAUFAEPjICBjRqUAyMPvIDSHFX2cErrfBCmdY/FOdWE+3oh499RiCrAP1lXGvlwQgZfw7Q1mc5OvMMR+dNj+i4O2dA2n8VUbnP8yoqW6k6FWgvzLR80xW86P2fnt6ZxAiQYLAR0UgUFyt+0MAYyJnz0g26B6ru4e+zqzAqf44M8aVBuoa6ExfS5uO9Llfp0GLtxnv9XsB89pzim4q3Wz8Sn2azcYaRjXTlWbrnFdZDUgsmYobf46N5a59QwuBNxuBOM/Oqsl5fDwrroQJAtcNgTfx93WjV+j5zBDgLIZCdkrQTo0HPjNQpLo3BAGyqRaxmmXPXZHSEadY/YZUVcj8RBHQfPuJVk+qJQgIAoLA9UVAjwvOo1DGC+chI+HXDQEZTFy3FrkQPdfbYOWUAOTDlMQ89f5C9f3okUjyKbKnqvTRCfwcCbiQ8GIkHfFUC74BsRNV/hsiymtB4EYicNIbYtJNyzUDOHdh7BrU9oT2GDG6m8eC5HYGAufhNBPUGekl6KMjoJtKXycEnde2kwg37eZMDS9WgTcke8Pri5UhsQSBUwjM6Hya0fhKfoLAtUZgBv9ea3qFOEHgdQhooTuDr7Vcfl1yeScIfFAEZvBpVL7m5NeSQ56+UMTX5iIvBYErRUBE7ZXCKZkJAoKAIHBxBN40Ljh/2HHxMiTmlIJYAHkvCMhg4gph1ZMFXvX9FWYfy+p6G6yQ0FOMFXuI3cbqM3sXzOkIV/t0XvvMom+qPYMAMHQ8fWUcfX+1lH4iuV1E/f16ECfwnmm7yZsJVmdziuLotGeTTNKqvhsYYDvrn2pvpnldOh1ZroLAdUDgbCeI2Fcz8RRDB0Cg3aqQ1XW0eF10mL7G332se9ZT025EdE9V7WORdi3L1W2nryQyfn8tiRaiJghofuc45Ly2O689Z8iESb7X6IZk8qfJPbeeOkIUXwHCyDqDeLi+l6sgcFUITPiPN/pPdz59jcnXab68KjokH0HgnRDQvMur5lt9nZHxNB+/JuqM1BIkCAgCgoAgMEEgLn8ZSIF6IlT100nIJOHpmzdGOB1dngQBQUAQEAQEAUHgE0YgPi6Ynrux2vFp3ycMg1TtOiOgx8CkUTOsvl5nuj8V2mYJhqup2/U3WHljPePgzGDK+GudVzzarPc63mWus/KJl8O8tDI0FpenyhjTNhj6/XT6y9Dz2cYleBpADcIFgGSSqWjxnIyJEiGW/VT86Y+1UtpTca/J4QlCbG9L0yVXQeCaI6B5NyLzpJtMM/9JhGm+J89PfrxnH2BAPHwS4SPc6Dr6kaFN5P07MGOy+brQ+hHgOVMksdCYnXkpATcGAc3vHJdootm20d9MQzPGY2Sd4Br3C/3p1eRymDXz06sj8Do9FtP10/XVOMlVELgqBE7xlmZGdjEetccfmVIzYlTo1ONVkSL5CALvhsAJ/558JE7zL2Mo9tVRNS/r67sRIKkFgStHgKwa/qLJQewQVP1GroLAR0dAMWrErRMhy5vwT99N06n5WyWZfinPgoAgIAgIAhdCQGTphWCSSDcdAQ4WNLPH68IwGUjEEZH7D4oAGVAzZpwZhSnfXzNozDXu76ekm2GwojHQV/LdhPd4wxe0BohA0lc+xpUxjMVoXHOI/+LxGT7JO4o0/T6edvqecSP6lCFKlFZ52XAC+EMgGAYIvJA2IwEYaQNm1ggNGaK009nK8zkI6LbhVbX1aQBVU6r/ovTx+3iWM8IZNAn2zdOeUpj29DpsmJtWAvqAz3YeBAjc8BUNVVRbZwwYyXjmcULkXhC4RghMOsA5NOl+5wGBFwAuEDhAMArCqx8ADFeGeQZgGzBSBswUYCT5HPNkMlUEs35T8VNJLvc4RXswBPx+WAdFV9KAmQnpNPilfK/EXI70jxqbuMV/0898J1jFEbp292r8wfHIKOqrY3482TdPvlGTNpxuy+nna1e7E4JIqrZBMXR/157tZtUjzsvx+5MshbfjWMj91SAw4TV+KEPGPGHP2NxmVmknEWe9lTBB4AMiQGkbMbOa9JI5YwyqjZXjFDF6LEr8ldwLAh8cAS2LJzzJY30nDx+cHClQEHgjAppnFZ9GI14dZoTcO+HgSfhJrpN3J0FyJwh8MARmsORJ2frlSYiMF+JYyP21QmCmLJ3Fw6R6ZuRrVR0h5nNCIM6nb+LNeFyN0ZvS6HhyFQTeGwKxNQhVRsSU5Ffhz/eG+ofI+PobrMSMS5SShago5WfEfGRAbuH3AwRcIFPb4qN3WqDqPCIPF2o9je/Ue6Y7gfoi/ByLfpJQ32lliBn65NBrd6Ahgwu4O304v2+hcVxF4PuwShmk7yyg/PVtmPnkidGKzk+usxHQjcAm5z3bmIpx/swTAaVC+Bj7U0ZLUVSlGziv0RknikfeC9wABvMmH9H4JJ6O8XwfgW8qOqgIdHdbcF8eYXjcRGs8wMgClr+8hdIfNmBVaKk0u2oSKghcRwTI4qdYln1P9TsgGAcIRqHBh9dw4e0ewjuqYdRoottqwzeAVDaLwvwczOUcEusl2CslJf+CRGw5OFbAmfKuEhRmHtGvaB8DzosG9n/4FYlhgHHagLGYReHeEuburMIq0LLmKgn4RPIijvpHfCJMlRepWXjp+LPe6Xzk+n4RYJ91oYxVvKMxnGebONrcRTKw4C+kkbk7j/LDFVj5HJSh1ixqbkr7kd+i8d9knEA2Jf30oBSrR/y9/u6rqjPOiR3BLDQkTBB4ZwTC+U04vtTG7npu8tpx6juXLBkIAu+IgP6uq2xCA6uAgz6Ga/mpLQd1XF7V/O1kXvWOVEhyQeDtEIh4USXWPKuvOsfYWEEHyVUQuFYIkGenJ6oqLKKS8ja6VexMER0FnBpjCK9fq2b91Inxo0maGW0vOMV+5M8438Zfxu8/dZCkfjcTgYh/42Ph11bklIB+bUx5KQhcHQLR2i0zjK+LnSpAy1vyqL4/FUEeBIGPg8BployYU8teTdJ5slV4WSN0ra/X22AlYq5QKRrA74dC0rB8IBEACXsiWNXimDIq8JRhgdKQ2qZSuBiWAX8UhIoLzdVUYuj81YxtimPDdePJZO7CrRilgxmEyh7aJdB4xQ+9q7g7LTR/eIaXz5/Bcx0kl8uYGz1E/vYiktmE8kBw4bI+84hqgd8NPZgEYxqUKDcP4QycxiN0qc4vL/9MC4ZlAhZ5woCRCL07nPvRJW9o/nAArxPAb3TQH3SRKmaRLBdgFa2T7duMzHJomEQyBgG8gzY6P77A9rNX2O824KRtpeHP31qCWU6EvBtb0/3Mm1Oqfy0RYC/SQjMmI3X/4CDXC+D3Arh7QzjP99F79BSdnUP0Gy2MBkO4ozE87lFMJGBnM+jOmUg8rGD1zw/x8L/9LZSTOuvoGnW9ScnvAxol9tlX6RFmGMDdruHlv/0Aq+1gkDVg3a3glvk1ykvzCPL2uaLifdB2k/JU32fygUNZ7AOeDyNhhd50LGNm+96k+n0qtOpeTL7neIRekNzDNmo/PcPv//tnpH0b3q08FsYPkFkrIZvNxPyThJ9D3U1vAiZKP6oX5PldJn+OPQSep8YCHAcEtqU8PU0m6LF4NEANxw4BjKQNJM3QoFgDGQ0RbhImN6HdPisa9SJR7HvK+quxrf4IkofJZNGf4jfNdLzG+PGzwk4qez0QiHhXjac0z0ZhE94kD3POHefXCQ/rSfP1qI5Q8ZkiQFms+ZYQaF4mn0Z/imX5nza++kyhkmpfIwQinlW8GydrFi/zvTbU1mPjiM95mfD35CGeodwLAu8HAWOWoQqL4nwszsfx4S6ZVfj0/TSI5PruCJA3+YuPK6gm4NhBCdrYGCOKKhdB4IMjoOWr5leyJe9jOtIJTeTbSKWmZK+MgyfQyM01QECPF0iK5us4WVruah7mOz2O0Pfx+HJ/rRC4vgYr/Mjz2BwvPFrFb7lwd2pwjbFSHqYXcrDLxdDAg3Hp3r4XwG/24TXaGA1dpEsFWJUSzLwJZ7sJv9VHMPZhmFa4eKYZOj7T0wx9qWaKJWJHsAKMk4BRziC3VIZZiDoPaWwN0d09xvGrXYydMdKDAcxb8/BGnqIpltOlKPgsIlMY8SOqlMw0YArgd3347QH8VhdBf4BgNEIwdgCXZ5NQGBmARaVUIjRwSidhFrIw53LKo42Z5rvwGIRTu8kn/BfAawQYPztE/ddnOOjWkV2ew8oXGyh9c+fkKCdqFHTjMS2PBOoM0Tmoo/ZqF4ftYzgZG7e/eQCMWYHPosWkkp8AApqtJ1WJ5CZ5nN5JaEjobPfh/PQS1b8/xrPHv2HYbsNjHzQMmJYFL/DhIlB/9aqHLFZhrpbwgBO4ScaRnIx1pVPv4vGu4J6iQSkCWSz7a7uP4809mO0heikDKXOE8hdrSskt/TUGOBslknFKFtNocAC41Tb8WlPh5aWhZGx6sQSzmD5ttBLLSm4/HAKqL+nvDq/sv90x2ns1HL7YQiaw4TtlJDbm4Q05HjGgjtGJOuH77IvvDQU9XnADuPtdeMct+N0RkDTgpSwkSlkkl+fVUX2kIegD7tEQfqOOYDBQi/tOCsiszMFemgPS5qkFpxuJyXsDWzK+NALshxwOqqPzQi+Myvja4VF60feQmZLR6NWPjr7okYyG8AmGGSE/6jiXJkASCALvgICeJ6lrAND4Vx0LGR2FSvnLP/74MbG4kYNHopohD9ucn0Xv5SIIfGgE4uNYLzrGdEwPdDH+5WCXC/NU9FP+JsMNL2rTS6R8OqWE+tB1kPI+XwQ0/6pxbnjUeCh/YzwczdeV/OVGulS05hVtppvwNscY+u/zRVRq/hEQOKX31DzNcbE+ZprHqpOPuUcwBQQcQ0Syd7I2I5Oxj9ByUuQZBCJ5q3QVXGPRYwnyMMcQau4WbeSKJ9b8yyvzkJ8g8AEQUHyqZS2vev7GcfD0GkQ0/qUMVusRHAtr4a2vH4BmKUIQmCCgxwvU4VDWOgjXIdQG2ilZqtfQyLe0fiA/W6HCacLHOmMtj/WzXD86AtfaYEUt5FIh2hjBeXGE7f/n72gGAyTXy1j77gEW0nkYGTP0aDECvKMRnGe7ePX7C3RaPdy+t4HKVw9gr1XQ/v+eoP5iD0FnhKSZgBlEyhg1uAh9CHCUMFFgRk3zZp6l1jOc6PlGAC/w0Ld9dAsW8l+s4eu//gOSmaIaqCjPG46J9NhCyjUBx4A1NmA6pEVW7V7bGyiU+DGlENJHjxyO4B3U4e0cofdyB61aTR0/Muz14IwcdUwUjZNM20Yyk4GdTSFVymNudQnFjSVYK2VYC3lYC1lYlQyMbKQpj3YDqoHmGHD3B2j8z5/xH//X/42aP0BisYQv//Uv+OeVFRiJVHhMEIlXx1LFauEayHg28kgihwSGnomkbyI4s0IbMVAsqdwKAtcDASUgI1JOpCFt/FT/GABe3Yfz4+/Y+h9/x96T5+gPu7AsG5lUDolsGlYqCTdw4XgOxr6LcSZAubKAytxC6GWIeemj1FgcfzGjlSjkai8nVQkVKhyYO0CSf74F3wtgOYDFARAXTbTS5WqpuLm5RWxBbIIhecBFn96kfv0dZneERtrD3Dd3cO9f/ohMJqWOaZssMN3cWn8alOu2U8pEG2mPfybSgQHftZDwODa5+TyvDZ6VnHIA5/E2dn56hlG1jX7Ch19MYe7OCu7/059hLxWVzHFrPsY/baP69Cla9arStnaKFtb++jU2kn9AYjEcy+kx36fBEFKLj4WAspXnf8own0bOAfz2GH6nj2DIM0RVJw2/hzRUyVrKANAsZWHmLJjp0Ohy4iHoY1VEyv08EdDDQ44D6OWSmwjanjLW9wcOgpELOLRiobEKYKQMGLkErGIm5GN6riPzTi92xsdnnyeyUusPgQD5Vy3Sh0bXatMT5W9viGAwRjDm6r0fKZoMGGkz5Nsi5W8SRipc8AzUwueHIFjKEASmEFBDhHCh3h8E8DscRwyUYXYw4DpYpO2nsjRpwCyko78UzGxovKIm4iJzp4CVxw+KQLTGq8rUm6GGQNBz4A94xroHIx0AlVD2BvTay2VzynD+hH8jIOTysRFQRgBq7RDwu9xYO1LjCSQAM2PBzKdhlk5OCJjQKzw8gUJuPhAClLVarzbk+GGMoDsMeXZMj9mRgDVNJX/NvA2zlIFZysHkvI6nFcS8tn0gqqUYQSBEgOypDQOpG1ZraI4aAwcDnrjiAr6nNswoY8GsHc7hChmYuQSQDNQ4IuD6GnMUGXxtOevaGqxQDqqjJgY8aqKF1k/PsP2fv6FlO8iP1jF/b00t6AaJpGJWfxgoo5bq98/w8tff0OsNUEnkMHf7nhLGzZf7OPjlmdpFbwfWxGCFvMmyTka9ahis1s/U7mLVdKEhC+eFjBqJ71ONqvqMwZM4fXQsD82ChUowwp37d5G4XQwVs2piaSGLBLJGEqbhww5s2H5Iz6kM9QMz/tw7EHlBT2C6AbyqC3frCMPfXuDo+RZae4dwmh3l6t93XXWl8QjFD11N+qaJgdGFbwG+ZeLw8QsEpTRSSyUU7q5g+S8PMP/NBpKpYjgBIvbEPbI2dbePMNitITcMMAyAcWuI3mEDo4MqMnPrCLSVqWnAUI5dSDBpNmC7QIJ/vgHPN2DxXHfygS7jc2/bCAq5XHcEYoyq5Bh3cIVHZfEonfqjTfT3a0g4PhKGjWJlEUsb61i+exvmQlmZbXuDHpqdFo6MAfIPVrF2+3aoqKBxis5eC1f9/DpYGJf9LLoyqspHp51WgszKi3G46KHS2MiYSaQMdn3KDxuJ8EswK6WEEfdosuP3gP6rKg6+/x1Wd4DjbICRFaByZwXppXnQk5XCmGl0+wiCHw8B3XYu1Dcq6RtIwoDvm7A9Ewa/U4xzY35TjKVP6KMRKRWpwwDHj7fw6t9/RtDso5P04c9l4Lge7n37bWh85wFedQj38R6Ofn6K4+MDwPDQKCeQXK5g+av7sNXA1DiRVzcGn/dLqHTpS+BLVo2+W2qhqA94bRfefgPudhXtrUM0D2sYtXpwhkN4ngvLMuGlLVilLAprC1i4s4rC7RXYqxVY5QT40VI7RqQhLtEQEvWtEYi+DWoTwSj0dunVh/D2mxhuH6GxV0XvuIlhqwtn0FdDLO6K9rM20pUiCusLKG+soHR3FRa9sBWoOJ2Sq8LLb908kvANCESLOdxNyrUjv8njEVvwdqvobO6heXgc8e4QjuuoXaRewoSfS6B8e0XJ38rtVdi3lmEWbZiceJBf9d8bipfXgsA7IaDHELH5l98Yhjy8Gcrf9lEd/WYX49EgHHCQf1Mm0nMFFNfmUdlYRfHeGqz5AqxCEsjEvFa8E3GSWBB4CwTI0zR85RrqGPCaHtztQ3R39tCuNTEcDTEomrj7z9+gcm8NZpbb/GU94S2QliTvCwHqDCKZrDZyNX24L7fQ2NxFq1pDP20gsz6P5S82MJ+/De5TPrVmGY2r3xd5kq8goBDQ4weuQ4wDeG3q1Ybw9o4w2NpFc+8IneMWvP4InsPVcMCybYwzBtLLJcxtLGN+YwWZ2yuw5oswcpTDMn4Q7vpACGg5Sf6lTbYy1HbhVXtwt6robh+isXek1tDGgyFcz4Fh8OQTQ62h5VfnsXB7FfO3l2Etcw2NRrBB6LmNVeA8jmXIGsQHatCLFXM9DVa4mMBBK62lWgHcnRb2Hm2he9zCOE+rAwOJTCY8DFAzLAXuq2M0X+yjc1jF2Ahg2SaMLI8jMDCAi65H4TuAScMBeljR3BgdS2HChGWaSHiA5XswfR9BEBqheAbgmgZcy0Rg8C9u6EIPLaGXFsbuej6GjgXHG8JT/omiM48j5me5llKHUiVKnS2Vo9ziP/WLdUpN6lSMT/+Rg7/IswotlZ1XQziPt9D85Qmqz7fQPD5Gr9+F53lIJpOwbAtGIgHLtGEEhlJmsw1dz1VxPMeF23fQbwNGu4GiO0JytYzS/VUa2qn4qpki7NVg0qahiwE6xWEruYav/gJalUZ/k/Zh4omQi5R+5K+AbRyc2UjIBpxE1+0db9XJy3ig3F8OgZv65dEMEfHR++YFXRzBnZTFG/13omhTCwtjwG+4cJ/uob69j163A49WfsUc5r97iLV/+Ar2nXVwNzi//oEzQno4wIIxglXJIrVUDo83oNGILk9f39TA7KuUDfTARSO2egfuoAM/ZSNdzsOez4cKvPOMVljXM38mbH4DwC+BNng7T3E/E6w3Uf1x3mtSL4rtZahk3lE7BO0RnAaPZhtiNPQwbNPTFZlEE3CZjK8ori76fdT9ykgMRw80rnzvZBIPjQmv3DysNhAbyoiXAxv1L3I1xz52akHliup85dlo13jMOPqOqzI4duCEvBlguNdE77AGe+RikAjUIn0ilYKRp9cUAwGPlGx0MaKxQK2FIY1gLSO0xrcTMBKW8qZ2I/C4coDPZkhe1X+apc7GkpAJAgSJnxPOb7ijiV5V2jyqagzn9x30n75AdWtPLWwOun1lrOK6Dnzfh2mZytia3sp6Lw7QerKD+TurWPnqDlJf3Ya9WlRHnyovFuKscQK53LwDArpT649SxL/qe8+JkBfAp4c9LsrvNOA+3Ubj+S5q24fotFoY0NMlDa6o8Of8h8dXJWwkMmm0nu2hubKLhbvrWPrmDpL3l2EtFWCmo13Tusx3IF+SCgJnECAPR2tG+hhp79iD82Qbw2ebOHq1i9ZBVc1lRhHv+kpgA4FpALaF0atjdB/von17Bbe+foDEF+uwb5VhzVnhWCk+nzlDgAQIAu+AgOZfLX85veoGcDdbcJ7toPNsG/XNA7TqDfQ5hhiNlWdTlsj1Ko4PEpkkWsUcGktbWLp7Cwt/uIP0AxpflcMjrvWg7h3IlKSCwGkEpgcT0TxUB/NRKZ5CfvYO6VV9H91fn2J/exs9jicCB82ygeLqPMor8ycGK6cLkidB4MMgEMliXZhej6SxldJb7XfhPNnE4aPfUd3ZQ6/dRiPto/DlGpKpJCpf3Aq9C+r5mox5NZRyfV8IxMYParMMDQOrPpwXVTV/qz5/iebhEbrNFgb9AVzHge9xwQKwTAsuxw8vM2g93UN1paLGv3NfbSBxdxnWfEJ5W1GetM9bd39f9foU8lULi59CRd5DHWLjBOaupmRc2+UaRIdraEO4Lw7QffISja0DtI9qaLdaGI9GcN0xfM9VczPfNGCmk+gW8ugs7qCzsY7FB7eQ+/IWErfn1OYDegxSntuEh99DQ75bltfSYEVtYlU7Y6mIDOBtN1F/eQBrDGSsDPK5IkqVCmDbinH9UQDvaAhv6xjjoyassYtcKYtSqQCzmFNnBuYrZcytLMJL55SChp4uQn4MZ2c8IsgyLCSNBFIjD0avD3fYw0gZnQTwLQs+jWSKOVUuuT8U4z4CdRdu6fX8ABk7QGHORnmxgnSavmKjlX3dASIDBo5TLB6PrHoftX7RID4+cGEYn/X13dr7dOopIaDKOR3jgz2dVz01CKRrPR47UQvgPNrH4D+fYPuXX+B02vADDyk7AbNQQLZUQDKfgZ1JIplMK+B814c7HsMZjuCpvwHGvR4ClzunAAw9mNxlzsoTZzaDxpu15xHr5RLmludRqMxh6PaQKqVQmp9DqlxQCveZuDEPgwpIKr9D0yg2Pxduw8aMQ8vIYTBfT75bUXA85nvhg/MpmhR9XvtMIsRvNF/F6ddhjBcPj6d7L/csmD1Vof++4HsPlGu6uboauQBhUBy7OKbTFMTjRe+mk59Kwpc6P6adRJ7KiOHsI5GRglcfwHu+j/5xE854CDORRHZ9AYt/+wKpv3wJezF5YjhipAGjFJ4ZyJ0FWh7q6ymCph9CgiZksUlJA4+CO+xh/PNzvKzuAuUslr64g6XiA4AKkuls9LNWaut8VJ1MpbRXvTYIECirROYwnQsTkRL9C3lLPcWDo2QzgnTCU9fz4jF8moJTCS/6cF4BF00/Kx5xVAaFBpKOqY5ASwYJpNwAthf2Ogo0Jddmpb9MWJz+89LFgdLx9TX+7rz0Fw4PDVTDLGdlrAtlhqff840OCU1VaOZKAymGh0Yr4ft4HmfzOUWqjqozPvVyxgPjR3/hVyo0olUUqPFJJDZ1vIvke1kaZpA1CdJ5MWBW2fH30b36tDK+fscreZNjh2oANEZIjDykYWJgGsgVy1hcXoZF17xm6IUlaPWAVhf2yENKbX9KYm5hBXOVeSSzmYihJ1R+tjeTJiHGcbw/W0RmVJy4TIA66W/kSX/Eb1cAZy+A89MeBv/rZ2y/fI52swbPGcG2bNimCdMgb1pKfnpjF95gCL85wPHWAfqbBxjs17E6GKBkPIB9aw5m2uSEIjaInEGXBAkCb0Ig3q/jcaNxkz4uVe2CftnE+O8vcPzjI1Q3dzHodOAFPizDQMIyYZupcE7FhU/O5fp9DI/bGG5V0Xt+gH6thdXRVyiYd2As58PjA+Nlyr0g8BYITItflYUer9LQvR/APXDgPN7F6N9/xs7T52hUq/DGQwSGgRSPE7YsmGZC7WTxHR/ByAM6NbR2j9F7sY/xTgOLrQ4qeAgztxQu2FPmx+X+W9AuSQSBMwhEMnmyJkbFqFqsdzD+fgudHx9h99kLjFpNOJ6n1jNTNo+sTitjq8D34Tlj+P0+xtU2jjcPMXi2j161iaXBEJXEQxirhdDTyrVcGT6DiATcCATIuPqn78ONQGrpm0F+uEvaawXwdodwftvE8OfnqP72O7qtGhx3CNf00an7GHDn/9BRmz11riJvJ0jIzXtAgCx65pOu1xD5kvdOAHoaprGKs9mE+8tLVP/zZxxsbaLfbcPzXfRtR40RBvdvKwMt5RWT9J7J/D1UQrL8vBHQfEpe5TyMmw1aAcaPD+B8/xTHP/+O3Z1NeA7PYQNSlo2kXoMgcp4PZ+zB6bXQPWyi8XwHo4MmVuttrDsujORGuGlGnzjweaN9odpHTaLiigg4BzKCxJ/tS6cAAAAgAElEQVS+8j7aiEjjQHffgfPzHob/9Ribv/2GQbMJ1xkpFxJcf0hpHqZNAQ1caMTSHKC5W8N4s4bBTh0L3T4WzYdImAuwipbSHanypFFC7K/J/x9/WkImjDOF7sHR1TvqYbhzjH6zBdPzkbBsZLJZJEulUOFJl65qh8Ee+vtVOL0+bNNCdq6IRDENpSPNAMtf30GhXIQ54DkWvjJaYbFKpakUkybgh38Bz+B+tYfh7hbG3bFaLLZTGeTX11DibsZcBrAstQgX7ralApOZmSqumzLgFC3YK3mkFstKYTvZlavqSmVo2PvUbmZtoRANgKIoJx00jHqaZeKYnX7zdk/T+U2XOf3+7Uo5N9W52StlaNTGOxwEPsP+7y8w6nbVIpKdziC1MIf5e7cxv76M5GIJRjELI0XluKnOPQ0GIwS9vvobNtpoHhzCaNcxtA3kby1h4dYKTCqj+CMh/KMOmnYCPmCtlpH540PcH4+R6hwjsVDE2p8ewloqKTfWpyqlK6Lw0w8Ri080aqdSvP6B+ehsdJvMCtO0vz63c9/qIs6L8Kb3k3SkTdMXv04ivIcbljPrp4jWL0NiLlwPnZ9Orp/j10tnFk/8pntmHhXAy+vomJVVWN1Tb15L7vTL6WedE/PlH5VuHNe2+hhWm3CGA4w9B6lcBov31pFfn4c1lwjlrxVzlx0/6/JS9QoJUv9TTtLAhBNELjrv1vHq337Az/svkVxfAJJJLH7zACdHumnip65K3gahtW6Er5LHoY+JsJ66vtH7kxwigFR4dK9fMmwqiK9mBOkUp64XjTdJdE55k/fv+4blK69kBhKBoY5RoiEmj5WhlytVc4XTJUCYRfMsYHS+s+IzjO/jcWblcV7a14a/xhhKpWNBLPhsgadD+MSPDccQp9+Ez3HiI4JmZ/taame+ZD5sJ5rJGKGpTDiSIR30BxZRoOLMzOH9BZIEXXV9fU1pZz6tTEMZoeRUAO+whaA/VsceJU0bqaSFQqWM0kJlMjmhMsrvDOD1aOHiwTB4TEUK8yvLmFuch00X1Beg5TVkfrKvpjn3k63oO1RM8ajyHEkD7EAtFjk/v0L1f/wndn7+FY43VsbvNPpMZLLIFvLI5vNIpdMYuy5G3R6GnS6G3S5cd4hhtY7dfg9HnTr+YAPLqT+ECicaasZsKN+BZEkqCJwgEM1PlfdTBwjUcb0dOD89x96/fY/a0SFG/YGSkXY6hUQ2g3Qhi3yuANO21KaBUacLhzv3ui0VtzscodFvo+cO8MA0UM5+TWdXMNLRmPGkdLkTBC6FwJlvUmzeoHdBO8/20fif/4WdHx+h3+vADTzlRS2ZySFTLCgZnEqm1E5TdzCC2482vIz64FrCzk+PcDRo4a7t4c5cHvZCNlT4X4pSiSwIXAwBNYbgIJ3jiAHHtSM4Pz5H9f/9Hkebm+j12spe1UqmkczkkSkUkcnnYCdT6mjBQbuFfrMOc9hTR12NqnVsfv8bmuMh7loGVqyvYC9nYGRjc/aLkSaxBIGLIxDNzZSnXnpWGQI+PbXtduD8/SkOfniMxsttBN0uLN+BbdAruoWEF0Rr92ek+8XLlpiCwCUROMNtXAfgH/UTPMaKxw5THre5qXYPve8fYevvv6G9u4/AGYOHVxmGjZwRIBFYsCjD1RpFuDHuTP6XpE+iCwJvRCAa/zIevVl5NHbd7WHwX79h//vHaO7vYzDuw7BMJDMZZAoFpItFZNLc4mWAHgcH7Q6GXIcYDeCPxjh49AyNeh31TgvfFXOwb8/DLJjqVIs30iMRQgTOLF4KMOchoKAiH7uhd1cabDuPttD495+w+cMvGA778H0XJk9KyaSRzGeRy+eQTqVAg23O4ci/g24P/fEQTqONqvMSx4MWnESAdZ7KkloE9yhQPivVPNfS5HctEPj4BivTMKiPeOQesA+4TzdxvLmjPvqmaSOTS6NQyMHgTkK1UzGAz13+L3fQqdcxoiBNAvnlCuxKDmbegJkzkPrTHSTurSgl50mR+rgHA/BMBL7Fc3/gHQ7gGAGM5iF6HQcufOSyKWTvLiPzT3+AVSkCydA9vNIxac1otEps2AGMpA8zZ8EsZEPhzXqFIxS1KM0d1V4QHhGj91YruqL663uVbIYqS71/11HOeelZqC5Yk824+k8Vfs5/THdevuckmRkcla8GgxwI1kdwn/EsyB10Ww2MAgeJfBaVu7ew8seHKP3DlzDncjBzNFKyAdMMFVHMnO7XXQ/ByEGiN0Cu3kSp1UDPd5AoZlG8swgzz50osTqqOvC/AFbZQPLbdSwsFlDhgkAxD2uJu1kjXaOuwDRul4XidWu08fZgefpZX+NhGn991fRd9qrzVvnwIcqQt/qdDtZlxcNnlafjzXp3kTCdv46rF9H1M6lkGWfKmQqYzif+mu9m5BsrQt1OyomnnY70Ts9kSBLzOsaYUYCum74yyjSN08+z4sSzjvJSl2iAxQmaTwUEd31zMAAfyZSFueWF0LNVMiqEF/5NG6vE87/EvTJW4SSRShMe41HtorN9BK/bByoujLGvvg3KBoAE67rG8YjKU6RF9PGMQ/VPeVcJI2vRriwRlfEFE8ZGMDPyVFnHy42R8LpqajKn45wKf115OuGpBLHCz0ur0135darAKUyupLjpuk5nyn7MMG10oWl4U7rpfPSzTq+fZ10ncfT4YkakqPzwQu8m2i/sCbuGqWJ5qIqciIQJXzPiResT5XH+HElnNGWQo+ukaZhVpk46o7pvFaTzi5epM4q/mxaPhCxm/6OUU/uHStlED2c8ItBOZ1FZW0Lx9opy/6jOTR86CLp9pUh1fQ+ebcLKpzF3axkFntPLSYz8BIGLIKB5Nt5vlBcvjkVpgA2Mf6mi+/3P2H/+DK4zhGMZsAp55FcWsfbwDnKLZdilAgwaU9M1b6sP76CJxtY+jnZ30G83MO4O0Xy5i90ffkeumEep/DUMM0BAt6Z6PHsReiWOIDALAcrZuKxlHMrQaGeT+2gb1Z9+R+vgCP1RH65lIFnIY+HBBorrCygsVZCYK8NIJeH3hvCrTQyPGjh+tYvG/iG6rSZGnQHqj3eQTmeQLRaQ/HIFRiIZ8u8smiRMELg0AloQhwlp7O48P4b79yfYevwUo34PNBcEFziXK1i7fwfltUWkKiUYmRwwduB3+giqHbS39nG4vYOgVoM3ctDZOsLeD0+Rmyth6V//BCtFt6yxcdqlaZUEgsA0Anr1POQrzn3dI1ct1h//509o7OxhMOhhbBpI5nOYv7OB+Y11FFYWYHJzXzoNuhT2q3WMDo5V/MbuPrr1GobNHhpPd2FnMiiUCshn1mFkMieefqdJkWdB4FIIcAARk796bc0P4FPRrzxduXCe7WH463McPn6Jzv4R/OEAuQQNrUx4gQeuxPNYCkMNbC9FgEQWBK4UAbW+oDbTBsrLc+jpqgPn8RbaP/6O/Wev0Kweq4WIfDoP2/MxdIbhceNq4XiKnFj3mHojj4LA1SFAPqP8VZ7Jh8qzyt5vz9E6rsL1XfjpFLLL85jfWMPixhpyCxz/ZpVSJeA6f41r7Yc43tpF9XAP1qAHp9rA/o+/o7KyhNv2d0jmKif77/Tc8epq8OnlZBjqaDBjllz49Gp7uRqRX+O/yNBPHR9I48DnNQx+eYbqqy2MhwOM4MHMZ5BfXsTal/eQXSwiUyYPZwDfg98O1yBau0c43NxBr9FUxiuDrSH2fsgglUlgKZeCmYscYgj/xtH/6PfXzmBFKSPVcUDh+e7+5i661SqsIIBtm8jls8gX8spric+F304A76CF1u6h8rrheY46Zzi7WIJZTsPMGGq3lr2aQuAlQ0FKxWk0hFbjaHYKLiZz14LL3V0W3FdZuGkTfcODS/fGKQvufAaJO2VYywWV52TcHGfqaJFYCR+tg4o+EqE88pXChIpRHkOr1jXUC/qkj+iI5xexiLYs0xzzPhajWbwuero8lqvfndxoat7DVU9qaJTUB7zjDrzNfQzrTfjOCK4ZILdURuXbuyj/7Qskvl4L25pKpQh3w440WQQaFgIvgWCcRTAoI9lfRInnmiUs2HMFmFkauMTqwaTkE8tQJ7IY6QSsxXkEw4o6Bkh57tEKeZ2MAL7rbwJyLKN4vvF7RtEK2Vh0pWSP5xO/j8V77S3LiZelM43CFX/w48Ef84+gPpWE4QzQ5etrlOxil1iO6vZEgTvh0VgU5smzmidFxW/i8eL3jMNnHZeYagv41xCpkrC/x/lGERDLK0ofz/41WZ5+pWgMgeWtzkOTqehVcWLJomeFDYP1+yjRqTHRJKN4RN6feFdQ6XXBUTTmreT0cIygN4A3GoPnvLs0+EhayFWKMLPRUUAsI/rTYo7ZXOqn66ATsW0oq3n+ZnMI76iJ8XELCSNA0jVheUZIn8fz1iLFnU4bXRU+mjaSGN2HRYX/k+WV2j6SRRPjlVl5TYWp/CLcmPWlf1HaU+lCslSb6vY91Z5sbtJ8nqKShMTyUHm/FXGnqAofov5PIFWWk0H42b4wI/X7CdKyMaqzbvNTVT718AYymI/G703p4nE1HbHs433htVlF+SjaddnMh/fEnInPzSBWcMQoKot4PjGa9C0NaFWUKN9J9lre64jx6yRSPPDy91G1Lp6Q5cYTxZ8ZznHdCPAPqvCGQ3VMhWGaSOezKC1XYC2UwvQ0gIsMVjjxcX0fRiaFzPIcSqvzMIvZ0N0/Kbuiul68khLzRiNAPox4kfyojjA99uF+/wj1py8x7jTVDtIgk0Xx7i2s/flLzH37AGYlGxpSpxKA58Jvu/AOelhYWUH6pxRqz1+i3aii3x6g/ugVDucryN29DXs9D5NjEvKp8OqNZp2PSvwp3omOSKQ8paEwlUybNbi/76K5uQ8uZlo8/meuhLkvbuH2375BkhsBFvIw8zkYSRvBwAWPkLSPOkgvzKP4ywscPn0BjDrwj9poPtnBwXIF65U0zNxieHQkxzLyEwTeBoHYuIDrLcrbH8O4pqB25r1C9bfnGDSasAPuyMsheWsBK//4Jda+eQBrpQKzkAZ4vLDrwe868GoDlFeXkM7lkH7yEp2jIwxpNPh0FzulAir378LM0Lg1mqee6kNvUwlJIwhw+MBRechM9M7s00PbTg3uky3UX2zB73WVZ5VkMY/KV3dx6y/fIPPgVugFmMYn5Ec3gNdYgX3cRerpEkrpAg5HT+AP2+jVumg82sHRrSXYCxnkSiswMnohTVpAEHhXBCJBqMfBcc/Ze2M4v25j9Mtz7D5+ikGtDsv3UUrlUcmX0et30B120Hddtb5xanL6rmRJekHgbRBQ+qLQcNtrBHBe1eA+eoXmj6FnoGG7BdsPUC4vYi6dh9Pt4ai5rzxVxIcEsSHK21AhaQSB1yBA7uIvxnFcEuSxKPSusnWM1g+P0T2oKm8pCTulxgsL332BlT/eQ/buGsxSBkaSSjUDAY2262NYW6vIz80h+SPQ3ttHe9RF96iJve+fYH5lCdbinDp5QK1Dn7cWHVH2uV90C53gEGurk8DP824KHL0GrpwYjAJ4NR4F9BK1p1vo1erKIMXIJFHYWMXqd19i6S9fw6pkYOTIw0mltPK7HrzjPipbVaQzWRw/foHj4wOMun20n27jeK6IwsocrKUczIytNjPGu8/n2RDXp9Yf32Blun9SKRItiHlHA7QODpWbSzJNkLKRLufVTkLFRJy4NVx4O8foVhsYj0bwydW2jcz8HMx8GjzokoIzHBgYJ5vjtYIt6hTsBBNSrDCNaVJxwz8TvmEoIwlYQWiwkGScaFFC9aSoURkWCWnqiOiS/pRCJXoZ5muos5KNgN5dTGUso2adOg1p48CI17ACoYKK9YmMKeJlKQpYiUlFLsBoOm9G5ceM5VHR60Zla8U9yyO38KrqF9Zz8lFiPlf103mpjysQDH34jS6aR1U4o4Ey9fEtA0Ue5/PlBuy7q6EnHe5sii1wGtF8W2XH9lbK6wBByoJZKkLZs/C4El2nWfQzvwgj1pWuUnV78DqBWuMUxVVpZuX3ujBqxDWxjMdHnZ/KPzq+JCorblQxaQcSRJojPlThzGtCaEQA89VhqszISCNeD52O9Y6Ou1L0aR5hXM2fUdyJApXYx/uCbhddZkTGmy9RApYTlQcatGk64+HMjLRaCI2MWL5lnih142XH75ku4v1T9Yv64aSsCKcJv7BOrGcQM4qYzvfNFbxQDF20ihx/iPMIwzU2cXyiEpQ8imTbmbZRDaczjmShOiotxoMsi2dfUj7zb+Aj6DvA2FUuLhMwkDZt2MkcAj+pDErUQcOUJ8SJfY3lEyP9d6Hax9qH7c2jgOhCtkeXyG34ezW4/T5SKRs5z0LOsRCMgvDcbicI5Zbq/xFPniqbD7re+o5h/EVH2vCouGiBJfAJQsj3E56JGw6ovsekUV35rLOLco2yjj/Nvmc5un0V7/MMxihMfyOY/aTMkP9V2+p2ni57+nl2yW8RGoKgstdGK6z4pLEjmK+y/Gl8Js+xNtLyie8icgJiE8l9FUb8+CNt59HHLHWfisqZpFHyMeonDJxqKyUjGRb92F6naGB60sB8o5+S+yoflhuE/Bcvn3WZ5HMiaydwhw1xfn10QVNVZjKT3ZW4cQzA/q7jxspX3z72aZWAzRwb91wET50nr3E8YzirekcYTPoBi2SZ6vsS3atGDHlff/NVfBqhqLN6HfjHNYycMRzLhJ+0kF2aQ36xDDPHcVck0wZjBHQVORpgFHgwC0UU760ivVSCmU2Fk5c43XIvCLwJgajzsL+oiTbdn3OxaKeK+qMnqNerGMGHYdsorC9g/R+/QuVf/4zE7WxoFK/7mGEhqKRgzWdhLi8gz2N2XU8dxZfqu+jtHePw15dY/eoe8pUvgBS9DL6JOHkvCJyDgPp+xN6Rj/XYix4vWwHcVwc43tpDs91QHwujUMDcw3Xc+9d/QObPX8FaToVeKKNsgkIS5lwS9loRZmkORSsNdzBEd3dTeWcZHNaw+/glCg9XMVcpwkimYSRjNMitIPCWCFDZz7Gzmjv0ArgHfQyevMDh3q46BihIJJBermDp2wfY+O//CPv2AsxCOK8L5+CmOuLUWszAX56Dkc1iPbCx2RkgOXDRqTZx8PML3PnjK1TmvlResaa70FuSLskEgXBawrGwOlIw3MznblXRfLWLVqsJw/cQZNPIbazg7r9+h9x3f4C9Rm/D0RolMQwMmOUMgltpmJWSMsRa6PTR2dvCoNfG8LCGg8evkLldVmNeIxFuDDx3TiTtIghcBoFonqfGwuNoDMEjgH59hcP/eoS9Z6/UeDhhWFgqL6A8v4rk3BKM/R0Mq0MEY18da0WDLZGtlwFe4l4ZAno+pza4AF7Lg/OyivH/foKtn55gf/MVfG6MMW0UKxXc/vIrmEEC4+0dHDR2Qc/65smKypWRJRkJAq9FQK0/hONf5V2l2of3Yhfbvz9Db9xHYAPpchbzf9jAnX/5Bsmv78FasMK1c52xb8NfyMBcLMCcK2PN8xD0RxjWHCTdAI0XOzh8/ArZjSXY80uKyyfrkToPuZ5FQE0wzgZ/9iHxj3xc7nINrQd4h010f/0dxwf76Iy64C6txEJRbfha+W/fIfmHRbV+QB4MIbZgVRKwllLwlspAIoklJ8DQGaPTGGFca6P+fBfZ2wvI3luCMV8AaLQi62jXhhU/vsGKhkIPZpWSAfDaPpxffkOrVoPrjuFagdrhSoMVu5gPlSncKdMawN+vo99owXUdpa22s1kUlngsRT50464VVVNKIVU0OwXfK8OUIFQ2MYyD4phBgjIwUUoZQyku6L1DKUeUQiw2KdT56SuVSqwbJ5uTAuM9IFKKejyWKFQEq2icmEZYBH0PgevDsCwYGTPshPTuQUxYjjIkiRleMOwiv5hySC1GUrGsFbNj7jgG/IGnLCuNhAmzlISZNkJjGWq1WK7GIV6li5R9kTgRbsHIQ9AZoN/uwHOHIZhJWymdzKUSrLIFU7vhjdpaZR/hoBR7zIvPFvGO7iOl3ySuDg8/teECLduX7UDXlVR+U9EZNbdBrKbroWZjUXvH+Gc62uxnJog1HjMnH1BhqeiIjLm0go00jegSyICZIW/QqChqHx5LZSN0Dc/CYtlOytbEkw90WZoHNO1sV21soI0hWG+WrXEZuIpGxZ/JiD+5UJIM+xAV6MxeCf5ZdEwIOueGdY8Ul8oQQRtTsXwaToxo1MS+b8BI2QoHxQ/JCDc2GfmUdZnm0wgDVX8aAUR1ZL4Ke2LNfjAkD7APmjAyxJuemIyoX1PDG1ZMyYQZdZwRdE5lp4JjCXkbewyNGeIKXfKJ4peIdnUUVogPG4CL/zS44tEWBmWHMmqIPAgpxRhzJyD6z0RAoxWWwSDmT7zH0TFbjgU4JhKBiSQMpGAhq5iOxiomgnG4q1FBTNztmMyY1RZTVVePKnFYPvMjHWwPHgXEP4+LdtsHSMMGu0JmbKLgcDcv4JsBDOJBQzUaidN4UdU77MNni4vqrbyD8C07Og3U7JD/IjxVXxyHRjM8bkz1WeoHU4Ch5RDLTtAwISxLdes47+l6nWrQiCL9TvdL9jfN68SeFvJ94kAXY56ygDeyJky2LQ2J1bcr1q5kbsU8swo7i8KVhihI+d95mL9lacRG8z6xoUyIh1FmaP6PDC+U7GcfSJEfQvmojNvYOOcZ+OgmYVnaWMiN5ErU9JN2ZlzSEBl8KgXJMDxrMxhEie3wW6VkdVoNV8JxRCr0CqT6mc4jqp/Kh99jyjn+jUPPYKoeGahvcmhlEn6PScZMOaT5SteJ5ajvVbgASKMTcohJ/vF8hZ8yfmI8JXNj8nbsK69k2tNYQH4nf1OO0BL0PBqisicXxR/ELMItwlm1rfrORN9e9a1z1FF/Bo9jTIf1puGPHqhxbKLK12zOK/lg4KBJw+dhH0PDh5Ey8eCre8iuLoR9lN2IR4vVW+g2mhi5Y4o1pObymHuwDpPHAfEISvkJAm+DAHlc8zV57XgA78kzdI5rGNNboG0ChTTu/cufMPdP3yBxJxvKcu6KjrOdEcA0DNrjA999gaA7wnyjjdbOCIY7xmDnGI//4+/4x283YJZotav3ZL8N0ZJGEIgQUPxLhX84P1QeL/da8DcP0Ks3MPLG8AwfKxsruPXXr5D96xewFlPhGDn6Fqic/PAbwfGYvZZF0N9ApddDrdeAXx1h3BuivrmH2qtDFFcXYc6lJl4FpC0EgUsjoMcBKmHIv8orY8OF8/ff0Ng9gMtNTpYJL2vj3p8eYv2//wWJBwuhsaCaJ0XrBTwGKwGYnEcmgcTXG/B7Hkqv9tE6GsEZdpWXoEf/63v85d48iku3lBLgFAmXroAkEAROENBjZMXDR134O1W0j2roO0O1uDK/toQ7//wn5P7yBeyl9MkYIspCzQs5J/UM2KsJBMM7yDW6KI166NGroOOhvXWI9nYVyw9uIajkwvWuExLkThB4ewQ4j9Tj4BHgPNtH/T9+xvbPT9HbPUav18XYBjLL87j11TfIrd0FrBSCQROoc+NZoJZa3p4ASSkIvCMCeg3FBdxqF86vL7H7X0/QfLyLzlENg9EIZiqB5Y07ePDtn2B980d4z7Zg16rgBuhowfgdiZDkgsAlEVBzOL1+R+8qVbRebCuvw67pw8inkb63gLv/JxX966GxCtezybOxQaxJvR8D7paB4G9YqbbQG/TR7zWB7ghHv28hc3cZG98uhmkvSaZEFwRmIkAeVDwcrsPTS4r3Ygud/UOMB3048JEo5PDwn7/Dyj99i+SDhZP1B7Iw03IO53EfvQFjAcA3dxC0h1jq9dHsteCOBhge1HH4dAu3/vYVTB7HHeP9mXRJ4AdF4HoYrJCZ+CNzUEnhBvDbAxw++R3jfleZkngcrCZNZHh8S7GgonPhzK93EdRacPp9BL4P006oM1ytAncoJk4mXNzFrJg2LEoxf4wZlccSvo8tEKtbpdQJFe4BGV15bQiV7z7Xk6noYjoOxPWP+eo/ZhJ/F0n/SEWkFuRMKkXpYYWKN54tVxvCo5uuehOjdhe9Hl3U01jCQiKdhJ1OwswlkSrnkKHb2koBVjGhFlk4IVCdLFY3TdaZq8YjUgz5/QAeLS8P6+qYjWGni3F3AIeuwBIG7FwGiXwaVi6FVCGL9Oo8rKUyrKIRGq6wriyX9dX1P1PoJQKYh1KWGcowAW6ooA3gwzMBP2vDzLHekTJal8nr1E8FTSztIoxiba344VSayAW2cqM6gLtdQ2+vhpFhwE8mkF+eQ/HhiloUUEoyYhnKxND4Q2N7Ks/XPTDBacLZlspYhRaF3NXY8OAd1uAdNeB1+hjTunU4VLvb6b7KzKVg51MwiinkVspIri3AyFrhUQYaG02CLiqiW+HscedvG6P9Y9Tbbdo+YGl9FZmNZZjZUClMV+Beowd39xDDGvmzh3FvDIN8Z1qwkrbiT6OQQnIhj8xyGdZiUbUTUmbYV3TZmhZ9PQuB4iW1UEPDER4Bc9SFV23Dq/fgdwdweyM4gxAH0zSBhAWkLZg8wmkuiyTPsFsqITnPXfLmWWtJ0kJ+JdbaKIB98KgHd78Gv9bBsDnAqD+C4zjhWYNpE3YhiVQpA7OURW5lXrnBM/NswsiQTdfpXa7n4RTlqYxs2D+oSFBWpwHc/Qa8egdBuw+3P4TbH2M4GtPGAXYqBTOfgFVIIlnOIFPJw5ovhm6vqcTn10CVqYReaKASeVlR/MEyugG8OvmwBefxAfzNHYx7fZheoOxRxp0exj8/gt84hFNMK0OewAgwTPhwsibsShYLD+/ALNDE5YI/ksM2op3AgG3jqF0N3lYNgx8foXdUQ9qwMPA9eI0ujp5swk6klXGfnwjgmT58K8AwGcAvJNXusbnlBdg5WgycpiLU37PvB6ENADX/voVgEMAnX1S7oXystTDo9NUZ9oq0hAUzmYCR55+N1FwOhUW9x9YAACAASURBVI01mGXuMo6MyFjU6eLCQdh0GKsa8SQbjsYCdDvqHrbh7dfhNVrquzAejjD2PVUu68IBW6qUR6KSQWK5CHshByivUzwjU3f0C2J+mWixeqlSFGOyTGYSK3dGPS9TzCQus+Sf7rf8LJAvajxjtaXwCbp9eMRnNIYzduH7gdr1wsWEIJeAVUojv1JBgu7/5nle+5RyeFJYeMPXijcoI3c7GOxXMai3MbQ8LN1bR3J1CWZk1OA1eXRHA+5+HaN6B8POEN7Ig+d7sG0bVsYGcglkl8tIr8zBXirCmuNW8tjkkEeH8LjD2gDuXg1+tYlht4/xYITxeKw8MtiZNJKFDEBZO59HlsabSwW1q12NVaizjupxLvQqAhXbYQzOR+kpiZYfHIsoQ45mV31zfNa308doOILn+jA5FsmmkMhnkCxlkSjnYC0UYS/zOLDQE9mZcllePFDJ3XDMpz03ecc+vMMGxoc1jDo95a7UHztK/ioZb1swUgmw/lbORrKUQXo+D3ulBKuSCo3GtLxgHVodDLpdjFwHXiqBzEoZSw9vK9mnx3tUPvnNDjqdjmKrRDaHzHIF2VsLsIrp0KX6FE/IoyBwEQRUF9N8zp2l9S6aL7YQjMbqO+OnEkivV1D8cg327Wg8nZgeR4TG0twJpYwSxwlY99ZR3DlGsVZDottBrz1E9fkOnMMGrHIWBscj12OGdxGYJM41Q2AiqqPh4GQzAw2FD+oYH9TCY3jpISifQeHOIvL3l2CvZNQ3yOCmCj3HYmZqwgwgEcD0DFirJfj3V7HwalnJ6FavjXG9i85+Df1aB8X1CpCa+l5cM4yEnBuAAHmPfzRep6F5a4TGkxcYtbowggCeZao5QenhKhL3lmGVzJBvtec7VjGaB0X2+LAWAfv2CsoPvkCz21ZjTWfo4/j5Dlr7x8g/XINZiPKJj3duAFxC4vVAYCJ/FTncOBKugSjPont1OJTB3Z5ajDTyWWQ3FlH+ch32ag5mjoZV0ZyeGVGMqnXGaL3SA6ylNKyH66js7KBXb6HXaqBZa6O7W0f/qIXirUVlpCXsG+In/18RAlQc0UvucQedl7uob23DHHlIZ9IorMzj9l//iOIDeqkqwztuw00EcOGpY6c5CVf8GPH0FVEk2QgCb0Yg4jm1DsT5XH8Mt9pAa2sfvWoN/shBJl/Aypf3cO/br2F/8QWs5RL8/QTGhgefR6aTebnME607TmTr5ObNZEgMQeBSCETzN6VH4Ro+9aZ7NbT2qkiQIU0T5lwO6fsLSNxfgrXAI1nDuVvEpuGaHXnUNKLjhrnBvgj7wX0sHtXVGNh0AgwOGqhtHeBWO4BZ4sY5YexLtZVEniBA3978Z6ovfuQlgLrqEccO9Kx/BK/bg++56sjh1FIZS9/chb0xD7PMsW9Mp0XZTV6nnpIb+emheAGw7qyicFjD3N4e+sdVtDtD9PbraB/UML9QhpkthJsgJ1TJzcdE4HosZ1Km6QEohSs9GjTa2NvagjkeqGNzeCqDmUkhW6KCNafiU3nq15roNbkrlluPPSCZQbqQQSKTgMGdi1pe6vw12jqcz/F7PvLZMNSG6lDvQUdu0WBDWcmGmei1OPWk85guJ8peD1SosacC10IAbqDM+gaK9EZAN+EeDUZacJ+/wMHmFlpHVQxabaVwpqcDw7Bg2hYSiaRyUZ9fLGH5/i2U763AvsOFwkLoZWPKKlJX+dSVOKujPbhbn0YIAdztKryX2ziim9G9I/RpsDIcwPM8+KYJI0ElUQqpYg7ZUh7zD25h8csNJO6vwZyzYWbCRXY2w8wd3qcIeM0DsSSOqh2Yl6kUdLATCKKMKcQMJ9wFrsrjAJLNrdshnn0sbHI7uYki8nnSdpFXCS5wOVSQ91H/6RVefv8bunAR5NNY//YBvl6dh5lJRvwSKjQn5TO/6TLiNJ25j0VmXdg+XFwjj9P99l4H7uY+2i+2cbx3gBHbZjCcGFEoTw7pBJLFDFKVPOY3VrH81V0k7nAAkgsNa4gPd6RrxmV9o8GMuo4B52UNuz88wvbuLhzfh/3nf8CyXYE1b8Oru/B2DzHa2sXh5jYa+0fotzrwHFcthhj8tPAc+0RCGVQVlipYvLeOuYcrSNxdgLVUhMl+GR6JeBoBjX3sqgZYVNj3Aa/pwd1rwCNvbh+gc1jHoNPDqDtQSmnPdRXcXBP3KNVoOLNYQG4jPDZq7Zv7SK2WYeRjOLPuxJoeEyKDD/YDGmy5T7dw/GILjcMaeq0unNEYvu8ro7ggYSCRTSFdysGu5LFw/zaWv9hA4t6achutdvpHR5FdjgdOQzLhx0iPPaGcdEeGfVyEJY+4R2O4m4foPX2BzmEN3VYbg14/VNg74dm/lm3DSNtIFrPIzhdRWK1g5eFtJDaWYS+XYGQjTzQWGcWfeFdRhjxDwD0cw92pwduuYrx9hP7WMUY7NYyGPeXsKcWjzToDbP/9EfznSXhJEzQ09C2gl/ThVdIo3FtBZXkZZv7ivt6V/QPbyQGcV204j7bQerSJ/qtD9Per8Lod2IaBlEcXnR3UnrxA/6gGJ0FeMBDYAZzAVTSYy0UsfXsXKTuJPPk0yQ4R9jfirTGmI5akZyDvWfCbDvxmAG//AOPtfRxu7qJ1XMeARhFjh0spoecm24KVocFkBoWlOax93UTh3jrstQqsiq2MRyZyURc01eSqH1LuRAZaymihOob7ah/jF7uobe6iVq1i1O/DdV14BMc0YKWSSGYzyBSLyKyWMP/FOso8Lm2tAjNvApHySHnBiJd5Hh3xOJe552IU40f5Kg8el0n/prhaZtG7Go2I2lQAD9Xil/tyH829A7SPjzFodTAaDuGMRwonwzdgqu9nAsgmkSjlULmzgsUvbiP/8Jbif7NohJ66SPsULtp4kDw4eHmE7e8f4fjFNnoJH/g//ooV5GAV8/BqXbhbe+g830H15S6atRbG/XFoVEZ5atADlYUgY6O0uoD5B+tY+PI2EvfXQ+NLtpMPeHUfzqtjuC+2cfBiG+39Ywy7PYydsaoP29xOJpHMZJTxV2F1HvP31lD56jbs2/OwysmQ3/QIT8vVN+Cb9E0Uxjb8Do20hvCqxxjv7OF4ew/Ng2o4Jhg76qhEHreWSKWQzmWRruRRWJ7D/N1V5L65B2s5DzMXeltR8p7lkoY4rkr2Aj4nIPzO1emqvw1vaw/Hmzuobu2gzwkJ5ZfvKfnLhR6OyVT9UzSWSSlZVlqbR/beIopfrCBLQ82krWSk3wmPAxpMjvnJY+7+OuxbSwo3TQ9dm3rtLuq9Doa0e5wroLS2gNJyBUbWlknLG/hGXp+DQCSv1ICO32x6/qq1cbx/CLiemmNQHpXvr8BenQNlkPJaqMdok2yjjsNPs23AzAVKtvv31pD4/TcMe3244wHaxw3Udg+wwrFOLjcxRJtkIzeCwAURUBwXmx9Q8HJc4vdG8KvHaDfr6I968CwDuYUyiuuLsBZK4fg+Nk9WxalFej1HoydELmiasNYWUF5bQWdrD612A/5gqBY9B9Um8v01WEW6RLsgwRJNEJiFQMTDagzHtaVmHwc7e3CHA0774CZN5Dfmkb81B7NsTry0TebyzJP8y3wiPqbRoLmYgfX1faRfPoHVqsFzHfTqTbT2jrFS78DMFife5maRJWGCwOsQOCX2aGyiN9TQG2C1hnazicF4iLERIFfJI78+D4sb2LgOpz3KThfATDkmsaCUUvbtJeTWl5Cj0rVZx3gwUGsr7f068n0P3AglRq/TIMrzWyNA/os83hp5C+NiEvWEh3w+h4W1Nax+8xBrf/0jrPlFtQnFb3TUGofHscdkEf2tS5eEgsDbIxAJZF64jmdkLHilFDpZoJM3kSwVMH9/HXf/5Tsk79+HWcmHugEzgENjQ66P6kUQlQkz0hu73p4sSSkIzEKApn0Ry4bffLWGHq6Z8vi/eqOm9JBewkJ2oYTS7ZXQmzA3OUbjXJWv5tWQXdVat5kKEJQM2A83UN7agf/qsdqwOuj2lP5hvF9HKjUvBq+zGkbCLoRAyHYR80XraNR7+co+oIdutYHxKNy0G+TzyG4swLq1CKuUnJyEcdIBwjmcemaWlN8pwFopwr+1ivxcBaN6E954BKfRRXOrirlbqwjm8uEGyAtRLJHeNwJanfG+y7lw/lxUoJLOP6qj1agj51AdyG2/Nux8BuliuIOYi2d+qw//uIZWo6Gsr33l7t1GppRDgt5VtMEKmV3/yKzTzwyL/6I+opz408Ak+qdGH9T4RbutT21an85X5xflpTuKQUUwfHWOIX0MZGEi75jwGg78x7to/fIIW0+eoddqKhdFgced2SSYrmmVmQYcw4JvGWinbBz//AL/P3vvwR1HkqQJfqEyUmcioQVBUBerWNUluqa7RnTfzN28t79139sfsDfv9m5vdnpmWpVmFVmUIEFoZCJ16HufhXsiAAIkKLqr+jqCTIRyaW5u4e72uVnrnVVc+PkNNGkKdKWRmqJ9bsFbF0idufhClDuV3dsJgh+eovMfn2Pz/iN09/YRj6noo5JeuTIgLM20EZgmhpaBjmOJv7rW/Yu49b9/Buf6CoxZOsJTu+pO0vRE9tlbUUApecLnEjX9ZqY3nHyXCEIqw7cdgK5IYgPj7hBJfyQ7wSc78VJSZZNPr7PlyYTJXKqM0+CyuMUFAgrIjo/h/V3sfvEDOvCQ1EuolIupSw7aCVYrW8yCO9tlZUvlx/Rf6WC7kCAcXIxpNSBBcK+N8e+/wsZ399HZ2kHojxHT/ZW0TTooCZNYzGINaGHEdXBw+zH2vn2Eq//bJ6h8sCZ+41OgSGYQw4LpAjI/7v7d7GHw/TMcPnqEse9jaLcQzl1DEkwj+OIH7H7xFZ7eu49w7MEbDBDS6ohgII/S9diClolxaQv9757iydoUVv/hFmY/ugZjYTqdsKY4gZTmugyqPBOAAukvNIjh392B95vPsflgXfgzGPKrBVHexlEkPEMLS1ESITBixIaB0bqN5PEmdjf3UHCLWKiWYNKqhjqkjRUwgBY0wp0EwZ0NHPznH7Hzw0P09w4QUkHsexKDNQzj1AVTaNnwHBsjG9i/s47Ow2e49ncfwb25CkwX5WMo7mEYM8t7OvNXPE+SYDvxp10V0fLOTgLvP+5g+z+/wObjx0g8T9rFj8IU7BexD3OcSQAJL0wcuI64IWi/cwlrn7yL+sc3YM9VgIo2A8iAaa5i2WY/Qu93t/H4D7ex/8M6iqMQ1iiAHcQoJoZYZbAMCwQO9Qns2E7gI4KfEOwGHNoRwpkyWnGIeEj/Hq9AAOkT6a6c/X/7HA//7Usx3eaOQthhiAIIBDBE9IzHI4x3PAz39+EjRkxEuG2K2fqOE8JcaopVimjtEjAdI3Es0SWyNOQ7ynSajKOViWJsoOzRokYb0dNN7Hz5FbYJFhvQ6pQP8h3lY8wGIcvbJkJaPLEs9Eoutr69j4X3roKuHiqfXoNFZC8XayaNmdKApNCPJm3rp9/BcN2D/9tvsfHlt+g/3ULQ62HgDREl8lWUBGLuOjJMBJaFse1gu2Jj995jLG3s4MLffwznwowsxIvVC+Z11LRpO+jMX6FJXimodOhXinF2YPKCtgjWY3/dQvfbh9i6/QCDp7sC5AuUfEySSABm7DAmZVRsIDFMxBa/nyb2v1vHky/vYenWVVz5p5+jcE3tKCQ9+P3kmY3DH/MU92MJvKcH6H77BJ0fHqBjhujNLGG2tYqkVYL3b1/g8Ve3sbu+iXCYWnhhm1qGLZNDVozjCS4yjx5tYf/eBvYebePGr03YFy7ArKcIcv+rTez95g/Y+P4ext2eWGMgkjxmnVQatJrksz4FA4MfSujceYSth8u4+o+foHL9AqwWtSoZ5jqbqpM3hcREaWQg2hkj3n6Czp272Hj4CMPOYWo+NAzBbw2ZiM3qmyY828GelQhIsb4yh+XtHSz/6hM4y02glAKqBCh1gs8YX+QYLRdtefC/WcfOv32J9tY2+u02RoO+WKUxTUt2JLGvEQBFfmfmtLaS2Cb2HROPihbChTIu/P37uPnZh2isrUq70bpKuL0FysLATFCarmPh3ctiGc7UnwK6rBsF6PX6aA97gr6fmm2iuTIPd35KLCSlQC9+mF82sJqQMr/IKZB+5lJ2FYUTAXa0AjU8PITLsX2SwKoWMXtjBdZsJeU1cXOVEi/7bRB5xD7EyXbBELO91uocjGIhlQkc/3g+dh5vYObqMpxFIsbyI6fAa1JAf/t41uNOjkto2XB3H8N+F37oIam4qC5Ni8Uys0ardWfkl06d03EWp5MVA9ZcFeb8DEqlCmwYsIII4902RjsdJIMRkuQVLPGdkW3++K+YAuRdHjxzLk+rddttHB50YAaeuP5DpYCpK/PCiwSikH+fA3YzDfK1/NKNOVY9gXNjEaXfVGE/s2VObnghus92Mdg+QGO5fmZXSAuV/80p8CIKcLzJxUa1u1St0cT9ABEtfBLAHvoYmwEas1WUF5uwpiqpq+KsDOa17gcqO1G4lg1YCxbMxRlUm3W0N0wYYYTh3iE6G3tY6I2QNAl6zce8L2ql/N05KZDhQyqL7LUlXPr0Pdw/3EFreg5rN29g9mfvwZ4rCb8mu1yQiWXjHOe6KZKQ5yxznzPvPFhOgbdFAY4PuIdluo7KO5cwtbkFmxtcGlO49ukHcK6mm49oGZkbFOiXPKQA5lpNzM2DJ4Tx2ypXnk5OgdMoQHbjGgTHD9yAfeCht3uAbv8QFbq7d4sozU+hcWFONjcK2FWLWJ71NdPmUEANSzhWttemEC7Pybq2w02BnL8ddLHz4CmW56ZgVrWi57SC5c9yCpyDAop/xcIg1+GHCeLOCINOTzakUsdkN8qoX1mE1SqJ7k3WyLLDVsXD1H3QsAH52HAMWC0gWphGudHEoVWA6QF+b4yDh5u4cPMK7BUye87D52ilP0uQHxWwoj/bWXlI4Rj3Ohjsb2EIX5BSkWkiKFlwZmowuDOffrEpgMd9dIcd7Pld9IwAoRHCLSaoNVzYZYZTHMuMspnwWmd+JplTN0BUjvAf458J8M6mrdM77Zl6x5RNKhmTCC77wzhG8NsvsLu+js3HTzDu9xBFdMiSwLEc1Mtl2JYjH5zYp3+IBGESIhxHiP0udr99AM8fY8kbYfkfPoSxXE3BOros2bPq/KnSDwieBAi+uIet33+J/fuPMR70gSCEGScomA4Kbhmu68K2ixiNPYy8MQLPk4VFL2hj2w8wHoxw8RfvYvGT6yhcIyAhm+GLr1VxjgUS0qlFftKdQAujlLoAIBAhpOLOizGiNZrdHuxeApPWIYRizyuEj7U9c8q0zUnWmBSEL9g2skhgoeJZqIcW/CgR9zDGiFq2FNDCMBMlMNPOpD9JTy7OfJEGY51FMZpa/Aj3YgTfPUPnf/weWw/XZfdW5FPZT4BGjIJdQKVYhW1a8AJPFNnjwEcceoi8EJ2+hy9GQ8ztbePCpzdRe3dFFISwjszNs5rSl8jnHGB7FqojA5VhjFJkwN0bIfx+A+EPT7H77dd49viRLFTz48FmLjlFuIUiLIs72mOEVOSHIeIwgjXwkHht7PYOME58+FGAlV99CouaQloS4P/st0ANqoTmQQpWCR714X95D/t/+F4UskP6ufU92W1vmkQAJALEKDqumAYLogBmwp3LMcZ+iMQL4cQmWrUpGAVXFsul77MpiOOQLXZA+NSH/8cfsPXvX2D3yQb8bhexorVjmlLHkluCSWBAGMAL+PPh0FXF5j62Rr4A5y63P8T8+1fgrCrlt+4LL2n643xyxl2GPiw3rcEEP2yi/b++wP7thxhs7yEaDeTzaiKGxaUmuwC3VIANU3YA0kIDXRuFfohg7GEneIDewSGmnjzB9V99BHdtHoYAbjKykwu9wxF6Bx0MD7uIPA9GbAotxOKfMtNKUICs9NIaExUSZgJbgUY8O4DvuMInp68Gn1FndicWhTt240SsTATjMRzDQpGuQWLAjIkkoNgxYFumAEYISrBMQ8AyLBbBJIFNC1xFsa7iED0inVblKx0h7bqkW9G0UEosJO0hwq0vsbf+FJtPHsOjdYkkBf8UCi5si2qWBFEcCsCPqkOLFnv6HvqjXTwbBWLl5hIitH55UwAJIh8UP6hs00LwRgMjRgn87/cx/u13WP/dbYwO9hEP+0hCX5DsBaeEguui4BQEOMPdnQKuIlgpCtB/8AwPugN0212s/fIW6u9eElP9HKQxmwk7Ti7Opv9P7g0rIHKZ1q/a2Lm3jmd3H8Aa+Ejo9oUgIpGPNkp07eKWYCUmYi8UdzpjKtliIOh58IM9POl78s1aS26h8vGF46AiTSw2OtsmBNzARjN0EMUukiCCsTVA8O1jwLuPB5//Efvb2/BGHkyCP5wiCo4LxyzAoEu7MBA55SYRwhFlRxu7XoRgEODdz/4WZmtadk9u/vtvsfNgHaPDQ/kW01UP5axDq0CWKZZ94oBpRSJrjd4YfrCLw1FfFOCXTAOVmxdhNa1UxrKdWZdTDr6SX5KgEBsI9nowv7iD7Qf3sbezjUG/iyQMRZ6U3RJsmy5/LHijUaqkoXsgKUeAvr+BO8M+BsMBLv78XVRursGa4QdcZSwoX7UAT3rSzdteBP+rH7Dx/34ufOt7NOfkg4a4XMcVsGHa1xyxMOP7qeUcgjaDKE4XgULA79Lqjg+Dq5u6rt4Y270D9IwQY9dEa6mFpZ/dEPdwEoTfAAEtDtH2+zhIRnArDpZWWqjROlFJdxadIM+6MqcQM3+UU0BTQLMM73ktlpN6SA4OAfYZLmAaJqyqi9mryzAqypLPC9hLJtyKBY2iAbNRQqlZg7fdRiH0YIYR9p9sYXhwiGK0kHOqbov8/GoUULwra+tyncpUWuNMOn2xbkiAuIyhChZKc004rRqMSuo+Tcb1euyr2F/Ymn+YniwYcV5nwGzWUWs0UXEr6IdDhN0hhnsdhJ0enKguO/perfB56JwCRxTQPCybT7q0DnQoLkypiKeZ/ti1UL8wC7NZSi1TnJiTSkpaJvPMH0GDlL/E5DarKBWLSPwx7BhiTrq7dYBGuJauIWTTOypWfpVT4AUU0AI4lZfCw5zz0MVldwSPLn85/ufc0zJQnmuiNEu3x8oPqObTEznI+IGyl9NfFzAjwGxWUWs2UC6UUQpCYBTAa/cRHw6QLBSVWawTCeW3OQVegwLpWk46j7ZaZbjvX8U/TJVQKtfgTDVgTZXEGjTnheLGmG6eOc7gWhatkx7bJfoaBcij5BR4EwrocYBliM7BXprGu//4N7KWRIu39tIMTO7w5xIHwd0B5XgiLtllY6d0AJ3ImxQkj5tT4BUpQFb0EkTP9uH3huIOk+NfFCwUWzVU5qdSy0HUCnPupoYgz+XCdxzTcjNoHTBaNVSrNdi9ISxumhmM0X62g6UBdxuXcyuDzxEwf3AuCojCPV134PhXxsARPU5ESA4HCEayNV7AgG69jMZFWu6noQql2jlNzFLnKPxriGtiGQNXC6i2mqiUaigFI4Rhgv7mPgb7XRRGHqyp8rmKmwf601PgRwOsPCcLFXMRtWfNFlG9tYKbwa/gBgYSy0RQc1C/OC8WREz6tTYBe6mMqV9cQjxbwOxoJAsJznQN1SsLKE5Xxdf7meODLDM/VxgKa1o0OQokoBWR4KcFPqWhGIzRec782OmofKKak0cwGqG/8QztDQ+bnV0cjgdwGkUsrKyhMdNCuVYR354W3eKECaK+j9F+D51n2+ju7MEfdBG0A3TuJbBtB3NrKzBrDgy3pBS9mfx5ybG/mFVKIICIbx5h/4+38eTuPXj9HkL2Y7pLWFpAc7aFarOGYrUE2yjAG44xPOihv7WHncePMR4NEOwfouOF0h40H7rYLMNqFWGcxy2RIpEm0zEqqodc+OROPHOmhPpiC/1HLqw+4EYJxo93cPDNI8w26jDcZZhVA2aRLpeUtQ+mkZL5KOkTmWXWVI/C6PbiE17HkIUo+vujqxCD9ikjukzJ0Fbno59NUtOF0AH0/STA0QUXJKg8GykwwvcbGPzxNja+v4N2r4sxwRhFFzNLy6jPtVBrNVCv1WHQ6owXYNzt43B3H5uPHouFnqDbR/eeJ1Y1ClYBpeY0rNmSuH5h2UVfr8ur6onIhBOZKESJKP5G2/sYffs92l4fT/eeoRcO4EyXsbS6imqjAbdSglukEtMW/gx7Y3R3D7D/+Cn8gw5EqdoN0Lu7iZ1yDQurl2DcWITl8KuSaR9dDlEKK8s/WxG83/2A7c9vY+uH+/APDzFmY7gFFJtN1ObmRFlTqlRQLpaAmICZAOPREN1uB/HwEH7VQelCCzbdIpWOuiTblSZ2Y1kASuB/+xA7f/gGm7SaMDwUQFihUsbs8iqmluZQm2qgWGKfMgUEMz4coL2zh50nGxj1ugj2uxiMx7BMG7ZhYbbKnU5lAeaIDGIraxY4avHzXZE2nAAp/qAboKiTwP9uCwf/13/i0Z27GBy0U7SpEWNqfh5T83OozbTgVMui5LZo0ymIEPZHYqVnf2sL7fY+wsMeet4Yw34PCCLc+MdP4RZXUz+V6TqByFG6GGm8uwSjYiHaX0MttGHujsQlUO/ZJjrjHnyEcMs1LL93A+ZMBXHZRmimLoGGToygWoC70ITswj1fzY9ophaIWx9dglMro9xLYA8MdL6/h/bGEwT+CEMuxNUrqK0sYPnaJfgO0vzN1A3bqJAAU2XUV+dRnK7DoDUi0paHtE0qN4wkhiXuhfrY++ExOoMuDnptDA1P3Ci1luZRm2rCLZVgFxwIbcMQwWiMwcYuehs76B3sw/R9RLtdHHz7GEnRRn1tHrbdkgUZgY3Q2orKnovpAsIgUGuQwL/fQ/D5d3j8xTc4ePYUQ4IsLKDUqmH5yiVU51oosm0tW0AL9Kc7OujiYGMLu3s7CIcj+Bv72ApCAbYUXfolvQSrcgTXAAAAIABJREFUQZcTKlNmfqz+6vlP/cRys625G7ZsYejG2Iy6qBYLqNen0JhOf6VKCfwVi0VYoYF4HMLrDNDf2hcAUm/YA0Yeou0Im1//AGeujMsX6L6pklqBYp/TcpI00TIqNkRGuomBWmwheLqHnUGAnjfA051NJI6J2uISZleWUanVRG6wnexxjPBwhOFuGwfPNjHstBFzl+9eB71xBN+dhlUpo72ziXv3v0dv1Be3YysXV1HngnK1gkKpAMu2gGEIb3+A7tYe9p5twB/0EQzGGPpj7Hz5EI1mE8W5FqxqI/0ekmaa2U60Lx9z/kmASDQe4eDZM6CzhyftLYyNCO5CHXPzC6g26qCsLRRd0L0YLch4vQEG7UPsPHqMUaeDsDdAEPjYiu/ADIFV00K1dkncE0mbcVxA2UtZRgtvwwT+1+s4+PwOnj14gIjxLQPuTAOt1SVMzU6jVK3CdgsCRAvoBssLENHCV2+ATreD/fYehv1DFBYaojgt1CsyaWF+1kINs59eR2G6hqVCjKnry7Dmm+nYkFZirEQUT/ZqFXOfXcN7ywS0OZi7cRG1i9OpOUgOEoR2LyDiCZrmtzkFJhRQ/U54/nCEpMvFolSXSQtghYqL4sIMzKIaE00intllZWwvQO6qi0qzgVHBhTM0xeIY5RvljHxT/hLle6b++eVPgAJa9hHUPubYfIxxf8ThtiDOTddFaaom4zpaFZTx7omJleoCk8pMFKfEMlZL4s6QQEgEw9QyWW+EcJS60pN5+MkEJinlFzkFzkEBysE4QdzzkBz0YJF3yb50p1l0UJul68zS8Q0UOlnNe1zs5PBFbbSg/DXLBtxmFW7RxaibyPrAuN0Hf7L5hONH5q3T0Gnm55wCL6SAZhh9TucitIJLt7seXQAGfrpb37ZQbFRRqJXTOdFRlKMcMs94KZY+rQQJrReXXZjlMlxuwAhNBOMAweEQCa3YhlyQ4cwg5+EjYuZXb0QByk/l1tIo1DDVuAK6exdFE9cm+OPmOWai1uC5XiLrlZMFizcqQR45p8AbUUDGuAUDtP7qXFwUS9MwTXAvJvlY5l7MQeRuuubBG97KT8/L3qgUeeScAmdTQFiPrzO8xg138e4BwtEIpkGZmsAiYKVegdmsiVw+NladJHJKPpTjBL0SLNCckjkhogjB0EOPFjI9WhPKx7+nUC5/dG4KKNlJHhadHQHbQyS9gWxcp71xWvC3qy5qs00YLjd9KS8fZ+Wh5m8Jl9sKHP9aMOplmcPZAwtJSAv9XUT0BMDNx7r/vKgvnJVX/vytUkCrrt5qoi9LTLf/aeG4A9xZIujhBm59sCquX8TnJSdWRQuGY6dC1QScS3XYi7dQ/tt3kIQRDMsCHEuY0KQfNlpikdHBaTmpZ9nC6Gt15vCC//TjF6Ry9OoUppb4/KN+3MFPBZFlGAi8ATY3nqAd+xgXTVQWZzB1Yxk3Pv4AlcsrsKYbKcKFSAn6D+8GqG0cYOqLe9j5/C7a97mr3ENw0Mfg/jYO7m5gbqEGs1EUJZEslrDP6UoQEEFvMr0E4ZM+vK/uYef7B2Lyn4qw4lQdzWsXcPmzD1C5tJL6JC/bsrpe8hLU9sZo3X2Gyv/8rbgKIDDA6IzQvrOOQrOMynwTzfLV1HfdiUXLIyKd44pNR1nFRaGqIebS3YsLqN9tYnTYRkJXLTsH2Pnqnnxwp4sW7JUWMO3CrBxZwzkVsHRKG51aIkU3+k61qaQEfyYImpYFL7pq0nTlOZuuaut0iMrUX8KIuo3YNkO6Iekh/OYBNr68Da99gNhMYDXKqK7M4+IvPsDUO2uwFqdhlFxZZGObFjsRqus7qP6v3+PZ13fQ291F0Pcwur+NPbeMucV5VMqXYRbpKiLD16qsVJgjslCILJRiE1YSwescYGM4xJ4/RFCxUVmaw8yNFdz8m49hLc7ClEUSRzSeMhjqxqg92Ub9N19i/6t7GG3vgIYFxgcj9O9uYePrB1ilr+X688oZKQat1nDXfYfWQ7bR/f1dHPzwEKNuR9xoEaxQujCPmXcvYfH6Goor8zBbDRgFaq+Jao8Qd/tY2NxCdX8LAyNCdXFWlNCULdIMarFSgFt0ifVohAH7wZ2HiHp9hEkAq1VD69oqrn72ESo3L8OabopskUz8BKX2GLVHm6j9x1fY/+4RDvf3cHA4RPv2Y2zXamguz8JqLsOwCumcmwO3195pp5gjMsUiDEE24dMRws/v4fG/f44h2wYREtcRxezcpzex9v47cC5dkPaRAlDZQDdq3QDNe4/R+uoO1r/6HsF+B6OBh+HTPTw87GN6ZgYLcy2YlZpUVZTMLvtfAY3aVdTeXRNAVRLZCO7uw/rtXQG97Po99KMIzlQFjV9/Avv6guyWoaCTPugYYoJNlGz86mT7SprT2X/p6s0EzJKJxmc3UfvgndRt3GECupzvtPfge0OMbAOluTpan1xB/b/8EwxOHk3uAqYZFtLfhOGYMApWqqFne2heUOWhvKT1Irpi83uH2BsNcJCMEddcNC4v4cKta1i6dQ3W0pws8vF7I5XhRGAYovnNOvzf38bG13dgHOyCwmK0c4itb+7j4OPrmKkWYbplJOYxD6MpWIWymYabDhMEX97Hzpd30HmyDnhjxAULhcVpzNy6irVf/RL2yiyMipuWn1YmhiHK24dofPcQzm+/xuHjTXjDAbrbhwKYqTXqWJIy0/qW6gdnU/yn/YZtJfxgwF5ooXV5EfX9RbSqTcxfWMLC5VVUL6/ArJbVAFa582G/3fdQ+WEblX/9HZ7+8ACHh204fozDjT3s/rCOmRtLaFavyhhiQgQt5imgyC8h4ERAkS76DAfJXhsHB23hE6PmYubqBax8eANzH78Ds14Tlx0EusWjBHHbR+n+Juq//w67X99Bp7OPUeDD6PTQ/vYeYivB9uEO+mEP9nwTc+9dxY3PPoazuigTQ45/RHYPEpS2Bqje2YD173/Ewf11xN027LGH4aNtHC5vYv7aMuJFWoVyxHVOuuD3fN/jZ5pjEX7fQm+I8d4IYzNGUDJRuTgrLnQu/ewWrIVZmJUywD5ErBd3EHXHCJ9uo/avNex+fQ/d3R2MxgHC9V3s0JVPtYjr11ZgNZ2Upvymy3dGx08Qfv0Q+3ceY3x4CCOOUJidwexHV3DlVx/BWV6CQfCfsqrHNhFFkBeKnF/c3MKz9XUUN5+hfHEOi9fWUOSYqZCKGOdCHVOzP0Pz7z5IF0RdM5UL7LaUCQS3loDClQbmL/4M88F76STbNWG6HG9qLngVgaXj5OecAhkKEKTSGwmwjONGTrbp0soqu7DoK5eTZ81mlDU89L26nTwj7/KbWrRRatZR4O4+9uEY8A/6SPocRKV9TdI8LZ1smvl1ToFTKCBzMPIi+YcgQz9GMvDFqmYcJzBMC5ZbgF0rwRA3vKlcPSWp448odzkG4jSA85GiC5PAe/YRPxBLWcmYZgRjJLGZjiFP4+EX9ZPjOeZ3f40UIH/oH+Vh30fSHoiFFW5CosLJLrmi8OeGG44JXniQB/nT8tdNZFxGa4cc2HCYEg7GCHscyKvxok7zNP59YWb5y79uCmQYRvGwjLn7Y/jDUQpYIQ8rd+l2mX5WUv6cjCOyBNTJ8cz0ZE5M+evIOhItQXJt0vd8RHS1PQqQBJS/b7J2kS1Afv1XTQHNd1xjIZ/S0jOX7SqFo3VURaCEylSyKefYeuOoIL7JuPmRU+BHpoCsHVAuGkjIw9xNpg+yKHUDPETmcgAgF5mzDpyfcwr8iSigWFBOiv3Erfl+BzEtRzBbI4ZFvQHXpbWFVz5nJM2ypxSP4wtZ1+f8reKIhbZgY0s2c8fjAKNOX8YOks4p8fNHOQXOSwHFxilP0tgCXRIPR+IxgBYG6UnFrrgoNMrpmlhGFB/jP/Jzhqcpu41CIlZejXop3RTJTYzcfEwLRF4kY18Z/56Ie96y5+HeLgV+FMAK237ChNn6cCArIAUDpphcUz7YGYEDBD3x15G5m4tKaA549ZEN+yZMJopLnWh61tkef5q5O61ijKR+k/gqbXEvksTo+gN0EWHx4nVc//XHaP7qQ1HoG7QsImN5Lu6xIyUwqzbMKVo6aGDOacDtBfB2niKmq49OD/e+ug3n+gxml7mLOF0AZCeVoomFhhQQEe2FCO+tY+/RExx29uAnoVgNWPvkFpY+ex/uh5fSjkzFj1YwhzQBVoLZuIy5xQto/Pf/G1//x2+RDIYYHQ6xe3cd7kwV9bVVGKWCDOayAiJDqWOXLFv2IJ30M7HUUuCikAXr4hyal5bE9Up3ewt2HOHg6RPs9/fRePoYN//+E5RuXYG93BAFF9Gf/CoLypOAJ6WkmjCfzuRk5tl7uWbA1JMvJ/RnRRNw0HNx+eBkjJP3ase5uCZQ7fPkAN1Hz7C3swMjDmHWaph/7xqu/eoTuOJmwU3bRyVFsAjNwZrNRUy1/gvK1Wnc/u//goI3QDT0MFjfwcMvvsO7l+eQ1FtpXzop2IVPaUUmBVS5dMUVheh7A4yMCIuXL+Pqr36G2me3ZDelWWL/S2lMmkoZKias1iLMch3F4hT2/vU/0d9/BjsMMN7t4PbvvsT0L26gMV8SZeFkgZB5K2UwFbvR3hjR9w/QXt/AoNuBH4ewy0UsvnMFFz/7AOVPb4o5fLNipj7rSAeWIbKB2SaSiw1cjq/THI64xzIrCqTA5pCyJojHQNxO4P/xW2w/WE9dXxCsUCzg+i8/xto/fwbnyiLMWmqVQkhNfgpSU/xW6xJapQaqTgNbX3yN3s5jsRBw8OApHn/bwpULDTguzeypXaen8sY5HiaG8DAtwsiAc5Qg+OY+tr66A4w9hHQvUqRZ63l88H/8Heof34C91BTLAbIgoNpV5vuNAqypa5haWkRlZh7b//p7bG0+he8PMdo7xOPPv4O72MTs6s+OrCRRfrD/VC0YRVMU1QIqqhdlsQt0UWKmYNSAO2YrtlhZsmYIEEllOpXsx2T4813gxYRQyg2C19LFjrQd6SosMg0xCxdwt6RrIqCMbJhimUlAjMxLvh8KeavzpjzUE0vSSB1U3odJjCCK4EUeOJhZvnUd1/7hY7gfXIdZtyfKdy0bqUQ3RzaMTy7BqLRwwW2g9//8DyRjD34Yo9/p4c4fv0F5voHa3Jq4dZHspG3UN0JZvhLev/MQ7c0tjLzUclhjZQHX/+kXmPnHvxHwEHd2yoxDlP8WksCC1ZqFNdvCSrGGmd98jfXvv8cw6KH3dBs739cxe/0CrNlLsLiTnwfpoGmRPvnp/yUvsszUDLgJ7AszuFj9BBd+dh1mk0CrogAQZKc3q6nXCrhjKzSQ1IowG6sw3DKWoxidP+7Bkp3jEbo7+9he30DjxpUjOrB9NI30Wdz5cZycOt4KwwCjJMDIjnHjo0+w9qtPULhFazZE1ajFXl56hgCfjMpFGOUmZgYhojshgsO0DAf7m/AQoh97KLQq+Piff435X/8c9nxFQJvS3iwP+Zayt1KFOXUd87aLelzA47vfwxuG8Adj9DZ2sbu+iaUrLRitOswJ8OKoavpK74IwCaBNYoyTCGPDwPTqCi7/+mM0PrkJe7GRfm/4zdBlIM/Xi7BmL2JmaRW1qd9h6ze/xZNnj2GEMcK9Hg4ebaL/aAu1qwStGKn/UFGkE5hIX6QRwt0Ogv5APtSJZeO9Tz9B858/gXtrIf2+6DZkgdkGjB86SGam4Kw2cemj61iLIpglS9DyqQW+tLEIWuMYiqA9GT9qy2+6LZkmQXV68VTszJyQVZpQ+TmnwKtQgLJKg90Yj9f9dHd0ktBpGWA5jliXoLwSWaXCTbI5Rf5o+SfWgbhgVKukrsKYX5wgGvmpdQoxR60YPZvOJPH8IqfAOSiQYSHQzebIw5g7/KOQ+57FPZxRsAUQPPneqmRPYzuZ4/M906XcLRZglEqg2zc+SoIQ4cgTl5V0M3r2vOocZc+D5BQQCiQyh4qHHsaHPQGkUyDTUpxdLsMouum84iS1FO9nH3McQb6WTUC8KLliaZFhaD2XloHC4VjGNxJwMoDMppJf5xQ4JwXIYzx4JoBkSNk4FtentCYsY4hSUdb7yJunsKxK4JQTw7sOjCLdjRYEsEK3qpS/4dhDISLqKjMHOiWJ/FFOgXNTQDMnx6qMlOVtPVbmMx1OXR5tr+NLHencueYBcwq8fQqQR7N8rNhSxgXqXcrHGWY+ztrH+PztFzBP8a+eAllRKXoPIGl3EHgj2YpP/RotJhe48bmgmJlxjrPsMTLK/I3raNxEz/GDbcItFWHRZRsjRrGMHxKfpuCPRc1vcgqcnwLkwRP8K4tmYx+h58n4l8oAq1CAww0v3DycXSs+R04yXubGxEoJTsGBQTfdtBI0GiP0aGGFi+0v7g/nyCYP8pYo8KMAVlj2U+UhH+oBABmPjKKZhfc89L1KI+Fz/U4FkQXdUzNQAX4CJy5WEx3mI8GIyqzpKax8chONT2/Bnk/NI4qCim4j+FEQ2hDNk4CKJXuhgvjyKmr3nqE67CHqthEEIQ62dlL/4tmOxg8VM1QfrLhLCx776H1/D71OW3Z6GtUy6jdWMffhFRRurMCspwAPUcjq3c1KHqiVdkTv38DaXgfrX30J0w8R7ByifXcD/TsPUXMvwpwtpZR+k7aQD2LqFsi+MIv4/auYHo5xOB4j7tNnXojocIDhd4/xzV4X9T98h+nLK5i6dgH22lLqnoigJlf8MKULpCf45XRmzDIJmU4dwp/8LL9JpXRimTM/7LTSMCZYw0f43UPsbWxjFPqyIDx7aRlLH92A+8EVWFOOWJERsBaTYPFk8MDFC6ZjI3r3Cq5v7eKLL36PZDwSf/C7D55g5+5DzJcd2Iu1FFCUKUJ6SV+x3OmbgiS8JMTAMOHXXVz8xS1Uf/4ubLYrFSUEI5CWwpwKhcW4NLiyXIZ9ZQWtx0+xN+wgGnThj8do7+zKYEZ4iCRUpJVBPi2AcGPwDsFUT7B37wFGPYJVAiRlFzPvXcXqL2+h9MFl2IuVFKjCcmglZKYoBM+kUoZWECgj0vaaLDKyLxAYs72P/t0HODzYxyjyZbfoyvtXsPjhddiX5mE20l0gUk9NK+FJLtQbcK62EO9ewuz+Pp4dbolbnsH2Pta/u4eZDy+jVS3DKhRTcEFGdumkXnqe9F1u3UsQDxIED0fo3XmM/a1tAfJ7hoHqhUWsfvYB6r98H/Z8WYE1lGKf+fIXpYhA4RG7CtgfYo7mqwMPg60nKEbA3oMnKH4zg9bHN2BNUfmf7voWOUuKSnsrmctrM8UZCW4iiQU8w1Vbg6422DaUHfxP8uufavOX1v1kAKbDxWJttUX6cSLyK04SxIZBA0EIqQTR/EnzcNL+af5SDpKDBT55KBamEoavA5LccYS2Cx/egPvRDVjT9kSJPkmL8cgTisdwpYFkdAP1299gsLkjZu4jP0R3t41g4E94Psv7dI9CMFK04yO4fR/tjW0Mh0N4TLdcwq1ffojWxzdhzbup2VH2c50f25Wymfc0UXpjDfF2D82dPbR3hvBHAfobu3j09R1cWqqi6NBSBi2VqTY5SYef+n3alQWsatEAWbmOZL6aWs9RoEThU/IeD/Ib6Uj8JgFnEeBcm0Hl3goajx7C399HFIbw+gMM23SNFdOXFGhZi/EmMoNpSZKJfLtjgjtiHz753rVRXZrC2t9/nIJVWrTko3hC9xkjESCulGe1gXBtBZW9PRwOejDDQNw+DWgrqVzAtU8/xOytqylYpWyIaxrhfdaFzMm02N78rF1ZRuHRDlp7e9gfduAkCUJaTeoMhadoM5F10PyaEiX9S0CXaZlihIl9iC7X/IINZ7aOxQ+uof7OGuylBibgRCZCgA/Tkz5miBU0m7L7k/cw7/nYPNiGMR5jOBii+2QHG9/cw7VmA6a4rzNSkBjlGmX9/gHGgwGCMBDLZdxlX1laEGsusluUtJP+rhtd3bMtWQi2UWzJj+2rzUHquko5FZ1EDk3aQvEFkyA5mTyBgYo4wj8qyyy98uucAueigOYd4SsVg7w2PlI2kd/sgiuLPedKUyejv13kZRoFqNVgOwUZkwoH0zWi5yPxGJCB8iOnwCtSIMu3Oir5l2MNupYMA3E1Z1l0P+vCFstfitdEoOpIz58pk+XgN5my3XEA1xXAChc9Q4Ku/AABF4yoMNXhn08qf5JT4JwUUAzt+/DHIyTKZr9p23BKnJ+dISdP42UmJeN9QMxKl0twXTedNxMAH0aIx77M5c34ra8UnLO+ebD/X1GAfCjylybUfIRBgDiOZJ3GdhwBrAoPZ8ezJMBp/JslDHnZtgFaaHMcsThkhLTaHMIfeyjT71suf7MUy6//3BQgj8qRM6KmRH7+C6OAkt+nytIJf/+F1Skv7k+bAoqvjm2aiRMEQ7oTDNLFTMOEUyzA4vztnHwo8ze1nijGrwqOuAo3LTu1wBmGMIJI1jq4pp0fOQXehAJqyTkd/3J8SwuAtMIaR6J4cQp26qae6wgvO/QQQp+pHrJpddsV4LdpEnTFNfYYwdhH4mf0NS9LO3//J6fAjwZYeWnNyDUvEqDq/bEgmglfmviPH4BFpYKIyq5xwcLUjQuYfvcinAslUTaLUoprKFwYYXHVeopYCaFHlypgzjRg0M3Lk0cYD/uIIx+jbg/BiOZo1ao2OzgzUz/uao4OfESPd7Bz/xHGgz4sfnDmp9B6/xKKN5ZgzRdgFpWiVyuHJ7RNLY1whzsVZfX1K6h8dxfDIMR4GCDaOsTugw2U1mZgzdCKxvlozWCTLHQUPuSP1na4m37GQnLzIhoRcCkC2nceYdztIAo8mL0xhr1n2H/WFtdIvQdbaF1dRGV1HtbClLiOMGtcXE1SV0k6Q33Web7szC+0VCpVFk6Cs/BM61UPVWltPYNgoujJPvr3n6DXbiM2LRSmGpi6dhHNGxfgLClfrwThaAspTEOATYmYJzQDA/bqLAq33kH1/h2MgwCRF6C3tY+9R88wvboAe0G5fHmuvGmBCFhhg3APZVh2MHVzFY2bF+CsVGCWWdfMjmBJQwGr6Bc5Aqy6AWuhCfPCAtz1hwjHQ7FWMe724A9GYo1FLOBo+stiDF2iEETSQfRwA7vrTxB5QwHW2K0qpj64hNK7F2BfmBJAFek9USyeoL0AJXTdpA+oBtLXMd0ODRA+eoruxpb0n5BuxpoVXPzkPZRurgq/iRKU1kWYfqqrTXd8kvZWAmvagLU6C/vpEqbWH2DYbmO/P0Ln6Q72n+6gTvc6NTe1BHWijLp4p55ZXJaV3ZjXcSI0oyuv4JuHaNPly2AICyZC20T96jKW/+ZdFNYqoJEAsYjCMmboKzzCZzSDRvcDtoPovWtYODhA+2AXcRhhv93F9r11bH9zFwsfvSv+3UUG6fKQEBwBM13KJAKqkSBSYBVaJhEZJaCRjFWjbCVVWpLGOWjC4DykDZRMFLAGtzNmADMhwXxIEIgihHmnlnEkriqrzpq3E8WJfphmI4o/4p1CKrorRUxfX0XznVU4K/RPltZ5wneMqxWIfEXvQ1OAtdLC9IVF+AKAGApYaNwdIhrR59fxOLxnn6ErMMrP8Z2H6O+14XGwVC6icmUJM7QcdXEaVjXt69Imuk7s+5TTBAk5BpKVEsKLS2g9nBfwgBV4iA762Lq3jtmfX4U72wBo/pxlPwf9FVl+GidVXuExsqJ286WFYbYtpZFV/2E8+RkwiaOcAczlaTRmW9g72APiSHZ0j7sDJJxsxdwxfqLKOg268qDvTPJaHMGjWc1qHXPvXYFzYxn2gpkCtZRS44jGlJH0eWAgaQLm0gxK003YWwXEoS/WTULHRKFVx8UPb8JenRfLKtm+LNVnvZgoQSu0hjVrw5yfRqPZQGHTAoE08EL4vTFiL5Ub5Evh3RNVYqfSKhXWyUtiRGUH1dVZzNxcEzd7tDAlfU+Bz6hol3JoWpPvQn5zyoj3LmL6q0UMN57BC8YYHfSwe2cdq1fWYM3VYdHqmeozojMKE7EKwWJFhgGPXUNcgNENGFFYdDvBsvNMAUz75Ky7kcph7hNR6WmeOKK3Ym9dzkn7KSJIJVQY9W6ym1+9O0mu/D6nwGtRQPNgGCIOQyRxjAgmRGGqJ9oyNlHfuRdkQtbUw1D5xnGybafCiubTxcoKLWEE/Ir9dKd4L6hi/uqnQAFhNFUQzb+cV4aRADxFVlqWLHZS2UmFqYhNhn2Z/OR7/aObX8eBbdnyiGM4umSMggC0sJIfOQVemwJZPqR8DWPE5CuCbpmoaR5frH8Z76r0OBYRC1ccdxOwVSjI7jwCBo04FtmbeB5N4b120fOIOQUoEIVPSQpekLfCUOQv1w85DqaVIJPu0LnJKCt6yat8cOzh8WccWxt26mrdsh1YpinAK8pdn/yby9+cCX8sCqTTvMz89McqSJ5vToHXpIAaL1AEZyT5ayaWR8sp8BoU0OMARqXeg5tZCESVtTZucubmRcWo50iey28CVGFYrj3TMkW5dLRwwbSDCLHnIQm5EztfgzgHWfMgp1Egy7t8T12JHwpgJYpSwDYtDL7KhhmOh1OdeEaPRbA2x9ECWOHacozQ95EIsOu0guXPfgwKiOrjx8j4lfPkF/9la1dk7pO/V87ozxBBTUJjGAh57dq4+P41uMszR+5NREmT7kCTpRX2MJl4ptYDCBgxigbM6QbcagU2zSZwZ+XYE3ACfc9K5ybN+GNcJsFNa3tdxOub2NvewjgYo1AqY3ZpEStXL8NqTaVWDKggl91CqasXKpfECkB41Mmtlg1raRHVcg2u7cBhnKGHw+09Mamk83wZRXWTscr6WuKoCYs8FNPRgHOxBPfT62j989+i9cE12YkdlEsYc5e4aYmp1P0nT3H7X/8T//7f/k/8/r/+Czb+5XN4nz+Bf7+HcDtGfJiIcpjgHalXhj6nlZVlOvN44cszY8kLEZq8UrxNsEbcHiARXZOnAAAgAElEQVRaf4p9ts1oCKdQQGthDstrKzCnWxOlI/V2ovDTZ3GzoNKixR4CRi4so9WaQdWtwE4M+P0BDnf24fVHaZ66eJroPFNhmVAVSysTBg0NCIjjnc9+BmuhkVqXYFso6ypacS8LH9SZECTAg21Hv4jzMyiUinAsBzZMxF4ArzdAMvSP9+cJDYB4v4/R5h7223sYR6GYqy3PzWD6ygqsuSbEHYqWXLrsui48My0e+p0ojpXSle/EpQStWXTQv/8Yww5BT4GYFqvPzaBx4xrs2boAggQwomgr15P4guaROpiNEsyFBcxMz6HiFGHFCcL+CO2NHYwO+0h8KsBV/1VFe+lJkfFYOHbpfozR7Tvo7e8hikMa5YFZKWH20grca8spEEW0y6r+TEBbRFAyRdypsP1cQxTs7pU1VGp1uJYDK45xuLmDb37ze0T0Q8kdtayzpqUukJQvLSQXf6kkTxeBtXwgA+jAJ84n0zrx+vTbk4kpBbZpTQYZMRfzZEdYagFikg7Lz1+2GqeWIRU4BGsRhBPYJsxmBUvX12AvzKTj7iwtmIYs+KmdwgSp855rhyUT5ZUllNyimAm3iVMYeIiGHASlAyYR6apclNECVts8wPb6Mwz6PXBAVqrVcfX9W7DmZ6W9pN9TBtN9GNMhgp3yi+6imBbzrwDm7AyMuQWUi2W4piM7Ptn3/d5QFu2P0WZy8zYuUjDDydZ6GylP0lB0f44nGUC1q9CWboB031V9XoCBmk61GppTLVE00KoU+6lP3+2BsmNJWuqK6LMUgvIxVXjQCo9nAIXpBi5/+B6sZjEFizEuwSkEtiQEdClwHUEX7IfEYrSqsBtVkTtMLTYS2OUS6stzsC8uwaw7KWiGVl70d5xnHmRVThaJoaIFljqtKlVg2w5sw0x3SI5GCIkSD0kIFe/kSRQ3aV2YdEgQTKWIxso87NUFmHVXgQJT+Sn0UDSWekg9lUWjkgFzroELN66hVCwL38eej87GNvafbSPuDoSeMjlm+UmDZg12yYVlWeKMx/YDHD7ZQvhkR6xJJb4hLtg4UEoI9JFf2p/pVkV4PiVzWrNj7ZSprC5z5tGxS/1en4+9zG9yCrwmBbL8SD5V4+r0klaSTFE4ndk/T2bL9DjO0v2Z/c+yUmWpsozHPAg+Bft9fuQUeFsU4LdCgNORuANK5wnkR0s2PVDxKUBi5qf582Temf4gl/zDbyLBLmp3kxiW1jwsA56zEjuZeH6fU+AlFOAcQSnhOV/hOEqU/WRZstnLWC37nrzLcYzjiAyXsREx9JwyhREEsKJdjr6kWPnrnAKnUiDDb8KenN/Sgg/5WPiX7tO54JMRrEzoxK3wtSSQyYX3DCcbO2wBvdJNucAOoxiB76e7WF/aKTJp5pc5Bd4qBY4zsvB8pk+81azyxHIKvAkFjrPqiZRypj1BkPz2z0GBLE/q779aI2f2dCnI8S9doZz7YJqZ4DLvc4symNCvBFTr02S4WkvN2f/c5M0DnkKBLP8owHYUR7K2bpqWrB2kE7hT4p72iHM0LnirdXnysMmfXoOgioX8K5u+Tksgf/ZjUOAvB/pGSZgVvj8Gtd5Snvq7QaVoYppwamXMX70Ac7qcfghEGXX0UZBdlKr60m+5i9JKxDWEOVUVhQ+VV3EUIg5CUbrFgzGSqfKRuxTpoAmSMRDvHWK8uSeLKlSmmQUHjVYL5nQKmKGSTyumRJWVpTsBEmJ+IHVDgVIRlWoV1nBfmcL1BJBAix6iSM3GZeGz96fQ87nXpAUf8ie76RPYsyYMZxHTzV+jce8yDr6/j43v7sLojWCOPNhBiAIXPA9DjIZbuP+0g6e/+w7N1QXM3byI5vtXYC+2xLWOUaL7Eu6WUvlky6jzPaWc532kk9BtLu2XjawFJxXWYyBq9zB+toXI9wQywsWIIpWRraa48RCwBePL6lpmfY0JKwEsyVN5Xiqg2ZzC+NkOBnEMMzIQ9sdHLnkYkAXUZ1VYqmOFN2XHfQSUbcx9cBX2TFVZ5lB5qXiTsY6qnNwT1OKaMCoubPpIlh3xCai4H3a6iAcjWC1XKqA/HPx4EEQUH47h9zxZHA9goFivY3ptGaXlOZj1UloGKm208jatwdFfXSf9RBFfSMY4VF7T8sDhEP3dNiLPk0WacqWK6eVFGFVq/NNwPE/AJvy4EWCWTY8kpLWSSlmUtI7pwIEJKwT8zgCxsqghFmsygzxdtBeeNfNIPVNXGsnIh7fbThdDad3EMrH6zjWxmmNyzMiDvJxuuE7vlTUCoXP6RP6KwnjKgLU0jemFeXQOe3ADH6ORh/7mLpJx6oOS8Sbp6TJJCmywlIWE5LqvZt5JXN5n4/Fa8YoEfckfbQFCJytxM/H5XifPXb+CE9CBJ1BwlSfLyHeMryPp/FkdARckYu0hcR1YrRrclQVYDTvltyyIQcfTZ7YTaV+gFQ8DRqMqppoNMowfAeNAXLXQkopZVQVhGZRMjds+4v0ugv5ILOcYsFAsVzB3+RLosk3kIMf/mgc1ETUt9Jnym6j5ckV4MhwSpJKCZTCOQDAjeVroIInqCrzmWeebppgmMnn2mmm+LNrJ9NW97psTHuFzBdiSJLUOl+3ollAoVWGZNgwiOcMYiRelgB6dPomUvRaiJbKQG8QB/CTCmBa1Z+oo3LqeWkRhP5dwaVROCuVgk4sbHbYPXT3ZMErc3cAJo4GI3+JKEfWlGRhl5RaQC9H8zyRUMkyLMlaKxT9Ms2DBKNiCEk93YAYIx2OYymx4doI5qY/id/J8CqoxAMdGoVlFld/HRhkGZYrefaHkl5RF0UROpCVdk5QBa74G653rKH5+G+gdiGlQYxzCo+Wa0QgATQQpYFXZgDVfQoGglYIDu5+IO6P1L28jrphYLDuw5qdg1RygyOozN+WmSYiRIYiWrSpImokqpKZ/+vDor3qdpeux66OQ+VVOgTejQPqpTMfcUSqEyH5cLNLWUc6VgeZZLdPYBaiwUgJC3DnSJVhACyu5Pd5z0TQP9GIKkOf4YeVZLKyE4udZvqvkO1qbyCpMM98qLaEZVV+fmplpCWiR7zheIuAq4ILRyxT+L0z01Jzyh39tFMjwIxfQ6c6KcpJDaS5QFgqFlDtPMinvs4e+l7O6YdpqjMYuIuxI2cx+ohfrs2nk1zkFXocCZDf+ZBMZN8BwUk7WMyfWVTgGmIhDhp3cnJGhfs8z45qmzAO4ks/0KX9j8rFMqnTgM9LKH+cUyCmQUyCnQE6BnAI/XQqoDQcyd5MVNSO1LME1hPOOGXS4zLqkTANpnY0bSLker/LRllx+ugTJS/aXQoHJ2n7E8WmEiDzGOZeV8t0b1YNAFVr0Fl2SLIzLJvYwDOHm4983Iu3bjPyXA1h5m7X+CaRFUACPhJ2t4sKoFWC6qRsLWXtW3w+G0roYTkBFUcWH7FtiJcFEbKcL1qInI1DDC2W3+OQDxPDKwoNYSel5CLoDmFxY4UeGCjPTFJ9zcZdbtkMYljK9P5n1cnmHk1cDSWgjCSyxVAJvnPq+ZdmoHAtC2c1PSy+JHwNFZSaalT1rzpuSIm2VTBh5zHRVXHHPQiAE6y6AjAbMqRJml2cxdXUN0YNt9B9tYbC9D9/3EBC8EwTw/RHQ89BuD9Df2kPt0VMsfnAV9XdWYF+Yhlm2xHqB0DQtRZppJm/9+JXP2bqdElkLYbGWEiRIBmMMDjowwggWBxSWKYCPJAwQ92geJ0MgsclNiU0By+dkGhPx2BRrDclwAMe2UTAdSctie3sRwlEglnaMclpNbV4+JTR3+qe6eRadblHMkgVrpgIJryWGZsps/VgE+aWAKqNA0IwN0FIFqH434BgsH33DhekOIWbONPjxIXuNgaSvlOuJKRZerGoZlflpWNP11J2HBmPo/E6h6+RRtnx8KIuJdL8CoWfYp9nbSEhI6wSFchHJcIiobUoYAd/ouupEmSb7QWwiGZliCYC7mblcZCWGAFbsKEHQHYIAEwG8sKyvdbB9Uxqx78b9sYCyjChElEQILRu1uSm4zdQVEFmA/YNtyEM1R3qRyV/cazhUkAPmdAX1mRYG9xw4gQHLCxAc9MQKDsFpms0y0Y9d8v2kenJxkujHgr/+jdBdLdwxFek8afkEtKK6wqQIOnw2x5NFEwIdBUijELACRK4FNIowqwWQl1lJkc0n4hzFVvJZAAQEJKSWI8iutLoDAkXGlM1kdMVUMjZKxEJKTJDW4QiJFwpgpWAXUCi44mM8obu1ToEJpQSQemQrI7WWQsY9A8lgIOZFCMZgvzOiGNHYh0cLK2Mi3zN0zFbgTa5VcXRJ1CfuTVI8Oy4zSRtLs0F6TzAarc1QJ6ysqog1Gvn+UYlgIPYMJWeGArKUQW9iwOQnj6ZQtFWhs3I3CONMQR4BLfE4FpJKAeI6J+uqLRN/0lKqfwpwhd9G10otslDWIoFdtOFMVVKwH2WtfNRVR9bpKQJPgIE2XUHRFJZCmrNsYSgmDY0oSuWsjqvPukCUh/yRnFy0dmxY1aJY1RLAIb+3Wv7phmVgXQaeKZ7I866h5EkTBbcI27BgxglML0IwGMEfjUGPQLofUU6JlbjFOUzPzMAb9GH4PkZ7bTz6w210vSGWbl5G7eIirLlGOk4qpd9q6Yci7I7S01V7/pwpMF/quuuAul76Pj/nFHgLFJhwneY3nvXOaMWG3NlEpekrHel8WqKk3yM9UGXXSv9RYZqa432llPPAOQVOpYCwsPAvTcWl3wwKUnlOwIr6Dpwa+UUPteyltTBaOVNjOi50xlT4T3YD6oAvSix/l1PgJRQgX4kM5lg+XaScyF8lV2W+k01GmDzzgPfZZwJYIZw2BYFzXUXkvIwjswEzaeSXOQXOS4EsC3EeMxlDcGxupOMHLX/TgXGacjbeWXlpsSpLDJS/ynKLkr/sK0cTrLMSyZ/nFPjzUECz+Z8ntzyXnALnpAAZ8zzy9pzJ5cFyCrx1CnB8y0TVN12zq2wm1gt6fKjHBNnrbGHUOvhkzscxh/pxJYNgcLEyKGMVlalOM5tOfp1T4FUpIHM0ZemVG1tE7GrFzCsmpgcTZFrlUlPYVPRZiYC1c8DVK9L0Txxcq5//xNn8FSb/AgHN7wB/7Gz0m1Uql2CpXTrysVBxdX86jXoShH+40MdJq+yuUFYYOKHlx4LfHmIYOOfkDc9U4o0D+EMPTmLCNUyUvAT2dh/+549h1ooQzR21d1qzNCktzYAQzWAhiUwkQwPxkzbghzCp8IOFOAKCcYBg5MkOT6MoKqrTqnD0TNX36EF69dxjyiXWR6wYcGOpAdN1Yc3Mw7kyh+DSHqz7z1B4vIX+7gF6+20kh13Ew4Eo8LxuB8Ggg/2tbYw6PSz1+pj1rsC+OAtrqnBkjeZkQf5U92wUVpJnCmJ6bhiFGPcGMOMYZcNCHBpwD0YIv3+GuD0U6w0y4GA8/ZME1A3bN7CR0OvPwQhBZyhoRGZAJQZd8oTDsVj+MBsFiSl5qzKwqunSW7oYzR3AhaKbWmzI7qLUNGG2Jw/9jMpZ207NzbHthF1N8U8vi9EK6MD1PVmnJn/Sv+JgiGA8JAJK+NspOSjTygytDmiwis5T01DfZ898x0Odmb9UM6R7FoKDCNwJZUenkyQoehGcvQGCbx4jXHfFasCRwlhXSifCs4HEs5CMDMRbbfjdgbhxkXrGCYL+UPxFvr4faFVw0odKAgHzDBBw92kcw+cH2wTKzTrMclEW/IXIijVY9Ump9TddJykoCraPAaNcgNVspKYBidoiuGEQQKw0+QDKKRknf5noJGEF4jj+KCWSymsSj/eZeJPn573Q+U7OKrFMmnKZzTfz7rRsjr0WoFY6CKJ7lsQ2YVUIVqHJZRX7WISTKWaQO6Q3fSJyUVwHo/UWKmH4I2JBHxS15P3RGMlwBILT+LZi2CgNI4R3NxFvd2EUaXJfV45MoRNQPKlySsY24t0hkp0DJL4/sX1HEMOIgBXKZsoJNod8hDLp/CVcsuDSJ4jySMEp4h5pnCAeJyJDE4LyPP48AWKKtQFaUGF4+vAZG4ie7MLbORSSEmgm3zAB0CkQHWmRpXGWNmxU8gt3ORYKMItE56kIGi1G5TFlYJbttawjqxDRbSkzhEwriWG6DopNWrJSERlXMxDPqu7PlSudKU5KGNE9IHcTJ4m42pm8OOWCSdLCCqvO8YhTLMKplFJT4brwWTrocjAtVTYBtbDIBNGWXPlm2JYNK/FhBOk3J/QoTFQccSuXCADRuXkBU702Dr0RRtu7iEIf/ac7OKS1sY0u5tZ20bowD4fWVuaboFU5s2ILiEyAPxbHBOkHRsqh85hkJhfpn2w9Mo+zlwyiqpV9nF/nFHh9Cmi+U98Yrdw0ntOOvloWMm6aDKC0eKA7x/zIKfAWKaD5l0mqsbxAo0RQErDymhJTp6vO7BfyT32C+V2ydJi3WJ08qb8iCmT5Z8KnicyXUotULxjnnSRTNi2+E0Gb4X2CribJnQx8MrH8PqfAOShA9sqwGD/3tELFIT9lsBbIOoiwOFlPPzgjC74+xqEyjsg8lLmKXjw8I5H8cU6BnAI5BXIKvDoFXiKfXz3BPEZOgTMokPnQ85JzLB6T8UM2mg57Gn/yXfY5r9WYWg8fjpLSCR09ya9yCrwRBdTag+Y5Kg6FBV8lUaYhg2euW6Q6G9nArdJIx8/k3aONOceZ/lUyy8O+TQpktGZvM9kXp6XFWFbuvTjG+d6emS5fvO3MzlekY6F0EVgcwX0Q2OU4KNWqqTnkdAZ6LM6xGyagdKJULknHkh3QZmoalIslcQxCRya7hlh11TljglgCKkYD+GMPBYGYGDD6QxzeXUdvcx+xTVO5tDCQLuikZWaJUw0h72n0i8o9NzRgjyJE/aHsSrdgI05MjEcexsMxEpp0TvdUH6vGG92w3lS2CwFSt0RW0QAaBqzWHOJ3Z1E8/ADVBxuYuvMAew+eorexC+/gEOZoBCsMkHSG2PriDnqdDoa9HtbcT2GWppEUCK44UbqT9ydev/GtZloFJoIXwae7HBqngYlw7KN7fx3jvQ7iogvqWrnrRYrFP2q3f7rwRrMQMYpJAW5iw/QTDLv7CEZ90N88XUZFdCnTGyFmm8VKyaqaNx3DpDdpsQzYTgHFUlHcJk2U9rrMp9CGj/RrKaRlpoMjxbf8wMiOfqJ8udgndVBU1ICV4RCBN4LEpIWZog2bllocMp/KVGtkTimDpKYLoc6yyKPDUmndC5D0fRhhDMe04NISQa8n7qX6TzbF1U6oLSnoeJnGlu9cAhRiU37wQgwPO4hCunKi8jsBXbGINRnudtb1z6Tx0kvVxxlXrEUMQ8SdHnwFWAlIQNNAdSoFrEzANUyY+WXLra/VWfRkdvrBNlwbRq0K00wBFlZsIg4MATsVRj7QTF03TdJnGjo9aW1RXUh15DH/SKHPqKFumzNen+8xiZMS9WRyadE4JE+H40cFOyVlJjORu+kwnn+DJEZoxuKaRdAjkzDZumfT0w3MncLqOtMAQiFZzGZpU3POEpu3CsRAIEkggJVQSF0gwGWvjWf/87cIDchPDCyxgmklNQnSbiSgQaAQmShEBFMFGPY7SJIoBfcBGPeHKVgxJGLFkr5piCzN1uUnfE0Zwb7P4lOWEI8zTBB1Y8R7XUT7fcTtHpLOANFwBK/fx5i/oYeAYDgvghUANr0NDD2EA1oaY381YcbpzoAJcU+SQbUVH5NkVNLFhgHLLcBRwExh+wxDkhWI85s8yqQhHZRAU8UntLpmlwqoTjfFvc9E3jJD1d66SM91L+nQ5Nx0h6S4+CFoLo5R0pHOODNW+o/fVhtWsQCHADiC2TRA8ET+J8uTmsVnZWk1xUGBoBfbhhXQ0lqAaDQWiy9SFYJO2UWYfhEofDQH2B9iNYrx5HchgsMu4PuI2h52/ngHB98+RGV6CtOXlrF4cw3uxUXYCy1YswSuGDLEEDURXfvpbvicAEz7mZCAbaDro8+KNsea5wx65Y9zCpybApOOr2Kwr/M7IAonQxRPr76Lg2PzDA9zXK8ED08CHqDVlsz359zlzQPmFDhJgSwP82NG9z00/WxwHqIE6GvwWjZZgvDjOLUGJnhP8a2uPhQny5Pf5xR4VQpoZlOuTzgAkCE9Qf8EkIvgfIVEuVGIoyamq7oAZboe58uY7jX6xCuUIA/610gB4V/hssnEMbWKovgwOy4gfTTfa1opXtW3k7NalyHLioin/FUblDg3OCvaJH5+kVMgp0BOgZwCr0aBk/L61WLnoXMKvDoF5LueztxkiKrHvmosmxnSvlLa2jqbxJd1zXwN4pUImAc+HwXovketm1N8kt849zr3oWUuJ4DUJaQJKMUCp3PpaFfcbbKvvELS5y5DHvC1KPCjAFbOU1LySMo25wmteO4V45wv5ZOh3g73MhUqqLjL2nELMGx2wsyk82S2vD+ZtZpdirJJzKgwvXQxXJR6Kg255mIK+x7N1Ho+At9XbmKAOPDgdwKEvUPxFCGAFRZl0gBHC+J8JCa/eE4M2DHEDco4jhAYhvzYcARIiCWQbD1etVGzcdW1YBy45s9d1RRa6jnLGlsJLLolKFMRtgB7uYnirWsIH+3C/+YB9h8/Q2//AOFohEM/QLDZwc43j1CabWKhYqJQnkHiHKV5PHuNtjv+9JXudGFPRtLkpYUVz4c3GqEs7RkjDgLESQjf9+EbJrwkToU1rXvIP3KRRhmmvgMHBne2W6LMTuIASezBQ4TITBAiQhjTxIg2saAKk+EtXuofAVVUPqYrcyfCnlWfE/WbmJVVi4OksGVZR8pQhicNqIwOItDNVBiM5aFJl1lO+hPl6Wl5nsVXmTrpIsnYjKw5DJAMfER+IB8ouioKQh+H+zuwDguiiI64iKOpqzpDmlWasOASiJmi6wuCCwJfrDWMEcGLYlHWRhFprdA1Z5VTF+65Mxk9/RhLd6LFiMFQLCeQ90VhbiZwSi6MgtYsp4kImV6Sn8gCURxbMIpFWLZDKJoA0ujuaNjtozz2gCRjJYkJT9pA0YFZih6OLate6jBpkKOanbw/evN6Vwphq4qgcz9XWiIPpV1VobK7301TgAjFWlnJZgILsnU/kQXf6brxWi/2yUIgQQSpcoeyUxLS9NEFJ/9TJo3pyswHLf6wz4XjEbrbvshluowRK+NqDMUgukgcexHgRQMsAlgkCidOMPL7AqAZGxEC04YfBvIrC0/aGbDOifq8tdtsRd8wUSUjBKwSJmIlKdoeIXy2h+DpDvYfPkN7aw+Dgy6CoQe6zaKco1sctg2BOVZiopBYsGNDQIFmFIvrGgFhkppsQ2noE2XVbcvHk2v2QVpYccQyyoSV9HtWXV3rdzKu1u8liyP68HFkAhEtiGW781GQtFDH4mfLqcCAmUzPDDqJlipdGE7CUhFjWzAdW4wACY/xxcky6PhsE7qX4pEyoYxs6GZQQLPkszAUS1NRQD9NWlZoN0LpeMK5MgPg51hzimjff4rDrX2xNDb0BojGIbzNPTw77GH38VNUF2fRvLiIqbUllFemYS00YE+XYZQSAcGIBTYWOFtmTQh9TkuS/31LFMjJ+gJCZohD15tavsgIbuJeRfULJsPw7EuZeMdTJ2Oz46kw4tM37fsyGuS42DLldzxefpdT4A0pwA+Z+CrnJgk92jvBqLzNyt4Tt4p7jwrC8HGMSAG7BT/LPJSZ3qOA+VVOgVekwAnWFN5VVu0IkCJYJdGAldOSfo5ZM4E0jzMPJY6F7zn2F5/oaqyfiZJf5hR4Iwoo+SsL9oKaT02XH1ufYQYn+T6bqeZbHU7494SrIcOEZXEDVzZwNpH8+jQK5NQ6jSoveEaCvYhXVdRzBHlBJvmrnAJ/Bgqck5f/DCXJs8gpcCoFZNwgY1O1DsH1aVmDODX46Q851tUCWY8dxFNsnFpq5nROxsD5+Pd0AuZP34gCXKNWc7iEygCuMQsfqnmY0otIHlom64GZPvOl3vhKvWvAeSBXz7hYwZW5dA0tVZC8UWnzyG+RAj8KYCXLM2fV5TxhsnEZ/lXjSHxG0sI3m4gwLUNkesIkYDbnE9fZ9E68yt7qVKnY/P/Ye9MnOY5kT+yXZ93VV/UFNC7i4M05+Gbe26eVtNo1fZE+6A+VzPRRJpNkuybt7JudeTMckgMQBAE00A30fdRdlbfs5xFRnV3oBhoYECSH2bBCXpEeHh4eEZ7uHu50VmEmDrXlJ1/q7HODrjydKA15xSccbPrPLCz6qIxlKZBEiGK6MNAcT8W2i6zkIys5yCQihnIGUeGSlMmeQA15CF9SIdDwT6cVMZUmcLhjuu4jW2nAblYARm54qeLdIHrGcdKI3DPqgcwl18LJhep87tiWAm4Gx/HgzHlwWnU4iwuwW7NY+XYR1ftPcfDoKdygh/EwxGDrCNvfPkHp6hwWW3XYs5VXM5LgpqlxFp4GR/aItoHmbp1/ylRONChHAUo0VsNCxL6hQ0KlJNFvUqSyuzGjwwqFBO0iKIZFQhbFmY0ks8VhxbFKsFGF66SolFOULs3AnmWKnymDnulcGu3pcKSxlIXB80QBNxlgLPu6fwY+lwJDP3HGyA0x7hSOY2Xg1IsHd3LSkYLKk0n9rDvf9+f1AevUz07pXXg/joA4FOcPudS0tmplGQtMtcPlSzJrSR8qBxG1i06NMjoI8Ey5ItBZqKIcPpwYpQrgrs7CbpZheXlkX5NwrII/Lq5hAoxDOWdIYHr4sHbHdwGXVm4NW6F1cm2qNPd5beDyaHuwvLI4qhCEzCBZgmA8UillzPv5I9/Tf+odVbm4TgmxNX+ph6qkKqLOc+8bOK885uYzxfzC7OKkQflYmU/ylUxBNI9YN8/PwUEeM3KG48DzmeNkCs6Zl2cD1LOnvMESeVCGTBnZgw+YiimmQxlTuTCiioWYKbUaNWR0RLQVNNXrCn0lWmmnJs7n4mRF50fNc4kvDiMuFAkAACAASURBVCuOl6E848BdqAFMrcW5Mo/MmW16Wzf/horYGebH00hFVUl7GaInXUR3H2L/8QaOdvfR3j9An9GZ6Czm+XA9F27Zg+uW4bqO9KdtOfBSD5XMhcOIRd0Bxr1AKpFq8hO2Yq/TRGAhEk7LB7zkTkTLpKRhU/mTcvrVc5tvOl4VMG6HyqB9Tv9oWDKHTq8tmqGU25h6KNFzXtbRxJMw2R5ZRLizkhHa1PrCeiZry3nt4H3tkzchFj8oLFvmzzRhKiz1YSzPdZ2CLunmZ3DmLcCZh1X6BK0by6g93cXhxi6s/X0M2x0EwwHCYR/j8QBBp4fRzhG6j7bRWGth9sYy6jdX4V5pwZkvicPquetDvg3581xXnXF70qziRFFgwt4kFn+GjwoCTSgw4SN9IvxOWcb11BCXFHGxzPlKA6TH/OTFCaiTE01rkpvjkuMui2KJuqf6RKX2cugA7+U+714G8wR6cVZQ4EUKkHf0j98DYOQsyuNZIOkN05iG/5zSiBDOmw/y93kuP73Q8py3GICOBlOmVHTdifOvelr8X1DgNSkgMozmMTqiC++SX6nsjBHHTFXINJ0arjmaasx1nnf1/Gs2WUiUFt6zLCSUpagL4fxr3jWwimNBgdehwGSOVHMwHVFBZ3KRr7k1IYOVKp2KyAMG9qv4znw7EL44lXMs6AhX/I60bbi+0rtMviUN7OI4+bxTSxaJrQiu9EPqbkGm16QASThNOvJpjpenH79mDUXxggI/AAUKrv0BiF5UeRYFqGbjxlTbEX05dYX8dqPTivwZtePJkqbu51mY5+bHU74fhrIhkOsgN1ZajL7JjAW0bebm77NQ+rnfm0QLoS23oNW57CC0ocqcNhHqBmwHKSLhP7WTlxFZz5YhXgBqvgmN0woDA+iNC7SvcGtlRhmbdjW9+fgFGMUNTYHpyeL7I0xOo/n9VfKDQ37VJDB5zpO8e9ZpzJUrSH7mnnynnC54wSuOFRkLHIRM1P0mc7tGxzRBjtqZQdAwhiSWm5zTEzKWKCh0VilVG6iutuAtN4Gyj9S2RelCS3BmyZ7NSeQE5WzANUhVrIzbkAgeGQ3zNQ9Rq4T68hzssk45Y+hhkDTXZx2nyKurOV2ScMwv98TS3GylVBip9jLlgF0vwVm8imhhAc1SHUl3hNFejGQ8RNod4+jpDma29jBzYwllOtpoW6+APhNnXfk0rjlcTk4vVEiKM3UHlQZplorDSGq7sKol1C4vor4yB6vuIWQUGZvKYht0WmFZ/tGUbWhFI6UhD/vHtjIkboZRKYW7WENzbQFWTRvip9tHZZukuhAbpiguuON+koqHlU2/Ixic/58xoJqjWFok8kQOFMnEJlDxTY9fAccFXBnfX8gxl69uGp88yc0zWaDUS7LwSf0qugDxiZiOo17H3M2rqM41kJVsiUjDr2UqgmhMVmm8qCRKVTQLScBx8kHNqhjhInBSBBUbzkIN9eUFWCXmMlLtk6PBSd09/3/Wy+7V7RHlaJxKZCOCoLMK3ZqUB1Cu0ReFT9B8TTwmKMDqYUVHmIxCbK7yPJamKnk314ecFaTunOBlgObf5/lr4HjqVdZJtKhv1uhxuMov77xncDQv5+vj+fRzUy53pNFepVYzDc0DyRU0py+RNimUSpUalFwQHD8Q6PCgPRC0ClLm5sRx4c3OYuGDG3DKnijBmapNmm8ZdylhTsWTmeJLcWjR45g8QmIFToJh3cb8e6soz9UkIs8pZ8JXNM008fWOuoEX7WzSJo+HEOzEMCudzKAp3Qzx8xTRn9ex9ce/YvfpU/THAyRI4VeraMwvoLbUQrlZhV8vw6uV4JVc0IjrWw4qKaNP+UifHWP47SZGvSOZR8VhY7qBeZw0PlJEIlypEcjxJ86diglPPjpM8/MwCWMCRxeQ65OGm0thp7NgEN5JcQVdF5b5kqu1RJThHG4003kkps71u+QU+eMrHFx6fJl7+ukLB/KRRFmZtIuVkxjGqZLI2hL5h/ONVEcovE0/O45bRkfxLThzC/BuzMPbXkN1fRv9jR30NnfR397H+OgY2WiMZBghHuzh+PkhOk+e4/hpC4u7V7H8+fuwbq3A8irKgSiPKXEzNDPH/PPi/LUpQJKa32u//HN9wSvB90pKrskSxHRQZuS0HHteiDSG8JSVmD6LEfMIhbxtZXB8B5avHWMKfr8QSYtCZ1BA+EnP2TznrnuPTleucuhmmlGmfGOKwZzSUyAZpuYx93fqkheiMFUOL5R/uPBYdCz1fOFh+UgueDhHweL0tShAHtNMZ7meOKFTihbpLY10NE8l/Uxkk5NXJmKDnOSZ18ANCIPbS1Q13OwAz4FV8pWCR4+h18K5KFxQYIoCMgVydzQdSeg0KJFdY5k/OYfK/Gt4Ms9zeZ4VIBqwLkuH1ywKJWU05X6ODDg2vFIJDp2uGBWu+HuBAhOy5mnKiWby4IVXihvnUYA0NHQ7RU91W7PqeW8X9wsK/DgpYHj6x4ldgdXPjQIW4JUrKnqaqAoykV2TeKL90zqEHGHyPGzOeTT6wShCNhopG6G2HUW085e4MdIY5XLwitMJBWSpMx8d4rCibHeTAsXJaX6kvYTyr+fJhkiSRzbMRNQf2GqzyymaUS9GKmsZNs+/WlQTZ60wQhbHYvMiW0d8y3cleroorKdkklNVFBfvjAI/D4eVv4Gcir/5v+b0t8C4BprZEaEMlnpcXRS+Rud0005efmHa4yP5ZYCrdq9xZxEjWFRmZ7H04S14n9+B3ayJkkUcBCS/hFaCn6rPXBgFORWahGvB8m3Z4WzPcLHK5TU4Qe00ymddEbwpz6OpzpTNP9f3Tt0yUddZPReDNIPdsOCuVZH2b2Bmu42jbhvD0RBWnKFz1MHgqIOwP0J5ui6hmal4+miQnL5vrqeBmftnH8XYyEgArie7ZhPqDaplLN++juYvP4CzOgN4nIB1OB6z9WAajVPV0hHKhmWngBvBrruw62XYtZKaw/NldVt5y7iMKJ6ZruBs/Cf9pIvLOqwXYXaoqUqiQIj3RQ6OeYfKEY9REXxxzElTClQJEqYK4p/GMffm6VNTyem76ko1TLVbDDrcwemIcxA9KmuzTaz94kM4N6/AnimrKBSskO+xMUawoKQm+GuAXAsnz5jOIwPoD0QD7EIDdlWNg1PG2rPwO+ueoQsNu9xZ5Xni4MDoBUxjwrQ+8XCEUsgl1lPDRvA9C5geS4TJMvovi1Jkw5F4BujqpI5avQarlEsHZF546XEC4WQMv7T8GzzMVXHytr5p+iHvvGLKnxR+5ZkY+rUPutDK0OuisHR5McZPxsAZ1bKcMfR4DlzfhUujUBpJWpbmSgsr//Y3cOabsCr8CtAWf9NOAUkg2ruJfMlndBgQXMXVCszdZvmcBz2JfmWV1HMKa1LsFLwz8LzQrRdWHf0W8XsF4TS9XqhGN83czxgYaTdD+NU6nv/hKxw+2cBw0EVgpZhZWcTSe9ew+N411NeWYc/PwJ6pyXxnlRkFRZEpDTIgBIIvtlE+6ANP6YJmZjzi+QpcZS5jeY00i5s5wLxuQJijaUD+KG1TUDg36MtT9Z/ZLYSpq54cifPkg+ekUrqJvKo9LCHpHQw+cYIkpGeQQvYEWh75HA68nceH7zENEHdsErbtwvMZ8UY77hkwmk1JO0YQYv/Q6ZRHu1aHe+kWvA9voPHsAMnmLuJNOq/soL17gG67jXAcYHg4Qn/YRefoEINojDU7QaN8HZZD+SPXdNMIczQ4FMe3QgHT/W8F2N8jEPId+b3swyuX4FAmo8MKUz0G49Oh91/GozJGT5RF4ig2Gqu0FqzAtpAyCAbTjNJhvPgrKPA2KMCFiCKDZ4shvuSXEPJeGiOlw9Q40DyYm3NNveTn/ARheNiILFxrwlhFaSFMGmJ9T/EwU1u9bDyYOopjQYHzKGD4h8eSB79SFYdBsiF31YVBOOFP+UbTcKbZ9hR4yuyaj7NRgDiKRP6isT+RqHHcbFKW76hT7xUXBQVehwKGd/kOp0buWC55Ev2EGyrSKEEUjJGEoUS7kh0oMofmKjmPkfX8C/JuECIOmX6azvcqOpBfLsluVqPrz0H8+Z1yrOf74udHge+vxTn+zJ0W9P7+KF5AfqcU4ORR/BUU+IEpwMnVsWDVqrBdT9Z6pksPx2NZ+0UPLal8cniexbqEI5vauFEtQxbECAcj2eQmIrFjw2Zk67KvIvnnwBWnBQXemAJaX8wN4JKynrZCbu6n/oEOJ4m2ZbxQwRlMLIyqC9KcF0RIdaRiyazgOHDLZVi+fzqTxwuwixvvUlArHFZ+BPwmxtG3oJXT6whUupATZxI2URxQnEw+eJleplSuIh0PJbJEyEgqNQfu5Vk489wVpAxwepP0+RRihfzjUaIEqPfoT2HTQKi5i34Vpqh+4+UHU3j6yLfOmHsMCqeA8l2W5VFnS7GqFuyFOuy1S6g8qMPpHCPmDsFxKgtuzHQrYjk0FecganLm7ry9U9KHVXJXVqWKSr0OjDuIaJ1FjIiG5sU63LVZWLRBnIHeK5Ghspl95NOZgn1jqYmYk/U5NFUpIjRu+Qn+lZXlccwjaxp60o9st/jdsBiFKSpJymW4HqPzWJLXPg5jRGGMjMFE2H7izCq00+QL6OSr5EPNB1JeQjUDdqMMpv/hwse9cTFShHYM+CmcxRqcpRps7ashSkzCNL88Lcy9fJ1yjwbYDFaJXke5iCMvIPuKG6xL/9EZTFIW6bps2HAzG8N2H5VRJO186VjL4ShgxUM6QzYOkXa7SOJYRfdhFA/XQm22AatSfpHfcnDeiBdNgy58zFXIU/7Iz/qcSmLTJRRZqHBTfaWOUu7CdemCZlDkqn5dECflNRCD18kDdcb75JWyB6/iw3EdZGGAIA4Q2DGcVgXOKh0vtNOFwYnHHH8IMHOdL0My8EOEdTiJiq7C59oh4cUOnkbwjGtTzxmPCFpVb3rljELn3eIrBndTxlwzBdo4Q7J5iODL77C/8QxhMBLBsrHYwLV/+hSrH96Es7Yijpd2mbtcaWRj2zVcRn73CUffF2TVgqeaRC3uVOOmLg1aPJ4ZlSVf4JXngsArS51VYBpNw16G3+X55OIsCOqeisaSweEEmSRIqLymAdLwh6G/ATFND16zMh5F+I8RMxJKHKtIEp4Lv1IWpxUDYnLUsFm1AStyA7MklixYJQf2zDLStTm4N1fgbR9gdnMP7fUdHD7ZQrt3jHE4RrR7jOdfP4Q1U8X1ZhWV6iXYlRMZROqbbscEieKkoMD3TAE9zK16GeVqRdYuh7uRohDRkKn3Mlj05/KnmHQyKPRcb6YnHhOVlSXtdBFHTGuhUlJQUeTSWEpYut7vuXUF+L9DCpD1ZE3J+2yXHKDqo1QpI6HCiOmAwgBpMD4xmJLp5OUcUQxbc02Rn+QzBUP5ZkGIbBggCkO1u5/RAyoluBUaTPkxmYNTnBYUeF0KcBoUGRiwq2X4MzPi0Ap++0eJUrYHkYpUIfKSYdZzKjJzMPmYc3C/j2BEhX2GhN8l5RLcehV0ki549xwaFrcvTgGzhvMoOoCSyNOS2ipMxeE1JP+FSmZ/Bfeerlcr66kDCKj4p8OKGJx8eJQhuEnmtQCeBl9cFRQoKFBQoKBAQYGCAj8wBSgDcyPt/Iw4kzA6v51aCEdjpHRa5Z/RNxhUz1r7Wcb8tPww6g9UhDZG9PcclBuUf32ldzWwimNBgTelAPmQegDacmuUTUvwdPSeJIoQUf4d8xusMmU+OIOBtapaUCH/hgmywVA2jyWpJLSCVymjVKuoKJkEcQaYN21K8d6bU6BwWHlz2r2dN0WZ8nZHhGSXMNhpZY0Yr6hMYdqeCj94K0i7FsZJhF4yRscKUW9YsOeUkcfiLqFptM4btPn7esGT3exab0lUptfBHHrm9PQxDzP/hPfNYpm/z/Ppd9gEmeR4kokTDY2+9sIsyvUafNcHwlR2g6cBw6LGJ7ZKg7CpL98C8wU/Xd80PkQp7+hiYJ5RjriLwaJSQaVWx/ioiyiOEaUBuskQS0xt1LQkes2pdk7wOwvo1D0x8k/1K99/oR0GUc7m5nwK1jmXUjr/isBnnaYi41ygC/G2OSV+3BlcqcD1S+LwmyYZoiBCOAqBmEpuS2WwkUgSfOEcRPK3TR3KLi2Lnl33QOMRd3LSwSHOUgziMQ7HbVyqpHCE1qSNroAH/giD+OaNueZZvt1yrmgtPKjBXAjfKdzFmMvAGSUXVqMqAiGj8Uj0hNjCoD3APOnDOvkzdeXhnHWuy9NDGiJwqhzWkiGIzlMNRljRURHOgqn7VJpq4E/xy1mvmaKvfdRspByctDKYRj9ptqK1pLNiZBRWnC+vK5vgw5NTiJ/GRkhjjPAa1ukSL7nKwWXMEVOnOUrFnBtNOZ4Q17IHp+rDdV3hyTAJMUiGsLxQnFWcGe58U+2cAD0PDcI2eGs6CFGEeHympDZxZDlB7DxoL9w3qE8eyA1T4eSuJvILpfMFLn5O48AISJ4f4+jhBoJeTyLO+I0GarcuY+k3t+HfXIMzVwFTwQlNpW0nDcw4hzBlmswlEQbhWKW/JMOYtE3Eml1iaPgqDC/UvFcBI0Oc4PmqKifPp+s285Xcp/sWYU4Xmrw9OWEpSQqWpMphhfOJRP7R/GZKElQenEZZbnG5iJW3ejQOkCSJOErRKZBRJVz/JREfWI1xWqFjJbuP90oWnBkga5WQLi0huTKDZK2F+aUlNBuz2Hv8BMd7uxiO+2g/28fBw000Lrdw+fKKKPcZ9U3+9ME0ozgWFHhnFBBm1k6WtbJEzSODM1hWxpRAoxHSIed43ywNaoyZcZY7ihMZ50EuejGN/QmiThf8aOcf1z+vVtXGfr0GvLOGFhX9vVCALMefTJuGf3mkH3nFR6nsI3AcWHEq6SSS4RBZwCgrDbVu6vcNDIFjiEO+51pBHuZxHAEjOqwwNZaFzPdg03GX3wLOBWV8A7s4FhTIU8DwruwczSTFrtVswLIp7TBCZYrxYIxgMILLtD6ul2N8zf95eDzXzCw8zLm4N0AwHqsErVkmzlZevaI2V/B78RTzTwMrrgsKvIQCWvwWEYDnnnK6osMgvxPJrIzuwygrcRjAMxPuS0DKI5bjj/MvFfbjUM+/ECdB7pB2uEO6mH9fRcnieUGBggIFBQoKFBT48VJAy8HisDLXlOiVjO3Mze1REIjBn5GnuclL/b1EkNByA8uJvm8YYtDtIM0SiRTLNIK+yL+usr8ZkD9e6hSY/cgpQF2wbMamOqBWgl8tScTulA+SCDF1aIMBEM8AqZv7hssxn5F5hXGV/Kv01bFyWAmUzpoZKbwqdWjKYbv4fvvxMEfhsPJ99EVujLwKvJhmjJHpVYVf+ZwjUo1V8/8ENHHijx6WtbIotS3bFs/KaDhE0OsjHY7hJMwbrj3ZzDvmeF79ZiLgMa+g4TXffd2/i7yTL6OanZukchXqcsY4LGkHSiWUSyX4noc4CfV3eyYh0nJvGhJOKGqeEaRJGWLunXk0lb6MDAJMGzMY+YS7F5t1jB1Xdt2mjC7SHiPrBcgiWlG1BoOHqbadiUPupimfu/XiqaYlQRuyvljo9e4oNE8QlmQaBJ7/ESQXI9+DVa3AYzguZpmiUnsYImwPkA4i5dDyqp3DhKtpc6oRukE0jEq0imZVlIuy6MFCNBqju3uI1WEou5ellZqfJ04nhjA6vYiUeUldusoTfF6HdCQZjbiM0ELP5aoNe7amlK5tGzY3VccpBodtxL0RfAmJxggjJ7xxZnVEiooqKlvp5zIIkPb64rRD/9LYdUQosCl0MsoMaXDen3ZKI0iy+6S9pryhjbnm0RQ661m+3HnnfM+8m/MII5VoBBRDoKmDRXluyhuYuefm1ukjC2S5CBrTAE6XfuEqD1/SE+mUWPn7fIn9IPfU2LfqFTilEpLBEGkaIRyOkRz34KzMAw1ai3RbOFbM+XTlCnUh86SMQZ83pD4SytycBvCK6+k2vLT4GUjy/depmuWFXzNkowzp0TGOD3eQMKVdyUe5UcPStcsoX12FM68MBbav6U3c+L6GwSPTIImgOhhj2O0jTZMJWVXEFBbWfzx9HVzNez+hI8cHh7iwQ5IiHcZI+iEkwJeEvueEkmsQzw2JzJFzCYM8DTNk3SFC2a2ZInUcgAbIsq/COBow5r08XJLaXPOo5zEahmRsNyFh9u1GCfbcIqy5RSwTNo2mOyEORwO0t/ZxtLmL1U+GsBt1JcsYmKbu4lhQ4F1TgLzMYBFMNdgog+ke5eOLESYGIZL9Lix3HpanIkoIz5/Ht1wwOB/SOWwUYdjtidGKoUwT20J5pi6Oj5Ox9K7bWtT390cB8iJZk5lEaz7cWhkW53b+hQmS9hBpb4xsDGSMaqajWvLxKTbmBX+c/438OY6QjccYRwFiK0VWovzpwy4zP9ypt1V9xf8FBV6DAsJu/I/fUY0SrPkGYpsZjG3mBEI6jBAc9FFZjeHQqZbCEPmT7xj2M/KKuc+jOAxmiHsDiXLBHavk30q9Ap87TDkGzPuvgW9RtKDAKQoYPuQ3H6MONsqgQxQdwflnJSmyfgAMItEZgUan877ZDR9TryIyOyOsxsA4AFOEU4YA0yVXPVicf83GtVMIFRcFBQoKFBQoKPACBThXmzn2hYfFjYICPwAFtPwg8qhvwW7VYVeVLpkSBKPXB50BkuM+LK8h0fdfJrgKe2tlfxZA9H3dTgd2miK1bdglH6VGDWD6wkIA/gE6/O+zSmEll9kRSrLZ3Cl5KoJ3CiTDEOPDHtzVBFbF0TzMuTj3AabnZrG30A6QkPlpUwhl00EUBoizBKlrw5upwqYMTB4u/n40FCh644foitwYetPqXyoTWcxDm1O26HMOeO7Qt2fqaLTmYds2fN4cB+hs7yHc3EHaixVKxq75KgSJiEGG3MSfqVvbuczlq0DJcxZ+1V++jKn7vHdM5WwPjQM00Eex7EgJo7F4hZaYIqlShuPpaBLnwXqd+zm88uieAmEekE5c27l7pl7G7OIiHK8Ey3JkB21/6wjjZ/tIjsZiqDgFg+Tm+wYGlWSENSUr8Ln0i+mfXLdN4GmcBa3cef56Uva1T7hCTBHFtN/wDXGuUiFTQ6lShQMbfmYh6gzECDl+vod0SE0LFeJ8Sf8RrPmZe9PHXNViDKLivVGF36ghtRw4loN4EOBwfQvpUU/oLujm6DVRXuZpmXs+2XHHunhf98XkvWmcLnLNfqVdgMpWSWnVRK01Kw49ru3AyyzsPN5Ad+cAIjwmU14j03QRgz2VVZkorNJ+hnTvCHtb2wijELFlwa5UUF1ugc4T4pFt2vsSfOmEdDIRvKTg3/Io14fCz6SxpCLTjJRlsFL+NCoGb/14gqJ5/rfg8rbe1WOX/GLP1GDPNeBUysjY6ZmFoDvE1t3vkHYGgr6kSzlprtqlnG+neUb8zJAj3dhm024pny/4thozxQN0duDPOMawfgqJ03/mNdO/09faWYX4p90hsm4XaTwGpwDP91Fp1jGzOC+GtHwKoMkcyKbqcSpjlGtAmCFtD9BvHwuhsjQVR5Zp1N7etab32wP4diAxUhN5UJMoDSOM2310d46R9pjqQQv2prYzmiHfrjSehxmSToj48VOMhiMkmaUcVipl+E1+JJfUEGR/Gl5kX5MneG2QkDoMM6jb7D/yPvuXzobOkgX/gxbsm1cxv7iEilOS9GhJf4zxcQ8pP0KokDe8ZPAvjgUF3jUFtGzGaD/OYh328hycUhk25Q44iHsB9u8/RdINwAhQ8iGdx/GMMSfGpgBI+wF6xx2MgxGiJEJiZ1hYW0Z9fkZ7xOQBFecFBS5GAbLchO30haynlEHnamgstQCPedCBNIjQ3z5ActRHOopPZI08jHy1Zi1m0UGG9LiPQaeLYaBS1Jbmaqi2mmL0l3U7/25xXlDgdShgGJnygwM4Cy6cy4vIPFdkEUaptMMM7Y19JNwYkv9GNfUYUUTL0yJXkHf5Od7O0D9qYzwaqtKOjUZrDrWF5vlOAwZucSwocBEKmPmXDoMV8nADlYUZMQzRwYRRrjrP9tHbOhSZnY6swtyEbeTfqaM47Ueah497CNsdhNEIKVKUGlXUFmchG1aMU+JF8CzKFBQoKFBQoKBAQYGCAj8eChj9A2XgMuBeXUJltgbbtuAwBeAoxGi/jXD3WDmxGnn3rBZoOUL0qFGGtMfvtx76vQ6CNJYNM26tgoVLKyqdylkwinsFBd6EAtz461iw58uwWzPwa1XZZM0oQePjPg7Wt5ENKdSeofedKDNyMnGi5d/uEOPDI9Ghxdy8WnIxs7YEt1EWG96boFq88/1QgKqj4u9dUuCijiAvw+mcBUWtJRK/Qo1KM0iNUZRGbzpFzDXhLC+hXKnCZx7ycIzezj52Hz5FvHcoRqrJB6/+WD4XHfNcKyGlnF7Uzn3ndR/k4U233dRvYE4/N++aHanjDFm7IwomptyIkcAr+yjXq/CZM/0t/5kuOA/sxJGEzg00xs014F25jHKtAcf2JGXReL+LvcfPEW/tIx2p3bWn4E7T4NRDXfM0Xc5DSN+fBjkp/ppwJu/ldCi8N0FRV8TUJOIBXKJCvIna/DwqpSrKdCTpD9He3MXR0x2k7b6KNHMKiFqgxHBqjKE0gGqjqAhXObxF8e5asGfrmF9dQaXWgOt4YGqc0V4b+/cfI9k9BBhlX/tvnSCcb1HunPAN0Xhu6jvrXu61C51qgZORj+zZCuo31lBtNkCHlRJsDI/a2F9/jvjZ0aTNUr8xCrMSwSmbGIqFJnGG+PkAyeYW+u22irBiWSjPzeDaJ+9PvLBf1XZp6pSfzIXa9YaFhHf4nxixHTgMTU/yExE64sT0bNAITdNfCzPCK6aP3hCPt/KaxoHzgN10YbfmUF+Yh+uVxYkq6I/w5K/fIXi2i7SXSP8J/7Lt+vcCHma+54NcG834F9r28AAAIABJREFUyN97Vd++APtlN0ShryuUyli/dcKT7Au9KXvCn3keNfgShDEOGPwN2DiRFG6OJA8AMjtD5tqyG9DizkBGqVIbDxWm5mNNYOrIBMwuthEg3d5HNBpKKhzbsnXUrJc18Kf4zBDwHNwn/ZSJbdBOM0T9MbrbR0i2D5ANKNVrp5UpUBN+YuQvFhsC6V4Hh989wmhM5TcjZpVQa81jZqkFu147zY96rhY4hG14wYxZAuU5ZQv+KL8YGYaG01l+vNRg16rwHRcly4GXWnBFYT8laE3hfg41itsFBd4uBcxczI9t7g5hqsFWE/W5OTiOB4cOud0Rtr5ZR9pjGi5V/UQuJP/n/8jHZG3uKjlOkDzbwajXRxRHSDgnei4uvXcVbmtWxkr+1eK8oMCbUkD4kd8oZW54qMBeWkC5VofLSJBBhN7zfZGd075KJcc53fDydJ0Ci+t7QKfRBOnuAQb9PmJReGYozzdQW5qB3aiIjDP9fnFdUOC1KGDmYBr8a5x/G2i2FuD7FdkUYQUJDtd3kBwM1IYWOt8aWYR8rH8TnmY6SZYZZIif7GHU7iFJGJuS8igwu7KAmaV5heL0/P1aiBeFf84UID+ZP5kz+b3HHdINB9bCLCqNOjzXh5tZGO4cYbjTBudfcXolf9IR3PAvT/U3lcCk7E2n/aNENqz0OyqkP73Xy7N1NJbnZGOb7JI2SBTHggIFBQoKFBR4KQWKJf+l5CkevkMKTGQIrQelDcFZqKDWmkG9WodPvWcYY7DfRuf5vsi/ItuaTWRn4cqNYJQvYiB+foR0Zx9pHKvPPddFZaaJletrskFN9HZnwSjuFRS4CAUMAxs9MHVo3NROGXVuFr5Xhms5CDt9HD3ZRtody8Zt2dBl5N/peigHM3gBHa46IdL9Y/SO24hFh5aKLWF2bRGVuQas0lsMYjCNR3H92hQoHFZem2Q//hcyibBiRrrGlx+7WuHotJpwrq1iZmUFfqkii01w2MbxvaeIHmwh3hlK6gV+0JqPXjlSiZP7yaLF3eoBB79OCSC713MKnxy5OOdM/3KPT59q9BkRJR1mSLoZUv6GGVI6nbA+LpzTP21ck/tcVFmOitGB2g0Vb3eRPNrEsNdDkEYIbMBp1lCio0i9CjpOyB/rN8jyhuDDDOu5v/zz3O1Tp8zic+rGORfsn7IFZ96Dc30Z9cst+LWKSAVJt4v2o0207z9DvNlXXq26b8TxwNBguu3SfkaVyZCyPPuGxjw9kU/wmmKVczB849sEb6pQkThOg5oYZ8wOopU67CvzqM3T6OIgCQKMdo/QubeJaOMIyVEsjjumPRPeZLv0zwhU0l622Sgg2WiuQR7gtOpwb15GfWkeXsmHk6awemM8++JbdO9tIN4ZCb/JOND8PYGjGyV8piM2SHgxoXMG5oOUuk29eSKcbv6Frvg6x69dt+G+fwP1lRa8Ep0aLKSDMQ6/28ThV4+RHGdCG+lv4mx4gsfUUmGsxxwPmZSNv32K7pNNDEZ9DNIYadlDc62F9355B3aNIdcmXHIhPL/3QkSHOBkldKkk0WaojLOzDPY4BLrsN6WQEz7g+GD7Kaich+A7a6bCwDRjYognPzYsOKszmL9xWdKCeZ4Haxxh8HgH+1+uI/puF8lRquY/rZB8Yf4zcx75kOHuOPY5/5EfQ+VYMBkv59HiDe/nd6famQU/tVFObKlXcNHjUPqEY4b46XH1wjE/ZomPdoiUlBlMm2H4UqJmcT6IBNZEUDV8b+ZG0oA7Yrl+dDJEXz9Ed+M542FK1GtGazFT/xs2/8XX3hlPvVj1yZ1zJp48boxMpEnsZYAzCBA8O0D38Rbi/Z5eb5UskB9P8mEraws91TPEOxHix9vY2djEIBgitqlcL6N5fRnV1TnYNfdkHmb/SDhGhiTP1BotPKsV7ZyrMudE3mAzpE91OifNO5JbhWydJWKwZzhzu6JDOebbeEKQ4qygwLujAHlQDy5Zv2uQ3SGttUtwXLqb2pIS6OjBcyR7PZmfZA6blhu4donsrfk/yJDsHCOhk/loJM6miWPDbVSweH0VzlztgoLnuyNFUdNPiwKnpk+j8GRK2boHe3kBNSqM/AoQJxjvddDbOkbKKCuSqvLEOXoidOXXZH7XdTPE2x0Mn+9gOOwhZWg8z5LoKvXWjOzwv9jH00+LrgW2PwwFjP6DO/RWblxBpVoDI6zQYaX7dA8h+ber5OZTc7A29su3H+VNyticfw8DxPcfYdTtgjvzIm66qPiYW1lAqTUzEVF/mNYWtf7UKTCZf7UMIbopiTLISG3zaMzPoVyuSpTV6LCH4dYxkoO+irRKJ3LyqXZS4VHmYTMHM0LQkAanNoLtffR6HdDklHg2SvMNNFbmRHa33AkWP3VyFvgXFCgoUFCgoEBBgZ8NBU6t3pRPuaGvYcFfbUkkf9kREKnvt+4TRvEP1aZoE+l1Sg8hsi91b4y8TBvCo+doP98W0SLiN2LVR3WpgfrVRXEsKBxWfjas9k4aKpt0ZVN7XaK8lit1eLanIg0/2UNMGbjHdPZ6c6rhX31UOjS1EZ3pi5PdLtLne+KwEiSMEGTDqVcwt7aI0lwdtv9OmlVUckEKFA4rFyTUT6WYiq/ClEDKMDqt8GMOcmfRhnvrKi598hGas/Oy6T1jFIsHG9j+4gGi+xtIuqlOn6MMnROjPT+CzY8GJhoCtVMJo38o5xVlGJVVzHwov5SA2hplyphLGhvpBdfPkByMEO8OkbQzZHQ+GZ4oluTDnLuedJoTcVrQjhnyPkOn9xhNYoz43mPsf/EndNrHiLIUSdlFdXEWtcU5eMy7xwgAmnQGHUXD6ZuTpxc6OSU46DfMPbG/m53jTcC9soD6xzcxt7wI17LgRGN0njzDzpcPMP7qMeKdsTjgyKRMBw0xZJw4a4jjBAUO/mgYpnAx0jRjn7H/tNOKKDIu1IK/vZDwphhH2XLT+pMd9LJ7vmLBuVSDe3sVy+9dgc/Q+Qxb1x1g94sH6N7bRLzVUaHEDQ9I+7lA0aia+1GwYnvJF1TU0HmIjkEU2uggsOTAe/86WnRaadRRsV2UwhS79x/i6R/vIvzmqdTDhU3gaCFOnAT0GJjQ2oTXpaPESNOc9Zv+Mc19XTZiefMOjeo1wPtwBQvXr6DenJHoEG6U4Oi7Taz//i7ip4dIzdg14yHHH3QMSEdAcpQhenSM4OtvsLuxgXE4Rj8JUVqYwcr7V1H58CrsJmNon+4q4QKDz9/OEq8HwbCNObIv61XU6g0hEcMbJoMhhnuHSPbbJ3OEjgAh4yRPz1ztpntyt77H0xMvNuFH+l8wpWjNgrs2D+8XH2BudQnVShWVzEZpEOPpf/0r9v94H/HmQOZDM8/KHMC5Tn5UUmpHFeMIwvFPfuSPfCwx/LXx8633YzYJqc6YNyVxWHHVHKSdusRxRjvPyNwkTgoGZ300c5fhX/FRUj3E/raqFYCRwTjlxTGC3hA99jd3F+q+5hwnTME1hHWwfjpqcS3ZiTH809c42NyElURgtBbtD/M99vlbBv0qhpX1n3xmfmocy2tT71o02sjjDCVYcMcRwu0jHHy7gYRhxvt6PZc19mSd4fqi+E7xV/zgGXpf3kevfYxxHCLzXZSXZrH04VU4izPC4/k5mu9KSgimJRvoOVPWLPKBljPYf2bMmsWKczn7k/N/d4C438MoGiPMIrjVEiozdUi0nUK6fctMV4B7YwpwgHGeL1lwlucw+8H78Cs1+dh2wwzj58foP95Gsj8Suc0YTMny5icyDMeDHjfJs3301zeQRRHo32WXS6gvzcFbZXq0qQhDb4x48WJBAS0D6iiINmX0pXlUl5dE9nJSC2lvjKOnuxhs7mmjv3Yw1IoirsUyffM6UU7VyUGE5MkuDp5tYTTuw2bu6FpZHFaq8w1JgZn/TCj6oaDA30QBfi9wh+msh5mP7sj3k8g+YYLR9hGO1rcRb7fV97GO0DiZeymDUO7Q/Ex5Jdk6QPfuNxj1uojSBInrorE4h7lLC3DmSuJ8VfDv39RjxcuGApQfyL+ah+3lFrylRdTrTUmFmQ3G6G8eYPBkR30fymYVpfeQ7yAjQ4vDipKvqRNLNg7Q2drHgA6DNpD5DsotRriaBXWFhcHJdEBxLChQUKCgQEGBggI/UQpoHQSN8M6VVaxeuyob/7I4xvigh876LqKne0j7icjA/E6TjZ5a7pXzvMP2cSYbZg62d0TM5QY1f7aGxuo8nKWS6DrMvsKfKMUKtH9oCpBnzZ+Rgcm/803Yq8uoNmdQckvAKMLw+SEOv32K5KCr+Fc+3tQ3m+geJt9wmq/Hmei3R0+ei86aDivUoTGd69xaC3azAqbxLv5+PBTIB8//8WD1Y8BERykx33nMbMCf/Jnd/eb6NfGdwIRyLzmxSL8EEF/inznqS3PjFExjrDLlDZ404HCHRpUKRwfuB+9hfmMLg34HwaAtxp/nX32H3niE5cMDtD7+AO7lBQljLgpH4wRDuFzE6EzS5S6NHexvPcPxoA+74uPKJ++hebUFp+kb9BS2Bo8J7uZkqlE55SaN69HGHrbvPsD6w8eYqTfRWlrG3NoyypdXYM9WYZeprdf9Y3DUBKFBLN4aI/zLd+j/9TE6j55h1O8gTCNYlTLql5Yk/cnC5RVYtZIoBEQpZVCTo+rwExqfbtapotMX5CPTz5Z1wkMsl6cHFRGeJTmK7VnAvXUZC9tbaB/sITo+xmg0wv63TzAajXFpdw8rv7gF971LcGY5e59USpAyOUuKBrb9AL3NLRweH2Pgxrj22W3Mra3AmS0rZcQU6Q0kidIjeJs7Fzia9kyOPLHETyDNMjkKDdXtU+2nYMPxxbD5zpyF9Oo8GrevovdsF4NohCgaI9nv4Lv//AUWez1c/e3H8G5egdO0kblkGI0fj+TNkDv+t3F0dIgwibH43iVUlpqw6hUVNYROK0yxM+/A+8dfYOn4GEFvgGjQRylOsHv3MUbDIS6vP8Ol33wK98qSGgc00hoa84QGJC58nRTJzg76W8+x2znEuGLj/X/6BWorLR2pRPe3oY1G96UHIVauhCirLNh0avrsA1w76OOb/QN4mSsRJo4ebOCr/+3/wI1/+hjNj27Du9RQtCFd9dRB56X4WYDo7mMc/P5rHD9cx2DQR5ClCH0bH3x8C1c/uyO5Lrm7igqyU3+GzmyObouZG0+jmyt4CsBbuGB7SAtGnGnWxUAtAQnFYWWM3uYeOn+6i9nSL+C5DRVRRwz3Rumn5otT40+jJWNVs9Pf1AKhjXIcFJgyD5ywqVTHMuYn3u+Z6tsrLVRvXUGj28ZoNIQXRAj223jw//0J+zu7uPmPv0D1k5vqo8DjeNeYmgN5kt7vRwGSnS1sP3+GYRqitDqHK5+9j3KrLh8TdKYiDc+iw5v0EvHgP/55mQV7EIERrey5YxVdwzYTe77O3NiVN7WDhZ2p1D4VF06jDruiRBXOD1ajgnK9jn43lDB+/aM2tr59gpn3VmHZN+C0fPUuaUvwNPCOMkTrI0RfP8bhv97F/tN1pHEI33aQMjqH/NLT8zPxkX4UxHL/qVaypXJm1pxciZeeTmBOIKgVQmQOzoPaockMsJcC0w81TNUD6n+VI0vVMQExqVvdsWwb/HEMx3QmtCzYaYqwP8DG199ikI1xvdPG3K8/hT3rw/JV/8rbdNqLMnEiHf+nL3H0+3vYW98QGIz2YDeraF5ZxOIH107WKTpm8qOX0/QoQ7y5jyd/uYcoijCzvIDlO+/B4e7kqsaP+Jq26ehZfC85zBDd20D3q6+x/fwJhmmAyM7gNiti8LRqKv/oVHMnZChOCgp83xQgjwv/KfFRUlpROHPmXbi3rqGx2MLxqAsrGMDqjvDN7/6EmyULy9VfwZlzlBNjbv0VR2M6Xw8zhH9+hPGXf8Xx7g7CKMAoS1FdaOHqrz6WlC2SFq1g/u+7i39e8Clz6ZC8zlIV9uVFNJ7Po9I+QBIGKqXsTBm3Z6tofngD/I6hclS+m/ntQwdU7Syf7CWIvnmM43/9M44P9xFFI1iVEuYvLWHx2iW4882TaJc/LyoXrX2bFMiJKypCRQa7YcH76Bbm/ngfe3vbcII+sn6AjS+/RVbxsDbzz3DmS7ArmcjHAoL/Ua7Wm3PCuxsY//ErtHd3RXaJrAx2vYyP/vHXqK+2lPyZq/ttNqmA9TOlAGUB2dxgwWmV4VxewszyIqpHu5ImtfN8F/f/5Qt8zJSD71+H03KVMohyAH90thKZHUi2Q0T3nuP4v/4Z+9tbGIQj2I4tuqGF65dQWqQS6uQb/2dK8aLZPyQFzpw/C6H2h+ySou4LUGCaRTNLqWLM9+CZfD0Fl2Wm4UwVKS4LCrwWBbR9Bdw0s7oA98ollGoNeKMUwSBAe30bj/7zn3DL/w2s26tiJ7SoWyYfap6l/YD6h/jZEOP/8/fYfbiOgBFeLQtJycHiNW40vgyJzFbw72t1T1H4FRTQdh/Kpfw+Y5aQ5uoSKsMOgihAMgxx/1/+DMyWsVKrwL1Ug0XbIOVm8iJ1ELKJXQUwiB4eIL73EHsbzxCEAZIUqMzNo3XrmjitWD4VHq/AqXj8TilQOKycIrc2nglzZ2BW4tTKkMgPsotRGaheQ5gQWHqngiiuCTdBnCXIGAJZD4g0pZX9FDIvXuRse5NxZKWIrRQRUokYktrGKUIBM+uNAcYoFnY5A2YteLeWUN66gdqoi6PHARIuPEc9hPc2ERyNMH5wjObSPMrNGizfg+WpkZ/FCTCOkIwDjHsDtA8O8fRoFz0rRu1yC61LSxJS1NQ5OZ4rhOW08ixsGsfykmdsiOHTPex98R368DFobOJ4YQ6NhXlU6lWUyiV4JeLnAS6/6PlegiwMkfbHiI4G2F9/hs7uPka9HiyGsC7ZKF9ewPIvb2Hlk5vwFufEYWSCq1g6deQCWEgsi6jIwpxy4jsjf4SxGwsM4jDhnVTxEGgQ1QZZFjL0kLK6ZiqE6xbc6wuoHFxH67iDnb+GSIMR4m4fnYfPkPZGGG7sY251EY1ZGvcqgMehbDHkALIwQhaECAcjHB4c4uDgAO1oiHC+hJmVBTQX5+FkZVUh61asoulOxxL1SzJ1lhAuy/HPHPXlqYORbUx7ZIEh7dRG/IRwxbEoFwHI0MDAZvsrFtylGtIPr2JuaxfdaIjB3i4QROhtbCMZBYh3u1i49AiVuTrcahnwSkCUym7jLBxjPOjj+fEeOvEQzmwFtVoFpWZFaCVOGMSNC1/TAm4vofn8Fmb7I+w/ego7GiHuDNG7/xxP9/qINtpC68oMd30yRJEjiqAsSoAwQTYMMez2cHS8j+OjfRwmA2SXZnDt4w9Qa+UodIrOufvnneZoLafkOT+DbVnwbs4jPbqN+Z1tdJ88hh2MkB31cfDlQ7i9AIsP9jC7sgyrzpAsVFylkv4k7QUId45wsPEc2+tPEfX7GCYhxlUPrdvXsPKrW/CuLcNyqKzVjh15/AxO0sfqucyTtoXU4Y9znXR8/q3T5wbG6bsXv+J0QWWa7FafQXltCV6tjtF4AAQpop0+nv3LfSTdCM2rq7BnZ7RTV4DQjZDVHDQuL0vahEnIY42yZE1iaGQ9KBgyXE5lcF8QcaENlYt01krFGYJHQuX/SnM4NZbEAUc5rDktG+6vbmJu2EV7NMBgdx9xGCLY2kfUHQCHI8zf3cDM4jy8mpqbRRjjGiJjP0LaH6HX6WCvvY/doz2kDR9Ln93CyntXUZqlw8pU/Ren/jklOfhTcZ6h8weTXSS7Pez8l3tofLeD2LNlDqTjGv0wVEwPBUq5LygyMy0Af6GTYlQC3IU6bv3yY9TfvyI4W9zdfbmFxVs3cPDXHhAESPpD9B9tY+P//hJLjw5QXZmHVadDnqvWgWGArB+it7kjPL+9sQErTXDrylUslup4/OBrhEgRWhloeJCdjPmuNuemXzmH2aoXySdWloLtutBfHhYdeGzOtcphRmZbW8kbImQbQftlgFmvqVqcTlKRLVRgJz0hTwpMARJcUoR2htCyMWYap3IN1XIZjpPhsNPGwdePgaMh0vU2qnNN2PUqUKJjpy3zHnfWZ0ddbH59X0KD9gc9DJHCbdbRunkJqx/TsaouDijmI1ZIReNPAAlh3r73FO3dQxxWKhj+ZRPzi4soN7TMUfYB31X10dFlNEbWHSPd7+L44Qa2NjfQ6R8hdlKUVxawcHsNc9dXYDdctZ5PiRZTFCguCwp8bxQwQ10q4AV5kU6WNQvOlTpmf3kL+6NjdJ714McZButb2P79PdQyD9VPP1COdxXO01yDyPscLymi9S10//BX7D1+gu6wi24aIa6V0bqxhNVffwi7ThnFUs6I31vrCsA/OwoID1uwShnsWQvu7WXMHlxC8/gQg909DDo9tO+u45nj4uYY8G6twVnwVKQJTvoxI6sAyX6C6MsH6P/lLnaerWMUDpC4VBbVsPz+dcxLSqu6Gi8/OyIXDX6rFCDPmm9Mzr+Us5l6d9mB+/F1zBzt4fDht2CUyuHmLnZcF7OlKqqfvC/pORnRUr55KWZRXmlniB/tYPSHb/Dsm4fi7N9HiLDiYvbKAi59/jGcxTnZmfeCs/9bbVgB7OdGAfFf5ze5n8GZteDcXER9/zKqu88RHnUR9kfoPHiGjf/nT7g5zOC9fwNuiwKH5mFuYmIY9P0Y0f2nGP/5G+xtPBUZYmyn8JtVLN9ew/w1fjM3CofBnxuD/ZDt1cKyfBvmvmepQ1VTuIWMhv+JPmZab/lDIl/UXVDgVRRQfKzZ/ERnw9eoEuQD89Dwv5FbXgW6eF5Q4CIU0DruTAz+Vbg3VrD4wQ0cf/stwsFAIlPsfPEtmpUqFkcp3OuXlH3EWIlTIOlkiJ90EH31AOt/vovR0SGGSYSRRyfaBubuXEHzxtqJ7sHw9EXwK8oUFHgVBbixlxHRGxbcyzMo/+IGqv1DDEcDxOMRhs/2sP27r1GLbdT+4Zdw5suwufmRcjPtmfyGO8oQPz1A9K9/xe53T9HudGXDdlrysbC2iEt3rsmGGbtki+rtVSgVz98dBcxU9O5q/DHXRAHBCAtWJo4gNEjJxl5LGdtpVJI5WBvh1begvjeROM5o5EQgyVS+eTqsUFKhkY8yy0UcVs4Eq0yhNBrzJ9EOeDTtEOD6ReKgd2jYDRXtKP3lTazGIUZhhNH2AYLRGPHxGP3j5xg+2ILjOvB9D9VqFW7JE0AJd/yPxxiORgjjQOo6yEKkszVUqw24TGaX8QPjjB0axOeiixjBiGErQ2WYonIcwBkNEaCLfWsTe3Qc4eRl2yiVyqiUK4Ir+ySOIolKEocBMrrOpUJt0KHHKrmors5h8Re3cO3ffALv1iqcRi6ahOlgGgnY+cyIYAER/WBIZ4uG6EmHvtgrfKSVY6lNZxVjruZ7ymDLl/Kk0DYJURJwZ5e77CL78DqWxwnSQQhnaxfjfh/ZIML48Ra2nm5j13XglcqoNxrSfhI2CMfi8RqOA8RRiHEaI8xSxDVudZxHNo5U6qRTteeaYFF+1g4rckYD6pRAnRsmLG1JY3M38+XpeSs/DZfw2a+GAIYIxseBIbi4W22GDlXLqB3fwWoSIk1SpMc9jMcBwo1dbD8/wLZjoVStoFavo1FpIAljBAH5so/+qIt2FiCeKWPu9hrST8cSdUVVrPmSEUQqTJEFeJ/dwqUIsBIbR1vbSIYDpIMIQW8Xjzf24HgeSpUKao0mSnSMSlPEQYAwiBDSOSgKEGQhYivGqOahVKvAzhxkEsIiR9/XPSUtzZ/4YlniNcq0Xv7HV3E5+C1G/zFGd2ML6WiEpD1A+y8P0L27Dq9cQX2Wea7L4uE/Ho4w6HYRjkaI4hBBGgMSCaGOmWuLuPUffoPmpzfgLFRIiEnaFVP95Gj6ShxaLMQyN1pIM+W0ohRkCnGZAyYvvvmJ8It+XRTB5JNyBmepAufGJSxdv4Jw/SnCUQCrF6F9fwP9jT00W/OYW1pC6jo4SnsYlgL4q3V88t/+FpXyVTh1Oh8ZwNpBkfxPXxxGm2BlskX49XAX5SKdGqyE7gjiqkJHDjonnFR4AnOi3PYgwljp02UgeB9rYYTd1Ea/fYw4GMM6HuCo/Q0OvroPzy+jXm+gVqvJHJjEEYajIaIgEAeXkNdWjCEilC8vwr2dwuVgZns5NxtF5gkab3YmXa1jpXN8ZxnKlovkuIvd4w6Y4XTSavKOXgDMNKHRkdkmsRSNhnaCng+4q3OYr8+h9t4V2PRBYZS+6yuo/+pDNLafYbh/iDSKJQXUbucuOnefojbbQH1uBo7lIBpHGPQGGA66GA37GEZjBHYq/LLy689JbFjr95HEKkKHOKzkxxynPrWcCW2EZnTOom8g+V7PleKIyBKGl15GSdNwOqwIDDo1peLMl9hAbKewJlFWCPOshVTVNVnnhdcyRFmsosWIUyVXR0Ndg5xqnKAg/Z8isDKEDOdpuZhfaGFmeQlW2Yf17TcYHnXQPniI3r2nKJWraDQaqNdqcF0b494Qg84Ag35P5IA0i9XcXvXQuHkJlz5/X5ykaPSRNGwcStpfS9b1MEU2SFA+DoCNA7QHIwy+eITnlSp8/qoVNOp1mb9c14GdZhjRwa7TxaDbw2jQwwgJIkbLmq1g9dObuPzZLdTosFKbMtgbMkz17cu6qXhWUOCtUYB8R2MpJ45SBnfBgv+PH2LpcA/940M4vQHG/TE6f32Mx+0xbncz2UFCw5E4Ycch0s4YycYewq+/xe7jh+i3DxFQWeSmaFxpofXZDXh3VmSefJuRs94aDQpAP10K6CWIjpomyop3s4VK+xpWDzrY64yQBX2EO4fYG47RCC20Dgdw6DBc4wKQIRunSI7GSB5vocfIWE8fYzxoI5XIWA00rq1g7bPbcC8xKuHU/P3TpVyB+Q9NAc69XP8pe/KbxctEiel99h4ud46xt7sNqzdCNBhj+PC2V4G8AAAgAElEQVQZHvUD3OnEcBjBc74JeL7SH/QCpNttxH+5h92Hj9A92EeSxAjoLLs8i5VfvgfvfUY91boEylfFX0GBt0UBI0NQ3q0C3tU5ZN2rWHq+i/boKdJeG+PjPnZ+/xUakYPFdoDk2qqS5fmZG6ZIOzGSJ3uI7z3AxoP7CHpdxFmMrFFC6eoilj66gcraEpw68w+/LcQLOAUFXoMC5luNr5Dn+ZPJ25LonXKpU7a9BtSiaEGBd08B4WVuatJsTIXN5KfQER0On+cipRt55d0jXNT490oB0WPS/uUyGiDgXmth9rcfY659BGszkU3OYxr8/+Vr+J0AjcMR7OU5WKWSUoKGCZK9PuKvvsP2V/fQ3dlFFkdgKqC0UcbcR9fQ+ug63EvFZoO/Vx76Idul+JeigNo0wywh/uc3sbi3i6TTx3A3kg2snbsPsdELcHPswL6yDHtuRvFwmiDrJ0ieHSD+5hGe3buH3tExoihG6rqorCygdWcN9ffWYDcZTfyMDds/JAGKurlPvfgTCohgrIzl8qHmxBg4MfpWgsiK4TqpGJIkMgp3iSspWhNPROjzCUmhhRY7FvMyjH1g5GSIuTPSilB1UliMDJIXWM6Hpp7wY5K951sICM/LMLSBkZ0gFCMpLUPaqKVPaWwTQ6qkgVDGev/ODOzGb3F7dRXt//IXbDx4hOHhIawkBaMpIY4wDkYI+l3lviCJ7Cw4li15m+kkA5cumzYs7oQue3Do2MJUA4Ys5qgwOm3Um3rGKuU93mcbiTMjFbgOLM+FFVD3yRB7zEOWIOWPjiRxIk4GrIKvEg4FQxoUGYmaZZimwKlV0Fhp4cZ/80vM//YD+B8sKQ+83EiQiZFAjGHeTia8wN3jY0QYM7aqGBxNZUrgpEJX4UzFboahl6Bvk48iBLYjfaOw0QKsoYmuS7XfUgq169yZ/hnWli9h4Q9/wdO793G4tYUojuGSn9JYjNjMoS00YztJFzpDCQGA0AYCcRLx4HkObN+FzUg5EgVD9MjKHi90Jn+mGLkJ+laMyEpg0xjiaiebqb463ZG6IWwHSSeRNkiLBIEHDN0MfSvF2EpkHDFykdBZ99eEDDRqsn+5i2jeQukf72BhbhbNhRYe/+4LBIeHyIKxOIzQiWUQBhh2OmjbHhgNI80YaShGlESInUToQRuRGgJETiPICukAIj5YjFgyA7v5a1y5/h7K/9d/ws63jzA4PobFQUMeY4QLRuYZDIQt6SrFjxC2NqHzSpYgonOSmyFxVaQRUe6bL5UJU5qWvuZRoy78lVqwafS67MKqfYjbMw0c/Mff4/FXXyMaBnCTVBSp4/EQvU5bnBnELzXLECfRhCahY0nUhJu//hg3/v1v4X9yRSlbPY0smUgY6wRXYQH2EWnHKCd+ir7MkzEGdgLf1i5+dIZJ/SkmP4HzpmdSP18Ww4kFu57BfW8FS//un3BAh4Tne0jjFGBfMUpOr43nm5sIAXTtEbJ5F0vOFdB5p5LEOsxOpuZezs1uhtRJEbgWPJ/zDttoKw/diyA9QZAOIQl6ToIuOP7pkBAjdHRduXICltcyp+sdzLaF0m9vYHHlMmauf4f1//d32N/YRDwawSanpymiUR9Hox7ah44419A5gfyv+ppLTobMs8RJL+WRSwznaJn/pwbeRdp2Vhk9b1hlIChlGDsZMkbfoBJUz0E8iEsn26gDgpjmm3VUDSUVrURGK72hk0zCrbNNQhui7DOcZQneZzdxfW8HT/58F8c7u4ijBG4Uod9uo9s5RrbJedBSDhKcKiXaVQqUXNRXWvj8f/kf4V6/jXjzGNZsU5wvRk6KukQW0n1BJHW/MFWOOH9wbHNedxL0ECNgRKiyA/jOhRyAZDhxLIuzV4LQppyRYmRboJOOZycqCpV4dEi+J+lTcZwSZE46QZxn6HTnKYeQkcyxCTI7Q+SqZTnIYtTNHHTyqqInx3DJxsjP0HdTuL4Hf62Fxifvw15ZxntlGxv3vpW0dHTOC4IA/c6xOKtz+bDhIEstxEkqa0XMYdKsonVrDb/+n/8HlD+9CXfFhV0yva2dpMh/nJC5fvqu4k9ZrtnnQDAciNMljoFjm4NCTyMyfzGmueJx0mTE1FGzVbQ+uIY7//ZXaHx4A85c+bTyx7T7BA1zpzgWFHhnFBB5x8iHFQvenRnMH36KbDDG+h/+LPJs3O9j57uH2Fxfh1OrodpsoFGvIQzGCDs9pP0hvDCeOKaFnoO0WcLtf/srXP6nz2BXaZAtGP2dderPoSK9jsvyw3MqPX061XvIPryG2W6E0W4Xo73niMY9hJ0O7v7u9wj/+K/w61UsLi7B930E7QGG+0fAkA7olEMjJFmMsedi+foqrv7DR6h+9j6cGR0V4OdA26KN74YCnBLNtEiZlfPv9Qay/gd4v93Fw9/9SdKqpqMhdh+vY39nB3atCr9Rx2xzBlEUIuwNER/3YDO6bMI5mDohALM1XP38Q7z3H/5ZoqNOvpk4Vkyd76aVRS1/5xSgzC+f5Zwi52y4ty5hefA5Ms7BT0IEww6y4Qj3fv9HxF98gbTiY3l5CeWyj2QcYrhzDGcYIwlDRHEgOsig7Ehk5Cu/+Qhzn9yWiIgTGaLg379zjvoRNe8sXuOGDtFfcpOIjYz6hbwO70eEfoFKQYEJBTQvy4ZXxJPNzxES2HTepgJF/4lOSKk5zK3iWFDg7VOALCf6WgvU1zpLZXi/uIWbR22sRxn2nz6VdOBHm89wtLsL6/d/gNuooVavwYGNfq+HtDuCNQxhRQnIy9QxR5US6teW8Zv/6b9H7f01ZeinPo8p53Wdb78xBcSfDQXIQ1oHIfzEa9cCN0J615to/MNHQD/EeqcLaxTBjlMcbjzD7v/6vyOtlFBrNmVTO+k1Puwg7g6QjgKxg9F2Fzk2spkKrv32E6x+/hHcy3NnZxf42RD8x9vQnJn+x4vkO8OMk7nPydyC3XQRzpYQNDwkGZXCPtKyA9ulIwZHjLJxqrNXYMhChM2dETUb4xkP4UwJcWjB5rHqIJHoCa9QcGhbu4zdicE4wajmYNTwMY5KyMqsoySGOROC/wWlicHH50RgwVmxYZWuYH62hPKNFp49WEd7ew/xaIwkCJFGkTiGZDpKCQ13tsOd4C4y34XfrOPS2iLmb1/B8kc30LzBXXUqGssrKHPuYzHGMWNCjelxVnAp+SXsSgXt9R30d48w7HUFL4meQsWRpJpgRARl2eeOdYlhQ8cZ30Op2cDi5RWs3LqChTs34F5ZgLvSkB374rwxjYmmEWlnVS3EMx6iZgmW4wl/oOarFEksZ36EwfIUPtk/VQuDuo1R00XqVJG6FpIqnXum3jF163dFz+WpCdldokG0Bav5OW5fa6Hx7SPsbj7DuNdDPA4kugCjfcifOKwwpYarnFCYuqZeweKlZSzcWkPr02tYeP8qnJnaiUGPuBMd0o1+RjVgPOMimCkhShx4c2VkdU+1J9dO9Zp+mQD0giLARCgCbDrm1hwkcyUETR9jy0dStWQc0ZAuYevlBf2fAUcjpI60QqcV3FmCVfVxa2kOBw/Wsbe5jd5BG+FogIxOB3RSSTNxWElsG5HtIbIdePM1NG8sY/WT9zC7xl331VN9ZQy+9DxhGibZOVqfw1L1v8PcvTXsfLuO/e1tDNodRIzUI45quqniBEWHKNKb80YJ/mwNrcuLaNxYQfODNcxeWoBdswVuvplvfG54i/yV6bg2jgX/0zUs1v4Z83cuYeu7Jzh8toVeuyN5+ZSDACM2cHeKjcQpwSlzPNSxsLyA65/cwfyHN+ByzDLKkHY0Ip8rjwOTtkx3vhyISAZ4CYIq+cVDEHviNBbXPMQ0ULP/OAamPsSEt9+AAIY1zKtkVwY+YVogd8WH9Zs7+MhNsPWXb7H98Al63bY4riQ0clsxhC9cC07FgyPzo6P6hbwqTikWUAWCpocojRFUbFh1B6FPBxOzPcLU/pIjEZXxn4lSfDTjI5ypIillyGbKMv5ZnxjsSev8n+lf0o5zLFODXfVhVT/EzcUKFr5+gOffPcao1xfHKUZSSekYJIPPKHIcxY+OA6dSRnWxiZW1RbTuXMXqp7fgzlPK0/0yTdQ8Lued63EuGJq2cl0rJxg1LAybrkTs4KMT8CpNnZkihBf5XJNVRQVTJMksWxRToe0gLmVwZnzEFTU2yVMWd8dyTbhcQ+Pf/wYfLC9g694jbDxalx2yKVOicfcVHVQyOo/Ri8KBVfUwu7yA5ZtXcPWT2/A/vgmrbCMdenCuLSHbGQClFHbdB2zF87LMG/6V9VuROqg5CGZ9hM2SOAFxd2JGp5Xp/jyPhlRW0DHGzzCmPND0MWxUkNIZZ6Yq41McWFm3OIacpuYErLlN3OgsVHNkjUrsBEnTQ8q2VJgax9HzPRlMd4xuDyNMBeyz2RJG1RKs67Nwby3AXWthdubfoH5jCc/uP8LzJ0+F79IwEmcpQU28VlxkFAZqHporC1i5cw3X/uEjeDcvwVlwT7zUuUSZuskvyOA0XXi3LuPWv/sNGiuL2N/YRntnDzHTWiUx2JcqxJqaiuh0lLkWMseDW/IlHd616ytYuHMZi+9fRYUpKOYZgmdCoZMT1l38FRT4ISnAQcOpyczvNQv+h1exECaI4wSbjx5j0O0gi2M4jAoYdhF0h0hdDxnl2ThCRidlCxIlClUPtZUF3P7Nx7jy+YdwV2bU94ueN37IphZ1/51QwCzaujkid4nMpNYwZ7UizqMrowTxlz6SzacI/3/23vy7lRzL87sRJLVLT09v35fMrMqqzKyu7uptxmOfOf7J/p/nHP889rinp92bu1xdXV1bbi/fKj0tJMPni8ANgiGSoiSSIsUPM/UQgeUC+OAGAgHcQOy/C0aMjf2P1jk4sjdvjyzPGuVOaMdH1tCYTJ/Ca+aWb27brRcP7Olffxk+kdXY0YsJcYcK+uxrokTzUQ09i4Tnj6DEcVvpl/ds5+jP7MVh2377L78Mn3S147Z1Dg6te9QOE/Tff/O6fDGm3bGi0wnDwxO9x7DestbNbfv8P35lL/7jl9Z8qM8Kl88T4RnAxzzzUX1KsegEYn9YPj/q87GFNe+smv30ud19d2jFestOfvlLO37/Vm9wmZ2cWHZwaB8OTmxfut/RZ7I7eoco7PJ8omeQrXW7+eKBPfzzz+3ZX39ljXvblmteUMN1H68vOjfKvzAEpHJhyOG6l5djBe18emz6dG1h7fBSTHyeXJiaUdBlIRAMCqXHGifrnb0VsxO9DJd1wnNbnukFnziv6nqewhnkl4ZzDIELEgjz8ZoG1DuAmke9t2HdP/vcnp/IuqSwb7/52k4OD604PDY76YZd5jvZu9Ard7oaOHQs10vRmdlhw8L49/5PXtrL/+VPbePTx2ENIYx91T1rHoIfBCZFQP2ifnK1tqS+taEXvx7Z1mHbnprZr//hH+3o7XsrTk7Crundjye2/+6jHTW+D0m77bYVeo6LhirZxrptPLhtd798aY/+/MfWfKrdXeMzXJkb/84RgUFT+3NUvBkVRZPIWgCNE8qlkcQ9e3b4pa08vRUG0Ot3b9jNl49sdVOmieVnFcID3RhF1PUVFlf11vODXXv8Z59b88EN65y0rXlzw7Y+vW+r2xuV3HBBDpMb15zCW276nMntbXv4s89sc2/XDj8c2Lv1tj35/BPbuBG35VJ8v9AlMx4HPy0AaqcLGUdsNaxx+77ld1fts+d7dvDHV9Z+f2Tt94d29P6jHX08sI4WkiSv1bTG2kqYsMm0lejejm0+vm07T+/a+qPb1riRl295pvkOq88A/3JSqQzI1zNrPli3fP2FPdzdsbuffm8HX/9gB6/fWmf/yLpHJ6ZFNJWto0l+LfA1cuu2cstXm5ZpEW57zVZu7tgNGRM8vWfNJ7ulMYW2fNJidVpOP9ZgU/ogW43bW/bwq09C53i8ktlh02xPC/wbsvAomboMubKeVmfauHfD7v/sE2ve3rb7J4d20jB79PlLy7fWqnbw9khdFSFw1idJ9CbNlgyb9qx5f8MeP9+zW797Yoc/vLeT9wdhkbb94dA66oiL8vNIems9X2tZvrlq+c1N23p81248u28rz+5YYze+8Z4MJkJ+cQGz8ey2PfurL2z15V1rW9fyvQ3b+ex+NCDp6U+v2RxY9PHTRrTivb1tT/70c9u7f8c+Hn60t2sn9vDTZ7a2rbtCnEB0YTFt4C6jlVZmRZwAzDd3rXFn2+49vW17v//e9r95a4fv3ltn/9Dah4d2eHhojUbDuisN66w1rLupLb5u2M6T23bz+T1rPbhtuRZvvXzKM0y8F5bpAVgL2+syVNPniG5Z486mPX5xL2y3K6vM4w8Hps8snRweWzvu6pM1GlasZIF1c2fVVm9t2w3tUKBr4MFNa97aCHrg1avauPIY4yBcDDGeyu6MZA+WZ5a3irBLQuPmI+s83bMnL+7a3X/7o73/9rUdvNuP10Un7IDUWluxfGfdGttr1pJe3N+znU+fWOP2jfBmtni7Ppf5KLO4hWXIN3Y+csJuCqu29fKevTj6ym7sP7eDbts27+7axt2blq81y+snllc1UFXS6oxR++FR1HYqri4lcdjU7k5f2ONb27b75I69+/Z7O9n/GD7XdCSDrlbL2lu5te5v2K3P7tuNO9rqML7JGwc/rSe37Kf/+S8tf3dsJ/r8zP2tMJmXy8gsqcfwQsW+QGLX86ADT//6Z7b58oGdrFrQyXufPrF8Q5ZcPSl9TOJ1GIy5giGNhTeOG3sv7da9bdt+ec8O37wPb3sef9i3o4OPQS+1k5QMMxorK1as5JavrdjKjQ3buH/Tdh7ftg19Euj+rmXKOhiD9fK/0FEsvwaMYTekGy278+Vz+7xh1tzXTilFtQGVGkr9WfWT8VQ0VpGrsKAbcvWJPD3YN7q2v1pYd3fNbj67Vy4EhP5YRol+z7pp+e6X9uThLdt+ft8OX723thbJjo7DzmAneWadVtNsfcVW72wHDrdfPLTWy/ITcIXsIe5v2LP/+U8tf/3ADlsd29HnCLbiDh2xb3elDRPFZnbj+X17cfyV3X322N43jq1xe8PuvXxSGkCl9awqHA88LPQ7atc12/v0iX2iXUpefbRHq0e28fKW3bxzq9ytLMSP19wgWVFxVC6NA+799KV90exa9+DQTjZyW3m+axu31OaJ8ahkxus33Pdvb9qDP/nEjndb4ZN6tz9/aa3nN61xK7fG7T1r3F6zZ4/3bOfXD+zguzd2+O7AOodH4cG1sbpqzc11a21vWLG7ZpuPboX74uqncdcyjZNincv7WVkJ9R0yPMk2Cms+WDH785/Yo4d37e4fvrPXv//Wjt/vh52EuvqsnRaN2jKRzaxoNsK4I1tfsebOmm3cvmF7Lx/Y+rM7YTylsUIwavJ7m/jo59zjKQ4EZk3A+/je7la6eRXWfNi0wl7aPctsZXfLfvjVb+3w1WvrHret0KJTp7Cs27FG1rSTLLN2S0biua3srtvGk9t296fP7eFffGmtF3d7Bqfo+6yb93rm5/1nWrt47yj7VBlYmtnzDcvyL+3JZtO2/3HL3v7bb23/7dvyPtzRd6O74dkkiGmUY5/Oxoo1b++EN/Pu/uyl3fqTl9Z6dju8+RcMDr0PT/PmGALnIeCdrqep6a6e+5r3tNvoI9s9+YWt7WzYN//ya9vXs4PGkJrD72TBmFAj1G7WsKLZMmvK4H/dVh7dtL2fPLUXf/WFtT65Z43t8pmsGm/QDzt53EkRiDoVdoZt6FlIU1gNK37xY7u/2rL1jTV786uy/z3RTrTajfCoa93wCXI9dMkgsGEdPRNo7vHpHbv3s0/tjvrfl3etHEPHOTH0d1KthpwzCFRddapzek5uZOGlSFtdsexk1fK1tpl2FfRnc8lNxylp+jPyJBgCEyUgPUx10efyVjMr1htWrLbCztj5hj43EXfkVQEG6Gx1PUy0gAhbKgKui65fiRvmgNf04nlmrU/vmnW69mJ1xbb+6V/t7e+/taP3++GFT41/9bKMXs3VPG5XL//pawfrK7Zxeyt8BujRn/7Ydn/+Y2ve1guFcQ1hiF4vFX8qe2kCVT8o3fUTPceFl/7LhQPt4m3dp7a70rLPttbs7S9/b/vfvrKDDx/ClxhkqN1td8JLklojsZWVoMft7TXbeHrPbv/kuT3+sx+F8W9jb8VyrQv7tXLpGiBgkgQwWBlAs7Fjln1xz+58etfuxPByYc7C7hrhYhmQbqiXX2yySHy+azcf/cJu+jqwtjbasHIrb7/BDBLkMhSmY82zKN3TLbt99+d2+yR8sCBsv6/tvsINKRrihPhKEzfi8IsxLBzpAxJxYFU0zVY+2bPiyU1b65ppQa/7obDu60PrvnljxceP5XbQG2uWb2+GtzPy7VY5UR5lhO9+1Y1ABtVniJ+K6T+tM4u1HqK1KJfv3LbWZ7ds7djs5rFZd7+wYv/ICr0N9fHIisOPoYPKmvpUyrrlu9thB5uwe8ZqsnuEyifNV5k9s9RVf6W+UItquVnz0YY1bn1uL/7D52VHGeqqT3eUD1TBCEKCkj+FaQH85v09uynuesAKi7tx5xGPm+brxwqLnbPaUTsGaPcL7TDTfPjcVn/xzAp9FeegsO77Q+u+eh22gQ07YqytWa722dmyfFfHskSMu6fozRktVg+aDFadm2arP71nKz+6a7dUFi0sa8MB1VNrnirXqF8aLnlrZs37Lbv/v/4i1EcslX++Xu42pDr2GZC47NQzlktGZPrkVPPOQ1v9kwe2eRTr/7Zj3bdvrfjwPnwDQwOpfKs01GncaJY7p/gNKO4e4tkEV3mpngoLzGWAYtZ6uW6tZ89srf3UtivWx9b94bUVx8flJ6/W18u8djYs38qj0Udp5KR2C8wks1t+EUyTS2nV+sox7CRl6nFi36H2CPlIV9VOmxvWuPuZrfz8M9v60LXuuw/hEwL6rJc1G5ZvbVp+eydYVmtnktJCNVqrhm9bxk1VlE9goX/icdr4akftzHRr1bLN5/bgx8/soQyLtEAdPpFS6nqZeEr/iov+xFlZaPL5YW6NW89s5U+e2e77rnVf/WBdDVra7fANw3xv0xq39bdW1l1v8ipxmPUrwufBvnj5v1txJIOIcvcWN/AYt928T9U3Qlsvd+3Bg/9g97VTVLwGg9x0UD/oslK7hgFZeQ1qYbNxM7P8Z3dt5Sd3bFObqqhv3i+s88NbK96+C2/PhU+mbW5avrkWDPVkhBh2zVFfJ6MctY9cZ3fJppGcYIggo75bTdv6T1/aj/7yiyA1bIWa3m9CI9UyjOoVfD08uqq/P/w4s/CJoXBvEaDSaKX1bM2aD57bvT9/bp03RXgjtviwb8XBQZjYkrFGvrVhjYfb5fUZDHbi/VELxg9atv2/fWVfNH9WXq9qd+mD849FDu0a7wcrXz6y+z96WO2S49d62J1tUN9aq3a4biOz9V88t2dfPQufLlJ9vb8N/bQzSa+9mqwQRWsoj1etee+l/fivX1hxXOqPtr0P9z7VJfmFdouGRq2Hm3br/p/YLft5qXPiq/iqhx5mn21a8+Endv8Xn1jnTde6r99a99378AaGxgD5noxb1sI3cXt9SuxPq/L3+j1/+ygwCEaNhbWerFrz/kNb+fKBbahff19YV2348bDsv45Pyj56TcanW5bvbFq+1TTtDhPGBhrv6JpK9Sk9TurOIQSunICui9DHZ2ZrhbUeNqyx/YndfnTLbvzTr+ybf/6Vvfn2e2vvH1rnWJ93lDHiihX6NONqbq2ddbv18qE9/upTW/npS2vc1H24vGar6+vKK0kBrh2BpD8PddP4XcaSuzIYbll++wu7/XTP1v9ux/7wr78JhrUdfT6lXVhX32XVbpfa7avVsK17N+3OZ0/szlef2cpnjy3X+EY78+nZapx76LWDS4VmQsB1WDqmF0K0S8W93PL/8Kw0MH9x3775h3+xN9/9YMf7h+WuQO2T8BKMXlDQGHt1e8tuPLtn9794YVu/+KnlNxrlTq1xnBa2Qq+NH2dSNzJZHgIaP0iH9UyybtZ60bR89zPbe3rXtv/2H+wPv/6Nvfv+lbU/Hllbu07GyRZ9rrW1tm7bd27Y7vP7YdfN1o+fWfNO3A1xgs+Hy9MY1PSyBLxbDnLiXLI+b5nvNa37aNvseM8a++u2eaNjrd0NM33WvC/RZUtAeghMhoDUMry02izfxursrVnxZNcah/dsc70bXjRY29sp1yCG6PAQ78kUECnLRaA+FxbPfZ5Pc8vZV/etcf+O3X/+wDb+/l/sm3//g+2/eW/dsK6oz6vrc/Od0O+uba7b9r2btvfjZ3brL7+w5tPdcvyr+WWe3ZZLt6Zc26ofrOuwj381B7GqjSC0Dv3EGo/u2sbf/Yu9+qf/z05+98fwcmVbXwnptC3Pc8tXVqyxtmqtrY2wucKjrz6z9Z+8sNaT9XI9V/PIVaZTrhziz00AgxVHJj2VoobFYC0cFeXClYdrfBwXrM6l0FH5/eFSE9RhcSWV6xfJoAtFF6r7x/KFpCqPrM/1OZN1M5MVb5xArBb6PJ0S1C94+cWLXunC86xuOF4+GazET/IUe+tWdNYrg5dwowsLW3GHEmlRkHWBG1ZaP2cSXe0uorecwtv5epAOhhOZFWulEUDjRmZFZ82ssxYW/PoMchRffzLQSBbhQjuoocUvTFrVMnVmzkbrACvlADQswrqeRNmVjFq6sOC4UVim/du9joqjOomzjj1NrQiBZVw0LIJha+SaF2VdupkVq+XOK8WtDSser5cWEZLj9ZKuer0Di7hYXR9QxDJU+ql32X0HBoWpbf28Xs5h51HXwsJvq7DGVqmbXi/Xzz4GQ1gEXfP20k4oHelENLbYyMJCftG5Zda9Vepg5FsucseFTNVfb/XrW7hxLTYU3dslqUfITxPnmmBXXr6IrDrcWjV7cr+8zv/X6rsAACAASURBVFResdSia+Bbsgocg2VKcs1pN5Ikj0sfRt30azpUQzoRP5ei66TQjkl3dsy6O73yqpyqjxswqPwqq9xUD0JnIFLumTRO1P8iygrfY5XMICsyUXslSdL6DvFOo5x9nAgJ7So9D/XQbjmlbjS2G1bcuW2F9EI1kUFObEsZBajeocySpboEHVN6Gf/EBXf5DzPwGlbK2DahTdRfqVw6kSzXTbFKfwP0MASrbKFtomFN+IxMqcShb97OrHF714r2biUt1Mmv/XDtet1KWSG8in2BA5UpLW+8D4UtU7x/Lje6KuMpC28vd+UnGe6m/tFbzRH6bdU/XmOBhdJFP13MYXekFd0Hyk+w2d0NKzob5YXu14n0QwZa3h96fxIMh4rwFm1oH78WvA+NZekrv8JWS/0p84/ljMZAIe6A+rgohSv7QnVSvNCnZ5ap/LG9wiR0aPcq1ekD5ycZktXSora2+izvDWpn9YEDy6MwSQzjDu2GEwvsvCIHyQiL5OKrHYc2GlbcvWlF52Zov6DPqa4pf6X1W57ERtFegVBneStO1G0J07mutZCP+trOtll3q+zvY13L66cc90gHyvzLdOHYmXhmOq/l70G4EJglgT41lK7rXiD91LW3VlhD19HqrjWf/Jk9/evP7fHr99bV1qb7H+3kuG3NViu81ZRvr1t+Q0Zbq5bfWLd8W4uo8ZqQ3L6MZllD8rp2BAbpkvexUdfCfUzdbLOw5v3cGjsPrfXpnu18+HPrSofffLCTDwfWbncta7VsdWvD8hs71tjTywTrlm/os0Aas8XnNOU5KN9rB5cKXRkB6a7GHprrCc8LheU7ma18tmPNRz+zJ3/5uT3+cBCMcztv34WdQVvNprXWVy3f2LRsdyd8ulGfc27satATx27+kCf9ZexxZc27VBlrHKyt0TV+CJ8U3bXWs7+2l/t/Yt0376zz6rUd7x+YvpLaaDatubZmjc0ty2/qb9Pybb3gE/tf6a/rriDSDy+VKs1FZX0M29Lcnl4cfWSfPL1rLw+6Vmi3wbW2NfZWTS9Kop9z0WIUQgR8XKxj9ZsaFoQXIQvb/vPP7aeffWLdg7ZlrY5lGvPqc80ae2gcwg8CsyLgehrn3rTDtenT69qtba1pjVsvbeWLJ7b35kMYPxQfj+348NDa+hRmntuaXoa+sW35zZ3wQrR27C/nQ5OxA+OGWbXmcuQjHXW9Tcen0S+s0azq6xKZtdbXrHHnK3vwF5/YvbcfrKsXet9/CDvtN3Lt0L1m2c6WNW7esHx30/IbK3H+oTeHthxQF7OWWiLhJwLeyfqAWQsvWvDxC0VR1MmHjj76K8zTjaKoOD6pIblpOvkPG7QkeVfi4wUbJqbjAmgwKvCijJJXL6uXS8Kr9elepHAzCxnHMnshYjqdhnLoPJY1GM0k4Z5kpOv17GXdix7ZhAVlbxtl52k04a9jP++l7LVNLE8Qn5ZtUH6e3tOEb01qm5XwUn8ZqrwUnupDmi4s3JUdoD5p01c2b59ReSeywsKG8gn1K3eTCIz76jtCmDPzKO56HnLdzxf+PczLOkw/PV7qRlnS6YBJCiK5HkcTLJJXeXjACNfTyHhAi9TS1QH6WsmUbK+3FoTD+0V5sD9RLqE6pXd/OTydnoXDzjYx2FmrLiPKHa4FZZDGSY9HVPHcQbGsIbugH1p47lrRyssF6FDmAZl7m3o5B0QpW84BK0KtwdQenj4slid1VoCzj8deN506Sve7tKu8Qj8Y20xtV2USSlm1x8D20WK5W4bLldGf0qf18LqOU9gknfos7VoVfkOYBCBVeROOSuR1U7j6FDcsEEdP4/2f4rufXy9BL8rsvQzJ2cUPI9bAKqpGUURDKBXby+HxouunyjhE8Xj1kri/6u+q54mD2w2GKnrgDwZlYhANLEvrxpiBp5F8MdF53U99iv/q4e4v18sSjkuDJu18FQw0Yn8foifi0uR9x16WeP+WoUqYjNPYwGWNkuN8Qlmi/vu4ItVfz0fxE3nhVGG6N0WdD/XzOHL1F3Wuut+EfAUiqU16HL2r6yyJduow0W0x9Ptkv7j+s758VRbVVQzz0rZQVQm/GHYqTzwgMC8EpKu6BnysoQWl9cwau4UV97ase7hpxeGeFccnttqRNVtu2UorfN4rW9O2vOX9Ttdo+ElWlDkvVaQc15yAdE7jG+lglpm+9lNsZtbY27DiZN26H7es+HhiK8f69p766UbQ37Aluu8CKkR+n0J/r7nCzFH1vP8N45DMTLobXoDIreg2rDhet+JwO+zytnZyUhq4a+fWlRXLNOkpI4Fo/O9j8dD/hjFSrKeOlQ8/CEyDgHRXz0cyPJeqyZB+S/1vbkW7acXhhnX3b9iq+l91sup/m3EMIQN+f6FC43zve1P9nUaZkQmBUQRif+njYu0QXdxcKR/w1NHqOTfOCYz1nDkqL8IgMGkCrr9ypaemnaj1XLdqRUfbYKuvjS/MhnHzpAuAPAgMIZDc26v7vaKqT9V4VjsWb2dW3F6x4njPisMdK046ttpuh13+NUjQSwfZ6krYhT68+Kn5f1/SjLo/JHe8IXB+AonOhsTpM5X3tRq/avMGjX81HaGNDO5tWnG0bt39bSuO2rbajpPFrYblayuWrWsuIs6hSU6UW40p0nxKsfw7BwR8OW0OijIfRQh66g+CWgRLlDlMPnin7P7nKXaU2zfBITlx54d4eFrioIsnRq4WVxVHP1/gi6eV4+GVR+0gledB8tOv3NkunsSHW52lBdax+gRP4+Fn5etS03TuF92+G2LMM9Tbuah8tTTpaV+Z0oBxjjUjIKZav09/assRZfaJqhAljRfLn4oa6ziR0SdzUMWTuF6O4Cqux0/jqACu8/L3ONH/vOWr0ktWGLRHmTqv5zuW8BgpKWOydt8ns69NqrxKQx9f26myrMIrn74DNX24VmO8PtlJTMWpRFUHSYRZHFZlTC+GAYVxL3eHlk0R/G9IJA9OXaWqnaepFTTNn/I+pb6ead2tFcSDQ/r0+vaAWvyRp0qjh1MvjM5HyVE8hafx6xl4+lo/23ctpPyVPpXpst2/Lv+i52IV04Ys/CT6VddN4u9c0iJV2bs8xU/SVOGVZxLB47nbixyORulkLergUxU0yg465hMPridD8h0sLJEV+5jQQ0nGuHJq8cKpdD8BWnGvFaKWNORZxVWgR4hucNyvJmvo6Vnx03AxTPj2yUy7MxVN6Xw8ksg4b9/elwcnELhiAkGt9U/YCUxvQ2vxX6uo+ouFU5+j8ZQmmNJfvQ8adi2laTiGwKQIBOUt++VwKD3VG9KNlhWbsoosf6Hv1j9Bh5PnJyWqqbSnwYXApQiM0xe6/sZn1fBmtHZxXV0xu7FSjqm8D1Zf632wx49uKKfLQqcv1WwkPoOA65m6Tp+n8CSx/80aTcs2tvq7VvW/rpvRLfvlmFh+/CAwTwSkk+EBLypnfbwrb++f56nclGW5Cbi6DprjiWHLDYjaT5VAvV90nav1lX3R1LfKCEXj3zVNQmiJOFoCqLBhsBCf13w+QmlcptyQT3Uw1SoifIkIuP66q6onahZUU3PD0kvtONiQccpGb2jg6yY+Bk70N5XTP2BeIr4LUFUMVmqNVOm/LgopdFww6Xuoq6UZ+zReaN7nh3TRT/n6L70e3a/PTSP4sQvw874EtZOqkom/p3PX5SlKekOK5/0XdRToaROxIV4qKw3z40HpPCy6FX+PK9ePdTgoD4UP8nfZsdh+OtSND0h9ojxvdwclVtio8EFp6n59mQ6QN0p+PUznvnFGPR+dK9zT1PLtQ9V3MkhQKadPVDyp2nFIsjO9Yxn7riEv+9DGLjP38oQ8dKJ61OpZ5R8j9+XTJ6CXtvLWQXVSSbrwgRftlMhh/ENhdVf2lJ61F+yUJI9QYShjpPH9uIrad6AswwBBvmnUUlBf3CoT9x0Ux8Mu4VZtVpdfPw95RE85+kvx1ePXz88qo8us4nm7DBE0LHhIdC9+X3DfSczY/SRff35eleuCB5KTlrl+n3Cxnp+7if9AVU3C/bDfdUHulnUKZ+7lblq+fiHjc3AZnl719EkI5ZMsWHj36tl7kqGuZOkzVPEeMzTeqABlpj8fkHvcerndP3Gra0V+LicJH+iXho/KYxwIHsfdVLaO0+vRw8RKx4PS1MszKI7LwYXArAnU9VP5S0el026IFeLET4YpPKYpF0qTAsddpcI1XNdzpan7JUk5hMBZBFxVx1Ijj+R9c7g/ZuVQ1HWxfo9TmpjuXHmdVXDCIZASGEe5ot5Wxt8a08XBUVDRqMN94yXlEQawUYnlJLqeFoFjCEyFgKtedEMeUd/L5/JkHOFx/HpQZPdLjz08DZtK4REKgREEov6d6nPTJOhoSoPjqyIgPfR+U2VI9VLrSApO/XScnl9Vucn3ehNwHUt1s17jdO5B8au45aC4ml9IZXkc93OZ9XP3x4XAeQm4jimd65W7LmvAefDyOXnt/O5xE7d6sVOB/peEn3seP0nL4fQIYLAygK2uk6DkUdOr43h+OolfWf0RKjlpgv4oIcRTp9HCseIODazFrsk9lXdd1qkIo+UFIEma5LCW8JyntXKfSp2Gx90TPO9ekFa16imjh/sr0QV+npfkp6Kqm/g5ZFayzpGm4q40XgB3zyMnjTtOQS6bh5fXuY2TZ1rGs46r8kmw/vRzV4Eewd0YJXU8yJMNCkv9+o6jzp1K60L7IpdFGxJUi9l3KvHnT6ZUacEkIQ8+pazBEvvz8jv+4LihkDEoOB7N3b5aJMXpz6Qe6/Lnnr+7AySOLEI9Xf18gLzxvDQE8pwvKXREcs9hvDJNIFa9LDpXIdJfPY7CPF49rH6eyuk7jhHTvORVT187vzCfVE7MJ12rUNEk24uTRu8r9qAT9ZF6g8wTK86YAvrqMyhNIrMvrpdjUJoQ1h9bZ6lPlaw6cIGXc9M8gqRR8tMwJUx/aVjqz/FUCIB7DKx1HU2TRIDV5HwK1D935rdkT6eJ0PqT9ag8PB0uBM4gIDXSX6qGZyTpj5yO+11QqptRlz0fj1Ll5x5nZkoECAwhIGVKdS49dkVzVyKks5pj0BfY6jNjmuEMOhknIYZkeb4LZpgQ/CEwBgHX71SHlSyeB6evH40XQAhIEvXF6aUfowREgcD0CSRdbqK1/fkqIKp3fwBnEJgRgQHKWXWtGlvUn99mVCyygUAYE3j/WNfTqKSVtw6qk3RtLXpWYXCFwIwInFfnFF96PajPlUoP8o9VUbJhl8qMaks2QwjUH8uHRLvm3snFcEqPk7BTFKqw6qAvymDfvijhRPE831NpTnmcTj/IZ2CygZ6DUg/xS9Inh+ndbXDC/siD44zyTdLrMDmNqU77nBI3RpRTadK8kvTVosKgBCP8EhEjYg0JulTiROYF5fQl6ztJZPthGl7v+dMwxVd43c/ljOUqsQuoZzaWgF7yMaOX0WKenrU80+O6rFFh9bjxXEkGJhvqqQAxcA4uuEww2Na0jKMYPbG9I5cw1E2jpsf1BApL23pU3Hracc/HlHlmtDMjjFugNJ56+BRADBuU1yC/VNSI45FJPdDdEXIuFTSufMVLVXXcdGnhzpnmnNHLnIYlqvnrVK1c805LO/p43IRJvOTwtGwPjK6fVhHl4fwHBlYxQ51ORekFT+xoYB5pOZXToEiD/CZWKgQNIiDk/udqNCgefpGA62gdlvvLrYd5Uo9Th1n3dxl1/3o6ziEwgoDUZ6AKnVe/UiHpccxbXi6yrzgD4vaFcwKBcQicV4+GLiy5BVbtwggWLjW/ccpFHAhMgsBZ+t0X3ndS5q7xxgDvSRQNGRC4NIGom2Op6FiRLl0iBEBgbAKVSlYHSdJBfkkwhxCYKAHXt/ocg/sPymzUYpenS8cQ7jdIFn4QOA+BaenSGXIVfKl5/PPUkbjnIoDByji4zlDwcUScFWcGWZxVBMLPInBVjTTJfCcp6yxeCj8rv7PCx8mjijNRYZXUMw+uKNvB5VJhZlygcbMbN97gil0D3yEAhnhPpcKzzGucCsxbecYp84g4c1edswp0VviIus40aFHKOVMoV5dZ1RyarPAJEHevrliLkXMFb0BxB4W5NfuA6ANv9YNkDEqLHwQuQuC8+jVG/DGiXKSkpIHA+QkM6m9HKeiosPPnTgoIzI4Aujs71uQEAQgsLwH62uVt+3mq+ST0MJWRHlf1HOhZhXIAgakTuIQKXiLp1Ku1zBkMejRfZh7UHQIQgAAEIAABCEAAAhAYgwAPeGNAIgoEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgMJYDBylA0BEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEITIMABivToIpMCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIGhBDBYGYqGAAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBaRDAYGUaVJEJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIDCUAAYrQ9EQAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAwDQIYrEyDKjIhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABIYSwGBlKBoCIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASmQQCDlWlQRSYEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwFACGKwMRUMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMA0CGCwMg2qyIQABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQGEoAg5WhaAiAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJgGAQxWpkEVmRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACQwlgsDIUDQEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAtMggMHKNKgiEwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgKAEMVoaiIQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYBoEMFiZBlVkQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgMJYDBylA0BEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATORyA7X3RiQ2BpCWCwsrRNT8UhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGDSBAorzMLfpCUjDwLXiwAGK9erPakNBCAAAQhAAAIQgAAEZkJAj9z8ILBIBHizaZFai7JCAAIQgAAEIAABCEAAAhCAAAQgAAEILAMBDFaWoZWpIwQgAAEIQAACEIAABC5JoDJQ0aq/r/y7e0nZJIfAtAmkqhqOC7NMf5UyT7sEyIcABCAAAQhAAAIQgAAEIAABCEBg2QgUYZOVwoK7bJWnvteOwLTm0TBYuXaqQoUgAAEIQAACEIAABCAwHQK+kWllvDKdbJAKgakRkLFKeLh2JU4tWaaWK4IhAAEIQAACEIAABCAAAQhAAAIQWFYCRWm1sqzVp94LSiDMoWWliUplcBXm0SY/mYbByoIqCcWGAAQgAAEIQAACEIDAVRLw9f6rLAN5Q+A8BMrH6VJz/dHa3fPIIS4EIAABCEAAAhCAAAQgAAEIQAACEBiXgAxWwt+4CYgHgSUjgMHKkjU41YUABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgekT4KWv6TMmh8UmgMHKYrcfpYcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgXkkUH1PZR4LR5kgcPUEMFi5+jagBBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgcG0I8CHia9OUVGSqBDBYmSpehEMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwLIQkKkK5irL0trU87IEMFi5LEHSQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCKBwswKPgeEPkDgTAIYrJyJiAgQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBMQlgrDImKKItOwEMVpZdA6g/BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQmDEBDFZmDJzsIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwLITwGBl2TWA+kMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEZE8BgZcbAyQ4CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgsOwEMVpZdA6g/BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQGEogGxpymQAMVi5Dj7QQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCwZASKJasv1YUABCAAAQhAAAIQgMB0CGCwMh2uSIUABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFrRQBDlWvVnFQGAhCAAAQgAAEIQGBMAtpdxf/GTDJmNAxWxgRFNAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIHDtCRRmxZSMVFJ2GKykNDiGAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCAwkoLdK9XM3nuJAAAIQgAAEIAABCEDg2hGYzZgXg5VrpzhUCAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCFyGgH8GaHrGKxisXKZ9SAsBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASuCYEiy6wINiqloUoxxXphsDJFuIiGAAQgAAEIQAACEIAABCAAAQhAAAIQgAAERCB9M296b+fBGgIQgAAEIAABCEAAAhCAAAQmQWA2z20YrEyirZABAQhAAAIQgAAEIACBpSHgi01LU2Eqeu0I6J2Q+F6Ivx7i7rWrKxW6vgTKSSNU9/q28HWrmXTV/1S3oLuzmfu8biipz7QJDO1YU4VNj6ddIORDYJIEou4O1fNJ5oUsCEyAALo6AYiIuFICodtFka+0Dch8bALpCFda65pbWGb6m+YPg5Vp0kU2BCAAAQhAAAIQgAAErjWB6T6sXGt0VG66BNIn6/hOf5mhB3Qts254/M6K8p3/6RYI6RC4OAHX2jBJpC15LTdtzRs0N7gXl01KCMyKQNDjzILudoNbana5xXRaCtf41I9jCFw1AR/zxr73qotD/hAYg4B6U/2C9haZZfrr+fRWocpo/AuBuSQQet2ozKX+Sqddu+eyyBQKAn0ECiusyPTXLV30t48PJ/NHwPvaXslCT5zsltkLmeRRc5LCkAUBCEAAAhCAAAQgAAEIXHcCyYNKWFM6/SgzksBVzi2ds6gj60Hg/BIIehmfpXXc1+6FZVlirBLfEZHRSpg36os7v1WkZEtKwI1UglJP/w2nJaVMtadAIHSx0VhFBirhL3S7Ckl/aWesYzrllA7HMyQQ1U8aWGphZkXRW9/Pqv54hmUiKwiMScB7Vu9Rlaw0VSk74J5eJ69O092OSZdosySQqmV4XgvPbq7hsywJeUHgDAJ1tYzKG7xlrOJGK8HtjSckNdXzM3IhGAL9BKRgU1Kgnlgd6U/7n2gc0V+ESZ5hsDJJmsiCAAQgAAEIQAACEIDAUhHwB5cBlZ7iQ8yA3MbzmuLD3HgFINbUCYyrd1myoWk9Tf3cC917YncfXAjMlkCqm74z0OntKWZbJnKDQJ2A9HREf6ngoMoj4pQxRkao58o5BC5PIO1jozTX1zJIOpn+XT5LJEBgGgSkpQPUeRpZIRMCUyPQGwX4kdy8tHqdWq4IhsA0CdAzT5Pu0siuq1H93LvM8wKRnAFpg5fCfP7hvHLPER+DlXPAIioEIAABCEAAAhCAAAQgMAaB+gPTGEmIAoGJEvAHbXcnKhxhELgqAv56f+xk65NGQyaZrqq05LukBIb0u6m3jv1zbKn/khKj2nNOIMzR63NsmT4mmFk3y31/tp5VwFmKzNh4zlt5wYs3QP9Sr/IjmEGTy85X2wXVTVrklSZKkUxLf4fll+bN8XITCKoqRfF+V66MVvSmPz8IzBGBkf1Z+bKMl9bHwH6OC4GxCIx7L/Z4I3WylqOnSbyVPHjHOYfwKbb6/EMSfxKHGKxMgiIyIAABCEAAAhCAAAQgsLQEzvMUVIM04KGoFmO80/MUYVJ5jlcyYl0lgUQvBjW7/MJ8/bhlHCRk3LTEg8A4BBKdHRS9/AiQJjz9Hf/k2yqeYJieniHbk+NCYGoEQqcb10Ojnp76HJv80dWpNQGCz08gqGrY/Tz2wDJcib1w+YmVKDPq9PlzIAUEJkDA9a/Wf+rUg2QlWFSdbv/iaVWCKnLlM92DWec33dog/TIEarrbL0qdsAxUorHKeT5LMVJufy5zcTbNa2IaLChvT20GFb63hAAAIABJREFU8K3jURT/6yW8Rkf1Cs9z1Qa01zwX90JlU3sMqqe3k4f5uTKppSn1VZ5FfNkgjXyhUo1MhMHKSDwEQgACEIAABCAAAQhAAALnJjDowcf9XJg/CE3qeacu3/NJ3YvkNY7cNA+Or57AWe2scH1GJW5WEQ6zrPcwT5tffRtSglMEemopxS3flQ5KrIBe4Kl0eEBgHgi4mgZVrd7S01ntDWn1xSj0PDQZZagRCGYqYajQM1PpG24E5a4lSk/7IqcBHENgQgQG6GAY8qZGK+G4COtRpeFVkveA9CF0mro7LM+kWBwuL4F+9SgVMaveNkie3a4TIlV6GtdcP8zJEaO8JcshfN07KzLr/SmJh0yuKeZCkldrGjo8yQp6OScpcxayVO5x2Y6qYz3MzyXbj/vqU86daSw87R8GK9MmjHwIQAACEIAABCAAAQhcUwJ6XNGUZ3eM55bwQl98+Ol7Buo7OT+oMbLuCVVe50rQS8rRYhAIeuZF9bYun69j02dhV5Ww3BS29zfrxm3+Ff2S6ug540Jg4gTK3ftlrFIarBTdTtn/TjwnBELgkgS879UtN/a/lUTpb7drRbewQoHh0yoj+t4oq+qfz+qkPe+z4lUFYlyQopjocdIG3iwTlT9hYUlxS8mnPGKGRdeK8CfLq3MsmA6TN+F6IA4CKQFXO12Dscu1LI9G2+PqrwtJBXMMgRkRkPoFFSz0dn95N8niM9wi3FvOi6nvWfa8iUfFF6z6mGxU/DHDptY9LGJ5BzGLw4TM8miwkltY9M9qBtuD0i6y39QUY5GhTKjs02Q7VHYZoPFvnmWWaxwxpR8GK1MCi1gIQAACEIAABCAAAQhcSwKaPAhz9JmVk0VZmAA9T13D5NL0nnFGF+Wq8h1dKkInRUCTQkHBegKjyobJThmqdLvanUJHeblYqknPOGfkcXupe0eoTo8FR9MjMEzPNC+UZ5rk1AYrnXKXFZ1MccJoerVE8nkI1Lq08ySdadxKd6uDcpEp6GwsSTAWjBXKNFmfNeJK1Omi1uvt54n4kCj4x8A0Tj3e6RxKH08zLBz/ixNYJLYqa9/ykRc+KpK62t7HgGSrUhrAXpwOKSEwXQJSYf+TGutPLxoU1g3+vlfbuH3ldEuLdAgMJ6DF0aC/3W7VDw/5qNVwIQsSEgzLVFa/B02q3FMwVvFihs3xlr28bljlUNJ2C/MTmeVZw/KwS2Y5F5FG4RgC0yIwzqU5bBzgY4iwI1s4KUcRE++fkspjsJLA4BACEIAABCAAAQhAAAIQOJuAHlPCRL2mjIrxJuzHeVA6O2diQOBsAnrgDpOaPtenF/rjU3jbGtYpmlZkLSuypnUt/ilOTOcT+INyGvYwPygufhC4LAHXY+lkx6THTetmDetaIxxLj7uWB929bF6kn08Ci3bvrPeRPtEp/W2rn81lIChD1zz8dSwL/oPsrkLdU4EubEBTxZeuq5A0WeXJwWwJROXV4ttC/HwcoHLHhT0dhv63MOsUuRXR0FU7A3Wz3NqWW0ebXi1KHReiISjkpAh4lxkvRSuyRtDhbpGFsUTH8tD/XrX6evkmVe+Jy3FAc1/Qidd85gIdtWes/ret57m8aZ3Q75Zmg+p/Nf7V2OI6/uovX0yijmGcVAc8CcG6ZU7h2li08gql43VXWHxeQfqqPribdUx7ZLaLLOh2mm5Uc9THuaPizkWYQ5iLwpSTQvNWpDqaiV5GsbLjylR0/3NjFKXV+FZup6txsM7zsNlrd4oKicFKXTM4hwAEIAABCEAAAhCAAASGEpCxiv7Pc03cm7U7hbX1JD7ub9ynpnHlzfuT57j1IN5ECUgtwkN2lCqDFX266qSTWdZcNWusWDc7tk43t+OTwo5OCiuyzBp5TCc1j39VweLu/9U5BxCYAoF04TN0b9Hg6rhjdhT629wsXzHLW2EB9aRjdlzvg+kXp9AyiBybQDlMqKJLPTXJeXjSsbY+BaTxQ57bSVHYUbtrR+0ibC2tDVd8/jO8reuT/1GfPawSPOqgPtaon49KS9jkCcyiTxrWxuPm7ekVPx6HifowdjA7Om5beLc/y8IClHRZRlgnolXT+TrAqgieRz0C5xC4LIFKyaL6ug77glNYdGpYljUtk9F2ISPCzE40th1DL71Pvmwx+9KrTGdcO33xr+okljFBfFUludb5uhqq33WdlJ/GuMdhLKzxrxb8szCmUP87zhyEy10keF7/iZY5Xm8TlRmFUd4ShPcRwY39hvRZ8xDqb0tDK+lvZiedrh2eaN+KmMoTD2igRdPhqejDAC4X8zpNcwT6kVmclpRGP4fUGHW0vJ7sku+w2FFYNDTppRp9pFT65Jpkp5LDcWZ20u4Go5Usl+Gr7t1prNGyzxuaFdOUft7SEB8CEIAABCAAAQhAAAIQmDsCehzR200fTgr7r//tH+y//Jf/I1jXr6+s2M7mut3c2TqzzOHBKqseyc+Mf54I/iB1njRnx/WnvHM8bJ4tlBhTJ+Az7+VDdNi+VJ8B0qdU8pYdHB7bd9+/sY9Hbeu2j21jNbdH925Zq9W0RkMT+eWOQaVhVhEexsvJdD29lzsLTb0KZLDcBLKgfeX3zUUiTNK3rMhX7ODwxH7729+aFo5WV5q2vb5qt/duWFOWVn2/UZNIfo30JeBkXgmkFkyTLuNUZpTVV3ZLw5Nw+5Q+N8Pb0TKu+uM339nB0bF12m3T/kBPHty1jbVVy6XCil8ofvk+qnZwS2dNpdVB5IVuy5J1oYQDqLscdwdEKadzBwVc0k95jsr3ouJFd1S/cVG5c5LuLF0PuqE393MLn6pSDxwwSx/zYGh1eHRi3373nR0fH1ur2bCdzVXbu7Flq6utschlE9O/OWFKMeaPQBg/lGNXV0r1oxoDW9a0777/wd7vH9hJu2N50bYHd2/ZjZ3NcOmf2atM4RnOr7F57nv8OaJs7LKPVE/Bb7IExFl9rd7eD/c4PXOFz1dpF6s8PLe9+v57Ozj4YM1GZje2NuzG9oatr7XOKEip2WU7nhF1joLPumVdqKj9Q6oLiRiWaOHKq4pM+DIu+4VSbwOnOK7Q7piN1op998Nb+/71Ozvp5tawY7u1s2F3bu3Egqgw6Z/3yHK9x3G/Ya0wP/6LU9KS2UX1d/SwzhXM3TPaZ8zrszeWHEbZ84tuJTfGTwtdVVyGKlkwVinlK67+9IJiZjJS0bPb63cf7M27D5YXR/byyX37n/7qT+3Tl89tc32ye6JMVtoZ3AmGAAQgAAEIQAACEIAABBaXQHh00UqpHny6hR0dHdmrw4/2/bffDqlUfDCKoeGs98+QNIO9/dFrcOiYvrE448nysrs7Zh5Eu1ICejNET9tqtdDOWjsND+qNait0TXx2izy80fTx6Nj+7Xe/D4YpSiODFf3Kl0bipH8494d+9OFKG/jaZ+5T6tK3UuekcR1rWt5aN2voE1bS09yOTtp2cnxsb96+qVGJutrn25PlcvuCw8mgdKdj4XMVBBan3wl9cOhApU/qj7Udut6KblW7AoVza1m7e2z//rs/Wq4+W+upWpySsUtwXR+rmdYAPpC40Kv+E2ToRQt3mmH6UNZ9WOiF/GO+VfYXEjIq0fkkj9sWpdQx+Zc331GFvGDYWXXz8pUGK+VnL5VVabCiz7CZtvLvqgfOrX1S2Ju3H+zd27dmmfzG+J1VhDFEEAUCowhIxbz/DBodDAD13NawLG+aPgUUjAL0mYoiCwaEX3/z7YCebICyTsNgJY5zBhRgVDVnHFb2DWHRLpR3AJsZl+i6ZifdtMKtV+OYIH5GJewq2OlYUTTCW/6v336wt2/fWDZW/+sL/otBrlpDnkJx07XqSYmfZnnVN0z6ipt8ecs+QloWxsCxxMpH/YYMBvPWSjC80mfYtKOrhhKv3ry1169/iGOIsvfur62PS+Qujg57qSelX1OXExXsvOWu9HJkQm/X8WpRyRwRvXz9b2SmMXVPWu9oWLpy/kE6W14fyqXsi0NInln4hGu3fIlxpRxpjCjl5YIwWLkcP1JDAAIQgAAEIAABCEBgeQgEW5XyQUcTovrEij4N1NInVvp+gx+GyoclD3O3L+HAk95D1sDgsCA2JOSU95myqhRePnerAA7mmkD5wK1HbN9MVG2uBVJNzmsL3qKr6SDF0LpTy5qt3Dqddphe0iJVuVAVZpnmuqYU7roSkA5La92VgUrTulkjbIGurdDDf9rWv6E4zQFrPYN6ukF+zjCGTX4W1zPAnScCfSsWk7vHlZP15WRnqcPdcCIjwSIrd1lpWMOKrnay0i4sHctbjfAptnKGVP1up9zNquyRwyR9OTlbAvTSunHh5LC65NMS/V5yOuRqfKZXnlF9xKC66n5Z9+/3KCX2+4UUA7zkH+p23mLUi3Cpc30aUNZTvQJq7JB1c+sEvVUhs7CmmuW55XnXsrgr1oCOuFcSN+KaQt36LudejhxNgEDQgim02QSKNlBEMNCOw9eyP9bTmq7ThuWNlh2ftMNCqXS4W3St0VyxRl7qevmvV9bdXjbBp3dZ9AIucTRhcZcoyaikXkq5onCazajUhJ2DQDBWkdFK+XJMyTq30mCwZZ3uSbnYX6jfbVijkYfdVs7KYdFazDXurHpdNHzSPJa6vNUNWCPV+Nym+321Q1DvM0Aybe10tYegDK9kqN0M/bJmKXp9S9o6TlauH1+01Uk3aQLjtYjaM23TSZTiPPrgecsdnc6f4UrdlT6XY+GyBnqhwCzLCys6XesU3WjAPYn6nJaBwcppJvhAAAIQgAAEIAABCEAAAgMIhIX++FkUzeU3sszWV1dst/ZJoHIhqXyM88ckF1ee+yOeux462K3mAgYHl4/wyeLCkGhhcqBenmFxS//xyjdaBqGzJKAWC9/fjZl6ewcDlUbTDo/b9vrN+/DN86LbtdVWw27d9O14M8vjxL0e6usq5bJmWR/yWi4CZY8jTXNjlfK4k2k3itwOjk7shx9ehx0pms2Gbay2wmfZZDg4+ufa6+6g2B7m7qA4+F0PAn5vc3cytQpv5skOMKiQ/tEkvKbmG9XuKup/uyfdsFiaWddu3bwZPqkSDFyywrrdciJfy6zlHG8sY9x5ozzz/nky5S/7+lGy5uuaCGshE58AP78OnCbm7VLKUi9W/kLrVhmMNDYKO0JUUWd+4CVOF4cKa1i7Y3Z41Lb9/QPrtDthoXR9rWlbGyvB6HW8gkqP5kuXxis3sRaDQKm95X4/Zf9Z+uj602cxG/b23Qf7+PHYTjrqlXPb3dm2zY21snpBNaN+DjFeXU7t9V5BrhNwdzE0YzFKGUyxkx1WSsbSXRm9Hh537MOHD3Z83LE8b4Rxw/bmmq2vn/VJoMWovZcyaFtUL9c8D7usG8ROeMcSyisCsaWCkYpayQ1XZHulka52pzB7827f3n44CPNRmm/Y3lyPc2hqGd/aIh67TLnVRNikNeKyGkX60QT8Qo7u6MjnCw06MY4+JGU4Q4/KkbrGDnqzq+yPgzbqnzyzk27X9g+P7d3+vtlJu1L78xV8vNgYrIzHiVgQgAAEIAABCEAAAhCAQHic1oKSnlwy29rcsJdPHtrPv/wisDm96ONvSJVTfOGxqvfPWDzjY9bouGFXjNFRFOqLPGPJDOLGeRA8O19izI6AjFXCBqZxcVMTRdJV7bCi76J//e339nf/zz/at69eW7tzZHs3d+0//eWf2sqK3jLVhL7e7NOkkR7UlTROoEZVmN6b7bNjRE7zTkA9VGqwYtbJmvbhqG2//cPX9n/+1+/DzhRba6v25MEd++qnn9vaan2Xq14dS9X1Xs/dXnj/kXS/34ezeSAQ+6MJFqU0JphsYwdpYc691OEiLHrqbVItODXs/cGh/d9/8z/su1dvrGif2Gors7/4+Zd259bN8Ekg6b12XilHDF7ZsowpAfmMNHrwpGO6k5Q1ZpaXihau4rMu5UvlcLHEdSNPSSmLWdOz2ump3EbWbVDikQlOiT/bo8wjSA2LTQ07ODyxb7/7wf72b/+HvX330VZba/b4wS37yWcvbXdXRq9n/crPXp0Vi/D5JDBI6+atpH4VBDeMgcuVaY19ww6DXbP/62/+u/3293+0zseOZd1j+/LHP7NPP3kRqjJWHceKdD4yZXnPl4bY15WAK1jpSjdkMHh8UtjX37yyf/rnf7Zvvt0PL8w8uX/fPv3kqd2/d3ssGC55rMhXGSleyNMobxQ92XE+5Q3zDOWuQKXGSmt7Nn+ZdbRDpmX2d3//T/YP//xLOzzRh14Le/H4of3Zz7+Mu2qWOxKWquctJS2IFkZxPuMqVZO8p0nA23zcPKbTQ8hoRU9t4ZkrfEJQc7mZZY1GmDv71a9/Y7/813/THkFTnS7AYGVcPSAeBCAAAQhAAAIQgAAElp2Ano3Cs7j+yWxttWX3bt+yL370uCJTX7CoHr+S56rksEo37MDTuzso3rjyXIa7g2Tht9gEpAv1vSbU3vrTMqh2pPjNv/7KXr/qWNE9ts1WZp89fWhbm+vWaMa3wxXZba0S1+UsNiFKvygEvF+T3nUys7f72oa3bf8961i707HVRmG3dzft80+e2OZGXevLWroMd4fVXXnwm18CZ7Xf3JU82RxFuqW+t901e/Wmbf/y//6zvX7dtbxo22ojtxeP7tnjR3es9hWWsMNVtDusJkWdQ3D9ZO4qT4GuA4H0fi/9fXdYhDHvP/2jdnE7tmajZXs76/bJ0wd2/+6NaJgzvOZ6sVUqOw21nYbM4TUhZN4J1O/nOtfb/Scds1/+csP+GLbAOrGsOLFH9/bsi08fjFWlaehZep2NVQgiLQ0B12O5H48LW2017Xe/+ZV91z22Rt60vRub9uLxfXv+9M6ZTKahu2dmeokIXl53LyGqL2nKtC/gkideTncvKa5KvjDljQWtNrCINRAPBan/1d/Xf/zGftlo2MlJNxis3Nndtp9+er+q79ABQvkeTS8eRxCYIgG/7jwLjYGlyxsba/bDD6/CZ1y7GKw4HlwIQAACEIAABCAAAQhA4MoJ6Ikls/DdUh029HnTEYUaFTYiWRXk6d2tAi5wMAkZF8iWJFdMwNtdS/paFA1/Db07UljR7Zh129rp1Jrpmr8nSso+wCsJ5RACUyAQX6zT5JH0s9loWLOR20m7HXQ3K7rBv3HJrNHtSwIkeT8BN/RLfDVWaKjflbIFhSu3nc6KTngTNfTPdUWU/kv54+xpn0FsfUY1yevch/V8zy2ABNeNgKuEq5nOs6wIf3r7VGZYGkOoD+69ST2aQvXFwdHRCIXApQi47qZC5Cf9K8O0k5V2Iywsrz5hkcae3XG4rnpd/OwyJqeFIJD2v41gaNUxK7T0n2nfNsutqw8OjvUbdF2MlfAaRVo0BgtTXhV0xAaVGt+WuqxxQzd8fjjX2CHV31GVrfrua6SMVGUuCUhPvd9VAXXcU82i1F3tuBIipTEnWx12WJksT6RBAAIQgAAEIAABCEBgaQjoAUafYKmebOID+0AA/U886dPPwOh4QuBSBGr6ptPysdqPSjfLupZnRfgrV1Jjrr2n8/5iDPPvj8UZBCZGIPSzLq0oP8kmcyt9xkQLqOXkfWpt5ZFxITBnBKIBVihV7EtlhNJbSD1d3p6By+mw4FN27EMCa9703zUgnF6UgI8nghHKWYtJZ4VftBCkg8BFCBTlYpOGDzK6qp7hXNaofvI8/a3LG+X6fWBUHMKWloDUI7zdH/SkNBQsfcrFfi34j1LXpQVHxWdH4Iw+MdVP6avrr1wZXVXhknPK+rUKnV19yGmpCUjjXOtctd3gSm543WvAsGHS0DBYmTRR5EEAAhCAAAQgAAEIQGBpCOj7pfrm7pgVDg/jMa4/BY2Z9Mxo45bhTEFEWHgCo3QrLJjq+7yFFWG2XjP2PoFU2y4InVp4VbguFahUsSjfjHaDlbDSVEh/+UFgvglIh2VkpfFCkYW9KcJx6R8+kT7dClQX0XSzQfq8EPCBgLv1cgXNq3ueed4vTWPgxOdMHVPcJP6ZuREBAuclcFqvK5+gn66kYRRhYdOKehazVNHz5OVFr5d34LkLdndgpAt4VjRPp71MVqPqdhm5p0s5po9vF3EVmZd5Col6WO2lEl4oyLqmlwx6z2wyVtFuK9f0Nw30o/Tsshgpb3l7r4xOerB1VJ4JUjluKEfByfNbmIuoQ+zJuGzzkH5eCHibujsv5TpdDpVQfz0tLXvkchjroafTTcIHg5VJUEQGBCAAAQhAAAIQgAAElpBAeOTOaov8s+Yw/897syZCftKJ8B2J08ohndUUp08XWZaXi6dxITXAO50MphC4UgLlFKcmjTLrFpkVod/Vu07T6H/rE6ZXWvUlzPz6dUCqkf5y7QoULFaSZg1+yfl5D68frvMSIP5AAq4Y7irSmH2boiW7okiC27fmYXeKLCz0l+tSY8qsypiWp/LkAAITInBav1xDgx7HXMJxNB6cUMYXE3O6uBeTcyqVC3b3VITJe0wrq2nJHUnAM3V3ZOQJB6YaG0XLq9BHrOLzW7X12lWUb8LVHSZOVXMUw+LMkz/lja0xGETZlOXLMnphJqh03xya75SZNvo11u950l3KMpJAqtHqevVJQflNUzsxWBnZJARCAAIQgAAEIAABCEAAAqMIhMfqaT6xjMqcMAgMI1DTyTAxFOKWj9jVpGdYldI3KXyiaJhA/CEwQwJS2AE6HIxVNGEfDKwa4a3TdGrzciWcnKTLlWPZUw9o/EVD4qoUdbjsdeMuKuHF7dJwJfinxoKLVk/Ku2AEap3qqNJHHXb1dIMVudqVIpPhStpNjyV6rEijSkUYBCZEoFwwHSgMNR2IBc9ZERikgD4uKj9KoQHy9JdMZ1XfEfkMQjEi+pUHLWN5fbwr+FX9q4PQJB4l7Owa7ZDkV+pwvdX609ZDOYfAVAm4siqTmipWp0F504iTLxGzcpNnikQIQAACEIAABCAAAQgsDYGwcLo0taWii07AJ4jCJJE+SxE/TZFOGpVxyhfb0uNFrzvlvwYE/FMq2lklGq4U4TNX16BuVCEhMN2JwCSj6R7GavRNcvoyf1jw7xmu1CdGp1swpENgTALxyxjS4fCXGFwt1NvvY1aXaNeDQP0O4uc+1vXz4StS14MDtVhsAtXYIVQjbAlkhZU7rYQx8GJXj9JfBwI+OBizLv454rIv7tfwMUUQDQLTJeADhOimWpoeT7MQ7LAyTbrIhgAEIAABCEAAAhCAwHUioAcXbQMZt4LU7L3eceIHgfkjMOSRWq9LxwXTMFlULZr2ajAkZS8CRxCYNoFBShgWSqW1vSWn0BfHBdXLF2lQppeXioQlJpColA6rv1Rn3XOJMVH1BSLg+hpcGb1GxV6gKlDU609Aaln/9XTVlbb2BDcoUV0I5xC4MgKpvpbKWu5zdWUFImMI9AiM1X9mYXc2DRzGit6TzhEEZkfAldPdKmff4arymNoBO6xMDS2CIQABCEAAAhCAAAQgcH0I+DNLOV1UGqmUC6cYrFyfVr7eNQn2VnG5X2/o6Xvo4f1+TRyhxte78a9B7ao+OBx0F+zj9tegAajC+ASko66waaqyE042REeNUzwczyGBRI9dfYMbdHyYos9hPSgSBAKB+BRXlE9wVUed6DmgIDCPBHo7UsRF/4GDjHksOWWCQCRQWQ2WHW7ZG0MHAgtCoNLf6ZcXg5XpMyYHCEAAAhCAAAQgAAEIXDsCPtV57SpGhSAAAQjMLYGeZVVcdprbklIwCNQJFIU2aYs6HI91Hrx6ql1PxjkEroZAsoh/Wj0zy6od266meOQKgfMQ6KmzL5PKp+d7HlnEhcCVETjdGV9ZUcgYAucjQJ97Pl7Eni8CsxsvYLAyXy1PaSAAAQhAAAIQgAAEIDDXBKrFJiY557qdKBwEIHC9CIQ5+r6J+nLBdHbTR9eLJ7WBAAQgcCECmfperfXT+16IH4kgAAEIQAACy0YgDBmCtXacRWMMsWwqQH3HI4DByniciAUBCEAAAhCAAAQgAAEIxMXSYLQSJut5OQ+lgAAEIDBLAr3pzT7rlVkWgbwgcCkCYaeVS0kgMQSumkCvJ77qkpA/BEYR8Me1epzCyv/q/pxDAAIQgMCUCMheJYhmDDElwoi9BgQwWLkGjUgVIAABCEAAAhCAAAQgMAsC9eXRzMr/ZpE3eUAAAhCAQHyr3z+jAhAILCQBjSbqI4qFrAiFXiIC4fNVSX31SSA2WUmAcLhYBOiCF6u9KC0EIAABCEBgCQhgsLIEjUwVIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwDwRwGBlnlqDskAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBCRAIH6BgS6AJkEQEBCAAAQhAAALTIoDByrTIIhcCEIAABCAAAQhAAAIQgAAE5pQA346e04ahWBCAAAQgAAEIQAACEyZQGq1MWCjiIAABCEBgLALhS2y9f8ZKQyQILBsBDFaWrcWpLwQgAAEIQAACEIAABCAAgSUnoJdMs+RN06IIs0dLToXqQwACEIAABCAAAQhAAAIQgAAEIDBpAoUx5zBppsi7XgQwWLle7UltIAABCEAAAhCAAAQgAAEIQGAogazPUGVoNAIgAAEIQGBiBJienxhKBEEAAhCAAASWkwAbZC5nu1NrCEBgaQhgsLI0TU1FIQABCEAAAhCAAAQgAAEIQMDCzirMeKIJEIAABGZBoDJWUbebGe+WzgI6eUAAAhCAAASuAYEwhojjh2tQHaoAAQhAAAIjCGCwMgIOQRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgMDFCWjByf8kpTJiubhIUkIAAhCAAAQgcE0J1McMPm4o4jsHfn5Nq0+1IAABCCwlAQxWlrLZqTQEIAABCEAAAhCAAAQgAAEIQAACEIAABGZMgMWmGQMnOwhAAAIQgMDiE8BIZfF+qSwQAAAgAElEQVTbkBpAAAIQGEUAg5VRdAiDAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEJg4AQxWJo4UgRCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACowhgsDKKDmEQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhMngMHKxJEiEAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEBgFAEMVkbRIQwCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAYOIEMFiZOFIEQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAv0Eiv5TziAAAQhAAAIQgAAEIAABCEBg6QlgsLL0KgAACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgMFsCGKzMlje5QQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgaUngMHK0qsAACAAAQhAAAIQgAAEIAABCEAAAhBYJAKZmemPHwQWi4A+CeSfBXJ3sWpAaSEAAQgsPoHYFzOQWPympAYQgAAEIACBSRGojQv8tHpqc49J5VeTg8FKDQinEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwLQIVNOe08oAuRCAAAQgAAEIQAACEIAABCAwCQJZ+drBNJ/iMFiZREMhAwIQgAAEIAABCEAAAhCAAAQgAAEIzJjAlF9ymnFtyO5aE8h8erNge6Br3dBUDgIQgAAEIAABCEAAAhC4TgT8SW6adcJgZZp0kQ0BCEAAAhCAAAQgAAEIQAACEIAABCAAAQgknwMCBgQgAAEIQAACEIAABCAAAQhAoCSAwQqaAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEyRADurTBEuoiEAAQhAAAIQgAAEIAABCCwsAQxWFrbpKDgEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBYTAIYrCxmu1FqCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgsLAEMFhZ2Kaj4BCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGAxCWCwspjtRqkhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAwhLAYGVhm46CQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgcUkgMHKYrYbpYYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAILSwCDlYVtOgoOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEFpMABiuL2W6UGgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCwsAQxWFrbpKDgEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBYTAIYrCxmu1FqCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgsLAEMFhZ2Kaj4BCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQGAxCWCwspjtRqkhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIDAwhLAYGVhm46CQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgcUkgMHKYrYbpYYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAILSwCDlYVtOgoOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEFpMABiuL2W6UGgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCwsAQxWFrbpKDgEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhMhEA2vpRi/KjEhAAEIAABCEAAAhCAAARGEMBgZQQcgiAAgckT4IF+8kzHkijw/jdWAiJBAAJTI0BHODW0sxGsBuzGTnU2OZILBCAAgfkmcMkbG2PU+W5eSgeBSREozM5hCzCpXJdbziW756WFlyiq36KKxA9FXlrNuAYVz+iIJ9WK3jlMSh5yIHBlBBgsXBl6MoYABPoINPvO5vJEHWb6VHC1hfTue35KdLU8ppa7g1YGY8CeipZ4GWL+U8ljagDnU7AY+t/CWMu5HjjSMfTRo16t66QFPSv/0gJNoR6OqrCuZfG/NMvlOHYKUwA8CKCym1FWg7LH75wEXD2UrGq76qAmLI1ca+QRQTUhnJ5FwFnWEHuyvuBwopUWGavoT3eyhkcNTaqTUpTC9VuYu10sL87CEpB+DtHjha0TBV8AAlI8/5MC+l8seug34zH6uQDtuexFdIVFWaelCUU1AB6HsdpjnHjTKu2Cy3V1VjVAefnGjKo4aGrl8sKRUCqpc8jOVtlUv+kmHNxAN0WlCMKl+Tq614G4xvcU2BQuejg+O2LOmEA5+kov+lJdXYHdVbH6FVkh/T4zLjrZQQACS0dgQWbR047zattofkpytRyWIvcBd2Ta//It7wzdvbzEGUrIFrHUUuT4MBoPzyR2wWr2LhlNMJSTDGfmtXARBOcsQOPEWbiKU+CZEqjrWP08KcyIoCQWh6MI9DqvU7FOd5spcB2n52ny0ynTUI4hAAEIXA8C3oG6m9Sq3j16l1n3T5JwCIGrI4BiXh17cp44gSHqPMR74tkvg0BYTrKVnaa7k5SNLAhMmoD0NOpqqrLp8aSzRB4ELkUgmKgNkDDg+a0W6+wYtQScQmBeCNAnz0tLnLscc7DDirTndPd3yneQkp1Odi4AFxGpLC+Z7bnKuLSRBTkqgZy0rWbVBiHf2Ni0+SU1MbZl2nYLwbRP8YJGLFAPkNCOsPuqM6pJL9E4ZdLyX+WnPQZ0tiDWkaOoxDCnmEDq89JJn8cYMi8RJSnGJaRcaVIn5tRUGOnLNajaaK4jK6jAlEgtcnrqANPc0vDUf8mPheoUmj6PgTF61ILRoifo7aziAxYPKRP0n/WEcASBKRFA5aYEFrFnE9Bd+4z+82whxIDAFRKQ/vqPztRJ4C4ogVSdvQpRrdFuB4I7PwQGKeyAZ7Z6gVHmOhHOZ0YgUb7kcGbZkxEELkRglNEKinwhpCSaTwI+LYFaz2f7jFGqOTBYUSldk8qj/rMYLM9eQK9qF1Q+iRr0c/9hYof5D5KF33gE6k1bZ6wF704UpTD9aVp06AL4ID0ZryhVLIlQnuli+9D8qlSLf+D67zWpt4X7n8v1BlaiRdh1sg4hlNsr4a4+sTNo5fNcZKYfOZbRryGd+k9Lrelya6iPAi/a6C48trFO9efXkF+7nv/CuaF+XqtEkaNXoa+EqJMIqqFPIulEgRcFOoqQqOq38FRDLURJNXK68tSx9HMa9EKmV/1PUjHtRpScVkxU+7r/VRd7kfN3HYuX6YiqjHPdpiOCrlnoALy15PrxiGwIggAEIHCtCMR+T13ooJ93i2eFD0qLHwSmQKB3t9eRn7mijpehpxovNrEgMCUCUkT/1ZXyfCrtUnAhMCMCrrzuKluUdkbwyWaSBLzvdXeSspEFgfMQSLtTpRvVpXpcd0fFHyXnPOUjLgSmQSDV4WnIR+ZMCVyJwYrrUNnX9fd4OtNfGSeGeRR3HVFcM3Z57n2WO278NDsdp+dn5TFv4b5Qk5br7EWbNPZ0j52tuyG3zKxtZsdmdlKUBiQyIlG5VzOz1WELmn1Czl9u6ceJmR0WZocxD+XZzMxWzKw1yljm/NldWYr0OtCx/9ULNC5Oj+euy8lqHrVTjzY/7sACZiYTBF1HCg7XzsB481ONUJKsvH6OzOxIBhWx/DIE0DXkdZlIqSMPXbP+p+tWx7rRKD9dPwtrhODtrY9W139uvxLuSXn4HFLd1EDX14CUdUljnRfRXC+9ZiVberloP/Xp0hH1udIXuaqLdEU6I91RvRaxbgPboqYE3oZy/ed+fu6uJ5XrfyHMAzwi7kAC42HqxVI79M5cZM+nF54PiugJcCEAAQgsF4FeNzm43meFD06FLwSmQqC8l0spUcypAEbobAhIfaXM+qHOEQTOYhDwvtfdxSg1pYRAIJCqbXoMHghcJQHpoo8JVI70uK6nHneQ/1XWgbwhcF4CdR0+b3rizxWBKzNYUX/pfeagxSjXs2BokSy06gFML7L6wrHL8fjj0PV807iD/DxcYSqjFlrzesTzZOwCZ+yKlRYEtTCo4utPxVZ95sH4YhBCx6zFyw+F2Q/HhX39w0d7f9y1rNO1G1nHXtzatpsbTVtrlnWZBNagbzHPrw8L+/UPx9Y5+miN7rFtNnN7fnvX7q43bUPwFunnQNX+WXn9qK6+CKxgr3sSNdRwUPsMqrrHc9fjKI9gtJJexx44565YqD6+qK7rSE2vjtNVoF7feamS2lPlftc1++ZjYX94e2Anhx/NOie2tdq0l3f27M5aIxgFVJ3xJSojVspPRl4Hhdm3B4X97rv3dmSZrXSO7dZqZk/3tm13oxWMzS6R1RUidsuUWISidz8q8vJ6Kq+f/trJT+0hX7/mLloJyXLW4u3XsRt2XFTuVaVTHT4WZm+6hf321bG9OTqxbvvYtu3Enu5u2p2dTdtslPvVXFUZp5lvqS+98ZC3rffHdX1RuPzU/8jtFmZ5dnm9mmYd50W2rr/+KzOWTFBjgA71J/5+zYZ0MY63h4d7UvkrnofPS50px2ITkH75b6DueiAuBK6KgHeC6ju75U5z8lIfWf/RR9aJcH7VBLyPTdS4KpL70fdWSDiYdwJRWet6jQ7Pe8Mtcfm8o43PX05C3h4k/UWHnQzuXBKo9b0qIzo7ly21XIWqK6EPDgZQ0BpRumFwPWmVxDvmyoMDCMwngXQutyqhXwNDFbyKycEcEJi5wYr0Q4qjRarwV5Q7V7TkCoh3gFGBtEDc1m7rXbOGFgS1SpOZdWS0Endh13n4FMM5gLqejkqiOOq05WpBqNUxW/NVCS/rKAFzEibO2qVEi4KHJ11rtztht5Cd1VbgJqOVebtexVx60i7M9q2wrw9O7B9+/4198+7Iuscndq9Z2Hr+1NZaN2ylmVXGA96uF62P6+ZHM/vjQWH/7de/t/bH95a1D+3GatPW11ZsZ2XbNhoXzeEKlCKF0i0XOMN1pXZXNeLgRHqiqB7dSzpuTQfFy9xIRdevDKTy8jp22cprUDoPv0pXZRMT/WQ4pevnXbuwvN2xtSy37ZU81GfmnWgs0ykngellV7nfdQr73ftD+/vf/9E+vH5lefvY7t3Yshvrq3ajtWOrqoAaQYku+NN1oz/1M9rJ5YMV9pv3Hfubf/2N7XfN1juH9mxnzTZaT2xtdddajZ7BzwWzvMJk/RorQxVdT2Idbg/Sed2filLfw/VVx5u01XkrEvrFyHn/uLCPJxaM9rZbmW1FK6r+Ep43h9nG1zV2YKWB0z/+4Tv7/bt3dnJ4YHt2bCs/emFbG2u23pibq2yicPy6kVC/ZsVD9z39dYoijEGkQ3kePzJVlNeOiDR0nFhIqN0Xqe0nCvMywrzvCzsk9Xb7URvoVp93y2tb/ZZ+3la65jtxfKoxonZh03hKaeo7i12meKS97gTUE5y+eqVniWoGCDrnGr/u+rAg9YsKGiY3/T4U+1BpdKq/qpHO/RG60mF56ld5xHMcCMyIgFTPdVV66z/5exjq6VRwF4GAP1u4Xkt/1ffGIWx/FRQpUfDaaX9cziAwaQKx0w0LpVG2661c/Vx/EzWNITgQmA8C0lUf96pE6m/r+iv/1G8+Sk4pIFASqMYNsaP157WUj4KqfljK7CfpcZqAYwhMlUBh+q/c1d6VsZeh1FJ6rZBKn+PLxszT9jjN+9HMV4E0wa9FTb2B//6osIN2YWtW2G4jt73VLEzyS4GkYHpT/32Md3TYtpVmbhubuTXzLCwOvjss7LBdhEXlfMwd2SU3/E7r9KkVhhBFCYrCmp2ubXbb9nBnzdYasX8eJMPlX7Hr9ZQr3m8Lsz98LOy7797Y4eGRrTabYYeSx1ur1tIqy6CfC1HYkCiDkk3CL2CPBgMq/7ujtv3hhzf2u+/fWfvwyA5XCvv84Z496NyoBn+XLaby9Jv1sRX29qhj//b193ay/96yk4+2t96yDy+fW0cr0bMGchmoSdudZKXhxYduYW+OzY67RVhY72rXlVgtOelPi+/n+SXZhWQy9Gp1O2GXhL2tpm3ERcDzyLyKuKp2WJCMu5R8/aFrv3n12uzwo+2tNO3x3g27e2PdNmSEcxUFrOdZA6/y6++oa/bDwUm4dl5/820wWGkfn9j7Z2070YJ4vM1fVqVdTZzbDx8O7de//8Y+dApbbx9a8/aOfX7vrrVvFlYsksFXnXN6Hj9btl+YvWoXdnIs48oiGBKsFx27s9my9eQuWzVRdZAKO/tY/ZM+76T74rf7XfvtNz/Yh+O27a617PHOpj2/tRYMP5WlBmZn/tRYFyzLmbLHjKA6qb99d2T2h+9/sH///gc72n9v77Jj+/LRfTtpF1bo+29Rn6+4uGPWqj9aHbPfZ4KhU/xslsY7+93C3h+aHbULO+6YHUun2idhV6xGnlujkZs+ONXSJ/HyzNabuW2vZbbRzMKuRfJf6M9u9WOb6ZnugWqPMF5ql/q4327bquXWKjLbbGZ2cysLfGVUpGv+bbewg0OztnZ+M7ONvLCdVsO2V9Q2Y16DM60lmc0ngV5vrb5B+pX2ERqbuTGU305O9YN+Az4VMJ81plTXgICeG+KOjdJZN7bUCyXqS6XDUkv9ScP1J/3VfUqGffrTIpX8UdtroA8LWIVKb91QOOl/VR3ppf/pOc91V8eus3J7PfgCQqDIC01A/Wvoe6XDOo6fVvU+2PVYOqtHKemw+mHXYRlbp7/aaRrEMQQmSkC62817+pt+/t3HD8pQ/avGCytZqbc+ZpDrxxMtGMIgMAaBoL+x3w3zB3rRKI55ldyf49Snqu9Vn+s74qZzNZJDvzsGcKJMhEDYaT9+rcLHwD5u0Ln3vWn/qmONGzQXIdf1WP7hhwI7CdyZEnDFc7fX7/q4WOMKLTuFca+Oo/7OtJhkdikCaruZ/XRD1g1dOxV8fVDYr//wnX379p3truT2fHfTVu7dso3wun+5aKBPwfzuoGu/+/qjff/N721jrWmPnzyxnZtNOzgu7Fe/fWWvPhwEo5Us0zKOlLWnsCMrFt6AlYGMxy+NZfrSFIXlhT5B07bVzpH9/+y9Z5ccN9Im+qTP8qZ904oyo5l93e695/7/D/fTnru7r5mRNBIl0bQtb7PS454nMtFdXeymKJEckWLlOVXpgEAgEIgEEIGIXSuH86fPsNf04ZXhH26k/8Bu9AeHHXWQKvx/P17g6Y8/YT4PUK94+L8f7aP25X002vVXMWdjrR//sNFUDiUquULgiIETFz8tF5njIXc95FmO1MqQmgaycrH0bdFjfk0vnsmnEUwkpofEiGAYGTLLA/i74pl1An341xTcNFihscrLhcJfX77EIkoR5wZy0yl28t/Vfzb5QbrN5sOCBusGLgZyuGmIah7jqFnDvzx5DKdmXhl46N5X5HzbVnx3baD5gbxAjyFn8xz/+6dT/M9vv4cVBbjfquLfntxH5euv4PvXHn7eHQZvB4n46zqkBpBYLmKLvOwCKkOYm8gMW5QN74rqbEv+hM8U6WZilZuIYcJUScFnH+2yLqmkj5tcSwX3SCn8vb/EYDAQg0AuCO45Fv7Hk8c4ahaf2Zu5NKw3PxMD0jakcYxS+M+TIf7f//m/EBsWdqsu/ul4B61/+Rqtqi0DszeC/LZIvVEhr09EZSzrlVn87ltITQeZ6SBVOVJlIlOG8PLroXy4b9c5R2PJZ/KNUQC9edGLWJ9huyYKZ6M5ZoslFqsQqyhCGIZQHItYBmzLlJ9vG2hWfew2GzjqNLHf8NH2DNRNoOkUxit6MVqXuT2/hgJmadxXhgN8NlZ4PuijP5/BjCLUTRv3uh3885f7qFuGtN0UCj9MUpxf9LGYLcVQbcctwp492u3gqOX9mhHpa5DbvvrDU4ACYU3xT9lA7z40TgxBw/zCEK1mGmiW3sluiO51IaOvbyT4w1NwW8HfgQJkNfl209Cv5NcVlHjVCzMgyYEso1F0sVDkWQaqLlCDgSrxLZVPvwPq2yK3FBC+JP9yDE/+FaNhKAQxEGXFZg7tJZSjeCqYxEDYMuCXC/ccZ/G3PbYU+D0oIGOFNf6l/OWaxSqlR2UlHqqJFxfryb++aaDmAHXbQLVcwOdQYTtc+D1ab1umHj9wrZe6AXrnXabctAFw7YpCmjKYSlFXATULqNpAxTLEeEUrMbYyeMtLvwcFtL5A8y/Xcjhvi/OSh7nhqgzb7BsGKiZ5lzxchBenERZ5W8tfff496rIt849NAc7D9PKAXFM3UOo49FrDinzLuVtW6MTIj5StZFdPcVNWIX99yxADwlv5lcBvffHHpu+2dr8nBQo9AVmPMpnjCo6NqS/hGnuoFJwUMm/j3M01DXE+IRhv+fX3bLg3LluP9d44w9skJBPReGIChRejEN+eXOJyMsFR3UPDBrKD7lVYBS7WcuL1916G738+xWxwjnbNg793CBM2hiHwY3+I89EMqzSHYVgAYzNsSslSaF4Zs4ixQTGAuFaDkVv1cX1tUEmU5zDyFJUswrGr8OcHR2hWPThu4Z6fuXSOD0k+Eyf+9GBqnlIBM8MgyBBlFrLExPl4jiBOJN2HgbvGWrfFmuAxDPEUwUEgBRE/svQOkRmFh4jrHL/tKkeODDSvKLhCJlFkKbeC3I5g5Dly00WqjMITyW8r5nfLRcrqieEyAy7mS/xw0cckCBFlJpTlSNgt6T80vdWHeDvi/dozecceRe4i5LsP9iE3WaKaRYj22njy4B46uQfPKgbp1znX236zrOtU/6grjQFryIHcOFI4n62wyF2YeYhxlKA/WyBIEqS+KwO3fxRub1qOUJQW1Ny9QiMAy0NmuYCVIacBmGEXodXK1tV1flP4m+l0fs1nmeUANocGJpTKxCgqV6b0n9dzzSbkD+Fe8+eVLblwPp9yoXuSAM8nM7w4u8RisYRjGLjvO/ji6BAHzXo5IS2+Qq/2pTerH8sibcVAJlToBQkmqYHEMGCGKQaLAMPpDFW3gwotZl5zkK/5Y6r132uy3PlKU4YJNKw7E9/ygniwXilDK5lmwSfk1yyBMi0owxRcb8n60TwijXiQPrzmQFr4RlGucDwU48VwgovpEuMgQpyk4slL2ukq9GEO0yhazjYU3GmA570xGg6wW3Fx3Krhs702vjraheMU45PrMQ5Lvj50m/HMN9dcfZ3mU7siD3JyM0oVfh5O8XcxqJ7CjlboOI7Q6Umyj6pV8CsNqn8czvDivI/ZZAE2TddK4eAAe6066JuFE/2blP/UqLqt7xtRoDRWoVzg2FbLhv4sRX88xSxYwc1iHFQsfHW0g2a1Bs80r3lLCxYWpjv1GxW8TbSlwG+nAPmV42MqmuZQGAYKvdkKF8MJZqsIQZggDGNwQ4lLYxXHRNO3sd+q46DVwH6jirZvoFJ6XNl+i357W2xz/joK6HFYEcIUGMUKvXmOi+kM00WAeUCD4VQ2yFiGDbdcqCfv7rfr2GtWsVMz0bAL45VfV/o29ZYCb08BPR/kuFXk7wq4GMcYzpeYLFaYrULEcSybrBiO2TNytKsOdupV7DcbOGw30KkUhu6lE8u3R2oLYUuBN6SA5l/OpSapwmCpcDFZYDRbYEr5G6fIuL4PA7ZhwDdSHLSqMnbYbdWx03BlgwbHD7es/r8hFttkWwr8NgpwNYbrcZS/NLQaJsD5NMJwtsR0vsJsESGKE+QZQzqbqJoZWp6JnUYFh9029lsNtCo0wtpuMvptLbDN9aYUWF/zI9/KWgP5VimMQsjcbbRYYbTkZrmVbJZL0kyMBW3Tgue4siGuW/Ww16hip1lBt+6gzg1ypTHsm+KyTbelwLukgObtYnW81JOUY+LeLEdvOsE4COClCboe18qbONppil5BDF23i7TvsjneG6z3a7BScpHEpSwt+WjpdLlSeDme43QwxmQ2R8fpAoYD07QLYwQFBAAmCng2HOFFfwC1WKBiKtgwZOFAxTHiYIloMUfEbVw0WDH0kFWzL9duqbKhBwkbOWzk3K3N0YP48cjFlwfVYQZNuGkGy59EwyrORp7BYtiMPMYqs5BnmSwIX3mRKIxkf0Ft/wbtx+J4vKOOQzB68Y9UMeULFcPI+Eth5AZUZkOVu9dZ/I2i129eeVni+s5PGuMCsEahaAkUCjwq77QCr3yh0709OteQCJoEEb4olYRU8RZ7Bd++pN8LAmtI9k/jCPEqQLhYiTUt+0fpYqVEjZYqBpRBhbGF3DCF/tI9DAOWwYFMWpr4kFTsZWVn0JWTnREKWRJAZSGCmiP9hyFT7nYBcd0GAkYaYoM59TMm2Eiui34XZ4LWilyVp1DsN/S4RCM28XmeQGV5Qbb3gYeu5+tgM80t7/n4Kjvfy4/taYryQBmlKpsi7x3ouAhel0fayzWNA00DBj8AUr6CMtiHPsZDc8JN3FkXfk2otJmEGYbLBPN5LAYrtSxDnHJaUFb/9qa6CfAN7gSTDGJIaUEh4TcpM2DwnCYwy91It4Eivpxgc4FoQC1TFKJqGWhVXFEm/RoFOz8puhUbyPsAACAASURBVO7jRCGJGDovQd2zUfMscTt9O9VuYkac5CfGcYztZ5ZGKppxr3mLTz7Wg/TiQYU06T+GAj15/HA+wc8XA4ynUyxXIZKUhikGbMeBY9uwbBp5cQySIS9/WZ4jyHMwZM00jzB1TISLhnj/+GynAzh22c9uUozcyDbjAsssoueGHI6Ro2kb6Jbe7Uo0f/2JjcjjZpHlww/7RNS18nUcK/SDCP15gOF8BS8KUKv74m2P/MzqMT37/OU0QH8aYDlfinGzRwdsKodvvQnnf9g02WL3/ikgcq8shrv79cKnzH0GGV5eDtDrDxGsYvjZCo+bLnbrdXheHVRAyWR7E82PsP9tVmF7/+FSQMtK/R2hp6nLBDgbFWFbL8dTjCcTrFYR4jhBElOy0jOYAcc24dkm2vUq9loNHO908Hi3g+OGhY5bhFuj5NS/D5cKW8w+dAqQT28ThbIUUo7DOATmBqqXU4UX/QBnwyGG8zlWqxBRSMPhBHm5IYre7TzHxIvJDN1RBQftJu7vdfB4p4o910B9zVvFh06bLX4fGwVucrMex2tjq36ucDpWOO1PcT6cYDpfiIfGMIqQlXNQuu+3aXTlOeKdsVtf4mgnwePdJu63bOx6xlWYoK38/dj44+PCl/zLH8e7soF1qfByGOO8P8BoNsNiuSrGD1FShmA3YZo0WklxMq2g05hhr1HHo4M9POzWcFAz0VoLFfRxUWOL7cdGAT0G1mtpPaVwNlN42Z/hYjTGZDrHchmIsXaSZKKzoNE2+df3TJG/e+MFjna6Elr+XtvHnk+DrCLslXVT3H9s5Nni+wFSQPMs520B1/9oYLVCYaQ9DtCbzDBbLrCkXigKEScxspSpuYxvwrEc2XRQ8z2Zvx20a7i308JRu45Dyl96vNI6k9sG3h8gTbYoffwUIF+TS/njmIJOMWg/wHWJ5yOFFxcDXAwGmK9C+MkKx3UPyHPs7DRQuXWG+PHT5I9ag/dnsEIuojKUxgXc4W8UuwbpLu1soXDKncSLSNxOeZUG6vW2LGhRD0xFAHcLnIcKZ9MFpmGEmmmhVqmiUfHElbBvWWhWKljWU7g0WCl3YhcNxcJpxFJ4zKCRCkO7hKmJNM6QJzFMo3Cx77vc+QVQv8DwP1QK0ShBDBOIfJ6JK6xK5qLBtI4r97JQzGJ43CWcf+m9qD1ES1doQO6CUxbzyknDvwUHguKkk4szPHu2gZ12E/PJWIwLOGltN5twnTfYV3EbXiy7fM4yeHlbsk2c17KVap/NXGL28AqswulHoWxnjbSBRJH6Dcq+WfAmWuB+dF2HdbKKeakwcWElI9huovwKtNc8WAf+hnB+DX1fU7LwAYuka1hay7bqdeSGDbruzsRghbzP3QxsmmLJIjeK8ByJMhEEIbIsF0txw0jhmBzEKDgWxUihZDVooMDsYvxFYwV6WLHkV/F9WJuKvBvtskGQdVrddi0F/QLjrefbAH9NqxtIFI9pbKPtLNh/XBvNehUVjx5WHNR8C41qBY5lSTpdzJ1FXBf2665eB5jvXve+LEkn08ZGNFaRVi5DWxHnN8ObkH455VV5JWBBUeyfSrmq7ZpugJNUpRwsyriV72/kWSNlmf0u9G5k+4W0a1Bfe0kwxJE/CWdDD0yWj9Qu+hC9P+UljQlonXI38HltKdcvmV8vJFZchoSpoFmrwogyNCoWWrWahHrjTmYpS9ezLFzjy0WiXqbwH2djpJMBDmouPj/cw1GHjvpZBo0juCNkDeMNhDUsbXzx/SjFZNCHl4Z4vNvCvf2uWN4TQgFFI7MG87pqwsZMQYOqIiQca1rk1jnXkr/TS8K/wurGzTst5opXSH8Opn+eK/zX6Rx/f3GG3ngK5KksKLueiarno+r78D0PrkensUqUJ9wtSWOVOMkQxSniOIWKgDBPsUwyxHRjrw06y6HFVd3KAT29N4xzhR8upxgsFqjbwGetCtqHO8Jfv7rW6w20fr0JaB2RzXfv6n6z/Ksy2UvJU+tHkVgMmtmHZeypME2BeQqscgupwUm6jXq9LuMn3xMbvDItMF4m4nqd30eOCSu1CtqtJlr1yiulrZe8vf60KUDO409/P3itF+/7Cvipn+PbF5c4vehjNp2LkXE1W8FJU8xWGXbZxzdJqHn9lRebCbf3Wwr8dgqQZ7UXIC56Xirgvy5z/HRyifPBCPPlElmayBzNNCwYrivG7KlSCNIMWZjiYj7C8+EcO8M5uKsvPt6FsVNB2zXglPyrx9+/HdNtzk+VAmQhLQa1WCQtyLtc1OQaE+XtiN5+5wr//myJH09OMZyMkChuZCrmsxbnxaW3QuZZpgr98Rwvx1M0+2Pcn8yxio6g9lpw68W4+/0tqn2qrfmp15uczOV4Pfsr7rSxykApfNPP8cPLHl6e9TBdrJBycw33i9gmTJtrfPQqXfymS8rfOdzBCu3hCsNFF9G9Duz9Gmp2sVuaMngrfz91vnu7+t8mfzVEjh/0PPjZimsRMZ6enKJ3eYFUNoUaMBXXD20Y5dq+rB4pA9PpCi+nc9SsAS4XEZbRIbDfgt8w4XMdXxeyPW8p8J4oQN4mD1PxT/n7w1Thu5M5fn7+UsI5J/RqpXIJ32zYHP8WnqXDHJiHMfqrKV6MOP5d4slhgNXxDqy9Jjq+UcjdQqX1nrDfgv0UKUCe1UaCg3Lc+1M/xMvLES76E0xmS+R5ApN7Bg0lG1sN8eJqyKbcIM2RxTlUEMOaLNAeOTiezPHFQRfpwS6clg3Tfk2YoE+R6Ns6vx8K0LagnNhxTsfRMQ1VaEBIG4MhnV1MFP7rOcfElxhPJ+LZ34sXSEIXO42qeDHWYxSZLK5PFN8P1luob0mB9zu3pmLSLEJSiKAEMFPA2SSWRaqUu64cF9VaDZVKVdJSacCBANN992yO2SpALpbVlijnOCCl+7+Dho0/P3qIo/0IUc6ZGT2slF4DxP0Jd/MbyA0LLGeSmHh2MUbvYgDLpp+WFK1WHQ+P9tCtexLPigYrhsqKjiDKniIkEFVoXhqjiwztZh02g7m9xcFOUmJaLqv8RnjMVgC7FRu+5uCdBgpNF/jTw3tw8xSrMESzWsUXh100qgzZ8SuPq15+M99rULlaPLqZ4/X11m8JtyiSTzigo+EKxZROcRPqrXdvkHQziZRJIyYp/RoLwufdZvpby918+JsybQL59fcsljhTqVZ3DRx36gg/ewwOQlJ6UKF3o4w9j3Rl5YoQW6nlIoSDeZLjp5+fYzaZF146VIJuu4HDnZYYPtETEXmt0M8X3lZYJruim0bizn6/6qNV8+BohfqNamgMbzzUDX87sXUj8Mxjnbb6Wfnq9af1jGVKhtIhDBOy62inbuLz4wOEKeDGDRzVHDw56qJZdW7f5fz6Al/7VlfrtYn0y1tQ16/Wz2I/xAeyCkVPKyVpuTC1nvAtrzXZ5bxWhl4sI0MUPPIWBd1FoF9TkV+T9jWoEhX2GAm7xNA1hg1luOKLKTc4jCroWxTHf02h1wC94xVzizwH0HWBzw53EAaPMFyE2K86eLLXQKfZFKOH20BoC+QlgNOZwv/64TmS4Tm+Ouhip9nAbrsma/NcdhdnZbcBKWsgdS5DV1Bp9c1pHxcvnqGhYnjWI3S6XVSdora/RGpSZPPHoq+8AJU0ZJpfgnUHyr/7Y+JOmrENaP19vlL49mSFn857GM8WUKYJ17bRqbk47DZx0GXYjyq4m8H3i9FCkirxJpckKVZJhvkywmS2wGo2Es9pB40q9rs7qJbebUgrTS9NX47D6OWuHyr87cUZXvYH2K97qD46xFcHOxIT/C5i3Up/PtTH+rV+ps8akfJeJ914rFP/g84svfi6s21IG9kxlSgs4wRxrmBYNqy8MO7d3+nC5ZhSxqgKQQIJHRDGKTix92wb7XYL7UZDQnLxW/v71u8fRMZtMb+KAuQ1zf/kOU60qQylx6WXgcL35wG+PzlHb7pEEMbIDBeGYyFBhsSwZU7D/BrGrYVvGe9Wsmwfvh0FyHP8htHocUEPUxnwfS/H356doT8cI1itxGDStmxUfRfVig/P9WWHXprn4h6dO/iiVYgwz9GbR8ifnQAJw64e4ovDOprlTr0tC79dW21zr1GAjFvORzT/ctxKY5X//XSE7573sAgCcBZLj3Y0+q64BQ97fkV2+YdxLB7wViE3WqWYrGIsX14gj0JYyT3UHu2h7RmolPPstdK3l1sKvBMK6HmXKEsBDJXC94Mc//70FOe9EYKAxio5LNOA5zmo1SrgRiF6bMwpf5MUQRghWkUIkwy9+QrJTy+RBDM4eIDH+w3YjvHaecA7qcgWyB+eAnp8uv4d5zP+0tJd/2mg8B/P5vjbywsMJ1MxzLZMSzYJVKgb8DxYjicbrOhJNkpjBNESaRRikSb4/uUFIhrIBodo/emeKPy5RsLf9thS4H1RgPKX3tk4Z/thlOO/Xozx81kf0/FcPJWThz3HQa3iiW7Lth3kucIqjrAMFwglVFuK/mKF5OU5osUMTv4QXxx04FcNUWe9L9y3cD9NClDucq1hqhSengX462kfTy/GWK5icRzAhXluUqxWXPieC9dx4DoWTLuIxBAliaxHBFGMJE0wizMsT3sYjydYTedo/NOXsOrFOJtb7Nbl/qdJ8W2t3w8FCmtq8pfM5crxBNduOac7C4Efeyt88+IMZ6M5lqsIDNRu2BaSNERiWshEV7OG3ZZZ14jx4V6+P4OVNQYQRYC46VEShmAwnWMerJCrHBXXQrXiwa/QLKRgQFpIMVzB89NzhEEgu13oCWW/XUPTAeow4LsKtf0aIlUTRcMVictFCcISJaIJpAbwcqkwnnnoMSQOUthGhpZv4vO9Jp7sWPAkdEWxe1ZglfhrBaGdA36Wo1ktLAivyisv1qq7+eqV+0L9tJFj4/aVTHzASm2m27zfyMjX/Hi0YOCrPQv7zmPZqU23zPeaDhpOsSvoF8DchMrExKXEZz0vH20e+hnTrafdTPcm96JMfUuNNyf9m7jchZfgLtr+oha66LvSv0kdfm2adzX5Is5isGICx3UTVbsjfYfOVGjzlaVFkxI/PmONYwtYmIzvbaJ/bmOWx2IcxuBd+7UKvj7cw4ODCiyGKGGG0gBC7CLKa/YdW+WoWqbwW7lh7fVkKMh9O8PcRXydZx2yfnZXnrW0N5KW7vbZd+imcYf9ZM9Hw30AJ4vQcVAoeumd6R3w9Roa15fE+XV4v+7dNZSyqxa7q9YeF231hjBu5Lvu+lePN8Fc3YtbF03Zq+S3XJQ5rjK+2aLDFWTtteUWyHy0Bnbj5o4Mv+LxNezrq7uzv0mau3OT11waICoDj9tA/fP7WEQZmvw+VizQ88q6YxSBZBTGEpxkc1ceB3WDZYL+bAkEMYI4R5JzT2nR50nT12HJdBwo8kfjh0EMXMxW6M1DZHaGVaZErtyE9ypElrP+e6XW5Y7AV56/yYMrxigTbxS/Xscbr27cvElBb55GK0rmucL5OMfLXl9CJ2RxCM+xcdyu4/ODNj4/7KLTqKPGhWO7sMVlu9MMkAqVVNlglIVVVMUiaCNYNCXEW8OxcNSqo2pzP8/Ng9ViexTuEhVGMfCCuyqCBBXXRcBoUmsulTfJsElOga4famKuZ1q/LlFhMp3lltc3Ed640/n4+LV5+VInvpHwmiJ8rV9p7yqap0mfRQxZWEoTjhUVqr6HTr2OVs0XWc/xLI2+JoECY/wqlYOhuejF7mCni0ajfkV/XY5UZ73gjfptb//IFGDD81fwIHmC/EY+ojymN0nZETLK8fR8hGcXQ4zmCzGY4nYnk4Z/WblD+oq5/8j02tbtg6NAKbv4DeM4guGrRkrh+TDF05MeeoMxwmAJW2Wy4Hm008Zuq4pOvYpatQbLMBGlOWZRjMkyQG88w3CywGKxwngZ4efeWL6BtaoPt+VI2N/3tzjxwVF3i9A7poD+7spZi99yHExl6QoKJxHw3ekMT1+8wHwZQ2WJhMXsNGo46DTRrVXQqHqo1GpIc4VFnGIchOhNFxiNxljM54jSFKeDCZqug0algj/fb4jRN8dr22NLgXdDAXJxscpAVtbjBipLn08U/v6ij/5oJsYqSGM0XAcHOx3stRsif5uNuniCTbIc8yjBaEH5O8VgPMViFWMeRngxmKDmueL23+v6Mo/Ufejd1GEL5VOjwG38o+dZsmkjBb69TPDDyxNM50uoNIZn5Nip13DYaWO3UUeTilO/ity0JHztLIlxOZ2iNxhg1B8gSBmGcArfMmTO/Od7Lex7Bt7Ab/in1hzb+r4jClAG0zsQ5e+LBeUvPWFOJCywkcYScm2v08DRTgudqodGvQHH9sRgZRZFGC2X6E+n6A8nWAQRZqsIJ3mGuteHZ3uo2lWRv+8I3S2YLQWEAuRbzt+iBOj1xzg7vcB8HkJlGRriGbiBVrWDdq2CesVHxfNkbdBybBlzBGGCUUj5u0B/PMVkNEaYmRgsYnx/3sfhTgfmg10cVowrncj1qtu2EbYUeFcUKDToeiyh19AYRvtkpvDj+QTPLnq4HE4kegQ3E9qWJZ7JiQH7wfb4OCnwntaEyBIlUxnlBAvAQgGXU4XxfIkwpuspE77noOK7cNyCkTgZEy8sU4XheIIkTmAbChXHxH6rjpZjoCaKFQMdv1j41aTXA2QyMvWkhMXFNVpepXWg5pgwVApDRbCMFHUzwZFv4HPfBP2MML/+aY0GBS6f8Uyn78W5LFEXqBHYPK+/L0iymaK4Z7pb3t94xBse+lzCZl3XDz5eL5bvqHRnfGU6ptvvULViyQelWsZcftuPikZfo6bPm3it3/Oa6TZx3Uzzyv1VhkJDLYYRryS6+4HwxtrrK3Drba/f31YRcW6tE9xx1vnWgZdJ9Std91uS3AH07R/rRTS2tyi9K4WxEiETn7zsg7zhwIa/AAr8ELjcOUZpkbNHKThGio5j4EHVw+eMTW8VfYT1kV9Zsav7su8QB81vQoMy3Y3aaSLdeLh2o/Mw3Xpa/VxXaC3L5uV6Nv1u/ZnGm/hy8ksvRTQG2NuzYKMqz7xSwbterIb1W88aB33+JTi/VLbAuQJ2dSF0K3wLvFqCTnUN++YT3rEfsR0lDR+UiXVKgUq3bWUT3Xi+3mxreV/F5PVP1mHzmvho3np9znf3ltXWddPGbNdP3l05GhLLY5xQOvmqGAzzxp33lsh49ml2Ud3PCwuyomFE0VTu5J/EwGC5wjLlt8GWsCeZsqRNN78nulx91jTX8CgfevMc4yjHMjdQg4XUtMVYVMta5inZQ4O54gud5upFeaFpKqHFNp4VIws+3IS6CaVsnLVkGn+dcu2VfvTOz7oM1pVjkXkM9GcBhvOFxIol5ZuuhS/22/iXe7v4YrcG3ywMY2l8RJzZpoTDazEudIDMMZDUgajdBj3Q8svOHb6US5uHrjel91wB/RWNlkKkykRiOkgMR3a9Uc5pGX0LmGuwBMhj/cwMG5l0ufrMLDqJPheA1oHdfLNehE7FFDdTXUO57cVdMDRebBtRBNAYJWb4pRh5nopngFa9hk6tLp7JWArTLblgNc+QqcJjFL2G1X0Hezsd1CqFFLqB3zoCN16s4b29/INSgF9amkaRCYrG5xVl6BzAWQo8HeZ4enKJl70RxvNAYpR69TpMy0ISxQiXKyjjri/2H5Rs22p9GBQoZRdP/FHhH0LhgptKhlOcDkZYhhGsPEej6uLhXgdfPzzEUauCDo1oGUatXOSfpS4mUQ3PBy38eD7Ci6yPJMjQW4Tw+hPsNuvoeDvwq0WI1quxzIdBiS0WHxEFruQt5yElD1Pm0psVvQP9NMzx48UAw9kMyrDFK9p+o4Kvj/fxcL+Lvbor33zHL7/5mYdRUsXJtIOfPA8nhonpYol5FOBZbyxhHA9366hWDBmjbz/zHxGzfMioCu8W6128JA9zo0AvAp4NV3hxOcQ8TMTzX8118HCvjT89OMbDnQZ2fKBWNcTrJsNWTlMb/cDHi1YDP1creNEbIp6lGAUpnl5M0KnV0HX30Oo4svFhy8MfMmN8+Lhd8Q8nWNwYV67JL6jsnyn8/eISvfFExhSuY6LrOvjz4S6eHO7hsOmjYRlgRCtuPg2VhUnu4HRWwVPfQ5pkIn8XSYTT8Rz/+fxUPMw23dvnwB8+tbYYflAUKMcMxZSNN8WIgvKXBtuDFPh5FOBFb4DJPEKWZfBdG/e6TXx1/wCfH+2i4wI134BjFiGHZ5mNYVjF6bSLp/6lyN9gNsc0pPydoltvYLfiYXfX/oevZX5QtN8i884pQNkrXEzn+YYtu5Jty0Gz5uN4t4UH+10ctpto+y5qriHjYc9mlAwDCRSCzMMw9XAyr+GnXk3Ckc+DCKs4lPnbt6cXaDVraPpVWYPknK/oNe+8KluAnzAFyFN6vZaymHO585QhgHL8dD7C84s+RtM56Im82mjC9jxZp+UGAx1G6BMm30dd9fdksHKTJnqBa5YCF8MZFvSukiv43JXie6i6jii8uahAwTiMFM76C6wiTsIULIMeVWx0mlVUGeOaCru1GFZ6UKzPIpiNwljFZGiP0mjDNrn7nK4kcsDIwTAmdp7Bhy1hhihgNQx2Cn2sX1PhQ4UF0zE9f7+4qLYGgJeZNuYpCxA4pYcAwtU46LPG4+q85tqWHZaHLoK4aIVT+erGie+kjHK30Y2XazeER7oJLcvnGjeNb1GX63Q6PV3ma3yYh0o3TSeeNRyeeehzefvK6eZ7hgQq4zz+Ys4C1BVea3WSOqy13c0yCvx1HQiFvLmuPH0Fydc80OWzrfQ1YfOny9X4rNNHP3sN6F/1ar0sZtS8wGuNj0aKuAq+ipM/BU+VfF56PTCQw85zuHkGD5YYwBAeD11OeSsgWW+WQZjsPzyYTvMq63qVUwNghs2D3beEI8ZKZb/RdJPkt+Vbg6PryrNuD77mvcZJ12UdV4kERjqUSt31PGvg5fK2MnR7sgy+56HTafqs48P3TKvrtnnWcHj+5YOpipTCy1cY/HJO5tN46TPxJo34AdFK9TshyWqxrvEVGhqdO7PxxSv0KWXfOh4agDbWIF66tle0IaCrG53jHZ7L6knoo3cI9gYoLVfLevBEniR5yZOaP67zXE+waSjBSTYNTAaBEoMJhsozLRex6SIyPXmvv6ukIaukeZZtrPsd+wR3mEgYixy4nCkEcSaGM5ltIrZsxHaRhu3EvMRVw9J9S+MpbVzSr3gmwla/vuotTFJUvSTAWopXLsvvG407iIPIshKSzk18iIvGi8/1u1fgveUDwmU7RVBYJEpCHdLFpniZs03sNqp4crSPR/sVdIxihwLx0visk0foVfYDufYM5KIULHaWSbQ8vii9ZLH+bDNaorP8ERe75xFWqYIybaSmh8ytSHuy0fX3WpdPHPS1Pr9CDr4o6atlPMsV/Mq662vyKmGS9vrH+2vzyZvQ+Y55eWgY+sx3PIr8BZ6853uWv/7Tefh+vVzNG0Ij9pEoQpomMLIMjg3stZvoNOtwHfrmK42OVgrjyRQqz4U2VdvCfq2Kg2YFVVawLEMu+LdeiauH24tPgwLiF/CqN1/xGQ34I4W/n6/wnz+/xGWvL+76wXAqzQb293ZgOi5GowmSkDMPze2fBtW2tfx9KUB5KRxXsp2WpeRfGuydjTNcDKZYzBZixVKxDRy3G/jXx/fx5T0fO6YhG0F0LejBq03PhPSSWvNg2AcIohTjLEIYrXA5W+Lnix6Omj5qTh2mU3wHdf7teUuB30SBcrxBCcoxEMfANNp9cTkSz0AG15gQo1ur48vjPfw/fzrEXtUEvZtzfM0w1RwjtGygYwNdn5s39iW8avDsJVTmoD8P8N3LM3z18AE6B654udtK69/UWttMd1BAj2nJj/QQdDnPcTaYYDKdIUkUfNvAYbuBf/v8Ib66V8Oha8hGNQ2OPEwP1e0W0G66qDYY4jjFIA4RLQP0FyFe9Ec4rnvoVrpoloZXOv/2vKXAr6HADflXrh9wHYKhVLgp9WQY4OXZBdIkkTlnw/fxeK+Lf/v8AR50DTRRGJ7IuKOUwR0A7R0TNb8rqHz77CXiBTBPYnzz4hSf37uHw3oLDf9VL6O/Bvdt2i0FblLgei2NcpQG28NA4WVvjOl8gSzK4Fomdmoe/tvjY/zToy6OOU7QaydlZ2hawI4L7DRs+NUjpIaJc4YKmszQmwV4MRjjqOHioL6Dpm/I+upNPLZ3Wwr8NgpwzYubHRly9bP9LlarAC8HYzy5t4fHR3u4v+ujYxdriNSx6nU6yt9MGWhZQMNSaLkm6vUGlotdPLsYYZWmiLMEP1/0cW+3jXvtCtplGPPfhuk215YCJQWuFiFuUkTW0EodRC9VeHoZ4psXFzg5O0cQUjobqHgV3NvfhVOtixfBVRDCMNgLtsfHSoH3ZLBSfNxF0JXKElpB9RdK3FBGYYQsSQAzRcv3wV0Bogwura/7U4Xz3gAZRwaGCdf10KR71rovroLJclSq8CA/81gfHFN3ovmcz9d/ckczK8ZrK32mMD1h6nQlyCsYvCcqHGxzwYPpSv2MWBKu3+u8ctbI8YY6uHLQzoUTup4nzHU8uUNeds/fAHL7DWnLTks4MqAvkzHcihj06IFSmY5GQ8SfB/G16DWjXMgpH1+diBN/xE8MdMo6MwHpLh+zUhmkceBZrmX3h5K8pBHTuqrYccRr4iftV+LHNMTnrkO/k3AzZdp1st6VTz9nWo2X0EmMogpFO3mOHYBlvL4jaCwKqL9YfpmchjsErnmHbcBr4kPFIduDaWh4UeHkrMRD8zdxIs1Y92vvDcShBKwr+SvPRI9lrEO5ui5x54k/4sCftBMTCS9QC6xg5DT6yoUndJrbUCHdmZ9nXWem04pR8rwu70b+EhdBlC/oNckoYGi+ZxLWRbel5q0bldNArypZPNDtQli6KL7R/ZCweOg2Y/uxP1Cpz/ZgnUqSFPQpHl37PgAAIABJREFU0/MZ66rzcYGJ9xpHlqVpwjS6LoRN+sSyH7uA76A00Ct5QYdTIm7rOJdFv3K6SqMRLXPxOdVoYkvySq7iAXHUddFygPjyGfPTk0MBp6gPn7/RUSrb3yQty9N0JJ2urqGkLVgm24Heo9h/tPcRPtO8QKPFNyLWmyD0e6YpG5PVEZlW0kNQKg0NWOf16rIN2XZ6oZ7eki7mCv3pUqzs6Q0ltlyEti2TcAuG8CPpx+IIjxMXyiFdLmFxhx93SQ1ihZPhWOKh021vattYGRYCg2FT2EaGyC/dHvI9uNW4piAsjRHXG6swEiwrfhvt1yqreVPjybrrPst+RWNYpiEudskvInM3vkksjWl4KMo54vQaFMqkv3hi2TQaWibAIlwhh2LED3j0HteuocXYsbg2VtFtoAGvVVXaYv0563qFt0Z+TQ6RB4o2A86mCmfjGZRbEW9rqelgpSxpTxrTsr8UNCrObH96oGP5lBdXTVQixLJ5sH78iRwrZR6TFM+UyDym81SxmEjeIv3XxwSsgya1PjMPrwmLB8/k/7h8wO8IYTENz8SdZTINZS/x4T2/uUle5K+axWISZT0PphNjolBhuQplfGqpVOq926ihVXNllxSNtDIaHUWQ2L0qTWHlDMnl4dFOGx3PkBByxOOVY71Cr7zcPvjjUuC64bU85oL998Mc355cgrv8z8dc9AxF0dnpVPHw8yfo7jcwWwGL8ayYPJDfteH0Ncg/Ltm2NfvdKFCympSvWU2+p+U3bJwAF6MFJrMAaRSLcd/x3j7+fP8IX+756DAWejmW1rBkLqE4ljCwayg82jWwCI/w3WKKSZpIyJWX/SHOdhvo1jzU6XZ1e2wp8LYUKBlYxsxitA08vcwxGC8QxxlMlYsnlS+OdvFPj+7huGqKooljA37Hs9LomfM+jhs5XlE7JmK1J2E1p4MUSZZimub4z6dPcVj9Gp1OMQJYG4q9bS22+T9FCpRjXFZdj2klHFsGnE9CDGcLZGkGI8ux12nj6weH+Oqghj3XAD0oc76lD62AsmT9UyHumhjfO0C8XKAXRggyhiqdobfTxINuC42KLWNtLf81nO15S4FfTYHS0zrnbFQnvRgpXAwmiJMcNBisuxY+2+vif3z1BA/bBhowZB2OYwY5ynVUzk05H89rQPy4K55ie2mKaJlyNo0fzs5xWHWwd792w1hWg9metxR4YwpsCD49/uU4YpwC5+MYA45/E8rfCM1qDX95dIQ/HXWwXxqrMJw8wfBHUc61CruUv591TUyW+4hXEc6DEBEynE+meDFw8Hi3Cc93ZS1jO4Z44xbbJnwNBShLtfflz3cdNLwH+PK4i+OdJtpVjhcKmcuRK3+a72T+ptfsS97NPAP/8tUBhosIwXyOFAaWSYrhPMB0EcLwK1drcq9BaftqS4HXU2BDBjMx+ZFjYXonPgkS/PWkj+/PRjjpz5DFKwlL3G418ejxEzy4v4tFrPDTuUKeAhaFOHl4e3yUFLgaD7419npitcYLmrGoMGA4oMFCYTiZIUlT0XzQhTDjBNddF1SUkZdofT2aJ5jMl4BhiUcU17ZQq/jwHfNKeS5cy4HAWnm6DnzEHwWu6Jo0btS2lAxrKLocpsmKKcYbWnmuYWye2UE42D6dZ+L5JUtyqJw7xgwcNjx0q1q8lz1qHUC5YVzTg7vTL5cJJlGEKImBNJN6HTSbOKh7aKyB0vW8Ald+OEQBo4B5pjBdKCyjTCzVnWSFQ7pirvvwXdauqDKVVRfLHMtFiDxN4JoG9ioODuo+qloLXqJOPPlju40zheFCIQoiqDRBs1rBUdeRyQRxYHsRh9FSiVu7RRRjEUZIlJK280wLNcdF1QKaHneSe2gyvExpnMN6saxbmvGqym97ofGcpQrTZYZpsIKjFHyYqDkWOlUXrYolXn7uKmuTz94EZ+7u13QUvo5JI4VZmEks91UUSVw1hsaq2TYajoO6Y0kc7VbNAl1bki/XmucW5roL47XnRILHBpHXb4trJqRGzxSvRnxGAaEHMEZe7BSm9SLfGSqHqbKr9+Q1XVRZ4lXbSt/Oi0H+PMmgshSOyuGZwPFuU+rKsphfw7jqBhrRctIqsmSlMF8kxWRB0UtSioN2A+2qhRoz6jwakbX6ExceVGJPI+5U0iEgGCosw2G9ht2KK/2CYMg/i1RhFChMghxmGqFq5uhUHXSbvtRBF8cz8Wce9p9RqDAOFJZxBidbYbfmodPwYNEtX2lIMEsUxssc01WEeRRhGSfyUbUsG65lwbeUKLJano1u1cdOxZT+xzpc0aio0q3/QlOxTCkYsuDlwlPRrXQqoTAfacUJGmUWPV9Ngwyr1YJ2hKiZBlqOhZ2KD8+3hVc1AlIma6jL5QsSZ60jMc3moeknzymuS09ZAcuPFcarRGK+LsMEcaqQ0wuCYaLqeajZ7DM2mp4tuxMa2jWsbpzNwj7Se/IW6UGZOxjNAdNGxWQ4GBP7DR+ebcAqGYPtp+PtngaFG96fz4eYLiMoy0FuupiEKX6+nCJFSxZ5bMpu+gHLU9h5IiH4ulUP9U5F+IGGKn325UDh6UWCi9EUEfs0TKzSDGeTKSqndbSqvngwo5woPDJl8PMMXd/DTt0XJYHuqmtscaNVCo81t3CKfqTPpazV/EqjmrlSGK0gfWsZRQiTFGmSinLNp6x1bdQdW0IFNn0HzQpQs2/uzKI7zCuBdAOzX3fDehI3yoRQKcRZBlV6CmPIw7pjoGIVC8x6srjJtuv369fEhHk2D5JGt/9UQjgAL1YK353M0BvPkJkulDIkrv3JYIK/+Q4824LFMDcql++OmWeomIbsHOr4lsihdZkh4YnW5AS/c5NEYRRlCJIMUZojzoAkScRlLnHybUeUlr5hoGlbOGi4spuT34K76q7rxvyUR+T/wUphuQhEBniuK0pOji34vdRppqnCeJFjskqx4KIQkYGJhplhx7ex16pLWC3C5fdgGQLL1QpZmsK3Lew1qpKmXu7Y0+kWK4XZdCYeVlhet+Ljs92ujN2oJLitPXQdtudPjAJkGh5lpxX5DYWX8yWeDca4GM0RZSYafgX3d2p48uAQxw+bMF0DJ3EOymORQTLRppGpBljC3Z62FHjHFCCH8duxPr4k39KwT3aXLuilbYXlKhGFU8O1cL/TxKN2Awd0J22UoTTXxvQCs/yccsde4AH3dy1cdttirBIGC4yWAXqzBSarCHs1V+C846ptwX2CFKDopME7DU7pXeX5xQizIBZnuz4U9uo+HnUbeNgy0N4Ilcx5KfsCv/M0sKUhFu+POyaOj48QLOcS4nqZKzy/7GEwPcSD9s6V195PkNzbKr8LCmx85slzNMCnYTXX5BhWlC75GYqiYls4bNXxZK+Do2oRNl2M8Us8CIo/8rIoUZWByFR4uOugP2hjGkSYLVYYryIxwpoEIXba9WLD1LuoyxbGJ0sBzXuc+3KOxbXok8ECw9kSeRnOeKfq41GnASrxO1axwZFzKP7W57qUwdw8waeLBnB8dIj5MkCworofOBtNcDltIzyuSRgs8vv22FLgXVBA5G+pB+JaMMe/U65Bpxl8C9hvePjyaAfH9WIMIZtxROpeczDXBiSUvDKQGAr3OiZGkzZG0wWWWYBJFKK/CDAKQrQaDtyN9ah3UY8tjE+QAlwzoEFKKVNt10Cz7SCsNdHwzRsb5a+59VU6icFVqf8JWkCjXkPfdpBlMSJlYRYmWKxi+re4IbdfhbR9sqXAb6cA1yHoKfM8WOH5cCSG1kGSivOL424Hj48P8dnDXbSqBk4mgJXRYMWAJXr/13H4b8dpm/P9U+DdjedewwMcSC4jhfFsgdl8hkzRPEXJjtV2tYKqV8Sr5mB2kQOzxRxhGMAwqBrP4Vs26r4LR7yilERhea8pk6luJBHdKfc0c9jBXxFTnmW8zuBqfbBNRd1PgwnOekPMGVNeGTisWPjvj4/RrHauB9ebeHGRrvRwIspDpfDtxQQvhhMZbKs0hmeZ+Mv9Y1j3DlGtF0ozAaNhEZHympcc/M+gcLJS+Pmsj+F4jngVohLN8X99+RAV9wC2awpONLQZpQp/O5+hd3mBOFyh6lj4rF2H/+g+qu2bO9kInxSi0u9iofDdyz5Gg77sOr53sA+rc4waDHFLOgw5Schx1u9jMJ5gFiwRUzEo7kUA2zRRcT1UHFOMaO4f7OB+u4WjhomuU3iPYOm6mprlN+/18zc9sw7kOy5Q0X31pVJ43l/h5dk5BqMJ6irHjmvjuFGH++AAdbcGh1YEv3gU3oPuSqZpR/pReReIJSAVpwoveitcjsYYzueYByvEcSzKB9M04Jq2KN3pFrNTc3F/r4t73ToOqgaaax4ASJfCXOQuDG55/sbEJKNqFXKxwKF7i/QYk6G0lBgryMq1KFLYj67bj9ekAQ9dLPNSgTiJFH647OPFcIYwCsGFwiatyP0v4XU9WRzXsHTeElShrylpehErfENe7g3Ehb6RJqiqBP/y+QN8frQPt1ooLSWvRqYERFzIF8RnniucLBX++vICi+kYWRSgYgP//YvHqDld+I4ladl/+kGG788G+OlyAiMKsONxR0gHfuUBXK804Fkrg/2T/ed0FuPp+QBnoxmcdIWvHhziC/8ItG/jxJ1GB5ejECe9fiEfgyWWYQjTtGEYJizTEg8MVc/GTr2C404Lnx/uoVs1JL4vd1FRiG/SS9PtBg00cclBtBAoM22Q6CqLLDCUPDxRCj8MYpyen2E27oOU2au4eNxtoXp0AMdp0G7i5rEBuDB3upnkNrzZRiyb3nSomJ5AoRconPRmOB+OMJrMsVgyrJyBPM9hGSY8j6HlLLSqFey26rjXaeN+18OOX7hFvinhbuLwMd2JTKM8zxSeDmb49oefkMJCx7XwuFNF5eEhjHpFPJGRjpR9F5nCs1GOp70Jng/m6E+5wB7DMC0oy8Z4FeK7k3OcjsbiLYIeI0yGzFMpvDxBx7Pw5GAXx9X7UF7hKe3HQYLnw6nw9WS2QMowe2Jln+NFfyaGTfxuc/eqqWh/z/B7CWp5isc7Xfzp0X14uxWZKJH+wprCkrdwRMlHSjwPldJPJysVYBw7sL9R3nMgO86As2GM0/4EF8MpgtUKSZqIIUKeZvBcB1XXRc330KgylIuPB4zjuuPLzkQuil31K13WWzKK1FHqSlrxxxrxil5tDGTKLJ8WMvRtiiV09iH+6DLx2TjHj4Mpfh7OcDrhQjf9OFny/VkECZ5djrGaL0s3oDRYUbC5nJinaHH3225bdh+7detKTtNYRdN9phQY8pELOfQW0ZtOJb54QOPVNEOaZRIGksp226RxqI2K7Ui83CfHh6Ko368baNnFpPqK9rfQnIqnSa7w43CGs9NzhKsV6rUavn7yCEdu4aWGPHAZAqf9BU56Q/THM1nU5OISOaiRx3jYquJPD47RaB9JndIcCIIMwWIJlcRi/PZwt429Zg2+e/09JJ8xrCVjTxu5iaplSUin425DDIHXlby3oL999KlRYK0j85K8TU9K1TSFu1rAi2aoVWq4v9vC14+O8eWjHbgOQG9Y4l2P+5cY58so5i9isLLxbf3USLqt7/ulwPUsoPg2F9+p4nsSZNxUkmK+WCKJQ3imEkPsw1YVuzVTDC853tJyUH/3+E3Sz/i+YQCHdQP7nQ760wDL5RJBkmOwDDEKIoR5Xca4a93n/VZ6C/0PRoGCczTvFusBxaaRi14f4SoSw1yXmyZaDRy2amjQ2PuO+ZTmY/IwPTq2LIUnxz7OXriYGyayPJONVhxfT1ZdmYduTon+YATeVud9U6AUfuRh8i/H8/SwMlnkWMwXiFYBjCxGu1YHN6odtCzZYEDF6Lrc1Nf6TG+vdQUc1AwcdFvojeeYzpZYpTGGDA+0DHCc1iTMkJbZ77uqW/h/TAqQ50QGl94uuQbeG02wXCwLPQBy7DcqOGrXJVyFnvsxn+ZXTRneF/IXErLt4YGFy/MqRobBfZ+YLFYYzpeYrBTcWrGOvuVfTb3t+W0oQB6mwSDXbbkJkfosjiGQpmhUbRy36ri/46NuFqF8Cr4v16vKgjX/Uv76ysB+HdhrNXFWrWI0CxCnGaarGJfzFfa7DdS5iWmzE7xNJbZ5P00KrO0I1PLTtoCKVegI+ewVNiPD66N8yRPHxzJ/A1CrVmDbFqKVQm6aiJIUYUS/2m+/hqmL3p63FFingPAgNw8owEtTOMESfrSEbTo4bjfxl0f38eTBHlpeIa8pa1WeQKlMmFw2oa4D3F5/NBR47/Npyjy64p8HEAVGEidQpgJ3FfsOd8K7qLjFR52Tsf44x2yxQJ6nssPbBGOz+mhUfJjc+XuLup5llPL0hpTUz/RZlO1iBMMceWEQU6wI39pgRapCtUQlCQcqg4SKkil6/ZHEdeu5CnutGj6/15GFaE4UNwfIop7Su36pQIqA7y+n+Kk3xWwRQKWRDEoUXLT8Ku7XW4LPjY9IWQmNE5XoUwU8W6b469klej0q3GO00gBfPTgGXTgwC8umEm+cKvw0XOD0YoRwFaDhObLo8qfjwoRnHWeWwR/bg67vno1mOD29RBSGmCoL3c+OcFADzpYK3/w8wY8n51gslojiWBRTsgHUKAw7FHfXmytpb1oLf3vew+d7O/in+/fwl+M6uq4hoVLWy9eNodtNn/XzNznTIEEMoLgTWyl8N8rxzbMzPH/+QhZG910bZruJ+80matUabOcOYxWpDKlRHkSmVJLehhdTcmGBZZNfzpXCj/0cPzw/w9lgILu8Y8b84+xKPA+UISdoNWUsYRsGajbwrD/AV/cO8Zd7h3jctmRXDAcJdKf9To61Kl2B5DPBoyhBbktekOQGBT61KDTAKrxn6O5zE6uinxIKnzOvhFwwgUkG/MC4y5MpnCxBu+Lh/sP7OOh4UscbfCCFFkBIU7bpEgA9RfznyQV6/SHCMOb2fVSyFWzXRrVSRbvaKAb5zH8TsQKX0kqevP18usQ35z1MhwMgDNCpuPj6yRNkJn1MFGWy/wzDVIzV/npyDjMMcFjz4NkOviqLWC+GxXJiQx7ohzGe9sf46bwPOw1h1mqoHR6i5Rp4Pszx08s+Xp5fYrJYIowjpGmKnHLOtCRcFK2iHdsSWeUZY7T9Hl4OJ6I8/mKvBtcpZOedi6xErORXIUfZL694SNP46sH1Bfs/6c3+82Mvx//56QQX52dIllNULAXnaBeG7aBarcCxXyH1NSBBYZ1CN14VN2t4yG7I0jippxS+ucjx7YtT9Ic0FAwQRjGylB6cWGgB11gk4lXEsWeo9oY4rPfx5wfH+Oq4gwcNU3Y7atl8Q67egsqH+oiynH2A/MhdoqeLEN+c9ZHAxq5vwVYtfHm4iypobGCIR7CLSOH/vBjhm5NzvOxPsYKDFPyqGlA01jQtBKlCMl3icroAQ5wgKwxMLFPBUzH2qg5s34VFDzszhW9/PscPZz3ME4VYGciVAYPfG8NFohIMFwlGs4mICpMhdSQwUAo7i1DPY3HLd7S/h0xVhGlucAbFoHgeufH0qknIJsLH5ZkvhCaq4NUxFE7nCt+fDfH8rCdu34MV+YXcTFlb8uliJcZgtm3Dth007QyfH7ax+OwePj9soc09AqUx2FXhb3lBvmM/pWEkJ3m6XyZZjulihWWUIIUn9dssStf5+rnuMDwXUnM9DXmFNebi4N+eT/Dvz8/xkoYqiUIEm+ZDQgvyQJzTw1OK6Xhe0If0p5cdlcDMYnQrjhiefnZ8jPaa1y1Nd4Z7/OY8xQ+XI5yOp5gsFljF9IDE3UecKOSFLGOFxWueKd8ZjudcZHg+nuHxXgdfH+3gvx03RTbe8TWW6rNeswR4MV3ih/MelvMF2q022kcPZPczjVX4jfiPH3p4fn6B4XgqBlo0MDZp9GwYmCUBqpbCA/J7yUtxChnHLKdz5FGEVr0usX67NRNeKWf4TaArSlHWrkJYlotus47DdgudahFzXUKQEegNFmbr6Ba68ULK3/59GhRgy5O3Gdv8L/fbWI524CYrNLst/OufvsDDvQrqliG7qGnsTEktI0oZe7FXl95WPg1ybWv5O1JgU0qR+zimXcbAeDYHPUTmeQbPUtivu9itOmi6xQ7oG+P4sg58VnKw9IEqDHQthZ1GBZ7rIVcmEpgYrRKMVjECOho0r4Z4vyMltkV/zBTQX15+u8UD3AoYTSYII8DMU1QcA/c7LexW/RthTnWdmV8f7BN6HFc3gAd1A1XPBb2k5oaJ1DDFIJyhWnarjasvvs6/PW8p8FsoQLnJkSrlLzdxTBcBVqvCW7KjUuxS/tZc1J3CCxB5dFN+a95lP+B7Gl21DSXeLqsVXzbVcd1isgrRny8QJHtobhWmv6W5tnlIAS04y/WnYvygMKNn4/lS1pMZE9dBLh6u9qqeeLCibTb5k+FyNw/yMH8yhjaAY98Qr9+yCmZaiOIVBrMFLidL7Nbqm9m391sK/GYKkJ0pf+dQGC0CzJch0jSHm+doV1wcNiro2EUoNirW9LiDBa7LYs2/XM+nN7fdhoFmvQbTHiONgUkY44RrItyY6BriveU3I73NuKXABgU0/1GGUia/ImZvY1w+K0O5Mz3z0hCA0S8syyp4nSHhufEu4xa869WujeK3t1sKvBUFyFuUr3Sa8KjbwWR3B1VYMG0f//zlZ7i/WwW963M76II8K/xMTqfOv9AvvBUC28y/GwXem8EKmUofnCCFQYY4oEKeCqwUvqnQ9Aq3/HQFTyFIw5bpeIZoOYNJxRbVKlmGqllDq/SwcgW3vNCy9eq5LrQ88/n6OypoCsOVIgE9rNw8Ssm8NuDgk4LdgUq3A6M+QjQJkDBMSLREf7ES7xEd7+buHHYZim7Wn5NNLpaMlcL5PEcvzDHLHKyMKmA5SA2Fi0WC8+lSQjPwg7CJO/EkHsRHK5N7YSLu9xe5Acv24VRdWF6FeqGrARPLDk0DCzhYwkEEB5wmBHmxQEh417UurlmOKLwMYGnYmCsLMVyMMxMv5woXS4XnpyM8P7vANAiRcXsyAzvZNhzHgcmPmFIS25m7q2PkSPIcy1WG/GKELM6RR4f418864qHiLkZcbzvWf9MbzuZ7SVPiTnqPoPDjTOHffzrF+WCCgMpVv4Zmp4l7R/t4dP8QtaolO8tv8sHaXamsEiONskDSa/Pg/leqerWi8EIp/Odpgr+f9HDZHyCIaJBA5Z0hin56heAHn4YrQZjIDoFE5WK0EI8XCOMT2UUTPriHx/s1GbySL14ZYGwi8qb3utH1eSMfH9/8lVt+r9JRecJ2/+WDqQwuZnd3kfojhFaIKAfyhCF5VhhHLTDsAg/WUQouwTKvLNZwoYbeNlY5+mGKWUZaO7KinaoEL0dzPFqE+FI1JJQM3dddwVlrN73wM8+A/irGMMoQ5QY824dVqcGtNcQdP/stDdUYHTcyHCxgIbRdGGaC0LARGbbgRRqtdyDiy7z0YbAyLCxNG4HpggYAvUjhx3EEP3Hx96fnuOjRiClALCFVLJiuI94faLDCfkPvBDRO45c2URniVYbwdFAYPKXHaD5oXwm4W/uQIFcS8upUEmNNMupkPPPHOohnmVzhu4sc//79zzgbjSWkhu/4aHebYmh0eLQH36N5xKsH+4v0Gb69SnB1cf1IF16CYPvQM8h5pPBfpyG+Ox/gZDDFinImYyg2C6ZpwXI92K4rxhf0nhEmEcJMIWR7JnOEyQtM5wvMj/fx5V4RgoZuOoW/XkX3PT25ru/bFKDbhO1C+RKZQGA4WCoLiWHDyw0saXiSK+E93WdojDKPUyzTXBbTYdkiRFWeCy2ljxgmlEkvZwYMkyZxuXhYoZcVpBYy20Rq2oW3qhQSQoYeQXKL7W6KcQNXOwvbvmLh3nZsMOweLRUKg5UEtGw2MoEui/ukDH/EdYMFXiFV8V7/FzTV+UTe0lsRvQD1E/z9fIBn/REWixCrKEeSAZZpw3VtOC4NVGxEIWVx0e/zOMcqipGeD7CKQ8wXh/j6aA9HdVcUyzf4hYVqxF/B8vUPmI1GU9zR69qknRIPQVGeY7RYYREliJSLjAYjJSieWWt9f7MEvtG/IsV6Wjo5I224Y4e/iK3lVWDmJnK6SCy94ojnGtOG6dfKEF6F5xfQE15mwvBckYvkEVafP9KEZ8q5FRR+7g/x9GKE/jyUb34mBo0mDMuFbZtwRW7SwMxEkjB8YeF1JclTxPMIQXyJeLVAp/IF7B0awBVjqfX66pqyTPL/0nAQGC4WyoFKDIwyA/6C/V/hb88u8LI/wnTO74yBzHQkZJlMpWlAYxowHQe2y+XSgr5JpBAv51DRCjXLwG7FAxdRa1YxOWeVGPhqPlMIlwuJlUrveEfi0akF32R86tu+zxpznnnc3prly+3pD04B9h0aw+35Bv7ts4f4bK8Bv+rhsFuRHaaWQbFJyUpPR/QIVHpXKecqmpvuJNOWve4kzfbFb6MAeY7fE4ajYEzoRRhKOFOVZ7AYXq3mou1bIgNpWyWTlFv4kI8Iq9hhClDp36oYqFarsGm0kgWYhSkmQYRFlCH37Hc33/ltVd/m+ogpoL+4rAK/8zQCnARp8c3nYqahZHPDbrUi3lWKORS/8gXzFiMxfV0QgndMRy98DQCdRh0X/hzLIERuWKLwZ3jXDFuDlY+YdT4Y1EX26k0/HH9GqghLTsUQPRaqDJ2KDYbsZPhOrg8VHPtqFfic4w/+qDCl4WzDN9CoVeFVKjBpKJhmEhpoHqXYpas3Jt4eWwr8GgqsC94yH+UvvQP1GWKe612ynEj5q7BT8dDxi1DzlqJn7tsL03zN13qXf7Piw/d9JCHXPkzMwhi96RyfH9fFAPEOULcXsH26pcAdFCD/isEKPaxG3JBDHubmnlw8xe7VfDG4orgkzzE9u4Hm2fUbvucYQuSvA9TFU4WNzLKxSnMM5hwHx0iqHl6vnLgD2e0mUIZnAAAgAElEQVTjLQXWKPAKH5bMKOu/a+muLsm0zKTl+BUTF7xN/uVPZcWGNOr7DK4hm9y4yDdrfH8FdHuxpcC7oQBlLNWFe66Bf6Z+dHdXohIctKtou4aIzFAZEr642FRfRIeQtW5RVLwbPLZQ/rEUuFXH+a5Q0B/lujLw0AfS/TqO3SNRonBidVhxcNSqom4XoWGoMrtXMREdNHG/7sDmYpjKcY/GBe063GJT8ivorcnSO6WkyF4xPCBWVBWJqdVr1f+Eq3/MxQHyQdtE3fdhGpYoxcMMGC5D9GYrNHarcG8gcy3zWT6VRwxVcDpLMAlicGd1TuWUWCYqjBcBLsczLNSxhCXQZa+D5CCICl1ZOEy5020lk9c0S+E6DrqdJioV54Z1uv7uMG44F8J55poil2UK9SbVaK8e8tYEUuJnOEgtGzMq3E9HSMMFxoM+Vos5mp6Lzk4TdU4cHAeu48KyLTHWWWUZZqsQ4/lSdpEHYYzpIsKzZAROTA66TdTbFrzSy47GQtdd3/Ms304J4VR4+VinC9+TNvyRzvRAQM8QP80V/uP7U5yenIuSiUql3WYNX1OJfbyPg64Lzyo/vuuFrV2vl0O+ETxKfPQ7lkvVAne00ysFd/o/7WX44cU5Ts57iJYLydmq+mg1amjWKxKKwnepIDYwjzPMuVA7X2I8GmJJbzXxAklEjxs0BLoPe8fDriPqi7dfxNWIs553XPOx/hXk4F3Rf+RerIeu6bFGsutLKvCMwq1hVQF7XQeu50NZFrLUFEOq/mIhrjwf+PQNURKWFyVe5FUaT3CyO4yAwSzAMmTYKROKgyPTQGo66C0j9JYhuOOfccbJ1aWt0RU+BMtdRCGN42Jayq8QxhlUpuDZJnZrPhqeLXmZie3KPGJ4pmhJTAOZwkMF3/AdfxpXXZB+zhi99GaQGw71v+ivMmTnA1hKoXd2iiRcoek6/z9779kkOZJkCT5Qh3MSPCJJsS7SbGb3Vm5P7j7cb7/7cjJLZGele5vUTFXyjIgM5tzB7eSpwTzgHh4kSXVXdwOZCMANZmpqampU1VTR322Dp5yCmo+ax00iR/qHeZyKX0iai5zO5oiiBJdpgh+PL9FyfRz2O9hr2TfWNIJTiZS6Iot+T+qQ9agvgyuf7FtEAYDtBwrfn+X4/fM3ePXmjbhUqbsWDtoN/PrxPn6xv4u9tltYh9K0WocphJFKWOUkxmN+RRXLC+uZ+bPtHqcKvz/N8KeXb3F8cYX5fAHEC2y1m2LRoNtqo1EorPBkY5gkslF8NZmKha7ZbIE3YYqYQg/xbfgUj/qOCMINjp/+acq4LNWtWdwf42bSchrSTniSbpFoDShXSBV7c37RdOVY1HMsfNFrwrf3cL6TY2J5ODkf4vJqLL7PaU662wxw0O+g29LjF7mcHE6FFTeL0fYsGYP7noV6AGS7HQw6DUwtX8Y+Kl1FiZLFS83Osd9rY7vXQ9P1xMUQFVTpFogugep5gv12A1uNmvCN4cK75pC6la3SQ5dfK0ywz6V7vGdDhT+/ucSzN6cYzhbgcZWmY6PTDTDoddBu1BDUPDiOizCMMIszXM0icRmUhBkuJgniTCFJ6K6Q/oN3sBtYIphY0t68rDDvKm63/aIgmmZgO54CXSHWPAeRbUs754nyZ+dDdGm5qe9J/0Vfx2aCZrIV2OyU2KbWOrdyHNJV/H0r4HGnjvRwINbRwqCLF+dTnF0MESdUYQGavovddoCnW21Yubb8xfrnCE6XQG3fxZN+Gx1Pm7o1+9fMgzdx9NIImI9gz+cSv9lqot2oo9UIUPd9OJ4Hy6ZlFwuLVGE4m+NyPMVoNEKSpriaJniexdh+dYogeIp6R7t1M/xhaGrqne6I2K9mto/Y8mApB6PURjJUGF1c4sXzF1jQ7ZxtY9CsyYZQgwqiNIGqMtTTCI97bex1mrLJRPj0nf5Vrw77s11RUnky6GC/00LD0eXmcNdQFrZthW+2G+h+fSQWY759tIPDfuN6vLnBG6Qy7xsfTLGq5z8QBaRtst3xhP62h/3+DmxXW+fhnIXNe3lxfiMbQJqHyIPL4XQZqXqpKPDTUoA9F+dmtBpJK13TMEKWpdyxFOWTjrj29fX8SsanzfiQi8n/HEM4blBts1VXaNQ9USaN5hZCKtiGiVjpSuFKXKarrooCH0MBsiXdWY3mieyBcEh2bEss/faaDXF/YvhMj9S6z9V/r0dvfuNczlO0wqfkhH9Qq2E6j5BZNsZUEA5j7TXXAPwYxKu0FQWK2SMFptOYAtMIScYVM3kY4gqXgnuz/yjKAPy4gf8YZISq7H/rPvvfGmoU+kczLBKF8SJFmEDceN7YXKhqo6LAfRQgkxXLHT54s//lWv18rGSdzUDOBWquKxbUm8WBT/LzbRfBCv/ydH/R/7brvuydTaIFlGVhHsVisZjzFeZbXRUFPpYC5CP2tjxUTYVtWpClzIVuWrlXQg8A3WZjpas0vMqnXMsXzcNkc/a/gafQDAJ4fg0q5L5vjPFigUWSIFHa4m4pqYFWPSsKPJgCN/lnLaTcUZY/lcN5Pln23a5lBItYW6/mUTY7z0HjA5Sj8CqDeTCiVcSKAg+gADmM8njKFt1BHWkWCMPVbEvmwCt7aDIPMJ4sKE0gU2vuNOx9HfKAzKsofzUKGHnIT4IAmYobsAMbaO94+Gb7ALE6QFaYlvIVULevFfiJzD8/6uK7R10RvnGGS6UG37aWfrHXO8H13+WCGGY0YRTDaUEPpxj8RwyvTzPreJsh6gZiYctRaDcC2I4LlfHkrovhIsLJcIyn2xST66bAvM3NMKbnZOcypoWVEJMwkQmQ7fLEO4X3VOyY4nQ0xvlMiW9Z8b1F3YwSSoRpNg6HM/pRXGjrDCqnjBtbWz00aWXFWVMkELOMIoaUST2FnVrfQDdfwmU25kmc+Z7bkDLmLidO1F7PEb18gyyeiIuHQd3F53s9fPPZUxwO2ugEejOHGzrEk5YS3i0oSJzg+9dneH02RraYi8ln+2yIL95dYK+2hS5P5JfKWXoVmsofBvKmR4EiVGvP6YPh5CvmyUURLXE8ixX+58sx/tf3z2RxX0OOQSPAb4728E+fHeFJzxHFIAPrOqO73jRm62mEVoUGNvN+vVD4w8t3eHV6iclkAV8pdAMPX+xt4+sn+3i800KjZi2VZUQZIwb+/TjFv34PnA4nWCwWeDeLkbw5R7MeoOPvoj/wC67VpLgL03u/rRN57Td/rgTRYoIwDSGv8ddKZiupBF/2AzQB3q8BtUZDLMzkaYLMc3AxnWE4ncPaZozlIX+ddzFB4kYNT/KfTXKcDaeI4hzK9mG7NqycVmtSTBKtsHI+ydGjEkdhuamMDeuJChk8aXe1ULiaLJDklpgNa/gu9rpNtAo3VSySqVcKSHPLhbJYEor0BdOVUpv4TGPuXJRVXCjbk9N3l/ME48U58vkYAVJRkHm6t4Vff/EUu/0ATSrUF4t7aT858Has8PtnV/jxzQnOkhyJ4+J8tsCzk0scdHvofLuFxrqrnxIOgqQRdClLTgUo1mWpds0AT9qwzZKHfxgq/O7lO3z/6q0o1jRqDva6LXx7uIP/+NkjHLRtEbrJwe8ykZdUKQLJM7qzWcmzHM3UCxWTqOz1fKzdPp2eX4npYyr4dOs1/PrRHr59+giP92poFKMX6UT+oKu1P72O8McfX+E0PhdXN6fjCE52ju12C61gFy0eifzJLpbXEEIrB5mTmp82S23NhCoFVNhSFp+59OumebJ2SZ6DuoX+UQvfHjUxVMAZgH/5c4DZeCpWr2xaiGh28X98+QhfPaqhSSsRRRkIg7xIjudGKN16JXULn/e3REFkrCCu1v7f4ZVYAkKeoFNz8JtH2/j1l7vYqVsyqSR/EBZN87nFmB64135+N9GG/LB+caQuj9bkWdY9hWgXCvjzaYgfj69wcTUTha+m4+DJoIXvHh/gq6e76DY0PqZtjugS7CrHf//zS7w+tkQIdxlZSE5GaAeX6NRb6B40RPFjOVFi9W5Cbh3Z8u8iPsdEnqjpBxb2+j10CqtB8SLHOE7x59dv4aoEvv0Yh10HHdmQ03VguIob0eKKsyzHLufF92LOwAk9x9TfPOngqydtUfi7VMD/8yIQ5bdsMZV6GTQD/ObJNv7vbwda6UKGWG3hhHXGtsd6C+ierJjLMBvhj2Lj8Giri4vhCDVk6HdaeHq0j6OdHexSwZeJlhs+un95PR3g+zdT/OEHrZCXLua4WsT41x9eg+6i9qgoopNJPnw19SZPGu+xPVFUpLIilIuLRYrT4RTDd2cYTyboBC4Otlp4ureLJztb2OnWIZuiVLZPNE/XafWrIO5h08LOF9v4T19sayUcbqay3yxaNfOlRYAv2zaefHOA/OsD+ciT1oTBtiKXqSzze/m89cMyRvXy908BcgH7E/ILrS0p+3ouxW/s0/jU/C49p/aNYjixPFH++ydXVcK/IgXIg7w41nLDnlYrpwn0CX+67aX7QcdCM/AR0HoZeZcse8dF3ibvc7OJ6/vAt1CvaQtodHeYpMAiyRGmmSiXU/Fy2bfeAbf6VFHgNgqQ58jDVJblwRAe2qGrQse20ay56NTry7V42UYF+Z9peZkn38mPnNbQYmOLirC+bJ/qE/5RjDEFWjpZ9beiwCehAPmX6+N5CszCSK+5uGFv2+jWA7R9X9YqkpnpuMtMW8LCzN1pEdB3LDkk4/lU/rZFGDuNcsS0TJjr/ZhqylEiXvX6MAoUvKfnsYXCCi2hzhRSmmuz9Enohu+hVfNlj7+Y7W6GX3TGwrvFHFr3vw4ajQDqai57IGHCw3+06V1dFQU+ngJkO5n/FhZrZwlAHsvyTFy1U9bC+W8rqMm8wHS5fJr3dSwYzv0g6X+pNOu7CII6spmLNA6xSGLEtLZezLs537gN1jrs6ndFgTspsM5IZq5gnkzMOLyLPpdBNKbPQ7dcB/LAPF1jzxcLpEkEW2UiFeHht2a9VvHqnRVQffxYCpA1l3tolM1RWF5iW8qxdEixjyaW3osQrv02yMvKOJXYvhxcvf+VKbCUw/wkeGhDGLKw56YTzWDrk7E6N25YcfJpGIvIMF6TnSNXSPygeNK72OD6aCQJ0GwO61xFoLimFGKy0bE1GqLJpSAKDr1WC81WG+OLGdhN09z+u9FUBOHltGR63uzgeVNIPgq5YRKLOWX6PfZqdGsRYDaZAYmPWZLhxekV2k/7oDWDJXEKwJw40U0JNw5nC4VFmMopCFqnCTwHg24b9XptqelryiBw2EiloZon8SOGmy/Bn7Jm2xJhO83d5rmCCkPxO3q4uy2WFv7D53sY1LRPMXPCg/XKMtOLaLMB1L/qwGu2MIqeYUzrIVmOaarwby9f4xe9Gg7bPannMia6hnTIJiwpCGW4TCYtDqJaWYWn7V/FwH/5/Rl+fPlGdlAdlWLQrOMXB7v437/5DLtNno7S5DX5bMqjjI/BxAiFGZ9plzgUgnMKhl9cKLx5d4FwEcFzbfS8AP/pu6/wzdE2jvoWuoXpVtKJFxVW5KTLExet3jf4lz+/w9vXb7EYj3AZJnh7NcZpt47D9kD4gvxo8C5AfNKHKRvzuM7H/DIh5nl31oxlFpkUAPa7XZy2WhglEXLbwWQRYUJ3L0U82fAuKoO0Zv1y4k4ajRYK08J3KE3PNZst4fXRYorU8cSU+MnlFF+0O5LO0JcYEqRpi7TiMZqnmEc0aafF4M2ah8OtHuqu7m9M6ZhO61sUFl1Ep16rvJWIc4MIq+loHspCknGxnsDLMxztDfCbzx/hV0+3sVvTlhzY/zFfWoFhuTs20OwBjV8O4AdNhNkPGIYpkjjG2XiBPz1/jW8/30KfkvDSVZBPyqzfdWlW27tWWiNNpA0VVmyorPJqrvAvv3uGZ6cXiONIXLntdpr47ukR/rcvH2nLEwWuMpEloQ3BSnjcEigxytFZv9yMo3Wik4XCi/MQJ5djhFEMZAm2Ok38X7/9Ft/utrDfttC0tCYty0ZaMb1fU3Ce1BAEX+B3XoA374ZYzCc4n0d4fn6FQbeBdq0lAhLmXc5/BeWP/nET8s2Q98vE4FuGo5VhCs0FiaDrk6+sDhEe8cl6Upa4naMykkc3EzlVFhknRx0pOkjQtwPprw0PEkPC4c0FMxUCyScU0Nuc9EGhYVtwLe0bklr23LZvWplYdhlAWw0wfZWgyGZAH9VEu+A7Cec4w/7cuJEqnuJSqphHbFJWMdZV3lwpvDkbil9hMj3HoW8/e4zfPtnFN3va1Ya4g2IZisbRdIFgx0av+xn+v//Vwp9fneBqNMU8S/DmaoKd8yscbddRpwKbqS7TsMzvhz6p+FFYmmrDwuf7Hl4PB1hMZzijS7BMW476ffwaw+EQv/nyM3y538O2b4lQhHXCOiAt7QIZ9kmknVwGLyGmDme9UahCSw41Zcn4SDdlPAXh0DWjogFlzQ8NlaBX1BfzkbzYRmSOZomLwWVeRZYGPhVLvnncxaD3aynHoGWh5XOupHEv1hK6/6XAk0oubcD/rAXL+xq/+/EVRkmMKIlxOYtwTP/NW030muUavy4meZA3lbU4J+EcjC4Jf/jhGawshZUs0Kz5+KevP8M3j/bxeBCA1oFY/6QjyZf7egHO8pl2QlpxTkBSFmQsSnr9IF0Ig3E4l+WT6YsquY5YvVUUuIMC6/xFPuLNq9zO2CfKnHstvIhaPSoK/GQUMPxonuxzZZ6WAXGaynhtOzZ8jy7fHLFWUebduxAz/M8nFct9j9bveGjBlrVhnKSI0lTm6yb/u+BV3yoKbKIA+Yv8w5v8G1EZKkr0HJCKuI6NurG8thz3NRffxXeMwTGf9lHrvgfPo2tSPbdcxCnmhcLKXTA24VuFVRS4jQLkJa7VY3G1TQfUXEc5orBCy4FuyR3QfQom5F+zrqPlalpnpqtUbe05EWVBWrpKM7r21uuA2/CqwisK3EcB6RvJuzkwms1ljGcaKgzWgxpqnrakRp7ceK11pORfvU61EDgU+PvFos1CkmeYc4+ZLndXZtMbIVeBFQXupABZz/Av57+LhPOIRMtwuKfjODL/dQtXzuRN3ndd5f7Xc4CaD/h+DXNaHldK5r5xRivimofZLu6DeVd+1beKArdSwDAWn7KxVmI2Mn7xnXMKs09PWea7sUIU86iogq2495uj3wzQbxcHj6+T3pp19aGiwIdSgGxpWJdzgfIUwYQTtvTfSsHi5vkyhc61nMbgsSnMfKuef10K/LT77GWuKVilPPCus4/5vWQYCdBCizVQfzGqMV89MdbCHzkh3a6h2+ng+HIuLnYocD8fTUXrkCbvDVGZlmXhIjNS2tXFaKown8+g0gj1RgOdTgO1Vg/RPJLBYLqI8OztKb446mPL1YtKU1iOJUbgzknTdJ4iXMTI0xycLFG7kWbpqLhSprNJL0+uPnnJc0lpHbb2t0xzDlZKZVBpClfF4vf2F/t7+O0XezhcE6oxHW/iKkI1ZSGxFB7t2DjY20GymGOa5wjzHCcXV5hSgaegcznPMjrLcKJcCDAJmzcHUQZTeEnrDK8jhf/6h2O8fnOCcDoBLatsN2v41dND/McvjsCT1BQ63bo4Kmd8411nzvwEp+JFNnMpjIPCyUiJZZXZbAqVhmIZ4qvDbXz3aBuP+hba1k1lGZ5k92k/wFJIOxbOj3YQTad4O50gzHIcXw7xoungsFvH4y1z9vwGcj/LAFN3fLJt7PTqeNNsYnx1KdYhpotQXCFNE4UuK4ZXIQwkeWWjkS58WL+TCRbzmfgN9T0X2/2ubDqOTt+IMtZwNsPx2RnSJ52lJSe2X3MROnltHCuMZiEWYSxCcm72tOo17PTaYhqavMG8N18U1ZtSbY5xM5TS+BzIYth5Im5WfvnkEL98tIX9miWKcKQNcRU6FcoBPIHKvA5rCuN9H+fTA8xenyKNHNHCP70aiQnfrK4V8daxMmXgU27FJXwmWgOiPGhpizNU+uDUk+3nh5HCf/3TG7x5d6YtqzgWdtpd/PMXT/HLxwfYa2h8TfuxygQuCk48boqbi49F+zU0Il6sEyokMf/jocLphbasQlptt2v45nCArw9a2G/aolRBIbjpZ6kIQBTaVKLwFLBrY7rYx2ge4WIxxTRJRWFlZ9AGfRxu+c6yTzc4/M0/peJJcc0Bhg90PWie4rvwl7BiBkXNjZxOq3Jw0UyBPWnJeuW9ntb8Nt9NXDKWons7HscTV5G0/WI29FfhkM7lE9ise8J9yMV4ho/JL1RwYn97Fiq8PltgOB7L6RRa3nm83cF3hzv4cqeObZ4gLHhE8ioUPYgjFW88X+G7p1uYxDlGkzloaftiNseri0u8PGuhu9+96Y7woUiXCmZowt572wK+O+wjns+RhDQBmyHOElzMM4TpFMP4Ob4/7uBw0MXRVh8HPRsdnsQprHkwe96mLtaJSDrxYhy2E47frG6tAyuDuZ4DCC1YX3reIjxg+OSeumHehE3rIgOeMuppd3VGMUToW+BYoCP5sxLJp4puIp/U8OqsifloiMSyEWUpRtMZpvMQaK5p4Rkg5acwEAthI55Pxe3UoOnj10+/wm+fHIgbMNKtUdS/wYmE4WkR2YwvlfO2ajX0NFnzN8vP67Y0xefqUVHgoyggSnuEQKZbZ8SPglwlrihwOwXYr62zG3/LMJ/xdCkNmTlwXQeu464eODAJb+kc+Zmf2Idyyu/RfaBLP+ja8kWSpUgSKpObmAUyt8C7vRTVl4oCputUSDLoTfZiDuLYDmq12lKB+iHsZTiScTmfqHnkXypb6TVYkuda4CQq3Q+BWNVQRYH7KUC+414Xla7iJJY1FwX+5F8qTNHCMi/huAewHaPIHF76X6tQWOGhHFrTzBEmdGulXVLcj10Vo6LA3RQQ/s2pMBiKdQouvnjQkspSnEc8gGVXMjD867vsg2X3VPbKROCfUeEVyLmgra6KAp+AAuRf7qFEdBudpshTSns0/8r8tfBl9RA+Nrwr/a8NiBd4zn85h1CcYytQaTvjZHu50/AJClGBqChwGwXKE1sThwxaMDTlBZRjzqDEWve/v7rEbD4Xk89yILwRiAvtPjfbeBEer4c0iCJq9ago8FNQQFjR8ONPkUEF8y9CASPz+4tkxkzYd93Vf933/S+GaJGRwZVrQRKLxq76TUsUViz7TATuYZJiOA8xngODhjYjb/p+PulKgcLg4VyJUG0xmyFPY7RqbexSQN5r4PTYRWppIfTLswtMQoW0sFVvYHHqwgkTzR0tYlqaiBAtUqhUIai5oB9m+lL0XW3UlkVYttGSAoDMiJYfHkBRgwAF7nkKT6ViEeXL7S088i0xk0/68Ob4Zi7zTsW2prLQcxUO91q4uGiBp5DSOMJosZDTHOa0sqG3gVF+iusRbjRxRlfgL0L3whqFWFYJFX73aow/PXuB6SKW03ttz8V3j/bx26dH+HLLR6dQBiBsrXpwV65lDBSUxXuZvXwkKlI3tACSAG8vY1HEoWUK386w3fbxi6NtPO5Z6BWWIWosRylbCn8phCXwyFJ4smXhfKuLy6tLTOY8eT7H20sXJ6MedgcNERIa+pYx/Lm+G3Yjj+z2LXSaTbyh0FLRZ30ipy6GkxjNQU2sNJhyMB2VKajMcB7Thc8EYRiKjZNWPcDedg90mfjMoX0Hhel8gbOroShx+MYnbgGM5Ca8DPTRDIznIeKIjpqVnNBg++k16rKBbuIyKdMIy5lCGOTe80mLBmw/gQsc9bv4em8Ljxu2nOrnutrMDQ1byFKlQIQLo702sL/TwY9nQ+QLD1kSYhonmMXa6hI7VqJo0ht0Zc9/iSuppBVWZGOKCl9chAGYGFc8zy/wh+dvgDQCfQJutev49eMDEQA/7mg3QOv7AMzT5LvMqvSiaahd2ZSCBV+WkxtxIZUPIoU3F0OcXw2RJhmatsJRv4nvDgfYb1qgRx9az9CuSrTyA2GTr3TZtdLK030Hz8+6GM+miKcx3s1CHE9muGSdd5ra/+E9OJfx/Hm9U0BvareEWVEJ5Xow9WKemsc0o0gYra2oDC54X/fhpKe5TD/DJ+tK07k0lvMUSJZD5bwt6Z+lfkpKIib/DVibbO59EobJn0+Og3RLdDKlguA7MU9pqRz9hoevd7v4YlDHrkdrPIXSxloOMl4RR2XhaR94tbOFFydniEahKDmdTiZ4fX6GLwcNNOueKPTcyeRr8Nd/kn4yh7CArrLwVRdIHu8jVQ5+OJ1gPpvKyZ04zXF1OsarqxkGZ2M83p7g6c4AB+0GdpsudulqydGwzATO1BHzJI3LdWS+iYJRMX6a+mB8vtu5VjIiTYQu68iXf5tKLNGVijBGjZKfCYNwecnTpCkCiJP0/a5Ct1XHue8hsW1kuSOumeYLsTmmAZT+lsEwWP/WGkhUpqWLn8N+G//0xRE+72hLZuyrDG+XQGm8DJLlDxve16Ot/96QpAqqKPDRFNDzjuv57qqFtI8GXwGoKPBeFNA6rjkyanXKGsYChf4UnK70ieyYVwI2Z8MoMubYdOFJhRUbKs8EfkqhADPkwFVdFQU+kAJkRTNvpPwnTemcmccALFG0osC0vBZ/aDacw/DmfgsVtgiE/TVXWHFGBWSaqn4otCpeRYG7KUA+5h5RIjycynqLPOw5HlzHWVUYvBuUfGWvSv5l/+s6gEP35BJIi8p0zZbKfuND+vEHZFdF+QenAPk3pfWIJNHjOk89S//pwSmfYnkgnQzv1sRCEE1m6jmH8C5opaJSWHkgKatod1CgPPvkPCJl/5vxwFcO26J1QV8r+xURy/HvAKu7Wva/ovhtaQsA3Jor3G/TiiHdDundixKkgs9LIdVrRYGPogBZircYoShDKpiZXMibcgJ6EHgzUXjx9hTzBRVWcrHa/Hiri/1uS9zKE8SHzKnLWVfvFQU+HQUoeyKHV9ffMgX+csvpTbzy0JH9Z6AvC7YAACAASURBVEBhTo55EozKJ52ahX6nqTfpHBdZnmKW5Hg3nGPXa4gLAV00feKYgjUKY6+mGYbjCSJ28kmMbs3FQb+NYGDj33wfqeciiSDuhS6mc8y7LRFmM29enCzpQUOJH3FapojjGE6eohME2O820fa1qwyThoLg2y7i+LAqoPsAbVKJAxrPXHx9tI+Djidm9g2cdVj8TTzMTXcROwMLrVZDTlHnWYI0z2URw3KxfAYWcd7EMjpcuzTKLAexo92IUAnhOAF+/+wK//rHP2M6GsG2LHSbdXy+3cf/+evPcdTSygEGL24sGQEAF/73XxTGalkxYxM/DsoyiS2E7vTPenp5heFojDzL0GvVsTvoY3+rh8DWll24QcBJKS8Dx5SVtGKjbNcsbA0G6A7GmMbniBYzjOYLnI+nmIa7aNavLfncj/dfPwbLSd6hAYwd3xLlKoZl9JOsLEyjFGfjGbZ6NQS25hnSxNCW1nNOhwqjyVSsfji2h367jsOBJiStrSySVHyLUnnsaq4QeBa8oiEsYRUKX+OFwoxWjRKtsNJt1zFot9BgI5eKXaOZDHbXgqNbmXMt2cpP+kxHhm4jwHefP8FO01kqq7DOdUmus1+2m0KppO4AnaYFn8JdHqdyHCjbxmS2QNRt6A6qyJCweBu+0sF6Q1U7c7HA9pPYwALaUsVpCvyP74/xxx9fIg4TBHTt0qzhq90t/OdfPhZLSq2SEoK014JWfJi8TDlWyr78oWOV4zCE/SonwzQzeHx+geHVUFpC3a9hdzDA0W4fdVjXE+qS0pkBTTikGfuZraaFnd0+riZTXIZzcTcymkUYThcIkwZcb7NFGgPr5/y8pt31WxlfUw/lMMbkTfrQ5y5N+dt0oMtAcb9zbTzXxGP9livVwCD8ZR4CghtPum3waeKtPw0+y7QbedTEuvtJ2MRvlAJvz6Y4Pnkn5qubtRoGnSaOdrbRa+n+lvlxHGTZmY4X5WCmD2Z/TKsgg56FrZ0dHM+GSNIEkyjG+Wgi5oUzo7BSpH/vh8m42CCmpZQtWPjlrotGax+OU8OrtwnG00TchuWWhUWU4s3iEqcnp/iD64i7si+P9vDdZ48waFoyD6GSCBW4qJTBcpjL0F7XlOb15bfiI0+zL+ur0IHieGYUQ5fEMgk3PQtm4OEi4ZciDnVbSXDSnK8yTtLAFG9Hu9PhN/Z7jUYTfhBgZk1ko5wm+0MxNbopwxLvyWeFnJs6dHWVJ9jpdPHl/hYOuhZomNQoq9jsv99nBW2YtFRvN7B5SJwbiaqAigLvQ4Fqof0+1KrifnoKmC6Q3R3HTW6iU2ElF1+QekzlyVAZO4rsuTnEsE2XGSeKYUiicG1AZRWxsMIxQuWyNryeaGyCVIVVFLiDAmTYggX5Kvybq0IIpCcldMtMVyg3NurvAFvmW75T2E84xRS4aCM8hV1Mygoc7gBZfaoocC8FhH9lrax3rnKl4BSb8NLXvgefGR42T87faeWC4Lgu4s29Ofldakf3IllFqCiwgQKGNclPnD9w7iBzBCix0KbN9es+2sTdAEaC+J1rXfbZPBjjuZA+3OzlGrf3Scq14Qeb0r4t+yr8H5QCZg4hsoo810pXPLQjllE0Ubin8dBL+LzYb5H5bzFHkT6Xe1ZpJm6HmG91VRT4KSlAXlwu19bGe05jKcPkwdY5PQjECv/2eoaL80skWSIH2Ds1B988PsBup6YP9v2UyFawKwq8LwVEPFHIO+6bYLwv7Cr+X4wCfzmFlb9xJiH6nCRTnt2lSf+ejZrvIkks5MpGmEMUFb7sBsh5/LkQkLGzpyB2poDhLMR8FiJLUrhQ2Kr7OOjYcGk5oBlgMaTCio3IcnByNcZndF/R0ifXmD/HEbGEQFhzhWm4EGGNZ+Vo+Ta2GzW0fI0jJ07lceeTkJ8axcjRqrn4fG8L9ExjXHMwP8mjPLsq1gqkBhmNwiMqEThWDiie3kjlNNJ4PsNkniNo6ukeNzQJpgxK/yhOMCkOnhZy10PsAqPCRNl/+/4c//byLRZhAifPMGgE+OZgG//5u2/FOgMtlREHXro++XdVmFd8vuNRKK2UYogSEeuESkmLVE6IJzSZrRQaQYBuq4V6oPOhG4vM0kJ1nbsuJ2GYm+EUoDbqQC2oA/S5YrtIlIVZnIhiRkY7xO8zOy7h+1O/Ev/yxd9ElTxAC0VdS6HfCNCoNzCOc+SWi1mS4XQ8xWd5H1nhi5l8wJttSCysjOYI6cInS+HbNvb6bey0LNF4H/S7ouiVk/5Rih+OT1H39hFQaF3wEulLS0dzBVBhJZIdRQUrT9Gud8S6Cl2HiAWPIu9yOcy7XlBoQdJykmc+3vG08kzcATW9Or44GqBbs0S5gu2jTDPzzie/kQbSdmipqKaFzNTuz2l6GjlGsxnCqC4N36S9HQ3GsMU0Cc8ZhrDEldnrUOG//PECz9+8QxiSvi76gYdfPt7Hf/jisSgZ0eCTgU+cBFIRYMJX8l0GcrZw3ZoZbD6Zdm7a0MU0wiyif1haMsrRbrfRbbUReDoN4zNvPg3tDWTzNLxWr1nwPEdUKJTtIUwU5osE1FFSpiNYQfjn/8PQjZgKHVf85hZfLa0oSToYWpVLJnQSzTsNxChurMcRaOUMiwiEK3nzWdSrCKyKU9I8bc0NUBOvDLf8TjwMfiX2KEfR72s4MI3wCy0qRQqzKEWcZOKv2q810O100Wv7cIklT7wW6ZlmeRlFw8I6FzcMmg0LnXYLJzbVymwkctOaWSK+t/2iTEsYD31Zw58/lxZJlAW7AXR/O8DzvS6enbzDq9MzjMZThDHpoxXL5paDV+MYF+Ex/nh8iaOtLh5vD/Bku42DjqXdBRXjLenOW1/a2hppZtDQ7YT9F/03kUTXfRnjmHZlICwTLgNKL1RgKwAzT8Lmz1zLb2Shy9iSJ8MKxRY5qV+A8WoOLCr+MpJta3P60jeX8rnttVCYYg5138VBr40nWz10YIkVMo45MhvjnEO/3QZpNdwQazV09ddD4qymqH5VFKgoUFHgb4YC7OL0eKFRlrFX6bknP1BQL3cxDzEFu01Zhd/L3Sbf5S4EpkxHgROFsVSKoVCrnL+BXz0rCnwIBWTeIYJ4bWFF5q0rHPkwqIZvyZs0DsDVPedTbAsMy/JclHMr3n0YPatY91OAvMT+l4okum/MRSmQrtTkek9mMzzM5/VFIT+VBamUyD06Al2NcR23eqso8H4UYP9LQbw57cz+l9aByGEPYt+CHc3egvAwD14Ui1DdvxcHI7iH9H7oVbErCmykgPBZwaPsEsXyn7hXp3xG/zMJ36fHNHC55yIHiGQyoecQzOPOfTGTYfWsKPApKUCmLC7ONygzoSsgyh6vFPDijNZVzsRdlSsWrWv4aq+PXxzuoh9ce3cwMKpnRYGKAhUFPgUF/nIKK58C278iDPbhnCRTzsmT/t26QqfTxiieI0OGMFM4vRpidrSFDPWlwIgTaFpXob2Ay8kcizCSmXndc0TBZLtuifBt0GlhdF5DNLcRwsHJcIyraQdZq3UNSwYPJW4QaEViHkVyEoIuUdo1F9tNHy1HC+jNmGMmRIZ0Ev4hs/jCfKNrWajbFvo1B01bW/kolss3VxzFoWbSjTfXFDUK/eQINxctqbjYWcQxFvSXi0DiEUeiaG6DO596A5NSMhep7WKSAaED/OHlAv9+cobL6QI5XHTrDXx3tIN/fnqEX2w54raIzE481i+T33r4pt+WWlVYIY5UqOCgTqWkaZIiktOHFI9Z8F0XvuPSEwxCuhMiDsXgL5NUZkLhKa33WBZii0oEGiYnq7btQFn0K2wjoYZrmiESzeufr8LKRrqV2g+tAvTqNnrdLiYXE2QpFXEynE/nCDMg44mJQihtrBNNUq3wRb+enM7Trc5et4meZ4kweW93G1dhijAKschyvHx3ice729hperLJQ/oa5bFJDoxmIRZRLIoUDnJ0G3X0Gg1xgcO8WU93XvdGWEvN9kPXKypBw1ZiAYQKINcdsAEoLXSZWNpNIeCmGykqbsjyvtjYJ8/QGgEVpAx3lyHwXfhMXrizStOTVIBykFoWpjnEGs3vXrP9XGE6T2HbHrpNtp8t/ObxDr7oOWgXyl4GNrE17wbZ9d8m3JTM/F5/8juxJ99PYvoe5gkcJSdo6JvYtRx6J8LcV0gKobwUR/qDAho3QhyIshMn1qxrVqIlbY6JfMQp/SenSBIKXEiDdUz+hn+XtAxIT95cbJj31ZLpE3r8qnskxtpce+uh/E2y8TbfrpVWSG8uWLiAv/u6G79S2lK5GMp05sm+ge6w5lRWoQ4kN79sB77riWpCnCnMOUZlaqm0IvQgcoVSD2lEanBBxiYk2fGbZSOFJRabqLCSZDlyCueK/D/kYXA39OOTY6KvLPRpPenAwVZ7H4d723h9PsLJ1QTDyRxRSAUuC7MsxzTOcJGmuIxGOB1FOL6YyGLx8606dmlBjP6QCzqZOpDilhDmuELhCm/hETkpX2yOMLJBtJTmIa8mP6Fx0abZDqmIQssqMnbz3QZSC5jz5umNjFxoi7UolSnQB7neKH9IrloZju286XvYaQbYrXtiuYqWlvTcRNey5tiPqcGH4FPFqSjwCSkglisKTVF5mFb2CfOoQFUUuIcChuvYt7NvplCTF0d6Cuk57q9c/FkK2jSklKPICVPOaTXQIg9Fa9N6PCrBWsmn+lFRYBMFSgxnXvmUWyZAOtGHsNUyDV+KQ3sycZRTfFrJWASnJuNN+FVhFQU+gAJkqdzS82MR+nO9xf2h95y2b+Rh0z6g+3cqXRnFgg9AtUpSUeAGBdj1kq94ccogtx78b8S9K8Cs4qSLJTMXDE34pu+VPYGqD76LjNW3D6CAZqnikM+y571mNL4t+9cHwic/Xyu+6Hmw2C0Wc1cbAH5IJg/EpYr2D0gBw74lxmUvzf07WlahFXYeyP7xMsePx1c4H05FmarpuXi83cevP3+KvaaF+lIK8Q9Iw6rIFQUqCvykFLiWl/6k2fz8gd83/ps5sQiZADR9C7uDHsLxJeZpjCjLcT4caesaqIvlBJaagtiFAs5mdGcyQRwt4Fo5Oo0aBs06ur4eDPZ6Pt7Va5hcUcBj4+xqhItxH+FeS2Y/FLxQYYHC2HGkMJnPxLWQjQw110GnHqDbaIIWIhiX+JoxiE95L7ydvPdsigURDWAqm9hitcCzLcnHLBw21nCRn8GDODG+TYshyoLKtcWUNFdaIFgCUho3l6ECh4sb10Ee1DC2PNhzhfO5wu+/f47JaIIkTkVJ5HB7F7/88jP84qiFpWUVY+hhA9Kb8iPRlrQrvZvylJ90SUQBapyyLEznQDk1LOwaLjIbP05yNGxa79B0g/imZK56mipWDmyF1AZCG6JIcDHNtRUQChfFLUyuT5//LZ58KZSX2OF4sNCrW9gZDPD6YiYCzTmFsIsQk0Qh8mmFR5uho0UaCjUvJwrjWYgkSeBZCl2fClouWtT+tYGjvQF+PLlECFsUht4OxxiGESJ4ctLemLSLoDCaKUxmM8RRKEoktPpD11HtRh1U/vrQq8wPm2BwGUJfvTUqMd1Yg2zkwCUYfiVqnkd3Lgo2tZ5EgFWcRC02AZYJNrxIDlSAcjykboC5DbyZK7w6TvGnH19hNA3pnBWtwMOTnR5++9UBPu9baBdtnSA1x2rgd2O8AYEiaNmmCqUK9pHSdqi0kqS6L2A7cT2EtofjWMEd56CFC0elosjiWLScwstgQUsUtOCgENvAxKYVqgj0A8vWSNqnaSz9b5rRtsxSze52RP9WvoiwyHDfdR0ZypBv+G6ehZaCPr2htRdEcctQ06S7q/iMU45n3vXz7pMhq3FX4WzK08Q331hSbkqRZxaJEp6hMgndY8W2jasMeDFVOItoyUSJgiDjE2GtokP3dvx9vdE7dYCTqcI8jMViGkdRKhlGUYowJk+qj7bKw3Iw2/JFLqTVNj53YKHVBp60PZzubuPFcRNv3l3ifDTFdJFgHnE84OlHhfF0gflkiLMLB8PxEPPwEMlBDwddW5Qz1/Mp5ynjWrne5RS7HuAYvHKtE7/0ke2Wi1pzc2HLOrm+tR/xOAPiRLFrEcUVtkj26xznJi5wOY0RU6OFinRZej3mlvK67VVcFVK5DQqtoCauERueVgS8pgHfCga4DVAVXlHgZ0YBNj3T/JabmSbgZ4Zrhc7fNwXYe5qhQSseioaoCJxunjEtMe4aWQz7mufKZ47PpQFIv5tcV2JWPyoK3E0BMljBOobXzFMSFt8/hLtMGplCkdULwavML2Wiyby5v8F5sCzS7sa1+lpRYBMFCoY1/MYoDCrzMb/J3lHB7OVvm0CaMEnHtAZ4KaHu37VA1rQhk656VhT4aArIXmDBcMJ/Jea7DTijGF5dfdXhZg3LSPxf7NfeBq4KryjwoRTQ3Kr/yn6SuNQu2PA9gBp2FkgyTdAwpUMv2kUR8h5Qq6gVBT6AAmuMxr097tPJHmuhrPJiovDHZ6d4+/YM8XyCukpw0G7h6/0dfPOoI7JH7rStgfoAZKokFQUqClQUuEmBSmHlJk1uDSmLPQIAj/dbOH7tisZ4pBSupguMQ1p4ULCda1P84s5kojCdL5AmMXzXwk6vg36jJsoUNDjyaMvCy2Ydp7YjwriL4RRnwwkm8S5caqEUgiEOIuMQGE0nWCxmIgFqd+qirNJw6QahJJgsJvac+ywXpreW7v4Pcg7asVCv1+CWrauYmZd5ro1Ykn8Bnp/0GXzaidA+AsTnqEgQdSTu+ehF+CpOjGc5NmzHR+YHuEhzvDsJcXpxhYvhHFmcgZb/676PX33zNZ7se6LQYJhcDLusgDSYXQsuJaS84JGVTxFv+b4CRNZRHOBpZWVB4WbCM/s2MsvF+TRE8vodXl2MYWW0DqLdKtHahpwepD0COUVLn+88PWMhoUUJy8E8TjGdh5CIlj55HmYJ4iwVlyl/M1MDko/eJyiYpdKOAvoNKqx0YVlvkCsLYZZjGMWgS5gdP4DvW/pkPq1uJMDZVYjxeIYkitF2HOx2mugHWCojPd63Uf9TgDFs0LLCxSwUiy3TpImOr12ksO2IS61QIYwiZFkCx1KiPNZr1tEIKBZaWRevVvQDfrGot14KoqzSrAXwNfcL76w1l1uTs8V4DoXt5JXiLiyRPAwGza1T68NDYnsYZgov3uV48/YdriYLpLklClVb3RZ++9URHg8sdCzttojl0m3347hOWhKtCRX9FIXdfBf3RBRs0xSx1IGu/4vZAvHrU/x4YsGhCU66VaLBFFFY0dRWtJHBtkkGE8UFR6xUTeMEsyl1w4mzQp4mSGJd71Dswf++LzNesZRCM1qHEuWdEpMXDGvCTR0zzW08JXVYkO7GyWrhaP2xAF3E1I8yTOLH+pc8yx9WUvDHdbs0eTOdaP4nQJRkSytUkzDG83fnGE8muqzLI9qEUyh5iTskQrBFOYW6g1TgiiwX8yjVfq+VA5XliGi1Kc5EYYUpPvZaLyb1ztzCbVGduphKn1LoeMDjJ3WMHx3hZKTw6nyC48sxTkcTXIyn4q6IyitUqFm8OsF0FmI234f9i0N4bVtcDjEv0piXyZdPfVvaGo60Jc0fxQeZK0h8k6iAYR6mXtlOzU0FlCldNOWcnyiMQmAW5WL9KYwT8KalGlq1olIaFVQSuiOzPYzmERazucx7LJt1QpO7D6e2lediVrpVq6HhOWJhhuVeGe+pJFtdFQUqClQUqCjwYAqYvn6ZQAYPHjQolFKLCLcMFctkXDvqOOW/+jNB6D6fMOnmwrgaug/qEnz1UlHgQRQgRxnlP9ljEGtuXK8//CKMlfjCr9rSkNZXoebVTT5/eA5VzIoCmylAruKhMbqPYEdJPqR74A/Z35O0BS8LPxdWlNk+qIRosyNe5fTNSFWhFQUeSIEl/8p6jK7/tHtBrZz6QCBr+8N6W1bPG2Q/gvupyxXvA2FW0SoK3EEBM96Tf/Wt90/4g3NbuVYfd0BbiS4/pKdd6ccLHpY++F5QVYSKAp+MAtx54015CfdYua/3ZqrwP3+8wo8v32AynctBwLZr4VeP9vHrR3vo0rp10TY+GSIVoIoCFQUqCpQoYGT5paDq9T4KUBhC01cHWxaCmgfL4Wl/nibOcTWdYxLmqDfpaEQrMUxT4Hy8wHwRQqUJavUadnsddANf3AtRsrJbt9Bt1OB5rgiOoyQUCwEXkwQN35dZErUdQ0WBUIZ5GCKNI7i2i16rgV6ziYbHZaaeUJky8Ldc5sU8i0mWmYiZaLc/uUrQiwKXSiMUmK/ltZK2yIfwb+QhgYwgYiVx51B2AcAvN9IUmTFc6KqAt5cjRFGM0XiGVNHdCUX6ClmaYjwcIu5vA029qJecCpyWeIqQW5DZXJIbSFzjxU8GT4IhTknOk+T0XU1fPnJ2XrsgSacYzmjNo6AhMogKBTe1lpo5pK2e+hoFHsJJ6U8hz2AVbhtyuhsSYd4G5JYF+/m9UE+ClgRIJ1oXaTvAVtcWQWNC9zQqwzhOcTmbY9EN0CkmTLSIMo0URtMFwihBnmZotjwc9DvghCkAJ0oKXcJsNHHheUjSEIvcwtlkJm619gaBnOinssoiB4aTCPNwgSSJULdz7HRb6NRrYsFDGI9IkmE2XnfwSyn+eu0I+ypaWLHgWdeuRdZZkiAMbxlwJo7JWSmKibVLD9ngZ8SHLGwECVuUohZJhpenCYYXQ4wnc2TcQLAt5GmOaD7F5OIS+dY2PemI9QdTHoOLwe1DnoRFEutSaGGFKBOlClGSiNKJ4/tQliNC7iiKxBIOhfvit0Xare472GaopWZRU40beewBLK3skiqFLEmRZyk8lo9tJ4uhxLrRaj/5IeW4K82yWd8V6af6JhuPuqakyku9m4QuGalAQBT0dA3fWr/8XDosuh6PG066fyuAl+JuKqZJr2txUwwdJvFM5KJtGN4RnklysaJDRT9qw9E11nAaYzGfLUtNdpFWJXCYWt/cllXKFmNXludD2a70tyrLYFMRjP0s/9O6CpP8BBctSXGk4JDB/pFuwowSSIsWV+jurw/stjt4t9/G23GG52eXOL0YYjRZIAxjROECJ9MF/JNzdOseOk93UWuKOqhgXNTIEnuSQUjBei/+XY9st3clBo55mvnIHAqXKXA6zHA6nOB8NMPFeIZZFIm/8jTPRflQ6o0moU09WjYy2wUtsLD8nEdxMiDC0CW2d7+Q56huyolszbHl9uh2kMmk3k1h74Zjom6KVWK/TZ+rsIoCFQUqCvzdUsD096aA7A9lTJXxo+hn7+pAN3TB69FlaVQowVBQqpX4tQVOk2/1rCjwXhTYMHAzSJZKwsR6inCXsHQJggy7/KGxWPKwACVcrbTCyQ7nJVzrVVdFgU9FgYLNhK9EaYUBRuFKL3IenJXhXfNkQvOuRaVaYeXmgYQHZ1FFrCiwpMA1b7Gf1PvU5Dj2vVRauea+ZZLbXzjvKHWthM11rO7Xr1VVSlFuh1V9qSjwHhQgT8meVTGJYLdL2YXeuX8PQAXHm3bBlAJDJsLkYR5k1fuZ7we1il1R4MMpIHt0hWUVyhqprPJ8qPCHNxP8+PYMszCWQ8e9Vh2/OdzCt4c72Ktb14fEPjzrKmVFgYoCFQXupEClsHInea4/6kmx/s0JC09D7/gWOnUf556LJE0RK4XL+VwsRfQbVGmhOxOF4VzhcjRDFCYiBGu6Dna7HREu8Zx/DloyUBi0AjSbAebTUIQ6PHn8bjTH3pYPx7hAoMWJRYqQJ8EpiHUU2nVflF3qnt5TIa6ylr1Gv3gzpShPk25E2hDA+MQyk8UyFVbkhMd6TGa6YZVggs0nUdaQH4VUs1DUIDgGm8WIib+SjZy+VojDCBfn50jiGGlCN0AeUlqpzm2xcPPs5B32OwGaQRtbtHbD0+vMzpCAwMWqCwM25rSSrfmh1V/Mr+snoaR0e5CkUGkqCiZUWfKQiTUNGsmhUJ0WIqh8Igookq+BeL1oE1UgZWurAZ4N5Xnwsxp6NYW+Txc3dCvEHP/a1wNxKMhrKE0hbV0BvaaFdrOGSWwjTy2ECXAxnmO+00caWGKxZkpzdAsqrMzEOpFt5WjVPez3Wmi6QA0U+FpoQmG728JZPcBoNkGsbIF1OVkg6QeiLRxCYRJr10JRlICC6ZoD7LUb6AWuvHMdYvhvnbrEn+uJTdzyIEoojSs3MoX91jMofpfhb4KrHdxoxQx+p7BecCtxsoFhngQtcag0lCuE4QLv3r5GOJ3L75rvIibvWgrjMMG/nV7iYHuAxrYtFqHY563YKDCIlTIwQbcUayWYyco3P6YxkNFaRq7bh8oTuK4D387gqlTqWcTdNLVdnJOUBm0UVgoFOFYge6uc5nzY7lxtcWLbs6WefccWl1MrCH2qH1IozSSk5XttxJRxMMQs6Gt+lqPceJfuVCcokq1EIQzpZQRHE4M1e6N2JR3jm1i649R8ZsIEzApi/MGaYUess16BsYLNA36wMcrGwBLcSiLCTtNMbtF6pAUrS8GzctSpwKTR0TgJ0mZjwfS1UoKivTPMQU4mD3LpX5u2hX5giSKoR6tGK7m/54+7CMETuiVw5RphX+kpCy0X6LeBvZaL3eYOnjWbeHE2xPH5EPMkwixNcDKZ4IcTC08GDbRqLbiudj/HrFcuUVCimocuEb/nRb+3jLt8uWYCBhklMz5nCjiHwtuZwsvjEU7Oh7gajTGj0uF8IYphrmOBt+e4sG0XNpVS2C4dB7nrIHEDTGJasqFyC086Z6CVlaLaV9C+6wfj2zYFRkuW0UQl0g+ouAdEuSv76ltFgYoCFQX+7ikgY5Nlw3HcQliaios66tXfJTNd71/ZLZshRt6ZnkOw9jQEqnS7HDM4AK4n/runclXAT0kBM9uTqQCVWWWyRcVYWw5/ZIUC+3qed7EdYRXsunR1KBZgbQtWTpetdN9amousUqAqbAAAIABJREFUA69+VxR4AAVMH0leJNtyeWIrvZ/FHjRXGdIs10LTW/YmNmVjeNs82ffmdMvJzQ/CEeUrp7DksglCFVZR4OEUIJ8ZHnaXizvuYfEcER2qm9XoLTBNQyh9NkHcz+KtDywRjjhkFyvg3PetrooCH0MB8pm5CYc85TiOWHvnYSbyHvtPw+MPZTkTj7DZ9S6tyhZTXipua+Xtagr8MfVXpX04BXgILVKUWwIjKLyaUVnlCj+8OsF4OIWTxui3fHyx28VvnhziqF9H09WH4ww/Pzy3KmZFgYoCFQUeToFKYeWBtDKTY0YXmRYsdC2FfqeBYFhDPE3FfNbVLMTVIsZhvy4bGlMFXE0VhqMZsiSHbzno+TXstFto+RC/b9zs42nx7W4dg0EXp+MZcsvGKIxxMpzia3RF6YFuM2YicE/EkoecLs5jtAIXrcBDzdMCKuJo8DVPXUz+KkIKe/lGXeI+MjAvWQjYFjzXFuHQSpo7Rit+Kt/XOBCiFkpSd8SAMHGJqQmTvAR1SxQNoixFsqClEoWa76Pba2E0miFUiVi6ef72Hfp10uUpmrv1pbky1t0KzLVfK2W68aOg3Y1wHZBlFLhTkSgDFE/pZ+g2a9jqNNFtN+CoDFZGt0WZPr1P7K1CcGjloixA2liyaUAsC6UV2KipGB1XYbddQ7fmw9W7brdg8pcJ1nS8myYGE8bizTSsA7rEabE8Wx3EsxHCJEGSO7gczzGJMiRtG7SuMlbA5STBZDoTFz6Ba4ty1nY7QMPW7mq4kUOFoMOBh+N2C6Pzc6TKweVkLvBi9EXQStdcdKc1mUeyyUNcCG+vFaBXo8KKXilwPf2wUunSmbi3PQ0N+Cx054UOmn7lr5vfCbd861bOUhv+ucaX8Qxc81yBatEMa4YoWiAJQ7iWjUazjaDVxnA4RJI5mCUJfnx3ie32MTr2ATo7jijoLeHI5oAW8C8JtTGzZYo7X4TebDsphRY0wZCL+x72DZ1GHbudGjq+ozfr+J0nGpi92FYo7FGIggKR4M3OpLjFAkMK5CkGnoVH3TaaQU0UVj4C5VvLY5ouDT5pZRrDFdddL1Es19MqMJbPNJb3x5C03HQqToOk8yTuSRo6aVppGxVEWF8MLWG95Cfznc9rzNhf8bcO0T26Tl2GUU5bfjdw+BS0Sh8ljL9LgJZhRXAmp7K50ap5ptbwsdtu4qATiOBAxi1K0kx9LIFpOmshm94403Th7kFC1RU0LIUt18JWq47AdVaUSkpo3v9awn8lsgmXQvGHZgyiyk0RhrC+aHmFinlNWOgB2OtZ2Ok0ETTqCFOIRaQEOSZpitcXF3g33JH5BRX6zGWyMr+hGVTzAvOyCt4o6EqUlleBGn8THy5qaSqUC9rnNBX6/AIvXrzGcDhBkiRi5YT06rYaMv61Aw+BX4PvumJRy3JdWJ6DzAuwqLXww8kQZ+/OMV3MQOtRtu3DoVLLhutGOUr1KW7SOH6WFfhWCrIBYBVUUaCiQEWBigJ3UsCMu+yVHduBV6sBeSwWyZIslyenZpuvmx8YUr7llKqe2smpa27UexwvyuNu1ZdvJm8VeisFDI/JvJe8S+VZsUSrv3CtzjkLf/Ey8flebJHoD6W/Wu1ZMyPhRgmQpJkobFFh2raV5FGjYdyKZ0uUq14/lAKm/+XeiUOFFa4VeNBLZUjSRNyS36UweF++3LKSw1bSELias0QoSyXwiofvo171/SEUIA/LAQyu/5hAmNpCnCbiCtAE3QurWBubeLLvmvKIkiV9thxasYHAt+RshIlXPSsKfCwFhId55sbVh2/SPEUqVs/1DEJYupQJ5wfrYTLJYGDxjfPmJFHI8lT6dFGyUkr2Smz72hr3EmyRdvm7eqko8D4UMJPdNT5icEwr9ACu6AYoAf71zQT//vINLi6GsCwP3ZqLr3Z6+OfHe/hquy576tyb5LUGrgitHhUFKgpUFPg0FCiJND4NwL9nKNyoYLfMSbevtFug3UEPLy4nGM/mSJWF4SIShRVaVqnBwiwBRvMEk8lUFBlagVZW6dQ8+IXfN7pIoXB2r2tjd7uPP788Rp5bGFNhZTTBMNGndejSZDQHhqMp4iiG59jo1n30m3U0ap6WyRUVYDZo+JNYC+ay+LS0LJerUI4wD16NaoUVTqa4iL3tYj68GMO8F0HL8OsvBRwRsl7H2gTdwLLF1U4mVkxcB+h1W9jd2cbh/i6evT7FybsrTMYzJLDx48kZaqLg8BWetLRVBdZdGTED1zyvsbjtTVRsloMzwZnb5cFxng5nUs5C8wTb3S18+9kjPN6vwaWFjRywi9uQnnkbQTeTcqOM7k8EJxEmAl4O0BpPwwH6gY2AOxd/9evhVCOqjE3ayKY3lUwA7G/3cXZygtkkR5TS/U+CSZRgBhfk9/OpwvlwgtlsJu2u12lip9dGr+4LPXy9byNuhg77Nl50O3hlO8hgYxYmuJwucBUqOAFA5TFaaxnPZkhTcohC3XUEFt1psf54mfrUvz7FX12ZUq/Sf2xYhDwwG6GhKCtp1QDty7pA+hYYwo+lb1IHliUukLZ7bewfHKLdb+HFSwunb2MskhiLFPj+1Vt0bKDpHeJpX/M12U7cv8hurqnREvD3fDX8QOtHpL/v+dpHN9uK42Cn18G3j3fwZBCAJ3PYhq4VVnS5TTuSrPmdwaVCE1WVp6hTgcyxwT5Y+gEd9Sf7q1EpEFrLpYRe6YsU7u7KNLGFpUqwuVvJn/dY77lOTgyKWzofzfxlvErQJWb59/W7VvZY6VRNJg94XsPRdWYUX6Qc5UoswSKOxJZP9reyqcpTVZaFTrOBz4528U9Pe8Lf5FdxJUVY5cIRnmSu6UYSSARGy3hCi0pwFppWjrZtIfA+vM2WUC9emXEJGdadMOnqThxRJh686faMW3JUXvEA7NrA010bF4t9jEaXyOYKaRphNJvjajbDLE6QNTmzuL6Yq7nLDUSX/f4aZFrOK6iswr759ULh98/P8YcfXmMxnUFlOer1BvYHHRz0OtjttbDTaWDQ8lBzaWUFoLseVp4IeqjMyzlN3MXoaijKp0IVUUJibqvXzRDzXZuW5i/DMoTPm7SrrooCFQUqClQU+HAKsO9l38x5Ws114LkebCeHSmPEtHJGJWN2uPdeBpIeb5iEljHTVFtL4wAl+dgOfM9bzsnvBVtFqChwDwXIV74HBIG/nCfQukoUR+KykMkZh7eeGxY/ViYetAFHiwCuzC/4toiUWKeU7Ona0XEQeC68pRr3PYhVnysKbKBAme3Ik5w6cz7r24BLgam4Uk5EWYrbTZuWOEs+JnxhbJ0R+10Dn/P5JAUyfQpEonEtVfM9GkKsrooCH00B0696toVWo44LWqiwHTmwFydUuGJPurZmY0CJZwWJ0m/yPI8rRTy0EfMYhVaucm1L9n0DT7eZj0a+AvAPTQGyoWFF9sFUhPKoSF3swyaJVlp5H4VB0/+KskoOxCn74FRbWeEhOyps0xL0z+Bg6j905f/dFN5w8M0C8Qv5kT0o3QDxINob7u29GuPPz19hMpnIfnnDt/H1owP89tE2frHbRd8rDgwTAK9S33zdYopv1aOiQEWBigIfSYFKYeWBBORERasq6H6Zv6mQstv30Wk38fbknZhGniwSXM4jsQzBE9rjUGEymyKJQ7h5ik7QxH6vg3agBTiEQyEUJzu9GrDVbctGncpihGmGizDE8Uihvg0sFDCeKcxmU2RJjLprY7fbwqDVRMPXJ8DN2MFi8V3/LoeWCmykO6Wgu181HHOC38BfGafuBrDxqxZSrkLhr5tYWxDrvZmCp3Lsd5v4/GgXT452sTew0XQP5PTJs0WELASuFgmenQ3RffYWtc8P4bRsOblOepur9GqC7nlqLf5yOhEsKiDgAM5FvkuRYgRFl00UePo2dn0LDQrlZeNB28ggDAPH0JKZmzC+GxpooSVABQ1CJ9/8LV3lsvKd+FMB53Dg4McgwKVlIclzTKMYo3mISVYXIwB0p0UhbLRYiECz1+xjm1Yyaq60G9KFAle6zei7Cv1mA/V6gEmSY5FEuJwt8G6SoF/zMKN7oSjHZD6BymM0AlcsJPVbTdT9T0NTU1/meaOOZFepXMM3YtwIuIZVvBU7U7r5WtJ36D83kt4IoLlq7s76ro2DQQdfH+3h6IAWViy07X3Y0QKv4xhRDAznCX44u0KzXkejNcCWZ4GOzlasH1wjt8K3NzJeC2Ayk1R426JSgELNC2BbjvgosR0bNbFI5YF+MqngxLM5hUrYCkTCMjy2Tl0d7ku7qSla9/npBNmix0NcCqsjZsy4gSwD1hGVSIViha7clWTlH0K7QkelHC5986reg4FajnadtQAiIhsxXdYRvzLqjVuIW0iZRLmOFnw2Fmwl/3U4Kx/v+EHI7Dt4sy8NaBnJp3sCbl1R0SRHw7Gw09D9ra5rjY8UdQ22nMpaC2PFXPe3jihJUVD3wX1uiRwah0Ith+ESIITTWJi45imhum4YxJu4ce7RdhV2unoDMIpTcOMkEaU/KpwlSJWvXf2Yeiv4hdUjVVTkIbwqpzY1CkRJ2I8vGmHJmNFJZZ7A4KL22ekUr07OMZtHUHEEWlJ5vNXCrz5/hCPOS5o22nUq9mqToYROGATJBXJoKSRKK6pJaKF0RYU43u97FcV532RV/IoCFQUqClQU2EAB01/zE9+59qAgSNY5cQ4VWcjoDjeOwZPOcpW7bumUyz0zP2oHABwDmCTJgJDjV5yIiJ9zvBrnfjyJvUHPtMilelQUuJcC5DzeZs4YuECjHuj5jW0hzXPMwxAxhUXwZE4pQMssW35fm8PIPCZRMvdSMrGiQpeLuutJW1lLei++VYSKAoYC5B32luUn1zz1GpVJarAdOjnWVq7Iv0nhBdWkv/E0wEpzcPa/EfvfJJN+nDNzUVZxXTSCGngo7IPXPTcQqAL+oShQ4jeWm3zMQwvtZkME8VzjZbnCPNRKr+xLV3it3HmW34v1o8wdoDBPFBZhLC7eqHNFC+AN34Vf8e4/FLv9JQrLfaCGp8d47oNym4KKJmEaI6KlFTlapHmd+Kzws0GQbtmLuS8t64sbFlq4yhSywremx/1Q3we3tjbCWGtbBnT1rCjwIAoU/SnZiDzIm/LFIff15grfv53i2eu3mE0msLIY3cDDZ7td/OrxLj7bbourdO6tyn53xYsPInkVqaJARYGPo0ClsPIQ+hUdMicOnGiwr6fQiJt3Awp6mw1QuEo3PvNUYUjLDgmQeRT6KkxnU6g8gWspsYhClwXtmlFe0E8CbakCXqeNaRqJqcRJkuLtcIztQRdxDkwWczkRpLIMQc0R0//del02Edfm9MuSEf3ltUGYufx2z4uBw01EESoJJe5JVPpM/DSOa5JWbkoWAtrbyqDBXAvn6zbweb+D7/YHeDyw0aIyyJ6NON7HZBbi3UmISGU4m8X406tj7DQbCOw+/KYl9VZC68ar4LkRkUJZpfAwsaRHMTHlAinwPbiisOLAynOoJISTLNCwfHRhicIJ+Wj9JhIGXvlp3okO05DvOEm+w8jNjfL8XAIo8jZicZaFbnx22haa9QC24yJRCrMwBt1qsd14LVosUpgtIqRJIuace81AK2i5lmwuCj3ExRCEB/oND51OC9PhDHGiMAojnIyn8Lb7mFDhKwoxX8zFhUij7mPQaaEd1MQCAHHiRZobuhdB7/W4K+3tvPXwLJZWKNiehE+17acHQaBrr1whcF18truF7/a72GXjAdDes7GY7GEeZjg+nyLMFd6OFvCOzzHoteFseXADnftSPc5oaLxnX1DGlbmL8oGnN+IcUVhhZ2sBcQw/TtCGJ8oyVDZjPWmMNRRDb4aZex0+w8kr0m7YiTNRGUg5wUe+i0svwhDEtKKHgGR+Blnz5IclDQ1SD0CMXagI+MuAWKSbaRmib3418a9DBQVB0MTbjCZTlm8hoyg/UGFFH/HTii1FP30LicswimyXWJnfm55MV8aamwf1GoVnVFjRToR54tuOQgQp0PYsBIWS4CZ4DDO8RLjli+GmPyiGpvLnD343ZTf5LgGVC7cMNC/8SHw0lvwrG9cAug0L7XqA0SRCokKxwrLgBkqWyiKUdcTL5GvqzPCcVNu1vtEG7ikAFA/up9B63JkCXl0McTGaIVfaXdd2q4Zf7PXw20cd7FBB075WrmT+ptnxKfsyYB+ukKUZ8izRAxs5uBD8rOZc/aooUFGgokBFgb8WBTjucM1b44Y9BaY2xxgLWZYjCiMt8FSMUZrnGGSXA6weCWihgtMXCp3iXCGKY3HNImMbT/c7DnyncB1gYFTPigLvQwGymtH/LuZP5F26BNWK3ZZYlaCyVEiFq5WdgSLxhvyMrT3OY2SjPyLvsiVYoGfSmu2h7mjllyXbb4BTBVUUuI8C5B/DibpvhByuqdUC2C5n4rYclFvEdBNO6z8P2+Azc3D2v2GqhP/jJAbNZHm2jbrnos69rL/Fjab7iFp9/6tQgPxLC5vthiP75dwrSJXCIo6xiGKx3Mk1/UMutgn2vXRhMU8h6bXCoILv2GiSdzfuhjwEehWnooCmAPnMXGRN7iHWXQs1zxXXmNyDS7MUIa1SJ5xD3C9SI0ze3AMhD/PQTpzRQq5CnuWSR81ztCzBud7zMnhUz4oCD6eA4WDzvD4BYPiQPEjLKmNaVgkV/nQc4oc373B+OQRdrncbAZ4OWvinJ9v4aqeN3Zo+uMr2sNy6voHQAzvyG+mqgIoCFQUqCmymwP2j6+Z0/3ihWh4nAh1OWjixprGCFoBOs46g5iOKPaRwMIkyvJvkyDo2LqczsYhiqQyBa6MT+Og1fDRsfYrbEJIweWq61wD2d7bxcjJGlOUIcxunoykuo44IfKYhT7JxWZojcFxsNeto+g5oapFDBHEqu7sww5TJ577nw4cZDZl/19OY3+Z5f54P9ZOrYFlK/JLSuszRoIejro8+NzgB7PHE9qGNJD7E5PIdZjGwSHIcX03w+1fHCGo1NBvNpWUOI5C8gR8Rv41wIqDVKUz5CIeLI59uoBxbFkyyzrdsREkmShg8/UIJKNNwkGcdMR3r3cBZx4Mo8GZSxmFcXp9SgFqA/OkepcJJ2YuysOyssy4VJeoB/JqHOExB/r6cRbiYKzSpsDJLsAgj2HmGwHPQb9TRazZE2YXwDP/JyU8A/YaFnUEfp5MFVGphGmc4Gc/RRh+jUAn8KIrEpGPD99BtNKTO7qqHT0occdlSIspHAteKXpvUFG4BTHOTdC3le3i01cd+20LXYm8CNKEQPmliEe/javQDskhhvIjw8mKIzvNXaHqP5WQtXaSw7pwl597WWG7BoQg2VGBq4iQKX44lZuAtm3WrMJsvpP6ztA7L1ZYaOGiZ+mJacxv+InjDG+adkZZ7bybju9H78K+UxBQ4iELAJkhlZJd0/HDENA2KfG8Bw2DS2SwySE1DO2Vdq7IY3A3aZXCMX/6t43ARJJCXpjsKEsjn9fjl3xrjTTBN7puf7AM9uuWjkpNrg+MrLFeUH+ZhJGbaO961pRTmafI1eRIyw4i5eS9ef5KHofUKcCJgEDIIrkRY/WHwZRK+B1w8NupwnGmhwARxPZgXx90NeD7NrSGWM2VjM1Qo5ccEpYt9hAhoFHA2U7gazxBFMSybJ4Essdj0xf62LGibdJu4gbaEwRNF3Cg3dyzWYTIRYFo0gcs+8iMHudWylgpRvVYUqChQUaCiwHtTgHMuKgzXHQt134cNKn5z493CNKGSpJLxoWzB8mYm7Jn1vEM262kjQCysJEiSWNa1vsN1FC2Q6nnt2jB0E2QVUlHgDgpwZsOb/EurnhRmkgM5xeAJ/0WSYppmWOSFW9IlLDPLWgbILIpcyS/kXxGYisKKVri1uE51aGHFXq7Xy6mr94oC70sB9n/kN/Iw59RNKuJ7Llxxj23r/jdOwcNtkfJlbb5c65ppfilTBpm5PPmXLlWoMMC5PBVWuA5vuLYcrmIbqfrfEvGq1/emgOEfw7+9piV7pFzjUWmFrsBHixiTWMGrFfviZFLD9Gs5MphrR64jyb9UuIqSRB+HU7kou9LCCveJTN5rIKqfFQUeRAHyD28zfyBPBTyYyvHd5sEsJYon8zSTOUREC84PsMZr+mDDw3GqkMQxVJ7BdyyxVlv3nBVZzgrCFWOvkKP6cRsFDKOYp47H8d/sv9GyCi0mH0fAn15N/n/23rNJciTJEnwgDgecs+CRkayyaHfPzM5en8jJ3Ynsl/t4/3tlb3Znpru6uotX8shgzgnoyVPA3OEeHiRJZGZFGkI8ABjM1NQe1BRG1NTw0+PnOD45RTgbo1x0cH9jC/98dxd/2K2ixrm2nBG4VrAX4a7Df68IsKZQP+vj40OA3+FP/LimaGb6Pt9wkcaLATRKNmrVikza0QtKfxbiaBDiZJqgO55gPB4DYYB62UO7WkK1aM8NFkiWNNW5bAJ3tpooOg5imJiEwPFgiqNxguNxgu5whNAPZMClJBP4DsoFUwYSSYeGNKSVsfvB3+274iMtUywDmiYiWEks2wI5cToIxY9oBQZ2bOCr7SK+fHAXtRqNfCyMI+CHVyf49vkhfj2JMKYbv6zT/roAKWlRZ6Yn7hxIoKMKWqNyZQoHxNgZ6419vOqPcTZKB3NZDg4myES2eEm4nAPmwx/z+P0fiwFoGuyI628D6DSqqDcaMGwbkWFjECRisHJKeR+Nxd04V/10Sh7anotKYeFdhYOQaiCdBl80WNlsN8B5T1qwj4IYr4a+0DvuJ+gNxjKpbUQxKk4RrVoNnp16LHhXsvoh39PlZUhg0GAl4ZZaqYcbLwFKYqyS1p+9ooGv9qv44v4+3GIBsWliGCb4/tlLfPf8EM/7Mdgp42AtG71yZJUhXyfUo3VnqQOZjuI1fzQ4otekctFE0THBsbgwCXHS6+Ko18dgmswNt1QZWSf47pUBC+8VPZ55nw+TiqQqlCKyjsG3CmO9p2/mWNzeLmUjAClvJBegdUHwRSwp3zppuZdymydRJPlU1Rd666B+ImLUqqkJ5HJDjU8VfvmzIiyp2TmnRxxSTkxxdBIGsZxVvHXnNOcF/XVxLgtjeuZaKdLgrYhCgdsHGOBKw7PBEK/6U0zihcyQFsugyq/KpsrF55Rn+SnALmPgDZ4xTx4kz3zmRj3isSzfSmaMPBMplyqUHU0epMGVOb4fyN7H/K5wEKXieagUXWkT5MuZ6twUAyPhOyLFFBWasc35yejLiQQyF7bCc9bR5daEARWs8B4iCWdoeA626q5MCvF7qOqllXkkU1iTf+oPOjTvAakHrTD9Xqo886XPs6OvNQIaAY2ARuD9IZB9AuRLwS0h6y7QqlVREE+ShiysOB6N0fN9jKNEFk3MG2JkU334hGW2NdLB0vQbANk2dzyZyIC9iRj1iodmtYRS0VlO+v6KrHO6ZQiw7cH2SBXARo0GtgxJW89+YuBld4C+Tw8V6bFofyyu+IR3/LHpM0MiWz93ByOMRyPEoQ/LiNGpV9CulWWMZ0n0bxmmujjvDwHKEdv31Ig1J9W/HCNkmz0CF8ZNcTaeYcoJ1Ky9Ltwp5c2bTBiVDLM9Txke+QkmswBhEIGem0sFG81SEdw+i/0lfWgE3gUC1LjcFn2/Y8IrZrIbJbKV1el4irPJTAxQqIOVjObzZRhlVvqPMoabYJQAJ70h+r2+bMFuGglqJRcb9Ai8EPk8GX2tEbgeAhS4bISEsqv0L9sQjWoZpZIH0zQQGQbOJlMcDceyXfJVcwtKtnnmGEh/kqA/GCCYzbjaCKWiLe0Hr2DClgWO12NXx9IIXBcB6lExuE6AARL8Ok3wP3/r4q8//ozj4xMkgY+2V8TXuxv4p/1tPOI2QFyouurhXDVw1fm6DOh4GgGNgEbgNRHgd1gf11G2WeOFYKlJLxqHcGKm7hlo1WswDFM8rIzCBKfjGU7ZEGEj3J/BQoxGxUOr6qHqpFbkzDb/I1026A82uE2KJyvG/dhAbxricBDjsB+iNxwjikI4toWq56DhOjLhrhrnip56qdcpGuNeN56i+zrnd0dbTa1GMJMIBVojR+nEu6ycMoAWDBxUDHx9bwubm1twShX4sHAWxPj5pIe/Pz/Ey2Ha0Tk/OLVSqjnj8wuZFF3dsYDYc8K9JB5yDNQrJbiuC8MqYDgN8fJsiOfHQxkYyOb45hPB6ULynHCtYYG5zzm4OOpKyo/3lmWh4hEjBQDtmiPGRYZp0RQJwwA4nYRi5DMYT8RdOFdtbtcq6NCjUCFNTxoKG74D8djiAa16Aa7riAEMByO7swiHvQQnPe5XzlWc9IJjoVosou7Rw8qiPrKrrGh+rAjKMGsmBzLJzH/Zth6X8ZzKEI1WYthJhCJXo8SAx31ZDYBeERowsF8z8NXdNrY2OyiUK5gmJo5HU/z48gg/Hh7hyE/gZ+7cL8tv3bOUh8V7U1jzXbIx3KpaqFU9uCVHFO3En+G428fh6UAMZdjQZhq+73RUg0Bwuj2GyYnxbHJc0Z2f5xeZ8K1j7h2FSVb8R75WO5xZeJwNls+rc1qMlIN54IKhfFD+WjBQ0YS2ulmQUqSJMb9ZtmnCpEUXD36z4hjTIJCBIEVbypCrX4yq6hux57XQswDbdsSzCl3yxhEwGc/EvamilWZ0/n8+j/NP14eoNJI3DVYcA42yh2qlCuqPWWaw8uz4BN1RKqdCKVc/SEOVReWi6K7nWSG4/qmicd3zUl5rSZ/Ph3U+H5X1gAMjowkwGo8QhlxrxndroVIsygp46lfWB5WfKjdXrtuWlcqmYXFRJaJwvecu5qkGCXlW11wVT4NaIc6NIZIAJRuoOWmnlpNDzG+J6Qwg2skI7wlw2KUXJV/2bxbN+5aeVbIs5CT55wP0tUZAI6AR0Ai8NgLUpTR4pFF4tWhIP9bltkCGDT+McTwY4Ww8xThIB0LnX7B1SjhslJpLAAAgAElEQVRhay1dHS0TTv0AkxmnTmnoG6FWLqJZLaPipR71XptZnUAjkEOAIsgf2yTs57RKBurVKmzbFmNttkVedIeylTPd81M22RaZy3BGK98W4tgBV6YeThOwj0qvt5ws5ThPq1YSg6t1op9jS19qBF4LAfZZON7I/nqzWkTJ82AXCtIOPx6McTwcY5TpXzXpP89gRRgpy6oN3h34GIoMcyFVIqv7W2UXJY5TMlN9aATeEgGKH0WJY3Q1jplXynBdjnFbCBIDx8OJjJlT/1J2uRt02oFcZLyqfym/J1wgOpogpNdxiws2MoOVemW+YGNBQV9pBK6JgPr4Z2fKrhrn5zhprVSA5xZhWhQ6C2fjCQ57A1kkFc7bEOfzIrm8HHPxbHeYoNcfyLwOPQV7jolOvYRy0RJv0+ep6BCNwDUQUMKWyTBTKNlTxiojAE9mwN+eD/D35y9x3B8iirlooIxHu1v4Lwc7+KJTx1bRkN0kUs/qK3mvtC1WnupbjYBGQCPwThBgH14f10FgrpSp8lNPEWkjxkDdAzaaDfzECffExCyI0RtNYbseRuMpwiAUN29cNdaoevDcNL36jqgzG0T0drBZMFArl3FYGGEWGxj7EV71hmJBPhqNkMQhXLcgWxFVSy4K2T6His6c1euU6yOLk6J7EVOqF5Pix1jKZSmfED+646slBh62TZyMO5iOhng6PJP3ctQd4AckaJU8GFYDtmfIIKzKcxU3oS3bE6QZyTYFGQsKa7YAaHnNiWA3AdplA5uNKg7PBjieDTAJprKl048vj2XlldUC6okBN2epytJQllT+0mHLNS6YFxsYrKzM5zYcLIa8Lxr5VE1UqyV5l+yojsNQBr+DoQ2uvIzCQLb92G7W0Sx7KBLw7BCDjcyrELGhx5BGxUC9VoE/nSAMYwz9AK9OpxiMxxJmGQnKRRu1UhFlz4Sd1Z+U5IK2yuOjO4t9SpJuAZJNZF+Xx3SLmnQ41jIMkV3KHg+eafjVMoDPN0z0D7YxDUM8n44RxAmenQ3huSeouC5K+3UYnMEQmTw/uCsPLvintslhfhwc5kFSrD/bNRPbrSrOhiOMgwGCyMCL/gTfH/VQb1eBsolqZijI953TBDLIwbqS/5E2uVT1izJ3sweZsmBw5QViRBa3iUvLSbFNkV/Gi/wpPaZ4YxkkPAvgff5Yvc8/43X6ZtJQFZdhNGJwLBu2RW0SSWebrvzPJj5mmZ5RWOXzV/QZxufEMaUFcL9d8h8bhqzwO576GMUQoybqY0WHZx7qnN1e66TKwMjMn3m7MFC3EmxWXWx2Ghg9P4bvRzibRPjh6ASNVgueV4Jlp3peef0gDSV3KnPSZxh1L8vCPBa/hZSp+K97Jn3SplkJz6Sdz4c7KrEOpNjw6eIgXzQOUfxxQGRE7yQxcHgWYjAcI5xNQXM/DnJUvAK4Oicv68xf/RwbKBaLYqzEraBmcYxRGMr7z6chB0zDg3yRKz6Xd89xGgYKwwYS04Jv2LJiM8i+Z3xH85dtpAPklLEJUhekL6cJfnkxwngyRSzeXrKtgAiMPjQCGgGNgEbgwyFA5Z99k5Tu5zeUA/bNioFqqYShU4DvA0ccsB+OsDero+EV5LtB/Z//kpEcv330AECDZ37DTvwEr057mE6n4pWS37CGV0STE6b22393Pxx4OucPjkDWjFDNFLZb2Gas2Ql2Njs4819Ju8lPTLzoDXE0mmGn7kgfiHJLL4Tq4BXbX2zL85e2YYBfX8ay5S9dCtpGAs+iB5cSmmV6WV00fxQdfdYIvCkClF/22thH57YqnFQ6dl1MxxbORtPUk+8kQdtJt9imzOYHeJX+5ZleUodIcDhLZGxxOBwBcQCbE/6eg2bFQ6moDQbf9F3pdAvlp/Qvxz8KiYGSkWCz1cTL/gSzsy6C0MDRkO2HKe6iLkZZkmbeH170XVUfmv1IGrs+Po7RH4+lL20jgWdbMkbYrpZF9klHHxqB10aAgkNFmTuof8VgkONOroGaV0LBLiK2HPTHgbQhujPqT2Muw0yjDqV/RYYTtiESnCX0oD9Gf9AH4hC2EaFStLFZL4n34Nsy3q8w0OcPgECmBCl/NPJj+3WaAH0keBUA3z3u4ocnz3H06gRG4KNZLuHBRhPf3NvDo20PNdOQeSfKrejlrGpo3foB3qXOUiPwCSOQ78/cOhjemULNEUqSCAknJLPBDDZIqiYNVhwUHAczPxaDlf5wCphDjMYz8f1fdh0xVqmVHbjmYiAvnYxKPwScAComBspGgk6zgWe9CSZs1AcRTrpDxOEM4/EEhpGg5DkyKd+olVEwFwYP2bzP+32XOXyul/HlCVbaiQuSdG1icBiJP/6nB4MUS1KkMPNMTJuGgT/d4RYVB+ieHqPvhxhOpngWdfFX5ykqZRfloiveNdRg7CKji6/Im/qpWEwvFYnb2xSBgy0LR6MW+v0T+HGA/tTHD89eol6twLI7MGtc+Z5OoqYDY9lEaUZQTUzSGwsbCcyP5eLkLw+VJov+uzyxKCwHB7WrDlCtlGAVbFkpQYOV09EQEwPiXcWyTFRLDrYbddRcI91bOVdqGq1wjpN1sQBDGvqb7Tb6vQEG/hCjWYDj0zMZFJ9NJ7KKU7wTVVxU3LQxlkE7p8p79ZsHvuaFpF8l/Jo0rh/9uhkttgXiikBVRnWml5NqwgGvBH+676I33UC338c0CTGKDDw+HcI1n6NTdmE2i6hbqSa8sM5eUADFrTpTFjiYvFVIsL/RwKvhRAbSuPrmZX8M49kxao06Cgc1WAUDysuRSs8z6w35UDqVulDxxefs/FHeZCL9Ar5eJ1jRXk5D7CxwT6oIIQJYYqTA/FlGplmXjs8Yhz91MEyFqzB1VnP63EN3/V4uaUyVH8+CAXF2CnAcB5hMwL2XhrNAsA6xKaudlE5Vne08T+o65c0Ap6aK3C+6YCK2TEzjAC/7I4yzjlG+k8O0+Z8qy3XPLEMeD1r707jqTtvE2WQHL7tD+LMJhsEMP708Qr1eR8k9QLFjZjK9HnvSJZ+r+pbywjxUmdX5uvyqeKRPmVQrGmkgxNfGCTlizJ9jprwpfJiW6dSZ1+xocqKPg3VdDtYdxfj5t+cYDEaIQl9W5rQbVTTKruxDr9JnZOb1wy0acN2ibCNE7zqjaYKz8Rhn0wQGjWlXBgqZnnwpeaS3qzr3IZdtmFJG6fusO6Zb3ATVSvptYxqWjfSE96yjPEaCoxj45SjBL09fYMImEnOg1Y6ALP8U2/qsEfhkEGCdXa23n0zhdUE/WgSULqfu9xIDtB1u1+vonp2iNzpD3/fx7KyH3WYV2/WW9MtE92clYvp5v0a2ogAG9FDRT/Di1THGw5Gk8RxbJpxqrrM02frRAqMZ+90gwPEVboXKrXsf3dvCk+4Iw34fsyjB89Mhnp70sdeqiMdcpYPZ5lEHw1QbjMZWR6MEPz87FK+d3ErFNmK062UxuG8W07adSqvPGoG3RYCyKH2SxECrArQaFRyeVDDrdaUPd9gd4gVluFqHbaVjgkqOmTev2c+RCSsAwwR4Pkjw8qyH0XAA04jhOZYs5KHXSm7/lpf/t+Vfp/+0EaAsceyFfeqDHQdPe00cn3YR0cPKYIwnJ13c2dqAWzNlMQTbD/lDya8ydu0C+OXFKbr9AZI4ku1wm5USNmo11DP9q3uSeQT19dsgoPSvky1M5bZTlUoVg+kAY9/H0WCCp8ddbOw3UTLS739efyr5Ve1gjpG97CV4eXKGQb8PIzFF/7arHnaaNZQyz99vw7NO+wkjsKL8KHf89tOLFY1VX0bAfz6Z4D++/xVH3b6MB1YtEw+3Wvjj3V18vuuJx3X2+fJyrBDNty1U2EqWKlifNQIaAY3AWyNwqw1WzqNDFZtO0qbdN3V9PuZFIen0bKqW2aDmjy46G56BcqmEcZy6tx+NuJVJgGA8ke1HWtWybF3Alc9KqafcLCbxpEGUraDo1D1pDJ0OfXD7iF5/hCTkSmSu5AnBgT3u1ekVVibcSTTLQOWjzlLm3NYI1xkZl7TixkJ9ntQ5RWhB+yLEluOl8VXJl2kxJkMupcmZahVDIvId8i9NSQxp2EFPGxsG8Nm2heMvHuE/fnqCSRxhHIf49fgUzafPUTJ3UN4syVYkS3meZytXuGQ+WZ7nVXWuuC1Qpwzsb5RwfNKQScrZdIr+LMS3vz3DZDJCd7uNz7eqsn0CLbZVY0DxwIbFFMA4TsRd4NFpH/F4gLbr4E6nhWa1gKKa28txdpOXijdVb9Lzm+dIemoAkRhUPKBWr6Ebnkq96Q6GGM+4tUiAklNAvezJyksaVMx54QvIjFWIIW+p0Oi9ZrNdwtNnRfQHI4QxMOQ59BFHEQomVyi5MjBeKqQ1+qKSMC+VHz2DXFRueZQRUfHX08yYzh7y7vL4acQ0Dv8vp1+fx0WhmXFDQtfrMdLy5OgnmacgYsxJCSPBo/0ahrMD/P37nxBwe7JZjCfcWuvJS9jmHorNwtwzRL4cvJ7/Mv0hdZTvS+mg7N2RA9YfWb0LAzsN4M5WR7bS6oURAj/Eq+EM//aPX9DrNfD53hYebLmowJD3reoPkVGdQRoHDMIE3V6EQe8Ms/EIW/Uq9lt1bFbSz16e34sQu074nE5mIEOvFfSIMY24tVWAQ98m3HO8yaeAIxdpDoJJhouiRz3Ges5VmwU79YazzA91X7pVjML6IvlkOuJkwwD3JqfRimlMkBgmBrMAT0+7+P5lhEcbFpqWIe50ObipvnEKY+JLtnnPwSfXNlApFVPX0SZktdTJxMf3z05QdTq4V0oHTmkEQx6ZTv0UzzkYFvItMrNc2vyd5M+trBIDDTPBbtvBztaG6IvRIMTMBH58cYhw5qN31sHD3SY6JRqDpgMJKk91ZkeOkxDcRujspI/peAjXMrHXqmOvWYabKXj1bvK8XHXNPGSgOAGORjEev3yFl68O4Xol1MtltGs1bDfLKDs0AEo9dpEm0ykY1DehmyR4epLgh6M+fnzVw8lpXyIWbROtiof7exvoNKooOebciFLxTMwo+RWX+tZNDVMME9MgxGG3j789OcGXB21sFFODMFUu9d4IgeCerfBknnQhzr/YtPHk1Qm+LT9F4w93ZIso5UWMaehZhu5vuarjyTDB31/O8OPzV+hzi0PTyXhJv+SpRlZcKy6Wz0tP5SWqN7kcT99pBG4bApR0ZbB428r2vssjekSrjothF2FLv93U/7wVL3wWsNkwcFhy0TVN2Xb1yfEpao4pHlbudKpzT3hKV/MbqAwuz5IEvx7H+PbHw7SNHvhwCxY2Gw1sNaqoubZMzqq0FzOon2gErkaAckT5ZfuqDGCnbqDdqOHs9AzhOMIwTPDTiyPU2Nf8bANVK10YwbYL0/LHPgU9U9C7ypNRgn/8NsDRaQ/BzAf3wiy7Fj6/t4dWxZW8tOxe/V5WY2jMVhHJ7pUn36yfXAXQqabbT3dtWyaiXp718e3Pj9HxHmB/o4wGZ5qyg1fsQ1D/cmzpDAmeDhP88PQM3cEIcRyBW4W261XstBriYUX12RQNfdYIvCkClD+lf7kwabsKbLSqePK8CD+0MAoCMVj59pcnKD28A6OSDnCyv8o2B39sP1D/Un4PfU62DsXYld7ZDI6LJwEONvexxYVNmc5+U351Oo2AfPQJA4WP8ktP6rylF3cT2Kyb2Gg0Me2fwkeE3iTA//zue9TtR7C2W2iYalZiIcMcB6EO7nIcZJrgh8cnOKLRFrcUlO0EK9hu1qQNTfW90OApD/q/RuBNEaD+pLEK26/PQ+A/Ho/xnz89x+nIR2wWUK54+Hy3g6/3N3C3U0Q5kz6mU+PqlEe2iXmsyqa6z7eZVViWRJ80Ah8WAdHlhsxv6TG0D/sq3iT3dObuTVL+ztJQcXIaUX4J9xuOYcqkLSdv0y0NrlUk8fCRxqRiZsOYKxHqBQMbjRr6s554dPAnE/iTGOZsCtcxsFktoV0uicvCvBLPK3e+DE7a0uhhp2GA1uLPcIw4jOCHARD7MJIY3LW24hTEJR0nhZhO0cyvjmYY6YtPkjiBGYcwkwhGnMBQE6gJY6QNKkVjCQeZRI3muKXeTRKY5vqJ/rU0MoLMibyacQxkP054MXeesnbhUvaqDOSX+0PTb0GMdL9olYJxFDfMgx0jTvZWEgM7LvDNvQaO+328jKaYjUOcTQKZLKvYFmrePnar6eAo06bcpCxwEpmW+ylz2WR/jjvGVUpP8cnB3I4BPKgb6G82EPo+jn0f4SzCq7M+fD/A2XCKF90WatUKSq4Dx7bAHTqIQRRDtl8ZTafiTWfQH+L05BReOMP9dg01z0OtkrrcvgzrHJtvdMmyqclpEhCMRW747iKYSebdJmtUM24qSZdnl76r9B1RFpgPjVBqjoFWtYThSWqwEocxwskESeCj5NloVypoeA6Kue2vVPkVTSVfrI/bdbosd3FiWbIt0IzbTrBTECYomjaaroemW5TB9yWO5aUqycrKreoRPZKw/vAHa63M5nkSuiLYMeiZiS4fjZgfSzYBU3wVbirdKi8M58AR41GuLdbHOMh4oMykpV81vWMx1I806Y3I4HtDyj+EhzTVat6q/tDTykEVGO+20D0+xmFvjGA6xskoxHcvjuCVSig5G3ArmVFCxrzwTJmhfmV+tNAWvUO5Cee6Sl2o+OygcVugDRN40Cygu1HFz5MBunGEWRTi6UlXVjIcj0M87m+gWiyk3kIKlnQmpUEeJZiFwCwI0B8OMex1MeuewgxmCA92ZCAuEbONJaTf+kb0aaZT2U2NkwSD2MC/Pz/B02FZvPqIwGRaTsxMDHkrqfYSPUv5yjDjtm9xiA3PwWdbm2jXHTFcUVhRJtIBRX7HwvT9IkrlLHv3LJSKr74tDNusGNhp1/Ds5AyRkYhhzYuhj//+4xMcjzaw3eD+uZmsBQkKkY+yxfpZBA28mK8yNqsVgP1OC78edhGMRbowCBJ8+/SVdI6OdtrwChADGDMGzDAUD2ONUkG8JXG1FWVbDhHY1ANQ2tVPLXhoAKSOxVX6LRHdkRjYKwGPtlsIxwM8m44w8QOcdkcIJiHOhj6eDXy0axXUSh5c16QDHNADTxQBYZiIzh1Op+j1exicHCGajtEpe3AKjuwnXMxWLMrryzOhGLvgzCKxfPxxsKIfJnjSG+FvT14hsYsolytoVMdo1Rsou658CwqWCdu2YIr3IgNREiMIA4wDDor4ODzr4fB0gLPBFL4foogAnaqLh50GvtjbRrtclFVseX2s5MDjYKEL7FU9/Fq0MQxjBFGCV2Mf/+PxMU4TG7utGmpuqm9oaGUnEcpI0CkXYNiG0N4AsFWv4PDUw+lwgth0cDgM8Jenp4idGna5+ojeq8hEDExpNOgnOB1FsqLo+fEJeqMxigUHlaqH6XiK0Wwihl2ZaspqynlgWRb5zdtvbNNYmUHYYpSH2L/GqzqfkQ7RCLwnBPJymr/mN0NM3HPNT/EuthTpPTH5e87mEmXAb036vfk9F/DmeacqZ9uQ3+yaARzUDJw0PPROPfRGNPYMxYjScA4xLpSxxza4tJRTPcwJf7pB7yfATycxvntygqcvXoB9ZSuJUfVsHHRa2GnUUNMeKm7+hX5COVBdqn4NjZw7RoL7nSoGpyU8Hw/gJxaed0coWMcoelXc23VR59aJ85GFtP02oGeVEPju2Qi/PX+J6XgEw5/AM0JslWr4Yn8HzVJB8mJ90Wr6KiFTCEnj/4rIKu4V0W7bY0KTjUEoGa6BbXgDp00XZ2UHvTHb11P8fNyF+8sLDK27ONgooMZtfzM5ZB+E+pfeGX/rJfj+2QDPDw8xGQ1hhQHKxQL2mxXsNitolrSHoNsmRh+yPEr/coyHiwnpGfWgaeBlu4wnw2PMIuDVYIrvnh+jWK5gvNfEpmfIWDj5ZhXgohLZxioCvns+xT9+fophd4DE9+EZMZpOAQ+32thsFOcy/yHL/DHnfaEmzXTNx8z7e+UthwcxU21gLpbbrQAPOmUMThycJhyfmeHXwx7+v+ohpqaH+1sealn7lzxT/86y9u/zYYJ/PB3g6ctXGA2GsOIYBdOUtu9eq4Fa0UoXL73XwurMbisCSn8OADxLEvzn0zG+e/wCh2d9JLEJt1RCs93C5v42ik0DExs4ShIZ5zVzdYDyzyOvP/LXVpx6MaSep9F33kN1llSfNAI3goCSw5y4zvMxjHQxJBsSRsQ5tMUCYkZSaVWC1XsVrs8fFgHOY93qg4InPwqqeBbghG2cGj9k90gKc8ODq8FIRZn/qbzpotNNDNSsBLudNp51JxhNZojiGEkcwolDlOwCtioeWiUXLmf6skP4ylUWvgzxIJEAW56BdqUk2/1M/VCsemncYSeJTNrXi0XUXU8aNezEzj8kC/JpuVUji+XnRHfM8iuDlZSD1QrO+3mY4BYCCX+ZwY+RZJNpqiRXn5kT+WQZaQ1Po4eUXjYST7xyuCiK8+JwsjMzWKGtPbczWTrIZzZXxTTiuYSebxIDd6vAV3f3EAU+nvs+ZiFdAPdQsoBGyUG9sgM369gvlZ0ZyAQ7m5qZHC1lurhR5eP7Y55mCQh2G/D9GGEQ4TQMZcLyaOjjZHCMv/32SrZUcL2iWLaywRAEIabTGWazKaazGaIgoMsCJFGIbddGq1yGT+jmoCzyf9dXxIGlVihTvqT+UH5iU4y9LGLGbRwYjxGvy1eSTniTPvezLyZA3QHa1RKeywR6jIgP4hBWHKBS87BZr6JRMlDM9rVn2lxVEvknC5SxkgFsuAaa1QpeFLvwJ6GsIOLsNA0+PLsgxio1x17eXkgVVgqUlodFYgk5aWRxYiOrB0liIo7XG1qdg4F6Jg5FH7DAqcFLim0qWSnfS+9QjKXSEEIsBgpZ+YyIBiusi2n+8hFeSnz+hnnSaIR1iPVHpc/zKoYlOTmn4Y8YX7VN9B4cYPrzU5yEPiYzH7+d9FB2D1EvFLDpNuBwcj0jxhN/YqRCXWvSEwMNdnKGNissKt1AAzyps1wGud8GZlP8bPXFCpzGXofDEIf9l/j3n56j6BRQoZcpz4FTsBBEBiazEBOpQxNEUQAz9OHGATqug4MwQWrakeG6wsP1b1NhZxmV2LNRT0PA1IopQWzEGIUJ/u2nJ2m9mdekXC5GOlW2qDiZwUqSGic6swnuNyrw/qsLr9xe2r6Msm/J1nKpMVRqfEmdGklOeVGm/MqHPtOPezUDD/fr+PuTAgbhDEFioRcC/+unZ/j1VRfteg1N7gHNVt1kADecYqfq4U+f3cduvSB6U72vOr1Y7Tn4/lkdk/EUg4DmhAZ+OeziqD/G358do1mriqEm34UxHaLp2vjiYBcP96pSh8mr4jdFJP3OiPVCDq7VS5aJ6SrUVQbw9U4B4XQT/myGZ8dnCPwIp5MIZ5Mufnh+CrfooFTy5FfMtsTxZz6m1LfTKYIgQBwHMIMpyvywWzYmsGSgTPQNGVgwusrO2ntVNjGm4oCFbWBoOjgJLYxnCZLRCMarMZL4pciObXKrnQK8kosCt9wxaVATYjIey/ZoAY0GLRsx9y9K6F3NQLVg4kGngT/e2caDVlFc3vP9MO+sSgpvvOZ3ccsAHrZN/Nyq47fTHqaxgX4A/O3FGZ70xmjVSuLa3uEKoWAGJwmw6Zj4p7u7aLfL8q1kh/Sz3Q7oTWfw2wuEsSHy/uvREM+O/4rtThP1ag3FoidGQfSW1R9xr+Yh/OlI9GGpWsLuziZq7Zbsn/uk1xfjmYRbJmWKUcGdB1d0C8tGHSjfI54pMyxvvsT5VPpaI/BxI0DJVfKupFja6pRp1gf1eUkFfV4YNqfnu2nNQ/XFVQgojFfjic6+6OFq5Nt8TwwEjPRDkrb008UAnPSnMfNgq4HecBPDZ0cIgileDHz0fnkBv1jFIKbxa2rgwrQ02OSA6fNugm9/PsLjZy8wGI5lIUWZ3lUqLh5sb2CrVkDZTNvEK6J+m9HWZbthBGTsgXlk255+teVi1G/j5PgEo8hGbxbh56M+ZvET9PEA+5smasW07Uz1y5Wpx7MEf/9tJFsZHnd74Jac7KO2yjYedmq433HEs5C0t2+4PLeKvNK36nyrCveGhVGNgSw5oVF9Hq5+3i0Co40Kjo7rmB4NMB7FOJ3O8D9+fArfKWFkbmOnacp2o+wPcMKf+vdJN8aPj/v47ckTDMYTwPdRMhNseAXc6zSwXS+BDi7ScY835F0n0wisIEAZZP+T40Rlth8aJnr7Gzg+fI7BzJSxg/BsiOiXZxgZBTzYqaDlpXLIqqC2wv3rb1P89OtTHB6dIUzSSdFmsYAvNpt4sFmF2o5tpfqscKNv8wiIn1QZxM2HfuLXawSIOpFyzLHRzQLw+ZaJV8d1jMMYvWGMQRTi3385xDApYmTew27TFO+8TMcxIPFu0Uvwy7MRfvzlN/SHI8RBAM800C55uNtuYrdRkzaE1r+fuPy9w+JTlOldpZck+F9PZ/jLz0/x8riHICmgUCigWK6iXK0jsoBXwwRnIy6U4bxDbkSNY9wMXGmjyVQcxybCBHaUwEOAqg082PBkflS3hd/hi9Sk1iOgdPViveI8HsWVBityMB7naVYMVuaR9cVHjcCt1iUiqNmEMwtqhlMk0yGMyBcrKyv0YXKJdSq/oodXdPGlL4+NPAuGNMLLBlccF+GaMYxgAoPeGGjMYsSoOxY2yh6qjiEGKWzw8GDdmeeXc/nJFZRsELXEaMXF4bgnjR16WLGiKZrlkqz+rtNtcp5GRledSJvfF8axY66Un4mnAcOfyX6fsv3AFVM8RpDACqYwZlMYYQQjTD210BgnEXMZldvlZ/LBcpOXQhKK+0Yr8mHROwCNV/jL+CUuvFP4cO6wkEQocDIxnAIh+eeEVWZ8o7JmAlFOaT4sPz2ecDXg1/s2An9X3Pe+PO0hniY4Oevih18StD0bzmYTdbcwlwHhlV5dwnO/D30AACAASURBVAAxvdtw8jsyZSWgTE6rPHNnZi+NWfEoYeBhEyhX29jebOLbHx/j2fExJmLMxGnZghTZn/gY+iGMHjekMGTLp4j58YhoWEQZSxWuZZkoWJasWp/LTS7/m7hkPsxffuFMZJvGF3QCTsMJFoJx+A1IPT5cwQVByt4T41MG6VGoWQS2yi5+NBLM6EWHE5DBFEUzQbNoo1MpiXcHJe9L5Se97EOl9niuIMFG2UW9VMRoNEYSpvWcBi8Nt4i6Uzi/FVSOddZdkVnOW9MAKYrF+IEym1Buo0QaY4yjDsUTz3N5p9wmIayE8uPDiq1UhrJEjEts50eGjcKIz/ljMGkaAWBJPZ4i9mcwLdKjocOcglyodEKfW/1EAexoBuo8RD7sOIAtRi/r0zEveimil5UtO8HXd0sY+NtIQh+nh0P4QYTDV8d4bAF33AJ2Niso0EtQxicntAs0OApniOMZEEyl7Hx/6+oP85NnZMcA2omBUttBuXof9Wdj/O23F3j54hWCIBHnTJysj8MEs+EE5nCcrpA2C7IfchTGYqDEWmZnk3zyPsTAioi8u4PURKfxPYYBLBoTRTPEEb2nqIO5rzu49Yp6U2RUGd1ZMEwbScFBYhfgR/QqtTgUVpRF1hHWQXrjsGJXDPkURaZIS5t+pxQFDnbu1U386dEBvv3HTzgdjOibBWGS4GwwxmA0wVMzEYMj2x+jVQCcnQ581iGDNTaVWb5jTly1jARf3dnCbEIPPFP4lFQaMMwCTF6d4vD4LDWsjHw40RT7rSq22w0EUQUJK2xGz44AM/Clnolxi2AZZsZV5zFkCLnh95IDYPsm4DyoodX8Gn/55SUeP32B/oCGS4xjYhLGmA2n6E1msGy+tUS8LsVRiCjicG4sHTTTpMcrE9S3nJB4F4finoZGpmlmbeZ06yjDtFI5oMcXTuyFwHg4Tb2YGTRKo/ErZSAbReZ7jSN4BRsbtSq+2u3gm/0N3O/YYqySftfVu0+5VzJDuPnOdsrAv3x+H73/+BZH0wkimuNZBQymPsbTCV4eHaUD3TQYtIGo5uGzrTbacRkFK5X5B00D/YMdnPaGePXqBH6cGoSRzRcnXRye9mEYtvRykzhBFEeIQurCEK1aGQcHu/jym02YBcCeejimCx4a+cIU3bqKu5JrOUfUaTPRY4UkQCFhO4zehtJ6xPKmkrVKRd9rBD5eBPIyznaB6A1RYJlRVqYL5FuRFYPeovRxCQKXqPCldlOqOi4h9Ak+okASP/6yNjbFjYb5zcTAvQ0Lw3AXp6MZTs9CBGGIoR/h3779Ht99/zOqbhGNelW+GaOpD25xOwogxpe+PxMj7qIRYb/VwB/u7uDz3QpqhbRPrfX3JyhvN1HkrP7P+3TS5wR2C8DXdzYxmAT4668vpX049kP8eniKk8EA5aKFUtFGqewhiAKM/QCDWYThNBRPqWFAj5kRag7w5Z1t/Pmrz8R4eqk/dxPluVU087U8UzIXle+Kxxclu03h1L1s31P/tqh/m8Dw4QFG0RMchgGmUYhpkuAvP/yKnx8/Rdl1ZKtlGr+z/3YymKA/DcQDqYwzJbFMqO7UKvjT3V18sbch446UYdUWuU346bJ8OAQouyJTCcf8gA2LW7Z76H3+EH97/BLd7kDk98nRGU77Y/zluyLKHKfzHPbUMQl8dCchBrMYvh8hkW2AEpSMGAetJv7bv3yJrXK6rS5L+VqL6D4cLDrn3xECqqvFsdFaYuBOBfjm/i5GETDl4qfAxyTkVj8v8OzwGJ5lol6vwi0WZEz7uDfGOACmfoTJeIgkoYfcRLzp//PDA3y23UY7826V/zL+jiDSrH6ECLDpxDFkbqf2/a9PcdIdyCIzjj9HUYzeWRfjbhdPf7Zg0BO8WpzOwXqOS6sy0T2FjBaTGse4LRmAVR4rrGCCqhljt1FC4b/+CcV2GRXdIFbo6fNNIpDrH6i2q8itGKhwrpgLttP5VNmpIOMll2zB3drAxWN99WEQ4KzNrT7YwKC+pBeHKnxUjBDc8bViWrKKgI0FUbbZViOvA0bqdSH1suJxW4K6iU3PxNAKZGKG1omNArBZKmKrWoaXrRrLD/7N88u+CDyRZ07ib5cN7DdcTE58zPgRgQ/PDLDp1bBZdmSgRDWg5nSyi4yclJ0dXDfmpJ6PMncbN0JUzUga+rZsk5T7IGWdCtKlO9xKEqLKQRkjwYRbA8QxSgYnR1MLTJXPav7qns/540SdnRgo0AIzDlBOAiRGAC+JUKQXBq5UyvHO/PlJZJgjHZwIFTNCBSFivj/Llq0lCuYi3eKrmg305ybdE3ru2CghHHYw6/dkK4xk5qN72sWzZ8/RKRooFTfm2104EVCKQ1QQwRevGiFKsOHEkUyCk791ZWcYn6kGrW0nKO+YaDoH+Om5ixdHJzjrjTDzI4SciE4SsUwVVyOgMVAsBjxieGGbqJRddGoV3GvV8PlGU7anUDgpjG/izDJQZ/O9OYkB10gy+Qno1wM1m8ZYND5IV7Zfy1iFjCrQcoPfXHVRpcGXZ2KjZCKe+fDjELYRoOW52KoU0PQKMompyq7IzMueGcyIrGdGSrsVB4clC91kIh0D1te67WC76skeoZ69kBOhQ6IsdO4gDszTTRJUjAget+VCiLJpwTXOywJJMD75cGCglMl6KZ7BMSNUCxZK9mILHdK/6BBrZj5Uxji07I8TVJMQ0zhAmKR8lA2aPy1v70M+SJtGBZRlL/KlLoeyzop1P4Jj8AO+fORxZRmYdz0xsGsn+GKzirhfBs4MTKchopGPs6MYT0sOyu4BCnUPlpWWnYZirDv0PMVpeddMUDLpESLlK5+P4oC8MJz5cnCZk9D7ToLCfgkd7z5+8go4OhtgMBqLF5Uw8hEHsrmOJGA+NFChFyoTIUpeAQ3XxVapiPutOh7tbqJdduT9rLxmxcI1zyn3/M8f3ze/L/y2lBEiTALkNj66kOZ8QIWFJUPSMYizhlUEN4nE4NG10wl8hQ8/2ql+8kUnJqaJqm2jRCOL7L2nHKqsF/wyLd/NVgH43x9UUTXv46fnL/Hi+AwT7mVKPuhMi0NENBqLAxQKlrw/i+YUGZ+s76SlPEr9YduEZ9xDp1LEz88PMR5NEAQ0PEuAMJZtbcRQNAngWjXxNMb05Izvm64jSxH3Bo7QNyIUjFj2Cea3waZOzOKqEgkf+Xoh35d0RUCxRY9N2/ip5ODZ4QlOegPxCkQjqzCix6oEdGLDwojXLskjQrFgyzZinXIVe60aDtoN3GlX4Wb1VfImw8vgzllad8Go5F1khLiXDHy9swEjCPCyN0Z3NMF4OsPUJ0M0scoG2vjx4420T0LQmxU9PNEDS8ktot1pYKfZwJ2NNg5aZWxXDNTsdLCOea2yyHuGU9fyYds28PWejTB8iJ+eHeL5cU+2G0piGiHSGDVCRGPSJIJl2PCsMhzDQCFJv21kjQPmX2+YKPzxEX58XMGzw1N0+0PEoY8kTAugtBIndvi9KBUd7LRbuLe7jTt7HbS9tMwj18BTkwasU5SQwMlq0Go5yD7Lwa3DakaAquEjSKYoxiGKyQwF0CBm3ZvQYRqBjxgBymymz/I6g1ulyJZvUg+VZ0h2vs/X8Y+4dJq13zMCFMhMp6rvCL/Z9GTIVaaPtgsYz3bx/W8Bjk4DTINA+jczP0R/EuB4PJU2IPs7vs/t7dhni2GbCSqOiTvNOv5wsIGvdppo2Ya0T/jN1IdG4F0jQLmS9nM24UQvQf7DPfhBgicvjjAYjGUyPxz46I8T2DTO7XKyKUIYRgiiGHFiyqA++zS1koNv9vdlsn+3bkm/X8vuu35rnyi9dY1f1a/P9G8HBr7csuEHe/iHkeDps5cYT33pH3MCldtPOzR+t2gkH4M6OeS+03Hax/IcC/utCr7Y7uDr3TY6bublOcvnE0VeF/uGEJC2LcekOEbHMXMPCO5viEflXwwTZ92uyGd/PMNoEsA2TVgcNDIotyFmM+pfW/rUNHSlh+xH21v408Eeduscg0j722yucIxCHxqBN0aA8pP1yxQNBnH8gW0IypqRGPh8w8Q02JKxh9+ezjDxQ8z8AL7vo2+aOJv6sLgoKU4knEMjSRTJwmkaxW7XK3i0vSH6d6diSLtatyEU4vr8LhCg3Cp16I/HstuAkZgwDUtWG0dJOt47m+98oAYgKaysB/QMzxMXLfKnKgbHrvks/XFxe2hGKBdM+Fw4+i6Y1zQ0Am+AwEJXc76aC4l9mLGfLZhOZ2hUnVgiL/KehayNsBRb37xHBPjdvbUHZU2Elp4CbOB+u4bgwT6sMETJsnC300LD82RS/k3lUg2AcBJwowh8vddGzU4Q+IH4Lmk6Fu61qtis0GDFSCdTlK4/3x5K+c1czu1WDXyz34aXDDDlRHkUiMHI3VYDe/UyKmzI52itvkhVJk5a1wsGHmw2xfCE1unloou7rRrKRU6rLz5m6ppnlmnTLeDRRhtNy5KGWK1o406HE47Wucnu1fzVPWkRJ3ZSuFr77kZD9mvkJF1xVsduqymW9GwIqoPxOdfJInow0Ck7sj9po+TJFjkdx8RGvQqvkPHBTHIHb0mPE6HsuNBLhlEDrDubSGZTBOO64EnjAc/zYJimWOyzY8+09HBDF6nf7O8imHCfah+1YgHtUgnFxUL3XI7pJflW5eU8NHmvmkBrw0TH28LzRhlHvZGs0uKKrVkQwpdBMFquptu8cD2Ba1uoOjbaFQ87zTr2GlVslk2ZlBQPFSvlPcfImwZk8qTKQAVBDLntxsOtFgpmjMSy0TKB/U4LVdeVd/tancOMd574o3xK57UC/OHONrYaZRk0LMYRWiUP9zottMsF6SQQ3/lBXvOHkhcSpcvRuoXpbgt24qdeKGCg5jq416xis16GV1js75wnk79mfhycb3kmHmy0hA69htArz16nIdtzKZ6yYgkelFsaMWyWXTzabqPkWHD8MTa9AnY7LdE5Kl0+v3PXGVHhI6vHX+5sYMt1ELMeOw72WX+K5HJxMBnlnxDRvfp22cPn222MKbyBj41aGc1SEQVu5ZOr/3MKGZbMl+82SQw8agL2/gaqsS+rDQv+DC3blK1LZDkLvQFZhrxP8eJQ9fDN3X1MEqAQTLBXKaJRqcg2Z/Oyq3eYm6xjSVR5WRsrBWBr28SOcwcvzgY4HY4xmMww5ER/ECOggybQWCMRGSkYrD8JGpUiOhUPuxUPd+pVtCs2ShlM6l3Ny/sWF8RZ9Fq7BifZxtC/psEKvWdkjEj7nzqKBgqcpDRiOMEMO+Ui2vXqfCssRmedrFqQ/cb/+OCOeMZoFC0ctEpizEDZm+ObK5cKo+EAjQ2qDlB5UEerUsQvNQ+9wRjTMBKL+9Szk4mqEWLLs0TnV11H5JYkSUthSPkomgaqOxbapT3UXRMnvRFGMw7w0wAmkAmqImh0mODBZhvtWnW+xRH5pbx0ipCB00apBH86QS0JsFWryreaGKv8ckWSMOLB5zQS5Oul8WizYqB1r429WgmvegNwAGzI7aJkICFASK80NHKhwaOZSNqK46BVKWO3WcVuo4qNSkG+scRrnvf8Is/FxdeMzh/xonHPBo3/NmiAd4Dn3QmOBkMxWulNJuA4MjuOPIdqizwaX3EVpG3Dsw2UbQt118FOvYa9Zg07DVv2iuW3Veo7XR0qHtWZ7GX1jBgJT8TKMuDdr8h2hT/WT/Gix8kabmcYyNZS9MLkmQnapSLuNRuol8vynpkXja0qBnCH3rE2TWx5u/i5XsOL0y7GkwkCGmPGkXiHoTvGYhKhYptolV0cbG7iYKuBFmc8M9amdQ/T/U30xjNsVRx0avQDlMM9g5hhfMf1AnCvWYG510G/6sjA5sFGAw1upUTva1l8fdIIfPQIqG8gz5ngUl+IsTB1q2XKlnu2bYLGi/y+caugeeSPvoCawduEAGVTtdFq9NbGFQV3K3DMPfzsFXDYG2LiR/BDA2GUwA9D8VTGwVGzYMM2IvGWWHctbNY8fLW3iS+3W9ivOTJYT6NjObQSv01i8+HKsiJHbCeJ/BpAh23uBuA/2EOpUMTjo1OcjWYIQx9REiGkRyvuw8sjpuRD+jtly8ZG2cb9Th3//PAO7jZLsuhCtVO1dhao9L8bQoDyS1ljH4Z9ZGPfQRF70vd+ftLHeBZiRo+S1L/SnKcn3ES2cLYLNKyyxJPVVq2EL3c6+HKrhftNT7wqKt1+Q6xrsp8aAivfc9V+YPfPpPfiKhDc7chWuL+5Ls7G3BKdiz5jhEzL/qg0jC0Ytg3XcGTRV9s1cH+jij/eu4NHW+X5NipK3fOsrj81yHV53xECFwgQx5woxxy72oKBb7YLcMxd6ae96I7QH08wDULEhomxLJCS2X5YZhEGFwvaJtiG2GmU8Girgy93NnG/aaGaeReUmf60ufGOCqLJfOoIUJRF91oWCrKfPFfQc94rlU3iQ4/S3B1CBgy5mFrGo3mrDFY4wBjD4CSUGlTkIDZ/sQHTtmDRoMV2AcvRCvhTF7r3XX6KZTbGTVmXvh49r3OM34xQtGI4VoKCLKROmcuSvG9OdX5vgAC/u7f2UAqaQluHgT/f6+Bf7rXBjV/YKeNEFI0y+PxND1Up1Erz//NhG/HDltSaGNzPjd4pUi8hBFvaP7lGUO5SWCA9hnFiacc20Npz8V/2vhC/CHzmkl42+cWJo+sczHejYOD//nwX+HxXPjMsMyecaIywygNpcjVG0TBxt2Ji65s9xNgTR2A2vUZkadelW+VHxSEP7KDQW8f/9eU2AmwLHywL8SEfq8KYcIsIOzWWuFu1sP3HeyCm3IaJ6bgN01UYEDO+a35POUlbaRl4+Od7kjefMU/SotEErxW/FcvAZy0D+3++C5aZePE5MSO91UOlI00qQM7TysSeGlTghH/FwP1KDSFqmIIea4BRCAwnCaazdLKfdgelAg0rUreZfN/ERk3Gko/5hOQqE+/iXhUkw4L5UbbZsfxvf7wHH+k7oFzTwpxlZJlzya7NBdPwRzy5bVOxaGDzm12RMxIh7nzGPMgDeVl7qMyzDxXjUab2PQOdBy3864Om5EM+xetJ9lywXCWoaGW88ZblPKib2Kpv4l+/2hDPQ3wnlBnyt1p+pqGsxAbwqG3jbntH5J0DSzSEUmVhOh6coGb7cenI8aHCicOmbeD/+dM+EuynHSZVr1beAZPzR7KNgoEvtjzc3boDBwfpRzxLdxWmTM845HnLpD6q4p/2vhTaDCdPxIfl5T1ln9esm19tFXF/6xt5n6yzLDs96SzlSSaZKHcwSPBL0nzZKaTOlrrbqiNADT73g02A/gwYjRNMg0Q6ix7rT9FApZjyRU9V1Dt8T6Qp2bHNvYp3Lv9LLxWvJJSxzonFjmngzw+qSB5Upa+pol1GS9mwMy7ZIUn1YzqWWek5PldxiEebxld36vjjnX+V/PgelC5dwjdjIGNX7ogFM2I8fmMauy6+2T3AlHiGwGiUIPQTMehqFIFW0UCTxg0rOpI01TunTuIWAaW6gfv1XQy5X3oIDMYJgmkiRkoVB2i4BurOoh4IL5le9Sqs/3vitpK0WR7mqepYVpS1J8UL6yUHcellg/V/e6+EaM8TeWH5RjHQHySYBam3kqIDuI6BkpN+nygvZZYrKytlRmG/NuPXCBRaQtdAqWJgt1JGgBI4HDdKUnedMx8IogQT7nwXcYAZcAsG6mV6h4LwptovrJN874pH1qMLvw3sk1Lu7fSdMQ3Tb1CONm083NxALwEGM5U3n8eolyw03bT9pOoRsaZXuqKlsDbwVdXA3WoVY1QxToDxlN80lgGyVVjdNUBZqmQ6ne+JGHNAkjx/2Xbw4P/4av7dpP5W8q4gZr4ME5kwDFR2a/jj7jeiPhhOnUO6LBvj6kMj8HtEgLLMtkUpBupmhE0HmBZi+R40i/R2lXpb+j2WTfN8OxCgjLL9QjnltpF3HaD+sIH9zTp+fHYk3ra4vSBX+4fisYtbaBqyUMFxbGw1Kriz2cLDnQ08bBfRzPri6VjpZR+y24GfLsWHRYDyyzYC2xkcF/inTRM7rU388qqNv//2FKdnZxhN6JGP24umXkQ5kM/2br3i4k6njs92O/h6v4GGlfYx2O5Y1/b+sCXVud9GBFRbmDLH9vAO+7l3ithp3cfffz3Dk8MjHHcHsi0rvQsnbLDLgg726yx0qiXcaTfx2e4m7nVK2Cqk/R7GYt3Qh0bgnSCQHwjhddYx44myS/3LcYOvW8BmvYGDrRr+8fgVjk7OMByOZRsr6t104jRG0S6g7rrYaTXwcLuFr+5U0absZuMEuSxWh5XeSXE0EY0AEcjrSOrfbctAecfBna27+O7xGL88e4lXp6eYRtwmmZ4m6DmZBi4JXMdCvVSSRVFf7O/g3kYZWxyzzI955DPQkGsE3hIB0bfcOl3mAk3EBcDnojhL3GmnE2SGAcM0Uq//siBm7h8ZJs1iZXAx28Y+M2qRLdPZXYsNxFGCIhdaWyZargXPSuYLHN+SfZ1cI/BaCFB9cmzC5cJcI0THjrHhcHzYQsstoMKFM5kez5okr0VfR/4wCPCd3dqDgkjBVQ1jTmJx6IGDCmpCis/fRmAVfZ45ucTJtVgtm0e6BYZMKnEyPd94vwR18jOfVOREkzhUzsqSm0i7hMTSI5aXk1NiFJk94beHuDCv1fKzLAWDG2ykHQrGoxEmseMzobWUQ76bsPRAbkhf5cXJVl7ToIPfxLwhxmpKuuPlQV55yYV8xELwyfG/mm71fp5/RofbGiiO87RYNt7zx4kv5ksDG/VueSYfPK87mI4Hzxnrko+UPeOXmEZJanhEI42owB+xTktG2ozP908alFOVJ58p/rKsbvykyqHqC72GUA6UsQafq3K/CTOqvIIb609Gn/eq7MyDmMixrg7lGFDvlWkop1ytSaMfhRvDKXOkt/49StdYsuJzxuePdZtpiokx1x0Svqb8zEvyUem4Kwp1AcuRy1vlv2Q8wcTrypjxS96VHqF8KvwUrQylOf+8UGk40U0e8rgyncImnzZ/nS8L6wV1AQ+FicJS0cnLLussy04alBmeGW/pOBeQYkDe8r/UcIX1xxD9wQ5eywVi/lhPBY+0vpIH8qXklNfzbNaBtcTQNW6yORWSIm0Sp1FXOix44StcIkwYGZ+Hwk7xyHOef94rtvk9Udin9TF9p6rMikZG+tyJ74C05D1JHUmN4iIOHhUSxA3WF76zhcEYcVx3zOlkPJFmmKQGKQ0bCGuAUTMEm/m7yL6TLF8+PbFguagDeKhvgyr3EgtLNwvOGFfoZvWM3xklLyxfaAFRQ+lb5p/KC9MQF4U5rxVOF2S1yPSKK6bnj7zxJ/eCO7+DqcxQluWbWORu3QaSUvqtI+mUr/RdKMyUHiHe1+KPeoOEcpiTF6ULuP0WDVI2XAOxSx3IfG2REcobdanIi+KfnkRTcuk7ozwl6aB3yLJ4BmIvpcNo3F6QdPhOFbbkm+VgXmwy8TlppuVdYJVlI9jxWvDLZIXxM3ER/hS+Ko0+awR+bwhQhlkXWoaBb7Y87DW+wihKYEex7A/ddm2UHGoufWgE3hMCa4Qt+5yIQmbfUoyKq8D+o02M7m6gO4pkpelkNpU4BcuCZxdQdoqoewXUPUNWlKqFB3N6lGwq9TV5vqfS6mxuOQLUsepg24MuzR0bqO9YuN88wDQ4wGDkYzieIgh8WJYJyi893dXKZTQ8A80iULfSMR7VLlM09VkjcNMIUD1S7thWoK6kF+FS2cD25y2M7rfQmyQ4G44x9ektKIZpGvAsA1XPQa3ooO5y8p+G3mnbnGTy9eKm+df0PyEEVr7nSl/ymy/jNokhCyCaGyYe1Lcxmm3JQr7+aCLergx6nLUtlKwCqm4J9SJQ8wzxHC7jfBmU+SZD/voTQloX9T0hoMYalP6VMRQTaN4t4U87DzCY3cfpIN2eLRKPviaKVoJK0Ua1SB1cQMszULbSRc2kp/Xve3p5n1g21IUcv+MWgv/v//YlJhE3/QYSJXAcz87G4QiNGlNTMCldqs4SnnXTGFnmFuN0voX1gdtlblQsGTdUNPRZI/BeEEjScW2llz9rFtD5p0f485efoRBFKHMrV8dG2U7H/pdkmgyqAHV+L0zrTK6DANuNt/pQjQqeOVlCRcxrpaffReEVPQXm3F5Fyf6q9memKw34JT44+JdN7Mhkc0ZH1R91JglFWoUt0clu2CnganXSyh/keyVIHjOM22uoCUvJR32cMuyW011WmDRHxlf4iCJhMAPzhVDMMZyDSNmkI/nnx5ZhKqeLeFck8meVN/Nn3vR8kc+Wz9U7VOmYH+MzLp/zyNhKbxQj6d25//k0pK3KLvnmsFzES6/U/byg5yjfbAD540E+1I/888hjp/Cf85vFufiUp7wcSzqtGX1mmod2ib4ioZIvPUwnOxlF8Ua6lKH8wVtVnnw40+XJMx5/fG88Ux7kOW+ysHz6/DWjMG8lt0reFF8ZiXyS5es1EZj3HKcVmWTiNUkkTDBgOTLvA+RBhS1nevEdafPdSx3MMluXn6JA+vwJZtlEPOOr8qt4F57zLyNrfHBinYc8ovfB7JrBYl6Ryc18En01Pya8jOkLmbn8gSoT5YQDJzyY1XUOVUzFWlqWRUrS5m9+ZISVkQHfCb81Kn0aVyGgUq1QzWjQ6InvyDPTb410XiRJatyl8mZ09VviRZHPYKUxKPmiQRNxVu9HGRhyUIpHlr28ijSImznxLz0oM6wvPBiWXaYB8/95Kovyq8d5+ZOwDCNVjjR1SnmVPu/V7xzl5WxVdleeSU+Vj9eqbii9QAIkzWf8n57V/TJ5xQLpCU0VsBztfGJFNIun0lNuSULJUfp4EZlX+V8+G0UjrxsVO0JTnJCn9Flmxl9QzlO63jXTqjzzdJSsvS3963GhY2kEbhYByjZ1O9vrHMzslFLDNnav8/KfrwM3fLMf6QAAIABJREFUy5Gm/ikiQB1+mYzxGb8f1O2qbymLK2wgsA1MXBOTmo0gKgGmCds0pI3E7QMdK20f5uV53tBQYF/FgIqnzxqB10RAyTXllz+u9Oe2t03DQOAZiDxgVi1iOiuKlzgz9Z4uW6ZwK1nKO9s987bPFXXlNdnT0TUCVyOQTTKpCXuu9K9TDh0DIb2ylQwMa2X4QVkWmbAPxglTym+RW/fm+/RX56ZjaATeKQLq26/0b5WLXbiAowgEjoFZBZjUy4jisrR7uYCRepdGV9z2leMNiobS5++UQU1MI3AFAhxH41wNZZj6lzLcMQxELjArGhh6RcyioixS5cJEh173bQOemcow+3ladq8AWT9+MwTUYFwmYJS1OmW0zEVy3CAwP+6YyqFKctGZpKhz1aHiqXAly+o+H1el0WeNwI0hQIGk8OX0snjRlIWYHFtPF3pRLhlNyes5fi58cC6mDniPCPA7+0kcSv54vmklqvKaA8sApdnngZdcZAR44mDgOXpZ0ovC11Fm3NVyX55+8ZRX/K1Oha7L543CSFwpmhUCKu9V+Bj+NoeiSxr56zxNhivFlg+X6zdkQOV1QXEX2bwh/QWBN7u6LFs+UzJ0Wbz1OV+egk8VJhfGzD9YFYiVepKPetF1nk/G4Y8D7jzyaVS8dWHq2UVnpiFmV5btIgJZOOkoGgzi/XX5UXHJx4XyfEX+Ks9rRDsXReV/XX6vKhjprMM0n8+18zrH7RUBJJyTPZVnPlXucT743DXj5eOu8rx6v4rLurzTTFRKdc5lzSBmqs7ZNyYXQx7xscKY0an7L5WdlaxUWtLNP8pfp3ky5uLgc37zFIuLJ/mrZSrLd/l4i2sVh+c85oyhnqnYi/vFlTxbl1gluuKcp8Rr/hRGyuCCJNQzRS6fToWpeHKfj3A5aPnk8+vV/OYP1uCUf7Z6rdhQZ4WxOl+WzxItlUAFKoLZ/cqthCoc1z1TZPRZI/DRInCJ4Oa1I6PxnudLkny0xdSM3U4ElFyq0qk+Kyf5vSIHiewluVXxlRyLLCu9LzcZpfy1Iq7PGoEbQGBJz2YGzq5hoMZ9THNtIcajfPOs0mgxvYEXokm+FgKUQfVjQjWBWnQMJM5i7I7bLFNuGVfJ72tlpCNrBN4EgSuUJB+rfr/IJ40AaLDtLi/g4DP1IxtLZNmGyALWNSfehG2dRiNwKQKZvCk5zOtUMSSk/s21H7gImHKu2hAq3aV56IcagbdBIKcXRbfmvv1KT66SVzKbf05ZVfKqzipd/lk+TF3rs0bgRhFQApgX2JV2bl43q+g3ypMm/s4R+CQMVpRyVed3juJ1CDJzVZl4vpSZNMJSFJV2KXA1YxUpH75IsLjKP7/gOu8mhlEya3aVQ8rhBWnXBV+UgEytPlvD6JqgdblcGab4Jz31yyfKP2d42tx8V7kvcrqQ4ioWkmSVqwWdm706z8yFfL8DRs7RVsUm7XMPz2d4UZSLwucUcsW8Mu480fUvSPMquvmikvL5+KmvCsXq+ecX88O4/Fgzbf6jfXGK81XysrhXPWP+1+eX0/eZe6VLCKsyrUY5l88qsKsJ3uT+XCY53U7u1z1fk881o61JeVFQvrDXoJ5F4Ykd6HUpGKbC89cXcZAPV+nyYde9fpu0F+WhaPKskFJhF6VJwxk7i3m9BJeTU09z20plUi9P8lnkr1Wyc99L9WBtZPXwmmcFzGvI8TrKihV1XhfnXFgu7/mzHPTzsDUXr5XPmvQ6SCPwsSJA2V4daPpYedV8fQIIrNHJq/qXq/fZplDfNT7nT6n4efxVWvMHnwCOuogfHgElf7m2mApSMksm83JL9+fXbeN/+AJqDj4FBFb79WpSVMku5Tcv158CJrqMHwEC+Y/+FewwKuWWhxqvym7lRPll+Kqs5+PwOi/rq8/0vUbgphDItxeYB+V0dYItH4fX+tAI3BgCFDAqQx7Zme3Wi+SOUdQzXtPrtuq/UZbVM5JT1+qcZrL4n6e1CNVXGoEbRoACmcm6yknJqDqrcH3+/SGw+j39/ZXgjTn+WFUq+eJnIvtErFQ+qYz5mrdUDBVZna9q2ivwloicq/AqFs9KH6gc8qzk473WtSKiiM4Tr/DF8DVB8+irF4qeor/y/ILgub5Ln+fexTz9FYTn8V7zQpG9MJmKcBHnFyZ8jQcqj+UkDJVc1z9OI98EW/OMr/fq35iFXD7LJb/u3esQUCCe5/b8ExWygHg55Hr8Mafzua1PS/r8XTf+eioLfnl1PVoq54tTXJvOGoO7y/i8+plCfYUDFazOfMzrlWjXp39x2S+moTJXaa/IXPGYI3guxQrJ635JciTXX+bpKnZXY67gt3K7GvuN7tPykrL6rXTJGMzjHDBZ+OucFK3VNFm46kQyqyuzU+xmvPH2yjSr+a67Vzyqs4pzHeJzJlTiXCIVpOjxnHs8D16Np+7XxZ0n0hcagduBAMX9nKhndYD6YUU73Y5C61LcPgQSIIkBw0pldrXdcE7G36yxdPtw0yV6/wioNobSs4tRF9HF52X1/bOoc9QIXIgABVTJcF6PqrBMgHnSsnwhivrBTSPwGsKXxDEMk14ozidaDaGY87cazuKsVIGbLqGmrxFYQkC1ey+Sz6XI+kYjcFMIUDkqZcg88gKpwjMFOtejmUG2Mh5k+NrxB5Ve8c79sTJtPKelnumzRuAGEaDk8Sd6l8K3Kpv5ey2cN/gmbpb0J2yw8gGkVlUkni/Mng9ynwcVjxVOXedlYimM6fIRlx7mU61cr8TjrargK4+YUAWp8yJkhWz+dhE5H7p8fS7OuYBrZTUnuib5KkLzuNmFSqLOS+9iHnnxdB70Li4uJHvhg3eR6wqN9XnNQ+cXK8lu4nYlr6XbpZt3kPlb03sdAufjMiRfcxclWh938fzdX53P8e3yuD49xszpvjfO9hIz8rehuS4tWV59cdcvcI7iGyXK0r9B2quS8PlquXLcvvHlVfmS8Eqclds3zvp8QlLmb01BlzJdujlP5qqQdclVltkzntZFO0daRcqlOxfnTQJIT/Gk0qu81P1F53m8+cUi5jq6i6eLK5WUPPBQ9+qcBeuTRuA2IrBWzHOBucvbWHxdpo8QgQtl7sIHaSFez/tERownpfs/Qiw0S7cQgTVyrILUeW2pL324NoUO1AjcDAJzWZxfLNrON5OjpqoRuDEEuAEQJTknzRfmdVEchr+LUaQLM9YPNALXROAiGb1mch1NI/D2CFAIV8f2SPUi4czCeVJ6dG1UBqo+m0RYG+vt+dcUNAJXIEDJW5K+pZvs4bo6cAVd/fjjQuATNlj5QC9itSKtZWNNpDVBa5MuV9v1Ua4TekV+y4+X765D/kPGuYrb5efLdx+S708y7/cB//vI4xov7yNhQzj9cLx8uJyv8YrWR/kdsry+ICuht7VcK8Vcaeqee3ojASvYrtxenuVrRb6c1NLTj4HuTfGwVFB9oxHQCGgENALvHAHq79fR4fm4+et3zpgmqBG4HgJaDK+Hk46lEdAIaATeJQKm+Xradyl27iZ3+S7Z07Q0AhoBjcDvD4E3VIg0WLn0eEO6l9LUDzUCN4GAltWbQPW90rxSH71XbnRmGgGNgEZAI6AR0AhoBDQCGgGNgEZAI6AR0AhoBDQCGgGNgEZAI6AR0AhoBDQCGgGNgEZAI6AR0AhoBDQCtx4BbbBy61+xLqBGQCOgEdAIaAQ0AhoBjYBGQCOgEdAIaAQ0AhoBjYBGQCOgEdAIaAQ0AhoBjYBGQCOgEdAIaAQ0AhqBjwsBbbDycb0PzY1GQCOgEdAIaAQ0AhoBjYBGQCOgEdAIaAQ0AhoBjYBGQCOgEdAIaAQ0AhoBjYBGQCOgEdAIaAQ0AhqBW4+ANli59a9YF1AjoBHQCGgENAIaAY2ARkAjoBHQCGgENAIaAY2ARkAjoBHQCGgENAIaAY2ARkAjoBHQCGgENAIaAY3Ax4WANlj5/9m7z+dIkjTP70+kglYFlOrqqhbbszO7c3ckX5BGu/+cRjsj+YJ8c8Lm5nZ3RMvSBS1Sy6D9Hg/PDGQlUEAhUUgAX1Rnp4rwcP+Eh8jwJ9xna32QGwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4M4LELBy51cxBUQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGZLgICV2Vof5AYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEELjzAgSs3PlVTAERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHZEiBgZbbWB7lBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmEGBZAbzRJYQQAABBBBAAAEEELjNAgSs3Oa1R94RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEErkcgDckmqZke/CGAAAIIIIAAAggggMB0BQhYma4nqSGAAAIIIIAAAggggAACCCCAAAIIIIAAAgjccgH1phIfw6IQtDKk4AUCCCCAAAIIIIAAAtMQIGBlGoqkgQACCCCAAAIIIIAAAggggAACCCCAAAIIIHB7BRSMMikgZcJnDA10e1czOUcAAQQQQAABBBCYLQECVmZrfZAbBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgzgsQsHLnVzEFRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEZkuAgJXZWh/kBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQuPMCBKzc+VVMARFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdkSIGBlttYHuUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBO68AAErd34VU0AEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGC2BAhYma31QW4QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIE7L0DAyp1fxRQQAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYLQECVmZrfZAbBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgzgsQsHLnVzEFRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEZkuAgJXZWh/kBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQuPMCBKzc+VVMARFAAAEEEEAAAQQQmJ5AqqSSkJ6/nl7SpIQAAggggAACCCCAAAIIIIAAAggggAACCCBwjwQIWLlHK5uiIoAAAggggAACCCAwVYHEjKCVqYqSGAIIIIAAAggggAACCCCAAAIIIIAAAgggcG8ECFi5N6uagiKAAAIIIIAAAgggMD0BAlWmZ0lKCCCAAAIIIIAAAggggAACCCCAAAIIIIDAfRQgYOU+rnXKjAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3KAAASs3iM+iEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB+yhAwMp9XOuUGQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQuEEBAlZuEJ9FI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC91GAgJX7uNYpMwIIIIAAAggggAACVxZIrpwCCSCAAAIIIIAAAh8JcIrxEQkffCEB6t4XgmYxCCCAAAIIIIAAArMqkN5AxghYuQF0FokAAggggAACCCCAwO0WyK7mp1zVv93rkdwjgMDtEkgtsdOXjk6/u12lIbf3S2B4xuCVdvjufiFQ2tshMFY9VWUnnfKy/70dq5NcIoDA3RJg33u31ud9Kw2nwfdtjd+S8o6d+95UrglYuSl5losAAggggAACCCCAwG0U8Cv2+jUTf9HE59tYGPKMAAII3CaB05foT7+7TeUgr/dTILE0X2knRQDcTxhKPcMCpxqWkixwhVPfGV5jZA0BBO68APvgO7+KKSACCNyAwNi+VefA8Tw4/xPuOnNGwMp16pI2AggggAACCCCAAAJ3UWD4a2XsF81dLCtlQgABBGZQYLgbnsG8kSUEzhfQucN4X0Hnz8G3CMyCQNzvxudZyBN5QAABBBBAAAEEEEDgugTiea+e/fU1XgYmYOW61iLpIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCEwUIGBlIgsfIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCFyXAAEr1yVLuggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAITBQhYmcjChwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALXJUDAynXJki4CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDARAECViay8CECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCNy8QHrzWSAHCCCAAALXIkDAyrWwkigCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCFxNIAarxOerpcbcCCCAAAKzJUDAymytD3KDAAIIIIAAAggggAACCCDwJQQSs+RLLIdlIDBFAb9Er4pL5Z2iKkl9WYHQ0EQV/rLqLG26AjSXTteT1BBAAIGLCcS9b3y+2FxMhQACCCAwJYFr3P0SsDKldUQyCCCAAAIIIIAAAgjcP4GUNtP7t9JvT4lzP6RH7fujDxPT6/AIr29P0cjp/RIYNeyPXgUB1dzxz+6XDaWdQYHRrtUzF/e/obaGfXCShucZzD1ZQuACAux3L4DEJDMoEPbH2X5Y+WNXPINriSx9LKCKOqqsib9kP/yxE5/MmsDoHDjca6DzX6+5o+o8a1kmPwgMBeJeNrGBxasO8bPhRFN+QcDKlEFJDgEEEEAAAQQQQACB+yXAr+37tb5nvLSxOsbnj7Kri0T6Mj508SjNXwP9aA4+QGDWBM6s3rOWUfKDQFZZw8XNsN8N++BIk5ze/1K5IwzPNyXwiToYzyKUvU9MelMlYLkIDAVO11Gd74ZPTjWY6qPTEw7n5wUCNy8wuXk0BK3cfO7IAQIXEvB9bLazTXX9ITv/Zd97IT4mujkB7YFDwIqZ9rvh/fXlp3R9SZMyAggggAACCCCAAAIIIIAAAjcvoB/Wuh4ULnlOam7iatHNryVyMBSI1TFU2OHH13+JKLcoXiIwZYHhBU6v37GSjy3kozqf+/6MWXJTjF6el85oqtv56jIOFynhXba6SPk/Mc1pHr0Lj3CP9OlvP5HU9Xw97fqgXM5Asa4H6zNSvQO+KoIXw6tufJe1OkUS1nmU4HkmBcKGGKqpV+SZzCWZQuBCAh5tFer0haZnIgRmRkCBVte7DyZgZWZWNhlBAAEEEEAAAQQQQGD2BZIkMdNj+Jd/PfyQFwjcjICq46eu/+gavT9yE1KNb2Z9sdRLCsSKGp8vOTuTI3DTArk784a9XV20Ol90upsu43UvH4frFR7z9TMF/5++GH2pu6NH7643S2emnjuNOXOaz/lC6d544T4n41Oe5w76qkhqa/Jn/Z/1POVKQ3LTElDVjNVz/LXX4PjltBZIOghcg0DY12q/m4b9blZv9X5Ywa9huSSJwFUFhnVX5w1pwVKLj9N9ZV51OePzE7AyLsJ7BBBAAAEEEEAAAQQQOFdgdH3oen+snJsJvkTgMwVC/dVP8PAXL9zH9zwjMBMCox1tiMEaVtncF1mT00zkl0wgcI6A19oYrJKmHjSoBid1Me1/w2o9rOhZasMvzkmdrxD4EgJJNppKPlSF+vkl5FnG1QWGDU9JDBVMvQH145Tz+2Dq98c+fHL9AqqDo7r30dUGv7tfZxD5unr9uWIJCHyugNdUVeksSCVU4bPqb/x8tA187nKZD4HPFYi1MM4/ClYpWJoUTu2j4zTTelbq/CGAAAIIIIAAAggggAACCCBwdwT8otCoOKMf3fGL+EM7XgyKz6N5eIXArAicrp1hIIqQtxm4w39WkMjH7AjE3WwuR2EfHL5I01HDf75RKkyer+3517nEeInAFxRQ3R0FtmZnE7NSNa8rH9eV7hdcb1NZ1HU5XFe65xQ6LjK0l+pdfIzPNJxy/AveI/CFBGIdzC8u1tfx32/5aXiNwGwKeI32KBX9brtIHZ60Dcxm2cjV3RQYr4ExVFsBhDqP8D+/ESE3Zfz8iiT0sHJFQGZHAAEEEEAAAQQQQOC+CMSfI/5bJFXXkFP6VXJfACnnlxGIFTVbmmpprKl+P/8gdGtq2QUjXTgqFriX48usHJZyIQHV4VhpNUP2Ph0MzAbhQlHREismiSXZnaYXSpeJEPiSArl98XA/rLvykoIlSdGKxaIlheLpUQY9f7kZz8tvfhsZn+6CSYzPxnsEokCsXvFZn4fzXvUKlNogHczGeTB1Pa6y63m+5b7KfixCMtA5Q8HPGwoWniejxTkmf8unCHxJAT9/SM10CuyXHvzufv1u47fbl1wPLOuCAvmTBo2kndXUoq43WNGSdBAGFBxFwo520r4I9r8XlGay6xCI9TdXDX1Pm6Y26Pct9fobbp7JTTLVnBCwMlVOEkMAAQQQQAABBBBA4O4KJPrRrf/5pfrwa4aYlbu7vm97yfwCZ9buH397687+gTfw66e3mvw1Fi9/CMygQO4qkF76/lfZTNUpry7Th3/ex8rnVOJc+jNYerJ0iwVidYxVLBv0x5tNCx6goqAVfRtq8nAnHGeYRtljJi6S1jSXe5Hl3cJpLsOZL57fhXnLfGNZ47OXJzErFFVvwzAUumDvj1tWtvy64fUdF4gVONdgqnNeBbl60Ipeax+s6S5Sj2N602S7yHKnuTzSulUCqnKx2vmZby5QxZtLCdi+VevzXmQ2VthYWPVAofOHcAac2//qZCK3A9R8ubdxdp4R+KIC+fqbDePqy8/OI3SjTDHRvTOKHsx+3eXnmVJmCViZEiTJIIAAAggggAACCCBw1wW8fcn/FwJX9PtEF4yu4XfKpShvevmXyiwTfzGBUD/DxU691s/q3qBvfTU0qRWtkNggKVh3ULBualbOLozG60X5ehU/i8/TLER+OdNMl7Rur0CsZ/G5r7qpa5uFMG70QHf3J4n1reCPcy9yTqhg/tGEzy8t5o23l56LGS4pEOvBJWf74pPHKhWfY75DfxRZw1NSMkuKpsan/iCxfqpHyOrUghvigr+4wPUv8DqKFtdXfJ5qKbKL3FNN85oTyzvotV+SVzkKJa+3PqSVFWyQ6qG6HP7i8zVn71Ty11Ef4gJuojxx2bPyfJt9s59rXj+zZiXv3SqGvA5Uhy8RtB3Tm+a6oY5NU/NupBW3OdWN+BioV1c1+StgRefB2i+n5ue/N1mHbnLZd2Nt38FS5M95VEHUuJ/VZd0wE26aCc+n9r+x4udIqF85DF5eu4CqYL4axvrn1x/8OkTBTD1jFnUtIvY4ODbTlHJJwMqUIEkGAQQQQAABBBBAAIG7LuAXjNQfr+6L9obTxHppYt3hldCbEYg/qG5m6Sx1lgXizUuqor2BWbvbNTX2p8nABklqfUus00/9keqWEV0EVXSA/sZ+tfsP+fxn2WRXfaL+XlXwbs0fq5g/Z5Wjn5h1+qEOW7FkvW7XOv2BP7qDxDpn7INjWsMW1SlTxe1rysmS3C0WyNeJOMa5qrF2q+1eap1e39QZui7Ua5/c6abW7qahwxVd6NcwV1nFjc9xHxnfiyf/epwrDBkw/uktf+/DMJ4+LE0q0ef2epdfb0r3PN9Jyz3vM1+dw53ReVPe8Hexoo1lY5CY9fqqq31vJNW90mo81We9fuLPmmXccCyZa3sbt7NpLuCmyjLNMkwrrdvi69U3bmd61t3R3tBvpjqs71VnQ8N/0fr9bqi/8fwhznsG3BmbxxlT8zEClxdQFYzbm+qb9kPx91u3N/BzBwWtpArYThM/h7jpaxCXLyVz3FWBU/vg2DuFhrLSOXA62v8qaEXnv34Occ7+l33uXa0ps1ku7XvjPjjuf+OzLv92egPrakigJLFCsRh6HfQzi+nXVAJWZrOOkCsEEEAAAQQQQAABBGZOwH/IeIOSWb/ft2q1bn/92492uL9/o3nVD6dPN+PcaBZZ+JcS8N/Moctz70XFg1PS0BNQkli13rS9/V3r9buWFBI7OD6x/+M//d82VylbwQNWUksntvj5wCthzOmxskycfGya894Of+Z/orHgvDT47o4IZJUp9lsVq0TfStZLC3Zca2QXiwpWbTTt77/8arXqsZWKxTMBYhq+jxxWtjMnv/QXYf976dmY4UICcYXF5wvNdKMTxYZur8N+8TO1gRX90er07f2HHWu1O36ntIJX/p//9/+zpcV5S5IQtBICJcJ+WJuD9sf+rL14dqwP04TbWMOe+XSRXWtU8U9/Of4ua9Cd+XOI4YEmjBsfihHqxfArb6O+XF1xv8zqozmzz8PTRUHHgeN88Xn8+xl7n+Z9Q94GSdG6/cQa7Y7Vag0/n2g22/bbyzfWqtdtcb7yiZjAJAtmuQaD2Cg2ZcZLbUNTXvZMJXfLfNNEjUlqAU294V/bd2rqBUg9W5VDnW21LUmK3mPQf/3Tn+3lq5ehHyGPFIh7AT3H+hqe4zfTXT+nlzHdtO97anGNxefZ91BtLWQHNAVWaW/s+99BwWqNju3s7Fl/MLC0Z/by1Wur147tT39auOGCqQ4kk3lfAAAgAElEQVTHenzDWblTi4/1Nj5Pu3DTX2+67qB/qg6x8V89sllSsqRQsbfvt63V6vhwmKrff//pV2vUa1mUlsoZH6Oyjq5xaW8+7Xr28fJGS77qq+n7XjVHzH++gNdZDRkYa2JW3VQHddNirdmyg5MT6/S65iNkFqZdH0f5S9LJV+NGU/AKAQQQQAABBBBAAAEE7rWAfs7q8metndp//q9/tv/zP/1f1u2nViwUNKqKWT+7PeT6frec4x+bds+ZhK/ukYAq4ejHtl/8ye4sTQq6aB9+dGtICq856k5l0AsXlz7R5BQQJ1TyfGvhJaW1bfGHwGmBjxtMdWEzKZYtLeiu6L53f+D3+KcDK6Q+YNDpJD56N6q3/mpSxQtRAB/NeZEPJiV3kfmY5lMCsS7cEuGxIaJ8H6uAEN0RnfVKYd54WvCCq+4WrBf2xR4Mq3KqhsaAldTSgV/+D4GEWR0dXrT3mJVR3VaiQ6nTH58NPQxYOXuS2fpGdSIYxcIGoc/NZZbaJ7z868/ZR9w23wkBCqq/fs5QLPn+N9ZSH9jqEvvfGMz1uWtq0nweYzDpiyl85tvSJ+rFFBYz00ncPt/Qg6D2hL5l+0pUjxRFD1Lp+fhrRe8lM0l7lqTqZzAEuPiKGBY41vLR6vGkRm+n9OqeV7ApKX46metZe59e7uWmUG0YVkE/nivYT+cPRUsL2f5XQYUaGcj7aQv9tV1kKddT07gGcRH7q08z7fp7PbXBc+lJh/yGuqw6XLRisWza/+qRFEv+283Pf9O4/9U8p8uZpRL41Pvg1SHPSOH0cs+Y6BIfX19OL5EJJr2kQJKdpHq9Hf6ey/Zx3tuVAggTH5atZD377tlj+4//6/9iP/zD97a4kPWJoqo0hdVPDyuXXHlMjgACCCCAAAIIIIDAfRPQ7w49iqlZMRlYpeStRNmdz7pqVAoXRi/yA+VzGjw+AR4We5GFfyIhvr5TAqdqRPZGl3vUkcrw97R6pigWst/WF7hgcy0tTneKncJcSSBXB/Mvfb8ZKvGoN5XYIBUa/y+62GHdz6Wv8T889VMbzUVSDBeyLj3bRZJmmqy5RhD5lTXbMKfrQsi3alcMvQmlie/0jS5LhulGpYzz6dxjlOJFYgNHU1/W6fPnvOySPn/6kdDnp3FTc94G38k2o9qYem9Weh9Ko1eF3AnF5Pnjp9cqMM2qkWX0WvMbUW7L863xjfvWUS0NxFqbiZVK8Xwh1N2wjtVD26cLeF31Ieb4tlSF25XP26sbamQ8AwhnEYVh/dVa0G83TfXpuqupr+HyQ6gK1/G78HZVsmvN7dUCcs/L2vT3aKGOjdXHrMeKgQKtiomVs95c/ew2HZ3/TqrHp3N4+t15Jbvcd2P5vdzM50x9Xfk9Z5F8NR2BGGiercK4JrXH1RmEelwpeL3Wfjl+Gxat32n+yemPL50vAlYuTcYMCCCAAAIIIIAAAgjcPwH97igkqS3Pz9mjrQd+p7/GMx0MdLkoXgCNF/E/9hn+HL62K0YfL5NPEEAAAQQyAQ2topfZzviK15KGrFyrH1Jcy4vQIHMtSd+uRHURlPOH27XOyC0CCCBwjsAwnGJaJyTnLOtefhUbD+OJ371ECIW+lsAHnU/HgO97bHtdRff9A/uG6+IlXQROC1zi+kAh7dnmg3VbXFq0Ym5YYvV+NY0/AlamoUgaCCCAAAIIIIAAAgjccQFdL5grF+zF82dWrpS9i95+P3Tbr7551ZDk//zCwqSrC2OXivItp3fcjuIhgAACMykQ76K6YmNGdo1rJotIpu6GgM4qCFi5G+uSUiCAAAIIIPAlBXTn/9iViCsvPlztSK6v95Yr5/D2J8Dvi9u/DinBrAlMuk47nsfRNP5qbEPUUILLS/O2ufnASt5r0Pj8V3ufpOlFOtW82kKYGwEEEEAAAQQQQAABBO6WgEbc1UPtnLrpWT9m4uMiJR373XORWZgGAQQQQOAKAqPLTyGRq+6Hrzr/FYpyr2a9786x3sbne7XyKSwCCCCAAAIIXElA51HTPpe6zHWPK2X+Hs887XV2jykpOgKfJXCR314+TdxYLzLDJ3JCwMongPgaAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYLoCUxpZaLqZIjUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBuytQurtFo2QIIIAAAggggAACCCBwrQKx68dJC5lCd5CTkuUzBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbshQA8rd2M9UgoEEEAAAQQQQAABBL68gIJSJgWmTPrsy+eOJSKAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMsAA9rMzwyiFrCCCAAAIIIIAAAgjcCgECVG7FaiKTCCCAAAIIIIAAAggggAACCCCAAAIIIIDALAnQw8osrQ3yggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3AMBAlbuwUqmiAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwCwJELAyS2uDvCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvdAgICVe7CSKSICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBLAgSszNLaIC8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMA9ECBg5R6sZIqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMkgABK7O0NsgLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwDwQIWLkHK5kiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsyRAwMosrQ3yggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3AMBAlbuwUqmiAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwCwJELAyS2uDvCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvdAgICVe7CSKSICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBLAgSszNLaIC8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMA9ECBg5R6sZIqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMkgABK7O0NsgLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwDwQIWLkHK5kiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsyRAwMosrQ3yggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3AMBAlbuwUqmiAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwCwJELAyS2uDvCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvdAgICVe7CSKSICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBLAgSszNLaIC8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMA9ECBg5R6sZIqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMkgABK7O0NsgLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwDwQIWLkHK5kiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsyRAwMosrQ3yggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3AMBAlbuwUqmiAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwCwJELAyS2uDvCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvdAgICVe7CSKSICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBLAgSszNLaIC8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMA9ECBg5R6sZIqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMkgABK7O0NsgLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwDwQIWLkHK5kiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsyRAwMosrQ3yggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3AMBAlbuwUqmiAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwCwJELAyS2uDvCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvdAgICVe7CSKSICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBLAgSszNLaIC8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMA9ECBg5R6sZIqIAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMkgABK7O0NsgLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwDwQIWLkHK5kiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsyRAwMosrQ3yggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII3AMBAlbuwUqmiAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwCwJELAyS2uDvCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAvdAgICVe7CSKSICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDBLAgSszNLaIC8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMA9ECjdgzJSRAQQQAABBBBAAAEEEEAAAQQQuCGB1Mz00F8y9py9HX4f31/mOaY5nEcL++jD4be8QAABBBBA4EIC8diVn3jSZ+d9f9HDUZwuPufT5DUCd1FA29JZp2wztx2kZmmWqUn7gJjf+HwX1xdlmiyg+tBqp1avN/wxGAysUi7bwsK8LS0tWKWcWELFmIzHpwgggEBOgICVHAYvEUAAAQQQQAABBBBAAAEEEEBgugLdgVl/EC70FxKzJB2LJ0lGAS3jS05zrQLxta75Kh291wXgYiG8j9eC4+eeVpw/fjm+AN4jgAACCFxSIO5Y42yzv4MdmNkgDY/YcOjHFH2hhuh8kXLFyX/sr3PfqfSf+v6UUH7i+EV81rFRxzbFW6bhuajP9GF8xGl5RmCWBWI9H9tWlGVtg+nAbJCdEw5y5386rxv+JWaVbJvwz5Rm/vvhhF/gRbbsfhrOZX0/om3fyxm+jOeinsU0Oy/NzlFvLN9fgIZFmOkQ0uuZ7ewd26+/vbKXL19ap92x9fU1e/bVU/v+uxf2YGPFKmW0EEAAAQQ+JUDAyqeE+B4BBBBAAAEEEEAAAQQQQAABBC4toIv77W5qL9+8s529fWs0W35lN7HwTy1xia7yZ40SqVrpvOEwNf0btgT6dGq4K1ghSaxYKNnc3JyVy2Wbr1T8DsbFhXlbW1m0xYUktPTF3Kr1wBsV4gc8I4AAAghMVyBr0Z1uolNLTQ2Kh8cN+7C9Yzu7e9bt9S311uYQ+ahDz+m/JDtshIPT6Ovc57kZ/PusMd2PXcMEc3PGrhly851+qeNiakmaWjFJbH15yR4/3LQnDzetMl/+uLFeSd9UA/7pjPMOgU8KxEb97e19293ds6OjE+sniTf2D/ycMJ6rpZYkqZXKRft3//QHe7SxGKr5TdR1bWNxE9auoGD25vW+vX3/zqq1hn8QzmET35+EALOBFSy1hbmyff/ihT19vMV2+snacbsn6PfNDk869i9/+dF++fWlHR0eWb8/sP3DE6tW69bt9eyPf/idbawvWrF4u8tK7hFAAIHrFiBg5bqFSR8BBBBAAAEEEEAAAQQQQACBeyig6/yd3sA+bO/az7qIW615NyuJN9yNAlWG/WRnvaaohcDbElNv+nM5NQokBQWsFKxQKFi5VLZioWClUtHmKmWrlEu2urLsj8WFBVtbW7XV5SVbXiyFrrhp3LuHNZAiI4DA9QjcROvx55VEu371iFCrd+zNu1376ZdfrdPpeju0jjOhJFmwpPf2kC/bpNf6bDjXqUyFmBQtUQnnjl/66FR0ST7dURIKWCmkAyukqT1++MCSYsHWN9asZGXveWU05Vhyp77gDQI3LDChemtb6w1S2z86sZ9fvbG37z6EHo8UBaJtI80CmRW0VUitWExsdXXN5ua+sbXF0Hw1IdnrLWhugeoR5qSe2qs37+3Hn3+1k2rN0qQQQ21soO1W8dKJWalgtrY0b5vr6/bk8dapLf96M0zqX1pAgVgKzN8/PLJ3H/ZsZ//I2q2OaUigdqfrVbsyV/Z6sLhQssXFypfOIstDAAEEbpUAASu3anWRWQQQQAABBBBAAAEEEEAAAQRuh4A326VmJ7WG7ewf+N2GhULJLM0aKLwfFW/Jm9j6pgaO2NOKplLAinpZ8eAVtW/4OEO6Nzc0EsxXyra0tGirKyv26OGWPdratEebG7a1sWaLC2UrVxIrFLIhFm4HIblEAAEEELiigI4falQ88l5WDq3TVcBK7C3Fj1QhgEWfDRupsxce1BJf6zlOMHqORzEfy8cG5r2FTcizmuT9b7SQsalSK9jAkkHPkmLRnjaa1h0MrD9hqWMz8haB2RTwk7cQw6Wgj1qzY3tHVXu7s++9q4Qu8QqW6LxQQSs+HlZqhcLAXr5+bxvra7a8GII+bqRzimyT7fbMtnfVu8quByY0W+0w1GWmrh6b1LOKAlbKpYL1O4vWanXC0JWaJu4k4m5jNtcWubqkgIIhW52BHR3X7bjWtGa7b4O04MFY+l+j1bGDwyM7Pj6xR5trBKxc0pfJEUDg/gkQsHL/1jklRgABBBBAAAEEEEAAAQQQQODaBXRdXr2hJFaw/iCx3iCxcqEY+lX3i/cKR9H9ifFa/tiV/GGPK2rH0Hd++3s2fWhsVAOIpQMrFhKrtzrWaLX9TseXr9/aXLloG6sr9off/WC/++F7e/hww8eQL5dC4Mq1A7AABBBAAIEbFfD2cvV8oGDJpGiDNLFeP/R+orgRH9Ijy6Hea3qfR59lwSrhcBWOQX6UShXYEo9X8TlMr2breESLfazEKRQkc96feh/TIa2gNBIfIMjzMvDexobhLuclwXcIzI6AbzghO3qpGGM18A+saGmxbKne+Dah7aJgqQetaKK+aUjJdx+27aunD+3F11t2kw1YymW3l9r77W0PPmh1epYmReun4fw19BKYWt8GNhiklvR1ZhsD4mZndZCT6QvoN0iv1/UgyH6/7+tfdToERKZex3vdrnV7Xev7D5bp54EUEUAAgbskcJPH+7vkSFkQQAABBBBAAAEEEEAAAQQQQCAvoKv8HlCioJWiP1I19OnzNLu/XXfTZk0Wgyx4JSQRGva8ic5fqruVrJFRnbD7nbjm3canA2+NtHQwCI0h3oYwsF5/YN3esbX/7W/26u07e/xoy148f2bfPP/KlpfKVtLtumqQVPsifwgggAACFxdI1YiroXZa1mq1bNDv+xBty0uLtjBfMcWHzMJfDBHxxvHs2KOh5TwgREEn2bhAfnTRxEkWVqJpNRSdCuGv1Y7e9wOGZhuo9b2gHiFCr1/ZgS0LSYlLzY51MZ1PgSQaEsgXmx0VtbjECsPgmE8lwPcIzLZAOPNTg776I8kqu58Xqp7rM0WR6ZvUdvf2bXt3z5qdf7SFcmLlMJLkFy+g9nPVWsv29g+sVq9bt9vzgBTF23iPf9kuwoNvPOgtv/1/8eze+AK1u+x2zZrNttXqNUv7fSuXS94D4uLCvCluPe5TbzyzV8yAds0anrRSKXngvPcQlPX6qFqgoa3mF+atUtYwprl6ccXlMjsCCCBwVwVm5OfDXeWlXAgggAACCCCAAAIIIIAAAgjcUwFvHNSFaXX1rivURb+D1gNWsgu5fhG3qMbDENuSlwrBKrrAq0fqdy6q2/XQwBgu/Oq97mpUY6THxgy8CcST0eKb3YG1D07s8LjqPa8cHh5btVqzb158ZQ831029rZQIWMmz8xoBBBD4pECvZz7EzqvXb21vf8867bapMfL7b7/x4MDF5fIn0/hSE+hoUSwWrVIp2+LCghVL+SGBlAu1PKc+lI+G8wnxKWp5DscfBUj6XfN+/En8TvlB2vHpQstrSEON11pOsVTyoUHi0UgNmmHicFD049jEwqeWaGiRJLX5+Xkrl4Oh5uIPgVstMKzEPnCO9b17o3Dy5b0ceY9Cej/wYBZtj41my/YPDmxnd9eePX7o52tf2kDnlc1WavuHh3Z0cmKtTjecaypSIXuErdp3IWGgy6ynpi+d11lYnix6fbPjat3evXtv7969tW6nYyvLS/bNi+f21dMnNr9QDqf1s5DhK+ZBu3YFq6ytLNvqyrIdn1St0+laPxlYqVCwhYV523qwYSsrS8P9+RUXyewIIIDAnRYgYOVOr14KhwACCCCAAAIIIIAAAggggMANCWRBKD7MQtb252ElatQrJDY/V7IHG6u2tDhvpbIuTwxbNEKG/a700K26Gvh0R/tgMPDgFAWoqHvtbq9n7XbbWu2Odbt971Wlp5bUbEgFJaQu2ruDge0dndjB0ZHt7O9avdmwf/r972xzY80WvJHxhoxYLAIIIHALBVrd1PYOjuyvP/5sr1+9tGazYaurq7awsGBrqyu2uKQhP7IerEJ84Y2U0oNVzGxxvmRbG6v2/Nlja3c6IQAy6/kkdGCigJWBB614RrMgFbWs+j/1pGIFP+aoIf3g6Nha6mlhoME/9Jd6A+XSwpxtrK95jwJ+Q312B37sgSUGXfrRLkZvjskUCkV7uLlh6yvLVtLSNcyIenMZm463CNwagWHlHfgwjjYYmOq5bwI6BysVLe0POzzyHYd6zzs+qtqrl2/t8YNNC93ifbkSaxtVLyr1RsN2dnY92FkB0hrqUj3BJMq/hn9R739pfxTo5lkcFvjLZXgGlqRSd7qp7e8f2t/+/pP99NOP1mo17eHWps0vLNjmpp4nBDMK+xaSKRR/Ya5gDzfX7NnTTeu0GnZ0fOzHiblywR4+WLWvnz2xBxvrNjc3NwNriCwggAACsy1AwMpsrx9yhwACCCCAAAIIIIAAAggggMCtFPCL/dn9sn7XehJ6QUlsYKVyxdbXV+yPf/xH+/bF197Apwa/U9ers8ZEpRMaBMJ48Glv4HcwNhpNO6nW7PD42PYPju24WrNarWG1esPa3Z71FeCSmmnEIA1FpKEblM7e4Yn9+V/+aicnVfv3//wH++rJI1teLJnulGR4oFtZ1cg0Agh8QQHtk9vdgdWaLTup1a3V61svNeuph6tEe3j1hHXzwSp5ks31OVv6D3+wf/rDDzYIreT+dRjSw48yfrSKw5GE1lMdkdTrQ3jomFKt1ezd+w/253/7m73f3bdms+m9omgoiErR7Ounm/Yf//f/zdbXVq2k3sPSfghq0ch1OqZ5TxJntc2Olqd558qJzZUSS9Qqyh8Ct0lAm5T+cid13qlKCPGwgoaFVCByalYuFm1pack6LQUed6yvD33YyMSqJw17/eq9/fH3v7eluUXTJvWl/pR1FeOkVrO3795ZrVYzBUTrPFL7kIWFRd+eNURQp+N9/H2prM3scvzY0O5ZtVq3o6MT6/b64TxcFUEn2D4eUC77sZ7kPrptL8tFs/XVOfvnf/zOlhfm7N3799ZqtW11ZdF/X3z37XNbW122ssa04g8BBBBA4FwBAlbO5eFLBBBAAAEEEEAAAQQQQAABBBD4HAFdh/YRFPQcR1fQLffeBDCwJBnYwnzZVpbmbW1Jd6+fatv4aJFKzy/36gbd/px11pasvblhrc5ja7a7Vq037ei4ZnsHh7azd2BHx1WrN5vehXs66Mcb3K2bDuywWrP01VvrKbCl+we/A3J1pZJvW/lo+XyAAAII3HcB7YfVNltvtXzYh8OTmnU0BkRStIEVvHHS99RfsGH5k+tEjeIlPRJbnA/DUcSmQ39WcI0SiR9mCQ7bUrPP+2nBismCHR2UTI2U6kFFgSrqSqZgAysXC7ZQSWx1oWQPVuZCA6UfBP1omPXUks/t2ALVW0N2hPTeWfKT5l9rovFZ89/zGoFZEMjVUe+wKDv30/aiYBV9rYeG6tpYW7NGuWH1WmrNVteDvApWsE6nb4eHJ95jx8pC2ZYWJ/TOcQ1ljdthvZH6UEAKWul0uzbwc9jQq8rW1qYHRh8eHlqnwyap1aDdXSc7Hz8+qfnQT0mh5IEqHkDuwYLXsMJuLMlQj8uFxB5trdviQsWeP3tkvV7fhwpaWpi35eVFq5Rn6YB4Y1gsGAEEEPikAAErnyRiAgQQQAABBBBAAAEEEEAAAQQQ+BwBXfT3nlPS1NSJur9WI4UuWg/6fpetPg/3r19sCWpm0XAAACAASURBVLpJUw/drbi4UNJADD5ju7tmtXrPjk4e2IftFdve3bft/UPbOzy2eqNpvW64+1/DOqiB9fD4xDrtli0uLlqxXLRS5YktLxS/bKtDADp/mbIKY1ZcDGhWp4oNFXehLNM2Vj3INe5NO/lrS++25vvaQHIJ520usp3nZh2+zKcx/PCCL64y7zmLULLdfmq1Rt0OT46t3mz5ew3zpt5VUu3nC7HA5yT0Jb/KbVsTe2jIfZ/P1vjHIUBFPcgkITDH92U6IGn7DUOEqBcVDQ+illsPw9Q0F9nnZWSeVD4Tk16PZ2zSNPosrgZNH1/r8zj/pM/OSuu6Pv+cPGieWIYr5WtqCV0pF3dy5gnrR70LaVtQE79Cl9VxkM79KsWirSwv2nylZJb2rNNt6vTQV3Kv37davWnv3m/b5vqyLS2ufREu1Qxl4fCkavuHx1ZtNK3TV29JIV/lUtG+evzIqrW6HR7sD0PNQsjZhMJ/kVzf7EJkpqCUWrNtJ+rtsNWx3kDruGiWKpgxDUOx5TffK1Fpifq7UiJZGp94OnNXEZatqr20ULLFhVWzrVXf3eobPe7KKfwnhPgaAQQQmIoAAStTYSQRBBBAAAEEEEAAAQQQQAABBBCYLJDdSRvvYh92CW9WGKT+UMPF51xyjheEtdy5kllltWQP1h7Y061V2znYslfvtu1vv7y0dx+6Vu11h0tRU2J3oCEt2vbjry+toDvjF+Zs/umWlbIrJQPl7dzb3CeXdvKnE6526yN/DELhJwGkqXc/n6gb9Ys0ek5e+Jf9dEJR1cozGKhXHXXE8Llr+xPF0HL15y0EudfZy6k+TSrj5y7A60BWF/IV+jLpxbJPmmdSvfpougsWKL8cxZxlsVQMpfUR6ChAIF8fNVlufVxQfULin/5IHQH4eskt79NzjU0R13cuDbXXdnpmJ/Wa7R8d+X60r2X5/ikLzki8VTcMseMFziUwtojb9jbV0HJZo7uvzGyouRCSqSGAtC0PTL16qZF2uD+6LEEk1HznzTthHQ2DU+J3Qo6vlVb22juLUPL59POvP3flKP3LpJPP26eWqWnVAqwuO87qtOBC6WXHJI0FmEvo/GwrHCsmrozGqdWF3Jjjp8pxqe/jMsNyhovVxzELnq/hm0ul/iUn1tngsMe9bBjIYmJWKRVsfXXDBmnH6o2qperGSec/g9RanY69fP3Gvv7qoT18uGaafvh3ymD46ZVfKNlez2z/4Mh29w/spN7w4W00vGSpVDT1nKHhJN9/+GC9bme0UV15ybclgVydzLKsT7p98+E51fNWu5dav2++vnSMULCPD++ZTZ9fjR+VOiYfv/hoYk0Qd5Lahj+aIMzp0SITEonp52fTZ+PvNeukaWOSY89x9visr33/Op722HyXWMTYnNf4NmbKC3GNyyFpBBBAICdAwEoOg5cIIIAAAggggAACCCCAAAIIIDA9Ab/e6Y1L3kwxbNQJF3PV6JQOu4a/6lI9tkQJp2aLcwV7+nDDFpcWbWFpyZaWlu312w92cHRi/V4vtHdZYt1BaofHVXv55u1weKLVFY01b1MOVhkrXbwQrPwOW/vjh9m0Ht2Ru36ur/XweXLpjc2W+ya8DNgffXzlD/LLzS8j/zoupFCwgjcMxg+u4Tm/3PzraS0q+iu9aaavtK6SXszXZ6cTV6SeP5GR/NeT2ovVfnRWI/K01sNtSSc6hN3cKCggciu241NlOWuCmGZcRkwntwqHu5X43Wc+jwe+aBW3Om1vlIxDPhSKJQ/6077MgzliGX2Z8U1WmPhW3/lH+iALQDgVOfGZGb7u2Xy/HBrd40oNwSoqTCjj+Gr5ZJbyJpo4vo9D6Q1TnpBSxnrqm/hZfNaX8XXGfWp6vdHnmiY+fzTBJz6IeY6TXTad8fljOnqO3yl/Xo5YmPxEuenGPv74bbYBefiJhnMKa0yLOS/b2VnMcEHJEPU6g1WU+1x5cy/zH39cxln+JPSyouAF74wpC/Da2ly3dqdhOzs71mz1raCdWFKwvs7Tjo59CLJme2BL82EISS+hPGL9iEXOG8XPLvmsAItqbeBDS2q4yd5AoUoh4bly2TQc0Nrqih0c7HuA2iWTn/7k12Bw2UzKrNHSOfWJ6digngyTNPGh01SHk6QQd5kued62drG6HQt9Rkr+9YTKEGdTAfOvJ0waDU7FvcQPz3n+KEfnpa1sZPm4sUNgdMjnM//6nLLyFQIIIDBNAQJWpqlJWggggAACCCCAAAIIIIAAAgggMBKIV2FHn+iy9eidf597P/rm81+pIb1YsPmiWaG8aMXSCysVK1YuVazf/81OqjXrdhW0ohbj1Frdru0dHNrL10V7vLVuxRdf23p54fOX/9GcKl+8Gpx9ObHIEz8cpRa/js/xm/H3Y4uKk039WcvVssaXf4EFxdni8+RZYkEmL0C9e2jZ8dv4PEzr/MSHk93mFy6kgmeF/8jgQoXTXJOxPP1cGuPpfzTXpVvqc4lf48vxcmhRsSwflWGa+cgWfCp4RG3lWn7MwCWX50lmHZmMz/pRWbLlD6e77DK1nLF1ql12o9Gxk5OG1WoN0+g3hULRkqRoqRUt9aEfin7vu157fEfOewh/KlO5DXn4+ey+8GOYxzzE/jaylerrNaLlsHMvzy3VGdOd8fG5SZ35ZS4xr4OxjuQ+P3Pe87644PzxlGBY/+N88XnCMpRFPYZV8ZxpT82umXLTjt6GDcgDIjzl8I0m1Ss99DeaPvvAk9NUo4FffPpswtyiRjMMUwzz5b448+X48ienO56/s6Y6czFf/otTWQxoqg/qfa3f79nK8pI9WF83BQ3X6nXvjUMt+X0NM1Nv2O7egR0cHtvi0438ah2t4wh3xZIpmV4v9aALLU/7uX4/9QAarfnFxXn7+tlXtry05Ps+Bd7M3F/gHdZl5e8U/5UzfDo1N9N6arTs4OjQqrUTS1MNnqZ9o4ZJC+tZPRfGc7eYhdMpjdfrONX4s+YKA41qmM/4l18T4+mewogz6HlsQqWhx+d2sBjnn5B0fqnD17743CHw3DIM55ryC2Uiv+ApJ09yCCCAwEUFCFi5qBTTIYAAAggggAACCCCAAAIIIIDA1AWGDVdTTlnXXysFs82VghWfP/WL241G23rdN1btN6yXXZxN077Vm03b3tn1bufX1tdseXnBSqNr4BNzFq/tjl3rnjjtR1fEzUx3o6q7dDWE9LOu70O7R2gM09XjkHZqxULBisUke4y6GL8uuzMK8dHHivlRN+/dXurd56ss+vP/x+GMksQbvZPCwEplNfXqTtuilTQyUBoaBS7TMKC01e18tihvVND8Wl16HppcbMV8VKazPvAGl2wYHOVby9FjuMzLLi9LK3aVr+WqE5piIWsoOSc9lV3DFXT7ofEn5C3NBQeEuqMkyqWClUuJp31W2SbVzzitvGNdVcNiaHAa2KA/8AZFDZtVKhQ9SMw91JuObGI5Pm4PiklP5zlUuZBWzizmu6e6ogbRbFiq2LgY6mGoj8Vi0Y3K2VXSXDKfn8fxfGl717AIHuigoLpRMIi8xv/i7HrWNqYGVD080C4z1Z7Cy+P1MLFCMdzNXkgS84G3shFpxtMeb6D76Pv8B7m8aZ/Z1B30RzU7Pq5Zq9m1dOARgr5/Vd56/cS6evRCA2WSqKE33F2vZJXcKMnQZ4WGE/IhhUZf5HMwE6/j+vD9i4/Mkq3I7Iv4vUqnf5f6G598/P1ZicWF5qbXMaXXT63bHfh+ODQUq55kQ7LpWJIUrFhIfF9TUp1RZcn/Kd1cmvmvLvpaSSgv3jitmbRPyPKr+q793HAZZyxL86sc3X7Pt99o6/VbCWT73/mKNqYzcpZtc9pnyqJQUH0c+PNoplGBlYzeZVm1Tlfl6HtQxSA7pmmYGnmWsvEDtQ1qmJi5SimUy7OSTyWmlkHksjo+VdhuwlAq/f7Aer2eFQtF0z5Ki9O+QNtUWH9h3eX3H2cx5BZ5Yy/zZc1nwo8rvZ7NVcq2sb5uWw8e2O7unnW6PZO5ytTudG13b992dg/s6eONycezuPLyiV/ydcyjhj07PDy2o8Njq9dbvp/TUETaXlaXFz1gpVIphSFuVLeyrT6GsF1ysZebPMuk6nN86Ljs29sgHJeVDx96qViwpBCOC6oz2nZUXz51fnuZDCk7GnBTvavsHx7Z4eGRNZqNcB7gCek8RUFJfa/PvW7qedKK1T5Bqy2uujTuJPyzsI/K1+98vkLPVmFgoLjePB2lqW09OyccbsyaSBPk/vzcRsdXnYdn5woDRWG6UejJx/dXxcTm5orjsw9T8uVn53Ta54V6G7bT0lkFyOaO2dKzRi5VL2aphtVzH4XjxON6dn6blSHON8zE575Iw77a61IuDS3Gz+lu0YikuezzEgEEbqkAASu3dMWRbQQQQAABBBBAAAEEEEAAAQRuu0C4yKv/X8+fX7w2s+XFxF48e+wX9+v1mvX6PWu2OqYGIV2RVWNWo9WxN+8+2JMnT2xtddVWV+b94rQ3UE7I3th17wlTjD4aL6HeN9upnVRbVqvXrNlsWLvTsV637wEsagBTo5QacXWte2lpyVZXV215eclWVtRbTHbh+uPr76Mr/6PFT/2Vrzc1pKdmra7uRFaPC7oTuW6DgcrQ94AGLTjx4YDMSuWCLSzO2fz8vC3OL9ri4pKpnbGiYIrxBtNhjkfKWqYCVTqdvnV6asAME2moIW/QS1KreKPCJyKNhmlf/IWW2+2m3qjR6yloQwsfWKkYAonUiLUwp5VywTQVcKKy9FJrq0G2q+YeNYYpkKdg85UQZJLv3SLWIT232qkdV9tWrdWt0WxZs9W0drsVGhc86KVg5WLJ1FCysbFhjx9u2vy8ggIulsW4fvWsklYbPV+39Xrdms2m57fb7XhPReVSyebnF2x+fs4bHFdXV/y1GvMqHqAUTC5Kc0HB0WQTElae253UGs2unVSrVm80rNlqWafT9gYzzazhCVR3FhcW/U553dX/4MGSladZfXJ5a3VSa3d61uv3vf1M23axlHgj91xlNMTFuL22MQ2PUas1rVqtW6fdtXar7WVRPSwWCzY3V7HFxQVbXFiwylzFNGSFHsvzuYiVmJf4PBKc+CqfD5FoH1mtp/Zu58DevP1gh0cn5ttCqm8VOpP4NtntDazZ6lm13jPVkSRRoESoe3HnpKm9DU8N/cWCN/ZXymUrK4rtgvmbmOkb/1CZjwWIz1fLVAhfPCMtBQzmGnx7GpJD+4bjE9vb2/cgj16v73VO+2XfV3pwVtnrverL6vKybWyUhrm+Wm5Hc2v/3Gz3PehAQ7p4k36a2lxZgR1lK5RHw4OM5jodLHJU63hZdIzUfq7dbtugH4aKmV+Ys3nV+4V5e7CxbqtLS1auZClpcSLTfnagfYGOGX33UH1U0EqlXLSFSiUbqm7kq1m1/9Cz/g5PGp4HLVv7Pj331EObpbawsGDlctlUd5WPJ48f2vJSaGoJQUtZJjwzo2VkSfsygkz4pN0zOzg4sWarba1W289Rmo2GH0u1n9XyFCC4sDBvS0uLtrqyZPPzIfgzph6f4zJm4dnLGDMWnzNjf5sqoKFv6aBva6vL9vjRQ/v5l1/8PEIrI+4/Do+qtrt7YO3O91aYC+dGQjzrHO1zyh7XvwKV9vbVU0jDj3NhfaY2X6nY+uqKbW0+8PNJ9QzjGcgqzKh42Qefk4kJ8+RT02sPWu3qmNK3bqfr54/1Wt2Pczqf0DmkYglLc2WrzM3ZXLlkC9kxeq5SsZXligd0jPI7YaEX+ChuK/Vmah92Du23V299mE0FV2qoOAWOKFjFt8Nu6vuESkvbTzYMjsac80iJLNDGA1ZCIGalVLa5uTk/H/IAtwn50b6l2e76uZmOQTqS6rio8mpf4+eWES9XWAXzKE/1Rmq1ettqtZr36tNqNaw/0DrVuWk8H6vYysqSff3VYw8ay2dDSStYSMHDCq7rdLW/DeVTgFC5qIcCYnVOVwyBOvkEMgetz25nYM1mxzqa3yNuCj5kVrmgY2PJg2rnKxc8iRtbxqm3yrQHXYb9nB/bG13ft4bpJBmO0To+VyoVK5V0np07vJ1KkDcIIIDA9AQIWJmeJSkhgAACCCCAAAIIIIAAAggggMCMCegata6zLs0n9vXTR/bPv//BL2i/evPOm5GsoIvqClpJ7aTWsA/bu94Atr6qXlk+708X8bVM/enacF93TvbNmk01Jlbt4ODIjk+qfpFcjem6G1XDFOnCd6ej4YpSb5jSXdveIF2p2PzCvC0uqpFqxdbXVu3Bxqptrq/YXDn56CJ6KFiWgc99mnCRXxe29Xd4NLCDoyO/m/bo5MQDAhrNpnnj2mAQGkh1u6qZ3xmuMhRLRSuXFcRQ9gvgC3PzVikV7MHqqm1trtvG2qqtLM5bITY6jhVCbRonJ01792HbXr1+a90s2KiooB5LPSjj2VeP/Ll4ZgCMZ+lS/1OR1fD69t221416ven1R40RSSG1tZUl++rJQ2/MKJcvuOBEjf8de7+zZ6/evvMGJ61z3UW/vDBn//jD9/Zwa9UbK9QYp4YVBalo2Tu7++5+clKzWqNp9UZoyO33QyOVpi8VQuOIGkhWltQgve6BK5sPHnjdWV4uDO+wlut4g5/q70kttaPjqu0dHNj+wYEHrCgwptVsWbfXtX6v7w1RChZSg4Yawebmyv68tDhna2vL9ujRlm1ubNjSYsV7/LgU/PjEWhG5BqdJX7fVWF9r2/bOvh0cHtrxyYkPJdHuKMCj4/nWNqY/NfyqAd8bxOYqtrS44DZan5sb4S7/hYViCKwYX9hF3ufyqiX+/Otre/v+g7XaHd8rKJBrbW3Fvv/2a3v8cMMbtjSdHu2+WbXWtv3DYw8M0b5CwSr1WsOD2tQgqZ4XLB14zzZqzFLQytx8xSqV0IC+UK7Y1199ZQ83H9jaasXK5YtkejSN8uH7rHZqB4dV29s/8AbcvaMT29k78B5W1M2B9p3e01IaekH4+deXdnCwb4sLCgYIrXGh1wG/5z+U3RvDNICQ2frqqj19+ti+/+bZaOG3+JW2JW9gn1oZchVpLE1tp92BttWe7e8f2OFROK6cqL548GDoocPrS7/vgUHqFUTD483PzXtg2fLiku97t7Ye2Nbmhq0shwE2xvcJY4s+/VaVRX+5rKru1Jsd+7e//M1OqnX/rpD2bG1l0Z49eWIvXnztDbD5oDwFBlYbXTs8PrH9/SMfAkZ1X4EirVbL67z2kyqDAk4UsKJglT/+4R9tYX7ByvGom+VDx6t2L7Xf3ryz7d193xeocTy1gT18sG4/fPfCj6EKHIvbXlM9axy3bH//0I6OTuzg4NAUCKD9h+9Dul0PylTAW6lc8n22tj8PnFl/aw/WV21jY9UeP1aQYNl0fMqxZFAh4EBBdc1W16q1qpdZQWDheNr0HkV0TtDT+YAC0woK9JmzpBCOo/PzFVtaWrCNjTV79OiBPdzasGWNg5j783273k/KQG66m3zpayMdWNrvW5IObHlxwbYebPiz6q2CB7TL7qWp1ZttOzg6se2dQ+9lZT4GreQLcIWyxmrcaKa+/tVbSKvV8eWrCV/d22ysr9mTx49scaFsna53reK9loxCnMTtU+dz9enXceET8q+vdDal3ndqtbYdHte8blarVdN5lwJWdDzQQ4HP7qbzL50blRUsoW1ex2kFMpZsaWHBNh9smLb59bU1W1osXvr4rJhd9ahycFy1HW2r+4e2u3do27sHfuxKrez7QfXzpICVWqtvL99tW7XR8IA11U3lzwMj9NIp9WEWrG2pLS8t2jcvntt3Lx6d6aeg1B9//tWPldo+49b27Mkje/b0sT18sOrxMHF/pnMpBZccV/t+TNve2TPtL2vVup+/dvuqc30/JyormLJgtrayYl8/e2oPNx/68VbBlp79bNPS2tZ52E+/vrKDg2NTkGD4fmCLcxXberBuXz97Yquri1aeEJ3t6Q3M1+lf/vaj1Vtt66u+adr+wCqFog+TpcDjr756bHNz6m3xCpt1ro7VGgo02vNzagX1KthHvUdpUL+KAp7XVuz3v/9HD5Dz3Wss+JlrhC8QQACBqwkQsHI1P+ZGAAEEEEAAAQQQQAABBBBAAIEZFtC1WbWbqgOMjZWCfffia+9ufmdn1xrtgaWJwh3UgNSzeqvjF973D47s66dPbO4z72bUMpWmHhpG46TWt5OTugeq6AK5GhfVoKi7qNWY3tKd496QXggXu72HFQV7qAcPBa2ocT00OChoZX1txR5tbtizxw9t88Gara8u2cJCDJGZ4srIXdjWHcfVeteOTxr2/sOube/u2d7hoR3VatbpdoYNJh7IMFAwQ2i0Uf4LKkOhmPVqoSFkCt6biIZserC6Yl89eWTfv3hulaePbUEtBBOKogaOZrNr2zsH9veffvM75lVSddNfMA1R07P1tWXrb2q4gMl373+OjNZhq9vz4JJffntlR0dV75lHd5vqTv1HWxseqPH00ZZdNGBFaTbaHfuwu29//elX63kjaOixZXVpwe8w39xc9ewqLuH4pO11Rubv3ofeLRpN1R3dWa27i/veyOCBThpuQ713qLeWQsEq5UNb+LDrDVMPHz70xranTzbtwcbSMNBJjUVq0FG+tJ73D5u+jt/v7PowDGpEVR1VA2pfDWNanho2PPCg4OtXd+KqUUxbkxpq11aXbG9/34dOePJwy4NBKupV4XNWguY5Y0bFLakR6vi46QEVH3b27f32vgdUqUcYBYioAUoNMWo484ay0FbmQSty0t3P3uPC/JwP9/Boa9O3f/WYoJ5X5ucmVMhLlEOBBTt7h/bzr6/spFYP9bOQeCOzhpfYWl/zu7BVjna3Z9v7x363+vvtA9/GtK9oNlvWafe8twUNx6QeM2JwhIYCUu88CgrTPsPXe6loByc1++rxI3vyeNMebq7b0lLZG+DOoPQSqQ7oT0NiHBw2vNcpBUkpSEUN6rVm2+9o7yoiQdEGXm8S3967/b43WCpYKAw1U/BKpQCB+AiNuQPfbrXtPnn00ObmF25vwEo2bEPGdr1PHoEQ1p7qSr2V2vae6sqefdje9uAK9XLl26oCyjQUlobT0fBd3hAcgrQKiYIeQmCTeoxYWly0x4+27OmTR/bk0aZtri/b4vynhhGbUFRVnmw/oiE2avWO/fbqve3uH/jERevag7VFr5/qyaxcDFFUKpYa/3cPavZhd8eDEj/sHNhRtWGtZugVSb2aqM4r/ThE3sJcxfppYh0NPeRN36M8KSv9xEwxBdva9n57443aHrCSph5guLamAJ0VPzfQcVq907z7cGjv3u/ah+09DwCq1xvWaYfjm/YjYTig0PuZhlpRcJKGWVEgwIf5fQ/IefBg1aq1mve4ouBS7xUhy5rvf5LR0Fq7e3umx44Cjo5PrK6eZDpd0/al3iMSzTBQbwfatsveh4Rv4+r9oVy09fUle3y4Zc/rT+zZoy1bXZy3+bJ6cshOQM7b2EdcN/ZK60nHLdVVBeAtzJXtwfqaPXq4Zc12x9rdmu9btOLVm4iCNd5+2PZyK9gw/GUVbwql0L663uja3sGRHRwe+/FD61yBEDqv2FhftYebGzZfCcdKNe6rTnmghHrFy3o8UlY+iz5XFK/D3rNVx+vTcbVmB4c1291XIKOC0qrWbne8Zwxt3/pPdVTHZw2B5/nyHuhC7xjabrRf9l5i1lbssR8bHtrDrU1bV289lWyoxk846lT1w27V3r7ftd2DI9MxV3W3VmtZy3sRE0JJZwe+5+8nA2t2U3u/qyDn4ywIVDq5gBXPv9cGdysWUg/8Us+C3754dKZlu9239+937eWbd9ZoNPyY7qkOBrayvGRbCljJ9kk6V/B95u6Bvfuwa++3d/2coVlvWbvVsW67qzNJ/yfMsoJaLbXO1sA2H2xaXzuU3PqJTLE3qddvtu3t+23fdsVfSPu2trzgPTI92NywpWUF1WV/Sif+ZZ2maHtXEPKrt9tW97wULBkMrJwktr667EHDqvNbD9ds4awuZ2KaZz6PCtDqmu3sH9qPv7y0n1++Dj1YZUMnltO+ba4u23y57MMQDuvy8MWZC+ALBBBA4EoCBKxciY+ZEUAAAQQQQAABBBBAAAEEEEBg1gW8YdfM1AHG460le/Jw096sLlt7t+09q6jxXT2saMiOo5Oq7R+od4aabT5Y8TsZL1s+XdONd7/XGgN7/W7b3r3btu3tHe/SXnfF+vA/Wq43puv+WTV+qQE48QYcNeKotc0bxbztSd2km5XLuvv8wHa2t213Z8e+ef7M7958vPXA5uc1zMllczth+tE1bf/SG6+PW/bu/Y798vK1N+b5ne/tlrWyYIvQeF7wBlI18IQWhsQKffUWo2FBdNepGlXCQw1xhXRgJ8fH1m42/S5WdbO/sBQboU7nS54aCkh3sqpxpK3b8RWwYrobNLVGo+nBPgqUmfafhjlR7zu6u/vwUEOhaNl9DyRSLzhatje4XWLBCv5Q44TuTB71VpJYq9mwWqPhdwGr4eXopG1v3ryzl69e29u377zXkxCEEeqDxzlly9U60DAAuju30BtYJwl3/qpxS71k7OwfewNNrf6V/e5339qa7vgthxYIsakXl4PDuv3082/226s3trO3773ndLyxWGtAQTqFYaNtaATXLcs9r6Fap2pUVvDK4dGxB7scHJ5Y9cVz+/bb597bhw9nFfObPV/2KVZPNTJrqJ3jast+/e2l/fbytb3b3nVXGemOczUoavpCUvBtyberXPNXUuj58FtqWtOms1sq2O7uvj/+4ftv7Zvnz+3h1vqpRufL5FdqWr4aX49Oau5SLIchWLRt7x4c23fd1IqVEBC2f3jod2q/ebttewfHVlWDeTcEgIXhDrTew/AJMWAl6Zq1s4akUL7QcFqtNmxnd8+e7m7ZP3z3wp4+eWzrawveQH9We/NIJAAAIABJREFUu5PqgdJttfv25u07+8//7U+eh1ojBEh51U90e7d3JeKSCgTytz60WtMs7Xvjs5c8u3Pet3215so5ST2oqmQDm6/MeUOjnKax67rMuvnsaVUMPeKfMPPv4+fTfhZy1vNNtdbx4Zn+8uMv3kiq3oQa9bo3liorChBUo7Xqg2ZTwKPWkwL7vDE1aYcgJwU4FY/8mPdhe8fUS9UffveNPd1as+Wl+cu1usc6qL2jGoebTTuuqveQY89HMe1Z2u/4sVX778Wl0HyrbVi9mvz060v77dUre7e9YyfVhvUGCoQKWfDADT+CpNY11R99nvq+WD135Y97cfXE53qj6cFWe3uHvu3IQ8OjaN/0/KunVvCeXVJ7837bfvzpV3v7LgQFKhhAhgomHTmGWqr9vYJV/HCTDqxhqdVqBTs4KtiH3ZIdHO7b73743r7/9ht7uKWAtFAZdORQT2u7B1V7++69/fbyle3t7dlxrepDAfV8nYWhnpS29lshzE8BCBpqJAYdheCEo5NjOzw5tqOjI6s/f2bPH295jw5LS/O+3nO7uqw2xop61h4gm+zan+Lyw7POwbSOKyXz49K3L154wIj2f368TRKv2woEev/+gwe4rSzP+Xr3YJGr5FckWXa0f9My1bPOkc4zOuphJfRkpQAp9QilISN13EwU2pAFQlpS9GAVT8eHQMuiEC6ar8iRTa8s6RTnqNryeqKe5RQ4eHisQBUNA6gA0tirms77lEAYes3rfZr68JLKT/AJxzftqLTdqPcYDzbZ3bdnT5/Yt8+f2pPNNVtdXhhaxKx7wI7v4MMnOh9U8O6f//WvVq03veciBaoMBhresZItQaEeEvKzBA+i0fB8Ol8LSWnbUQBIyE+2IYXt0zRUnDb8gfcgo2PD6b6DYs7My1irN72uKFgvBis/PDqxRqPlu2UtZhissndo//rXv5t6WNS5nILDBuqKSTsa39n4WvXtfaDTcR/ismfFUinkO1tPcXXJWuecChJSQNHe/lEINutrHzWwQa/rPbZ5D4q+s8jyrgQ0s/6yY66G/FJQ8a+v3/v+SsNF6txWexzV+3anZSuryza3oN7Uls40yVI94ynkXPuhgxMFx7y3n379zYMeFdiob3UOt1A0e/JgzR482LBK3L/GQp+RMh8jgAAC0xAgYGUaiqSBAAIIIIAAAggggAACCCCAAAIzKRAbYGPm5spm6g3jxVdPbHdn1xujdMFZ/3TXuoIIdGetGtw3NlYudVHYGwqytg9dID86btrLNx/sv//r372Led2t3dadk/oyNi7oYrX3PhIagD3wQS+9n3A12KmhSpNrCJqC9QapNVod75lFXcJruBbdof37331vL54/s+VF9WQSS/uZz7n5FYuwvVuzn3556Q8NraDGd/Wm4I01aszTNXe/6K5hbcreuhAazsPysyYJb8CI7R7BSndN96zV6dri0rI3CkzKsabVn3rDGSQavklBGQW/U1ffdXuh2351oa6G2mn9KW2tKb9dvaDmdS2/YGkhNFR6gIbaOTKDiyw3lFtl0VAqSq9ofW/WUbNk4j1saJgoNdgOCiX724+/eDDGhw/bfoet7sLV8kbLDI1UsdEutokomEN3rIfmqoI1O31rH554Q1etUbVuv20//PAP9vjRmjf6HVdTe/v2vf3y20t7+fqdB+c0W2H4mr42DK+CWQOtGlG93VbvQ6O4lpPqs6RgCk1qdgbW7Wloi3emwImTas3+3R//yXsE0p3cqmKfU0+jn3LU7KS2s1/zYUcUYLN7cGDNdisEoSnDxcQb0dSw7CVQXnW3vAevZA3PyofWoSUeRJH21Hhes1q9ZcfVuq8Hb3j+5pE3kl9kHcdplFf9qaxK35sMC+HOc201CkQ5qTat2uxZZ1Dwnh3+/C//4kFF6olF/gqW8qAkBbN5hyaxt4wwbJjS17bmqzoszrdF1Y+jWtN7B6rW63ZweGT/8P039v23L+zrrzYsi1MKcyij2TYft081EqtnHQ1jpNWvXZbqqxruvFkrC0Tx5btu2D+p8c/3YQpI037M0w49Uajh0OuJDxOhPleyO9azJtUs+zP5FNelNy6OuD7Kq7Y/Xx+hqB99P40PDo8a9vNvr+2//8tfbO9Iw4JouBz1rKC64S3UWa8U2sZ8S8v2F3odjw9qUC7YINU+Z2C9at0aLdX5Yzs52rP/+Y8/2DfPv7LlleWLZTmrP3FibW86dingw/OVDcWjuqz6pHqt/Xi3k9r2btV+/Oln+/GXX703Fh1ftJ9V8F2o2wpmCLthlceLlPVqoeFOlpfUW1TIwHA9xYx4FQtlVXn1vXqgUgOyestSzy6NVtd+efXa/suf/mzVas1qtYb3fqZjsO83fOgSJRiOx8pDoSi7EFBaUG8S6cCHrRnouNjqe/CLekU6Pq7Z//Qf/r1tbi57HjXczPudA/vrTz/7MBwHBwceFOipa7/qx7ew7WgDUh8PWp62bwXV6hM1jms1q8cMDW9ydKwemNp2vHdg9W+/th+++8aeP//a5k71uqaSx4eW5jvrqHRDz6GuemFUNq3j1GxhLrHvv/3Wh3pJdI6m77Qe+31T8JGC+hQItb6+bOUr9n41LLiOAVnPNxoKau/g0IMlvDcxBdgVE2+8f7ChQK4FUwcXXjf9+HfODmG4gIu/iGup2ujZf/vT/7DXb9/bwdGxqVc1D1YNsbq+QSj4JGwQYR/r57I6P1Ai3s1Odsz2489wUmv3zPaPalZrduxAvbWcHNngd99a5flXNr9wOmjY9yFx+9a5Vz+1w5OqfdjdMwWzhsC4EGztAdg+TJzylQU2etF1LFamFLAY8qr17q/9XFjTh2CJNO15Xfd0J9F62TLPQsmKpYoVS3OWFLtex3X80dCaypufPhfNqs3UXr/btf/xr/9mr9+996C5rg+r5zsZD+JR0I+TDaQagtiTtO/b+vx8DI4arcdQmhCw4ue4aQiw0+apAe+0LlRkTaftLQy2NprfX2Wuqt/Ly/P2/Xff2e5h1Zq9xHsr0zFTdVA98qnXH52fKRhtfu5rW1sZjp85luj5bxVeXGum9ubdtvdMo97TOhrGaBCCmjTcpYaL+vr5M3v69IktLpZvUTTp+WXnWwQQmH0BAlZmfx2RQwQQQAABBBBAAAEEEEAAAQQQ+EwBb9yK8/ody2ZbG2v27Okj+8tf56zR7ntDiBru1A6gO791Z7h6WlEX9Brm5DJ/ujithjh12/7Lb2/sbz+/srcf9qzV6pga/nUXarz6qwYPNQaUKyVTV9+VSsWHatDyeoO+dTQ0QLfr3b5rSAQ1TOiuVG+vGCRWb2k4mGO/oN3tda3f79vzr5/axtr8ZbL88bThCrs1WgoIOPEheH59+caHsGm02h40o7zrT/mPwRJ6XyqWTL2O6I5+TaPGAzU89NV4mQU3eLnVgJmalSplW1xesdX1dSuVQxDBxxlSY7ku/KshITSy6r0aE71BPN7JK88ptcOpdErfS6kgGB86Ssv2AYiy3nBCo5CCTy77p+CjpFDyxyALWdHC1IvM4cmJvd/ZsVY3tb///ItpGCl1d69gFU0jBzWYFouqNxWbq1SsVCpbt9OxTkdBDn3vtUXuglP+NHSG2mf6DfUq1LPyXNHmFhatPLdoqytl7x7/599e2S+/vva63+qGISlCi08YnqpUGtVTDaWj9ajltdttr3uyUuOKN9R4UI1Z2uzZ7v6R14G5+XkrFL63xw83fGiay5rF6bVe1CvDuw9H9te//+y9/qj3m2ZbDV2hroRVooaxbPipSsXm5+atUq54vtVg3Ww1ra3tRrfVK+hIjcBZw16v07Pt3UNf32pcV68gXz/ZtDmNLXaJP5loe1W+lG8Fafimk2pfM7DDk7odayiF3UP7299/spcvP1ir0zZtz96g5g3L6mVBjdiJlXV3dW4/oTvEO+3Q+4nqhsqrZXoIXlK0Tj+1ar1t7fau7z80dJCGgNlanwtDM4SJQ4myauz59G2pYFpnlUT3xRc8YEVDk6ghUL3XaLgZ3Y0dk1CDl+pjIamo7THLue8kQpu07ys0ddabQiG1xcUlm/N14tm7hOzNTHrelq7vPmNXMLkgYtKfEtXrbMHvdxTc8Zs35msooOb/z957MDeuZFmDByRAgt7Ju/LPdc98MRvx7W5sxP7/iN2I2flmprufq1dGJe8oetAAG+dmJglSlESpJFXV60QFBZe4efOkQ+GevJchOMQQTAM681fGYHoaGPfXdBqem5L5hF5Dgp4KOcP+SyISdR7ymMbnRgthv410coRw2MeLFy+Qz+fG+WutpncyCCsFTVtgAhkXKVwIfryflHGI/akTDJDpRTg5beDtH++k7dMIzj5MQouEkCIrjpwGhjiTEGyKATUcDaWfMjROiqGNUt6UhxWjnLQ0NmYhqKmx1uEc5IRCHK1fNtFqBzg8PsE/fvkdxyfnKuTZYCBel5g55zIvxTzU3ExcOadxXub8rPqAIu3JfCdG6hBDmY+ZVxK+n8WPP/2ETNYTr0n//fMv4tmA4VGCXiBGbfZbEmn4z/ddeK4n7wJeypeQTsyL3j7omU1IFMyHGHOcHZBEMEDUD/DBAVzHgZ/JSEgid2zTNjVj9qwP3agMYF9wT+w415BgQO855VJKCCL54xP0LxvSBqivhE1pd2VOZGjEzHLxTqTia4uoeAvo9IbixYTeVZiXpuOCRvxatYJyuYSMz9BMZlMeb1QXnFw1d++zl3Yr3Z7kJIaU5Puo8rom5FEZm9knFCGExCq+d9HTkOepdwG+fxGvXjCQEGEMO8j5hDhTS5JHSKEIA7anFjAKkE4yDE6EnZ1nKryhKY7Z66GI7S7BdwHODWlFTOOrBkPmkIAsBEeSN6iBemWREIUMFegmIiFasb1TPxk6WGDZ+D7ME4Y4JIk5C9cjwXP+JjhpMiXHMVMHfEemxxfOjUzD99g/Puzj77/8Lv2u0WrJOxLnMg2GjIESSpGeVPi+OpQ3TcGIc1oulyEfWqWnOhSs+VYcn0QX4invhzxLyLu6emdVY9jkYV2eGK68Qu9zhUIar16+RLM3RKvXR7vVkvGLsyzfyY5Oz1A9OEIhl0UuvYZJ/56PUfyq0hHyXsn3+o97hyABnWEI1YuzIsHmshm8eP4cW5ubKBRy5r8rcVH22CJgEbAIPBoCd/tf1qOpYQVbBCwCFgGLgEXAImARsAhYBCwCFgGLgEXAImAReBoEGIqgUikjl8siGHXgiNWOH5hViAGuZG0227IKO4rMivTbdeMHYbpxb3dGePt+H7/89l48rNC7hXh2iLjukt+GI/gkamR9FPI55AtZZLMZpGlQTykLE41SJANwpSjdgTcaLVkBTy8t4rqbK9CjBHrBEGcXDTEi01NLIukildpCNhOnkdyu+1QKhvbgyvezS/z69j1+++M9jk8v0O4FkkwM7hKKZ4S0l0A2mwXdmZMQwD0NfFz9TSsAV5wzzAGNJTTEB90eeiQKdHsY9AMU8nkJmZAvZJBwx3aAWXXUuRgzmIZIq41GChryaaRQBtsrZgGT9E572hImP7XCW2lHa6o6py1WfneSrBLLc4RI3FBowyMJHxEEd7YjkpyODo6ErELzju+5gnWWWPv8+eBx2qdB2kXAEE1BX7yoMGQAQ1u1210hwYgBRRv82r0h9o/PkMkfIullxDD47sMudj/ti8ce1hVXA2c8F7lMVkKDZHV+zIvkKhrIWA9so+12W7wEtLo9nDOMQjCQqDBioA5DdDr0KlTH27fvwRXqNDS6/v0+SQqpjF5/TlsgwebXt+/AcCgBwyOM6HdnJMZrGrLZFouFAjLEKZsVogbbKIkVNP4ylFO31xv3r2arrQzAYjZOCJnt6KSuQ6hEyPppLNfKcO+oOltrJGFyFElLW9bEoHZ2fomPnw5xeVnHuw8f0Wg2hNDBFf05n94j8shksvCJOYltfkrqnqQVjld9GiR7AejNgd4H6CGi3e2J8ZfeiNgnSBDgOHJ0ci7jjIx92U3kUhOL2aRHqfaZ9pJYW67grz+8kpXy4iGGpKeQYYzquLi4RKvVHBtAabLzki42VqooFfLwUyrsUYKCdSchIUjlSGOgMkxXSwXUquX5Hf8e/eqLPjKB8/PVoCxTKY6aV1rtEX57+xG/MGzNwZHytqDHPyZ3Ew5obGSbL7AO/DT8rC/jBA3ZHE+krfQCGRcUYaMt/ZUKs63Q6099MMC73UM4CQ+eX8DzHY41rLE7bDI2qjmAtc4moAgrCQSjCK3uALgM8P7TvvRjkm+MN7FUwhESSi6fRdb3FUErmQQJKhxzgn4gfZ332EZTSWVTZRZTsBE/EnLkuiKv8YTG9U4wxMlFA3/s7mN/fx97+/vo97vSLv10Ahk/I95lsux72YyMIal0WogTJGv1ej0ZQ9gHLi+bEjJuOFQeFTgn0Vg/CgbA+SWS73eRLVVlDDo+PcG79x/FexHHThrHMykP+SzH2SyyGXpPyCCVTsm7APMkeU2IRsEAbXp+umyIx6puEEhZxFsXjfSjIY7O60ICLZRLQjQqJbPi6ELVnEGIZ3eszztU/V2TsppYr+YfG4ufdrC6UsP+UUlCAIp3ChLnQke8yZyenGFjZQnL1eLVsI0y5txBCyHDKlIhvVExzBLfuagVEUs6SeTSGSzVatKvXO2eisRMJiBhQdiOws7gE2aLH5trt+9NLXluQkgyKTeBaNgXRgbpsq6XhJ/2xLMQyYckVPC9kXOCeYeU+ZnvcZ2eeGAi4ensvI7LZkPO2UaV1x5FKD4970o7ZHsvVVdQLtIj0FVdqRt5xZsrVQy+fyEJCAOJNb3+SMJsMZQdPbeomzJzwPdS2FitolbKwU+lFLFSKl7XPoWQlEkvJ06EcDRAoZDFUqWoxsE5UKonSIQe6Z8K00U/U/Ta1xsMGVUT+0fn+P2Pd+KpjiRVEvM4Vso7eMZHNp0Cwz1l0p4QZOgRioSziITM4QBbG0uolPNwyaQymz7kTggrmpijSyNMHc7P/Mf7ego0T8/d81XQ9xxsrtdw2dyQcJnvu20MWFEctyKg3mhjb18RVirlIti/k3fg1EuYqcYAHz7uifc0hvaU/5sIFTxCzk9ja20Zz3c2sbxcQ+qOBN25BbMXLQIWAYvAHRCYM/Xc4Wmb1CJgEbAIWAQsAhYBi4BFwCJgEbAIWAQsAhYBi8A3goDYFWiASADptCfGh3qzJx4h+EGYH5vpnYJGPXpZEY8WC5aNz5L30qar7cMz/NfPb7H76UBWnIIhA2hMj0IhKKSSDtaWynj5YgcvXzxDbakiBgjKkE0fmPN6o4uDwyO8/7iLdx8+SJgBtTKeK9aVp4ZRs4vf3+0ilUqLgXtnqwZGSZCFrEbu7N5kEPsOTxBoWL5sBni/u4///McvEiKpO+CqV4hBgaYumg65GrecS2FnZx3b29tYXV1FqVSSVen8WM/V6NyYjYQYubzE6ekpjo6OcHR4CIZDWK4VsbGxgrSfFAs2CQk0cvPjfXyjTpRjVBY9aSyQi2I2EJqBnM88G5dzn2MlTq2VNccxTe4s0qhHjMRIqyUI+SbpYv/oFEfHZxhyRX1/ICQnklVooGCohO2tTaysrAjhifwmGnlYN5THdtENQtQvW/j3//hf4kKe4QRoNqLRj6VgaIl6q4eP+/TmMUAuk8He3r6EwSJRisLoWGipXMBrhpF5to1arQIasxJJuvHXHAQ6T0g4aLX6OD47x8eDQ/ztl99wdlZHEIzonwDhiA7oQ/SDELu7+xK2an11BX6qeGcvK6xqtne6sydRheGSTs7OpU9F4UiMXE40RCrtoZbPYmtrC3/5y48ol0pC+KBXGOpu9Cfe1HNv/1CIWT//+jua7a4Y3+gZgZ2nNwSOzy7RD7pYW1kSw3XZeDAyjdFU6ExL4OXJb5JY6oqG92AoRLD/7z//jsEgQLvZlAdoF8tl01hZquLZzg62NzdRq9WQ8lNSVmP8kt7FfhGS3AR83P2Ev//8s4wTo7AvxDaSVpS1zEWrG+Do9Bwf9/axslRDqpiBl7zakqlzIefihzeq7iU0CokvowjtIMR///wW//jlNzSbDSkfFXCTCRRzPv7y/Su8frGDWrmo+jAt92K2U+2TZySYsd2wnUl90NvQDHb2dIIAWw5Dybz/eIC//eM3kNxBDwIk6QmWDEDhANl0Gs+2NvH69UuZV9gPXEZzkNBYamxg26MHhIt6G7/++lY8tewdHivPXdJEEwgTHi6aPbz/dISk66NaWUbSzYoHgIlW8aPrak+NNyql8rgwhOpTjU4gIavefdjD/tGJeHhhiDU3kUAu62N9dQk7WxvY2FjDcq0mpBtOJ9Sf3k1azZZ49xFvQxyTYuoYbTgXyHU9NtJ4zA7GsZD4HZ1d4P/59/+FXq+Dbqcj/rtSqQSqlQK2Ntbx3evXqFVrQnTzPAdJl2FNaJyP5Ec9Dg8P8fsffwiR6LLZFcIPQ2eRPEoPV63eQMiBib//KkROEsrOL+i9YyCjMY3g1UIOz5/t4MXzHWxvbIhnFZJzZPpM8L3ChIFzcHHRxG9v/8Bvv/+B/cMjDEKGMlR1yz7VDgY4uSAJbh8ry0vIZNJIjy3aRMagEwPsix+S+BEhdEg1IOlAhfvbWF/Bp4NDvH33UZECSEccReh1B6hfNIR0xRBks7HaVC0vXiimFyIkQ91cNuTX6XaV5xuGKEqlUStXsFytCemInjb4jMzfEn6HLYstTeHLe5+7UVI65eDFsx28ffsWJ8fHbPjihU6IHLUqNtbWpN1Uy2VFSnMZPkoRKPi8vA8M+e6l+vwvv73F33/5Fe8+fhJPguKpR8L7JdGPIpw32tg/ucD6WV3IU64b71W6RCRVpBz85YeX+P71M8GB3gn5a3YG+P3dRzCMUbd3Im8cYTSUCSqfyeG7F9v4lx9fo1LKiTCl42T+4bnalMcitn96BLuihU4odaDfCem4kD+Wn1NObzhAp89fhL/98is+fNxFt9uVeYdjTNpzdb/bwub6KlaXq6hyzqIHJYb3Gg7FY12n3RbiH4m2HEvnbaq+SdlUnlbUOT22KWIzvfMIaWXew7zGB3SZOB8Wsw6eb62h22nj9OQIly22RaG+oBeMsH94gnTKxdJSBZ63hkKWL4HTcuZlxWz47rR/dIyff/0NJyfnQmLk/E3PU76bwHKliL989wobqyvI59I3//9hXib2mkXAImAR+EwELGHlMwG0j1sELAIWAYuARcAiYBGwCFgELAIWAYuARcAi8G0hwI/aXJFarVZxdNaAI275GU6GYU7oDWQkHgtarRYKWXqUmHxKv66k/BjcH0Y4u2jiP//7Hzg9b4i3lUTSUwwQfiRPuqiU8njz6pmsYFxboTGMK+AZRueqZEVCALxKFvnsM6yuLEnIHxpw/ni3h0azJ8YdflWm0arZ6oqbb1kdnsuhUmSYoRt0n3OLH/v7A4ZbOcG73T2c1VkOGpJoCIiQEJfyEarlAl7urOEv379EtVIWQ45a5cvQC8qwKOKVXRph5CKbrgoJ4sX2uni3YCgZPlMoFGRFNfPgd/dZAwWvz93GhsiZgvCBmUtzn7/lIkUoMZEixkj6iTbmvqywvmN+NCaNf9rWQMsFjaHtbqBwiEIkowjFXBYbayv48fs3WF9bRalYQDpFgyYN/8pII6ppjxo08qS8Iv73//lvqFTL+Ns/fsb+4Rm4clgRVmhASUo4GnoZ8RIOet2e3KfBJuW5eP3iGV6/eo7nWxsoFejhw5W8aLA1HAiTt1vgyu4VFMtFpDMZCSH1afcQne5A9yl65hgJmCen5zg6PpEyJD218viWahjfplGx0QrFOLy7dwiGEGFfpTcSYkcimuck8ObFDn747js8e7aDYkGtjDYkFWnIlKir0c+4SG1volgoolqtSWiQg6NTtHt9IawIqWUQSt/6r7/9jLTnIZ9/KYQeaRyT5jDWc7EDZV1jfVw2m7Kam+2BK+eXqyU8297Em1fPsVStiNeMVNyNhDGN6rw5TpA8xlXRhRxDgawIYe744lIM6Aw/xXBhNFDSC8vJ6RmOTk6QSa2hVEgJFqxTbqY4PCWsKZehJ9TNQVIZ5RgeSVgy0pqUMY5pSVohGc93HWRSysMTk8qRls885HDyR2Ucu68ufF1/qZ7BZmHN+MDnlEs/y1XxDN2g5pVLCQ1BA7EYGhGKN5JauYQfXr/Ci+dqnshnE2LAZf4cjxMkW7AMkSJ9+akcspkfUa1VhARDgkOr08OIIS2YxqEBuo/dvSP8+tsf+P67l1heyi5UdIOTCvuhHlHXHPQZBuuyJYQKzq9sh/QewjGU3qK2N9bw6gWJG6sy7mT9FFyPIX9Ue6L+qaQH3y0KDYrjFfv97EboJtCrI5lPmVCHzaLnIYZfG/Z7INEt63vSh757/Rwvn++gmC8g5SaFWKcjmNBJDEKP4dVouU/DT22gXMpjfW0T//6//oFPB0cS+m7AUFkSFiWSseTj3oGUUUKzDIZIOCHy+Qw2Vpbxw3evsbG2imqphFzWGxPqqKrwvXTnZL1kvCJymZ+wVKvi7z//gncf98SgLZWcZFiWUBHTTs5AomK1UkTaJ0FgGpFZvL7YuZ5MlEGegwUJK0rbWqUg5aTXmX5fefEgBnwHabW6uKDHkHoT/kpxSn0zlk1dvOVkEALN1gBHJ6eg1wmShBgyhnNczvexVKmgXCzKPEgkpS/FRgSO3WqE4Lw+IZDeku3c26bdki+Sz3rYWF1G0KNnvRE2N9exvr4qnldy9A6S8aWNmrmYKpnneRSxb9BpXwL44fULVColrG9s4P/99/9Au9sfh6oj4UaFQ2rh5OwCy7UKOEbEhE101fMNPc1w/mIi4jEcudKu1ZnE9RIiBOcLemUhOTLtOuJFRD016aRj71vyTqfCDQrJY07fnijCIxXKj2qIDIYA9Fx5ZyU5879//hXvP3zE5eWlePui5jCgAAAgAElEQVRLOi6WqmU8397Cdy+fice0XIb9mGG4FJFS/T/ARRimUC3SQxHDq111Y8IySDuQ0FF6Puc7mJBiqZAiDSmqyaSs0/pfvc4iF/NpbG+s4M3rF/jl7QdcNlpCgGMr6wZ9mQ9IQBIPVB49oejwRleETy4I+fb0HO/e7+Lg8Fi9G6jZWeZuklhf7Gzj2dYG8ll/7rg6kWaPLAIWAYvA4yAw53PI42RkpVoELAIWAYuARcAiYBGwCFgELAIWAYuARcAiYBH4KhDQhJVarYr0BxqSmvJxWYgHNDIxZEgQSLgQugany/WbNn5EpiHlvN7Dhw+74umArtgZesR8mOeKyOVKWQxhP37/CqtLNEQrwsF1srUtB2l+7HcT8NMF+D7dv6fhuin89nYXrXZHwu3Q4EMPCOcXF/j4ycPqShWeu4ay66uV2tdlMnM9GAIHx2182DvA0fGpCotEswTDGdGTghNhdamG1y+28SNXYq5x9XtaSAOmrCKSJ2ZzlPHbS9ENewphNoVKqSjGUxp66MHhqjnAPDzZi3yRyz/GQDS5/9hHimCirb7x8l21OSykihi5WAzZFIGF5RoxLpATiuGyXCni5c42Xr14JoaEXC4tJIIpVo+RoWwmIEeJBt61lQIiPJeQEs1mB81WIG2EedD8OxyMEDLslLjgJ/EjISGqXjzbwnevX2J7cx2Voi+GJs1ZEE1NuzSGQZKtcm4CbiqL8NkOBv0hep0AH/eOxKgstjw+GQKnZ3WcnF7gzStV6rv87fUinJ6diweR07MzCcshhmy9QriQz2Jncx3/+tMP2N7eRLmUFaPLVFWJEUnnSpjZtzwHXqqAtP8aJHckXQ/vd/ck5Ad1Dx32rVD6A0OxkBCyspxXTfAuBbiSVhE/Bv2eGPRINtlYXRLPS8+217G6XEM6lRyTY0jYMRuNWgZ/XuPYVaQx0K8hlUlDPFn8989itB6F9MahvBf0gh7OLs5Rr18gWKmINXNKjslAhGqPStpoyJ0yjGpETbuTZzRBhd5TkopEMIV7XK49XhgBQnxy3sSHT3viUaPT64q3KhpBk4kInpfA2toSfvzuDV4+20GtUkIuoyz+rK+xIVgC8qiBin2ZJDW3moLrbomBl54tPnw6BMcJ9oEQoXhfIunh46c9Wc1fLGWRvsbLwNwC6YZlmgn3JM0xhBfD3NDjQdDtiKE4n83IePP65XO82NlCqZjRZCmls0TFYIPiXBI5cF0VckomWGZ+TWPTjyjDNjuzScf+QHJq0EcSIUqFnBBlvn/DcW8NlXJOjR1U2hRAF5JzVVJbVLysh7RXQyZbwsn5JZqdtvQ5eZdwOKar8CIME6imrBBJJ0SlXMDO9ga+l3F2A8VcBil6cdF9zeAZnxdJCvPSDtxkGonEJsJRiHYnQDA8RqfXl9AezIPYMvRLo9GU8DnFQk7mWJFpym8y+ML7KXU0zmw2vM6wQLVKGetrK/i0dyLkRBmPEaFF4t3ZBY5Pz7G8RM8Y9ysIsyTpoN+PcFFvCJmPYRdVU2EfSwixiO9TxWJO6ujGnKQM7J2ft7H8rO9M2sHrV8+k/1Eiw7kVi3khV5HQMoUfE8xccGKWv1IpCTe9hCjhCmHs8PhEyE1UluQyehVptdo4P79At9PFsJibS6RWeVz1GiJzsZlMNCFXVJKQODq9CY8zq6eBS7/DOPGBy9yb2VOE/Ai2AK6vOEnxbHh0dIKg20X9oo7RoC9E3HIxh7/88Ebep+gxLZtWfY5Yy2ZE0B+deCaK98AZBeac6sev1MOcpDdeonedWrWENy9foNUJEA73pG44rvD/J41WG+/e74kHqEzGx8pycbbqx/KFTBQBF40edvcOsPuJ7zaBEKtIOeKYw/Fna2MNz3e2ZA7hu/q1AseS7YFFwCJgEXh4BGLT1sMLtxItAhYBi4BFwCJgEbAIWAQsAhYBi4BFwCJgEbAIfG0I8Ju653kolyvipp8fgdVPGYH5Qbg/6KPfD2RV623681s5PwofH5/ij3fvUa83ENLhPw0B9JKRcFDKZ8U7yk8/fIet9eK01xYjgBmNv3hP5ypGxiTEa4rnbsJL+eh0+/i0f4DLRlu8dfDZTreHk7NTfPy0i3IxK6FF+PF7kU2Fk4nwcf8IDBNRb7bB8AYMt8JyyIrfTApvnm/iLz+8xvNnG6KuGLEXyUAXjx/Ix4Y5vRp5oceNMSSWWOwUsfNHP2SlcvvMjEWMIU9Qlv4pY51at0yS0+bGKr7//hVePNtGzr+mHnlZlvoqgCmD4aBYX6tLRQwGz/Fp9wCDwQmG7Z5aUxsxfABd1jMvRazK+GnQ68+//PVHbG2sophT7vgp3hSbBA+jhbRvfUJ7YcoFlioZbK2vo15v4fDkHP1+Xwgf8pQYTVo4rzfFkwxXos+p0mursNnu4eDoRMJjNZotDEdDceFPLx5eMilhbv7tf/yrkKmyPt38zBE1a4fR+tMjSGrJQ+S8Qn8wFFLHoN4Xwz0LHDkO2t2ehGPZOzhErfp60obnZLPwJXoUQIR0Ki2rvX/6/o2EYapVMsoIyzKMAdf1O0c4xwf2Q4YYWV7OY+S8wqfDI7TbbbQ6HQVFIpJwKs1mE41WE72gixCFKe5TXDSznSIq6XZAaooY5GOJaaJVXgZiF/9pD02FfR4A7NLBCNg/PBbSSKPdwjAklUQNFolEhFIph+fPN/HXv/6ASjGloqOYdj/b1rU6xrZP7km5mICfXRfPO/S40G53ZRKkxwV65OmOAjHi0yvP0lIZ6aoK5zGvZEorfUcbns24ptqwCkfG9thq65A+JMkVstheX8WP378Wz0LVSlqSs1+bti+IGliN7HlKzF5Tjg70+KUEsr/J4KCJbhnfw/rKEv63f/2rkFWyJPxMkqhjk6cBT+djxr1qycWznU2c1+s4r1/IuDRinxhJz9Ch6+joIhLy6epKTTwoff/dKyEl0CxuijdbhKlz8W5BA3MSz3e2cXB8istWB93eufQ/jqdhGElYw2arLe8Dw2EIL6UVj2E6JfcLn7Ds8otxijj2kNjKch4dnUk4FKrJInSDAGcXlzg9u0Cv/0zep5h+IQxnysp+1ukFODu/wMXFJbr0rqXHunTKEw86y0sVZDIJkMOgm8aMlIc/ZVlIpNjeqMmPOSxcPio58wDfubJZR3kYebYtbaMTqNBxigjBMHV9NC4vwZBIDOM4z7OIlnxlJ01LT+hmLpi8V1+dMwTIeQUSQVq8KcdMWcyp8uClz/gu6SQkbFe/P8J5v4F6vU63QzIvlvI5CVf3Lz99h/VVFZJQsjd58ITH8fy1GjftzOM3pbnrPXnXzqWwtbUunvBI7ut02lo5CCHn5OwS7z58QrFYQL5YQG6OJ0jqRv5zJ1BeE0lYYRhFtiSOgxyPfC+JleWaCsG2tiKkuMUb2l1LZtNbBCwCFoGbEZh5zbo5sb1rEbAIWAQsAhYBi4BFwCJgEbAIWAQsAhYBi4BF4FtHgN+lXddBLpdDMpmU1aUMLcCNRngSVoaDoRh3Q4YyuWWTb9wRZMXvp/1D+d5NoxE/CLtuQrwkrK8ti3eVrc2SrCgXggEtJcyT3tPNR/J5H/B1/jTI0LV6IZfExtoy3rx5jmqlhKRLQ4qrQjlEkRhf6Oml3migPxyIPlrEtTvqMBwCvWCI47NzWcHJFbfqgz69fUTIZtLyUfuH719ja3NdCBE0gsRVNsSGazOac0NsHPGv/vHjOenlklnBe58Mr5P5RNdV8VTYAMMEoWGHK5QZzod4em4SpWIeL1+oEB8kHTEcz6IbMVVygEq5iDevX0p4HFpj2NbFHwYV0TLZVsvFPOhdZWWpgnwuCbPKjRAbmMUoaIy3ur8YnWh09V2gWi5hbW0V9GCUonci7SafpI+gPwC9D/UYhmvB8lDNYQjUL5s4PDoWgzBXCI9GQ4yGAzHqUfftjXW8ebUDIasYpfiwAlxdiR/zCnXgL5JoH6hVXKytLqNWLYs3IxrsZGxwHAxHwPHpGT58/CRhswTgeOM3eS661+G1PBrtcxmsry7jX/7yPa6QVTRONF5ylGKWs9lqO6HkzNAG5WIG21ubqFbLinQiCVSbIxGB4VhIZun3BzdqG5drEooRUmugvA4pskoYhTJ2cvyUNmMemN0rxzLT9TKb5ls9Nx4ErtTQ3QvEmafdHUr4h+PTU/SHQyGrcLz3vKSMETtbm7IqvlzSZBVmw8Yx285nKkQ3eWlPDOH0w3cvhXCRydCbmPIQIcZfOOLN4vD4GCcnp3cqBNvJPBjYZjivMoSU73tYWarirz/9gJfPt1Epp8dtnJlxXpKyzLNgzPbt67Qzg5fcVx69xAuQS08tjnis2FxfxYvn6+A4GxF4yqb6nN9i490srjJ3M0wagGc763j2fEfmY6MaMaCXDvk5DtwE+3oWa8tLWKlVxcOD6dNj9fkwy23KPr4xeVdgHy8WSGhcl7A5fI+RcZ1jLR8NI7RbHQm9NxgMJxJmB47JnSc/Mhgx43gVmWOqWi7n8Gx7C346pbw7sT4Y4iwkOTdAo0myzlDO71MA6sAhsNFsy9xCwhbDKiVkPgaKhZzMn/lcRpHBdCZGx/vkeddniIP5TT1r2snURX1iHuC75RAIhwpjztGFgoPv3rzG0lJVvI7wCfHAxVBg4rWnC3qZ4Rx9l02y1GEJqRo3mSs0IVxhSqKsuan3sztzXwTcknbqNolmkRBtWA72AbUfIZNOYWdzDf/3//V/CFmF79HxbKZUMDdMIWba51RansTSXbl3zwtUgSGUinkPO1vr2FxfA9sglZa2R2IOEviwuyekFXquCwYA/7siY2YsX3b/eqOH335/h8PDY5DARu+PfAFzE468e7x6vi0erkoFX5XnEcoUU8keWgQsAhaBaxGY97p3bWJ7wyJgEbAIWAQsAhYBi4BFwCJgEbAIWAQsAhYBi8CfAQESLdLptHxQN6tBTbl4PgpHGNFAONeqzq+5EyILDdkfd+s4OTmTEDrigUC+uNC7SohapYDtzVXxXpF2gUR8ObW4Y1dGMZP/dXvzoV+5igeWq2UxrOdzWTExMiwRf1xdenZRlxAml/XmQsQAGuZ6vZF80D49PZWQSIbUwKKkPA/VShk//fgDarWaIt3MUdToOOfW/EvGOBC/O+9a/D6PtRFkYv2YTfAw50oVGgEnX/B5ND5fRNd5quiV5JMVwsoqxfZGQ24+mxWPIXRbTzID2yvJItduvDfnPttK1nextbmBQiEvYX+U8VSRVmjUITkrm/GxVK2I8Tjre2I0ZjlFrPbeMVu3PJ+9loiAnJ+QtlIqleC6nghhyIFEIintsxcEEhaEHlIW2ajHZWuEk9NznJ3XxQMKjTY0hhETkqmebW/g1ctnSHtzQIhfkgKZgl3FjIasJQnd9UyMXIagQ/rZKHLQ6w/RaLUkNFGgFuIvUgTxfjA7zogq4rkowlK1JCFCMj5DEWiRRu8Fv96a5DI+ZBxsrq2hVqkImY3Y06MNQ4fRqNUlaagbiHFvoQLEE5mM4td091B1ojznzEsmj5h2c22CuOCv45iqPqW65Cr0ggif9o9wyhAdAT0VEQsVZiaZdMSQ/vL5DjZWV8bksjFas21GOuv4rvRvJjHlKuYdbK4vY3N9RcKRSTvhLBdRjz4u6pcSbo5zy6Ib+yjbvPzTBEMaSmkc54jKkEYk09GLFAkj+Zw3RQqgcpyXrgXeKL+oQjpdxFA9jtKDrJCNjVW8ePlMvGdIXszT4Mc82B/NNamDSYaE1ZA22XcZwmZ1ZRWeR+KPUp4EC1Nmn/NoqYTV5SUh9hnRE4mxSuHFOfmZPD1XkRHpYSHluZosxvYBjEhY6XbFWwgN1F/9Ni6nvHWIukSPYYFI3Fxl+JYMjemqfGHkSNnoZeXg6Bid7uLtMo4Fh63+MEKj1cHhEcMOKZKuVHsCKJUKqFUrQlxJcGDV2+y8Z64/6Z7qmHZ6Xca6D4kXPK0+H8mkPWSzvoS7ZNvkewDbDV93hazdJxl0pMgR18mOXSeOHDNir0mxu/rwvqBNYL8qc94VIc2QFKfaPQmh2xtr+OHNC+RzKSF9Tz0Wl3/N8U2qz94jFuO5XuQpEtnN4ExpJCd8lKSV5VoJL19sC8kok8nCSbriSWYofbyP3b1D/PrbezQaPQyH9Jo3kdUfMgxjB3//+Xfs7R+h2eooXMQrXQK1clGI9DtbGygWslfGm4kke2QRsAhYBJ4GgdumtafRwuZiEbAIWAQsAhYBi4BFwCJgEbAIWAQsAhYBi4BF4AkRcBIOUilPeU+I5Wu+9YajEPQwYj56T5LI5+jxskqeDUcRdvf2hSTClb/0JMGNu5SXxPJSSYgApaKvPgjzIbPxmMnNz1y/Yc+kXtIRV/VLtYq4BJfMJF+uPo7EmHNweCzuvweDWwwJzCsEut0+zs8vcHl5iT6t8criKPusn5aV3Ovra2LoEEPiDTre6dYdyj4td46b+ekED3JG9RQUmqYSM8wIOYkJ7rOZ8Afa04axGCWdBPKZDJZrNZSLBVn1f2sW8QSx9sXL9CKQL+SQyaSFvDBOynTa20Uhn5eQNCRXpb2k2GiZzvzuUjySRrK+D9/3J/2LbZPhpSJgMBih2+2BfWyRjWpeNts4rzeELML2zY0eaWiopUFzY21VjMBC6tHlulX2GAidMlL2v2LBw+bGmhhImYeqbgcjOOgPR2i1uzg5OwOJN4tss9nEn6FL/rTnCmGFYUlo/J7eTGFUmafvzT9jfuKJKZ9FNpuF67oKe9IEQnrqicSzyqA/EKPkfCl3u6qHPPUQyWTxx+epPpUgnvgrOp6n9xOpxyZOb1cHhye4uGwi4DJ5rq5n+3AA30+L5wmSVYo5kiMW2UxbUmlZBRRHkhl5XiRNbawvSZgwRepQhtbBaCQhg+iVh6Ep5nI452av5x12IBlqItALD10AMLRKLpsWb05rK8viEYjG2cfeIon2E6mwSk6EVNrF8nINq6vLkyn0Lm2TaXV6qs+waqVSUbyASLFNXwgj8a6Sy2ZknFquVpDPMijTDds8PUx+QtQDGLKG3kckdIuQgzikc8xyJLwLPSiZ8fKGnL6CW2xrVwtMjkgu62NjfQ2FfHZsjGfaYDASzyjsI91ucCNX4roCcgZqdQOZW0jK4tzEjeO+76dQLhfkl/ETU2Sqq5pel8OC103XjO8XfPTGZLH2wnRyqt9L2Vb5DswWo0hl6j1nyHff0UjmiYVBZVfX/fxGfR79ZuydUL9fFYt57GytCanVn0doNcDcQzdpBzc0hsn74QLv4DP5k8zM8TmXcYTs/vz5MywtLSOV9kHCFlsqSSvnFw183N2XuaLTGU48rERAszXC/sExfv/9PeqXLfT76j693eRlLKrhxc4WVmplITGpBjKjiD21CFgELAJPiMCV/wY9Yd42K4uARcAiYBGwCFgELAIWAYuARcAiYBGwCFgELAJfBAEaWGnIVyGBaOChMU3cNsg3+tGIHlZGiLRxfKKksQAo6xpt7rQlHh2foNFoKRmSOBKjHI0edL1eLudltbC+pRZbGlET4Qsf0bBdzCkSSaXMMEMmdElCr750cHh0juMTeqRQq2evFS5eWSIxSjZbbdn3B4PJitkoQj6Xw+rSEgo0gCdmDNLXCn7cG8KBYBY3GAw+R4N5YpXHgFmp81LOppmcK/OtfkYbVYwXAjYM1m02mxGCUDqdWrx4FBn/6SzlEtu758H1XGWcCrl6OhTjNMNjlEtF8YpSzOeRcj+jfrU3FoY2kjbJzNmvtFcFqkSj9ZChqhY0cNGo2Gi1UW/SYN4b90l+1CRhZW1lRVbBM2TVlY3537bF0vAwk6anBBqUs/ASrrgtkiIIOS1Eu9fD6dmFeJ5QdXlbBtfdV6uhaZCtVMooVwqTtmx0mmKCTKr3Oonx60kSetwkUq4rpAQjkmPacDjCYBhKyIT7l0G3ZBFspMc1sMf3RYCkkCDo4/ysjlarI+GvzHhHYzrH49evX6FULIyN+LfnxTqarie5Qi4ZgFIhh5VlkqZ4xrqlJxIH9IfCcESdXiBzw2hRxspUw1LyyIykRHo9qJSKIFlleakC7xbuxu1lWzyFDDsRx1mG58mjXC4jn9MB0ITQokq/uMQJqslEEhk/Ow7nIyOpxsFzXRRyOaytroi3K4H5LpkwrVSYekjEJiJw/FakNHWfZFz+BsORIh6YcZbPfnWbaRcxuorGy6hNLytbm2soleghjMQW9Y9e8OhF5vjkTLXLezhZoXe8i4sGTk7O0Wp3MJB5KVShogpZ8QBUyOdAbzY6auTjIGjqNb5/yJxidc95kxxGEp0UYUVnJOFmlGcSkknlfXhBHaTKNGFqwUc+O5m0nHF/1e3IicTjGr2uOU4EN+lIqD0SUKuV/GSs1G3ss5V4ZAGsNtdR7/oMK7qzvYFiMQuHLENxz+eg0+vh6OQUv797j7PLBnpDEvIA8tRPzi/w4dMe9g4PEfQVCd2JIqSSCdQqJfFspcINZcFXnfH4Emsvj1xEK94iYBGwCEwhYAkrU3DYE4uARcAiYBGwCFgELAIWAYuARcAiYBGwCFgE/uwIyKdt/hGnD/yjVpJLGISIoQLUSnBFTpj3ZdtYFZQMfge+vGyi2+tpJxm0nITwvATyeR9LXL2YSU0+lsceH2N9xw/E/KDjewwLVMXqyjJyuayI4ofqyGHolQTa3QE63QFGjOswbxMgJmVotdpoNFuyalNWaWtyAVdjlopFMWZmfFfclN9R3Xm5f9a1GTv+Z8n6Ug9PNQNZoay9DyQSsqq8Vq0KOeiz9ZMmnoCXSsNLpcTcNwrpPWioSSMRMr4PhpZiOKDPrVsa9hjWiJ5DSG5iW6WRhGZg1huJMv1+X/a3reDmUyRc1RstXDaaYjSnKIYWkZBdwyFI2CLp46p3kvshJyF1Ug6K+QL8lI+EQ3KaGidGUYSgP8DFZUP2xqi6SE7zCDrEulIqCWEonZpBfnzKg/HJIllJahKGaJT0GfqM2I/JUZAxYTQiYWUhcSqRgkBrolaMMwTQPN2uGXHukNm3kNQMoA+va78fot3uiacs1hPENQjDOZFEORIyGD0w+SlNtKAKHPzN7w4VYIwDvu+Ity4SVtSMSCN9QigmbPM06JPQSA8Mt27SPEiUnPw4OZKkQSOyeBWqlMGQZ5VK6bZh4NbsFk0gfYCeC6IEvKSHpdoS8tncld61UM2aRLpfUXYqmUYhV4CbZFg14pgAw6GxnyQ5JqY9GWdJXjFbTIy5NKdLTVcoa4BdN+EmkMn4Mv+zrpiP+iXEQ4YJ9TIR/BUdmQGJKmkQ4pd4jecki6yvrWB5qSpeeRCx5AzbGAppkOEPGY6x0VjM45VBgGN30AtxcnqG45NTDAYkUaoO5LoJyY9h8khc5BYf6dRofLcx2eT75PsZNTlkc472OD+7JLCq8HrSTscexVSJF33Pkjl5uok+ajFNVmavKodnqmdI+Vx6yfHww/evsbq6NNFH3rWEQzu59lBHeo58EHGUpb1gpT2gWs7i9asdbG+RvJWT0G2jaIRhOEKj3cLPv/2O95/2cN4IEIyA88uhkFjeffggfYVjMccgP+WhkMtia4NeZzbFs9Y4DOGDKG6FWAQsAhaB+yMweTu6vwz7pEXAImARsAhYBCwCFgGLgEXAImARsAhYBCwCFoFvBgF+BDYf4vmJmwYeMcyJFwgafIAEjUw0AF0xZU0XsxdEuKg3xSW9uN7nw8bLBA3Gfgp0SU7X/Ve2GUPClfv6gvkoP5Wcxhy6y8+kZZV9oZBHu9PBKOiDTmGSCU88v3Q6fYlbX8wVrhr0KZDCx2FaArTbXWWQJCYsPb19pFIoF4vyo33UYCfqGeWuU95cn1LeXLzbftrgr7/mU5lFdbhbdo+W+jooxNBIY6oDpD0PXNl9ZVW3Ket1Qq7RmivuPfGw4glhhaDRUwPJGaxpeioRTxzGen2NnEUuU0XKpkwvmUTScTCkVwZtxKaHlQHDbS3A9qBtvNdn2IaehEXhI8Qp6TBskYM022apJOGHptolFZ2DkYFvfJtp4hf1Y6wDGrJTHj3cJMRyKsZ342Wl0xMPBlKEOfkYnCaiJ0fmnujgAIVCTkglTHGDqPhj1x7Hi0MPEiQG8EfDr9qUQZt66yq5c558luKMSNH5cxU36n0ze8VSeKxiB8EAl/UGer0AIQmHMicJ7QgkO5BgRpIiQ+vIputE+kC8ESyApzgRczhnkBzgylwV0PuO8FKU97HBKESvPxDC2LUEyNm8TAPhdR6L1wN6d0iC3pCqpYIYTlOzRK1ZOQ987kTsAyTPOKiWy4IlITOwxdVm1nPreDYRqygiGceFn86AYd2EZKQKLjJI4qNHCz+TRoID75xNYJpzXV1Sd/nX/GiAptcskg9kKtQDEsd7jlccY6fnzWuFf8EbMe8qMS0IoamTjA9UKyXxeNLudoSQSxQ4rzRbLZxdXKDRrKFaTk+/n8TkzR4Sqm4vQP2igUajDYdWe/aDpIOMn0KlVEBRxubpuiL2ZjP1YM6v3U+LuDbZU91gW0nSKx87vehm3nQVVU2aEZX5yvSO42PqQcKXjXuEHggxAklHxXwWy8tV5HMZ9ajpHw9crmlx02eSsVE2XoCbjlkMeddRiSiR4YzWV2u4bGyAfaAT9DAIGOInxDAEmt0u/vjwEX42hyj5DG//+AO7ewfinY5DOevcdRQZenN9GTtbG1iuVZC01uGbasLeswhYBJ4YATskPTHgNjuLgEXAImARsAhYBCwCFgGLgEXAImARsAj80yPAVcBf8Eu4MTLQYBt3fS7GPq5kp370DKFXLGub05Vqo5xeb4Sz8zMEA3qM0B4kpGyOrLLOZrISvmG8otpkPueb9pUM9IW5SfXFVArIZbPIZTNgGBbiSof5NOqTGNDpddBoXGJtOS8G/it5kO/B8BPDIdq9AK1uF/QiId/XiYEDZLIZ5PMZZEV0EaUAACAASURBVLNzjEFzlbuSy2ddMFmospkziowff1YWcx8WDMQ4qPJRZjUeqzvKC81D6KBIBJDV+KGsgk2lPWSyKSEMjZWbZD2+dJcDhr9im5ZNVuIrYgk9KtDzioSVuIvAOWlpZJEuRDJMMiEGMbZF1pW0S6JHQ2pIT0ZzBMQvRcBoyFXwA/S6PQwHI0wAiYRQ5no+vHQGwyiBTj9S3ly0jCnx42pimBNlXBatdCJjMg0dtUa73QcSKU88GCi2h0movFz0gx5CxpOYyiSu/PSxyB/joJou2zPrI5v1kUq5Co+xntPP3+VMoa1W0SdIGHKVIVaNY4qQNzZ5L6i/5K94E6r9xytPxvMbNLxPmajXfZ67QY1v6RY9EDVaLfT6fTHKm7GGTSiVTiPt+2KkbPeU1xKWjeQEkjCYZl61jiEd16NKJ8SlBEBezHAQIuPn0A7ofUd7YBJykwoj1e8PxvPDbPXwfDpfc8Y7VEoNDipsThrFQl4Indp/0aR64o9Nrj7ckbC3FG75fBbp9MQ8wtHRZM8MZ8t4rRI6ofLskJT5l5JY5JBh3jSJz/NIYqU3KMM0UnnE87yah7k70YZH1JWkNBlnmTEvMin3Mo5pT3FTJboq/UmumCJw8DXb+NhR3B7Wi7ltyqHLSa88tWoZy0s1HB2foj9U7ygkCbe7PVzUL9FoNTEY1jCPG2yyNHuK70fAWb2JC3rH6wby7sRZimHUCrkMapUi8jkfHmOyGL2MANlTimYWTGFsEpv91EOLnxjMFhAjSdnWtDqyF8KZFsLrMkYAQxJ1+hEGgiHf8UhN1s1mPHgwtX52cY2fPuUYG1UCNiHROoqEfFcqFJDL5iQEmVJOPzB+7vNVluEkNlZQ9Bg5PRZ/FpqauEKOWzGTwObaKlrNDur1hvx6AcN3AoMQ2Ds6RSqziwES+OXXtzg6PRNPcHxvSiBCJu1iuVLEi+dbEiopl/Ovadufj4uVYBGwCFgE7oPA5I3sPk/bZywCFgGLgEXAImARsAhYBCwCFgGLgEXAImARsAjcEQH5tCy8EGOov6OAz0zOj8c00jG0Ad3ADwdDRLIgWn1WFk8XiYQYk2kAvGkLBgNZ3dsfDMQwJYZFGg5Goay25sfyTCYztRJ+bB24SfCC97gamKu2UymGHKIJXNAFo9iTGNDrdVG/rGM0WgcibbiekU27kSKs9NDudpVdkR/daW1zAD/tiWHRcx/A/cZM3gufigUgXj79ZMzGtbCsOySUbImFaQdigDAGNmOlUJjfQew4qZFPoIku65CeUNI0SqfUtXFiHtzcHKeSjk90XxMi1lQ5VH6m/ZDQMra03CefKf1UyfjX/Ki8Ol5cOENaBL1AEVaGQ62fg1EUgj5WokQC3f4QF5ct8UYhTVZlrYpvrEkELpbtRBOV2BACQgmn5aATDBCMhggd9qQQkbihINElQjQaYTQYqvAs8bzGgKsDVVYDKTOnDsokznv04MSwYQzbk/KSQg6bEfEAp8qwyz7O3xgOSibBblzht2c1gW9ydPtT90whS8Inz/L0CtTTVargnTwyPqJHEHr8Ifnum9hIHBmN0At66A8HGEahkEk4HtPo6HkpJF0Xx6dn8CR8j+rHqmurdqZIYhPMZCzXhZcUBkx5KCFkF+bDkHDZbB5eq4dAwoZF4iGJ/YqhifqDkaQ11TM7I6jcVUbqWIWiUn1PkVY4p9L7EkmW9DzGdE+9sVezM2TSHjivzep9qz5GaenIKjWhnPx06QmcTsM6oXeZjJ9GLCKQPMzUpkpuy9tkLTlw/pM50FjqKUcRZRjeRo5vE/ik9xUuajA2JZlRYOYyT/nqsbJUw+rqMv7xy++IOP6GLF2IIUJcNptCzO12u0h52pvGjFhzSpyHADqDCLsHhzi/aKLfp7yRhKvKpJV3lZWligqRN9vItSB66ZFa4571LNsC5dMp5+6MGO5ncDB9jg3FNCvK4DHfZ0k0JsGT3sv4Gw1H8g7Hvs/pi3iRlDYYRej1h2g02xgORzKWKHdKKlPVZowic7X8ii7O6mnegB34nqe8AiZj3g1nMH3Qgui+GO/vk0qc1fOWnPVYIqn0PMdmyN9SpYDe9hbq9Sb6wRDBoInhMJQ6brR7+OPjHk7rDRwfHSFgvFJ67UvSK12EcjGHZ9vrePPqBcrlnPWucks12NsWAYvA0yNgCStPj7nN0SJgEbAIWAQsAhYBi4BFwCJgEbAIWAQsAv+0CPDjuTGePea345sAZr78KD+ghwQSBZJJ0K04DdP83E2vByQN8Df2SHGNQHqK6It3FfUsTcDcEokkXM+TkCJcBS2bsgeo4wf6y4XaDAfA37SuzIyr4gcIer0x5vOypcGjPxohGA5UqBYmEvsXw7jQ+wax4KrxSLyxiM1dp5k1qsyTf69rMayIaLyt3PHT/72zNw+qOlVamPpV92Y1M0/cvFdlYQucFCxePgnVk1RG1JslLXZXtBQDiLZ8MFtaVlnxmiAjbd3EF4krs1gW41Sq1elysdnrzB2HpvZQDLqqMuXG+Lm5B/QSEEbKcN/vYzQaKcwkMUkrQKfbx29v3+FgPy2GZ96iZBbNbGa8Medqb8gaNFyzuVM/EpESMgowBEr9solWpyNjBZ8RWLR1kKQVGjg1j2Va9JwzpY5ImLrLaqB3FelfqtupfKZS3e9EcuMfKZ+WcVWF+wl/hKeIEdUbjYBWc4ROp43+INAWeVJrWEdMw3ZL82180wUdX2JKVcfEl+QIeqJiBIxxI3pELKTNmQKNdVr8YDQM0Qv6GGjD/JgJEUGMkMfHJ/iP//qbeO4YF0MOVGUza7PFj3lNERzUXVLiJHyM9tBCwuL5RR30pCLpSILUTB/2RRq4dU8QGK8rotIplvOYLaU8Oniuh2yG3ismnkaMvg/bAcZS9YEyx6sBIhLCDEP13Hsbg28ksMz6x+FWyAXCKFEh2CQsUOpqeD7z+B32JmtDvuOjUjqdpzrW7IY7yH2QpNc1DN0rTR4GLTmXOUo3L3NDF5K7jJ9EuVhArVrB4IRttA/OK2yPzVYbJ2fnQh6mRziGLrxu48gRjICLRguf9g6FpMVhnZ5GEk4oYRar5aKEk7mNpMvxWzbuqfNDbEaO2evx2xBWGIay0+6i1e4ISZPkFPZLkqbZbwfmPS4MhbTCeZN9V0grcISbEvSHuGy1cNloCrla9QBVGDVf6veThyjPk8ig7upn/nKMoYcVksQea5vkejUHU33cy3H8peRq8luvMC9uGc/BSq2C79+8kpBWDAfEtkxCIdtIs91FN+hjOKIXOnpdZKi3BPJ+Glsba3j14hkq5Rw8z0jUgu3OImARsAh8BQjcMH1/BdpZFSwCFgGLgEXAImARsAhYBCwCFgGLgEXAImAR+PMiMP7a//RF5Ldjem5gGAWGzpEQJQ5XMKpwOm7SlRAp0ySQq3oKYSUI9GpffpZWH4H5HGWQCEBygFzlH/MV+6qoe12hSBJu+HGexBjmZQz03NOI0el2xh4irmQixBQSAxgCYoQRl+rKpldp82O3Ju+IBw5VvCtinvKCkC0I5ANj+ZRlUHmxAOoXLwrr8bZ2dx9dWXW6JY4fJ5YMBcSfEKs+s35VHmPxOszC7NXJ/duOpH/1+2KEG4U0ljOkj9DKZE8PR3sHh/CSNDgaj02xQoybSRxhlWvcu4ghIpF2xpSyEn0wBA17HCvkKm9o4yE9vNBDEw2Bps/fVpbr7kvfNWSh6xLd57rwkxQW1J/+FmLI3Efi4z5DLwAM1TGMcHJxiYODAwnzofxEqHqRWmQMMzIB9GZKqE55Zu6FIP2oXMpjbWUZ6ytJMUZ/wWnHqHzrXryZ0PhMLz8Soo0h3lh/Dnr9Ac7ql+hIGJOYKDGIKpwEAl3Z5IqoTdW/EFEEIkVgE6M/yWFRJJ5dur2+zAcGJ+Gr0ODN9k7CSqhW+otMA/U4D5OX2RsWAs9VuCKObSRYpv00ZE4xSZ9qT5wEK0gotIfUQTDjHym28sTFeZjjLMudTLqgV7R52/yr81Jec00LYJXIb1xORZi55qmv67JpdDNasWgpz0GpmMfa6jIu6F2ir8jPxLbT7Up4lIt6A8vLyxKO8TqPSsSm2x3g7Owcl5d1DAcDIROxX9DbValYQLVSRj6XhbuAVzl5HzH9YEbve53GG4JDb0tAOxii2W6j2+2jcckwj00JGUaPMoEmtpHcNhwNwXmS77RsBEKqlPB7xusOxxEIaWUYhgjoXZD3ZU5VfdW0n2uq4l5FerSH5uFO/CL13lrIF6bCbz2aHrfNrOP3kM/XgPSbPEMDra8IqZbe4DoMm9gLpN/zHT4cqAxJRJKQj25C+s3W5gaWl5eQSs2+CX6+XlaCRcAiYBF4CAQsYeUhULQyLAIWAYuARcAiYBGwCFgELAIWAYuARcAiYBG4HwJf6Ks4V/G32x0x0NHoLMbwBA1MyijoJpOyMpNhO677Fs3v4jSo0+029zRMmXWpNGLQEEYiwLUC7ofY1FPUgSuDmRd/sho+ZkSnhxX5kE0Dxg0bdWcZaJTkp2wJgSMGL4wJDTS4xW0pN4h7vFvGsKLWkit95hktHk+DB5I8Qx7QZWD9JZIkrGjD8wPldp0Y1mcyodrOQ5Jk2P6VSYQ5mFYTP75Oo+nrNLwNGd5gROKI6V3S6EUuPaEEjRbXjSPBviueXGL5EVfRZVru9NkEa9XkDVlLEWCkHNrALSUgacX0l5u71XQ215wxvAuxf5RmHOcLKLqP9jvyubkZjFko1otm8ugyxu9eU+wrl/Vwg/4QOL1o4O3uPk7OzsbhcDisqRZA3SfAi8FY1DC5KgO9E5GwMsT6SlXIWJVSWQgr4+Z4RYObLxjp8VTSvKRtzLsbT3m3Y85JNEDLqnkpN+XT2ByKZzCGsWt3enCEjcL2O8ciqokTKudJfQthJaaO4KcJK0xF72BRpMglqlTsDwyLFSLiqv04/0EAiAmbORyroOd57hRxIynexzjOfYnNoMH5+aHGPVUSPeopzooMfaaEzIfeHjQUj1psGStNIR81p4cVbrCaJ5WOMgr5LNZXV/D+4x7anY7yGhJFCIIhGq0OzutN8TrhpzwkrrF6EZZ2u4ujw2PxVMK2LWTNUYRcxke1XBIvLr7v3neomKf+na9xWAkGES6bfZycn+Po5BTnFw1cNtqiP0k6QRAIKZnjwUiHSBK2hh73ZWaTdsA/HCN4hSjzvVZ5FVOXxcWM1vHuDWdSb3rsVbnducx3eYB5mt+859i3s7ns2EPUvDQPdc306cn8NEHkofKIy/ESQLmQxM72BuoNtvuW8sYliTgGURP1HkOCXC6Txs7OFjY2VpHPf9l2HS+HPbYIWAQsArMIXDN1zyaz5xYBi4BFwCJgEbAIWAQsAhYBi4BFwCJgEbAIWAQeFoGbPjY/bE7T0mj4DIIRGk26Qx8JOSCM6J1Ekz/EsKS8o5A8cNNGkgdX6NKQqAzq2jAghj8VbmhsUL1J0D3uGTsp5dMYJj8ngZEOvWKMVmYvFvHZ7+jasMY0QlgJR4ic2Ocih4QGusqfg8OsrHuU4S6PsJyzWVLvb3ebtBUak9j+iLV4O7ml3T1MmRV2qt0Y7yQPI5lSjCGc5ZpXd4vkxOrliuExUkrY2JMQjejETpVBTHK6jUxGF6PHdH6GYKFa1Lhd6cvjPiMPMRiN0AXEEKRaoTbim044LfxOZ0I00/1rrMedJCySWLMMaLOUsGyLPDM/Dasg3hEFghhZb/5Tt1+lHPIvuO/2AglNc3RyJmGa+LQYWrUhNk5YEcljvgaVU63FQYhkNEDW99Dt9oSUOG8Yu12zm1IoE6UYhm9Kdod71F55QdCev+ihQ7xzJBEOBwIS60Bwl9JKpaocxkSxWF+QCuNtZao2bYzEE7Vpgha9bNHb2IgGfBIrGL6Hz/AXApwb2N8mGRsBc/fS7+bcEbkx8gbVMDrNSf5ol5gn+x7HjsfcWF7mZepwXB0PmqlCcD6W44p+0ByfUpjgB0gIp6VaBeVSAe0OQ5/Qux09YoVo9wKc1RtoNDsSAmxeuCnOFqMQEgpn99O+Ji1rIm7EcEA+atWyEFYUotOlfHQkdabMpxNE+LB7jN29AxwcHeP07ELCAA2GyrMXvaew3DLcjWc95dVHLsqAqvqv9H0NImWTpKM8NyniCt/9yB2TsGv3KaSRHSezTUP3eGfX6Mu+nU6nJV8mmVefD6HU1DhnhuKHyOwGpSmeo9byUh4bmxs4OruU0FZDesLiOB5qX3FRJB4Sl5ZreP5sB7UaPc48RKmtDIuARcAi8DgIxL5APE4GVqpFwCJgEbAIWAQsAhYBi4BFwCJgEbAIWAQsAhaBuQg8juVmblazF4NggP39A3R7PfEEQY8qXDrOEBKu5yKXyyGbZZx3b/bR8Tm/J5vf5Ps0r9BAr8LzjI1746ce/oBeOZTRexIOSHSQEAyGiDDR8IoG/MguH7oZHkkRJ+TzvpBZ1PNqtSatETfIuSL4kS8oqB8lE5bS1K0Y+GX18sNkpdRWWE4ZO8SQZPK4Ss4xdx5nzxI/Ud0KYUJZdmhTk1yvzVoxSOhpwpCTVL1E4upewmExfFcCkEgb4/apBCr5mpUVB04qwWRq9iaBIiEoQ6C6JkZ7CckyQtp1kEmnlYFPPBd9IxYgg/sYI1Per2NP3oCiH9GorMKTiVcPgVfXIRktE6aF9FLDn5CWJBWueli8/qSEEiKDHj4esbxC3KF8rcM9slJPqr8yCvDQ9BMaIF0XvscQXgwOQbKJ+t2clSKrMD1b+xSElKIJMCpsFA34isBJogrbfiIaIZvxkctl4BqvKLPd5mYFJndZhSRxcI6cXP0iRw/dFeLyxnPmk5aMiLLBfGlkH77QbLOpJISssra6gstmC51uDxHoWS6JbjDE0ek5Ts4vUCxkkUnnZYyOa0JUut0IFxcNnJ9fyusZ+w/bI8MBMRRQuViU4/hz5pjPG4RVDzV3Hm4/jICTsx7effyEX39/J8S9VquDXtAXcjRzUu8NEZxQ6U7PPelUWt5dPS8p+1QygZTL0JSKzCyhqDhmOPwlhNxzcdlAs90VgpyURw+mmmK1eKHM+LT4E4+ekvVEMhr7JI//bJuUKYow6PfR63UlpKd4IWMdcq6L1P9nGOqT4U97vR6GQ8b4jCFhGvGfEaBYMe2hRcAi8O0gYAkr305dWU0tAhYBi4BFwCJgEbAIWAQsAhYBi4BFwCJgEXgABPg9Nwj6ODk+QT/oK+OZlssP3CkvJYSVTMaXj/03ZcnvvGI4HFuqaIhTvA4VasiYYG+S8nn3qEOc+CAGTLmmPMYwxAM3MWPJn+n81PdtvTTUGCzkI7/60i8rcUnYEIP99LN/5jPzDV/qkcuy5eM+rxojsUbVJLwXGJOHKV5TJZ6UFzRrvL5XMa55aFK6WAKHJnD1byHbqqQnNrpj6YdopCvkslhZqiLnp+C5miQmOCqii8p1rhba9MgU0/ev4qGN/Mw3GsFLRMhn0yiXShLiI1Yye/gZCEwZFrUBlOOaoVoY0WqIks6oL+lBzVzSp7w5PjTCTRoj7DH2Y9LK5wlnq2T5uefYy3GIbTOfzWJ1qYZyuSRkEjUeXZOXGsLHXlEU8Wq2xSucJLqQ5CY5y0VFWAmRiEIU81ks16rwvPubE1R5FAlSpszprndNIR7v8phY+nhZPLFkA+hTNPSnLRp5Zikd3mR5qaq8jpzX6fgHYeSgNxjhstXF6fkFVpcrqBTzV8ICEZWLi0ucnl5IWB0JoxMBSTeJfM7HUrWCYiEPV9iP15dP0NXvStenuvudEYCjsx7evvuIX9++w+7eoYR0HA0ZkosDQQgST1JuEn7GF1J1JpNBxveRzWSQSqXgpVykPBdpNyl4KcKKCjVItl7kJDEII/zxYRfBoI9mpyNhc2SckRFT6S39c4EiEIuvq7XpPiAeohRhZYFifBtJCLR+tRlFwNl5B6enp2g2W8pDouKqaJKOSsiwoM1WE0dHh6gUssilS8rLiqk4M2R8GwhYLS0CFoE/OQL3f8P8kwNji2cRsAhYBCwCFgGLgEXAImARsAhYBCwCFgGLwJ8TgeEgQqfdQf2ygeGIJgK10SjoJpLy8T+fz8vH/5tW4/N7rxBcUh4S+us+dzymLK5sHA6Hj/YxX4gmZmW8CUmkjSj0TKA8rzDEjCKsTKy3psS63OaUH67H5aChVAlnGYajoRhMTdIvt5/3df1xzSUM98R2It5nxhDpPMWwPk+nxRFiW6E0IUqMjWCfJ3Px3E3Gd3piocQTEpU0LHnmrqWSNqi9MZi2SUE0pKe8JGqVEv760/dYrpbhpz0xzNMoPzHJK+P4fIXnaaMIAuP0Us06HY3/UShemFKug1zWRyo1T8b46S96QM0eSzsjW/eCBysn5dJWnEm5KGTS6OTSUpvMZ0JcUWfSYaYUiGvF1Fxh7iDjp5D2XIgXrQfT9BEF6WJwKB6XiGGxuGI+ilDK5fFyZwdv3rwSDxLS3iWtYaco3VT/MwAZ9BTphXLjmyCqDaESbCJSbmhUX1Jt3qUXipQLLxVzUcN4LDdNkvFM9PEUaeUR2+icrMeXpPwyrswiMU5y/4O5Ig3+9xf7T/8kQ+YlGRbIQaVSRqlETyhpNDuBhHZk/wj6A5ydX6DVbKNfG8BzJ+4kTDigw+NjHJ+coj8w7zSOzCUMM7RUqyKXy8anmieBnf2PBITuIMJvf7zHL7+9xaeDI3S6ffF+R6IKxwOGc8n6LkqlApaXl7G5sYFKpYJ8Lgs/7cu7noRwTDrwEvSTxDFQdzK2S8cBPbh0giHO6w14hyeIIoaU1J3fDBdz2/A9oTAyzf6eYhZ9TL/CquRmXFz04a8p3bw6cFS4uEEIdIMI7z58xO7eHpqthswP7APcOMby/yZRmMBwGKLZ7ODd+48o5bMoF3LShtguBKt5+XxNOFhdLAIWgX8qBCxh5Z+qum1hLQIWAYuARcAiYBGwCFgELAIWAYuARcAiYBGoX/ZwdHQiXlbCkOQSWcIu1kGuUGU4oGKhIGF2brL48juv67rIZGjg0N4dHH4GDoWsMhj00Q+C8er2x0CeqgsxZjAQUoX5YG3ykpApN4Q1Yjoageglhj9+5J5QeCCrNoMgwGAwkA/iRu6X2RPx2Nd1OTS+6KUSb1CL92PP3pAyfks1jQgjGmbFdB6/S5HMn6nuus0QI/Tjhq8yU9K7Cr9DeqO72d/h0TskFcPIfbIQI2USCdeFI4ZxJYSG9Gg0QDjqy0r65VoRmZQjq+0NdsZwxb05psrxYylCTK/Y4VTpeJ3cNqog4YfYGkJd9XdvVlOyv70ToqF+bMUPVXy2fdZNznfwYmcTfiohK8NVBg4iqThWps6fex6yLSj3ILqPy0UhNZHYlM9nsbq8hEI+e1duxRerGk4jNCLLTwMs5JFwBCekl58ElstpaYsEzdTBVNseX5wUQy6ZIXNyWYUEio9wCkKVQkPOZwm5dtilUpvwQDFZtx6KoJlOeetDD5xAYzYh1T2QfJGrQ/OZwZyiDfDz5pAHyvqfQQzbt+tCvKBUSiUhajCkjRoDIISVo+MTnJ0vY22limyGXojUxnApwxHk3e/09Eze+wzRmN65Cvkcaks1GS/kNe4zAZVeaTK/QRa7Gt8uWr0Ie4enQljZOzxBtzeQgFy86ziheH0pFXJ49XwbL57vYG1tFflcDp6bFOKa62rvfjovISREk7GBQyTzSXDYdFwh8yQYHigynsnUwGAIZYu+Ly1QRKkDprvXq9IN2N10i23F/G5K963dYz22uyN8/LSPP959wOnZOQZDEqpDJPT/QxRpfnI+DIGT03N8+LCLYjaDVy935L1/3Dm+NRCsvhYBi8CfFgFLWPnTVq0tmEXAImARsAhYBCwCFgGLgEXAImARsAhYBCwCswjQOHB2doH9/QOM5CNvQnvOiMQzSpqElWxWDAHGMcmsjPh5MpmE7/sqJA+FG48nYSgkj/48osciX/jjmdxwTJvYKBxJXiSuTBNWVJlIqpkyZM7IozqJZEJ+XJ1Ld/FcZU9TB+WTrDIcjiSIy8yjT3s6ZZTWWQuW1Pem7bb785+NPxWGLL8xPJr08RTm2u378VNzK2V893ZBD5biS+S5mPJicEo4SLquWgWuq4DG++Gwj6DbhoMRXAfwEtpAZapp0X62QDoixNXt0le06mOjJm8uIGOxEn+DqVj+B2xCJNDVyllkUpsYDAPxlEKyivYVMCGsTGVq3IPECC2Eku3Cc5FJp8Ww+y2gy6ZkDK3kg4iHBCHlsOghBr0e+p2OhPswbX5u+7tjm7yuGk3znhanU88dw25HWcp4e7JHSBHL+Z6636RUHKP5XWL+1Ztk2nvTCBDjrO+hWimjWq3g+PRcEdYckgojNBpN8Z7XarclbBsjWPGZwRA4PmlISKBury/POE4k4XNIVllariGbzQghZjrHu58tSlYxkkkkaTRb+Ns/fsHx6QW6gSarCCFsJGNXuZDFm5fP8PrFM2yuryKfJ1llMvWocUJJjLdDM0yyuTMN3+nUT5groB8WpuG7I6/LNiVAX7vPjvJixML7iLjfM8xYF+ahynI/RR70KZao149wcnaB3999lH0vCBBFIfiOyneUXC6PQiGPs9NT+T8OWwiJ+e12FweHRyjocIb0TuelHlQ9K8wiYBGwCHw2Apaw8tkQWgEWAYuARcAiYBGwCFgELAIWAYuARcAiYBGwCHztCPBDLz2HdHoRDk/OsX98JsQMRUMgsSMSF+q5bBqlovrgm2Rsils2kkGy2ay4YqdBQH3054d/FUYm6PcRDIfw4UEH5rlF4uK3pUwjRShhPqEmrFBrWTnOEEf0AOP7yth/Q3FY1pTHVbcu+gGRonT16wd99PsDMQYtrt1nppzVlaoo+7PopTRTF8X3CY0ijg67MPvsPVWhGDECRRH6xJdeVkia4Y83lRJju8jdstEFuttDD5DaSp2i5gAAIABJREFU5GuUfwCR9xahcbzheQm55XlIe56E66JFTbVtiDGm1wsQBD2MRkMAntTLA1X/lFaUKf3XwMcLj5HRVK5f34kqtgq3w2Nj+KSHE7OW/96wxB5M+w7S6QyiKCM4G9inEImlN7bJ2ftM8gi8hKlsHuMkmUzAY6g5hnVI0HOQ6iv0MtTtB2h2WqrIxkodx+I6heaCqBPrrmjEzCYVHKfkXr0ydftrOBkXIkZmmttQHkZZg91EmiIAqOtUZqzQJMlCR1clL/TYN5HI4DKDzQ1FZpP3U0ClVMRStQKSjNHvixc0vn8FwQAXl01c1JtYXgkVGdcBgkGE958+4bLZwnAU6hBjEdJeEiSDrK8uI5NO6TBbnwneDfrPk9zvR7i4aOHjhz0hFoSMD8S5jp6WHKCUz+H59ga+f/MKm2srKOb9K3remOXMzVFIbzNDITuzXc4PWDVTJ/MUv/WaKgffhxUb5iFkzmaqC0fRM+WcTfktn/M9l83irN7B7v4R3u8eoNFsYzQYISmwhsikPWyuVrG1uYnfEyHOzy/Q7fYRRg76UYizehPvPx2hXNmH63lCDB0Tb79lcKzuFgGLwJ8GAfNa+6cpkC2IRcAiYBGwCFgELAIWAYuARcAiYBGwCFgELAIWgTgC/JbLj72M+75/dIlPRyc4bzRV6BsJOZKA5yWRTiVRLhWwVKugUvawiIcV10uiUCiAoYQYvkE+zIsLdx1Op98HV/oOaCGQzezjGt7lOPbB3wEGowhcYcmwPQxbw+/1NOiLa/AIYswpFotIJJLXfsunRDfJ8qfgp9NI0pvBKERI//kR0O320On20B/EgwXdRefPS2tsEKZsSpoiqEROhJA/TWMxaT8vx8nTlEeiSqfTwXAwRBQq085TurafaPPnOlL1eY2RiY0yYrt0xNtRzvfhJZNqsbZeBs7dYDBEq9kCw29J3ce6x4OgpfUQW/Otsk3iB8n56xVCQ6quNpo62T9oKOZKffndkyEi9WdKzRN6tElCGW0lRI4KycSwTPKjxwDzM9fie5IwpoQa4V/3niqnPE9C0yU9AkCOnExUEr+tR8JKuyVj3rgiFikSBV/3m3k+nozGA55/25shrTx1KW4dNJ5aoa8oPzNeqr2EvLpuYmUSTvKh6vNp10GRIXwqZQnlQ6It33tInONb0Nn5JY5OzhD0h2A4FD7a7Q/w9t0HNNod8HUs5FweRUi5CZSKOWysrSDrJxRJ9YlRajYHOD2to9XqYjSM5D2DHvP4DsbwX6tLNfzLTz/gxc42SnlfyJOmX5q+eq3Ksc5rYCTxoRf0EfQDxb8dpzEHpm6ulbrQDSVNk1bESxSJK5/B3bouV/G+Z3SPJZpzKXb3qz8kVGbjcTeI8Gn/SLyrHJycq7BRo0jaQzblYn25jJ++e47/83/+K/7tX37AcrUsHsqUN0kHne4Qh8cX+Mevb3F4fIpOb+b/I6Zu4hkbBezeImARsAg8AQKWsPIEINssLAIWAYuARcAiYBGwCFgELAIWAYuARcAiYBH4MgiIQRUA/S+0uhH+6++/4ODoFJGTGK+wjaIReEajxdrKEmrVsii7yLfudAool0viYYUPmWdovh2NQgT9Abq9AMMhNWCCh/sUQxsmOSUBPaAw9JCE8jFmY4YwcZD1M+IWX7zFUDmjoNJG/vIxz00im/WRz2VlVb8yHqn0w9EIw2GIkcjXD5oP2zE5j3koqjuO8q4xowI/uXO18HBALzCKZDPR5fO+vLPI9OLR7dGLhwm5pIA0cM6BdJL913gkRkFTgQqfL1qGeVWkr5EElvYUkYptVPSMhy4A0G4rMhHvSQSFx8J8np4PkZcm4HzROrhDOaKQ4QfooYDDCfukCh9GEaoM30pJ7lDoJ05KAmSxUBDvWAw7x41zCse6wWiIbtBDpzcSw/udVGOl3fa7k8BvIbFpj2b/1Do/1sDx1OX4evLjW5RPgnGxiOVaTbzDqfcfElGARquD0/NL1Bst9AcROv0Ip+d1HByfoN3tSRhIstlIfKNHvaVqGflsSkKqsJRP3VJIVLm8bGEoZBWOqgkhHbOc9C5WzGfFo0yCnkrC++tnuj4xEqJzvy+kvnDeHPSQIIgHp+n3t4dsTSQr8UdkWBRz/pBFeEh97yqL9UYPQR8/neL9xz0JGyUceJY1jJCMImTTKfz45iVeP99CMevgp+/f4PnOlvQRziFOIgn+T6fTH+Lw6BR/vP+Ig6MT+T/EXfWx6S0CFgGLwGMh8HBfSR5LQyvXImARsAhYBCwCFgGLgEXAImARsAhYBCwCFgGLwD0R4GrbYQSc10P88usuPu4eoNlqy4dtfhShqdV1aBRPolLMYWWpIsSV6xb6zqqR8hyUixmUikXlml6+mSfEiMsPyiQ6nF/UhbQys5ZxVtSC5+oTvDE8tDs91OsNNJsdDMUjirkTIp32kM9nkM+ThHL9p3ve8v008vmc/Ay5RX30TwhRg15iLuuXoJMVsW0wm6fctLcEZZMwZdSGiSghhJpgMBBjLr09TDapkHuboBhCqtmiB48BSNyhmT4ufZLPt3w0wfNJSsH6MSCa/TUZ01afybrI5Xyk0ykwVIqY6zRphW2+fsGV6Z3HN7yYLmT2V3Q2be3KjRsv3ALBjc9+iZsSFUt4D2IZVAQ3Wn4jZSyc6n5fQsGvIE8Jj3RtO7ldQXq74pySy2bhJV3lWYjsuQhCHKTHq/39A3S6d/R6ZZrovL1R61trkEbvr3pPUC2w966ieHvVMPppB6VSActLFWT8NPje4jB8Fj1R9Po4rzdxfHqObn+Ei8seDg5P0OQ8wfckOixKqDCIJKusLFeRSn1Gh713wVSrIKm5Rc8vIxKD6bGKKtIrUAjPc2UcKBbycJN8t7zf64xpgSSrBIMROt1ASCujkIExTdvUGMgcba7dv3BKwj0Vvle2ppQ65y9TpffS/LqHWJKeEK56+OP9BxwcHqPVagtpVJB1ImQyKbx4voOdrS1UywW4DkNmeXj5/Llc93317sR3enoWarY7+LR3iA+7ezg95/ttbHhS0F2njr1uEbAIWAQeFQFLWHlUeK1wi4BFwCJgEbAIWAQsAhYBi4BFwCJgEbAIWASeDIGZD600XNCc1+pE2N07xn/+7RecXzQkjAi/z9MgwPA3KTeJfNbHcq2CWqWIXIbu5W/RWufFte+57P/f3pl2N44ja/olJZHaF+9L2rlUVXffnjsf5sz//w1z5s7te7u7liyn90W2dmql5rwBQqJl2ZZsOSurK5hHKYoEgcADEKQRgQgHW5sbYuzBXBl+B1zNGI4RdHq4urxGs9nGkH7YH9ueOHXvkigdvxjiqFZv4ub2Ds1GC8PBSCajZaXxOEQu46OQzyKTZriix5UcPJXJuGLYQuOWBD1ZuFyxakJq0FDj7q6Gi4tLtNoDWGcx9+R64x9mYl70TFKSYBA38FTgOKKE6vX76A+HGHEV8mMbL4wYPpbEHmeyRitAjYY6wyHCWL4LZmGz+oa/40DetlZSUmSrMlWQPYImuv9on0LFJI2p6AEomUqa0Fu8bAxp96vrKm6rNbTbvUWb9pFCnzgsHZD30NsyekKCb+IUa+9E8XgkMBC9q4iHFVfCAomxyhO33zdRia8hxHPPj2dk8FOueFjJZzLwGO6EngPk+UHfVw7aQYCffvkZ9UZteS8rz5Qtp+VmjcZKu7/IdZrmGQJ/7PHjGTiLnY4QplJAIedLCMdCIWfCMoqnDRfDcIxGO8Dp+RWa7S6uq3c4Pb+QdzAaMXPM4ntOJuNjc2NN3v34HrRUV1/0JXGBWtFDHkP00CbNGKyMJ97k/FQS2bSPbDqBZNJ4hVkgy7lJWD++l7bagYR5pJHvaMy35Pv9UjjcPzQ3v8UOmocnvbjQWEa2V46Pi5XLVCurxOJFrjjlYAjUG32cnl3g6MuJGMD3+wOpGh02ptMpuQf+8ucfsLmxDj9F/zxAEsC7vS18/91HOe/x3UkMnlwMhiNc39zi+OQMJyfn6HTpnTDCZd91VlwPzU4JKAElsAgBjl+6KQEloASUgBJQAkpACSgBJaAElIASUAJK4PdPYGYSnHPjXJn465cr/OOfP8uELxUDDGcRjoZwxiESGCPjpbBeLon77GIhJ27hn3BIYjiJi3Ozy7R7u7uyIl7meqk0iRQDXDlL99u3t3V0gv786fMXzKnTe0uzNcTdXR31elNCD4lnEXqeCOkGJUQ+l5GPeEx5pnVTSYhSJJfLwvOS4smChiAUjat+7+7ucHJ6hnqjif6AK3J/i42lRiWLwZGxH3DGDkaDETiJL15QrFLkFSIyC87fs7431Sr6vb5x0U9lPY0WuPiZSuSpRK8o7Vu5dOYGWrVYYs2wRM+JxCHyAj0F5TLwvRRcKgrZz8HwD2Ocn1/g+PQM1apR3i9RwvM1lBs6nuyNGcWL+hb3HdriuWKUZ0Y5OlaJ7oOYMdJK2+Bb5LCITK+AkEo6yGUyyGTSEu7E9nnXScBxEuId4e//+AlXN1V0+xypoo1lxsud/c1k8fP2On7/Qbv2H7Ta8Zb/fezbhrLe1gB4KWB9rYK1tQpy2bT04WQqhWTKl/eU49NzCQ3EMJAXVzcSkpH3Eh8hqYSDbCaNSqUEei+xt4Ut5jkoJt2iqZ/Oje9u/NBQ2HVdMQrkOJtMJpHJZOD7vsj87HvpE8XwnYZ1pJe8avUWnW4X43EohjF8mtr6P5HF0qeYJ3OmsQoNcfgRo5UVF8Z31fvjl2ljCiyvHUtLvvgF9z36LX7dIinZu9rBWPrur0dfcHV9gyAIhGoyASScMTbXy/jh+4/48P4AxVwKNKLndVT60ph+d2cbf/23P4FGXQmXzw8X4dhB0BuIB6LPR1/E8J0hh1bcLItUUdMoASWgBO4RUIOVezj0hxJQAkpACSgBJaAElIASUAJKQAkoASXwtQjYSfpVT5Iyv6A/xuV1F//n//6M//jP/8LR8Qm6vS7CcCRmBq4zRtIFMn4S25tr+O7Te2xvrotr+WVUEEybcIDdnS2zitFLYjQaiiKdyvR+f4RGo4PLq1vx7sJF8lxF+6It0jhwJWSPLsKrdYlBX729E68ijiiSzQrcVNLFu/1d7GxvIpkUPc2TRXKSO5v2UCkVUcwX4HH5crTRYwwNb6gcPT45FWWp2ITMgpKD9qrY90vrG8uC+gi6w2dYmLTvGy8bVPDQow1cdNodCY3U69G3OcOT0JohlgF3eYwyz8o9k4yXMYxUszOWCf2b6t3Ue01kKDG95JnMpgl/H3sraKt5FV1aaRTDysnLtXIR25sbWK+UxFsQlXrU4NFwi3f06fklPn85BkM48f5geUuXGSm3osgr86rxRscI/Y3Ar1BiruamIUUmk0UqlRIlpDCWMW0s3o06QYBufyDt8u3XaIVw7mX1+vZk985mEjjY3xOFPEO2JRgfS7xDJBCOXQnr8PnoWLxITJx3PTa+zYj06L1hr4/df5Mx84/boPdaV38sQCDefxZIvnAS2z+jxzjHo7VKCeuVsoTNoQEG37vGYwe9/hDV2zp++fwFZ+eXaLUCjEZGKZ9McBzzcHCwh3K5YMIJPf9qsLCYyyb0fE+8vYjdRWR8IZ7yAPR6/cggecFc+ewLgbjjFHv7d/o0fqjhHz/+E/VGXYxvOa6b7eU3OMerlJdAsVgwnm5cdzJe8Xk6GI3kHZJe8OT99036RyxTu0uWv+MtGELeu49OTuRvGL6H8+8m/s3Bv19KhSze7W3jw+EeCtmEGNuzurbWTFcupvDdR4YL2kcxn5f7gyn47kQPggwN9NPPn8H33P4g6gMv7wq/Y9oquhJQAt8Cgckj6VsQRmVQAkpACSgBJaAElIASUAJKQAkoASWgBP71CYihSlTNVc2LchK8PwTa3TGqdwGOz67xz58+429//1HitNfqdfGsQi22g1BivHtJR0IAfTjYx3cfDlHmZDuDvy+58YpyMYHN9XVUSiXjAYKrSsMxBsMQrXYPV9d3uLy+ldWSMmE/W8akWBJ5hIp49YC4tWdonsurKq6vb9FstWUSm9dxfj6VSqBSLmF7exOVcnEyiT1bZPw3i2fooLVKGWuVCjLpjFnpK6uRHQxHIRrNFn79coyb21sEvYdeVh6Rejp7Hi9wyX3Kx3pxRTTDFqUkTIb1sAJ0gx7qtQbu7hro9waisHlQBDN5VEiTmqfpr4Ceec4vq7i8uhEPNvQyI5u0k1F60bNE7GC0/+19PaqcnhVVQiw9z2j2sud/k+oz4J/IhJQL2ZSsJN5YryDje8YDkNzJzNnFba0Brqan23zeG2yuSfM8kffsKUEQNauV+oHk9oBNMJvJnN+TFeAiFAswhfCn/cy57Js6RIlpM5H200ilPFmpzb7F8Ywr6DlGcCxqdzpTxdc3VYOvIEzkfUla1zTxiwrlpb5nVscz3Fwhz9XxjhhhcXU8PwwVcXx6IYrM27vuk6HaZAyw8vDb7i8qne3zi6bXdErgKxBg9MVsBiiXi+JBQsJniQcJGkmMxYvEl5NzefeiAYuYuDpmHMukk9jf20ahkH/Rs2KV1UuLwUo6koMGN6G809EbCQ1WukEXgz49lSxRauwe52X9EOJt5vOvv+L49BSdTkcyM7afy2T8UAYWZYyAMmKwQuM6+8xjzvR812y3JQxRn7Es32CbVJcFRtXhscnxNyjzrbKk+L0RcFfv4PjsHCdn57it1cTwh2+fbLO0l8TO1oYYrGxtVJCiPePMJobovoPtjTw+HB5gc2MDnucDDn1LJsSoi+9Ov9Lw8ewC9Xobo+GU30x2+lMJKAEl8OYE1GDlzRFrAUpACSgBJaAElIASUAJKQAkoASWgBJTAAwIyqRzNKj84aeabHz9rznNV+TA0E/Ht3hh3zS7Or6r48fMX/Mff/oH/+Nt/4eTsDI1WU7yeOI4xVqEbbRqm5DI+Dt/t4tPHQ+ztVuD59NowR5gnDlFGXuIlga3NdfFqQk8ldN1OpcMoHKMTDHBdreHs/Fo8djxwvX2vTP64d+Be6VQ8Bt2xhBhifnf1BvqDvswwj8cjOE4oK3X393fF+CST9u9d/9SPtO9irVLE9tYaigUahbjijUbawXHRH4b4cnyGo5ML3Nw2xEAont90pW786OP7VL6IhwyuBqZDlCikyGNXpFJJMGRRqViER4V5JN1oHKLb7+Ou0cTx+SVqzTYGdJEyb5OCHk7I8zANVThXT2OVWiPAz78e4fzyCkHQBVc705U6P7p9XQIknk45qBSL2N7YQKlQFA8f1O6ZO9pBp9vH+dUN/uvv/5TvdhCKg51HesGDCvC+Ytr43cffMr4Mge4AoPOe/sD02QcZzDlg72KbJ/ur7E+MVGwKXryopHMK+kqHKC3bwk/58kkmklIyDfP44X1Ybzbk0+kGv4MarR6cad/Z8BQvK4cr6DfW09je2hQPQ1QIOxhhHA7l2UKl43W1js9H5/jl6BS39Z543prbk+Jd7cknjJGVYzH7PsOH9Pvmeyll+cuqvNqrZuq82swf5jbh/jXLtUaGD8X5QxzheEQvK6VCXrzD8f2AIRBDGnxEnlZubqqoRwbLNKKg1wmGA8plPHlny9Hi5Wu22ZyWSac9ZLPpyXujkd2ECer1B2i1A9T5XjNYwHMYhx++yhKOYxzN0XFGoznE8YnxptFsNDEc8n3RheskgXGUOC7bEkyYlIaXDGHk+T4YlomhN7nxvhgMh6DReKPZRJehiOLlrGBfjGOifEyInlWXsIyQryubVzPKW73Vw+nFNX79corL6yoGtFCkwf2Yf7+4KBdz+HC4j72dTeQzqfse5WIe5viUzvkODt/tyd8nNGJnOzmu8dTF0EAs58vxKS4urxF0B/fzWqbqmlYJKAEl8EoC5i+LV2ailysBJaAElIASUAJKQAkoASWgBJSAElACSmARAqIwSCTgJqj854pxKjsjLwPMIJokj8+Vx6d/uc8P524ZFqcbuUuv1Ru4qd7i8uoaF5dX4g0k6A8xEM8YY3HFQUWfTKonEigVcnh/sId/+/P3skoxXt4i9ZiXZnengm7ve1xdX2NwXcWIsUngIoRjXG+fX8lkPifyt9ZLyGc9MA49F+Xbes/LV45Fbt6bzZGEgPjx8zHOzi4RBH3RTNA4hkYyvp9CpVLChw+HWCuX4U0j+zyatT2Roovxooe9nQ3c3FyhXqui15saa4Rw0Qr6+PHnL6LoSLjA5loeXiopbUm2j222DZmE+1R+cgKeygtyyuVyMonOOjDBbF48TMUUPayUiiX413dooY2RKEWonApx12zix8+/olAoIeGmsF7JwfNo1GA2EY96mZnNykMDqEZ7hOvqrXjr+PzrEW7vaiKny74ahpO8ZrLQn29IgO1Gx0eVYhZ7O9s4v75Ft0flPLV3IUZ0ke+6aLQ7+Nvf/4Fhf4B2qy3KmXIxCy9lwmSxnaUPzJHV9jeq2MR4JbI5GI5oINZHpxOg0+mKiVQ+m8b2Zln66GP5zSniX+KQURCnxANT2s9gMKRhClf+05sUDfOquLwuYW2tiHwuC5/j2xOQnmqT3zMwY5r0uhqQDZ8P21vrqN5t4+r6Bq12iMHAKLI5hvaHDs4uqhgO/h8a9aYoJTc3KsjlUuJZy3hPMHLIUOlAnhOPScYy+cjs9MbS5wN6y+l3JZQEw30wTNETzflYtl/9OOsx3e7/mh5/g73IC9ob5DzJ8mFtfg8tMhF/ZTusNT80QKFHuY2NdZTOL9BsNiOPc8ZomJ4kzP1oPNDxjSCX9bGztY5SMS+e235rgjSaodENjW34DmTeNBw4rovBcASGffz8+Qv+9MNHuLm0GNyI9eAzNGmo0g7odbCJi6sbfP58Il75Em4yuo8TcMYJiLEz84qBeNjPni5M2iKRkJCNvueh2zMebZhpbzDA9c01Lq8usb5WQqmUfeD5j+MTx6vf80Zm/MQwLl0dvoN0eiGuqjX89z9+krCfnaAnD1KGxaSxSqVUwMeDfdA75EbZhElkGChYLyszAvDnWimJj4f78h5Djz2NdiCeulwnIQa/7B/05LVWLiCZrCCd5sN7afH1AiWgBJTAqwiowcqr8OnFSkAJKAEloASUgBJQAkpACSgBJaAElMAyBDiZy/Aq/X4frXYbtVpdDAHErCCm2eQ8qRizRF5KbBghGhVwtWYn4PVB5Ga8h0ajiXqjiVa7g1YnQH8wFCWqmXSnooJKbQdZ38NGpYyDvR386buP2NlcR4aeVV44y0xjEy5yprwZ38Hm+ho+fXwvyvTqbQ0jCgxXQmTc1Zr4fHSMcDxCq7UFuvGmksL3kuJOXZSLc2D2RHkYot0OcHFxiS8nFzg6u8BdrYWB+O82SkhOZq+tlXF4uIed7U3QzfyyG5U/m+sl7O9uot1qot8fiBt3GgVQkU+lwvVtA+4vXzDod/DhnQk7RIOTdDqNZDIlShc7z805dPG+MKJCe4jhcChtH3QDdNodWfnMtv3uu08olWhoQkOm+VLTYIXlUDlFw5V6MoHBmEZBZklxbzDCVfUOP30+Qjgco9fbxdp6EX46IaFMqAux+hAxSojCmfQHDB0AtAJ66LkUrzynZ+dg+/UHA9C9vesmMaTBCj+8+M1MVx6p/HwkLzsqff0rlPMy6R5eRXlDwEs4WCuX8PH9e7TbDDszQisIMB6NMKKKb0RvRn38enwihlC12h32d7dhFO0MY5OUUEIMLUVDANmiPk3DFBpODcc0CBih1+/Lh2NUs9lGvd5Co9lGNu1LGImNjTJo4PVNb082MaG+bEslksj4aWQzGbQ7PZjQWMarQbvTxsX1FQr5NNKpFLbW1pCm1cqcjQYXvJdouCiGanPS/JEP2eajcvfd3g6qNKQ7PkGr1ZZwJzQsGY9dtLsjXN7UMRp+lpBM+7tb2N5eB71rpdMpUchzXOXzj5xl9Ir6PZ/FHJOHoyEG/YEolrv9Eaq1hjyb+QwYhyMc7O/hMPEO2Uxx4Ucl21X+2QE9ek7+K7fpy++qxag8zN/2El5v9h+mWSzvr5VqakJ6v8SXyM0a85NNuxL+cK1SAj2qDEc9hKFjPKKNWaIJ40fvFHBCFPMZHOxvSxhEvvO8bIsupOAvET4qlLnQEV4hn0U+mxEPFzwmBt1gOMaRhLz7+fMRPC+J3e11lIt5pDP+3HclvqPxWdYPHdw12ri4usPJ6SUuLqvCptejpw4gk8lKOD0afcv7UCSPVCdWtWj3SURGXhrYJZHNZuGnfYT1lmkch8+GEbrdAS6urlDIZZFMJMBQZwx7xm0WH40v7LPhd+fULt4fFoE3Q5bPxdtaE78en+H04grtbk8MS4w3HEf6CEMBffpwgI1KCb5nnq9xTnzvthv/RnHHgC8hUEs4eLeL6l0Nw/Nr+RsMoSPe0e7qLZycnqFIgyiP70pFpLxv/SXH1lK/lYAS+FchoAYr/yotqfVQAkpACSgBJaAElIASUAJKQAkoASXwjRGQSezIhbV4EaF/AhqcjMZodRjC4w6j0EU2W384ZS3GBKEYt4zCkXjS4Cp+etSgd4Wg20O7E8in2zW/aaTC/DnhyylwKieoquB3KpkAV7Fu0qBjf0/iuR8e7CLtGe8LL0VndXG8niEcivkUvv9wiHazIYr0eoPhaWi0EaLT7WJw0xdPBM1WG7f1phi4cFVjxvdFoW5CzhhOJtQGRAl5d1cXbx8XFxcSVqjWaEmInjFCuO4YiaSDYi6Dd9ub+PhuH2ulongkWVi7GAHg9HQp72N/ZxtBO0DQoQeUqsS6Fzf1cMSAheFXgqCJRrOOzY11lMtlFItFMVpJpVJwo6WydDtPhQu/TTt1QaU2XcN32i20mg1wNe7mzi7yhQKciUnJwxZJJoF8NiXeaTYqRWFcbw4Qjjlhb8KSdDpDfDm5xKA3RrPdx057E4VCFr5Pbzb0OsCpsDFCKmop1yiUviTtUavh/PJSvPTc3d1Km3kpXxSL4RxcAAAgAElEQVQwmUwOd/W6pGX3sn3bONyPqwceyj33CF3wSzgH5sR9uhkyt4G5V+Ze9fTBuNaHec1ukrFJ5Iwd2A+VGfe2edfeS7DAD8ljNmN6E3JEebJUHSMlOz365LMJvN/fRqtRw2g4kNXH9NJDgxPe+7QPo7KHXlF4j1TvGtjYYIirgij4PM+T/pbyUuDKYjFSGQ4nxlR2fKHhG1fqN5sttNsttFsddIMAVIgW8pkHCrbHiERNGp0mD66dt1yoPOUp+/uxXJY7bpuPuY4FNPun/Jopa/lymXfGT6BSzGO9XBJDwf4gFIUaY1DQiOj6poFk4oKB0tDtjcU7RzKZAL0Usb7ikSUMMRwMZGymYV25lFmukr9R6ilbjtH8xREzzjHyrWITvkJO5sx7M592sLu1juDTBwx6XZyHIzRbHQl5xZalIV2n18Pp1TU6/QC1dgu3zaZ4uMnlMmK4QmMtP+XBTdix0hiq0CCRXrRomNft8nnK8bkLGley//e7baS9JPLZHHa2tqQ2T1XNEhHZRekeDZZypfF0JmPmUgPAyyBKq4iwtly+D5iNxcvH/n5ZEfeuMr3AvHPIiakAcQj3rnnyhxV2TiJmbcrjDm8qPkOmfZHvBbzZTLonMpqT91seEomEvfF2wkHbtANldCXE2zLl8yp+6EWrlMuIt4nTTBo0yujRcoPGElGGjjxjQ3ipBMqlEnZ3tuEll/cYZMuMzLGMUZYMbJMWibfOQtVJpRwUS3ns7++g3e2KMTYvZI7DcIxGu4svZ1dwEkkxrN3aWEehWJDQeAnXeMLgOw3flem5jgbh3cEIF1dV8cR3fkGPeS0MBgMkky5yhTxKlYq81zHsISnRQMaUSEvCh2LbQ0w1b2Pvy3gu1stFrJUKuLm5kZCYvG4MF4MwFMM6xz1Fj57L+iEKhYJ46GN+9BIo779jGhsH8k6Uyfgo5nNicDyvTNP67P+2VSQnSSpy8qXga2xyD/KdyhZmKMlPK94j77YU0f4dwfeXZjvE2cU1jr6col5nONOxGBoyx3TKlb9haHD/bncb+ZzPyHAPN3b9aDigDMyfxoqFHJ8lFdy92zHeDYc9dLv0hOOi0+vjslqD/+UU+WIJnuejnMyYfB6WoEeUgBJQAm9CQA1W3gSrZqoElIASUAJKQAkoASWgBJSAElACSuCPTYATt+5EGcFpZaM84d5wZCbgf/lyhvOrW6SSychrxZQZV1fSQIKGHsNwJB9RLsuE/EAMV4xxiglHISU4rjGU4MpuTgJzEj4cibEKjTnodeT7Tx/w/uAdtja4MtFMKk9Lfdkec5GPA+TSDg7319ENPooMn4/OUGt2RalI44jhyMSLr9bqOD6/FI8RlVIJefFQ4oNeUgyjEfr9IXqDoSiFb+8aqDcaaLZaGA1HonikniIcjWQaPJPysb+9gY+MU7+9iazvigHNREO3YNXoNYKGN9vr6xhKyJ6eGJtwxX23N5Bc6G2l0+1LqAgaceROLsVYhUqgTCaDlOeJpxROlFNBQgUKvVbQoCjodtFsNVFv1o3CGiG2N9YR9IaimBHlEvvOTNPwJ+fls2kH9CBQr9URtNtot9voj+j9hSF7zKrc27s2Wq0TnF/VsHl2hVw+i2yWits0kqmUIBkNTTgietIIel3x2FGt1cSLDUObUFHMMmlMtL25g82NLfztv/+OXq8Pl73ZGkNRHSZKkYmm4nnSkpTtPGO0IloiU3HTf5/ParkUpMsAVdwigxXuiQLR1He5/J5KzUraj9kVAxkpL1Lq87wR5qmM5ByVMmz/tOtgq+Jh9P1HGVF63S5uhgMZJ2jQxrGF7dNs99DqXOH8uoq1SgX5fBZirOL78Dx6nUghkUxGBivWqGo48f7EflWv10Xxx3HHGY+QSriSD5WClJu1e0p8aeZ7acRUw/QXXkjwT+bwLJYnExhjFdMGYrgXtYcU/eSVj5/ktcW8I6v8a7VtCcPWaLXF+IvxHMLxALV6B93eBZqtPs4ub1EuFpHPZuElEnBcR8bywaCPfreLpONikwZF//7pwT3/uBRf8YxtRHZhVp6GXlHjy5hDAyw5HktotY8rEJOhsJhzpZDEv/3pA4bDvoz5vd6ZrIpnmDaaSNAzUH80RP+2hrtWG6eXV2KowjGP3qhk7EsmZVymWMaIMPJ2FQTyPO31aATaQYvGMGMXVPAnnRCVYk76qesuptyX/iVjijEWlRrwIGtix035vdreH2sBYWbv0bHDcY9njSJ+Il8kkkn8usaalG2fBfJt6yclRvd6VPHXFRe7mvmxrczzxMjBcYb/bLmx5N/ALsciPl/FUNI+AkRwdnbWI2quBVHxCt4nubSPjXIRxVwWrRaNsOhlhXFm2G9N+/OdsJAvyJhDb3iz7xnP4bkn0sRIIW6oQO7Lb/TyslbO43/++7+h1mhgMLgEPR2NHYbrccT4pF9vI+h/kVAxaxJWpyzGtHyu0diE9y+fVfwOgkDe0arVO9T47hb04MKV98tCLocfvv+EyvoGrm/vcFO9wYDPNPaYyYvH/L7Dut1jEKsq3xvzGQd7O1uo1es4v7wQo1Ex7HNcDMcuqvU2Or0h7hodXFbrkbe8jLyj01hlODTeF2kox2Ca+3s7+O7TB2QyiYm5h2lJc0eb4s37BOWy7EVG3oP2fnxU6lgFXrhrkNl3muj5IHkZUkZe7pvfs8WwjzL0E7tTfwjxqvL56ERCf/Kd2XXodXAsfZyhTA/f7eLD4T4qpez9EEqxxpmEVpop0ksyNFAa333YF+8qQZfP6q68FYahi1a3jy/n18gXTuRdPpelZzp6KnpU/Nnq6G8loASUwKsIqMHKq/DpxUpACSgBJaAElIASUAJKQAkoASWgBJTAQgRkMtXMepoJXFdiqLeDPmSFqM3ELu6kwQr/jY1BirmGk6acVE+IJwUqqLk5opCYxhtgKQx/kE76WCvmxSsC3Y/v7m5hd2sTxUJWVuTaIlf1zXI5aU/X24d7O0g4CaS8DH765Rj1ZgPd/lD8K3CVe384Rq3RQbvdw2VktJNImLANnNemdxUauHB1ZX9ojD0YUkfCQDg0zhiBSg7PS4h7+IP9Xfz5u094t7uFQtZDitp9CvSCjZdlfQfbmxuyTJOK/dSXM1xdVxH0+pOMh3Q9Pxhj0AjQ6gxwc9sUgyEqpe30PdtI2lDc1HPd9xij8UjqzxAuVLdyBTGVa5y4l27yiMyUiwY1xVwS+zubaDVbEh6jWm/JSmHqpmRzEhiMHOlfveG1KMnpgp5eBuhZgxu9c1CxE0bGUPQAY/iyA9JrDWQV9qcPH3Dw7hC5TA6fP3/GnXRB0xutCZYpdNn/beMYUvKLyjt7eNnszK0w96pplmb1P+Vmm/C4UbaYfVH3TBPPzWuRgyYLw4h52n+ywp0tbGLBxNRLi+Rq0rDP87NRzmH4/lCUcD/+8hkX1zeyKn0sWlCqME2pgxFQrTdRa7WN4odhUVxj2CZKokn/NAZylJr9kIZgVP4ZMjSBMZ5RqDhiG0VDz8KCT5jQqmqyMfcVAJ/kN2dHjGKitrBtTvWt1HtO+mcOUVqGUyoVsqKYvLy8FsOzRrsjXouEi+Mi6I1wcXOHWrODdMqDzzGEN5XL5g/Fy9SoP0Ahk0E4GuOvf/kEcX70xjieqd6Tp20bynf8fhOlO9vSHOR5adcV1YXZ0I6RIed++PQRqZSHdDaLf/50hN7QjJ8y9rmuGLHwOTMYddBsBxJ6g2Mfn7EmRE90Z4gulx4ZQqPU55hI49DhCOGI0huDNmukY41HnwTEk0QQPYqn1bdcjOGIGJI+m9EqE7B88zEymbYyRoerK8eUQANGFmdGDoPEKs5XV1Y8JxnDpUwOTIQflT1tgHjyr75PMawovEemH4pijZriqZYXkYYn2XQKm2sVVErFiZetkO9KUfkcfvhaRI9wG+trJgzjkkVRSpuftHFkcCPDrGOeOdMHK1MutjHfdNrB3u4W/vT9JwnTdXx6MXk3ojEHQ68F/SGubuuoNdvwvBsxDqbBtzH2GIinJBo0c4yVcF8DvtcY92OsPz2EffhwgP/x1z8jkfJB42MaEzaG9WgcsMa4lNu2mr17TF0m9bdVi6rJ5yLfz0r5LBiy5nB/F73ekRioSF5uUsI0ccxiqKJWcAw/dSFhjlgHuvKg3QxDlA16HWS8hISYPDg4QDqdMAaDtszJd2TRR1nZFvbvh0h66VWLN8Mk1+V3okLkuRrCpWGjdArD7qn8+N7PkZHeyG5rLfzjnz/h7PxSDL1NZxsj6TryrPxwsIfDd3vYWCvfN1ZhAdPmerQ43if0Krm9WcL7d7vodDrodAI0Oz3DH5D2Yt8r5nLiHW17qyj5SfYLlPFo4XpCCSgBJbAAATVYWQCSJlECSkAJKAEloASUgBJQAkpACSgBJaAElicgqgmrgY9NdNpdMVCgkgzDR2dbOQ0sytVotlSUbmIQ4cJ1QlEc0+iBE7EMO5FKJWTVaT6bQTmfw+7mOtaoxKiUUC4Vkc+kkHrj2RAqdMslH667g2TKE7nOzi9wc3uLZrslMo+pEA9DUGnbDagEN4r0OGVREhrdl7mGSjCmY11dB9mMLzHs6XHk4+EB3u3toMT487TJIK+XTtSHEIOXUi4J11kXbyJeKoW0l8LldRXtgF5X6EY8CvE0NJ5jGA5k/mZa3K4klS4xHsLlCv7Io4vr0HhF9NlPGm0wJxrjrFeKeH+4Jy7nj8+uUL2tg2FcBsNQ+LDPUPk6CgPpP5TLdR0k6OVBDIKM0pxg+Y+wmDfDlmQzHtbKRTBk1KcPh9ja3BaFbybjIZFwJKQVAZv6m7rNr/dTR22ZZGYVueaYXLVstkxv23vOtSIt+498WB4TjSIlO38bGZ6SeJlzRoRpHY3BhynDEl8mv9m0VN5vrZeQdF34DFmSy4KhDer1hqzSpgKfpXHrD/oAnQNJx6MiyUhHpbwgi7gZIxQjs/QHemoS5Tv71FhCCWXTaaR9X/rqHMxRieYryjY6FuUr9zAPWQ83Ytowbbt7Obz2B5V4k7sqanNT9qT/LqLlmhGDOWYyDjY3Svjwfl/4hldjCSVDEx+a9hhvUiMxJGs7AZKOIyvEWRxDP4TjEcbDIa0kxGvRfVYzBX4LP6PGNvcR/ZpQKRl9m/XpcMbWk8PqBWbxfK5Uyh7G2JdxjOHNrqs11JstUTzS4E5M/hg6bzDCyHEwcIamv4tnGMplRrtJ3xUFL+lbIyY+S9lI9Nw1FiM/emhJp/lcMcZ+T9ZukrFJxazYXyhXNGKa75l0T+a50EkzfptxbXo7GeMY1o/hRuhdKtpHfNxdqICFEonxiATY4PNMWkOEMf0mku4VnT2OLWo140mO/RFsM3qD4ntCKF6hpvVfSPw3TST6e8omIzNHZ/YMu/E345hEcOIVtUme+GZyn2GBCjlsra/h+vpOwrgNRhxr2KdDJJwQqYSDve1NScPSlyxGJDD9maKaujjC3Yx7pn8xxeKNTBmYmoaYDNny6f2BePbi+9J19U4803E8pREz7R3DET3W9SU048T40nHEwFKMLKW+5n7m/ZdwHHjpBBhG6PDwHT59/IC9nTV0Bwyxl0Ehl0en1cRwxHcyMkkKF9OXn4AePxUDmUk7Eury4/tDCV3GMarT7YlBI/nw3ZfhmsSjk4SqoaE2vYgYb1E0xg6HfQwzPnp9hnyMFzSzH3m8ssZnHN0oiulGHMfk18xFK/4pdefYMoLD8ZZjS3RMxp1o7JP+LSZT98tn9Wj8XW928NMvRzg+OUOj3pjw4lMl63tYX6vg4N2+eAfKZBYYi+8XM/kloRUz9JK2Ie/MDC83OL8Cw8PxfXg0hLxPfzk5RSGfRaHwF6R9tpG5YSTU0CQ33VECSkAJrJbAG0/RrFZYzU0JKAEloASUgBJQAkpACSgBJaAElIAS+H0Q4DwxV4RyMpZTyGbOOT7zPDbhe+ziY5nglf9ksjdS44ryzCq4OaFNJYc1OnBdY6jClaO+l0I27SOXy4hxynqlhI1yGVsba8hlM/D9hCj8mMObbVH1qCQQ5WIpCd/fRCbtoZjP4OQsjcurG7SDrkzEcwUsOTE8A+tIZYFVJ04m3CkslebycSR8kidGOR4210s43N/D4f6OeI7JZ5P3V11GOJeqb9Qetg7FbALe3pYo6XMZhtXxcXl9K94suHK3P6BCwbaubbX5JYqxDatD4xTHhZdMyKrotVIRGS8lXnEWEZlp8jlPvMn46Sxy+SK4IpQeYBrNloQfIluRS0QTNYlZcTyi4lKsEBCKMsModBiKyUslkfZT2Fgvi7HKhw9UDmwil03Kyl8arCRpYUMnM1GfNn17EaljTKR4q+yiYtEoUk0uprfH75TYlU/vzhGDh+RjV4JP7kYaq5jwQEaBGC1PfrqEJc5aMpEiUoxjIhMSkTNWwzlyPyiIyWPpeB/n0w78raLc3wypxfBNJ6dnaLY7CIKeWW1OD00MGSX9zpUbyZTMAUr0M6aoiI85ZwzDaADHj5dykU4lsFbMYXtrU8IY8P5+ajP5TFOYLkcmpkx+826JVWmaeEV7MoZIaBdTjpQn9Ywr8l4mAR0VlYo+Pr7fF0Upx+QLMWbroDfoSQg0rpgXj1A0YuGKf9ZLhGI/GMMZGc52PF9RtVefTQwRw9yJWlWMVUZwx1RS0hDM3k8yiK9cBtNfjAcvGq2kUgfIZbM4Pj7HxRUN9u4kZBxDSAwiY0hjAkjFtVFes7M5NBKQm8H0v8lNQOMsPoNcR8ZlL+HKWFjMpbGzvY5KpYJMOrNQvWzf5zhr5KYkJn8z5ljDkYWyWzDRpFRJb3mZyrI88zG/p21l30wWLOTRZJPy5P5if7DjeoR7cvzeMPZofouckDIjowkxVqGRUdQfrTGVKd2yWSTXt0lj+dhnjfnm0WjjuCChmzg2PDO42mvsNy+h95QExNh0e3Mdp2eXuL2tIRx2IwOesRirFDMZeVdaq5RfNfYaww4zFtAwyfQvvvNGN5eVbcFv20JU1O1ulRCGH8WIw//1CLf1uhh+BL2eGHXwZVCeJBICbwSM6EGFz5XIcxrPy/si5J0ml/HFI9YP333A+/cH2N7eQi7jAMEYWd8XLxrVK5pZsM9GhjjLuhCL1ZMG4eViRjyBdIIuUqkzXN3cot1uicdAMVQXoyp61RtjOKbBOh/NNFoxY4aMs24CJgzZ9K3d9pgpZXvEyB1ZdpubjInkHW+aOibmynal7aTv8gnHD41WKII1CGYKGmM97Ns8MxoD7WCEi8sb/OPHn1G9rUk4UKnE2PTbUjGPd3vbEiKpWMgtHcpqtrKUb62cljzb7UD6V73elHdn9q1O0MH5xRUyfgqbGxvgPZXNRCHhpMIzOU6bYeaE/lQCSkAJLEdADVaW46WplYASUAJKQAkoASWgBJSAElACSkAJKIGFCXDqk5P5VKQYLyqRjcBkYpyTsmauczopzcl3mbgWDweOhHShUtNNJJFM0AgkI4YDnpcUYxB6V1ivlMXdOQ1WMhlfjFeyaQ8ZbzqTOt1buALLJZwpgD/TvoO9nZIY0mxvbeD65hbnF5e4uLzEba0uHkFCZyyeQkJqd2XCPp6RWfHOEEc08iiVcrJSdndnAzs7G7JSuJzPShgiel559WYno6NvKoGo3Hi3W0apWMDuzjZOzi5xfnmN65sbqc9gMJgoS1i+UZzQ+41RSPMYV+1bpX3STYgBzPpaCbvbm3j/bg/rlQrSnve0+Fa2yNtAsZBCOlNBqVjG7vaWKKl+PTrG5dW1rBylhxXpX64xWKHS3MgXGQ2IhxdXvPLkMmlsrFewv7ONvb1t7OysC2svZfon+20uQy8zLoJghLEoB+klgyEgTL5PC2/OsonY042CkSuah0jQwxDzkLbn/cKKxiq7SMZPpYmUpaLEjJSavDMpu1nTHCncXqGkssWzfvKRvIdwYerI+rJO5rzZt9cs9U3FnEEl/clLApVSCn76HTY31/Hh8B1Ozy9xcXGJ6yoNmJoY03hJFGIsicr7KV667hfSovjlWRo00eAsgXwujTIN39Yq2N5Yw3q5iAo/pWI0Zj0vOevLjYpkUSaHAxnxGI5IwgZQObvCpo6Ki9qA/WzqDYReM6QNIiW39N0XFM46UaXMsGF723n4/g+olMs4Oj7Fr8dfUK3dSUggw5ptbj9yxMBn/2BIs2QCfiop3gUWhmor+Zt8G2ME42HFPNdM/SIDhUk/ivqY7QArkpVdhSvdi3kHGX8da6USDu52cHl9jZOTU1xVq+JxhcrtUchxatq/+fyQ0YxaabmPzLglHlVA71MufN9DpVgAn1X0VMExulDIyLM1m11sVb+5x6lANh4/eO/znqPXD+mDEwM9a565IjhzsmHtJwYFlEfqTi8kxmjF3HykupqGkr4gxipDONE7D1lzzDfvQqu52a20Uj8pj6yN1zKRQbzJmHF9DpavfsgaQvB5MxkLIxSi2Od989zzx6Kzlbe1iL06JhOOjNeVUgFZP4l+dyThE8nez2RAb3Tr5RLSqdlMbGaLffNq8zylpyWOA2Rt3it4f4rB0uSZ83yecWn8FLC3XRaDtL3dbfxy9AVHx8e4uLwSr3HmAcaxPBpKI268u003JkuGfklic6OMd/s78lzc29sSo85UypHxmx5pchkPhayPpBtiQK938o/v6vf7jpXPNsG9GtmT0UH+9D0Hm2t5pP76F6xVKjg6PsHR0RFqtboYa1NkMVwRgTkqOQhZvkPPiSYsEZ8LNESnMTHznBTD+4le8iLjmsl7jXhq5JgSwg1HcPiRe+OetCv/IbLZ9x1hyHsxevbyePyZMKd0Xn91eYGff/4JpyfHEsJJDAvZvOEAGT+Nnc11fP/pIzY3KmD/WMXG0EAbayV0D3ZRvb3FaNBDo9HCkIakCNHptHFydoLsf/pI/+//hXxuzQxkLNx2BH5PGmYVUmkeSkAJ/NEJOGPOHuimBJSAElACSkAJKAEloASUgBJQAkpACSiBFRLgqkHGZD8+vcTN7Z3xKBKaED4s5v4cp5mwnle8GK5QAUAFr3wS4mGFk9hUKqfEA0JSvCwwdIFMcHNCnituXTOxPS/fr3mME+uMltPvj9HpdiVsyV2tLpPD9LbS6fYlLEZvMECv38c4NF5ApH6phHg14Wp6+eSyKBby4CrLQi4rHkqoeKCXmZVsnCWyM0W2kaJv2n/0+mM02j3UG02wDlxB2253JCRFEASiUKEBi4QMGo+RTCYlrIQJ15SC56eQyaSRz+dQzOfAlaPlYkFWANP4YNmNs1qUK+iO0WwGqNUpVwPtToBur4de33764maf3mxokJBIJOH5HjK+L0qccjGPUiEPKrry+SzSGQ+eL+rdSV/98ZdzWSkcBH0JDUTlKw1dtjcrslKV4agW3RrNvoRXOru4Mm7v6ftAjHrGIsP25oa47Wcffu1GRv0RcHZxi6sr44WGxxIYw0u6orCisUelUkY2583enC8qPhiGuLq+xcXlNVqdrjHIGpK9i0Iug53NCva2N5BOL6h9sX2S0nDf9s1IOo43g+EYXfbPRhONVhvNZhP1ZhOdToBWJ0AnCERJJt53QnpeMfcZlWTmXkuKRxV6ESrw/irmUSjkJdwQQydwtTHHF3pcWaZdKO6PP5/h6uoGDJtlfOiwvcfiielP338QgwB6TlrlFvTGuK42cHF1g1qjKXWnoi9FY5ysL96nNtcryOYWbINZ4Vgx9q3hGK0OcFdvStgz8QjQbsmY1u32MOCYNqQy0hiuefSGlUnL+LVeLotxxMHhlnhImG3X2SK/6m/b52xfC4FuN5Qx5vTyCs1OF4MhY00ZZbWfdCVE24eDfQmh47A57bUrEtyKZL8HAz5nRzLe1RsN3NUb0vf5XOH41+315ZkyHAyl/a0HAj5L+WxNJVPIpPnc9JDNZUFPRfRIxrGQ3wyP5ku/5z2yQHUoGL2G9RniIsDnL1/QbLUxoqcjGuclGHItDbb7+8MDCeOyGjTWaHA6YHJM6PdDeQe5rtYlZJX14pFw6F3Cw1///IOEvXBXcOux6p3uGNVaA19OzhAEXfEgQc4M91LMZcH7bX9vy4QlXEHf6PXGuL1rybOEBrCGAg0WRsimk+BzZGtzA/m8vxrML8yFz5vBELi6vhMPbXxGi0qK4WpSSRTyafHIwZA+k5CNs3xsp6cMs+ciuVgO31FOzm7kWcd3EqblWOt5CXm2vj98hxyfc6/ZxkCzNRYj2fPLSwnvKOKxrISDnJ/Cx8M9eTcQ64VnyopXjUnlnXFo3qE5nlbv7nBbq4H16Ub3dr/XA+9rhgFiJX3fl7BdDFmXzWYlZGSlXDDhMIsFZLO+PLt4h7A82ke32iGur6s4vzjDYNiV+zPjedjb3sL6+hpyufREcl7Dz/QOm5x6sCP5R23e6gxRqzdwe3uHWq2GVruNoNeV+6PX68u7Ad8L+Az2Uh7SvodCLo3NtQr2d7exu70OL2Z4Hi+s2R7j6OQcd42mjHcMJ8T3qCTGWC8X8OnwHbL5NJwXvFvGy3lqnyF0Gp0evpycSxuxTvQuxnueYTtpIMX3q2I+CxoLzW5kdX3bknclhtwcO654/6OHQBrd8D2fLHa2NlDMe8x6ZRv/LmH70AidHlaCbl88E47DoYz3Gd8D340P9nZRLKan9x2Fjm8PqxU/q/tKQAkogYUJqMHKwqg0oRJQAkpACSgBJaAElIASUAJKQAkoASWwDIEwpAJnJCsGuWaPK75lxns2E4lDbw/OzHzyZ+Te3HigYDpjoJFwjJKf87dcWZtM0hvL48oMW8Jv+U2FUn8wBhW53W4fQW+ATpeGKgOIwUqvh5ArQyMvD1TmUIFOw4hcLishgVIpKs0TYHQaKs6ZdqWbnYy231Eb2DKoTKFhQLc7kNWYVKLQKGCewUoqmUQyZT4it58SZS6NVvyoHl6Sq/unc+G2nGW+6TyCehuuDqW+gDyprP78WIQAAA8ySURBVO32AnS7DMHEEDF9DIZ0186+khQ50mkPNEbgJ+3Rg0oSVnkpCueYEEF3hG5/iOEoRNJ1ZeU2w1JJG2X8pdphLMYVA3T7hiHvD9N5Q8PFS4kyd2WGSDCKvG4vxGAwFC8lNCBIUv4Ew2zR00wSzqztwlJ9ix2GPZzr6IHeYIRuj/VjeCZHQjKJkjyRgO8lkPVTLze0sn3Ttk9MzuHQGFH0+0O5zzrdQIyq2kEgisVRZKwiHo3oWcKNlGVeSrzt8H6j1yb2URpY0RCJ9xrvM/nYMpf47rSH6PcHwoWii/hcBo+xeF+iEcwK9VAiWRhx6A1C9If0NGC8x9BbUypBRXFCPJwsrcyz7O03a0HDseEYnT7EMMAYTFApGYjBSnjPYMUTYwgarHFcS6d9eL6LtzDwWKKJHiZl/WL9il2bjzDWMxgMMJB+bTwssV+z19MrAOsURa97mOcbHCF7Kp8HwxBBj8+UnjxXjMGKMVoZMnQbPa6EHGn4/OSz04WXovFQFmJERKOsdHoSdo7PU44/8ox5Su5YP5gkY3/gc67XE05yz9HzQYJjvfGqw/BrHINWs1khphnyCJ8JvV4I3gMch8b09OSMQYMVMdzKpFdqKDUMo+e7PCfIm2OG6Rs0rmX/oNcuGdenor4YAfPn+wQNc+Qed9gLqeg2CmdjYJdE4g0V9gsLHxmT9HqjKPSIuP6ahKFK+/SgF+Vm2dhvHrZNPK/AeLrIcKjXNcYcHLPFWM4dI5V0xQPfq5X+9BASGZT0ekNjB0YBo8c4+1cmk4Ln8cExT+DFjlnDlWAQgs+xHo3Qgi6CbgBrsGI983meLwZoHE9p3Mz7i2EMaXAmoQzpnYnyRQYx/Ob9wb4T9DoY0TuIy3cCV7zdcWxw+WIW29gEy1SH6W0d+C7Q6VB2Y0xnjYp5XzoOjc+T8GhARyPiXFoMvBjOiAwfvNdHgvC9r90do88waAytSY8mroPEOEQ6mZBnizzfVv1wjTHhc4GGH70Bn/FDecfkKGues3zGJqXfyd8n93FOcunxmSLGhQN5D+RbFMMl0TsL35PT/Hg02p9cMt1ZpkGmV032aNjXCQZgODmO0xLoSjjyvchFkt4F097DMSR+P75ShokwuqMElMAfnoAarPzhu4ACUAJKQAkoASWgBJSAElACSkAJKAEl8LYE7CR3fH4zXuKyc502n3vXzT0YL+Xb26fInCymhxCzAp2T1FG4GioGIkULV6Vb4xTWgvW+V/dVV82yjOc7p0Am44cyU5E7GnEVvZlop2KUm6zkFw85ZuUxJ9ypPLKK/5XVxSwtv7f81yjRqcxlyCUqTvhtwlPIKtwklSRm9bvIFKuvhK6K6jyn6rGUq9m1LJnbypgsIpppJlOo3Y9ft3Tl2RCmBrZO8W9mTZ3LI3qbeMmL7TPzR2RkF2S7cmX/kEYqDIMS1ZHf8rH9lF4QEsbwjd/sp49ku5hcs6micuXwSjOeLej5308ge/5ipojXxWYWqxORUnHPcYHGHRwbCJtJ2B5UKMp9x44QjQVyMpbHYoK8cSpbt9liovpz7LYoWBXWTerx2HWz+bzBbxYtys5IGc2xTwzGpBEoXuQ1igOwhBdyRZltx+W4PvTVzWHh2Ho6U16vztvmucA3xYh/eIm01wLXviQJ+cerbrvFm9Q51gfZpPYxaOv4Evnf/Jpo7KURguX0gM28E/bYPAFnMrBJZw7Pu3K5Y8yYH2a88szniBKVZfsUf9LIJOS7lrwr8gg3G3LRjEPW2EzGpCgFx2URm//Zy5gfjaqisZhJ42NAdOlKvjheSh/lOyK9Hw3G0TsjhTFeyygvjWpoUCnGNZax/aYkVv7omDzHo/1JsgjYxDBpcmIlVXk0E9ZE3jH4nHs01eMnbDvb+9iOUxPxY+02yWVycnLkRTvzsrYZPVoEL3r0pL1av5WAElACixNQg5XFWWlKJaAElIASUAJKQAkoASWgBJSAElACSmBZArMTmk/Nis7LOz4ZGuU1m6VcZvNl+rkJ5mX+2xyTCe1IXmsYYcW331YyOmHgJP9LJr9tHi/+jgsTbwdmaM/NHJe6zRYYSxPbNakeyWc2i2d/Mx9+WMCDQqbiSrKozHtco2NSTnS9URpQ0W6U7XNbwV43p8xnZZ5JwKxWkM1MruanVaLMzd9qR2ZPzv6em/PswWktLJr4N7O0n9krV/p7KoZkK3Yp9li0ypwnyMX2WSrL2CfeZLMQbOZvVY7Nf+bbVn3m8Mt+ztZlNpd43WzaGGc5HU/D6/l79thsvl/790uhvfS6Jer3WBG2L8/rx7yGH26m30ceIRiKLIZ/Zc3wmJCRDF/jy9bXlhUf6ibPVHtwcsCmft23LXtlPJ8R52uX94w4j59+TlB7njnE4cWPz+YeTzd7jtc9dX42/VO/Z2VYVb6Tl5dY4basOWXY51b8lNzz9oC91mZnj/O37e88Fj9u077RN2W24xLFs59JcZHMFCluaDM5P7tj6xgZat2rDs+xnvbgV6rnpF1m3iXoCUbqPm+MsfVg/SI5bRM9SB5PG0s/i2aVv+nFidu9/rXKAjQvJaAElECMwLfgEC4mju4qASWgBJSAElACSkAJKAEloASUgBJQAv8yBDi5+tqJ4tkJ2nlZzknzJMN4+tfK92RBc05y0t6WPzOpPSf16/nNzXTBg/fY2Cl0HmQF+H0vgWRqFRILljAvi4eXWl72zMNiHxPHXjGRVC6V/5hpLGMKTuuhKG9zlv+z3qE9fH/9sc1injyTkufs8LroGq6Stm7eH2QTSzcnl6UOPdkuswXP/l6upElqkmNW9sMTrNKbb7Zd+B1pfCYKsJm6/WZKmBW27aM8Yw0wU+3YJa8QZDZTautsx46VILt2rGMSu8Wvj+/b87/H73j9fgP5n8JoDO8iAZlwkphhIGiUZw9OTpga2DrNHH6uenEF9WzaF2Y5m81Svyk+PTvR4c9ks4JMDrx2Z5rhfVz3f722lMlAGsv2Kd6vLu/FGVgeMUFjuxyimGKux61YulcVzwJWldeLBXnuQgrJzwMThbkXsjqTZ7qtmx3v7RXMzm48x6xpOBilM9czkT1pM7IXRd9M8sipmZRP/oxnwf34b144KWb2xEyuk3Sx41HVpkeYx4OD09NvtUemk3aJFTLx9BI7JrusjN2k3qZ2c0WPp7XXvMX3DGDxhBRFc51Xt7cQQfNUAkrgj0tADVb+uG2vNVcCSkAJKAEloASUgBJQAkpACSgBJfD1CTwzGX1PoPgE7SLX2TT2+15msR/PnY8lXfkuy35p+TMTySuX7ckM7RR6VIFl6hC1o4i/zHVWnpdcY6999Duqh6hJmGhqrBL9gisNxXpbAex3LNM5h2Jn5+++5Jr5OX3TR613oHh14/tvLbz0N6uLY2Ffs/DZytmyo3th9vRv8ntWFivjc8I8mu7+PTS5teL5xa+N78fT/J73v1KdHi3m0RMWqk1gv81xGrPYPZvy3vf95PdOfcs/5oltn2QTueclmpx86Y7NlKZAbzD02Oxj4tnxNnboG9idI2hcqse63bzL5h2L5/XY/kuvm81vNh8rO9PZ/dk0s3k8+nvOhXMOTS5/6pxNFEsjxkwxGaeGB0wUS2ivjX8/czqe9Mn9RfJZIM0kyWTHlDrzc1qtByeelPJtTj4mA4+zXSbnJzvTQ3GJpqfjR1e3b/vITI7T/jJzQn8qASWgBFZMQEMCrRioZqcElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACcwncE9POz+JHlUCSkAJKAEloASUgBL4gxCgUbFuSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAEnhzAm/tLODNK6AFKAEloASUgBJQAkpACayMgBqsrAylZqQElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWwCAE1WFmEkqZRAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASWBkBNVhZGUrNSAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSmARAmqwsgglTaMElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWwMgJqsLIylJqRElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUwCIE1GBlEUqaRgkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSmBlBNRgZWUoNSMloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSiBRQiowcoilDSNElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUwMoIqMHKylBqRkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAKLEFCDlUUoaRoloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSiBlRFQg5WVodSMlIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBBYhoAYri1DSNEpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAIrI6AGKytDqRkpASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJLEJADVYWoaRplIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBFZGQA1WVoZSM1ICSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJKAEloASUgBJYhIAarCxCSdMoASWgBJSAElACSkAJKAEloASUgBJQAkpACSgBJaAElIASUAJKQAkoASWgBJSAElACSkAJrIzA/wdKnGEJBfSDDwAAAABJRU5ErkJggg==) # + id="0VfgNhh2spS5" colab_type="code" outputId="5571f1c6-9bbe-4c42-d611-e7d9f8562b58" colab={"base_uri": "https://localhost:8080/", "height": 275} import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.externals import joblib def extract_BoW_features(words_train, words_test, vocabulary_size=5000, cache_dir=cache_dir, cache_file="bow_features.pkl"): """Extract Bag-of-Words for a given set of documents, already preprocessed into words.""" # If cache_file is not None, try to read from it first cache_data = None if cache_file is not None: try: with open(os.path.join(cache_dir, cache_file), "rb") as f: cache_data = joblib.load(f) print("Read features from cache file:", cache_file) except: pass # unable to read from cache, but that's okay # If cache is missing, then do the heavy lifting if cache_data is None: # Fit a vectorizer to training documents and use it to transform them # NOTE: Training documents have already been preprocessed and tokenized into words; # pass in dummy functions to skip those steps, e.g. preprocessor=lambda x: x vectorizer = CountVectorizer(max_features=vocabulary_size, preprocessor=lambda x: x, tokenizer=lambda x: x) # already preprocessed features_train = vectorizer.fit_transform(words_train).toarray() # Apply the same vectorizer to transform the test documents (ignore unknown words) features_test = vectorizer.transform(words_test).toarray() # NOTE: Remember to convert the features using .toarray() for a compact representation # Write to cache file for future runs (store vocabulary as well) if cache_file is not None: vocabulary = vectorizer.vocabulary_ cache_data = dict(features_train=features_train, features_test=features_test, vocabulary=vocabulary) with open(os.path.join(cache_dir, cache_file), "wb") as f: joblib.dump(cache_data, f) print("Wrote features to cache file:", cache_file) else: # Unpack data loaded from cache file features_train, features_test, vocabulary = (cache_data['features_train'], cache_data['features_test'], cache_data['vocabulary']) # Return both the extracted features as well as the vocabulary return features_train, features_test, vocabulary # Extract Bag of Words features for both training and test datasets features_train, features_test, vocabulary = extract_BoW_features(words_train, words_test) # Inspect the vocabulary that was computed print("Vocabulary: {} words".format(len(vocabulary))) import random print("Sample words: {}".format(random.sample(list(vocabulary.keys()), 8))) # Sample print("\n--- Preprocessed words ---") print(words_train[5]) print("\n--- Bag-of-Words features ---") print(features_train[5]) print("\n--- Label ---") print(labels_train[5]) # + id="K2l8qoYG1WKp" colab_type="code" outputId="51bbccb4-eb47-459f-9b88-45bc080a55d5" colab={"base_uri": "https://localhost:8080/", "height": 279} # Plot the BoW feature vector for a training document plt.plot(features_train[5,:]) plt.xlabel('Word') plt.ylabel('Count') plt.show() # + [markdown] id="20mIiXNO1x6j" colab_type="text" # ### Zipf yasası # # [Zipf yasası](https://en.wikipedia.org/wiki/Zipf%27s_law), çok sayıda yazı içeren bir veri setinde, herhangi bir kelimenin sıklığının frekans tablosundaki sıralamasıyla ters orantılı olduğunu belirten bir yasadır. Bu yüzden, en sık kullanılan kelime, ikinci en sık kullanılan kelimeden yaklaşık iki kat, en sık kullanılan üçüncü kelimeden üç kat sayıda yer alacaktır. # + id="EbRkuLA515Op" colab_type="code" outputId="c6c92c8f-38ec-42a7-f029-17a02b77a59a" colab={"base_uri": "https://localhost:8080/", "height": 283} # Find number of occurrences for each word in the training set word_freq = features_train.sum(axis=0) # Sort it in descending order sorted_word_freq = np.sort(word_freq)[::-1] # Plot plt.plot(sorted_word_freq) plt.gca().set_xscale('log') plt.gca().set_yscale('log') plt.xlabel('Rank') plt.ylabel('Number of occurrences') plt.show() # + [markdown] id="gQk2qP352Mzp" colab_type="text" # ### Özellik(Feature) vektörlerini normalize etmek # # Bag-of-Words özellikleri, sadece kelime sayıları olduğu için anlaşılması kolaydır. Ancak, sayımlar çok değişkenlik gösterebilir ve öğrenme algoritmaları için iyi bir girdi olmayabilir. Bu yüzden, çok da ilerlemeden, BoW özellik vektörlerini normalize edelim. # # Bu şekilde her yazı içinde oranlanmış olacak ve uzun yazıların dominasyon etkisi olmayacaktır. # + id="mLgM2E2i2Vh8" colab_type="code" colab={} import sklearn.preprocessing as pr # Normalize BoW features in training and test set features_train = pr.normalize(features_train, axis=1) features_test = pr.normalize(features_test, axis=1) # + [markdown] id="RgnLnxDV2i26" colab_type="text" # ## Adım 4: Bag of Words kullanarak Sınıflandırma # # Verilerin hepsi doğru bir şekilde dönüştürüldü şimdi bir sınıflandırıcıya aktarabiliriz. Temel bir model elde etmek için, scikit-learn'den (özellikle, [`GaussianNB`]) bir Naive Bayes sınıflandırıcı eğitiyoruz. (http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html) ve test setindeki doğruluğunu değerlendiriyoruz. # # # ![image.png](data:image/png;base64,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) # # # ![image.png](data:image/png;base64,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) # # # # ![image.png](data:image/png;base64,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) # + id="d4wHXRU_2qmJ" colab_type="code" outputId="02426e01-e5b6-414b-dd6a-7e4658d282f4" colab={"base_uri": "https://localhost:8080/", "height": 34} from sklearn.naive_bayes import GaussianNB # Gaussian Naive Bayes algoritmasını train ediyoruz clf1 = GaussianNB() clf1.fit(features_train, labels_train) # Ortalama accuracy score'u hem train hem test verisi için hesaplıyoruz. print("[{}] Accuracy: train = {}, test = {}".format( clf1.__class__.__name__, clf1.score(features_train, labels_train), clf1.score(features_test, labels_test))) # + [markdown] id="litxxnD63JNP" colab_type="text" # ### Gradient-Boosted Decision Tree classifier - Karar Ağaçları # # Ağaç tabanlı algoritmalar, Bag of Words üzerinde oldukça iyi çalışır, kelimelerin süreksiz ve seyrek olmasından dolayı ağaç tabanlı algoritma yapısı ile güzel bir şekilde eşleşir. Şimdiki görevimiz, scikit-learn'ın Gradient-Boostted Decision Tree sınıflandırma algoritmasını kullanarak Naive Bayes sınıflandırıcısının performansını geliştirmeye çalışmak. # BoW (Bag of Words) verilerini sınıflandırmak için, scikit-learn kütüphanesinden [`GradientBoostingClassifier`] (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) kullanıyoruz. # # ![image.png](data:image/png;base64,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) # + id="46_RQIAT3fgU" colab_type="code" outputId="148ecd43-2dac-48ac-ce08-7c69661f3a10" colab={"base_uri": "https://localhost:8080/", "height": 34} from sklearn.ensemble import GradientBoostingClassifier n_estimators = 32 def classify_gboost(X_train, X_test, y_train, y_test): # Classifier'ı initialize ediyoruz clf = GradientBoostingClassifier(n_estimators=n_estimators, learning_rate=1.0, max_depth=1, random_state=0) # GradientBoostingClassifier kullanarak train ediyoruz. clf.fit(X_train, y_train) # Train ve Test veri seti için başarı oranlarını print ediyoruz. print("[{}] Accuracy: train = {}, test = {}".format( clf.__class__.__name__, clf.score(X_train, y_train), clf.score(X_test, y_test))) # Model'i return ediyoruz return clf clf2 = classify_gboost(features_train, features_test, labels_train, labels_test) # + [markdown] id="uVQiZtIv4iZb" colab_type="text" # ## Adım 5: RNN kullanarak Sınıflandırma # # Duygu analizi görevinin geleneksel bir makine öğrenme yaklaşımıyla nasıl çözülebileceğini gördük: BoW + doğrusal olmayan bir sınıflandırıcı. Şimdi Keras'ta duyarlılık analizi yapmak için Tekrarlayan Sinir Ağlarını(RNN) ve özellikle LSTM kullanacağız. [IMDB](https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification) verisini ve aynı büyüklükteki sözlüğü kullanarak geliştireceğiz. # + id="GpqZoSlj42ob" colab_type="code" outputId="d190b0ac-e9bd-4498-a9d6-baf75723d2b7" colab={"base_uri": "https://localhost:8080/", "height": 85} from keras.datasets import imdb # import the built-in imdb dataset in Keras # Set the vocabulary size vocabulary_size = 5000 # Load in training and test data (note the difference in convention compared to scikit-learn) (X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocabulary_size) print("Loaded dataset with {} training samples, {} test samples".format(len(X_train), len(X_test))) # + id="bR-6Wntu5qj8" colab_type="code" outputId="1cd23861-a333-4eca-a65e-e81755042d4b" colab={"base_uri": "https://localhost:8080/", "height": 105} # Inspect a sample review and its label print("--- Review ---") print(X_train[7]) print("--- Label ---") print(y_train[7]) # + [markdown] id="TSpQWvL456Q6" colab_type="text" # Kullanacağımız etiketler 0 için negatif, 1 için pozitif değeri içermektedir. İncelemenin(review) kendisi bir sayı dizisi olarak saklanır. Bunlar, bireysel kelimelere önceden atanmış kelime id'leridir. Onları tekrar orijinal kelimelerle eşlemek için `imdb.get_word_index ()` tarafından döndürülen sözlüğü kullanabilirsiniz. # + id="DPXA7VJu5-_a" colab_type="code" outputId="99d37fef-2b95-4148-f09a-6a0a2060fbe0" colab={"base_uri": "https://localhost:8080/", "height": 139} # Map word IDs back to words word2id = imdb.get_word_index() id2word = {i: word for word, i in word2id.items()} print("--- Review (with words) ---") print([id2word.get(i, " ") for i in X_train[7]]) print("--- Label ---") print(y_train[7]) # + [markdown] id="wO55nJHN6fxk" colab_type="text" # Bir belgedeki her bir kelimenin sayımını basitçe özetlediğimiz Bag of Words yaklaşımından farklı olarak, bu ifade temel olarak sözcük dizisinin tamamını (eksi noktalama, stopwords vb.) korur. Bu, RNN'lerin çalışması için kritiktir. Ama aynı zamanda şimdi özelliklerin(feature) farklı uzunluklarda olabileceği anlamına da geliyor! # # ### Pad sequences # # Bu verileri RNN'inize beslemek için tüm giriş belgelerinin aynı uzunlukta olması gerekir. Daha uzun incelemeleri keserek ve daha kısa incelemeleri boş bir değerle doldurarak (0) maksimum review uzunluğunu `max_words`'ile sınırlayalım. Bunu Keras'ta [`pad_sequences()`](https://keras.io/preprocessing/sequence/#pad_sequences) fonksiyonunu kullanarak kolayca yapabilirsiniz. Şimdilik, `max_words` ayarını 500’e ayarlayın. # + id="fdDXYA5A6pQK" colab_type="code" colab={} from keras.preprocessing import sequence # Set the maximum number of words per document (for both training and testing) max_words = 500 # Pad sequences in X_train and X_test X_train = sequence.pad_sequences(X_train, maxlen=max_words) X_test = sequence.pad_sequences(X_test, maxlen=max_words) # + [markdown] id="Q6f_Lbbw7C5z" colab_type="text" # ### Duygu Analizi için RNN Modeli Dizayn Etme # # Input değerimiz, maksimum uzunluk = `max_words` kelimelerinin bir dizisidir (teknik olarak, tamsayı kelime id'leri) ve output'umuz bir ikili duygu etiketidir (0 veya 1). # # Simple RNN: Gate yok # # Gated Recurrent Unit (GRU): Update gate bulunuyor. Memory bulunmuyor. # # Long Short Term Memory Unit (LSTM): Update, Forget ve Output gate bulunuyor. # # Daha fazla bilgi için: # http://colah.github.io/posts/2015-08-Understanding-LSTMs/ # # http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ # + id="6AgaQPlY7Qrv" colab_type="code" outputId="5bed47e9-d99c-4e83-bcad-e26f03f4dfa4" colab={"base_uri": "https://localhost:8080/", "height": 394} from keras.models import Sequential from keras.layers import Embedding, LSTM, Dense, Dropout # RNN model embedding_size = 32 model = Sequential() model.add(Embedding(vocabulary_size, embedding_size, input_length=max_words)) model.add(LSTM(100)) model.add(Dense(1, activation='sigmoid')) print(model.summary()) # + [markdown] id="cs0iYl3h8_kf" colab_type="text" # ### Modelimizi Eğitelim ve Değerlendirelim # # Şimdi modelimizi eğitmeye hazırız. Keras dünyasında, öncelikle eğitim sırasında kullanmak istediğimiz loss fonksiyonunu ve optimizasyon fonksiyonunu ve ölçmek istediğimiz değerlendirme metriklerini belirterek modelinizi derlememiz gerekiyor. # + id="4iLU7GtM9SVO" colab_type="code" outputId="f5285022-8259-4bd6-b5a2-e962b4234591" colab={"base_uri": "https://localhost:8080/", "height": 156} # Compile our model, specifying a loss function, optimizer, and metrics model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # + [markdown] id="_XTTg7Xd9485" colab_type="text" # Derlendikten sonra eğitim sürecini başlatabiliriz. Belirlememiz gereken iki önemli eğitim parametresi vardır - batch size ve toplam training adım sayısı(number of training epochs) , model mimarimizle birlikte toplam eğitim süresini belirler. # # Eğitim biraz zaman alabilir! Mümkünse, bir GPU kullanmayı düşünün, çünkü tek bir antrenman CPU üzerinde birkaç saat sürebilir. # # > İpucu: Eğitim sırasında doğrulama(validation) için kullanılacak eğitim setinin küçük bir kısmını bölebiliriz. Bu, eğitim sürecini izlemeye ve potansiyel overfit tespit etmeye yardımcı olacaktır. `validation_data` parametresini kullanarak `model.fit()` olarak ayarlanmış bir doğrulama sağlayabilir veya Keras'ın bu amaçla bir kenara bırakması için eğitim verilerinin bir kısmını (genellikle% 5-10) yalnızca `validation_split` olarak belirtebiliriz. Doğrulama metrikleri, her dönemin sonunda bir kez değerlendirilir. # + id="z8XlJwbU-PA0" colab_type="code" outputId="552fa56b-c899-4700-e6b7-dd3706e27cd1" colab={"base_uri": "https://localhost:8080/", "height": 445} # Specify training parameters: batch size and number of epochs batch_size = 64 num_epochs = 3 # Reserve/specify some training data for validation (not to be used for training) X_valid, y_valid = X_train[:batch_size], y_train[:batch_size] # first batch_size samples X_train2, y_train2 = X_train[batch_size:], y_train[batch_size:] # rest for training # Train our model model.fit(X_train2, y_train2, validation_data=(X_valid, y_valid), batch_size=batch_size, epochs=num_epochs) # + id="ZSfNtCL1OyqI" colab_type="code" colab={} import os cache_dir = os.path.join("cache", "sentiment_analysis") # where to store cache files os.makedirs(cache_dir, exist_ok=True) # ensure cache directory exists # Save our model, so that we can quickly load it in future (and perhaps resume training) model_file = "rnn_model.h5" # HDF5 file model.save(os.path.join(cache_dir, model_file)) # Later we can load it using keras.models.load_model() #from keras.models import load_model #model = load_model(os.path.join(cache_dir, model_file)) # + [markdown] id="_8lDfyO8O86s" colab_type="text" # Modelinizi geliştirdikten sonra, daha önce karşılaşılmayan test verilerinde ne kadar iyi performans gösterdiğini görme zamanı geldi. # # # + id="igfDR9bCPFgu" colab_type="code" outputId="81db2108-cf30-4ab5-a138-48a5a4e36c51" colab={"base_uri": "https://localhost:8080/", "height": 34} # Evaluate our model on the test set scores = model.evaluate(X_test, y_test, verbose=0) # returns loss and other metrics specified in model.compile() print("Test accuracy:", scores[1]) # scores[1] should correspond to accuracy if you passed in metrics=['accuracy']	4,725,117
/Dataframes.ipynb	e934b85b5c1de8137f9d59640d06999ad99e4798	[]	no_license	e10lee/Dataframes	https://github.com/e10lee/Dataframes	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	16,655	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="U3Yz3s2uio1F" # # # Q1-1: Write a program that create a dataframe from the data. # # Q1-2: Select people that are psychologist or teacher and their number of cars is greater than their family size. # # Q1-3: Select people who have at most 2 family members and at least 1 car. # # Q1-4: Write a code that get number of unique jobs in this dataset. # # # # # + id="yR8lcOtcjxXD" import pandas as pd data = {"name": ["Joseph", "Jacob", "Sam", "Jesee", "Ryan", "Lisa", "Lee"], "job": ["teacher", "psychologist", "data scientist", "software developer", "psychologist", "psychologist", "teacher"], "family_size": [3, 2, 1, 4, 2, 3, 2], "num_cars": [3, 1, 1, 2, 2, 4, 1]} # + id="Youi5qMaP34p" colab={"base_uri": "https://localhost:8080/"} outputId="67e3292d-3940-492e-afb0-98d66dc18336" # Type your solution here #Q1-1 frame = pd.DataFrame(data, columns = ["name", "job", "family_size", "num_cars"], index=["one", "two", "three", "four", "five", "six", "seven"]) print(frame) # + id="mbzOjeYhx2nn" colab={"base_uri": "https://localhost:8080/", "height": 80} outputId="453502ac-9cd1-41f6-c0a8-d25eaf2e8486" #Q1-2 frame[((frame["job"] == "teacher") \| (frame["job"] == "psychologist")) & (frame["num_cars"] > frame["family_size"])] # + id="wli5wqK1x9x2" colab={"base_uri": "https://localhost:8080/", "height": 173} outputId="fac483ef-c36c-4fd6-b78e-c3f2ef315083" #Q1-3 frame[(frame["family_size"] <= 2) & (frame["num_cars"] >= 1)] # + id="pRJMXnoTyA15" colab={"base_uri": "https://localhost:8080/"} outputId="4f1a5e60-15cc-484c-b6e9-370d21d8ad3e" #Q1-4 result = pd.value_counts(data["job"]) print(result) # + [markdown] id="Ow6nG8k4l91X" # Q2. Lets consider you have two series like the below cell. Compute the mean of weights of each fruit. # + id="u-X_DoOzVLLB" import numpy as np fruit = pd.Series(np.random.choice(['apple', 'banana', 'carrot'], 10)) weights = pd.Series(np.linspace(1, 10, 10)) # + id="S419nzYnpU__" colab={"base_uri": "https://localhost:8080/"} outputId="3a6817ec-3c1c-40ea-936a-f8b78fb19c17" # Type your solution here results = weights.groupby(fruit).mean() print(results) # + [markdown] id="g4lKaPIhtVrt" # Q3. Consider the below course_name array: # # Q3-1: Write a NumPy program to get the indices of the sorted elements of course_name array. # # Q3-2: Write numpy code to check whether each element of course_name array starts with "P". # # + id="RUP_jp2KtVrx" import numpy as np course_name = np.array(['Python', 'JS', 'examples', 'PHP', 'html']) # + id="U1i27POxv-o_" colab={"base_uri": "https://localhost:8080/"} outputId="105d99a2-0928-4b1e-97d4-4879d8bd47f5" # Type your solution here #Q3-1 indice = np.argsort(course_name) print(indice) # + id="TDEJJQtQMH7V" colab={"base_uri": "https://localhost:8080/"} outputId="0a780954-da26-42cc-fd4c-588263225337" Pstart = np.char.startswith(course_name, "P") print(Pstart) # + [markdown] id="sUFItNFOxwkz" # Q4. Consider the below student_id array: # # Q4-1: Reverse the student_id array. Print both original and reversed array. # # Q4-2: Get the 3-largest values of student_id array. # + id="Rl5qzEjFv0nw" colab={"base_uri": "https://localhost:8080/"} outputId="3465fdf2-bb48-4144-a659-4812de23c8c0" import numpy as np #Q4-1 student_id = np.array([1023, 5202, 6230, 1671, 1682, 5241, 4532]) student_id2 = student_id[::-1] print(student_id) print(student_id2) # + id="EetvHGrUxMCC" colab={"base_uri": "https://localhost:8080/"} outputId="65c557bd-f05f-41b5-8e71-0bba6e2bddfe" # Type your solution here #Q4-2 sort = np.argsort(student_id) result = [student_id[sort][-3:]] print(result) # + [markdown] id="uKRyDVuW6B19" # # Q5: Write a numpy program to print sum of all the multiples of 3 or 5 below 100 # + id="ZIixq1gz6OK3" colab={"base_uri": "https://localhost:8080/"} outputId="92e49bc9-9a85-46da-935d-84621cd8e34e" # Type your solution here # Hint: you can use arange to start off range = np.arange(1, 100) multiple = range[(range % 3 == 0) \| (range % 5 == 0)] print(multiple[:1000]) print(multiple.sum()) # + [markdown] id="2CWJOsP24c34" # Q6. Consider the below array. # # Q6.1. Write a code to swap column 1 with column 2. # # Q6.2. Write a code to swap row 0 with row 1. # + id="3OG6X5Eg5KSM" colab={"base_uri": "https://localhost:8080/"} outputId="8a66c6e2-f5f2-46e7-dc52-115509391296" import numpy as np arr = np.arange(12).reshape(3,4) arr[:, [1, 0]] = arr[:, [0, 1]] print(arr) # + id="Q-Q91DpO5YYW" colab={"base_uri": "https://localhost:8080/"} outputId="a17e754e-4f58-4fc5-cbbf-c05621901e1b" # Type your solution here arr = np.arange(12).reshape(3,4) arr[[1,0],:] = arr[[0,1],: ] print(arr)	4,952
/Untitled.ipynb	dec5b4926ca931754e771bbc82a18ab34fcde452	[]	no_license	piggy2303/chatbot_spech_to_text	https://github.com/piggy2303/chatbot_spech_to_text	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,028	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- print("hello") mport fileinput # + ### data from both ksin and ppin inp = fileinput.FileInput('input/kinases_human.txt') pkin = [] for line in inp: pkin.append(line.split('\n')[0]) d = {} df = pd.read_csv('input/ksin.csv') for k, s in zip(df.iloc[:, 0], df.iloc[:, 5]): if k not in d: d[k] = {s} else: d[k].add(s) df1 = pd.read_csv('input/ppin.csv') for p1, p2 in zip(df1.iloc[:, 0], df1.iloc[:, 5]): if p1 in pkin: if p1 not in d: d[p1] = {p2} else: d[p1].add(p2) if p2 in pkin: if p2 not in d: d[p2] = {p1} else: d[p2].add(p1) allnp = open('output_cheng_all_human.gmt', 'w+') fnp = open('output_cheng_fourplusinteractions_human.gmt', 'w+') uSubs = set() numKSI = 0 numKins = 0 for k in d: temp = set() for x in d[k]: if x != x: temp.add(k) else: temp.add(x) allnp.write('{0}_cheng_human\t'.format(k) + '\t'.join(temp) + '\n') if len(temp) >= 4: numKSI += len(temp) numKins += 1 {uSubs.add(x) for x in temp} fnp.write('{0}_cheng_human\t'.format(k) + '\t'.join(temp) + '\n') print('{0}\t#kins: {1}\t#ksi: {2}\t#usubs: {3}'.format('human', numKins, numKSI, len(uSubs))) allnp.close() fnp.close() # - x = [k for k in d if k not in pkin] ## 138 kinases in ksin that aren't defined in pkin #print(x)	1,742
/chapter_13/01_fid_numpy.ipynb	a14e552dd995e108c48915fe2eff55790dddd373	[]	no_license	fenago/generative-adversarial-networks	https://github.com/fenago/generative-adversarial-networks	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	2,482	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # example of calculating the frechet inception distance import numpy from numpy import cov from numpy import trace from numpy import iscomplexobj from numpy.random import random from scipy.linalg import sqrtm import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import warnings warnings.simplefilter("ignore") # calculate frechet inception distance def calculate_fid(act1, act2): # calculate mean and covariance statistics mu1, sigma1 = act1.mean(axis=0), cov(act1, rowvar=False) mu2, sigma2 = act2.mean(axis=0), cov(act2, rowvar=False) # calculate sum squared difference between means ssdiff = numpy.sum((mu1 - mu2)*2.0) # calculate sqrt of product between cov covmean = sqrtm(sigma1.dot(sigma2)) # check and correct imaginary numbers from sqrt if iscomplexobj(covmean): covmean = covmean.real # calculate score fid = ssdiff + trace(sigma1 + sigma2 - 2.0 covmean) return fid # define two collections of activations act1 = random(102048) act1 = act1.reshape((10,2048)) act2 = random(102048) act2 = act2.reshape((10,2048)) # fid between act1 and act1 fid = calculate_fid(act1, act1) print('FID (same): %.3f' % fid) # fid between act1 and act2 fid = calculate_fid(act1, act2) print('FID (different): %.3f' % fid)	1,501
/.ipynb_checkpoints/Covid_county_analysis_BARCHART-checkpoint.ipynb	289cd3abb71ac87fb2544f4b42f23525780c0b3f	[]	no_license	alexF3/covidDeathRateBarChart_nov	https://github.com/alexF3/covidDeathRateBarChart_nov	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	186,188	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ###### Analysis of Covid-19 death rates at the county level # # # # BLUF (bottom line up front): # # In November, Covid has made it more deadly to live in more than 600 counties in the US than it was to deploy to Iraq and Afghanistan during the deadliest year of those wars. # # ### Let's compare the rate of Covid-19 deaths in various geography to historical death rates by other causes. This will help establish a more intuitive sense of "risk" that is easier to communicate to a general audience. # # ### Let's establish the mortality rate for troops deployed to Iraq and Afghanistan during the deadliest years of those wars # # ### The commonly used metric for mortality statistics is "deaths per hundred thousand population per year." We'll use data from Statista and a 2018 RAND report to establish a measurement of "deaths per hundred-thousand-deployed-troop-years" for each year. # # ### That's a bit of a mouthful...we're summing all of the time spent deployed to the wars by all troops in a year ('hundred thousand troop-years') for each of the years 2001 through 2015. # # ### https://www.statista.com/statistics/263798/american-soldiers-killed-in-iraq/ # ### https://www.statista.com/statistics/262894/western-coalition-soldiers-killed-in-afghanistan/ # ### https://www.rand.org/pubs/research_reports/RR1928.html?adbsc=social_20180320_2212921&adbid=975928167633334272&adbpl=tw&adbpr=22545453 # # First, let's walk through the methodology and check it against the Johns Hopkins Covid-19 tracker page from IPython.core.display import Image, display import os display(Image(os.getcwd()+'/math explainer/math explainer.002.jpeg')) display(Image(os.getcwd()+'/math explainer/math explainer.003.jpeg')) display(Image(os.getcwd()+'/math explainer/math explainer.004.jpeg')) # # Change to monthly data 361.4(30/365) # # Load libraries and prep for analysis # + import os import wget import pandas as pd import geopandas as gpd import shapely from shapely.geometry import shape from shapely.ops import cascaded_union import numpy as np import geopy.distance import shapely import matplotlib.pyplot as plt import matplotlib.ticker as mtick from shapely.geometry import Point import json import datetime as dt # - # # Load summary of deployment and mortality data from Rand and Statista warDeaths = pd.read_csv(os.getcwd()+'/data/iraqAfghanWarDeathRates.csv') warDeaths # convert deployed years colum from string to float warDeaths.troop_years_deployed = warDeaths.troop_years_deployed.str.replace(',','').astype(float) # Check the math for 2007: 100000((904+117)/282546) # # Make 2018 MONTHLY rate of death by cause # ### US 2018 population: http://www2.census.gov/programs-surveys/popest/tables/2010-2018/national/totals/na-est2018-01.xlsx # ### US 2018 deaths: https://www.cdc.gov/nchs/fastats/deaths.htm # ### US 2018 traffic deaths: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826 usPop2018 = 328082386 usDeathTypes = pd.read_csv(os.getcwd()+'/data/cdc_us2018_deathCounts.csv') trafficDeaths = pd.DataFrame({'cause':['traffic death'],'deaths':[36560]}) usDeathTypes = pd.concat([usDeathTypes,trafficDeaths]) usDeathTypes['deathRate'] = round((30/365)100000 usDeathTypes.deaths/usPop2018,1) usDeathTypes # ### The deadliest year in the combined war data was 2007 at __361 fatalities per 100,000 troop-years__ # # Load the latest Covid data from JHU Github # + fpData = os.getcwd()+'/data/' ## Get daily update of hopkins time series file for confirmed US cases wget.download('https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv',\ out = fpData +'covid_hopkins_overTime_CONFIRMED.csv') ## if file saves as a ____(01).csv, delete the old file and rename new to "covid_hopkins_overTime_CONFIRMED.csv" if os.path.exists(fpData + "covid_hopkins_overTime_CONFIRMED (1).csv"): os.remove(fpData + "covid_hopkins_overTime_CONFIRMED.csv") os.rename(fpData + "covid_hopkins_overTime_CONFIRMED (1).csv",fpData + "covid_hopkins_overTime_CONFIRMED.csv") # - fpData = os.getcwd()+'/data/' ## Get daily update of hopkins time series file for confirmed US cases wget.download('https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv',\ out = fpData +'covid_hopkins_overTime_DEATHS.csv') ## if file saves as a ____(01).csv, delete the old file and rename new to "covid_hopkins_overTime_CONFIRMED.csv" if os.path.exists(fpData + "covid_hopkins_overTime_DEATHS (1).csv"): os.remove(fpData + "covid_hopkins_overTime_DEATHS.csv") os.rename(fpData + "covid_hopkins_overTime_DEATHS (1).csv",fpData + "covid_hopkins_overTime_DEATHS.csv") # + # Read in new US Confirmed Timeseries data fpData = os.getcwd()+'/data/' covidData = pd.read_csv(fpData +'covid_hopkins_overTime_CONFIRMED.csv',dtype={'FIPS':str}) covidData.FIPS = covidData.FIPS.str.split('.').str[0].str.zfill(5) covidDeaths = pd.read_csv(fpData +'covid_hopkins_overTime_DEATHS.csv',dtype={'FIPS':str}) covidDeaths.FIPS = covidDeaths.FIPS.str.split('.').str[0].str.zfill(5) # collect dates from timeseries file dates = [] for i in covidData.columns: if '/' in i: dates.append(i) fipsList = covidData[~covidData.FIPS.isnull()].FIPS.unique().tolist() # - countyPops = pd.read_csv('/Users/alex/Documents/Compare Hopkins and NYT/co-est2019-alldata.csv',encoding='Latin1',\ dtype={'STATE':str,'COUNTY':str}) countyPops['fips'] = countyPops.STATE + countyPops.COUNTY countyPops = countyPops[countyPops.COUNTY!='000'] covidData = pd.merge(covidData,countyPops[['fips','POPESTIMATE2019']],left_on='FIPS',right_on='fips',how='left') covidDeaths = pd.merge(covidDeaths,countyPops[['fips','POPESTIMATE2019']],left_on='FIPS',right_on='fips',how='left') dates = [] for i in covidDeaths.columns: if '/' in i: dates.append(i) covidDeaths.head() # # Make comparative rate bar charts # ### dataframe of annual rates of death in the US from most recent year available warDeaths warDeaths.deathsPerHunKyr.max()(30/365) # Data: https://www.statista.com/statistics/248622/rates-of-leading-causes-of-death-in-the-us/ # https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826 {'traffic accident':11.2,'suicide':14.2,'influenza/pneumonia':14.9,\ 'cancer':141.9,'heart disease':163.6,'Worst annual Iraq/Afghanistan losses':361.4} # formatting names for vis later (endlines give spacing off x axis) df = pd.DataFrame({'cause':['\nUS\ntraffic accident\n(2018)','\nUS\nsuicide\n(2018)','\nUS\ninfluenza/\npneumonia\n(2018)','\nUS\ncancer\n(2018)','\nUS\nheart\ndisease\n(2018)','\ndeployed troops\nworst year of \nIraq/Afghanistan\n(2007)'],\ 'monthlyDeathRate':[usDeathTypes[usDeathTypes.cause=='traffic death'].deathRate.item(),\ usDeathTypes[usDeathTypes.cause=='suicide'].deathRate.item(),\ usDeathTypes[usDeathTypes.cause=='flu_pneumonia'].deathRate.item(),\ usDeathTypes[usDeathTypes.cause=='cancer'].deathRate.item(),\ usDeathTypes[usDeathTypes.cause=='heart disease'].deathRate.item(),\ round(warDeaths.deathsPerHunKyr.max()(30/365),1)]}) df # ## So: an average month in the deadliest year of the wars in Iraq and Afghanistan had a mortality rate of 29.7 deaths per 100,000 troops # endDate = dates[-1] endDate = '11/30/20' dateTimeEndDay = dt.datetime.strptime(endDate,'%m/%d/%y') dateTimeBeginDay = dateTimeEndDay - dt.timedelta(days=30) beginDate = dt.datetime.strftime(dateTimeBeginDay,'%-m/%-d/%y') print('endDate:{}'.format(endDate)) print('beginDate:{}'.format(beginDate)) # + # Calculate death rate in the last 30 days in the # states of ND, SD, MT combined stateDeathRate = covidDeaths[['Admin2','Province_State',beginDate,endDate,'POPESTIMATE2019']] stateDeathRate.head() # - # Check population sums stateDeathRate[stateDeathRate.Province_State=='Montana'].POPESTIMATE2019.sum() # + # This checks withe the US Census 2019 population of Montana # https://www.census.gov/quickfacts/MT # 1,068,778 # + stateDeathRate['30dayDeaths'] = stateDeathRate[endDate] - stateDeathRate[beginDate] # - mtDeaths = stateDeathRate[stateDeathRate.Province_State=='Montana']['30dayDeaths'].sum() mtPop = stateDeathRate[stateDeathRate.Province_State=='Montana'].POPESTIMATE2019.sum() mt30dayDeathRate = 100000(mtDeaths/mtPop) mt30dayDeathRate stateDeathRate = pd.DataFrame(stateDeathRate.groupby('Province_State')[['30dayDeaths','POPESTIMATE2019']].sum()).reset_index() stateDeathRate['30dayDeathRate'] = round(100000(stateDeathRate['30dayDeaths']/stateDeathRate['POPESTIMATE2019']),1) stateDeathRate = stateDeathRate[(~stateDeathRate['30dayDeathRate'].isnull())&(stateDeathRate.POPESTIMATE2019>0)] stateDeathRate.sort_values('30dayDeathRate',ascending=False) # ### Look at national covid death rate over the 30 day window stateDeathRate.POPESTIMATE2019.sum() national30dayDeathRate = 100000stateDeathRate['30dayDeaths'].sum()/stateDeathRate.POPESTIMATE2019.sum() national30dayDeathRate usDeathRate = round(100000(covidDeaths[endDate].sum() - covidDeaths[beginDate].sum())/covidDeaths.POPESTIMATE2019.sum(),1) usDeathRate # + df = pd.concat([df[:3],pd.DataFrame({'cause':['\nUS\nCovid'],\ 'monthlyDeathRate':[usDeathRate]}), df[3:]]) # - df # ## North Dakota and South Dakota had higher Covid-19 death rates in November than an average month in the dealiest year of Iraq and Afghanistan (which was 29.7 fatalities per 100,000 deployed troops per month) # + df = df[df.cause!=''] # remove placehoders dg = pd.DataFrame({'cause':['\nND\nCovid','\nSD\nCovid'],\ 'monthlyDeathRate':\ [stateDeathRate[stateDeathRate.Province_State=='North Dakota']['30dayDeathRate'].item(),\ stateDeathRate[stateDeathRate.Province_State=='South Dakota']['30dayDeathRate'].item(),\ ]}) df = pd.concat([df,dg]) df # - my_colors = ['#968c81','#968c81','#968c81','#cc1212','#968c81','#968c81','#968c81','#cc1212','#cc1212','#cc1212'] # + fig, ax = plt.subplots(1, figsize=(30, 10))# remove the axis ax = df.plot.bar(ax=ax,x='cause',y='monthlyDeathRate',rot=0,ylim=((0,100)),fontsize=20,legend=False,color=my_colors) plt.title('\nDeath Rates (per hundred thousand people per month)\n', fontsize=40) ax.tick_params(axis='x', which='both', labelsize=25) ax.get_yaxis().set_major_formatter( mtick.FuncFormatter(lambda x, p: format(int(x), ','))) for p in ax.patches: ax.annotate(str(p.get_height()), (p.get_x() + .07, p.get_height() +3),fontsize=30) # plt.axis('off') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['left'].set_visible(False) ax.set(yticklabels=[]) ax.set(ylabel=None) # remove the y-axis label ax.tick_params(left=False) # remove the ticks ax.set(xlabel=None) # remove the x-axis label ax.annotate('Data: Johns Hopkins CSSE, NHTSA, CDC', xy=(0.76, .01), xycoords='figure fraction', fontsize=17, color='#555555') filename = os.getcwd()+ '/deathRateBarChart_NationalWithStates.png' plt.savefig(filename,dpi=300,bbox_inches="tight",facecolor='white') barsList = [filename] # -	11,631
/SVM, DT and RF.ipynb	0dd6575ca6fb03af2b21c5b53804cbd25c7e0fe8	[]	no_license	nishchalgpt/powergrid-stock-research	https://github.com/nishchalgpt/powergrid-stock-research	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	301,687	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda root] # language: python # name: conda-root-py # --- # ## Coursera Assignment: Linear Regression # + from __future__ import division, print_function import numpy as np import pandas as pd import matplotlib.pyplot as plt # Creates a table data structure which is easy to manipulate df = pd.read_csv("machine_learning_andrewng/ex1data1.csv", header=None) df.rename(columns={0: 'population', 1: 'profit'}, inplace=True) df.head() # - # visualising the data fig = plt.figure(num=None, figsize=(12, 8), dpi=80, facecolor='w') plt.scatter(df['population'], df['profit'], marker='x', color='red', s=20) plt.xlim([4, 24]) plt.xticks(range(4, 26, 2)) plt.yticks(range(-5, 30, 5)) plt.xlabel("Population in 10,000") plt.ylabel("Profit in $10,000") plt.title("Scatter plot of training data\n") plt.show() class LinearRegression(object): """Performs Linear Regression using Batch Gradient Descent.""" def __init__(self, X, y, alpha=0.01, n_iterations=5000): """Initialise variables. Parameters ---------- y : numpy array like, output / dependent variable X : numpy array like, input / independent variables alpha : float, int. Learning Rate n_iterations : Number of maximum iterations to perform gradient descent """ self.y = y self.X = self._hstack_one(X) self.thetas = np.zeros((self.X.shape[1], 1)) self.n_rows = self.X.shape[0] self.alpha = alpha self.n_iterations = n_iterations print("Cost before fitting: {0:.2f}".format(self.cost())) @staticmethod def _hstack_one(input_matrix): """Horizontally stack a column of ones for the coefficients of the bias terms Parameters ---------- input_matrix: numpy array like (N x M). Where N = number of examples. M = Number of features. Returns ------- numpy array with stacked column of ones (N x M + 1) """ return np.hstack((np.ones((input_matrix.shape[0], 1)), input_matrix)) def cost(self, ): """Calculates the cost of current configuration""" return (1 / (2 * self.n_rows)) * np.sum( (self.X.dot(self.thetas) - self.y) ** 2) def predict(self, new_X): """Predict values using current configuration Parameters ---------- new_X : numpy array like """ new_X = self._hstack_one(new_X) return new_X.dot(self.thetas) def batch_gradient(self, ): h = self.X.dot(self.thetas) - self.y h = np.multiply(self.X, h) h = np.sum(h, axis=0) return h.reshape(-1, 1) def batch_gradient_descent(self, ): alpha_by_m = self.alpha / self.n_rows for i in range(self.n_iterations): self.thetas = self.thetas - (alpha_by_m * self.batch_gradient()) cost = self.cost() print("Iteration: {0} Loss: {1:.5f}\r".format(i + 1, cost), end="") X = df['population'].values.reshape(-1, 1) y = df['profit'].values.reshape(-1, 1) lr = LinearRegression(X, y) lr.batch_gradient_descent() # plot regression line X = np.arange(4, 24, 0.1).reshape(-1, 1) fig = plt.figure(num=None, figsize=(12, 8), dpi=80, facecolor='w') plt.scatter(df['population'], df['profit'], marker='x', color='red', s=20, label='Training data') plt.plot(X, lr.predict(X), color='blue', label='Linear Regression') plt.xlim([4, 24]) plt.xticks(range(4, 26, 2)) plt.yticks(range(-5, 30, 5)) plt.xlabel("Population in 10,000") plt.ylabel("Profit in $10,000") plt.title("Scatter plot of training data\n") plt.legend() plt.show() # + def cost(theta_0, theta_1): """Calculate the cost with given weights Parameters ---------- theta_0 : numpy array like, weights dim 0 theta_1 : numpy array like, weights dim 1 Returns ------- float, cost """ X = df['population'].values y = df['profit'].values X = X.reshape(-1, 1) y = y.reshape(-1, 1) X = np.hstack((np.ones((X.shape[0], 1)), X)) n_rows = X.shape[0] thetas = np.array([theta_0, theta_1]).reshape(-1, 1) return (1/(2n_rows)) sum((X.dot(thetas) - y)*2)[0] def prepare_cost_matrix(theta0_matrix, theta1_matrix): """Prepares cost matrix for various weights to create a 3D representation of cost. Every value in the cost matrix represents the cost for theta values in the theta matrices. Parameters ---------- theta0_matrix : numpy array like, weights dim 0 theta1_matrix : numpy array like, weights dim 1 """ J_matrix = np.zeros(theta0_matrix.shape) row, col = theta0_matrix.shape for x in range(row): for y in range(col): J_matrix[x][y] = cost(theta0_matrix[x][y], theta1_matrix[x][y]) return J_matrix theta_0 = np.arange(-5, 1, 0.01) theta_1 = np.arange(0.6, 1.2, 0.001) theta_0, theta_1 = np.meshgrid(theta_0, theta_1) J_matrix = prepare_cost_matrix(theta_1, theta_0) # + from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter fig = plt.figure(num=None, figsize=(12, 8), dpi=80, facecolor='w') ax = fig.gca(projection='3d') surf = ax.plot_surface(theta_0, theta_1, J_matrix, cmap=cm.coolwarm,) ax.zaxis.set_major_locator(LinearLocator(10)) ax.set_xlabel("theta0") ax.set_ylabel("theta1") ax.set_zlabel("J(theta)") ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) fig.colorbar(surf, shrink=0.5, aspect=5) plt.title("3D plot for theta0, theta1, and the cost function\n") plt.show() # - # ## Linear Regression with multiple variables # df = pd.read_csv("machine_learning_andrewng/ex1data2.csv", header=None) df.head() X = df.iloc[:, [0, 1]].values y = df.iloc[:, [2]].values # + from sklearn.preprocessing import StandardScaler scaler_X = StandardScaler() scaler_Y = StandardScaler() X = scaler_X.fit_transform(X) y = scaler_Y.fit_transform(y) # - lr = LinearRegression(X, y, alpha=0.1, n_iterations=1000) lr.batch_gradient_descent() X_test = np.array([2104, 3]).reshape(1, 2) print("Testing on : {0}".format(X_test[0])) X_test = scaler_X.transform(X_test) prediction = lr.predict(X_test) print("Prediction(Scaled): {0:.2f}".format(prediction[0][0])) print("Prediction(Unscaled): {0:.2f}".format(scaler_Y.inverse_transform(prediction)[0][0])) 197536]['X'], cities.iloc[197536]['Y'])) ax.scatter(cities.loc[(cities.clusters == 141)]['X'], cities.loc[(cities.clusters == 141)]['Y']) # + from sklearn.neighbors import NearestNeighbors startlist=[0] neigh = NearestNeighbors(n_neighbors=1, n_jobs=-1) # - neigh submission_df.head() cities.iloc[47239] import hvplot.pandas import colorcet as cc allpoints = cities.hvplot.scatter('X', 'Y', width=380, height=350, #datashade=True, title='All Cities') colors = list(reversed(cc.kbc)) primedensity = cities[cities.is_prime].hvplot.hexbin(x='X', y='Y', width=420, height=350, cmap=colors, title='Density of Prime Cities').options(size_index='Count', min_scale=0.8, max_scale=0.95) allpoints + primedensity # + from sklearn.mixture import GaussianMixture mclusterer = GaussianMixture(n_components=250, tol=0.01, random_state=66, verbose=1) cities['mclust'] = mclusterer.fit_predict(cities[['X', 'Y']].values) nmax = cities.mclust.max() print("{} clusters".format(nmax+1)) # + #histo = cities.hvplot.hist('mclust', ylim=(0,14000), color='tan') # custcolor = cc.rainbow + cc.rainbow # gausses = cities.hvplot.scatter('X', 'Y', by='mclust', size=5, width=500, height=450, # # datashade=True, # dynspread=True, cmap=custcolor) # display(histo, gausses) # - centers = cities.groupby('mclust')['X', 'Y'].agg('mean').reset_index() # + from scipy.spatial.distance import pdist, squareform from ortools.constraint_solver import pywrapcp from ortools.constraint_solver import routing_enums_pb2 #%% functions def create_mat(df): print("building matrix") mat = pdist(locations) return squareform(mat) def create_distance_callback(dist_matrix): def distance_callback(from_node, to_node): return int(dist_matrix[from_node][to_node]) return distance_callback status_dict = {0: 'ROUTING_NOT_SOLVED', 1: 'ROUTING_SUCCESS', 2: 'ROUTING_FAIL', 3: 'ROUTING_FAIL_TIMEOUT', 4: 'ROUTING_INVALID'} def optimize(df, startnode=None, stopnode=None, fixed=False): num_nodes = df.shape[0] mat = create_mat(df) dist_callback = create_distance_callback(mat) search_parameters = pywrapcp.RoutingModel.DefaultSearchParameters() # search_parameters.time_limit_ms = int(100060numminutes) search_parameters.solution_limit = num_iters search_parameters.first_solution_strategy = ( routing_enums_pb2.FirstSolutionStrategy.LOCAL_CHEAPEST_INSERTION) search_parameters.local_search_metaheuristic = ( routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH) if fixed: routemodel = pywrapcp.RoutingModel(num_nodes, 1, [startnode], [stopnode]) else: routemodel = pywrapcp.RoutingModel(num_nodes, 1, startnode) routemodel.SetArcCostEvaluatorOfAllVehicles(dist_callback) print("optimizing {} cities".format(num_nodes)) assignment = routemodel.SolveWithParameters(search_parameters) print("status: ", status_dict.get(routemodel.status())) print("travel distance: ", str(assignment.ObjectiveValue()), "\n") return routemodel, assignment def get_route(df, startnode, stopnode, fixed): routemodel, assignment = optimize(df, int(startnode), int(stopnode), fixed) route_number = 0 node = routemodel.Start(route_number) route = [] while not routemodel.IsEnd(node): route.append(node) node = assignment.Value(routemodel.NextVar(node)) return route # + # %%time #%% parameters num_iters=100 # main nnode = int(cities.loc[0, 'mclust']) locations = centers[['X', 'Y']].values segment = get_route(locations, nnode, 0, fixed=False) # - opoints = centers.loc[segment] opoints.reset_index(drop=True, inplace=True) #recall ordered points cities['clustorder'] = cities.groupby('mclust').cumcount() # + from sklearn.neighbors import NearestNeighbors startlist=[0] neigh = NearestNeighbors(n_neighbors=1, n_jobs=-1) for i,m in enumerate(opoints.mclust[1:], 0): neigh.fit(cities.loc[cities.mclust == m, ['X', 'Y']].values) lastcenter = opoints.loc[i, ['X', 'Y']].values.reshape(1, -1) closestart = neigh.kneighbors(lastcenter, return_distance=False) start = cities.index[(cities.mclust == m) & (cities.clustorder == closestart.item())].values[0] startlist.append(start) opoints['startpt'] = startlist # + stoplist = [] for i,m in enumerate(opoints.mclust, 1): neigh.fit(cities.loc[cities.mclust == m, ['X', 'Y']].values) if m != opoints.mclust.values[-1]: nextstartnode = opoints.loc[i, 'startpt'] else: nextstartnode = 0 nextstart = cities.loc[nextstartnode, ['X', 'Y']].values.reshape(1, -1) closestop = neigh.kneighbors(nextstart, return_distance=False) stop = cities.index[(cities.mclust == m) & (cities.clustorder == closestop.item())].values[0] stoplist.append(stop) opoints['stoppt'] = stoplist display(cities.head(), opoints.head()) # - coords = cities.loc[opoints.stoppt, ['X', 'Y', 'mclust']] # + # %%time num_iters = 100 seglist = [] total_clusts = cities.shape[0] for i,m in enumerate(opoints.mclust): district = cities[cities.mclust == m] print("begin cluster {}, {} of {}".format(m, i, opoints.shape[0]-1)) clstart = opoints.loc[i, 'startpt'] nnode = district.loc[clstart, 'clustorder'] clstop = opoints.loc[i, 'stoppt'] pnode = district.loc[clstop, 'clustorder'] locations = district[['X', 'Y']].values segnodes = get_route(locations, nnode, pnode, fixed=False) #output is type list ord_district = district.iloc[segnodes] segment = ord_district.index.tolist() seglist.append(segment) seglist.append([0]) path = np.concatenate(seglist) # - path = pd.DataFrame({'Path': path}) path.to_csv('submission_JM.csv', index=False) path['is_prime'] = submission_df.Path.apply(is_prime) primes = submission_df.loc[submission_df.is_prime == True] non_primes = submission_df.loc[submission_df.is_prime == False] non_prime_lst = list(non_primes.sort_index(ascending = False)['Path']) prime_lst = list(primes.sort_index(ascending = False)['Path']) prime_path = [] for i in log_progress(range(0, len(path))): if i % 10 == 0 and len(prime_lst) > 0 and i != 0: prime_path.append(prime_lst.pop()) else: prime_path.append(non_prime_lst.pop()) prime_path_df = pd.DataFrame({'Path': prime_path}) prime_path_df.to_csv('submission_JM_prime.csv', index=False) e from Utilities.utils import gridsearch model = DecisionTreeClassifier() # Our model param_grid = {"max_depth": np.arange(2,8,1), # Maximum depth of tree "max_features": np.arange(3,8,1), # Number of features to consider when looking for the best split "max_leaf_nodes": np.arange(4,27,1), # Maximum number of leaves in our tree. "criterion": ["gini", "entropy"], # Splitting criteria "class_weight": [None, 'balanced',{0: 1.105, 1: 1.15}] # Weights associated with classes. } metric = roc_auc_score # Metric to use tiebreaker = zero_one_loss # Tie breaker metric. n_best_grids = 10 # 5 best grids best_score, best_grid, tiebreaker = gridsearch(model, x_train, y_train, x_val, y_val, param_grid, metric, n_best_grids, loss=False, tiebreaker=tiebreaker) for a,t,g in zip(best_score, tiebreaker, best_grid): print("AUC:",a) print("Tie",t) print("Grid:",g) # - model.set_params(*best_grid[0]) model.fit(x_train,y_train) c = confusion_matrix(y_val,model.predict(x_val)) sns.heatmap(c, annot=True, xticklabels=xlabel, yticklabels=ylabel) print(c) # This seems like a good result and we could naively choose this single tree, or any of the top 10 combinations as they have the same error, as our model. The problem is that every time we run the parameter search another combination of parameters will be the best tree because of the intrinsic variance of decision trees, additionally these trees will have a really good error as well because of the natural low bias of decision trees. Remember that the trees are tuned to the validation set and might, probably wont, generalize good to new data. # But there is some pattern to what sort of combinations work for the problem. For the entropy criterion trees the best performing ones the maximum depth is between 5 and 7, the maximum number of leaves between 22 and 26, the maximum number of features to consider on each split around 7 and using balanced weighting for the classes. The balanced weighting adjust the weights for the classes inversely proportional to class frequencies, basically counteracting the false positive hypothesis mentioned earlier. # For the gini criterion trees the best performing ones the maximum depth is around 7, the maximum number of leaves varies a lot but tend to approach higher values (≈25), the maximum number of features to consider on each split around 7 and using the proposed weighting for the classes. # To decrease variance and try to make our model to generalize we use need to use an ensemble. The first that comes to mind is trying Bootstrap aggregating or a random forest. It should theoretically provide the stability we need and reduces variance. # # Ensembles # + from sklearn.ensemble import BaggingClassifier, RandomForestClassifier base = DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=7, random_state=None, max_leaf_nodes=22, min_impurity_split=1e-07, class_weight='balanced', presort=False) model = BaggingClassifier(base_estimator=base, n_estimators=100, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=1, random_state=None, verbose=0) model.fit(x_train, y_train) predictions = model.predict(x_val) conf = (confusion_matrix(y_val,predictions)) auc = (roc_auc_score(y_val,predictions)) sns.heatmap(conf, annot=True, xticklabels=xlabel, yticklabels=ylabel) plt.title("AUC " + str(auc)) print(auc) #plt.savefig('Bagging.eps', format='eps', dpi=1000) # - model = RandomForestClassifier(n_estimators=100, criterion='entropy', max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=22, min_impurity_split=1e-07, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False, class_weight='balanced') model.fit(x_train, y_train) predictions = model.predict(x_val) conf = (confusion_matrix(y_val,predictions)) auc = (roc_auc_score(y_val,predictions)) sns.heatmap(conf, annot=True, xticklabels=xlabel, yticklabels=ylabel) plt.title("AUC " + str(auc)) print(auc) #plt.savefig('randfor.eps', format='eps', dpi=1000) # The ensembles seems to provide stability and decrease the variance run to run. However they seem to introduce some bias. Looking back at when we compared entropy and gini criterion and looked at the effect of the depth and variance. Remember that the gini trees generally had few false positives while entropy trees had few false negatives. An viable hypothesis might be that the two complement each other, and because we in the ensembles above only use the one or the other. To test the hypothesis we're gonna try an VotingClassifier using the parameters search result we acquired above. # The voting classifier consists of a 5:4 ratio of entropy and gini trees since the gini trees showed less potential in the grid search. The classification is done using a majority vote rule. from sklearn.ensemble import VotingClassifier mods = [] for i in range(1,100): # 100 Trees provides low variance. # A parameter combination that were sucessfull for entropy trees. mods.append((str(i),DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=22, min_impurity_split=1e-07, class_weight='balanced', presort=False))) if(i < 80): # A parameter combination that were sucessfull for gini trees. mods.append((str(i)+"gi",DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=7, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=25, min_impurity_split=1e-07, class_weight={0: 1.105, 1: 1.15}, presort=False))) model = VotingClassifier(estimators=mods, voting='hard', n_jobs=1) model.fit(x_train, y_train) predictions = model.predict(x_val) conf = (confusion_matrix(y_val,predictions)) auc = (roc_auc_score(y_val,predictions)) sns.heatmap(conf, annot=True, xticklabels=xlabel, yticklabels=ylabel) print(auc) print((roc_auc_score(y_train,model.predict(x_train)))) # The hypothesis might be true, this is a really good separation and a AUC of nearly 89 for the validation set and 93 for the training set. Thats not a gigantic difference and hopefully the constraints on the trees have prevented the model from overfitting on the training data. # # Pipeline # Before giving the test set a go we train our model on the entire training dataset with some imputing. We also put it into a pipeline to automate the work-flow. # + from sklearn.pipeline import Pipeline from sklearn.preprocessing import Imputer,StandardScaler X = pd.read_csv('input/Train.csv') Y = X['Outcome'] X = X.drop(["Outcome"], axis=1) model = VotingClassifier(estimators=mods, voting='hard', n_jobs=1) pipeline = Pipeline([("imputer", Imputer(missing_values='NaN', strategy="mean", axis=0)), ("standardizer", StandardScaler()), ("VotingClassifier", model)]) pipeline.fit(X,Y) # - # # Test # ## No missing values test = pd.read_csv('input/Test.csv') test.dropna(inplace = True) truth = test['Outcome'] test.drop('Outcome', axis = 1, inplace = True) predictions = pipeline.predict(test) conf = (confusion_matrix(truth,predictions)) acc = (accuracy_score(truth,predictions)) auc = (roc_auc_score(truth,predictions)) sns.heatmap(conf, annot=True, xticklabels=xlabel, yticklabels=ylabel) print("accuracy", acc) print("AUC", auc) #plt.savefig('test_conf.eps', format='eps', dpi=1000) # Eureka, this is really good. # ## Missing values test = pd.read_csv('input/Test.csv') truth = test['Outcome'] test.drop('Outcome', axis = 1, inplace = True) predictions = pipeline.predict(test) conf = (confusion_matrix(truth,predictions)) acc = (accuracy_score(truth,predictions)) auc = (roc_auc_score(truth,predictions)) sns.heatmap(conf, annot=True, xticklabels=xlabel, yticklabels=ylabel) print("accuracy", acc) print("AUC", auc) # Not as good, but still good.	22,125
/Deep_Learning_with_TensorFlow/1.4.0/Chapter11/4.1. projector_data_prepare.ipynb	5cec94aec4d3715eb284ff97d3ea39fec9b50e2c	[ "MIT" ]	permissive	hyphenliu/TensorFlow_Google_Practice	https://github.com/hyphenliu/TensorFlow_Google_Practice	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	46,049	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] colab_type="text" id="X1xALvaQWBX_" # # Práctica G: repositorios locales y remotos # + [markdown] colab_type="text" id="ciqUoT0yixVA" # ##### Cookbook [@data_mining_2020_1](https://nbviewer.jupyter.org/github/JacoboGGLeon/data_mining_2020_1/blob/master/README.ipynb) # + [markdown] colab_type="text" id="EUyHB8zC5ubh" # ## Resources # + [markdown] colab_type="text" id="ciPg12iW5wYw" # * [Git cheatsheet](https://ndpsoftware.com/git-cheatsheet.h) # * [Installing and using Git and GitHub on Ubuntu Linux: A beginner's guide](https://www.howtoforge.com/tutorial/install-git-and-github-on-ubuntu/) # * [How to remove origin from git repository](https://stackoverflow.com/questions/9224754/how-to-remove-origin-from-git-repository) # # # + [markdown] colab_type="text" id="703KlDUZIzsb" # ### Highlights # # ``` # vagrant@vagrant:~/test$ git add README # vagrant@vagrant:~/test$ git commit -m "primer commit" # [master cc2296c] primer commit # 1 file changed, 1 insertion(+), 1 deletion(-) # vagrant@vagrant:~/test$ git push origin master # Username for 'https://github.com': JacoboGGLeon # Password for 'https://[email protected]': # Counting objects: 3, done. # Writing objects: 100% (3/3), 257 bytes \| 85.00 KiB/s, done. # Total 3 (delta 0), reused 0 (delta 0) # To https://github.com/JacoboGGLeon/test.git # c1c7113..cc2296c master -> master # ``` # # # + [markdown] colab_type="text" id="GOljQUJIirjB" # ## Recipe # + [markdown] colab_type="text" id="UfPNo8E8WNHm" # ### Instalación # # # - # #### En terminal: # + [markdown] colab_type="text" id="jQXuWaLx4gdB" # Actualizar el sistema operativo # # > ```sudo apt-get update -y && sudo apt-get upgrade -y``` # + [markdown] colab_type="text" id="xQXis9JM4lqf" # Instalar git # # > ```sudo apt-get install git``` # + [markdown] colab_type="text" id="5kXkCzU_4pwM" # Checar la versión instalada # # > ```git --version``` # + [markdown] colab_type="text" id="L0PQqWAV9ufp" # Actualizar las credenciales de Github # # > ```git config --global user.name "user_name"``` # # > ```git config --global user.email "user_email"``` # # > ```git config --global user.password "user_password"``` # # + [markdown] colab_type="text" id="UfPNo8E8WNHm" # ### Del repositorio local al repositorio remoto # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # Crear un repositorio en la máquina local # # > ```git init test``` # # Respuesta esperada (similar) # # > ```Initialized empty Git repository in /home/vagrant/test/.git/``` # # + [markdown] colab_type="text" id="0SIlYoJsBgKN" # Crear un archivo # # > ```nano README``` # # Escribir dentro de él: ¡Hola mundo! # # + [markdown] colab_type="text" id="WVuSUCDDBqT1" # Indexar el archivo, o sea, agregarlo # en una lista para subirlo al repositorio remoto que se encuentra en Github # # > ```git add README``` # # + [markdown] colab_type="text" id="p8WcfehECDdB" # Avisar que nuestro archivo(s) está listo para subirlo al repositorio remoto # # > ```git commit -m "some_message"``` # # + [markdown] colab_type="text" id="JwXxpHeICeev" # ### En Github # + [markdown] colab_type="text" id="KNdbzZ3BCneb" # Crar un repositorio remoto # # > ![](https://docs.google.com/uc?export=download&id=1sgu4r1PcFb1o2x-Y8VhQe_g9aUVNiXVO) # + [markdown] colab_type="text" id="nHv42YcyETH6" # Sólo llenar el nombre del repositorio: ```test``` # # > ![](https://docs.google.com/uc?export=download&id=14XwhMn1DYJETVRz7a95E_oF4TgWdH080) # + [markdown] colab_type="text" id="xKyrbbLWE7sj" # ### En terminal (de nuevo) # + [markdown] colab_type="text" id="SwQy2fcVEo2-" # Agregar el origen remoto, o sea, la conexión del repositorio local con el repositorio remoto a.k.a. Github # # * Copiar la ruta de el repositorio remoto ```.git``` # > ![](https://docs.google.com/uc?export=download&id=1VnswhRavTcZzgju-4J814WB4wH_xaucT) # # Y ese será el "origen" # # > ```git remote add origin https://github.com/user_name/test.git``` # # + [markdown] colab_type="text" id="7EdqvO5DFqbw" # Subir el archivo que enlistamos para subirse al repositorio remoto # # > ```git push origin master``` # # Nos pedirá dos datos: ```username``` y ```password``` # # Así sería la respuesta (similar) # # # > ``` # > Username for 'https://github.com': # > Password for 'https://[email protected]': # > Counting objects: 3, done. # > Writing objects: 100% (3/3), 228 bytes \| 76.00 KiB/s, done. # > Total 3 (delta 0), reused 0 (delta 0) # > To https://github.com/.../test.git # > * [new branch] master -> master # > ``` # # + [markdown] colab_type="text" id="cj2F2aQvGk--" # ### En Github (de nuevo, ya para acabar) # + [markdown] colab_type="text" id="UW0okY48GwdV" # Seleccionar el repositorio ```test``` y ahí debería estar nuestro repositorio # # > ![](https://docs.google.com/uc?export=download&id=1pSb5V-F1sYvwMkRjq7CzzU-X2r8F2Ys_) # + [markdown] colab_type="text" id="UfPNo8E8WNHm" # ### Del repositorio remoto al repositorio local # # # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # En Github crear un repositorio remoto # # > ![](https://docs.google.com/uc?export=download&id=1poY44l8QYpM0lCtcbat7soodUQqILwHw) # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # En una carpeta raíz # # > ```git clone https://github.com/.../test.git``` # # Respuesta esperada (similar) # # > ```Cloning into 'test'... # warning: You appear to have cloned an empty repository.``` # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # Listar los archivos # # > ```ls``` # # Debemos encontrar una carpeta ```test``` # # Entrar a la carpeta # # > ```cd test``` # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # Crear archivo y editarlo al mismo tiempo # # > ```nano readme.md``` # # Dentro escribimos un mensaje, el que sea # # > Para salir, escribir ```ctrl``` + ```x``` y aceptar los cambios # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # Agregamos todos los archivos # # > ```git add .``` # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # Hacemos el commit # # > ```git commit -m "primer commit"``` # # Respuesta esperada (similar) # # > ```1 file changed, 1 insertion(+) # create mode 100644 readme.md``` # + [markdown] colab_type="text" id="oXw88Tp0A9Oa" # Actualizamos el repositorio remoto # # > ```git push origin master``` # # Respuesta esperada (similar) # # > ``` # > Username for 'https://github.com': # > Password for 'https://[email protected]': # > Counting objects: 3, done. # > Writing objects: 100% (3/3), 228 bytes \| 76.00 KiB/s, done. # > Total 3 (delta 0), reused 0 (delta 0) # > To https://github.com/.../test.git # > * [new branch] master -> master # > ```	6,869
/Image classifcation/Intel_Image_classification (1).ipynb	f47d2859fe7fe6ac49f619c26491bcf6a4387541	[]	no_license	Srinivas1258/ML-DL	https://github.com/Srinivas1258/ML-DL	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	100,187	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory ''''import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: #print(os.path.join(dirname, filename))''' # You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session # + _uuid="d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" _cell_guid="79c7e3d0-c299-4dcb-8224-4455121ee9b0" # !ls /kaggle/input/intel-image-classification/seg_train/seg_train/ # + from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.utils import to_categorical from tensorflow.keras.layers import Conv2D,MaxPool2D,Flatten,BatchNormalization,Dropout,Activation import pandas as pd from tensorflow.keras.preprocessing.image import ImageDataGenerator import matplotlib.pyplot as plt import numpy as np from tensorflow.keras.models import load_model from keras.utils import np_utils from tensorflow.keras import applications from keras.preprocessing.image import ImageDataGenerator from tensorflow.keras import optimizers # + train_datagen = ImageDataGenerator( rescale=1./255, # normalizing the image b/w 0 to 1 shear_range=0.2, # shear transformation zoom_range=0.2, # zooming the image by 20% horizontal_flip=True) # rotating val_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory( '/kaggle/input/intel-image-classification/seg_train/seg_train/', target_size=(224, 224), batch_size=32, class_mode='categorical') validation_generator = val_datagen.flow_from_directory( '/kaggle/input/intel-image-classification/seg_test/seg_test/', target_size=(224, 224), batch_size=32, class_mode='categorical') test_generator=test_datagen.flow_from_directory( '/kaggle/input/intel-image-classification/seg_pred/', target_size=(224, 224), batch_size=32, class_mode=None) # + from tensorflow.keras.applications.inception_v3 import InceptionV3 from tensorflow.keras.models import Model import tensorflow.keras inception=InceptionV3(include_top=False,weights='imagenet',input_shape=(224,224,3)) output1=inception.layers[-1].output output1=tensorflow.keras.layers.Flatten()(output1) inception=Model(inception.input,output1) for layers in inception.layers: layers.trainable=False inception.summary() # + from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, InputLayer,BatchNormalization from tensorflow.keras.models import Sequential from tensorflow.keras import optimizers model2 = Sequential() model2.add(inception) # model2.add(Conv2D(256,kernel_size=3,padding='same',activation='relu',input_shape=(224,224,3))) # model2.add(MaxPool2D(pool_size=(2,2))) # model2.add(Dropout(0.25)) # model2.add(Flatten()) model2.add(Dense(1024, activation='relu', input_dim=(224,224,3))) # model2.add(Dense(1024,activation='relu')) # model2.add(BatchNormalization()) model2.add(Dropout(0.25)) ''''model2.add(Dense(256, activation='relu')) # model2.add(BatchNormalization()) model2.add(Dense(64, activation='relu')) # model2.add(Dropout(0.3))''' model2.add(Dense(6, activation='softmax')) model2.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(), metrics=['accuracy']) model2.summary() # - STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size STEP_SIZE_VALID=validation_generator.n//validation_generator.batch_size history=model2.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN, validation_data=validation_generator, validation_steps=STEP_SIZE_VALID, epochs=5 ) # + acc=history.history['accuracy'] val_acc=history.history['val_accuracy'] loss=history.history['loss'] val_loss=history.history['val_loss'] epochs=range(len(acc)) fig = plt.figure(figsize=(20,10)) plt.plot(epochs, acc, 'r', label="Training Accuracy") plt.plot(epochs, val_acc, 'b', label="Validation Accuracy") plt.xlabel('Epoch') plt.ylabel('Accuracy') plt.title('Training and validation accuracy') plt.legend(loc='lower right') plt.show() fig.savefig('../Accuracy_curve_CNN_256.jpg') fig = plt.figure(figsize=(20,10)) plt.plot(loss) plt.plot(val_loss) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() fig.savefig('../loss_curve_CNN_256.jpg') # - test_generator.reset() pred=model2.predict_generator(test_generator, verbose=1) print(pred) # + import matplotlib.pyplot as plt predicted_class_indices=np.argmax(pred,axis=1) labels = (train_generator.class_indices) labels = dict((v,k) for k,v in labels.items()) predictions = [ print(k) for k in predicted_class_indices] ''''filenames=test_generator.filenames results=pd.DataFrame({"Image":filenames, "target":predictions}) results.to_csv("results.csv",index=False) 0.2.. 106 ''' # - for filenames in os.walk('/kaggle/input/intel-image-classification/seg_pred/seg_pred/'): print(filename)	6,249
/notebooks/Translate PWN 3.1.ipynb	54e1e32024a258c49911065278b68733187aea30	[]	no_license	khrystyna-skopyk/wordnet	https://github.com/khrystyna-skopyk/wordnet	0	0	null	2021-09-14T14:56:49	2021-09-01T21:45:40	null	Jupyter Notebook	false	false	.py	30,568	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # + import wn from google.cloud import translate_v2 as translate import logging logger = logging.getLogger("wordnet_translator") logger.setLevel(logging.INFO) consoleHandler = logging.StreamHandler() consoleHandler.setLevel(logging.INFO) # create formatter formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # add formatter to ch consoleHandler.setFormatter(formatter) # add ch to logger logger.addHandler(consoleHandler) # - wn.download("pwn:3.1") # + import pymongo client = pymongo.MongoClient('mongodb://localhost:27017/') db = client.wordnet collection = db["tasks"] # + from tqdm.notebook import tqdm from collections import defaultdict def populate_tasks_in_mongo(lexicon="pwn:3.1", filter_func=None): if filter_func is None: filter_func = lambda synset: True tasks_created = 0 tasks_updated = 0 for synset in tqdm(wn.synsets(lexicon=lexicon)): if filter_func(synset): words = {w.id: w.lemma() for w in synset.words()} res = ( collection.update_one( {"_id": synset.id}, { "$set": { "ili": synset.ili.id, "pos": synset.pos, "words": words, "definition": list( map(str.strip, synset.definition().split(";")) ), } }, upsert=True, ) ) if res.upserted_id: tasks_created += 1 else: tasks_updated += 1 logger.info( f"{tasks_created} was created and {tasks_updated} was updated from '{lexicon}'" ) def filter_only_big_synsets_with_description(synset): return len(synset.lemmas()) == 5 and synset.definition() export_samples = defaultdict(lambda: defaultdict(int)) for pos in ["a", "v", "n"]: for lemmas_count in range(1, 5): export_samples[pos][lemmas_count] = 2 def filter_to_have_a_nice_sample(synset): global export_samples if not synset.definition(): return False if export_samples[synset.pos][len(synset.lemmas())] > 0: export_samples[synset.pos][len(synset.lemmas())] -= 1 return True return False # populate_tasks_in_mongo(filter_func=filter_only_big_synsets_with_description) populate_tasks_in_mongo(filter_func=filter_to_have_a_nice_sample) # + import requests, uuid, json from urllib.parse import urljoin class BingTranslationException(Exception): pass class BingTranslator: translate_path = '/translate' dictionary_lookup_path = '/dictionary/lookup' def __init__(self, key_file, endpoint="https://api.cognitive.microsofttranslator.com"): self.endpoint = endpoint with open(key_file) as fp: self.headers = json.load(fp) def _get_headers(self): headers = self.headers.copy() headers['X-ClientTraceId'] = str(uuid.uuid4()) return headers def _request(self, path, phrase, source_language="en", target_language="uk"): constructed_url = urljoin(self.endpoint, path) headers = self._get_headers() params = { 'api-version': '3.0', "from": source_language, 'to': target_language } body = [{ 'text': phrase }] request = requests.post(constructed_url, params=params, headers=headers, json=body) try: response = request.json() except json.JSONDecodeError: raise BingTranslationException(f"Cannot translate phrase '{phrase}' cannot parse the response as json") if "error" in response: raise BingTranslationException(f"Cannot translate phrase '{phrase}' because of an error: {response['error']}") if request.status_code != 200: raise BingTranslationException(f"Cannot translate phrase '{phrase}', status code was {request.status_code}") return response def translate(self, phrase, source_language="en", target_language="uk"): response = self._request(self.translate_path, phrase, source_language, target_language) for l in response: for translation in l.get("translations", []): return translation["text"] raise BingTranslationException(f"Cannot find a translation for a phrase '{phrase}'") def dictionary_lookup(self, word, source_language="en", target_language="uk"): response = self._request(self.dictionary_lookup_path, word, source_language, target_language) for l in response: return l.get("translations", []) raise BingTranslationException(f"Cannot find a translation for a phrase '{phrase}'") # + from time import sleep import itertools import re import html from collections import Counter def sliding_window(iterable, n=2): iterables = itertools.tee(iterable, n) for iterable, num_skipped in zip(iterables, itertools.count()): for _ in range(num_skipped): next(iterable, None) return zip(iterables) class AbstractTranslator: def __init__(self, source_language="en", target_language="uk"): self.source_language = source_language self.target_language = target_language def generate_samples(self, task): raise NotImplementedError() def translate(self, task, sleep_between_samples=1): raise NotImplementedError() def parse_results(self, results): raise NotImplementedError() def method_id(self): raise NotImplementedError() class AbstractSlidingWindowTranslator(AbstractTranslator): def __init__( self, group_by=3, add_or=True, add_quotes=True, combine_in_one=True, add_aux_words=True, source_language="en", target_language="uk", ): super().__init__(source_language=source_language, target_language=target_language) self.group_by = group_by self.add_or = add_or self.add_quotes = add_quotes self.combine_in_one = combine_in_one self.add_aux_words = add_aux_words def method_id(self): return f"{type(self).__name__}(group_by={self.group_by},add_or={self.add_or},add_quotes={self.add_quotes},combine_in_one={self.combine_in_one},add_aux_words={self.add_aux_words})" def generate_samples(self, task): samples = [] total_samples = 0 words = list(task["words"].values()) if self.add_aux_words: if task["pos"] == "v": words = [f"to {w}" for w in words] elif task["pos"] == "n": words = [f"the {w}" for w in words] if self.add_quotes: words = [f'"{w}"' for w in words] if len(words) < self.group_by: chunks = [words] else: chunks = sliding_window(words, self.group_by) for chunk in chunks: total_samples += len(chunk) if self.add_or and len(chunk) > 1: lemmas = ", ".join(chunk[:-1]) + f" or {chunk[-1]}" else: lemmas = ", ".join(chunk) if task["definition"]: samples.append(f"{lemmas}: {task['definition'][0]}") else: samples.append(lemmas) if self.combine_in_one: return {"samples": ["<br/>\n\n".join(samples)], "total_lemmas": total_samples} else: return {"samples": samples, "total_lemmas": total_samples} def estimate_tasks(self, tasks, price_per_mb=1.0 / 1024 / 1024): total_len = 0 for task in tasks: samples = self.generate_samples(task)["samples"] for sample in samples: total_len += len(sample) return (float(total_len) / 1024 / 1024) price_per_mb def _parse_result(self, result): all_terms = [] all_definitions = [] for l in filter(None, result.replace("<br/>", "\n").split("\n")): if ":" not in l: logger.warning("Cannot find a semicolon in the translated text") continue terms, definition = l.split(":", 1) terms = list(map(str.strip, terms.split(","))) if self.add_or: for or_word in ["чи то", "чи", "або", "альбо", "or"]: splits = re.split(f"[,\s]+{or_word}[,\s]+", terms[-1], flags=re.I) if len(splits) > 1: terms = terms[:-1] + list(map(lambda x: x.strip(", "), splits)) break else: if self.group_by > 1: logger.warning("Cannot find 'or' in the last chunk") if self.add_quotes: terms = [term.strip('"\'"«»') for term in terms] all_terms += terms all_definitions.append(definition.strip()) return {"all_terms": all_terms, "all_definitions": all_definitions} class SlidingWindowGoogleTranslator(AbstractSlidingWindowTranslator): def __init__( self, gcloud_credentials, group_by=3, add_or=True, add_quotes=True, combine_in_one=True, add_aux_words=True, source_language="en", target_language="uk", ): self.gtrans_client = translate.Client.from_service_account_json(gcloud_credentials) super().__init__( group_by=group_by, add_or=add_or, add_quotes=add_quotes, combine_in_one=combine_in_one, add_aux_words=add_aux_words, source_language=source_language, target_language=target_language, ) def translate(self, task, sleep_between_samples=1): results = [] sampled = self.generate_samples(task) for sample in sampled["samples"]: results.append( self.gtrans_client.translate( sample, source_language=self.source_language, target_language=self.target_language, ) ) sleep(sleep_between_samples) return self.parse_results(results) def parse_results(self, results): terms = Counter() definitions = Counter() parsed_results = [] for r in results: parsed = self._parse_result(html.unescape(r.get("translatedText", ""))) terms.update(parsed["all_terms"]) definitions.update(parsed["all_definitions"]) parsed_results.append(parsed) return { "raw": parsed_results, "terms": terms.most_common(), "definitions": definitions.most_common(), "type": "translator", } def estimate_tasks(self, tasks, price_per_mb=20): return super().estimate_tasks(tasks, price_per_mb) class SlidingWindowBingTranslator(AbstractSlidingWindowTranslator): def __init__( self, bing_apikey, group_by=3, add_or=True, add_quotes=True, combine_in_one=True, add_aux_words=True, source_language="en", target_language="uk", ): self.bing_apikey = bing_apikey self.bing_translator = BingTranslator(self.bing_apikey) super().__init__( group_by=group_by, add_or=add_or, add_quotes=add_quotes, combine_in_one=combine_in_one, add_aux_words=add_aux_words, source_language=source_language, target_language=target_language, ) def estimate_tasks(self, tasks, price_per_mb=10): return super().estimate_tasks(tasks, price_per_mb) def translate(self, task, sleep_between_samples=1): results = [] sampled = self.generate_samples(task) for sample in sampled["samples"]: results.append( self.bing_translator.translate( sample, source_language=self.source_language, target_language=self.target_language, ) ) sleep(sleep_between_samples) return self.parse_results(results) def parse_results(self, results): terms = Counter() definitions = Counter() parsed_results = [] for r in results: parsed = self._parse_result(html.unescape(r)) terms.update(parsed["all_terms"]) definitions.update(parsed["all_definitions"]) parsed_results.append(parsed) return { "raw": parsed_results, "terms": terms.most_common(), "definitions": definitions.most_common(), "type": "translator", } class AbstractDictionaryTranslator(AbstractTranslator): def generate_samples(self, task): return {"samples": list(task["words"].values()), "total_lemmas": len(task["words"]), "pos": task["pos"]} class DictionaryBingTranslator(AbstractDictionaryTranslator): def __init__( self, bing_apikey, source_language="en", target_language="uk", ): self.bing_apikey = bing_apikey self.bing_translator = BingTranslator(self.bing_apikey) super().__init__( source_language=source_language, target_language=target_language, ) # [ "a", "n", "r", "s", "v" ] # a ADJ # r ADV # c CONJ # n NOUN # v VERB # x OTHER # DET # MODAL # PREP # PRON # Марьяна Романишин, [12 жовт. 2021 р., 09:18:00]: # Так, у цьому випадку adposition - це preposition. У різних мовах прийменники можуть стояти перед іменником (preposition) або після іменника (postposition). Термін adposition об'єднує одне і друге. # s також можна змапити на ADJ. def translate(self, task, sleep_between_samples=1): results = [] sampled = self.generate_samples(task) for sample in sampled["samples"]: results.append( self.bing_translator.dictionary_lookup( sample, source_language=self.source_language, target_language=self.target_language, ) ) sleep(sleep_between_samples) return self.parse_results(results) def parse_results(self, results): terms = Counter() parsed_results = [] for r in results: if "normalizedTarget" in r: terms.update(r["normalizedTarget"]) parsed_results.append(r) return { "raw": parsed_results, "terms": terms.most_common(), "definitions": [], "type": "dictionary", } def method_id(self): return f"{type(self).__name__}()" translators = [ SlidingWindowGoogleTranslator("../api_keys/dchaplynskyi_gmail_com.json", group_by=1), SlidingWindowGoogleTranslator("../api_keys/dchaplynskyi_gmail_com.json", group_by=3), SlidingWindowBingTranslator("../api_keys/khrystyna_skopyk_gmail_com.json", group_by=1), SlidingWindowBingTranslator("../api_keys/khrystyna_skopyk_gmail_com.json", group_by=3), DictionaryBingTranslator("../api_keys/khrystyna_skopyk_gmail_com.json"), ] # tasks = list(collection.find( # { # "_id": { # "$in": [ # # VERBS # "pwn-00006238-v", # "pwn-00009140-v", # "pwn-00014735-v", # # "pwn-00018151-v", # # "pwn-00022309-v", # # "pwn-00023466-v", # # "pwn-00050369-v", # # "pwn-00056644-v", # # "pwn-00058790-v", # # "pwn-00067045-v", # # NOUNS: # "pwn-00109001-n", # "pwn-00284945-n", # "pwn-00224850-n", # # ADJS: # "pwn-00102561-a", # ] # } # } # )) tasks = list(collection.find()) for translator in tqdm(translators): for t in tqdm(tasks): if translator.method_id() not in t.get("results", {}): res = translator.translate(t) collection.update_one( {"_id": t["_id"]}, {"$set": {f"results.{translator.method_id()}": res}}, upsert=True ) # + from csv import DictWriter def render_counter(cnt): return "\n".join(f"{k}: {v}" for k, v in cnt.most_common()) answered = list(collection.find({"results": {"$exists": 1}})) methods = set() for l in answered: methods \|= set(l["results"].keys()) columns = ["pwn", "lemmas", "pos", "definition"] for method in sorted(methods): columns.append(f"Terms, {method}") columns.append(f"Definitions, {method}") columns.append("Terms combined") columns.append("Definitions combined") with open("/tmp/translations.csv", "w") as fp: w = DictWriter(fp, fieldnames=columns) w.writeheader() for t in answered: to_export = { "pwn": t["_id"], "definition": "\n".join(t["definition"]), "pos": t["pos"], "lemmas": "\n".join(t["words"].values()), } combined_terms = Counter() combined_definitions = Counter() for method, r in t.get("results", {}).items(): terms = Counter(dict(r.get("terms", []))) definitions = Counter(dict(r.get("definitions", []))) combined_terms.update({k.lower(): v for k, v in terms.items()}) combined_definitions.update({k.lower(): v for k, v in definitions.items()}) to_export[f"Terms, {method}"] = render_counter(terms) to_export[f"Definitions, {method}"] = render_counter(definitions) to_export["Terms combined"] = render_counter(combined_terms) to_export["Definitions combined"] = render_counter(combined_definitions) w.writerow(to_export)	18,437
/Books/Patterns/Builder.ipynb	be948f1b116e145f2b9b9827567c1df127ca36f6	[]	no_license	Provinm/baseCs	https://github.com/Provinm/baseCs	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	4,888	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- class Account(object): def __init__(self,account_number,firstname,lastname,account_balance): self.firstname = firstname self.lastname = lastname self.account_balance = account_balance if account_balance > 0 else 0 self.account_number = account_number if account_number > 100001 else 0 def display(self): print (self.firstname) print (self.lastname) print (self.account_balance) print (self.account_number) ob = Account(100,"kartik","kumar",32132) ob.display()	812
/Abhishek_MT19086/Code/Abhishek19086_code_colab.ipynb	464271a9a7d3962e77fcbf73ca025a20461a831d	[]	no_license	abhishekvickyvyas/IMDB_score_prediction	https://github.com/abhishekvickyvyas/IMDB_score_prediction	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	571,205	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # SDA - lecture 14 - The frequency domain # + import logging logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(asctime)s: %(message)s') import math import numpy as np import matplotlib.pyplot as plt # Use scipy.fft and not numpy.fft which is slower on real numbers import scipy.fft as sfft # %matplotlib inline # - # ### Sum of two sine waves (different frequencies) # * Note the result when one of the frequemcies is an harmonic of the other x = np.arange(0,4,0.001) y = np.zeros((x.shape[0],3)) f1, f2 = 4, 2 y[:,0] = np.sin(xmath.pi2f1) y[:,1] = np.sin(xmath.pi2f2) y[:,2] = y[:,0] + y[:,1] fig, ax = plt.subplots(figsize=(20,5), nrows=1, ncols=3) titles = [f'Pure {f1} Hz sine wave', f'Pure {f2} Hz sine wave', 'Sum of the two sine waves'] for i in [0,1,2]: ax[i].plot(x,y[:,i]) ax[i].set_title(titles[i]) ax[i].set_xlabel('Time (s)') # ### Sum of two sine waves (different phases) # * Note that decreased sum amplitude as the phase difference increases # + x = np.arange(0,4,0.001) f = 2 phis = [0.1, 0.3, 0.5] fig, ax = plt.subplots(figsize=(12,4len(phis)), nrows=len(phis), ncols=2) for j, phi in enumerate(phis): y = np.zeros((x.shape[0],3)) y[:,0] = np.sin(xfmath.pi2) y[:,1] = np.sin((xf+phi)math.pi2) y[:,2] = y[:,0] + y[:,1] ax[j,0].plot(x,y[:,0],'b') ax[j,0].plot(x,y[:,1],'r') ax[j,0].set_title(f'Phase diff {phi} cycle') ax[j,0].set_ylim(-2,2) ax[j,1].plot(x,y[:,2]) ax[j,1].set_title(f'Sum') ax[j,1].set_ylim(-2,2) # - # ### Sampling theory # Plot a sine wave sampled at different rates with their corresponding DFT # Note the change when fs < 2f # + f = 4 # Frequency of the sine wave [Hz] T = 2 # Duration of the sine wave [s] fss = [100, 20, 10, 6] # Sampling frequencies [samples/s] fig, ax = plt.subplots(figsize=(16,12), nrows=4, ncols=2) for i, fs in enumerate(fss): x = np.arange(0,T,1/fs) midp = int(x.shape[0] .5) y = np.sin(xfmath.pi2) ax[i,0].plot(x,y,'.:') ax[i,0].set_title(f'fs={fs}Hz f={f}Hz T={T}s') p = sfft.fft(y) ax[i,1].step(x[:midp]fs/T,abs(p[:midp]),where='mid') # - # ### DFT - intermediate frequencies # # Author: Ori Carmi N = 64 # number of samples t = np.arange(N) # time vector fc = [15,15.5,16] fig, ax = plt.subplots(figsize=(12,9), nrows=3, ncols=1) for k,i in enumerate(fc): x1 = np.cos(2np.pii/Nt) X1 = sfft.fft(x1) X1 = abs(X1) ax[k].plot(np.arange(N), X1, '.') ax[k].set_title(f'f = {i} [Hz]') # # Maintaining the magnitude of the fft # + x = np.arange(0,4,0.001) y = np.zeros((x.shape[0],3)) f1, f2 = 4, 2 A1, A2 = 2, 3 y = np.sin(xmath.pi2f1)A1 + np.sin(xmath.pi2f2)A2 Py = np.sum(y2) logging.info(f'Total power original signal {Py:.2f}') Y = sfft.fft(origy) PY = np.sum(np.abs(Y2))/len(Y) logging.info(f'Total power fft {PY:.2f}') # - = False) tf1=tf1.drop(tf1[tf1['content_rating'].isna()].index, inplace = False) tis=((df.shape[0]-tf1.shape[0])/df.shape[0])100 print("% of data rows removed on deleting rows null values in `goss`,`budget`,`aspect_ratio`,`content_rating` is "+str(tis)) #only around 24% so now we have deleted all rows # + [markdown] id="ZxMj3KAMIog8" colab_type="text" # ['color', # 'director_name', # 'num_critic_for_reviews', # 'duration', # 'director_facebook_likes', # 'actor_3_facebook_likes', # 'actor_2_name', # 'actor_1_facebook_likes', # 'gross', # 'genres', # 'actor_1_name', # 'movie_title', # 'num_voted_users', # 'cast_total_facebook_likes', # 'actor_3_name', # 'facenumber_in_poster', # 'plot_keywords', # 'movie_imdb_link', # 'num_user_for_reviews', # 'language', # 'country', # 'content_rating', # 'budget', # 'title_year', # 'actor_2_facebook_likes', # 'imdb_score', # 'aspect_ratio', # 'movie_facebook_likes'] # # ` Here we can see in table data that bold attributes may take value zero so we convert them into NA and than replace NA value of these columns with mean value of that attribute` # + [markdown] id="a6Jwgm75OZ3K" colab_type="text" # Replace zero with NA # + id="BfjrALzFN_0e" colab_type="code" colab={} #Replace zero with NA tf2=tf1 ls_zero_to_NA='movie_facebook_likes','actor_2_facebook_likes','num_user_for_reviews','facenumber_in_poster','cast_total_facebook_likes','num_voted_users','actor_1_facebook_likes','actor_3_facebook_likes','director_facebook_likes','duration','num_critic_for_reviews' for k in ls_zero_to_NA: tf2[k].replace(0, np.nan, inplace=True) # + [markdown] id="G0HeukQy9bL5" colab_type="text" # Here we are filling NA of columns with mean value # + id="Q1Sm-zdd8ww7" colab_type="code" colab={} for colmn in ls_zero_to_NA: tf2[colmn]=tf2[colmn].fillna(tf2[colmn].mean()) # tf2 # + id="iYYj8iqs9-lq" colab_type="code" colab={} tf2_missing=tf2.isna() tf2_count_missing=tf2_missing.sum() tf2_per_none_value=tf2_count_missing/len(tf2_count_missing) tf2_per_none_value.sort_values(ascending=False,inplace=True) # tf2_per_none_value #Now we have NA values in only these columns # + [markdown] id="rjm_9zF9Dp77" colab_type="text" # Here only four column are remainng which have NA values we replce them with most frequent values in their column # + id="tpvyNoR5D_x1" colab_type="code" colab={} tf2 = tf2.fillna(tf2.mode().iloc[0]) # + [markdown] id="gjhaELt1T1tE" colab_type="text" # # Make Graphs to Visualise the trends in data # + id="iSFUJHdSUMgX" colab_type="code" outputId="5eef632a-a2ab-4914-8e1a-47dccf01b451" colab={"base_uri": "https://localhost:8080/", "height": 281} import matplotlib.pyplot as plt x = tf2['title_year'] plt.hist(x, bins=20) plt.ylabel('No of Movies released') plt.title('No of Movies released per year') plt.show() # + id="Pm0drwH8V0Lr" colab_type="code" outputId="e149bc54-87dc-42c0-e9d4-6b858532c803" colab={"base_uri": "https://localhost:8080/", "height": 281} x = tf2['color'] plt.hist(x, bins=10) plt.ylabel('No of Movies released') plt.title('No of Movies released per color where total rows are '+str(len(tf2))) plt.show() # + id="M9An3nXCaI-h" colab_type="code" outputId="4307ca07-2c64-4eec-b99e-960efea58c2e" colab={"base_uri": "https://localhost:8080/", "height": 348} x = tf2['country'] plt.hist(x, bins=20,rwidth=.9) plt.ylabel('No of Movies released') plt.title('No of Movies released per country where total rows are '+str(len(tf2))) plt.xticks(rotation='vertical') plt.show() # + id="6zxwqY04cpAE" colab_type="code" outputId="f66c6bb7-d45d-4ff4-ba05-5f28e1b5fb4e" colab={"base_uri": "https://localhost:8080/", "height": 331} x = tf2['language'] plt.hist(x, bins=20,rwidth=.9) plt.ylabel('No of Movies released in language') plt.title('No of Movies released in language where total rows are '+str(len(tf2))) plt.xticks(rotation='vertical') plt.show() # + [markdown] id="A-lB7Sond2RX" colab_type="text" # Add profit and profit percentage column for graphs and will drop these columns too before applying algorithems # + id="6GVoWcr0d0jd" colab_type="code" colab={} tf2['profit'] = tf2.apply(lambda row: row.gross - row.budget, axis = 1) tf2['profit_percentage'] = tf2.apply(lambda row: (row.profit/row.budget)100, axis = 1) # tf2 # + id="2NM51VAxihne" colab_type="code" colab={} group_by_imdb_score = tf2.groupby(by=['imdb_score']) tf2_avg = group_by_imdb_score.mean() tf2_count = group_by_imdb_score.count() # + id="OjgYXx0zlZaJ" colab_type="code" outputId="e17c932f-ac4e-45f2-e263-26f939a6b7a0" colab={"base_uri": "https://localhost:8080/", "height": 296} tf2_avg['num_critic_for_reviews'].plot(kind='line') # df.groupby('state')['name'].nunique().plot(kind='bar') # plt.hist(x, bins=20,rwidth=.9) plt.ylabel('Average number of critic_for_reviews') plt.title('Average number of critic_for_reviews per IMDB score') # plt.xticks(rotation='vertical') plt.show() # + id="wB33cFZNrEW6" colab_type="code" outputId="1ba0f03b-7ffd-474f-d417-fa86066625d6" colab={"base_uri": "https://localhost:8080/", "height": 296} tf2_avg['budget'].plot(kind='line') # df.groupby('state')['name'].nunique().plot(kind='bar') # plt.hist(x, bins=20,rwidth=.9) plt.ylabel('Average budget') plt.title('Average budget per IMDB score') # plt.xticks(rotation='vertical') plt.show() # + id="QN6-5QPjrlIw" colab_type="code" outputId="fac03c40-2412-4ad8-be76-3a349b617b9a" colab={"base_uri": "https://localhost:8080/", "height": 296} tf2_avg['gross'].plot(kind='line') # df.groupby('state')['name'].nunique().plot(kind='bar') # plt.hist(x, bins=20,rwidth=.9) plt.ylabel('Average gross') plt.title('Average gross per IMDB score') # plt.xticks(rotation='vertical') plt.show() # + id="kGWMRT1br_CQ" colab_type="code" outputId="8ae50196-d354-4f83-d8e9-e93d6dd4a87e" colab={"base_uri": "https://localhost:8080/", "height": 296} tf2_avg['facenumber_in_poster'].plot(kind='line') # df.groupby('state')['name'].nunique().plot(kind='bar') # plt.hist(x, bins=20,rwidth=.9) plt.ylabel('Average facenumber_in_poster') plt.title('Average facenumber_in_poster per IMDB score') # plt.xticks(rotation='vertical') plt.show() # + id="104W5-WJLsRj" colab_type="code" outputId="fe7bb395-2001-418f-925d-2fbda995c022" colab={"base_uri": "https://localhost:8080/", "height": 296} tf2_avg['profit'].plot(kind='line') # df.groupby('state')['name'].nunique().plot(kind='bar') # plt.hist(x, bins=20,rwidth=.9) plt.ylabel('Average profit') plt.title('Average profit per IMDB Score') # plt.xticks(rotation='vertical') plt.show() # + [markdown] id="oE2-RxhkLv9o" colab_type="text" # From above images we can see that most of the movies are color* attribute as "color" ,and country attribute is "USA",most of the movies in data have langauge atribute as "english" so they are not much useful for classification. # # Other Attributes as movie_title ,aspect_ratio,number of faces, movie_imdb_link (as we have seen in tableau) are not affecting IMDB score. we will also remove these attributes.title_year is also not afffecting the IMDB score but budget is increasing according to its values so we are not removing it. # # + id="5Tl5juLpEao3" colab_type="code" outputId="9491b13b-0a25-4321-d09c-aed6459322a8" colab={"base_uri": "https://localhost:8080/", "height": 34} unique_count_of_director_name=tf2['director_name'].nunique() unique_count_of_actor_1_name=tf2['actor_1_name'].nunique() unique_count_of_actor_2_name=tf2['actor_2_name'].nunique() unique_count_of_actor_3_name=tf2['actor_3_name'].nunique() unique_count_of_plot_keywords=tf2['plot_keywords'].nunique() print(unique_count_of_director_name,unique_count_of_actor_1_name,unique_count_of_actor_2_name,unique_count_of_actor_3_name,unique_count_of_plot_keywords) # + [markdown] id="3pjhitmtFUgc" colab_type="text" # As we can see unique count of above four variable is high so we will also remove these variable too # + id="2MKk6_nKZsPR" colab_type="code" colab={} # # df['movie_title'][6].replace("\xa0", "", regex=True) # s = pd.Series(df['movie_title']) # s = pd.Series(df['movie_title']) # k=s.replace("\xa0", "", regex=True) # df['movie_title']=k # #we can see some character at last of it # + [markdown] id="UaGFpwYg7OIf" colab_type="text" # checking affect of Splited genres on IMDB score # + id="sO157tjjZsRI" colab_type="code" colab={} # # Split Genres genres_tf2 = tf2[["genres", "imdb_score"]].copy() kp=genres_tf2['genres'].str.split("\|", n=-1, expand=False) kp1=list(map(set,kp)) new_colm=list(set.union(kp1)) # + [markdown] id="oSDPiUb0hH7V" colab_type="text" # Add genre colums having value 1 if it is else 0 # + id="pvV7hX7EZsSd" colab_type="code" colab={} for add_col in new_colm: if add_col not in genres_tf2.columns: genres_tf2[add_col]=genres_tf2.apply(lambda row: 1 if (add_col in row.genres) else 0 , axis = 1) # + id="X669zTUQwI4n" colab_type="code" outputId="203c097e-aa25-4a5f-d59b-f3559c36f987" colab={"base_uri": "https://localhost:8080/", "height": 500} dict_imdb_genres={} for k in genres_tf2: if(k!='imdb_score'): dict_imdb_genres[k]=genres_tf2.loc[genres_tf2[k] == 1, 'imdb_score'].mean() x_plot=list(dict_imdb_genres.keys()) y_plot=list(dict_imdb_genres.values()) fig, ax = plt.subplots(figsize=(15, 8)) ax.barh(x_plot, y_plot) # + [markdown] id="IapmIFoN7rC5" colab_type="text" # Here We can see that imdb_score is almost same for all genres so we will drop this column too during applying algorithems and do not and splited genres in main data* # + id="XPHK3XZxUOeg" colab_type="code" colab={} to_remove_column=['country','profit','profit_percentage','movie_imdb_link','facenumber_in_poster','aspect_ratio','movie_title','language','color','director_name','actor_1_name','actor_2_name','actor_2_name','actor_3_name','plot_keywords'] for i in to_remove_column: if i in tf2.columns: tf2=tf2.drop(columns=[i]) # tf2=tf2.drop(columns=[i]) classification_data=tf2 # classification_data=encode_the_data(kf_test) # classification_data # + id="2DE6eW_8Va9D" colab_type="code" colab={} # classification_data['imdb_score']=classification_data.apply(lambda row:'D' if ( float(row.imdb_score)>=0 and float(row.imdb_score)<5) else ('C' if (float(row.imdb_score)>=5 and float(row.imdb_score)<6) else ('B' if (float(row.imdb_score)>=6 and float(row.imdb_score)<6.5) else ('B+' if (row.imdb_score>=6.5 and float(row.imdb_score)<7) else ('A' if (float(row.imdb_score)>=7 and float(row.imdb_score)<7.5) else ('A+' if (float(row.imdb_score)>=7.5 and float(row.imdb_score)<8) else ('A++' if (float(row.imdb_score)>=8 and float(row.imdb_score)<=10) else 0))) ))) , axis = 1) # + id="3lUObk3mve7e" colab_type="code" colab={} classification_data['imdb_score']=classification_data.apply(lambda row:'E' if ( float(row.imdb_score)>=0 and float(row.imdb_score)<5) else ('D' if (float(row.imdb_score)>=5 and float(row.imdb_score)<6) else ('C' if (float(row.imdb_score)>=6 and float(row.imdb_score)<7) else ('B' if (row.imdb_score>=7 and float(row.imdb_score)<8) else ('A' if (float(row.imdb_score)>=8 and float(row.imdb_score)<10) else 0) ))) , axis = 1) # + id="LJ0aprPqVeLv" colab_type="code" colab={} # classification_data['imdb_score']=classification_data.apply(lambda row:'E' if ( float(row.imdb_score)>=0 and float(row.imdb_score)<2) else ('D' if (float(row.imdb_score)>=2 and float(row.imdb_score)<4) else ('C' if (float(row.imdb_score)>=4 and float(row.imdb_score)<6) else ('B' if (row.imdb_score>=6 and float(row.imdb_score)<8) else ('A' if (float(row.imdb_score)>=8 and float(row.imdb_score)<10) else 0) ))) , axis = 1) # + id="-olUYv8pLyRW" colab_type="code" colab={} # classification_data.groupby('imdb_score').count() # + id="kBrU_ArQ_n3I" colab_type="code" outputId="0c4b7aea-6483-460d-fdb3-603f174ecfc7" colab={"base_uri": "https://localhost:8080/", "height": 684} # classification_data import seaborn as sns corr = classification_data.corr() plt.figure(figsize = (16,10)) ax = sns.heatmap( corr, vmin=-1, vmax=1, center=0, cmap=sns.diverging_palette(0, 300,s=90, n=20), square=True,linewidths=.5,annot=True ) ax.set_xticklabels( ax.get_xticklabels(), rotation=45, horizontalalignment='right' ); # plt.figure(figsize = (16,5)) # Var_Corr = classification_data.corr() # # plot the heatmap and annotation on it # sns.heatmap(Var_Corr, xticklabels=Var_Corr.columns, yticklabels=Var_Corr.columns) # + [markdown] id="werMRCf_JEKq" colab_type="text" # From Attributes Correlation Heatmap we can see that few atributes are highly corelated to each othes as: # # 1.cast_total_facebook_likes and actor_1_facebook_likes(.95) # # 2.num_voted_users and num_users_for_reviews (.75) # # 3.num_voted_users and num_critic_for_reviews (.59) # # 4.num_users_for_reviews and num_critic_for_reviews(.56) # operation => # # A.remove cast_total_facebook_likes # B.add coulumn others_facebook_likes=sum(actor_2_facebook_likes+actor_3_facebook_likes) # C. add column num_user_per_critic=num_user_for_reviews/num_critic_for_reviews # D. remove columns num_user_for_reviews and num_critic_for_reviews # + id="f6X68OGyW8Yk" colab_type="code" colab={} backup_classification_data=classification_data # + id="gyh-JbzJXE6Q" colab_type="code" colab={} # classification_data=backup_classification_data # + id="-5pDDWLDD2KU" colab_type="code" colab={} classification_data['others_facebook_likes'] = classification_data.apply(lambda row: row.actor_2_facebook_likes + row.actor_3_facebook_likes, axis = 1) classification_data['num_user_per_critic'] = classification_data.apply(lambda row: (row.num_user_for_reviews/row.num_critic_for_reviews), axis = 1) for col in ["cast_total_facebook_likes","num_user_for_reviews","num_critic_for_reviews","actor_2_facebook_likes","actor_3_facebook_likes","genres"]: if (col in classification_data.columns): classification_data=classification_data.drop(columns=[col]) cols = classification_data.columns.tolist() cols.remove('imdb_score') cols.append('imdb_score') classification_data=classification_data[cols] # tf2 # classification_data # + id="GdPWIiqfFw3o" colab_type="code" outputId="edbe2b65-e95a-40f2-93a2-85b64dc8d169" colab={"base_uri": "https://localhost:8080/", "height": 677} import seaborn as sns corr = classification_data.corr() plt.figure(figsize = (16,10)) ax = sns.heatmap( corr, vmin=-1, vmax=1, center=0, cmap=sns.diverging_palette(0, 300,s=90, n=20), square=True,linewidths=.5,annot=True ) ax.set_xticklabels( ax.get_xticklabels(), rotation=45, horizontalalignment='right' ); # + [markdown] id="_A05VAlgHDb8" colab_type="text" # # Algorithem Implementation # + id="LFEwg8wS0Dkv" colab_type="code" colab={} # from sklearn.tree import DecisionTreeClassifier, export_graphviz from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn import tree from sklearn.model_selection import cross_val_score,train_test_split from sklearn import preprocessing from sklearn import metrics def encode_the_data(data): for col in data.columns: if data.dtypes[col] == "object": le = preprocessing.LabelEncoder() # le.fit(data[col]) # data[col] = le.transform(data[col].astype(str)) data[col] = le.fit_transform(data[col].astype(str)) return data # # classification_data_en classification_data_en=encode_the_data(classification_data) # classification_data_en=classification_data X=classification_data_en.loc[:, classification_data_en.columns != 'imdb_score'] y=classification_data_en['imdb_score'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # + id="YV8S2vv5WPGp" colab_type="code" colab={} # # GaussianNB Naive_bayes Classifier # from sklearn.naive_bayes import GaussianNB # model=GaussianNB() # # X_train, X_test, y_train, y_test # model.fit(X_train,y_train) # # multinom_naive_bayes,X_train,Y_train # naive_Y_predict=model.predict(X_test) # naive_bayes_accuracy=metrics.accuracy_score(y_test,naive_Y_predict) # naive_bayes_accuracy # + [markdown] id="95ralG7SWEdx" colab_type="text" # # Decision Tree Classifier # # + [markdown] id="PWhITcx08tG2" colab_type="text" # Accuracy with Decision Tree Classifier # + id="jJnLNsCm-J-W" colab_type="code" colab={} depth_of_tree_x=[] accuracy_of_tree_y=[] depth_of_tree=[] test_accuracy_of_tree_y=[] # 5,100,10 for depthis in range(3,10): decision_tree = tree.DecisionTreeClassifier(criterion = "gini",max_depth=depthis) decision_tree.fit(X_train,y_train) decision_tree_score_is = cross_val_score(estimator=decision_tree, X=X_train, y=y_train, cv=5) depth_of_tree_x.append(depthis) accuracy_of_tree_y.append(decision_tree_score_is.mean()) # print(decision_tree_score_is) # print(decision_tree_score_is.mean()) depth_of_tree.append((depthis,decision_tree_score_is.mean())) # decision_tree_score_is = cross_val_score(estimator=decision_tree, X=X_train, y=Y_train, cv=10, n_jobs=4) Y_test_pred = decision_tree.predict(X_test) test_decision_accuracy_is=metrics.accuracy_score(y_test,Y_test_pred) test_accuracy_of_tree_y.append(test_decision_accuracy_is) # + id="5IIGoEUs-V7B" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 573} outputId="1cb1316a-5dc8-4700-a7ef-dda36b187dd0" import matplotlib.pyplot as plt plt.plot(depth_of_tree_x, accuracy_of_tree_y) plt.xlabel("Depth") plt.ylabel("Accuracy") plt.title('Accuracy of training data with cross validation') plt.show() plt.plot(depth_of_tree_x, test_accuracy_of_tree_y) plt.xlabel("Depth") plt.ylabel("Accuracy") plt.title('Accuracy of test data') plt.show() # + id="edvzYF91KHad" colab_type="code" outputId="b296eb7c-a1ce-4b73-e297-cb286ac1ff28" colab={"base_uri": "https://localhost:8080/", "height": 34} decision_tree = tree.DecisionTreeClassifier(criterion = "gini",max_depth=6) decision_tree.fit(X_train,y_train) y_test_pred = decision_tree.predict(X_test) test_decision_accuracy_is=metrics.accuracy_score(y_test,y_test_pred) # decision_tree_score_is = cross_val_score(estimator=decision_tree, X=X_train, y=Y_train, cv=5) # depth_of_tree_x.append(depthis) # accuracy_of_tree_y.append(decision_tree_score_is.mean()) # classification_data_en.columns test_decision_accuracy_is # + [markdown] id="AQpgGIP7C6J6" colab_type="text" # Cross Validation k-fold # + id="F-bFpiTlsXUq" colab_type="code" outputId="daf410f0-bc6c-4862-9567-95527761b9bd" colab={"base_uri": "https://localhost:8080/", "height": 34} # by using cross validation decision_tree = tree.DecisionTreeClassifier(criterion = "gini",max_depth=7) a=X b=y decision_tree_score_is = cross_val_score(estimator=decision_tree, X=a, y=b, cv=5) decision_tree_score_is # + [markdown] id="d-u9tmrJDFYj" colab_type="text" # Decision Tree Creation # + id="qJRjODkJh7OE" colab_type="code" colab={} # from sklearn.tree import export_graphviz # from sklearn.externals.six import StringIO # from IPython.display import Image # import pydotplus # feature_cols=list(X.columns) # dot_data = StringIO() # export_graphviz(decision_tree, out_file=dot_data, # filled=True, rounded=True, # special_characters=True,feature_names = feature_cols,class_names=['A++','A+','A','B++','B','C','D']) # graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) # graph.write_png('IMDB.png') # Image(graph.create_png()) # + [markdown] id="CMpqEAiK9PNC" colab_type="text" # # K-NN Classifier # + id="KUOlpPXTt_yD" colab_type="code" colab={} from sklearn.neighbors import KNeighborsClassifier range_k=range(1,100) score_list=[] for i in range_k: knn=KNeighborsClassifier(n_neighbors=i) knn.fit(X_train,y_train) ypred=knn.predict(X_test) score_list.append(metrics.accuracy_score(y_test,ypred)) # + id="oLDB9Fhh0zTn" colab_type="code" outputId="ff6e21da-83a4-4358-b169-8ea19f9d9866" colab={"base_uri": "https://localhost:8080/", "height": 296} plt.plot(range_k,score_list) plt.xlabel('K in Knn') plt.ylabel("Testing Accuracy") # + id="SfBtBmKH6c6Z" colab_type="code" outputId="99d43735-251d-4c83-c8e8-6e013fb2c474" colab={"base_uri": "https://localhost:8080/", "height": 34} knn=KNeighborsClassifier(n_neighbors=82) knn.fit(X_train,y_train) ypred=knn.predict(X_test) metrics.accuracy_score(y_test,ypred) # + [markdown] id="PNfns9t-2xwA" colab_type="text" # Here we can see we are getting best accuracy when k=82 # + [markdown] id="Efw93gcz9o7k" colab_type="text" # # Classification using Support Vector Machines # + id="Ie42bpTQ3JZI" colab_type="code" outputId="eba53c1d-1ddb-4d8f-be28-43bb51f988b8" colab={"base_uri": "https://localhost:8080/", "height": 34} from sklearn import svm #Create a svm Classifier clf = svm.SVC(gamma='scale') #Train the model using the training sets clf.fit(X_train, y_train) #Predict the response for test dataset y_pred = clf.predict(X_test) print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) # + [markdown] id="iqobl_AreboY" colab_type="text" # # Random Forest with and without LDA(linear discriminant analysis ) # + [markdown] id="oET1Qc_ses5V" colab_type="text" # Without LDA # + id="hyRy0qz5bxug" colab_type="code" outputId="7506a4a4-a1e6-461f-bfa4-bab1895e9e26" colab={"base_uri": "https://localhost:8080/", "height": 88} from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(max_depth=2, random_state=0) classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) # + [markdown] id="wt-S7fZAe0xW" colab_type="text" # With LDA # + id="QGlHLaaca2xH" colab_type="code" colab={} from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA lda = LDA(n_components=1) X_trainlda = lda.fit_transform(X_train, y_train) X_testlda = lda.transform(X_test) #LDA tries to reduce dimensions of the feature set while retaining the information that discriminates output classes. LDA tries to find a decision boundary around each cluster of a class. # + id="XFUa4T3tbqAH" colab_type="code" outputId="5c6327fb-a80e-4921-f52e-62971ad2f6d8" colab={"base_uri": "https://localhost:8080/", "height": 88} from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(max_depth=2, random_state=0) classifier.fit(X_trainlda, y_train) y_pred = classifier.predict(X_testlda) print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) # + id="y9W1McdrKM7o" colab_type="code" colab={} # classification_data['imdb_score'].uniquecount() # classification_data.groupby('imdb_score').count() # + id="LSE3CwEcl_DY" colab_type="code" outputId="ebac800b-764c-46f9-896b-6ea6656fd494" colab={"base_uri": "https://localhost:8080/"} float("7.8")	25,920
/CNN 2D - FD.ipynb	7b2c37f092a059f6b0253c0fb85cc553cd1bcdc4	[ "Unlicense" ]	permissive	dmitryanton68/Tracker-fault-diagnosis	https://github.com/dmitryanton68/Tracker-fault-diagnosis	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	116,735	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:dl] # language: python # name: conda-env-dl-py # --- # + import warnings warnings.filterwarnings('ignore') from model import cnn_model import numpy as np import pandas as pd import keras import matplotlib.pyplot as plt import seaborn as sns import collections as cll import itertools import glob import os from sklearn.model_selection import train_test_split from keras.utils import np_utils from keras.models import Sequential from keras import regularizers from keras.layers import Dense, Dropout, Activation, Conv2D, Flatten, BatchNormalization, AveragePooling2D, LSTM from keras.optimizers import SGD from keras.optimizers import Adam, Nadam, Adadelta from keras.models import load_model from sklearn.metrics import classification_report,confusion_matrix from keras.datasets import mnist from keras.utils import Sequence from keras.preprocessing.sequence import pad_sequences from keras_lr_finder import LRFinder class My_generator(Sequence): def __init__(self, x_set_dir, y_set, batch_size): self.x, self.y = x_set_dir, y_set self.batch_size = batch_size def __len__(self): return int(np.ceil(len(self.x) / float(self.batch_size))) def __getitem__(self, idx): batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size] batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size] # read your data here using the batch lists, batch_x and batch_y x = [np.load(filename) for filename in batch_x] y = [np.load(filename) for filename in batch_y] return np.array(x).reshape(self.batch_size,img_rows,img_cols,1), np.array(y) # - def cnn_model_big(input_dim, n_classes, init,drop): model = Sequential() model.add(Conv2D(128, (4, 4), strides=(1, 1), kernel_initializer=init , padding='same', activation='relu', input_shape=input_dim)) model.add(Conv2D(128, (4, 4), strides=(1, 1), kernel_initializer=init , padding='same', activation='relu')) model.add(AveragePooling2D(pool_size=(9,1))) model.add(Dropout(drop)) model.add(Conv2D(256, (3,3), strides=(1, 1), kernel_initializer=init , activation='relu')) model.add(Conv2D(256, (3,3), strides=(1, 1), kernel_initializer=init , padding='same', activation='relu')) model.add(AveragePooling2D(pool_size=(7,1))) model.add(Dropout(drop)) model.add(Conv2D(512, (3,3), strides=(1, 1), kernel_initializer=init , padding='same', activation='relu')) model.add(Conv2D(512, (3,3), strides=(1, 1), kernel_initializer=init , padding='same', activation='relu')) model.add(AveragePooling2D(pool_size=(5,1))) model.add(Dropout(drop)) model.add(Conv2D(768, (3,3), strides=(1, 1), kernel_initializer=init,activation='relu')) model.add(Conv2D(768, (3,3), strides=(1, 1), kernel_initializer=init, padding='same', activation='relu')) model.add(AveragePooling2D(pool_size=(3,1))) model.add(Dropout(drop)) model.add(Flatten()) model.add(Dense(4096, kernel_initializer=init , activation='relu')) model.add(Dropout(drop6)) model.add(Dense(768, kernel_initializer=init , activation='relu')) model.add(Dropout(drop2)) return model def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): fig = plt.figure() if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes) plt.yticks(tick_marks, classes) fmt = '.4f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('Classe correta') plt.xlabel('Classe prevista') return fig img_rows, img_cols = 6410, 8 n_classes = 2 input_shape = (img_rows,img_cols,1) # # B1 train_path = glob.glob(os.path.join('dados/train/', '.npy')) test_path = glob.glob(os.path.join('dados/test', '.npy')) y_train_dir = glob.glob(os.path.join('dados/B1_y_train/', '.npy')) y_test_dir = glob.glob(os.path.join('dados/B1_y_test/', '.npy')) batch_size_train = 1 batch_size_test = 1 my_training_batch_generator = My_generator(train_path, y_train_dir, batch_size_train) my_validation_batch_generator = My_generator(test_path, y_test_dir, batch_size_test) init = keras.initializers.he_uniform(42) init2 = keras.initializers.constant(0.001) model = cnn_model_big(input_shape,n_classes,init,drop=0.1) model.add(Dense(n_classes,activation='softmax')) model.load_weights('PesosB1/cruzinho-CNN.ESSE.hdf5') model.summary() # + sgd = SGD(lr=0.1, decay=0, momentum=0.9, nesterov=True) adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0, amsgrad=False) nadam = Nadam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, schedule_decay=0.000001) adadelta = Adadelta(lr = 1) filepath = 'PesosB1/cruzinho-CNN-vgg.{epoch:02d}-{loss:.2f}-{acc:.2f}-{val_loss:.2f}-{val_acc:.2f}.hdf5' cb = keras.callbacks.ModelCheckpoint(filepath, monitor='acc', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1) model.compile(optimizer = nadam, loss = 'binary_crossentropy', metrics = ['accuracy']) # - history = model.fit_generator(generator=my_training_batch_generator, epochs=50, verbose=1, use_multiprocessing=True, workers = 8, max_queue_size=84, validation_data=my_validation_batch_generator, validation_steps=(len(test_path) // batch_size_test), steps_per_epoch=(len(train_path) // batch_size_train), callbacks = [cb], initial_epoch=0) # + active="" # history = model.fit(X_train, y_train, validation_data=(X_test,y_test), batch_size=10, epochs=10, initial_epoch=0,shuffle=True) # + plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('Model accuracy') plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() # Plot training & validation loss values plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper left') plt.show() # + y_test = [] y_train = [] for i in y_test_dir: tmp = np.load(i) y_test.append(tmp) for i in y_train_dir: tmp = np.load(i) y_train.append(tmp) # - #Predict Test classes = ['sem falha', 'com falha'] y_testing = np.argmax(y_test, axis =1) pred = model.predict_generator(my_validation_batch_generator, workers=8, use_multiprocessing=True, verbose=1,max_queue_size=64, steps=(len(test_path) // batch_size_test)) pred = np.argmax(pred, axis = 1) print(classification_report(y_testing,pred,digits=4)) cm = confusion_matrix(y_testing,pred) cm_plot = plot_confusion_matrix(cm, classes, normalize=True, title='') cm_plot.savefig('cruzin_b1_test.png',dpi = 'figure', bbox_inches='tight') #Predict Train y_training = np.argmax(y_train, axis = 1) pred = model.predict_generator(my_training_batch_generator, workers=8, use_multiprocessing=True, verbose=1,max_queue_size=64, steps=(len(train_path) // batch_size_train)) pred = np.argmax(pred, axis = 1) print(classification_report(y_training,pred,digits=4)) cm = confusion_matrix(y_training,pred) cm_plot = plot_confusion_matrix(cm, classes, normalize=True, title='') cm_plot.savefig('cruzin_b1_train.png',dpi = 'figure', bbox_inches='tight')	8,303
/Neural Network.ipynb	0d5ecf7871699df6a4388194aa163336767b6716	[]	no_license	WHaMoCaTY/Diabetes-130-UShospitals	https://github.com/WHaMoCaTY/Diabetes-130-UShospitals	2	0	null	null	null	null	Jupyter Notebook	false	false	.py	57,826	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import torch import torch.utils.data import torch.nn as nn from torch.autograd import Variable import torch.optim as optim import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt # - # Prepare data my_data = np.genfromtxt('hospital_ready_shuffle.csv', delimiter=',')[1:] X = my_data[:,:-1] Y = my_data[:, -1] Y = Y-1 scaler = MinMaxScaler() scaler.fit(X) X = scaler.transform(X) X_train = X[:60000,:] X_test = X[60000:,:] Y_train = Y[:60000] Y_test = Y[60000:] # + # data loader tensor_Xtr = torch.from_numpy(X_train).float() tensor_Ytr = torch.from_numpy(Y_train).long() tensor_Xva = torch.from_numpy(X_test).float() tensor_Yva = torch.from_numpy(Y_test).long() train = torch.utils.data.TensorDataset(tensor_Xtr, tensor_Ytr) train_loader = torch.utils.data.DataLoader(train, batch_size=256, shuffle=True) test = torch.utils.data.TensorDataset(tensor_Xva, tensor_Yva) test_loader = torch.utils.data.DataLoader(test, batch_size=256, shuffle=True) # + # define network # define nnet class Net(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(73, 200) self.relu1 = nn.ReLU() # self.fc2 = nn.Linear(200, 200) # self.relu2 = nn.ReLU() self.out = nn.Linear(200, 3) def forward(self, input_): a1 = self.fc1(input_) h1 = self.relu1(a1) # a2 = self.fc2(h1) # h2 = self.relu2(a2) a3 = self.out(h1) return a3 class Net2(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(73, 200) self.relu1 = nn.ReLU() self.fc2 = nn.Linear(200, 200) self.relu2 = nn.ReLU() self.out = nn.Linear(200, 3) def forward(self, input_): a1 = self.fc1(input_) h1 = self.relu1(a1) a2 = self.fc2(h1) h2 = self.relu2(a2) a3 = self.out(h2) return a3 # - # Create model net = Net() net.cuda() opt = optim.SGD(net.parameters(), lr=0.0001) criterion = nn.CrossEntropyLoss() # Test the Model def test(net, loader): correct = 0 total = 0 for x, y in loader: x = Variable(x).cuda() outputs = net(x) _, predicted = torch.max(outputs.data, 1) total += y.size(0) correct += (predicted.cpu() == y).sum() return correct.item()/total # run best_ac = 0 train_ac = [] test_ac = [] train_loss = [] for epoch in range(20): for i, (x, y) in enumerate(train_loader): # Convert torch tensor to Variable x = Variable(x).cuda() y = Variable(y).cuda() # Forward + Backward + Optimize opt.zero_grad() # zero the gradient buffer outputs = net(x) loss = criterion(outputs, y) loss.backward() opt.step() train_loss.append(loss.item()) tr_ac = test(net, train_loader) te_ac = test(net, test_loader) train_ac.append(tr_ac) test_ac.append(te_ac) if te_ac > best_ac: torch.save(net.state_dict(), 'model.pb') best_ac = te_ac print("Epoch: %d, Loss: %4f, Train AC: %4f, Test AC: %.4f, Best AC: %.4f" % (epoch, loss.item(), tr_ac, te_ac, best_ac)) best_ac net = Net2() net.cuda() opt = optim.SGD(net.parameters(), lr=0.0001) criterion = nn.CrossEntropyLoss() # run 2 best_ac2 = 0 train_ac2 = [] test_ac2 = [] train_loss2 = [] for epoch in range(20): for i, (x, y) in enumerate(train_loader): # Convert torch tensor to Variable x = Variable(x).cuda() y = Variable(y).cuda() # Forward + Backward + Optimize opt.zero_grad() # zero the gradient buffer outputs = net(x) loss = criterion(outputs, y) loss.backward() opt.step() train_loss2.append(loss.item()) tr_ac = test(net, train_loader) te_ac = test(net, test_loader) train_ac2.append(tr_ac) test_ac2.append(te_ac) if te_ac > best_ac2: torch.save(net.state_dict(), 'model.pb') best_ac2 = te_ac print("Epoch: %d, Loss: %4f, Train AC: %4f, Test AC: %.4f, Best AC: %.4f" % (epoch, loss.item(), tr_ac, te_ac, best_ac2)) # plot accuracy plt.plot(np.arange(20)+1, test_ac2, 'r') plt.plot(np.arange(20)+1, test_ac, 'b') plt.legend(['1 hidden layer of 200','2 hidden layers of 200']) plt.title('Accuracy On Test Data') plt.xlabel('Training Epochs') plt.ylabel('Accuracy') plt.savefig('acc.jpg') # plot loss plt.plot(np.arange(20)+1, train_loss2, 'r') plt.plot(np.arange(20)+1, train_loss, 'b') plt.legend(['1 hidden layer of 200','2 hidden layers of 200']) plt.title('Accuracy On Test Data') plt.xlabel('Training Epochs') plt.ylabel('Accuracy') plt.savefig('loss.jpg') best_ac best_ac2	5,016
/Module_08_電腦視覺服務應用3/Demo_8-3.ipynb	47dddef76a87fc73bb0441568218cc27f53bc3e5	[]	no_license	shangxiwu/Azure	https://github.com/shangxiwu/Azure	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	134,198	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # 特定領域偵測服務操作 # ## domain: celebrities # + from azure.cognitiveservices.vision.computervision import ComputerVisionClient from msrest.authentication import CognitiveServicesCredentials from IPython.display import Image from IPython.core.display import HTML # Set API key. subscription_key = '390b0e8c9f8b43d9a21e896e74ae1c7b' # Set endpoint. endpoint = 'https://aien08cv01.cognitiveservices.azure.com/' # Call API computervision_client = ComputerVisionClient(endpoint, CognitiveServicesCredentials(subscription_key)) ''' Detect Domain-specific Content - remote This example detects celebrites and landmarks in remote images. ''' print("===== Detect Domain-specific Content - remote =====") # URL of one or more celebrities remote_image_url_celebs = "https://upload.wikimedia.org/wikipedia/zh/4/49/Nobi_Nobita.png" # Call API with content type (celebrities) and URL detect_domain_results_celebs_remote = computervision_client.analyze_image_by_domain("celebrities", remote_image_url_celebs) # Print detection results with name print("Celebrities in the remote image:") if len(detect_domain_results_celebs_remote.result["celebrities"]) == 0: print("No celebrities detected.") else: for celeb in detect_domain_results_celebs_remote.result["celebrities"]: print(celeb["name"]) PATH = remote_image_url_celebs #圖片路徑 Image(url=PATH , width=480, height=240) # - # ## domain: landmarks # + from azure.cognitiveservices.vision.computervision import ComputerVisionClient from msrest.authentication import CognitiveServicesCredentials from IPython.display import Image from IPython.core.display import HTML # Set API key. subscription_key = '390b0e8c9f8b43d9a21e896e74ae1c7b' # Set endpoint. endpoint = 'https://aien08cv01.cognitiveservices.azure.com/' # Call API computervision_client = ComputerVisionClient(endpoint, CognitiveServicesCredentials(subscription_key)) remote_image_url = "https://pic.pimg.tw/anrine910070/1552868712-427881496.jpg" # Call API with content type (landmarks) and URL detect_domain_results_landmarks = computervision_client.analyze_image_by_domain("landmarks", remote_image_url) print() print("Landmarks in the remote image:") if len(detect_domain_results_landmarks.result["landmarks"]) == 0: print("No landmarks detected.") else: for landmark in detect_domain_results_landmarks.result["landmarks"]: print(landmark["name"]) PATH = remote_image_url #圖片路徑 Image(url=PATH , width=850, height=600) # - # # 色彩配置偵測服務操作 # + from azure.cognitiveservices.vision.computervision import ComputerVisionClient from msrest.authentication import CognitiveServicesCredentials # Set API key. subscription_key = '390b0e8c9f8b43d9a21e896e74ae1c7b' # Set endpoint. endpoint = 'https://aien08cv01.cognitiveservices.azure.com/' # Call API computervision_client = ComputerVisionClient(endpoint, CognitiveServicesCredentials(subscription_key)) ''' Detect Color - remote This example detects the different aspects of its color scheme in a remote image. ''' print("===== Detect Color - remote =====") # Select the feature(s) you want remote_image_features = ["color"] # Call API with URL and features detect_color_results_remote = computervision_client.analyze_image(remote_image_url, remote_image_features) # Print results of color scheme print("Getting color scheme of the remote image: ") print("Is black and white: {}".format(detect_color_results_remote.color.is_bw_img)) print("Accent color: {}".format(detect_color_results_remote.color.accent_color)) print("Dominant background color: {}".format(detect_color_results_remote.color.dominant_color_background)) print("Dominant foreground color: {}".format(detect_color_results_remote.color.dominant_color_foreground)) print("Dominant colors: {}".format(detect_color_results_remote.color.dominant_colors)) # - # # 智慧裁切縮圖服務操作 # + import os import sys import requests # If you are using a Jupyter notebook, uncomment the following line. # %matplotlib inline import matplotlib.pyplot as plt from PIL import Image from io import BytesIO # Set API key. subscription_key = '390b0e8c9f8b43d9a21e896e74ae1c7b' # Set endpoint. endpoint = 'https://aien08cv01.cognitiveservices.azure.com/' thumbnail_url = endpoint + "vision/v2.1/generateThumbnail" # Set image_url to the URL of an image that you want to analyze. image_url = "https://s2.itislooker.com/imgs/201811/06/12/15414798522851.jpg" headers = {'Ocp-Apim-Subscription-Key': subscription_key} params = {'width': '600', 'height': '600', 'smartCropping': 'true'} data = {'url': image_url} response = requests.post(thumbnail_url, headers=headers, params=params, json=data) response.raise_for_status() thumbnail = Image.open(BytesIO(response.content)) # Display the thumbnail. plt.imshow(thumbnail) plt.axis("off") # Verify the thumbnail size. print("Thumbnail is {0}-by-{1}".format(*thumbnail.size))	5,205
/final_project-master2/Data Cleaning.ipynb	4f9e46d01c601d7b0a85079b8f05412f19aa494b	[]	no_license	JChicatelli/Primary_Numbers	https://github.com/JChicatelli/Primary_Numbers	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	105,601	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd county_facts = "county_facts.csv" primary_results = "primary_results.csv" county_facts_df = pd.read_csv(county_facts, encoding="ISO-8859-1") primary_results_df = pd.read_csv(primary_results, encoding="ISO-8859-1") # County facts dataframe county_facts_df.head() # + #county_facts_df.count() # + # Remove all NaN county_facts_df = county_facts_df.dropna(how='any') # Set dataframe to show specific columns county_facts_cleaned_df = county_facts_df[["fips", "area_name", "PST120214", "POP010210", "POP645213", "VET605213", "LFE305213", "INC110213", "PVY020213", "SBO001207", "SBO315207", "SBO215207", "SBO415207", "SBO015207", "MAN450207", "RTN130207", "RTN131207", "BPS030214", "LND110210", "POP060210"]] #county_facts_cleaned_df.count() # - # Rename columns county_facts_cleaned_df = county_facts_cleaned_df.rename(columns={"area_name":"County", "PST120214":"Population % Change", "POP010210":"2010 Population", "POP645213":"% Foreign Born", "VET605213":"Veterans", "LFE305213":"Mean Travel Time", "INC110213":"Median Household Income", "PVY020213":"Below Poverty Level", "SBO001207":"Total Number of Firms", "SBO315207":"% Black-owned Firms", "SBO215207":"% Asian-owned Firms", "SBO415207":"% Hispanic-owned Firms", "SBO015207":"% Women-owned Firms", "MAN450207":"Manufacturers Shipments ($1000)", "RTN130207":"Retail Sales ($1000)", "RTN131207":"Retail Sales per Capita", "BPS030214":"Building permits", "LND110210":"Land Area (Square Miles)", "POP060210":"Population per Square Mile"}) county_facts_cleaned_df.head() # Drop all NaN from primary results dataframe primary_results_df = primary_results_df.dropna(how='any') #primary_results_df.count() # Set dataframe to show specific columns primary_results_cleaned_df = primary_results_df[["fips", "party", "candidate", "votes", "fraction_votes"]] #primary_results_cleaned_df # Rename columns primary_results_cleaned_df = primary_results_cleaned_df.rename(columns={"party":"Party", "candidate":"Candidate", "votes":"Votes",}) fips_merge_df = pd.merge(county_facts_cleaned_df, primary_results_cleaned_df, on="fips") fips_merge_df fips_merge_df.to_csv("training_data.csv", index=False, header=True) # + # primary_results_alabama_df = primary_results_df.loc[primary_results_df["state"] == "Alabama", :] # primary_results_republican_df = primary_results_df.loc[primary_results_df["party"] == "Republican", :] # primary_results_democrat_df = primary_results_df.loc[primary_results_df["party"] == "Democrat", :] # primary_results_democrat_df.head() # primary_results_democrat_sorted = primary_results_democrat_df.sort_values(["fips", "votes"], ascending=True) # primary_results_democrat_sorted = primary_results_democrat_sorted.sort_values(["fips"], ascending=True) # 8959 rows # primary_results_democrat_sorted.nlargest(keep="last") # primary_results_democrat_df.nlargest(100, ["fips", "votes"], keep="first")	4,320
/EDA/project.ipynb	ba5f957ba7e6df1d92efd801785d01a5d7a3d12b	[]	no_license	Karsenh/CPSC322FinalProject	https://github.com/Karsenh/CPSC322FinalProject	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	923,489	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Importing the Libraries import pandas as pd from mysklearn.mypytable import MyPyTable from mysklearn import myutils from mysklearn import plot_utils from mysklearn.myclassifiers import MySimpleLinearRegressor import matplotlib.pyplot as plt # # Loading the Dataset mytable = MyPyTable() dataset = mytable.load_from_json_file('yelp_academic_dataset_business.json', 30000) dataset.head() dataset.get_shape() dataset.remove_rows_with_missing_values() dataset.get_shape() # # Selecting Business Attributes id_ = dataset.get_column('business_id') review_count = dataset.get_column('review_count') attributes = dataset.get_column('attributes') mytable2 = MyPyTable() dataset2 = mytable2.load_from_json_file('yelp_academic_dataset_tip.json', 30000) dataset2.head() id_2 = dataset2.get_column('business_id') user_id = dataset2.get_column('user_id') text = dataset2.get_column('text') new_id = [] new_user_id = [] new_text = [] for id in id_: if id in id_2: new_id.append(id) index = id_2.index(id) new_user_id.append(user_id[index]) new_text.append(text[index]) new_id = new_id[:1500] new_user_id = new_user_id[:1500] new_text = new_text[:1500] new_rev_count = [] new_attributes = [] for id in new_id: index = id_.index(id) new_rev_count.append(review_count[index]) new_attributes.append(attributes[index]) mytable3 = MyPyTable() dataset3 = mytable3.load_from_json_file('yelp_academic_dataset_user.json', 250000) user_id = dataset3.get_column('user_id') fans = dataset3.get_column('fans') compliment_plain = dataset3.get_column('compliment_plain') friends = dataset3.get_column('friends') useful = dataset3.get_column('useful') new_fans = [] new_comp_plain = [] new_friends = [] new_useful = [] for id in new_user_id: if id in user_id: index = user_id.index(id) new_fans.append(fans[index]) new_comp_plain.append(compliment_plain[index]) new_friends.append(friends[index]) new_useful.append(useful[index]) new_dataset = [[a, b, c, d, e, f, g] for a, b, c, d, e, f, g in zip(new_rev_count, new_attributes, new_text, new_fans, new_comp_plain, new_friends, new_useful)] # # Exploratory Data Analysis # ## Data Preprocessing columns = ['Review Count', 'Attributes', 'Review Length', 'Fans', 'Compliment Plain', 'Friends', 'Useful'] final_dataset = MyPyTable(columns, new_dataset) final_dataset.replace_missing_values_with_column_average('Review Count') final_dataset.replace_missing_values_with_column_average('Attributes') final_dataset.replace_missing_values_with_column_average('Review Length') final_dataset.replace_missing_values_with_column_average('Fans') final_dataset.replace_missing_values_with_column_average('Compliment Plain') final_dataset.replace_missing_values_with_column_average('Friends') final_dataset.replace_missing_values_with_column_average('Useful') final_dataset.get_shape() final_dataset.remove_rows_with_missing_values() final_dataset.get_shape() # ### Saving Trimmed Data To New File import os if not os.path.exists('output'): os.mkdir('output') final_dataset.save_to_file(filename='output/trimmed_data.csv') # ### Summarization: Column Names, Minimum, Maximum, Middle, Average, Medium Values stats = final_dataset.compute_summary_statistics(col_names=['Review Count', 'Fans', 'Compliment Plain', 'Useful']) stats.pretty_print() # ## Visualization # ### Frequency Diagrams # #### Applying Discretization To Review Count To Generate Frequency Diagram review_values = final_dataset.get_column('Review Count') new_review_values = [] review_counts = [0] * 10 ranges = ['≤ 13', '14-50', '51-100', '101-200', '201-400', '401-600', '601-800', '801-1000', '1001-1200', '≥ 1200'] for val in review_values: if val <= 13: new_review_values.append('≤ 13') review_counts[0] += 1 elif 14 <= val <= 50: new_review_values.append('14-50') review_counts[1] += 1 elif 51 <= val <= 100: new_review_values.append('51-100') review_counts[2] += 1 elif 101 <= val <= 200: new_review_values.append('101-200') review_counts[3] += 1 elif 201 <= val <= 400: new_review_values.append('201-400') review_counts[4] += 1 elif 401 <= val <= 600: new_review_values.append('401-600') review_counts[5] += 1 elif 601 <= val <= 800: new_review_values.append('601-800') review_counts[6] += 1 elif 801 <= val <= 1000: new_review_values.append('801-1000') review_counts[7] += 1 elif 1001 <= val <= 1200: new_review_values.append('1001-1200') review_counts[8] += 1 elif val >= 1200: new_review_values.append('≥ 1200') review_counts[9] += 1 x_range = range(len(ranges)) y_range = range(0, max(review_counts)+50, 30) plot_utils.frequency_diagram(ranges, review_counts, ranges, y_range, 'Total number of counts by categories (1 to 10) of Review Counts', 'Review Count', 'Count') # #### Applying Discretization To Review Length To Generate Frequency Diagram review_length = final_dataset.get_column('Review Length') review_length = [len(str(t)) for t in review_length] new_review_length = [] review_length_counts = [0] * 5 ranges = ['≤ 20', '21-50', '51-100', '101-200', '≥ 200'] for val in review_length: if val <= 20: new_review_values.append('≤ 20') review_length_counts[0] += 1 elif 14 <= val <= 50: new_review_values.append('21-50') review_length_counts[1] += 1 elif 51 <= val <= 100: new_review_values.append('51-100') review_length_counts[2] += 1 elif 101 <= val <= 200: new_review_values.append('101-200') review_length_counts[3] += 1 elif val > 200: new_review_values.append('≥ 200') review_length_counts[4] += 1 x_range = range(len(ranges)) y_range = range(0, max(review_length_counts)+60, 30) plot_utils.frequency_diagram(ranges, review_length_counts, ranges, y_range, 'Total number of counts by categories (1 to 5) of Review Length', 'Review Length', 'Count') # #### Applying Discretization To Attributes To Generate Frequency Diagram attributes = final_dataset.get_column('Attributes') attributes = [len(k.keys()) if k else 0 for k in attributes] new_attributes = [] attributes_counts = [0] * 5 ranges = ['≤ 5', '6-10', '11-15', '16-20', '≥ 20'] for val in attributes: if val <= 5: new_attributes.append('≤ 5') attributes_counts[0] += 1 elif 6 <= val <= 10: new_attributes.append('6-10') attributes_counts[1] += 1 elif 11 <= val <= 15: new_attributes.append('11-15') attributes_counts[2] += 1 elif 16 <= val <= 20: new_attributes.append('16-20') attributes_counts[3] += 1 elif val > 20: new_attributes.append('≥ 20') attributes_counts[4] += 1 x_range = range(len(ranges)) y_range = range(0, max(attributes_counts)+60, 30) plot_utils.frequency_diagram(ranges, attributes_counts, ranges, y_range, 'Total number of counts by categories (1 to 5) of Attribute Counts', 'Attribute Counts', 'Count') # #### Applying Discretization To Fans To Generate Frequency Diagram fans_values = final_dataset.get_column('Fans') new_fans_values = [] fans_counts = [0] * 10 ranges = ['0', '1-5', '6-10', '11-20', '21-50', '51-100', '101-200', '201-300', '301-400', '≥ 400'] for val in fans_values: if val == 0: new_fans_values.append('0') fans_counts[0] += 1 elif val <= 13: new_fans_values.append('1-5') fans_counts[1] += 1 elif 14 <= val <= 50: new_fans_values.append('6-10') fans_counts[2] += 1 elif 51 <= val <= 100: new_fans_values.append('11-20') fans_counts[3] += 1 elif 201 <= val <= 400: new_fans_values.append('21-50') fans_counts[4] += 1 elif 401 <= val <= 600: new_fans_values.append('51-100') fans_counts[5] += 1 elif 601 <= val <= 800: new_fans_values.append('101-200') fans_counts[6] += 1 elif 801 <= val <= 1000: new_fans_values.append('201-300') fans_counts[7] += 1 elif 1001 <= val <= 1200: new_fans_values.append('301-400') fans_counts[8] += 1 elif val >= 1200: new_fans_values.append('≥ 400') fans_counts[9] += 1 x_range = range(len(ranges)) y_range = range(0, max(fans_counts)+50, 100) plot_utils.frequency_diagram(ranges, fans_counts, ranges, y_range, 'Total number of counts by categories (1 to 10) of Fans', 'Fans', 'Count') # #### Applying Discretization To Compliment Plain To Generate Frequency Diagram comp_plain_values = final_dataset.get_column('Compliment Plain') new_comp_plain_values = [] comp_plain_counts = [0] * 10 ranges = ['≤ 13', '14-50', '51-100', '101-200', '201-400', '401-600', '601-800', '801-1000', '1001-1200', '≥ 1200'] for val in comp_plain_values: if val <= 13: new_comp_plain_values.append('≤ 13') comp_plain_counts[0] += 1 elif 14 <= val <= 50: new_comp_plain_values.append('14-50') comp_plain_counts[1] += 1 elif 51 <= val <= 100: new_comp_plain_values.append('51-100') comp_plain_counts[2] += 1 elif 101 <= val <= 200: new_comp_plain_values.append('101-200') comp_plain_counts[3] += 1 elif 201 <= val <= 400: new_comp_plain_values.append('201-400') comp_plain_counts[4] += 1 elif 401 <= val <= 600: new_comp_plain_values.append('401-600') comp_plain_counts[5] += 1 elif 601 <= val <= 800: new_comp_plain_values.append('601-800') comp_plain_counts[6] += 1 elif 801 <= val <= 1000: new_comp_plain_values.append('801-1000') comp_plain_counts[7] += 1 elif 1001 <= val <= 1200: new_comp_plain_values.append('1001-1200') comp_plain_counts[8] += 1 elif val >= 1200: new_comp_plain_values.append('≥ 1200') comp_plain_counts[9] += 1 x_range = range(len(ranges)) y_range = range(0, max(comp_plain_counts)+50, 100) plot_utils.frequency_diagram(ranges, comp_plain_counts, ranges, y_range, 'Total number of counts by categories (1 to 10) of Compliment Plain', 'Compliment Plain', 'Count') # #### Applying Discretization To Friends To Generate Frequency Diagram friends = final_dataset.get_column('Friends') friends_values = [len(str(f)) for f in friends] new_friends_values = [] friends_counts = [0] * 10 ranges = ['≤ 100', '101-1000', '1001-10000', '10001-20000', '20001-30000', '30001-40000', '40001-50000', '50001-60000', '60001-70000', '≥ 70000'] for val in friends_values: if val <= 100: new_friends_values.append('≤ 100') friends_counts[0] += 1 elif 101 <= val <= 1000: new_friends_values.append('101-1000') friends_counts[1] += 1 elif 1001 <= val <= 10000: new_friends_values.append('1001-10000') friends_counts[2] += 1 elif 10001 <= val <= 20000: new_friends_values.append('10001-20000') friends_counts[3] += 1 elif 20001 <= val <= 30000: new_friends_values.append('20001-30000') friends_counts[4] += 1 elif 30001 <= val <= 40000: new_friends_values.append('30001-40000') friends_counts[5] += 1 elif 40001 <= val <= 50000: new_friends_values.append('40001-50000') friends_counts[6] += 1 elif 50001 <= val <= 60000: new_friends_values.append('50001-60000') friends_counts[7] += 1 elif 60001 <= val <= 70000: new_friends_values.append('60001-70000') friends_counts[8] += 1 elif val >= 70000: new_friends_values.append('≥ 70000') friends_counts[9] += 1 x_range = range(len(ranges)) y_range = range(0, max(friends_counts)+50, 100) plot_utils.frequency_diagram(ranges, friends_counts, ranges, y_range, 'Total number of counts by categories (1 to 10) of Friends', 'Friends', 'Count') # #### Applying Discretization To Useful To Generate Frequency Diagram useful_values = final_dataset.get_column('Useful') new_useful_values = [] useful_counts = [0] * 10 ranges = ['≤ 13', '14-50', '51-100', '101-200', '201-400', '401-600', '601-800', '801-1000', '1001-1200', '≥ 1200'] for val in useful_values: if val <= 13: new_useful_values.append('≤ 13') useful_counts[0] += 1 elif 14 <= val <= 50: new_useful_values.append('14-50') useful_counts[1] += 1 elif 51 <= val <= 100: new_useful_values.append('51-100') useful_counts[2] += 1 elif 101 <= val <= 200: new_useful_values.append('101-200') useful_counts[3] += 1 elif 201 <= val <= 400: new_useful_values.append('201-400') useful_counts[4] += 1 elif 401 <= val <= 600: new_useful_values.append('401-600') useful_counts[5] += 1 elif 601 <= val <= 800: new_useful_values.append('601-800') useful_counts[6] += 1 elif 801 <= val <= 1000: new_useful_values.append('801-1000') useful_counts[7] += 1 elif 1001 <= val <= 1200: new_useful_values.append('1001-1200') useful_counts[8] += 1 elif val >= 1200: new_useful_values.append('≥ 1200') useful_counts[9] += 1 x_range = range(len(ranges)) y_range = range(0, max(useful_counts)+50, 30) plot_utils.frequency_diagram(ranges, useful_counts, ranges, y_range, 'Total number of counts by categories (1 to 10) of Useful', 'Useful', 'Count') # ### Box and Whisker Plot plt.figure(figsize=(14, 6)) plt.boxplot([review_counts, attributes_counts]) plt.title('Ditribution Comparison Between Review Count and Attributes Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Count', 'Attributes']) plt.show() # Review Count is positively skewed in terms of data distribution because its top tail is longer compared to Attributes box plot which has normal or symmetric distribution because of equal top and bottom tails and median being in middle of box. Although, Attributes has median value higher than Review Count which suggests clear disagreement between both features count. Finally, no outliers found in both features which is good for data mining and machine learning algorithms. plt.figure(figsize=(14, 6)) plt.boxplot([review_counts, useful_counts]) plt.title('Ditribution Comparison Between Review Count and Useful Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Count', 'Useful']) plt.show() # Similar to Review Count, Useful also has positively skewed data distribution between its top tail is longer but its short in terms of box shape which suggests less dispersed data or less variability in Useful compared to Review Count. And, Useful has median value slightly above than Review Count which suggests disagreement between both features count. Finally, no outliers found in both features. plt.figure(figsize=(14, 6)) plt.boxplot([review_counts, fans_counts]) plt.title('Ditribution Comparison Between Review Count and Fans Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Count', 'Fans']) plt.show() # Similar to Review Count, Fans also has positively skewed data distribution because of its longer top tail and median value close to bottom. Both features are in disagreement because Review Count has median value higher than Fans. plt.figure(figsize=(14, 6)) plt.boxplot([review_counts, review_length_counts]) plt.title('Ditribution Comparison Between Review Count and Review Length Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Count', 'Review Length']) plt.show() # Compared to Review Count, Review Length has negatively skewed data distribution because of its bottom tail being longer but data is much dispersed and widely spread in Review Length compared to Review Count. And, both features are in disagreement because Review Length has median value higher than Review Count. Finally, no outliers found in both features count. plt.figure(figsize=(14, 6)) plt.boxplot([review_counts, friends_counts]) plt.title('Ditribution Comparison Between Review Count and Friends Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Count', 'Friends']) plt.show() # Similar to Review Count, Friends also has positively skewed data distribution because of its longer top tail and median value being at bottom and both have almost same data dispersion. But, both features are in disagreement because Review Count has median value higher than Friends. Finally, one outlier found in Friends which is not good for data mining algorithms and it should be handled by outliers handling methods as outliers show errors in dataset. plt.figure(figsize=(14, 6)) plt.boxplot([review_counts, comp_plain_counts]) plt.title('Ditribution Comparison Between Review Count and Compliment Plain Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Count', 'Compliment Plain']) plt.show() # Similar to Review Count, Compliment Plain has positively skewed data distribution because of its median value being at bottom side of box which makes upper part of box bigger. But, data is very less dispersed in compared to Review Count. And, both features are in disagreement because Review Count has median value higher than Compliment Plain. Finally, two outliers found in Compliment Plain which should be handled with methods like outliers deletion or replacement etc. plt.figure(figsize=(14, 6)) plt.boxplot([attributes_counts, review_length_counts]) plt.title('Ditribution Comparison Between Attributes Count and Review Length Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Attributes', 'Review Length']) plt.show() # Attributes is showing normal distribution of dataset compared to Review Length which has negatively skewed distribution because of its bottom tail being longer. Though, first quartile, second quartile, and third quartie of Review Length, all are different and higher than Attributes which suggests complete disgreement between both features, surely because of data being highly dispersed in Review Length compared to Attributes. Finally, no outliers found in both features count. plt.figure(figsize=(14, 6)) plt.boxplot([attributes_counts, fans_counts]) plt.title('Ditribution Comparison Between Attributes Count and Fans Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Attributes', 'Fans']) plt.show() # Compared to Attributes which has normal distribution, Fans has positively skewed (a.k.a skew-right) data distribution but has lower median value than Attributes which suggests disagreement between both features. Finally, one outlier found in Fans which should be handled properly in order to get reliable and accurate results from data mining algorithms. plt.figure(figsize=(14, 6)) plt.boxplot([attributes_counts, comp_plain_counts]) plt.title('Ditribution Comparison Between Attributes Count and Compliment Plain Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Attributes', 'Compliment Plain']) plt.show() # Compared to Attributes which has normal distribution, Compliment Plain has positively skewed (a.k.a skew-right) data distribution but has lower median value than Attributes which suggests disagreement between both features. Finally, two outliers found in Fans which should be handled properly because of reasons mentioned above. plt.figure(figsize=(14, 6)) plt.boxplot([attributes_counts, friends_counts]) plt.title('Ditribution Comparison Between Attributes Count and Friends Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Attributes', 'Friends']) plt.show() # Compared to Attributes which has normal distribution, Friends has positively skewed (a.k.a skew-right) data distribution but has lower median value than Attributes which suggests disagreement between both features. Finally, one outlier found in Fans. plt.figure(figsize=(14, 6)) plt.boxplot([attributes_counts, useful_counts]) plt.title('Ditribution Comparison Between Attributes Count and Useful Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Attributes', 'Useful']) plt.show() # Compared to Attributes which has normal distribution, Useful has positively skewed (a.k.a skewed-right) data distribution and also has lower median value than Attributes which suggests disagreement between both features. Finally, no outliers found in both features. plt.figure(figsize=(14, 6)) plt.boxplot([review_length_counts, fans_counts]) plt.title('Ditribution Comparison Between Review Length Count and Fans Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Length', 'Fans']) plt.show() # Review Length is showing negatively skewed (a.k.a skew-left) data distribution because of its bottom tail being longer compared to Fans which has positively skewed (a.k.a skewed-right) data distribution as it has longer top tail and also lower median value compared to Review Length which suggests disagreement between both features. Finally, single outlier found in Fans. plt.figure(figsize=(14, 6)) plt.boxplot([review_length_counts, comp_plain_counts]) plt.title('Ditribution Comparison Between Review Length Count and Compliment Plain Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Length', 'Compliment Plain']) plt.show() # Compared to Review Length which has negatively skewed data distribution, Compliment Plain has positively skewed data distribution because of its median value being closer to bottom of box and data is also less dispersed in Compliment Plain with median value being lower compared to Review Length, which ultimately suggests disagreement between both features. Finally, two outliers found in Compliment Plain count. plt.figure(figsize=(14, 6)) plt.boxplot([review_length_counts, friends_counts]) plt.title('Ditribution Comparison Between Review Length Count and Friends Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Length', 'Friends']) plt.show() # Compared to Review Length which has negatively skewed data distribution, Friends has positively skewed data distribution because of its median value being closer to bottom of box and top longer tail and data is also less dispersed in Friends with median value being lower compared to Review Length, which shows disagreement between both features. Finally, one outlier found in Friends count. plt.figure(figsize=(14, 6)) plt.boxplot([review_length_counts, useful_counts]) plt.title('Ditribution Comparison Between Review Length Count and Useful Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Review Length', 'Useful']) plt.show() # Compared to Review Length which has negatively skewed data distribution, Useful has positively skewed data distribution because of its top longer tail but data is less dispersed compared to Review Length, also median value of Useful is lower which shows disagreement between both features. Finally, no outliers found in both features count. plt.figure(figsize=(14, 6)) plt.boxplot([fans_counts, comp_plain_counts]) plt.title('Ditribution Comparison Between Fans Count and Compliment Plain Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Fans', 'Compliment Plain']) plt.show() # Fans and Compliment Plain both have postively skewed data distribution because of longer top tail in Fans and median value at bottom of box in Compliment Plain (two reasons which make data positively skewed but Fans has more dispersed distribution compared to Compliment Plain though median values of both are almost similar. We can deduce that both features have some similarities at initial part (e.g. First Quartile and Median) but disagreement in rest. Finally, outliers were found in both features. plt.figure(figsize=(14, 6)) plt.boxplot([fans_counts, friends_counts]) plt.title('Ditribution Comparison Between Fans Count and Friends Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Fans', 'Friends']) plt.show() # Both Fans and Friends have positively skewed data distribution but Fans has slightly more dispersed data. Both features seems to have similar views in first quartile and median but disagree in top part of box because of different third and fourth quartiles including whiskers. Again, outliers found in both features. plt.figure(figsize=(14, 6)) plt.boxplot([fans_counts, useful_counts]) plt.title('Ditribution Comparison Between Fans Count and Useful Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Fans', 'Useful']) plt.show() # Again, both features have positively skewed data distribution but differ in all quartiles including median value and whiskers so they're in disagreement. Although, Fans is slightly more dispersed than Useful. Finally, one outlier was found in Fans count. plt.figure(figsize=(14, 6)) plt.boxplot([comp_plain_counts, friends_counts]) plt.title('Ditribution Comparison Between Compliment Plain Count and Friends Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Compliment Plain', 'Friends']) plt.show() # Both features have data distribution of skew-right but differ in all quartiles and whiskers. Also, Compliment Plain is way less dispersed in terms of distribution than Friends. Finally, outliers were found in both features counts. plt.figure(figsize=(14, 6)) plt.boxplot([comp_plain_counts, useful_counts]) plt.title('Ditribution Comparison Between Compliment Plain Count and Useful Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Compliment Plain', 'Useful']) plt.show() # Again, both features are showing data distribution of skew-right because of median value being closest to bottom part of box in Compliment Plain (which makes upper part bigger) and longer top tail in Useful. And, they differ in all quartiles so they're in complete disagreement. Finally, outliers were found in Compliment Plain. plt.figure(figsize=(14, 6)) plt.boxplot([friends_counts, useful_counts]) plt.title('Ditribution Comparison Between Friends Count and Useful Count') plt.xlabel('Feature') plt.ylabel('Count') plt.xticks([1, 2], ['Friends', 'Useful']) plt.show() # Friends and Useful both have skew-right data distribution, whereas Friends is slightly more dispersed than Useful and have different quartile ranges than Useful which suggest disagreement with Useful. Finally, one outlier was found in Friends. # ### Histograms review_count = final_dataset.get_column('Review Count') plot_utils.histogram(review_count, 10, 'Distribution of Review Count', 'Review Count', 'Count') # Skew-Right attributes = final_dataset.get_column('Attributes') attributes = [len(k.keys()) for k in attributes if k] plot_utils.histogram(attributes, 10, 'Distribution of Attributes', 'Attributes', 'Count') # Bidomal text = final_dataset.get_column('Review Length') text_length = [len(str(t)) for t in text] plot_utils.histogram(text_length, 10, 'Distribution of Review Length', 'Review Length', 'Count') # Skew-Right compliment_count = final_dataset.get_column('Fans') plot_utils.histogram(compliment_count, 10, 'Distribution of Fans', 'Fans', 'Count') # Skew-Right compliment_count = final_dataset.get_column('Compliment Plain') plot_utils.histogram(compliment_count, 10, 'Distribution of Compliment Plain', 'Compliment Plain', 'Count') # Skew-Right friends = final_dataset.get_column('Friends') friends = [len(f) for f in friends] plot_utils.histogram(friends, 10, 'Distribution of Friends', 'Friends', 'Count') # Skew-Right useful = final_dataset.get_column('Useful') plot_utils.histogram(useful, 10, 'Distribution of Useful', 'Useful', 'Count') # Skew-Right # ## Simple Linear Regression # Formula for regression line that best fits dataset: # $$y = mx + b$$ # # Slope $m$: # $$m = \frac{\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sum_{i=1}^{n}(x_i - \bar{x})^2}$$ # # Intercept $b$: # $$b = $\bar{y} - m\bar{x}$$ # # Correlation Coefficient $r$: # $$r = \frac{\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=1}^{n}(x_i - \bar{x})^2 \sum_{i=1}^{n}(y_i - \bar{y})^2}}$$ # # Covariance $cov$: # $$cov = \frac{\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{n}$$ # # Standard Error Formula: # $$stderr = \sqrt{\frac{\sum_{i=1}^{n}(y_i - y^\prime)^2}{n}}$$ # ### Finding Correlation Between Useful and Fans X = final_dataset.get_column('Useful') X = [[x] for x in X] y = final_dataset.get_column('Fans') regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Useful vs Fans Comparison', 'Useful', 'Fans', regression_line, corr, cov, 45500, max(y) - 200) # Moderate Relationship # ### Finding Correlation Between Useful and Friends X = final_dataset.get_column('Useful') X = [[x] for x in X] y = final_dataset.get_column('Friends') y = [len(f) for f in y] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Useful vs Friends Comparison', 'Useful', 'Friends', regression_line, corr, cov, 0, max(y) + 20000) # Moderate Relationship # ### Finding Correlation Between Useful and Review Length X = final_dataset.get_column('Useful') X = [[x] for x in X] y = final_dataset.get_column('Review Length') y = [len(str(x)) for x in X] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Useful vs Review Length Comparison', 'Useful', 'Review Length', regression_line, corr, cov, 0, max(y) + 5.5) # Weak Relationship # ### Finding Correlatin Between Useful and Compliment Plain X = final_dataset.get_column('Useful') X = [[x] for x in X] y = final_dataset.get_column('Compliment Plain') regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Useful vs Compliment Plain Comparison', 'Useful', 'Compliment Plain', regression_line, corr, cov, 0, max(y) + 1000) # Strong Relationship # ### Finding Correlation Between Attributes Count and Review Length X = final_dataset.get_column('Attributes') X = [[len(x.keys()) if x else 0] for x in X] y = final_dataset.get_column('Review Length') y = [len(str(x)) for x in X] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Attributes Count vs Review Length Comparison', 'Attributes Count', 'Review Length', regression_line, corr, cov, 0, max(y) + 0.) # Strong Relationship # ### Finding Correlation Between Review Count and Review Length X = final_dataset.get_column('Review Count') X = [[x] for x in X] y = final_dataset.get_column('Review Length') y = [len(str(x)) for x in X] len(X), len(y) regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Review Count vs Review Length Comparison', 'Review Count', 'Review Length', regression_line, corr, cov, 0, max(y)) # Weak Relationship # ### Finding Correlation Between Compliment Plain and Review Length X = final_dataset.get_column('Compliment Plain') X = [[x] for x in X] y = final_dataset.get_column('Review Length') y = [len(str(x)) for x in X] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Compliment Plain vs Review Length Comparison', 'Compliment Plain', 'Review Length', regression_line, corr, cov, 0, max(y) + 1.5) # Weak Relationship # ### Finding Correlation Between Fans and Review Length X = final_dataset.get_column('Fans') X = [[x] for x in X] y = final_dataset.get_column('Review Length') y = [len(str(x)) for x in X] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Fans vs Review Length Comparison', 'Fans', 'Review Length', regression_line, corr, cov, 0, max(y) + 4) # Weak Relationship # ### Finding Correlation Between Friends and Review Length X = final_dataset.get_column('Friends') X = [[len(str(x))] for x in X] y = final_dataset.get_column('Review Length') y = [len(str(x)) for x in X] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Fans vs Review Length Comparison', 'Fans', 'Review Length', regression_line, corr, cov, 0, max(y) + 2) # Weak Relationship # ### Finding Correlation Between Fans and Compliment Plain X = final_dataset.get_column('Fans') X = [[x] for x in X] y = final_dataset.get_column('Compliment Plain') regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Fans vs Compliment Plain Comparison', 'Fans', 'Compliment Plain', regression_line, corr, cov, 2350, max(y) + 2000) # Moderate Relationship # ### Finding Correlation Between Fans and Friends X = final_dataset.get_column('Fans') X = [[x] for x in X] y = final_dataset.get_column('Friends') y = [len(f) for f in y] regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Fans vs Friends Comparison', 'Fans', 'Friends', regression_line, corr, cov, 0, max(y) + 40000) # Weak Relationship # ### Finding Correlation Between Friends and Compliment Plain X = final_dataset.get_column('Friends') X = [[len(x)] for x in X] y = final_dataset.get_column('Compliment Plain') regressor = MySimpleLinearRegressor() regressor.fit(X, y) m, b = regressor.slope, regressor.intercept x = [x[0] for x in X] regression_line = [(mi)+b for i in x] corr = myutils.coefficient_correlation(y, regression_line) cov = myutils.squared_error(y, regression_line) plot_utils.scatter_plot(x, y, 'Friends vs Compliment Plain Comparison', 'Friends', 'Compliment Plain', regression_line, corr, cov, 120000, max(y) - 500) # Weak Relationship	36,125
/Research/cifar10.ipynb	b8fd8ecd75c24d77244b03814149dc61064e113c	[]	no_license	marksein07/Machine-Learning	https://github.com/marksein07/Machine-Learning	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	228,800	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- from datetime import datetime import pandas as pd df = pd.read_csv("signals_experiment_time/signals_8.csv") #header=None, names=["TIME", "HR", "RESP", "ABPMean", "ABPSys", "ABPDias", "SpO2", "SOFA"] # + df["TIME_"] = df["TIME"].apply(lambda x: datetime.strptime(x, '%H:%M:%S %d/%m/%Y')) # - df = df.sort_values(by="TIME_") df[["TIME", "HR", "RESP", "ABPMEAN", "ABPSYS", "ABPDIAS", "SPO2", "SOFA_SCORE"]].to_csv("signals_experiment_time/signals_8.csv", index=False) df atement will fetch `GTC_Loader` module and name it `gtc`. import FPSDP.Plasma.GTC_Profile.GTC_Loader as gtc import numpy as np import matplotlib.pyplot as plt # %matplotlib inline pylab.rcParams['figure.figsize'] = (10.0, 8.0) pylab.rcParams['font.size'] = 20 # ## 2. Define relevant quantities # Before loading GTC output files, it is convenient to define some quantities for initialization. They are: # 1.The directory that contains all GTC output files gtc_path = 'Data/GTC_Outputs/nov6-3/' # 2.The grid on which all data will be interpolated or extrapolated. # # For now, only `Cartesian2D` grids are accepted. `Cartesian2D` grids can be generated by giving __DownLeft__ and __UpRight__ coordinates (Z,R) of the box (in meter), and __NR__ and __NZ__ of grid points, or __ResR__ and __ResZ__ as the resolution on each direction (in meter). For our GTC run, let's make a box that's larger than the simulation domain: grid2d = gtc.Cartesian2D(DownLeft = (-1,1), UpRight = (1,2.2), ResR = 0.01, ResZ = 0.01) # 3.The time steps we are interested in # Valid time steps are the integer number in "snapXXXXXXX_fpsdp.json" file names. We should provide a list of integer to `GTC_Loader`, if any of the time steps are not available, an exception will be raised along with the valid time steps information. We'll see the example later. timesteps = [1,2,3] # ## 3. Load data files # Now, we are ready to load the output files: gtcdata = gtc.GTC_Loader(gtc_path,grid2d,timesteps) # As we can see, first, our 2D grid are detected and accepted. Then, an error occurs. Since our output files only contain time steps 1 and 2, when we try to aquire time 3, our Loader will complain and tell us only `[1,2]` are available. Let's try again: timesteps = [1,2] gtcdata = gtc.GTC_Loader(gtc_path,grid2d,timesteps,Mode = 'full') # This time, all went well. Now, gtcdata is ready, we can then take a look at it's content. # ## 4. Exam data # Python objects use `__dict__` to store all attributes, we can use it's `keys()` method to list all the attribute names. gtcdata.__dict__.keys() # That's a LOT of stuff... Fortunately, only some of them are supposed to be used directly. Let me introduce them one by one. # #### 1.GTC run parameters # Some relevant GTC run parameters are determined by data in gtc.in.out and gtc.out files. They are: isEM, HaveElectron (MORE IS TO BE ADDED) # `isEM` is a boolean flag showing if the GTC run has electromagnetic perturbations. gtcdata.isEM # It's shown that our GTC run here has electromagnetic perturbations. # `HaveElectron` is a boolean flag showing if the GTC run has non-adiabatic electrons. gtcdata.HaveElectron # Our GTC run apparently includes non-adiabatic electrons as well. # #### 2.Raw data got from GTC output files # Second kind of attributes store the raw data read from GTC output files. They are normally named after their data entry names in the output files. They are: `R_gtc`, `Z_gtc`, `a_gtc`, `R_eq`, `Z_eq`, `a_eq`, `B_phi`, `B_R`, `B_Z`, and perturbations `phi`, `dne_ad`,`nane`, `dni`, `Te_perp`, `Te_para`, and `A_para`. # `R_gtc`, `Z_gtc` are R,Z coordinates for each mesh grid point in GTC simulation. `a_gtc` is the radial flux coordinate on this mesh. In our case, it's the poloidal magnetic flux $\psi_p$. `theta_gtc` is the poloidal flux coordinate $\theta$ on the same mesh. Let's take a look at them: fig=plt.figure() plt.scatter(gtcdata.R_gtc, gtcdata.Z_gtc, s=2, c=gtcdata.a_gtc, linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) fig = plt.figure() plt.scatter(gtcdata.R_gtc, gtcdata.Z_gtc, s=2, c=gtcdata.theta_gtc, linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) # It is clear that $\psi_p$ is not normalized, and $\theta$ is defined between $[0,2\pi)$ # `R_eq`, `Z_eq` and `a_eq` have similar physical meaning as their `_gtc` counter parts. The difference is that, by definition, they should cover the whole poloidal cross-section, from magnetic axis to the outmost closed flux surface, while `_gtc` quantities only cover GTC simulation region which usually excludes the magnetic axis and edge region. These `_eq` quantities are used to interpolate all equilibrium profiles and magnetic field. While equilibrium electron density and temperature are functions of $\psi_p$ only, i.e. $n_e(\psi_p)$ and $T_e(\psi_p)$, equilibrium magnetic field is a vector field on the whole poloidal cross-section. We use `B_phi`, `B_R` and `B_Z` to store the 3 components of the equilibrium magnetic field. Let's take a look at them: ISSUE #1 NEEDS TO BE RESOLVED fig = plt.figure() plt.scatter(gtcdata.R_eq,gtcdata.Z_eq, s=2, c= gtcdata.B_phi, linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) fig = plt.figure() plt.scatter(gtcdata.R_eq,gtcdata.Z_eq, s=2, c= gtcdata.B_R, linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) fig = plt.figure() plt.scatter(gtcdata.R_eq,gtcdata.Z_eq, s=2, c= gtcdata.B_Z, linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) # `phi` stores the perturbed potential at each requested time step. Let's see snapshot 1: fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.phi[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) # `dne_ad` is the adiabatic response of the electron density to perturbed potential. `nane` is the non-adiabatic response, it only exist if the run has non-adiabatic electrons. `dni` is the ion density perturbation. fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.dne_ad[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('dne_ad') if gtcdata.HaveElectron: fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.nane[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('nane') fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.dni[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('dni') # `Te_perp` and `Te_para` stores perturbed perpendicular and parallel electron temperature respectively. They are both small in our example because we are at the very beginning of the simulation. fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.Te_perp[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('Te_perp') fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.Te_para[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('Te_para') # `A_para` stores perturbed parallel vector potential for electromagnetic runs. if gtcdata.isEM: fig = plt.figure() plt.scatter(gtcdata.R_gtc,gtcdata.Z_gtc, s=2, c= gtcdata.A_para[0], linewidth = 0.1) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('A_para') # #### 3. 1D equilibrium profiles # As mentioned before, equilibrium density and temperature profiles are given as functions of $\psi_p$ only. These functions are specified by a $\psi_p$ array (`a_1D`) and corresponding $n_e$ (`ne0_1D`) and $T_e$ (`Te0_1D`) values. plt.plot(gtcdata.a_1D,gtcdata.ne0_1D) plt.xlabel('$\psi$') plt.ylabel('$n_{e0}$') plt.plot(gtcdata.a_1D,gtcdata.Te0_1D) plt.xlabel('$\psi$') plt.ylabel('$T_{e0}$') # #### 4. Interpolated and/or extrapolated data # All interpolated data are stored in `_on_grid` quantities. Let's look at them one by one: # $\psi_p$ (`a_on_grid`) is interpolated inside the convex hull of points given by `R_eq` and `Z_eq`, and linearly extrapolated outside based on the two partial derivatives on the boundary. fig = plt.figure() plt.imshow(gtcdata.a_on_grid, extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) # $n_{e0}$ and $T_{e0}$ are interpolated on $\psi_p$, and then applied to `a_on_grid` to obtain values on grid. fig = plt.figure() plt.imshow(gtcdata.ne0_on_grid, extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) fig = plt.figure() plt.imshow(gtcdata.Te0_on_grid, extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) # `Bphi_on_grid`, `BR_on_grid`, and `BZ_on_grid` are similarly interpolated. The extrapolation is done using $B_{\phi} = R_0B_{\phi 0}/R$, and contravariant expression for $B_R$ and $B_Z$ based on the extrapolated value for $\psi_p$. (ISSUE #1 NEEDS TO BE RESOLVED) fig = plt.figure() plt.imshow(gtcdata.Bphi_on_grid, extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) fig = plt.figure() plt.imshow(gtcdata.BR_on_grid, extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) fig = plt.figure() plt.imshow(gtcdata.BZ_on_grid, extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) # The perturbed quantities, `phi`, `dne_ad`, etc. , are interpolated on `R_gtc` and `Z_gtc`, but not extrapolated. All points outside the simulation grid are assigned 0 values. fig = plt.figure() plt.imshow(gtcdata.phi_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('phi') fig = plt.figure() plt.imshow(gtcdata.dne_ad_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('dne_ad') if gtcdata.HaveElectron: fig = plt.figure() plt.imshow(gtcdata.nane_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('nane') fig = plt.figure() plt.imshow(gtcdata.dni_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('dni') fig = plt.figure() plt.imshow(gtcdata.Te_perp_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('Te_perp') fig = plt.figure() plt.imshow(gtcdata.Te_para_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('Te_para') if gtcdata.isEM: fig = plt.figure() plt.imshow(gtcdata.A_para_on_grid[0], extent = [1,2.2,-1,1]) plt.colorbar() fig.axes[0].set_aspect(1) plt.title('A_para')	10,679
/.ipynb_checkpoints/Keras_step_1-both-checkpoint.ipynb	0043382f91b22bfceda45a669cb0a0e5107e2a27	[]	no_license	TejaSreenivas/fashion_mnist_proj	https://github.com/TejaSreenivas/fashion_mnist_proj	0	0	null	2018-09-17T12:48:27	2018-09-17T12:42:24	Jupyter Notebook	Jupyter Notebook	false	false	.py	12,134	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import cv2 #image = cv2.imread('input.jpg', cv2.IMREAD_UNCHANGED) image = cv2.imread('input.jpg') image = cv2.cvtColor(image, cv2.COLOR_BGR2RGBA) image[np.all(image == [0, 0, 0, 255], axis=2)] = [0, 0, 0, 0] = merger() from __future__ import print_function import keras from keras.datasets import cifar10 from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import SGD from keras.utils import print_summary, to_categorical import sys import os batch_size = 64 num_classes = 10 epochs = 10 # + n_classes = 10 id_mtx = np.identity(n_classes,dtype=np.float32) mnist['train_y'] = id_mtx[mnist['train_y']] mnist['test_y'] = id_mtx[mnist['test_y']] mnist['train_y'].shape, mnist['test_y'].shape n_classes = 10 id_mtx = np.identity(n_classes,dtype=np.float32) mnist['train_y'] = id_mtx[merged['train_y']] mnist['test_y'] = id_mtx[merged['test_y']] n_classes = 10 id_mtx = np.identity(n_classes,dtype=np.float32) fashion['train_y'] = id_mtx[fashion['train_y']] fashion['test_y'] = id_mtx[fashion['test_y']] mnist['train_y'].shape, mnist['test_y'].shape,fashion['train_y'].shape, fashion['test_y'].shape,merged['train_y'].shape, merged['test_y'].shape # - (x_train, y_train), (x_m_test, y_m_test), (x_f_test,y_f_test) = (merged['train_x'],merged['train_y']),(mnist['test_x'],mnist['test_y']),(fashion['test_x'],fashion['test_y']) # + model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:])) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.3)) model.add(Conv2D(64, (3, 3), padding='same', input_shape=x_train.shape[1:])) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.3)) model.add(Conv2D(128, (3, 3), padding='same', input_shape=x_train.shape[1:])) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.4)) model.add(Flatten()) model.add(Dense(80)) model.add(Activation('relu')) model.add(Dropout(0.3)) model.add(Dense(num_classes)) model.add(Activation('softmax')) # - opt = SGD(lr=0.01, momentum=0.9, decay=0, nesterov=False) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2, shuffle=True) scores = model.evaluate(x_test, y_test, verbose=1) print('Test loss:', scores[0]) print('Test accuracy:', scores[1]) and λSB on the wavelength axis.** # <img src="https://miro.medium.com/max/1030/1oUtYY0-j6iEc78Dew3d0uA.png" # alt="https://miro.medium.com/max/1030/1oUtYY0-j6iEc78Dew3d0uA.png" # style="float: left; margin-right: 10px;" /> spectral_bandwidth_2 = librosa.feature.spectral_bandwidth(sample+0.01, sr=sample_rate)[0] spectral_bandwidth_3 = librosa.feature.spectral_bandwidth(sample+0.01, sr=sample_rate, p=3)[0] spectral_bandwidth_4 = librosa.feature.spectral_bandwidth(sample+0.01, sr=sample_rate, p=4)[0] plt.figure(figsize=(15, 9)) librosa.display.waveplot(sample, sr=sample_rate, alpha=0.4) plt.plot(t, normalize(spectral_bandwidth_2), color='r') plt.plot(t, normalize(spectral_bandwidth_3), color='g') plt.plot(t, normalize(spectral_bandwidth_4), color='y') plt.legend(('p = 2', 'p = 3', 'p = 4')) # # zero crossinng # # A very simple way for measuring the smoothness of a signal is to calculate the number of zero-crossing within a segment of that signal. zero_crossings = librosa.zero_crossings(sample, pad=False) print(sum(zero_crossings)) # # chromagram # # A chroma feature or vector is typically a 12-element feature vector indicating how much energy of each pitch class, {C, C#, D, D#, E, …, B}, is present in the signal. In short, It provides a robust way to describe a similarity measure between music pieces. chromagram = librosa.feature.chroma_stft(sample, sr=sample_rate) plt.figure(figsize=(15, 5)) librosa.display.specshow(chromagram, x_axis='time', y_axis='chroma', cmap='coolwarm')	4,397
/lectures/L11/Exercise_1.ipynb	486eb51e9253c9e0c944ef4055d863a2b293f229	[ "MIT" ]	permissive	crystalzhaizhai/cs207_yi_zhai	https://github.com/crystalzhaizhai/cs207_yi_zhai	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	3,038	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Exercise 1 # Read and parse the chemical reactions `.xml` input file `rxns.xml`. # # # 1. Collect the species into a species list. My output is `['H', 'O', 'OH', 'H2', 'O2']`. # # Some notes and hints: # * Hint: For this `.xml` format you should have a loop over the `phase` element. # * Hint: You can use the `find()` method to get the species array. # # 2. Calculate and print out the Arrhenius reaction rate coefficients using $R = 8.314$ and $T = 1500$. # # Some notes and hints: # * Hint: For this `.xml` format you should have loops over the `reactionData` element, the `reaction` element, the `rateCoeff` element, and the `Arrhenius` element using the `findall()` method discussed in lecture. # * Hint: You can use the `find()` method to get the reaction rate coefficients. # * My solution is: # # `k for reaction01 = 6.8678391864294477e+05 # k for reaction02 = 2.3105559199959813e+06` import xml.etree.ElementTree as ET tree=ET.parse("rxns.xml") elementroot=tree.getroot() elements=elementroot.find('phase').find("speciesArray").text.strip(" ").split(" ") print(elements) reactionroot=elementroot.find("reactionData") for reaction in reactionroot: coefficients=reaction.find("rateCoeff").find("Arrhenius") a=float(coefficients.find("A").text) b=float(coefficients.find("b").text) e=float(coefficients.find("E").text) print("k=",aTbmath.exp(-e/(R*T)))	1,736
/HM6(2).ipynb	7a43482e0c44ab71ec117187ef1cc82ba059e6cf	[]	no_license	mmyd/Machine-Learning-	https://github.com/mmyd/Machine-Learning-	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	37,554	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import seaborn as sns import numpy as np import sklearn import matplotlib.pyplot as plt from pandas import * from numpy import * from sklearn import * import warnings from sklearn.preprocessing import Imputer warnings.filterwarnings('ignore') inf552= pd.read_csv("C:\\Users\\DELL\\Desktop\\INF552\\HM6\\data_banknote_authentication.csv",header=None,names=list(range(0,4))+['class']) # Choose 472 data points randomly as the test set, and the remaining 900 points as the training set from sklearn.svm import LinearSVC test=inf552.sample(n=472,random_state=123 ,axis=0) train=inf552.drop(index=test.index) #determine the ranges for λ that keep the accuracy above a threshold (e.g.60%) cls1 = LinearSVC(penalty='l1',random_state=123,dual=False,C=10(-3)).fit(train[list(range(0,4))], train['class']) print('the accuracy for λ = 10−3 is: ',cls1.score(train[list(range(0,4))], train['class'])) cls2 = LinearSVC(penalty='l1',random_state=123,dual=False,C=10(2)).fit(train[list(range(0,4))], train['class']) print('the accuracy for λ = 10+2 is: ',cls2.score(train[list(range(0,4))], train['class'])) print('the accuracy will not below 60% for λ = 10−3 and λ = 10+2') # passive learning # Train a SVM with a pool of 10 selected data points from the trainset(90 times) from sklearn.model_selection import GridSearchCV import sklearn.metrics errors=[] for i in range(50): error=[] testset=test trainset=train train_pool=trainset.sample(n=10,axis=0) # If all selected data points are from one class, select another set of 10 data points randomly. while len(train_pool['class'].value_counts())==1: train_pool=trainset.sample(n=10,axis=0) trainset=trainset.drop(index=train_pool.index) # Determine the weight of the SVM penalty C_range1 = np.logspace(-3, 2, 5) param_grid1 = dict(C=C_range1) passive_cls = LinearSVC(penalty='l1',dual=False) passive_model = GridSearchCV(passive_cls, param_grid=param_grid1, cv=KFold(n_splits=10)).fit(train_pool[list(range(0,4))], train_pool['class']) test_pred=passive_model.predict(testset[list(range(0,4))]); error.append(1-metrics.accuracy_score(testset['class'], test_pred)) for i in range(89): new_pool=trainset.sample(n=10,axis=0) train_pool=train_pool.append(new_pool) trainset=trainset.drop(index=new_pool.index) C_range1 = np.logspace(-3, 2, 5) param_grid1 = dict(C=C_range1) passive_cls = LinearSVC(penalty='l1',dual=False) passive_model = GridSearchCV(passive_cls, param_grid=param_grid1, cv=10).fit(train_pool[list(range(0,4))], train_pool['class']) test_pred=passive_model.predict(testset[list(range(0,4))]); error.append(1-metrics.accuracy_score(testset['class'], test_pred)) errors.append(error) # active learning # Train a SVM with a pool of 10 selected data points from the trainset(90 times) errors2=[] for i in range(50): error2=[] testset2=test trainset2=train train_pool2=trainset2.sample(n=10,axis=0) # If all selected data points are from one class, select another set of 10 data points randomly. while len(train_pool2['class'].value_counts())==1: train_pool2=trainset2.sample(n=10,axis=0) trainset2=trainset2.drop(index=train_pool2.index) # Determine the weight of the SVM penalty C_range2 = np.logspace(-3, 2, 5) param_grid2 = dict(C=C_range2) active_cls = LinearSVC(penalty='l1',dual=False) active_model = GridSearchCV(active_cls, param_grid=param_grid2, cv=KFold(n_splits=10)).fit(train_pool2[list(range(0,4))], train_pool2['class']) test_pred2=active_model.predict(testset2[list(range(0,4))]); error2.append(1-metrics.accuracy_score(testset2['class'], test_pred2)) for j in range(89): # Choose the 10 closest data points in the training set to the hyperplane of the SVM aa=(active_model.decision_function(trainset2[list(range(0,4))])).tolist(); w_norm = np.linalg.norm(active_model.best_estimator_.coef_);dist=aa/ w_norm; ind = np.argsort(dist)[:10]; new_pool2=trainset2.iloc[ind,:] train_pool2=train_pool2.append(new_pool2) trainset2=trainset2.drop(index=new_pool2.index) C_range2 = np.logspace(-3, 2, 5) param_grid2 = dict(C=C_range2) active_cls = LinearSVC(penalty='l1',dual=False) active_model = GridSearchCV(active_cls, param_grid=param_grid2, cv=10).fit(train_pool2[list(range(0,4))], train_pool2['class']) test_pred2=active_model.predict(testset2[list(range(0,4))]); error2.append(1-metrics.accuracy_score(testset2['class'], test_pred2)) errors2.append(error2) # Plot the average test error versus number of training instances trans_errors=np.transpose(errors);trans_errors2=np.transpose(errors2); passive_errors=[];active_errors=[]; for i in range(90): passive_errors.append(trans_errors[i].mean()); active_errors.append(trans_errors2[i].mean()); means={"passive_errors":passive_errors,"active_errors":active_errors} means=DataFrame(means) #errors.index = range(10,901,10) plt.figure(figsize=(10,5)) xlist1 = means.iloc[:,0] xlist2 = means.iloc[:,1] ylist = list(range(10,901,10)) plt.title("Plot of passive and active test errors") plt.xlabel(" number of training instances") plt.ylabel("test error rate") plt.plot(ylist,xlist1,"b-o") plt.plot(ylist,xlist2,"r-o") plt.legend(loc='best') plt.show() print('the conclusion: when the number of trainning instances is small, the passive learning is better than active one.') print('when the number of instances is large enough, there is a slightly difference between passive and active learning.')	5,935
/tutorials/map_expansion_tutorial.ipynb	2ac44370ee4295ffcaa42afaa5255885704b6202	[]	no_license	parkinkon1/nuscenes_test	https://github.com/parkinkon1/nuscenes_test	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	4,144,577	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [Root] # language: python # name: Python [Root] # --- # # MongoDB Tutorial - Monday 6 March 2017 # ## Introduction # In what follows, we assume that you have installed MongoDB according to the [instructions online](https://docs.mongodb.org/manual/installation/) and started the mongo daemon with the following command. # # > mongod # # Note that you might have to create a folder `/data/db` with appropriate access rights before the daemon starts successfully. # # We also assume that you use Python 3 and have the [pymongo driver](http://api.mongodb.org/python/current/installation.html) installed. # # Note To run the notebook yourself, [install Jupyter](http://jupyter.readthedocs.org/en/latest/install.html), [download](https://raw.githubusercontent.com/mmathioudakis/moderndb/master/2017/mongodb.tutorial.ipynb) the notebook, and [open it](http://jupyter.readthedocs.org/en/latest/running.html) with Jupyter. # # Note This notebook might be updated later. Major updates will be listed at its bottom. # + import pymongo as pm client = pm.MongoClient() client.drop_database("tutorial") import bson.son as son # - # ## "Hello World!" : Databases, Collections, Documents # # Relational databases contain tables that contain records. # # A MongoDB database contains collections that contain documents. # + # start a client client = pm.MongoClient() # connect to a database db = client.tutorial # get a collection coll = db.test_collection # - # Documents follow the [JSON](http://json.org/) format and MongoDB stores them in a binary version of it ([BSON](http://bsonspec.org/)). # <img src = "http://json.org/object.gif"> # <img src = "http://json.org/array.gif"> # <img src = "http://json.org/value.gif"> # # Below you see examples of JSON documents. # # JSON example 0 # ``` # {} # ``` # # JSON example 1 # ``` # { # "name" : "Michael", # "age": 32, # "grades": [71, 85, 90, 34] # } # ``` # # JSON example 2 # # ``` # { # "first name": "Michael", # "last name": "Mathioudakis", # "age": 32, # "grades": { # "ModernDB": 69, # "Data Mining": 71, # "Machine Learning": 95 # }, # "graduated": true, # "previous schools": ["NTUA", "UofT"] # } # ``` # In Python, JSON documents are represented as dictionaries. # The examples from above are therefore represented as follows. example_0 = {} example_1 = {"name": "Michael", "age": 32, "grades": [71, 85, 90, 34]} example_2 = \ {"first name": "Michael", "last name": "Mathioudakis", "age": 32, "grades": { "ModernDB": 69, "Data Mining": 71, "Machine Learning": 95 }, "graduated": True, "previous schools": ["NTUA", "UofT"] } # Note that we can also use native Python objects, like the `datetime` object below, to specify values. import datetime example_3 = {"name": "Modern Database Systems", "start": datetime.datetime(2016, 1, 12), "end": datetime.datetime(2016, 3, 26), "tags": ["rdbms", "mongodb", "spark"]} # ### Inserting and finding documents # # Our collection `coll` is currently empty. Let's add one document to it. coll.insert_one(example_0) # If we call the collection's function `find()`, we get back a cursor. coll.find() # We can use the cursor to iterate over all documents in the collection. for doc in coll.find(): print(doc) # Notice that the empty document we inserted is not really empty, but associated with an "\_id" key, added by MongoDB. # # Let's try another one. coll.insert_one(example_1) for doc in coll.find(): print(doc) print() # Notice how MongoDB added an "\_id" for the new document, as well. # Let's insert more documents. coll.insert_many([example_2, example_3]) for doc in coll.find(): print(doc) print() # Notice how the document we insert do not follow a schema? # # Let us now find documents that match a condition -- let's say we want to find documents that have a field "name" with value "Michael". query_result = coll.find({"name": "Michael"}) for doc in query_result: print(doc) # #### Projecting fields # # We can use find() not only to retrieve documents that match a condition, but also to project only those fields that we are interested in. # # For example, to suppress the "\_id" field from appearing in the results, we can provide a second argument to __find()__, as follows. query_result = coll.find({"name": "Michael"}, {"_id": 0}) for doc in query_result: print(doc) # What if we're interested in keeping only some of the rest of the fields -- let's say, only "grades"? query_result = coll.find({"name": "Michael"}, {"_id": 0, "grades": 1}) for doc in query_result: print(doc) # ## Loading a larger dataset # # Download file [primer-dataset.json](https://raw.githubusercontent.com/mongodb/docs-assets/primer-dataset/primer-dataset.json), store it in the same folder as this notebook, and load it into mongodb by running the command below. # + language="bash" # mongoimport --db tutorial --collection restaurants --drop --file primer-dataset.json # - # Alternatively, you can import the dataset by running the same command on a terminal. # > mongoimport --db moderndb --collection restaurants --drop --file dataset.json # The dataset contains documents that look like the one below. # # Restaurant Example # # ``` # { # "address": { # "building": "1007", # "coord": [ -73.856077, 40.848447 ], # "street": "Morris Park Ave", # "zipcode": "10462" # }, # "borough": "Bronx", # "cuisine": "Bakery", # "grades": [ # { "date": { "$date": 1393804800000 }, "grade": "A", "score": 2 }, # { "date": { "$date": 1378857600000 }, "grade": "A", "score": 6 }, # { "date": { "$date": 1358985600000 }, "grade": "A", "score": 10 }, # { "date": { "$date": 1322006400000 }, "grade": "A", "score": 9 }, # { "date": { "$date": 1299715200000 }, "grade": "B", "score": 14 } # ], # "name": "Morris Park Bake Shop", # "restaurant_id": "30075445" # } # ``` restaurants = db.restaurants # our new collection # how many restaurants? restaurants.count() # ## Querying the Dataset # retrieve a cursor over all documents in the collection cursor = restaurants.find() # define printing function def print_my_docs(cursor, num): for i in range(num): # print only up to num next documents from cursor try: print(next(cursor)) print() except: break # let's print a few documents print_my_docs(cursor, 3) next(cursor) # get one more document # ### Specify equality conditions # + # top-level field cursor = restaurants.find({"borough": "Manhattan"}) print_my_docs(cursor, 2) # + # nested field (in embedded document) cursor = restaurants.find({"address.zipcode": "10075"}) print_my_docs(cursor, 2) # - # query by field in array cursor = restaurants.find({"grades.grade": "B"}) # print one document from the query result next(cursor)['grades'] # exact array match cursor = restaurants.find({"address.coord": [-73.98513559999999, 40.7676919]}) print_my_docs(cursor, 10) # ### Specify Range Conditions cursor = restaurants.find({"grades.score": {"$gt": 30}}) cursor = restaurants.find({"grades.score": {"$lt": 10}}) next(cursor)["grades"] # ### Multiple Conditions # logical AND cursor = restaurants.find({"cuisine": "Italian", "address.zipcode": "10075"}) next(cursor) # logical OR cursor = restaurants.find({"$or": [{"cuisine": "Italian"}, {"address.zipcode": "10075"}]}) print_my_docs(cursor, 3) # logical AND, differently cursor = restaurants.find({"$and": [{"cuisine": "Italian"}, {"address.zipcode": "10075"}]}) next(cursor) # ## Sorting # + cursor = restaurants.find() # to sort, specify list of sorting criteria, # each criterion given as a tuple # (field_name, sort_order) # here we have only one sorted_cursor = cursor.sort([("borough", pm.ASCENDING)]) # - print_my_docs(cursor, 2) another_sorted_cursor = restaurants.find().sort([("borough", pm.ASCENDING), ("address.zipcode", pm.DESCENDING)]) print_my_docs(another_sorted_cursor, 3) # ## Aggregation # # Aggregation happens in stages. # Group Documents by a Field and Calculate Count cursor = restaurants.aggregate( [ {"$group": {"_id": "$borough", "count": {"$sum": 1}}} ] ) print_my_docs(cursor, 10) # Filter and Group Documents cursor = restaurants.aggregate( [ {"$match": {"borough": "Queens", "cuisine": "Brazilian"}}, {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}} ] ) print_my_docs(cursor, 10) # Filter and Group and then Filter Again documents cursor = restaurants.aggregate( [ {"$match": {"borough": "Manhattan", "cuisine": "American"}}, {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 1}}} ] ) print_my_docs(cursor, 10) # Filter and Group and then Filter Again and then Sort Documents cursor = restaurants.aggregate( [ {"$match": {"borough": "Manhattan", "cuisine": "American"}}, {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 1}}}, {"$sort": {"count": -1, "_id": -1}} ] ) print_my_docs(cursor, 10) # Same but sort by multiple fields # Filter and Group and then Filter Again and then Sort Documents cursor = restaurants.aggregate( [ {"$match": {"borough": "Manhattan", "cuisine": "American"}}, {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 1}}}, {"$sort": son.SON([("count", -1), ("_id", 1)])} # order matters!! ] ) print_my_docs(cursor, 10) # what will this do? cursor = restaurants.aggregate( [ {"$group": {"_id": None, "count": {"$sum": 1}} } ] ) print_my_docs(cursor, 10) # projection # what will this do? cursor = restaurants.aggregate( [ {"$group": {"_id": "$address.zipcode", "count": {"$sum": 1}}}, {"$project": {"_id": 0, "count": 1}} ] ) print_my_docs(cursor, 10) # what will this do? cursor = restaurants.aggregate( [ {"$group": {"_id": {"cuisine": "$cuisine"}, "count": {"$sum": 1}}}, {"$sort": {"count": -1}} ] ) print_my_docs(cursor, 5) # what will this do? cursor = restaurants.aggregate( [ {"$group": {"_id": {"zip": "$address.zipcode"}, "count": {"$sum": 1}}}, {"$sort": {"count": -1}} ] ) print_my_docs(cursor, 5) # what will this do? cursor = restaurants.aggregate( [ {"$group": {"_id": {"cuisine": "$cuisine", "zip": "$address.zipcode"}, "count": {"$sum": 1}}}, {"$sort": {"count": -1}} ] ) print_my_docs(cursor, 5) # ### Limiting the number of results # + # what will this do? cursor = restaurants.aggregate( [ {"$group": {"_id": {"cuisine": "$cuisine", "zip": "$address.zipcode"}, "count": {"$sum": 1}}}, {"$sort": {"count": -1}}, {"$limit": 10} # See comment under "In-class questions" ] ) for doc in cursor: print(doc["_id"]["cuisine"], doc["_id"]["zip"], doc["count"]) # - # ### Storing the result as a collection # We can use operator [\$out](https://docs.mongodb.org/manual/reference/operator/aggregation/out/) in a final stage to store the result of a query into a new collection. The following example selects restaurants from Manhattan and stores them in their own collection in the same database. restaurants.aggregate( [ {"$match": {"borough": "Manhattan"}}, {"$out": "manhattan"} ] ) # ## SQL to Aggregation # # Here we explore the correspondence between SQL queries and the aggregation framework. # SQL query # ``` # SELECT COUNT() AS count # FROM restaurants # ``` cursor = restaurants.aggregate( [ {"$group": {"_id": None, "count": {"$sum": 1}} } ] ) # * SQL query ** # ``` # SELECT borough, cuisine, COUNT() as count # FROM restaurants # GROUP BY borough, cuisine # ``` cursor = restaurants.aggregate( [ {"$group": {"_id": {"borough": "$borough", "cuisine": "$cuisine"}, "count": {"$sum": 1}}} ] ) # * SQL query ** # ``` # SELECT borough, cuisine, COUNT() as count # FROM restaurants # GROUP BY borough, cuisine # HAVING COUNT() > 3 # ``` cursor = restaurants.aggregate( [ {"$group": {"_id": {"borough": "$borough", "cuisine": "$cuisine"}, "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 3}}} ] ) # SQL Query # ``` # SELECT zipcode, cuisine, COUNT() as count # FROM restaurants # WHERE borough = "Manhattan" # GROUP BY zipcode, cuisine # HAVING COUNT() > 3 # ``` cursor = restaurants.aggregate( [ {"$match": {"borough": "Manhattan"}}, {"$group": {"_id": {"zipcode": "$address.zipcode", "cuisine": "$cuisine"}, "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 3}}} ] ) print_my_docs(cursor, 5) # SQL Query # ``` # SELECT zipcode, cuisine, COUNT() as count # FROM restaurants # WHERE borough = "Manhattan" # GROUP BY zipcode, cuisine # HAVING COUNT() > 3 # ORDER BY count # ``` cursor = restaurants.aggregate( [ {"$match": {"borough": "Manhattan"}}, {"$group": {"_id": {"zipcode": "$address.zipcode", "cuisine": "$cuisine"}, "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 3}}}, {"$sort": {"count": 1}} ] ) # ## Using secondary memory (disk) cursor = restaurants.aggregate( [ {"$match": {"borough": "Manhattan"}}, {"$group": {"_id": {"zipcode": "$address.zipcode", "cuisine": "$cuisine"}, "count": {"$sum": 1}}}, {"$match": {"count": {"$gt": 3}}}, {"$sort": {"count": 1}} ], allowDiskUse = True # this can be useful when data does not fit in memory, e.g., to perform external sorting ) # ## Indexing # # MongoDb automatically creates an index on the `_id` field upon creating a collection. # We can use `create_index()` to create index on one or more fields of a collection. # ### Single-field index # note that the argument is a list of tuples # [(<field>: <type>), ...] # here, we specify only one such tuple for one field restaurants.create_index([("borough", pm.ASCENDING)]) # The index is created only if it does not already exist. # ### Compound index # compound index (more than one indexed fields) restaurants.create_index([ ("cuisine", pm.ASCENDING), ("address.zipcode", pm.DESCENDING) ]) # ### Deleting indexes restaurants.drop_index('borough_1') # drop this index restaurants.drop_index('cuisine_1_address.zipcode_-1') # drop that index restaurants.drop_indexes() # drop all indexes!!1 # ### Multi-key index # # An index for a fields with array value. restaurants.find_one() restaurants.create_index([("address.coord", 1)]) restaurants.create_index([("grades.score", 1)]) restaurants.create_index([("grades.grade", 1), ("grades.score", 1)]) # The following will not work! # We cannot _currently_ have compound multi-key indexes. restaurants.create_index([("address.coord", 1), ("grades.score", 1)]) # NOPE! # ## Retrieving the execution plan # # We can retrieve the execution plan for a find() query by calling the [explain()](https://docs.mongodb.org/manual/reference/method/cursor.explain/) function on the result cursor. We demonstrate this in the following example. restaurants.drop_indexes() # we drop all indexes first -- use this with care! restaurants.create_index([("borough", pm.ASCENDING)]) # build an index on field "borough", in ascending order my_cursor = restaurants.find({"borough": "brooklyn"}) # submit query to find restaurants from specific borough my_cursor.explain()["queryPlanner"]["winningPlan"] # ask mongodb to explain execution plan # As we see in this example, MongoDB makes use of an index (as indicated by keyword "IXSCAN") -- and particularly the index ('borough_1') we constructed to execute the query. # What if we had not built this index? restaurants.drop_indexes() # we drop all indexes first -- use this with care! my_cursor = restaurants.find({"borough": "brooklyn"}) # submit query to find restaurants from specific borough my_cursor.explain()["queryPlanner"]["winningPlan"] # ask mongodb to explain execution plan # In that case, MongoDB simply performs a scan over the collection (as indicated by keyword "COLLSCAN"). # ## Joins # # Until very recently, MongoDB did not support joins. # It was up to the user to implement a join if needed -- as in the cell below. for a in restaurants.find({"borough": "Manhattan"}).limit(7): for b in restaurants.find({"borough": "Bronx"}).limit(5): if a["cuisine"] == b["cuisine"]: print(a["cuisine"], a["address"]["zipcode"], b["address"]["zipcode"]) # ### Joins with \$lookup # # This is a new aggregation stage that implements left outer equi-joins. # # "A [left outer equi-join](https://www.mongodb.com/blog/post/joins-and-other-aggregation-enhancements-coming-in-mongodb-3-2-part-1-of-3-introduction) produces a result set that contains data for all documents from the left table (collection) together with data from the right table (collection) for documents where there is a match with documents from the left table (collection)." # create first collection orders_docs = [{ "_id" : 1, "item" : "abc", "price" : 12, "quantity" : 2 }, { "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : 1 }, { "_id" : 3 }] orders = db.orders orders.drop() orders.insert_many(orders_docs) # create second collection inventory_docs = [ { "_id" : 1, "item" : "abc", "description": "product 1", "instock" : 120 }, { "_id" : 2, "item" : "def", "description": "product 2", "instock" : 80 }, { "_id" : 3, "item" : "ijk", "description": "product 3", "instock" : 60 }, { "_id" : 4, "item" : "jkl", "description": "product 4", "instock" : 70 }, { "_id" : 5, "item": None, "description": "Incomplete" }, { "_id" : 6 } ] inventory = db.inventory inventory.drop() inventory.insert_many(inventory_docs) result = orders.aggregate([ # "orders" is the outer collection { "$lookup": { "from": "inventory", # the inner collection "localField": "item", # the join field of the outer collection "foreignField": "item", # the join field of the outer collection "as": "inventory_docs" # name of field with array of joined inner docs } } ]) print_my_docs(result, 10) # ## Questions from tutorial sessions # ### Question: How do we query for documents with an array field, all the elements of which satisfy a condition? # # Two approaches (if you can think of a different approach, please let me know): # * Use the [\$not](https://docs.mongodb.org/manual/reference/operator/query/not/#op._S_not) operators: form a query to express that "there is no element in the array that does not satisfy the condition". # * In aggregation, combine an [\$unwind](https://docs.mongodb.org/manual/reference/operator/aggregation/unwind/) stage with a [$group](https://docs.mongodb.org/manual/reference/operator/aggregation/group/) stage. # # To provide an example, let's say we want to __find restaurants with 'A' grades only__. # Below we show how we can use each of the aforementioned approaches. # #### First approach: using $not # using the $not operator # "find restaurants that contain no grades that are not equal to A" cursor = restaurants.find({"grades.grade": {"$exists": True}, "grades": {"$not": {"$elemMatch": {"grade": {"$ne": "A"}}}}}) print_my_docs(cursor, 3) # ##### Note on the semantics of the \$not operator # # The operator selects documents that _do not match_ the specified condition on the specified field. These documents include ones that _do not contain_ the field. # # To demonstrate this, consider the following simple example of a collection. # + # simple example of a collection mycoll = db.mycoll mycoll.drop() # insert three documents mycoll.insert_one({"grades": [7, 7]}) mycoll.insert_one({"grades": [7, 3]}) mycoll.insert_one({"grades": [3, 3]}) mycoll.insert_one({"grades": []}) mycoll.insert_one({}) # - # The result of the following query contains documents that do not contain the "grades" field. # find documents that have no "grades" elements that are not equal to "A" mycursor = mycoll.find({"grades": {"$not": {"$elemMatch": {"$ne": 7}}}}) print_my_docs(mycursor, 10) # We can remove such documents from the result as a post-processing step. (Exercise: how?) # #### Second approach: aggregation pipeline # + # using aggregation mycursor = restaurants.aggregate( [ # unwind the grades array {"$unwind": "$grades"}, #now each document contains one "grades" value # group by document "_id" and count: # (i) the total number of documents in each group as `count` # -- this is the same as the number of elements in the original array # (ii) the number of documents that satisfy the condition (grade = "A") as `num_satisfied` {"$group": {"_id": "$_id", "count": {"$sum": 1}, "num_satisfied": {"$sum": {"$cond": [{"$eq": ["$grades.grade", "A"]}, 1, 0]}}}}, # create a field (named `same`) that is 1 if (count = num_satisfied) and 0 otherwise {"$project": {"_id": 1, "same_count": {"$cond": [{"$eq": ["$count", "$num_satisfied"]} , 1, 0]}}}, # keep only the document ids for which (same = 1) {"$match": {"same_count": 1}} ] ) print_my_docs(mycursor, 5) # - # ## Question: Does MongoDB optimize the stages of an aggregation pipeline? # # The question was asked in relation to the "limit" query we saw above ("Limiting the number of results"). # # Indeed, MongoDB does optimize the execution of the aggregation pipeline, as explained [here](https://docs.mongodb.org/manual/core/aggregation-pipeline-optimization/). In relation to the aforementioned query, see, in particular, the part on [sort+limit coalescence](https://docs.mongodb.org/manual/core/aggregation-pipeline-optimization/#sort-limit-coalescence). # *** # # Credits and references # # We used and consulted material from: # * the offficial [PyMongo tutorial](https://docs.mongodb.org/getting-started/python/) as well as this shorter [one](http://api.mongodb.org/python/current/tutorial.html), # * the [JSON](http://json.org/) and [BSON](http://bsonspec.org) documentation, as well as [SON](http://api.mongodb.org/python/current/api/bson/son.html#bson.son.SON), # * these [posts](https://www.mongodb.com/blog/post/joins-and-other-aggregation-enhancements-coming-in-mongodb-3-2-part-1-of-3-introduction) on the MongoDB blog about the new (v.3.2) left outer equi-join functionality, # * this [StackOverflow thread](http://stackoverflow.com/questions/18123300/mongo-array-query-only-find-where-all-elements-match).	23,059
/feature_selection.ipynb	e40f7a4e358bca79a2645e78a4575db1d84007b8	[]	no_license	fetihkaya/data_science	https://github.com/fetihkaya/data_science	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	305,289	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import pandas import matplotlib.pyplot as plt import numpy as np # Load INS data ins_data = pandas.read_csv('csv/1-vectornav-ins.csv') ins_data = ins_data.drop(columns=['.header.stamp.secs','.header.stamp.nsecs']) ins_data['time'] = pandas.to_datetime(ins_data['time']) ins_data['time'] = (ins_data['time'] - ins_data['time'][0]).astype('timedelta64[ns]').astype('int64')1e-9 ins_data = ins_data.set_index('time') # Load GPS Fix (raw) data fix_data = pandas.read_csv('csv/1-vectornav-fix.csv') fix_data = fix_data.drop(columns=['.header.stamp.secs','.header.stamp.nsecs']) fix_data['time'] = pandas.to_datetime(fix_data['time']) fix_data['time'] = (fix_data['time'] - fix_data['time'][0]).astype('timedelta64[ns]').astype('int64')1e-9 fix_data = fix_data.set_index('time') # Load IMU Data imu_data = pandas.read_csv('csv/1-vectornav-imu.csv') imu_data = imu_data.drop(columns=['.header.stamp.secs','.header.stamp.nsecs']) imu_data['time'] = pandas.to_datetime(imu_data['time']) imu_data['time'] = (imu_data['time'] - imu_data['time'][0]).astype('timedelta64[ns]').astype('int64')1e-9 imu_data = imu_data.set_index('time') # Load Pacmod Speed Data # pacmod parsed_txt/vehicle_speed is gets corrupt rows, so we will use as_tx/vehicle_speed pacmod_speed = pandas.read_csv('csv/1-pacmod-as_tx-vehicle_speed.csv') pacmod_speed['time'] = pandas.to_datetime(pacmod_speed['time']) pacmod_speed['time'] = (pacmod_speed['time'] - pacmod_speed['time'][0]).astype('timedelta64[ns]').astype('int64')1e-9 # Additionally, there is an factor offset between parsed_txt/vehicle_speed and as_tx/vehicle_speed for whatever reason # Use 2.237 pacmod_speed['.data'] = pacmod_speed['.data']2.237 pacmod_speed = pacmod_speed.set_index('time') # Load Pacmod Steer Data pacmod_steer = pandas.read_csv('csv/1-pacmod-parsed_tx-steer_rpt.csv') pacmod_steer = pacmod_steer.drop(columns=['.header.stamp.secs','.header.stamp.nsecs']) pacmod_steer['time'] = pandas.to_datetime(pacmod_steer['time']) pacmod_steer['time'] = (pacmod_steer['time'] - pacmod_steer['time'][0]).astype('timedelta64[ns]').astype('int64')1e-9 pacmod_steer = pacmod_steer.set_index('time') # Add some basic calculations to dataframes ... # Mangitude of speed ins_data['.mag_speed'] = (ins_data['.NedVel.y']2 + ins_data['.NedVel.x']2)*(1/2) # Add ins speed to IMU dataframe imu_data['ins_vel'] = 0.0 for t in imu_data.index: imu_data.at[t,'ins_vel'] = ins_data.iloc[ins_data.index.get_loc(t,method='nearest')]['.mag_speed'] # ENU Frame - subtract 90 from yaw yaw = (90 - ins_data['.RPY.z']) np.pi/180 ins_x_vel = ins_data['.NedVel.y'] ins_y_vel = ins_data['.NedVel.x'] # Local velocity at IMU location ins_data['.LocalVel.x'] = np.multiply(ins_x_vel,np.cos(yaw)) + np.multiply(ins_y_vel,np.sin(yaw)) ins_data['.LocalVel.y'] = -np.multiply(ins_x_vel,np.sin(yaw)) + np.multiply(ins_y_vel,np.cos(yaw)) del yaw, ins_x_vel, ins_y_vel # clear unused variables imu_data # + plt.style.use('ggplot') predicted_yaw = [ins_data['.RPY.z'][0]* np.pi/180] # Initialize first heading value to INS heading for steering,time in zip(pacmod_steer['.manual_input'],pacmod_steer.index): V = pacmod_speed.iloc[pacmod_speed.index.get_loc(time,method='nearest')]['.data'] prediction = predicted_yaw[-1] - (V/3.4)np.tan(steering/20.0)0.033 # updated equation. prediction = (prediction + np.pi) % (2* np.pi) - np.pi # Wrap to pi predicted_yaw.append(prediction) # - imu_yaw = [ins_data['.RPY.z'][0]* np.pi/180] # Initialize first heading value to INS heading for i in range(imu_data.index.size - 1): t0 = imu_data.index[i] t1 = imu_data.index[i+1] y0 = imu_data['.Gyro.z'].iloc[i] integration = imu_yaw[-1] + (y0)(t1-t0) integration = (integration + np.pi) % (2 np.pi) - np.pi # Wrap to pi imu_yaw.append(integration) # ## Integrating Yaw plt.figure() plt.plot(ins_data.index.values,ins_data['.RPY.z'] * np.pi/180,label='INS') # plot yaw plt.plot(imu_data.index,imu_yaw,label='Imu Integrated',alpha=0.75) plt.plot(pacmod_steer.index,predicted_yaw[0:-1],label="model predicted") plt.xlabel('Time [s]') plt.ylabel('heading [rad]') plt.title('INS vs. IMU Integrated Heading'); plt.legend() plt.show(); # Integrated yaw looks pretty good actually. Gyro data is probably less noisy than acceleration data. # ## Integrating Acceleration for Velocity # + # Perform integration, Initialize to ins_data[0] imu_vel_x = np.asarray([ins_data['.NedVel.x'].iloc[0]]) imu_vel_y = np.asarray([ins_data['.NedVel.y'].iloc[0]]) for i in range(imu_data.index.size - 1): t0 = imu_data.index[i] t1 = imu_data.index[i+1] v_x0 = imu_data['.Accel.x'].iloc[i] v_y0 = imu_data['.Accel.y'].iloc[i] vel_x = imu_vel_x[-1] + ( ((v_x0)(t1-t0))) vel_y = imu_vel_y[-1] + ( ((v_y0)(t1-t0))) imu_vel_x = np.append(imu_vel_x,vel_x) imu_vel_y = np.append(imu_vel_y,vel_y) imu_vel = (imu_vel_x2 + imu_vel_y2)*(1/2) # - # Plot INS vs Imu Integrated velocity plt.plot(ins_data.index,ins_data['.mag_speed'],label='INS') plt.plot(imu_data.index,imu_vel,label="IMU Integrated") plt.xlabel('Time [s]') plt.ylabel('Velocity [m/s]') plt.legend() plt.title('Comparison of IMU integrated vs. INS velocity') # ### Where does this drift come from? # # Let's observe some acceleration data when the velocity of the golfcart is zero (observed when the magnitude of the GPS speed is < 0.01 m/s) # + v_is_zero = ins_data['.mag_speed'] < 1e-2 v_is_zero = ins_data[v_is_zero]['.mag_speed'] >= 0.0 v_is_zero = v_is_zero.index[v_is_zero] imu_when_zeroV = pandas.DataFrame(columns=imu_data.columns) for t in v_is_zero: imu = imu_data.iloc[imu_data.index.get_loc(t,method='nearest')] imu_when_zeroV = imu_when_zeroV.append(imu) imu_when_zeroV.describe() # + plt.figure() plt.hist(imu_when_zeroV['.Accel.x'],bins=32,alpha=0.5,label="X Acceleration",density=True) plt.hist(imu_when_zeroV['.Accel.y'],bins=32,alpha=0.5,label="Y Acceleration",density=True) plt.xlim([-0.75,0.3]) plt.xlabel('Acceleration [m/s^2]') plt.ylabel('Density') plt.title('XY Acceleration Distribution with vehicle stopped') plt.legend() plt.figure() plt.hist(imu_when_zeroV['.Accel.z'],bins=50,alpha=0.5,density=True,label="Z Acceleration") plt.xlim([-9.85,-9.65]) plt.xlabel('Acceleration [m/s^2]') plt.ylabel('Density') plt.title('Z Acceleration Distribution with vehicle stopped') plt.legend(); # + # Code accelerations by stop imu_when_zeroV['group'] = None c_time = imu_when_zeroV.index[0] group = 0 for t in imu_when_zeroV.index: if np.abs(c_time - t) > 10: # Seperation time in seconds. Tune according to dataset group += 1 c_time = t imu_when_zeroV.at[t,'group'] = group #print(t,group) #print(imu_when_zeroV) for i in range(imu_when_zeroV['group'].max()): i +=1 # range gives 0 to n, need 1 to n seq = imu_when_zeroV[imu_when_zeroV['group']==i]['.header.seq'] print("Stop Group # ",i," Start Seq: ",seq.min(), " End Seq: ",seq.max()) # - plt.title('XY Acceleration Scatter during stops') plt.scatter(imu_when_zeroV['.Accel.y'],imu_when_zeroV['.Accel.x'],s=1,c=imu_when_zeroV['group'],label='data') plt.scatter(0.0,0.0,s=45,color='red',label='zero',marker='x') plt.scatter(imu_when_zeroV['.Accel.y'].mean(),imu_when_zeroV['.Accel.x'].mean(),s=45,marker='^',color='k',label='mean') plt.xlabel('Y Acceleration [$m/s^2$]') plt.ylabel('X Acceleration [$m/s^2$]'); plt.legend() # ## Subtracting out the bias, and re-integrating # + bx = imu_when_zeroV['.Accel.x'].mean() by = imu_when_zeroV['.Accel.y'].mean() # Perform integration, Initialize to ins_data[0] imu_vel_x = np.asarray([ins_data['.NedVel.x'].iloc[0]]) imu_vel_y = np.asarray([ins_data['.NedVel.y'].iloc[0]]) for i in range(imu_data.index.size - 1): t0 = imu_data.index[i] t1 = imu_data.index[i+1] v_x0 = imu_data['.Accel.x'].iloc[i] - bx v_y0 = imu_data['.Accel.y'].iloc[i] - by vel_x = imu_vel_x[-1] + ( ((v_x0)(t1-t0))) vel_y = imu_vel_y[-1] + ( ((v_y0)(t1-t0))) imu_vel_x = np.append(imu_vel_x,vel_x) imu_vel_y = np.append(imu_vel_y,vel_y) imu_vel = (imu_vel_x2 + imu_vel_y2)(1/2) # - # Plot INS vs Imu Integrated velocity plt.plot(ins_data.index,ins_data['.mag_speed'],label='INS') plt.plot(imu_data.index,imu_vel,label="IMU Integrated, no bias") plt.xlabel('Time [s]') plt.ylabel('Velocity [m/s]') plt.legend() plt.title('Comparison of IMU integrated vs. INS velocity'); # + # Differentiate the INS velX and velY imu_data['ins_velX'] = 0.0 imu_data['ins_velY'] = 0.0 for t in imu_data.index: ct = ins_data.index.get_loc(t,method='nearest') imu_data.at[t,'ins_velX'] = ins_data.iloc[ct]['.LocalVel.x'] imu_data.at[t,'ins_velY'] = ins_data.iloc[ct]['.LocalVel.y'] imu_data['ins_accel_x'] = np.diff(imu_data['ins_velX'],prepend=1e-6)/np.diff(imu_data.index,prepend=1-7) imu_data['ins_accel_y'] = np.diff(imu_data['ins_velY'],prepend=1e-6)/np.diff(imu_data.index,prepend=1-7) imu_data.plot(y=['ins_accel_x','.Accel.x'],alpha=0.5) plt.title('X measurements') plt.figure() imu_data.plot(y=['ins_accel_y','.Accel.y'],alpha=0.5) plt.legend() plt.title('Y measurements') plt.figure() imu_data.plot(y='.Accel.z',alpha=0.5) plt.plot([0,700],[-9.801,-9.801],label='True Gravity') plt.legend() plt.title('Z Measurements') plt.figure() plt.scatter(x=imu_when_zeroV.index,y=imu_when_zeroV['.Accel.z'],label='.Accel.z') plt.plot([0,100],[-9.801,-9.801],label='True Gravity') plt.legend() plt.title('Z Measurements (while stopped)') # + import numpy.matlib from scipy import integrate def xyz_accel_scale_bias(C,B,imu_data_): """ Returns acceleration with scalefactor C and Bias B """ imu_data_ = imu_data_.copy() # copy to avoid rewriting c_ = np.zeros((3,3)) c_[0,0] = C[0] c_[1,1] = C[1] c_[2,2] = C[2] c_[0,1] = C[3] c_[0,2] = C[4] c_[1,2] = C[5] c_[1,0] = C[6] c_[2,0] = C[7] c_[2,1] = C[8] #print(c_) b_ = np.zeros((3,1)) #print(b_) b_[0] = B[0] b_[1] = B[1] b_[2] = B[2] #print(b_) xyz_ = np.matmul(c_ ,( np.array([imu_data_['.Accel.x'],imu_data_['.Accel.y'],imu_data_['.Accel.z']]) + b_)) #imu_data_[['.Accel.x','.Accel.y','.Accel.z']] = 0.0 imu_data_[['.Accel.x','.Accel.y','.Accel.z']] = xyz_.T return imu_data_ def least_square_cost(imu_data_): diffx = (imu_data_['.Accel.x'] - imu_data_['ins_accel_x'] )2 diffy = (imu_data_['.Accel.y'] - imu_data_['ins_accel_y'] )2 diffz = (imu_data_['.Accel.z'] - -9.801)2 # 9.801 local gravity @ college station return np.sum( (diffx + diffy + diffz)(1/2) ) def minimize_fun(theta): C = theta[0:9] B = theta[9:12] # print(C,B) new_imu_data = xyz_accel_scale_bias(C,B,imu_data) return least_square_cost(new_imu_data) def integrate_imu_vel(imu_data_): imu_data_ = imu_data_.copy() # copy to avoid rewriting imu_data_['x_vel'] = integrate.cumtrapz(imu_data_['.Accel.x'],x=imu_data_.index,initial=ins_data['.LocalVel.x'][0]) imu_data_['y_vel'] = integrate.cumtrapz(imu_data_['.Accel.y'],x=imu_data_.index,initial=ins_data['.LocalVel.y'][0]) imu_data_['mag_vel'] = (imu_data_['x_vel']2 + imu_data_['y_vel']2)(1/2) return imu_data_ print(minimize_fun([1,1,1,1,1,1,1,1,1,0,0,0])) print(minimize_fun([1,1,1,1,1,1,1,1,1,0,0,0])) # + import scipy.optimize from scipy.optimize import Bounds Bound = Bounds(lb=[0.9,0.9,0.9,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-0.2,-0.2,-0.2],ub=[1.1,1.1,1.1,1.1,1.1,1.1,1.1,1.1,1.1,0.2,0.2,0.2]) sol = scipy.optimize.minimize(minimize_fun,[1,1,1,1,1,1,1,1,1,-0.1,0.0,0.1],bounds=Bound) print(sol) # + C = sol.x[0:9] B = sol.x[9:12] c_ = np.zeros((3,3)) c_[0,0] = C[0] c_[1,1] = C[1] c_[2,2] = C[2] c_[0,1] = C[3] c_[0,2] = C[4] c_[1,2] = C[5] c_[1,0] = C[6] c_[2,0] = C[7] c_[2,1] = C[8] b_ = np.zeros((3,1)) b_[0] = B[0] b_[1] = B[1] b_[2] = B[2] print("C = \n", np.array_str(c_, precision=2)) print("B = \n", np.array_str(b_, precision=2)) new_imu_data = xyz_accel_scale_bias(C,B,imu_data) new_imu_data = integrate_imu_vel(new_imu_data) new_imu_data.plot(y=['mag_vel','ins_vel']) plt.legend(['IMU integrated','INS']) plt.ylabel('m/$s^2$') plt.title('IMU Velocity integrated with bias and scale factor removal') plt.figure() plt.plot(imu_data.index,imu_data['.Accel.x'],label='Old',alpha=0.5) plt.plot(new_imu_data.index,new_imu_data['.Accel.x'],label='New',alpha=0.5) plt.title('New X measurements') plt.legend() plt.figure() plt.plot(imu_data.index,imu_data['.Accel.y'],label='Old',alpha=0.5) plt.plot(new_imu_data.index,new_imu_data['.Accel.y'],label='New',alpha=0.5) plt.title('New Y measurements') plt.legend() plt.figure() plt.plot(imu_data.index,imu_data['.Accel.z'],label='Old',alpha=0.5) plt.plot(new_imu_data.index,new_imu_data['.Accel.z'],label='New',alpha=0.5) plt.title('New Z measurements') plt.legend() # - np.mean(new_imu_data['.Accel.z']) en(group2))) for i in range(len(group1)): for j in range(len(group2)): con_table[i][j] = sum((df[a1] == group1[i]) & (df[a2] == group2[j])) return con_table, group1, group2 con_table,g1,g2 = calc_contingency_table_T(bank,"y","marital") print(g1) print(g2) con_table stats.chi2_contingency(con_table) def calc_chi2_contingency_T(con_table): con_table = con_table.astype(float) marginals_col = con_table.sum(axis=0) marginals_row = con_table.sum(axis=1) total = con_table.sum() for i in range(con_table.shape[0]): for j in range(con_table.shape[1]): expected = (marginals_row[i]) (marginals_col[j]) / total con_table[i][j] = ((con_table[i][j] - expected)*2) / expected return con_table.sum() chi2_stat = calc_chi2_contingency_T(con_table) chi2_stat bank le = LabelEncoder() X = bank.loc[:,'age':'poutcome'] X = X.select_dtypes(include=np.object) # select string columns X = X.apply(LabelEncoder().fit_transform) y = le.fit_transform(bank.loc[:,'y']) chi2, pval=feature_selection.chi2(X, y) # + sorted_idx = np.argsort(chi2)[::-1] sorted_vals = np.sort(chi2)[::-1] d = {"features":X.columns[sorted_idx], "values":sorted_vals, "p-values":pval[sorted_idx]} df = pd.DataFrame(d) df # - # ### Mutual Information # #### Entropy # # Given a discrete random variable $X$ with outcomes $\mathcal{X}=\{x_{1},...,x_{n}\}$ which occur with probability ${\displaystyle \mathrm {P} (x_{1}),...,\mathrm {P} (x_{n})}$, the entropy of X is defined as: # $$ # H(X) = -\sum_{x \in \mathcal{X}}P(x)logP(x) # $$ # pk[i] is the probability of outcome i def entropy_T(pk): S = 0 for i in range(len(pk)): if pk[i] > 0: S = S - pk[i]np.log2(pk[i]) return S pk = [0.5, 0.5] print("Entropy of a fair coin:", entropy_T(pk)) pk = [0.1, 0.9] print("Entropy of a biased fair coin:", entropy_T(pk)) pk = [0, 1] print("Entropy of a perfect biased fair coin:", entropy_T(pk)) # Entropy of a fair dice pk = [1/6,1/6,1/6,1/6,1/6,1/6] print("Entropy of a fair dice:", entropy_T(pk)) pk = [2/6,2/6,0/6,0/6,2/6,0] print("Entropy of a fair dice:", entropy_T(pk)) pk = [0,0,0,0,0,1] print("Entropy of a perfect unfair dice:", entropy_T(pk)) # Shape of the entropy function for a variable with two outcomes x = np.arange(0.01,1,0.01) s = [] for i in x: s.append(entropy_T([i,1-i])) plt.plot(x,s) # #### Some facts about entropy # # - The range of Entropy: # $0 ≤ Entropy ≤ log(n)$, where n is number of outcomes # - Minimum entropy ($0$) occurs when one of the probabilities is 1 and rest are 0’s. # - Maximum entropy ($log(n)$) occurs when all the probabilities are the same, namely, 1/n. # #### Mutual Information # # $$ # I(X;Y) = \sum_{x \in \mathcal{X}}\sum_{y \in \mathcal{Y}}P(x,y)log\frac{P(x,y)}{P(x)P(y)} # $$ # # where $P(X,Y)$ is the joint and $P(X)$ and $P(Y)$ are the marginal probability distributions of the random variables $X$ and $Y$. # # def mutual_info_T(df, a1, a2): con_table, g1, g2 = calc_contingency_table_T(df, a1, a2) print(con_table) marginals_col = con_table.sum(axis=0) marginals_row = con_table.sum(axis=1) total = con_table.sum() mi = 0 for i in range(con_table.shape[0]): for j in range(con_table.shape[1]): # Calculate joint probability. p = con_table[i][j] / total m = (marginals_row[i] / total)(marginals_col[j] / total) if (p > 0): mi += pnp.log(p / m) return mi mutual_info_T(bank,"y","marital") X = bank.loc[:,['marital', 'education', 'y']] X X = X.apply(LabelEncoder().fit_transform) X importances_mi = mutual_info_classif(X.loc[:,['marital','education']], X.y,discrete_features=True) importances_mi le = LabelEncoder() X = bank.loc[:,'age':'poutcome'] X = X.select_dtypes(include=np.object) # select string columns X = X.apply(LabelEncoder().fit_transform) y = le.fit_transform(bank.loc[:,'y']) importances_mi=mutual_info_classif(X, y,discrete_features=True) # + sorted_idx = np.argsort(importances_mi)[::-1] sorted_vals = np.sort(importances_mi)[::-1] d = {"features":X.columns[sorted_idx], "values":sorted_vals} df = pd.DataFrame(d) df # - # # Categorical (nominal), Ordinal, and Numerical Variables # # You can read this link for the differences with these types of variables. # # https://stats.idre.ucla.edu/other/mult-pkg/whatstat/what-is-the-difference-between-categorical-ordinal-and-numerical-variables/ # ### Be careful in using OrdinalEncoder # There is some confusion on the use of LabelEncoder and OrdinalEncoder in Pytgon. LabelEncoder is generally used to encode class variable, and OrdinalEncoder is generally used to encode feature en X. However, note that OrdinalEncoder cannot not assign integer based on the inherent order of the values, you have to give these mapping. oe = OrdinalEncoder() X = bank.loc[:,'age':'poutcome'] X = X.select_dtypes(include=np.object) # select string columns oe.fit_transform(X) X.columns X.education.unique() ordinal_map = {'tertiary':3, 'secondary':2, 'primary':1, 'unknown':0} X # If you want to give integer labels in the correct order for the feature education, you should do it manually. X = bank.loc[:,'age':'poutcome'] X['education'] = X.education.map(ordinal_map) X	18,473
/2. Algorithms/NeuralNetwork/Neural Network - Convolutional Neural Network with MNIST.ipynb	0f92405373aee43a02a8ffea038213290583ebc5	[]	no_license	patrick-ytchou/Machine-Learning	https://github.com/patrick-ytchou/Machine-Learning	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	28,381	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Neural Network: Convolutional Neural Network (CNN) with MNIST # ## Introduction to Convolutional Neural Network # # <img src="pic/cnnall.jpeg" width=800> # # Convolutional Neural Network (CovNet/CNN) is now the go-to model on every image related problem. In terms of accuracy they blow competition out of the water. It is also successfully applied to recommender systems, natural language processing, audio recognition, and much much more. CNN is also computationally efficient. The key advantage of CNN lies in its feature learning ability. THat is, CNN can capture relevant features from an image /video and detect patterns in part of the image rather than the image as a whole. For normal neural network, it can only see the whole image as a whole. # # # Convolutional neural network is very powerful in image analysis/recognition. You can, in fact, use simple Artificial Neural Network (ANN) to analyze images, for example image recognition. Then why nowadays people often switch to CNN for images? # # Say that you have a picture with 100 x 100 pixels. Since each pixel consists of 3 values(R,G,B), this picture will, in turn, have $100 \times 100 \times 3 = 30,000$ dimensions. If the first hidden layer consists of 1,000 nodes, you will have 30 million weights if you use the fully-connected ANN, for only the first layer! Therefore, CNN is a great technique for images since it reduces the parameters needed for neural network. It basically is a technique that utilize prior knowledge to remove some of the weights in the fully-connected neural network. # # # # # # # # --- # ## Why CNN is useful for image analysis? # # CNN is extremely useful not only because of its efficency but also because of unique operations that help detect certain patterns . It uses special convolution and pooling operations and performs parameter sharing. This enables CNN models to run on any device, making them universally attractive. # # # Say the neuron below detects whether or not there is a beak. # # <img src="pic/bird1.png" width=100> # # The advantage of using CNN instead of ANN is as the following: # # # 1. Pattern learning. There's no need to see the whole image for image recognition. # # In order to know whether this image contains a wheel, you don't see to see the whole image, since from prior knowledge we know that wheels cannot be in the sky. In CNN, each neuron only performs certain work, say detecting whether there is a beak. Therefore, some of the weights in the fully-connected network structure is useless. That is, a neuron does not have to see the whole image to discover the pattern. This property can be implemented via Convolution Layer. # # # 2. Weight sharing. Detecting same items of different positions requires only one neuron. # # Another reason why we can remove some of the weights is that even if the patterns appear in different regions, since they do almost the same thing (say detecting whether there is a beak), they can use the same sets of parameters, therefore reducing the number of weights required. For example, two images below shows two birds with beaks in different positions. We don't have to train two neurons to detect this two beaks. Instead, we only need one. This property can be implemented via Convolution Layer. # # <table><tr><td><img src="pic/bird1.png" width=100></td><td><img src="pic/bird2.png" width=100></td></tr></table> # # # 3. Subsampling. Enlarging or reducing the size of the image doesn't matter. # # One other reason is that subsampling the pixels will not change the target. Subsampling means to shrink the size of image to make it smaller. Since the number of pixels decreases, you need fewer weights. This property can be implemented via Max Pooling Layer. # # ## The process of Convolution Neural Network # # <img src="pic/cnnseq.jpeg" width=600> # # There is an input image that we’re working with. We perform a series convolution + pooling operations, followed by a number of fully connected layers. Note taht the number of iterations (convolution + pooling) is not a fixed number. The ultimate goal of the iteration is the limit the number of digits. For example, the original hand-written number is 28 x 28 digits. After performing two sets of convolution + pooling as stated above, we shrink it to 4 x 4 digits. We can then limit the size of the input neurons. # # In this example, since we are dealing with a classification problem, the output layer contains the exact number of nodes as the total number of classes we are going to predict. Since we are performing multiclass classification, the activation function for the output layer should be the softmax function. If we are doing a regression problem, we can simply give the final layer one node representing the value. # # Knowing the entire process of CNN, let's now dive into each component. # # ### Convolution (沒寫完) # # The main building block of Convolutional Neural Network is the convolutional layer. Convolution is a mathematical operation to merge two sets of information. In our case the convolution is applied on the input data using a convolution filter to produce a feature map. There are a lot of terms being used so let’s define and visualize them. # # ### Important Elements within Convolutional Neural Network # # The input Image # # Images are made up of pixels. Each pixel is represented by a number between 0 and 255. As stated in the beginning, since each pixel consists of 3 values(R,G,B), this picture will, in turn, have 100×100×3=30,000 dimensions. If the first hidden layer consists of 1,000 nodes, you will have 30 million weights! It's almost impossible to train these gigantic amount of weights in a fully-connected neural network, let alone the fact that most of the weights are useless. This is why CNN is widely used for images in the field of neural network. # # Feature Detector # # The feature detector is a matrix, usually 3x3 (it could also be 7x7). The feature dectector is also widely known as a filter or a kernel. # # Feature Map # # Say that you have a decent amount of filters. Feature map is the output of matrix representation of the input image that is multiplied element-wise with the feature detector (filter) and the input image. The feature map is also known as a convolved feature or an activation map. The aim of this step is to reduce the size of the image and make processing faster and easier. Indeed, some of the features of the image are lost in this step, but most of the representations can be captured by the feature detector. # # # Let’s say we have a 32x32x3 image and we use a filter of size 5x5x3 (note that the depth of the convolution filter matches the depth of the image, both being 3). Here we perform the convolution operation described above. The only difference is that this time we do the sum of matrix multiply in 3D instead of 2D, but the result is still a scalar. We slide the filter over the input like above and perform the convolution at every location aggregating the result in a feature map. This feature map is of size 32x32x1, shown as the red slice on the right. If we take another filter, denoted in green, and follow the same process stated above, we can produce another feature map of size 32x32x1. Therefore, the number of filters determines the thickness of the feature map, which is how many individual feature map that we will stack together to form a 3D one. # # <img src="pic/conv.png" width=450> # # # Stride # # Stride is the magnitude of slide that filter moves along the input image matrix. The default value for stride is 1. If we want to have less overlap when conducting the element-wise inner product, or want a smaller feature map, we can have bigger strides. This is the parameter that should be set before the model compile. It requires some domain knowledge as well as trial and error. # # Padding # # Another technique used in CNN is called padding. Padding is commonly used in CNN to preserve the size of the feature maps, otherwise they would shrink at each layer, which may not be desirable in some cases. Without padding, the size of the feature map is smaller than the input. The picture below demonstrates that fact. If we want to maintain the same dimensionality, we can use padding to surround the input with zeros. We can either pad with zeros or the values on the edge. # # With proper padding, the height and width of the feature map was the same as the input (both 32x32), and only the depth changed. # # ### Process of Convolution # # <img src="pic/convo.png" width=600> # # On the left side is the input image to the convolution layer. At the middle is the convolution filter. This is called a 3x3 convolution; the size of the filter can be determined by setting parameters. On the right side is the feature map produced by mering the input image and the convolution filter. We perform the convolution operation by sliding this filter over the input. The magnitude of this slide is controlled by the parameter stride. At every location, we do element-wise inner product and sum the result. This sum goes into the feature map. This is the result of this Convolution Layer. Below is a wonderful gif from [A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53) that show the process of convolution. # # <img src="pic/conv.gif"> # # Note that since we have done padding around the original picture, the dimensionaility of the feature map remains the same. # # --- # # # The image below clearly demonstrates why the process of convolution is actually a neural network with less weights connected. # # <img src="pic/convo2.png" width=600> # # When we move the filter frame to the right, since they are using the same filter, what this actually means is that the two neurons share the same weights, making the total weights even less. # # # ### Max Pooling # # What Max Pooling really does is subsampling. It is done by applying a max filter to subregions of the initial representation. See the picture below. # # <img src="pic/maxpool.png" width=400> # # # What Max Pooling does is to take out the maximum value within a subset of the values. The objective is to down-sample an input representation (image, hidden-layer output matrix, etc.) and reduce its dimensionality. This decreases the computational power required to process the data through dimensionality reduction. It also helps overfitting by providing an abstracted form of the representation. As well, it reduces the computational cost by reducing the number of parameters to learn. # # Note that in fact there are two types of pooling: Max Pooling and Average Pooling. In CNN, Max Pooling dominates and is our go-to method for subsampling. # # # Whenever you finish one loop of Convolution and Max Pooling, what you really get is a new image representing the original input. The Convolutional Layer and the Max Pooling Layer together form the i-th layer of a Convolutional Neural Network. Depending on the complexities of the images, the number of such layers may be increased for capturing low-levels details even further, but at the cost of more computational power. # # Note that Max Pooling is not a must-do step in Convolutional Neural Network. For example, in Alphago's paper, the author states that he uses the strucure of convolutional neural network to detect patterns on the go board. However, in this go example, the author doesn't use this Max Pooling technique to subsample the image. # # ### Flatten # # Flatten is the process that connects the result from Max Pooling and the fully-connected neural network. Once the pooled featured map is obtained through iterations of convolution and max pooling, the next step is to transform the entire pooled feature map matrix into a single column so that it can be fed to the neural network for processing. The flattening process is shown as the image below. # # <img src="pic/flatten.png" width=300> # # # ### Fully-Connected Neural Network # # After doing all the work, now we can simply feed the flattened data into the fully-connected neural network (DNN). # # <img src="pic/fc.jpeg" width=700> # # Remember that a fully-connected neural network can represent any kind of non-linear relationship between input and output. Thus, adding a fully-connected layer is a good way to learn all the non-linear combinations of the high-level features represented by the output of the flatten layer. # ## Quick Practice in Keras import numpy as np import pandas as pd import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers.core import Dense, Dropout from keras.layers import Flatten, Conv2D, MaxPooling2D from keras.optimizers import Adam from keras.utils import np_utils from keras.datasets import fashion_mnist from keras import backend # + def load_data(): # # X_train = X_train.reshape(-1, 2828) # X_train = X_train.astype('float') # # X_test = X_test.reshape(-1, 2828) # X_test = X_test.astype('float') # input image dimensions img_rows, img_cols = 28, 28 # the data, split between train and test sets (X_train, y_train), (X_test, y_test) = fashion_mnist.load_data() if backend.image_data_format() == 'channels_first': X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) # Convert class vectors to binary class matrices y_train = np_utils.to_categorical(y_train, 10) y_test = np_utils.to_categorical(y_test, 10) X_train = X_train X_test = X_test # X_test = np.random.normal(X_test) X_train = X_train / 255 # normalize the pixel X_test = X_test / 255 # normalize the pixel return((X_train, y_train),(X_test, y_test), input_shape) # - if __name__ == '__main__': # load training data and testing data (X_train, y_train), (X_test, y_test), input_shape = load_data() # define network structure model = Sequential() # CNN model.add(Conv2D(input_shape = input_shape, filters=25, kernel_size=(3,3), activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(filters=50, kernel_size=(3,3))) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) # ANN model.add(Dense(units=300, activation='relu')) model.add(Dense(units=10, activation='softmax')) # set configurations model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) # train model history = model.fit(X_train, y_train, batch_size=256, epochs=20, validation_split=0.3) # evaluate the model and output the accuracy result_train = model.evaluate(X_train, y_train) result_test = model.evaluate(X_test, y_test) print('\n') print('----------Model Result----------') print('Train Acc:', result_train[1]) print('Test Acc:', result_test[1]) # --- # ## What does Convolution Neural Network Learn? # # Before Flatten -- Convolution + Max Pooling # # So after training the model, we get a great result...then what? How can be interpret this neural network? # # We all know that when we are training the neural network, we pass an input into the model. Via gradient descent, we find the set of weights and biases that minimize total loss. Now, how can we know what the filters are doing? # # Say that the output of the k-th filter is a M x M matrix. Here we can define a matrix called Degree of Activation. Here we define the degree of the activation of the k-th filter to be # $$a^k = \sum \limits_{i=1}^M \sum \limits_{j=1}^M a_{ij}^k$$ # , where $a_{ij}^k$ is the inner-product retrieved after the convolution layer. # # The trick we are going to use is to implement Gradient Ascent to get find the image that miximize the degree of the activation. That is, we want to find the image that gives the highest total sum after the inner-product is calculated. # $$x^* = argmax {a^k}$$ # # We want to find the input $x^$ that maximize $a^k$, the degree of activation. This way we can find the image that best reflect what the filter is looking for, which is also equivalent to what the filter is detecting. If we do this process of several times, say for 12 filters, we can get something like the following. # # <img src="pic/cnnlearn.png" width=300> # # What does these images mean? For example, the image on the bottom right corner represents the fact that that particular filter is responsible for detecting that pattern, in this example diagonal stripes.* Therefore, if we input a image containing diagonal stripes, the output of this filter will be larger compared to other filter that is not detecting diagonal stripes. # # From the discussion we can know that the job for each filter is to detect a certain pattern in the image. # # So the aforementioned discuss can tell us what the filters in the convolution & Max Pooling layer is doing. What about the fully-connected part after flattening? # # After Flatten -- Fully-Connected Neural Network # # By repeating the same process, finding the $x^$ that maximize the degree of activation, we can find out that each neuron in the fully-connected neural network actually performs the following task. # # <img src="pic/cnnlearn2.png" width=250> # # It's very different from what we have seen before. In the first image that demonstrates what filters in Convolution + Max Pooling learn, it shows some kind of patterns. However, in the second image tha shows what neurons in the fully-connected neural network learn is a full image, even if you cannot recognize anything from it. This is because what you feed into the fully-connected neural network is not part of the image, but the entire image as a whole. Therefore, it doesn't only detect and learn some kind of patterns. Instead, it learns the whole picture, or you can say is a larger pattern. # # # Now say you want to see what is the does the entire CNN learn. Let's denote the output of the layer to be $y_i$. # Say that you want to find an input that maximize the degree of activation of $y^i$, denoting as $x^ = argmax {y^i}$. Below is the shocking result. # # <img src="pic/cnnlearn3.png" width=250> # # Each image is accompanied by what the result is for each image. The top left image is recognized by the CNN as number $0$, while it is arguably irrecognizable by human beings. Therefore, we can say that neural network learns extremely differently from human beings. For more information about this phenomenon, check out this great video on YouTube: [Deep Neural Networks are Easily Fooled](https://www.youtube.com/watch?v=M2IebCN9Ht4). # # So how can we overcome this issue? We all know that digits can only fill a portion of the image. A number $2$ can never cover the entire image. There must be pixels that is not white. We can see that in the previous picture, most of the pixels are white. What if we limit the number of white? We can use regularization! # # $$ x^* = argmax_x (y^i - \sum \limits_{i,j}\|x_{i,j}\|) $$ # # The term $\sum \limits_{i,j}\|x_{i,j}\|$ in fact represents the overall number of pixels. Thus, in the equation above we want to find an input $x$ that maximize the output $y^i$ but minimize the number of total $x_{i,j}$, which means limiting the total number of $x$. In this case, most of the images should be left black. The result will be something on the right hand side: # # <table><tr><td><img src="pic/cnnlearn3.png" width=300></td><td><img src="pic/cnnlearn4.png" width=300></td></tr></table> # # The left hand side represents the orginal result. We can clearly see that using regularization, $x^*$ is much more closer to the real number that we human beings can recognize. # --- # ## Reference: # # [cs231n](http://cs231n.github.io/) # # [What is max pooling in convolutional neural networks?](https://www.quora.com/What-is-max-pooling-in-convolutional-neural-networks) # # [Applied Deep Learning - Part 4: Convolutional Neural Networks](https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2#a86a) # # [What are the advantages of a convolutional neural network (CNN) compared to a simple neural network from the theoretical and practical perspective?](https://www.quora.com/What-are-the-advantages-of-a-convolutional-neural-network-CNN-compared-to-a-simple-neural-network-from-the-theoretical-and-practical-perspective) # # [A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53)	21,355
/python/crawler/page82.ipynb	2c3c2392372c593eee24572a418f6a63de14426e	[ "MIT" ]	permissive	groovallstar/test2	https://github.com/groovallstar/test2	0	0	MIT	2020-01-14T08:48:41	2020-01-13T01:53:31	Jupyter Notebook	Jupyter Notebook	false	false	.py	26,982	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/groovallstar/test2/blob/feature%2Ffrom_colab/page82.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="7dYuYlxLl_2b" colab_type="code" outputId="baac047f-c563-482f-b705-263a81c2a9fc" colab={"base_uri": "https://localhost:8080/", "height": 1000} import requests from bs4 import BeautifulSoup class Content: def __init__(self, url, title, body): self.url = url self.title = title self.body = body def print(self): print('URL: {}'.format(self.url)) print('TITLE: {}'.format(self.title)) print('BODY:\n{}'.format(self.body)) class Website: def __init__(self, name, url, titleTag, bodyTag): self.name = name self.url = url self.titleTag = titleTag self.bodyTag = bodyTag class Crawler: def getPage(self, url): try: req = requests.get(url) except requests.exceptions.RequestException: return None return BeautifulSoup(req.text, 'html.parser') def safeGet(self, pageObj, selector): selectedElems = pageObj.select(selector) if selectedElems is not None and len(selectedElems) > 0: return '\n'.join([elem.get_text() for elem in selectedElems]) return '' def parse(self, site, url): bs = self.getPage(url) if bs is not None: title = self.safeGet(bs, site.titleTag) body = self.safeGet(bs, site.bodyTag) if title != '' and body != '': content = Content(url, title, body) content.print() crawler = Crawler() siteData = [ ['O\'Reilly Media', 'http://oreilly.com', 'h1', 'section#product-description'], ['Reuters', 'http://reuters.com', 'h1', 'div.StandardArticleBody_body_1gnLA'], ['Brookings', 'http://www.brookings.edu', 'h1', 'div.post-body'], ['New York Times', 'http://nytimes.com', 'h1', 'div.StoryBodyCompanionColumn div p'] ] websites = [] for row in siteData: websites.append(Website(row[0], row[1], row[2], row[3])) crawler.parse(websites[0], 'http://shop.oreilly.com/product/0636920028154.do') crawler.parse( websites[1], 'http://www.reuters.com/article/us-usa-epa-pruitt-idUSKBN19W2D0') crawler.parse( websites[2], 'https://www.brookings.edu/blog/techtank/2016/03/01/idea-to-retire-old-methods-of-policy-education/') crawler.parse( websites[3], 'https://www.nytimes.com/2018/01/28/business/energy-environment/oil-boom.html') .com/science/1999/aug/11/eclipse.uknews4", "https://www.theguardian.com/science/1999/aug/05/technology1", "https://www.theguardian.com/science/1999/jul/23/eclipse", "https://www.theguardian.com/science/1986/jan/30/spaceexploration.columbia", "https://www.theguardian.com/science/1986/jan/30/spaceexploration.columbia1", "https://www.theguardian.com/science/2014/nov/10/breakthrough-prize-scientists-23m-science-awards-2015", "https://www.theguardian.com/technology/2015/jan/16/elon-musk-falcon-9-rapid-unscheduled-disassembly", "https://www.theguardian.com/science/2014/oct/31/-sp-rosetta-selfie-mars-saturn-a-month-in-space-september-2014", "https://www.theguardian.com/science/blog/2014/oct/31/pumpkins-halloween-yeti-mental-health-polio-blogs-roundup", "https://www.theguardian.com/science/2014/nov/09/steven-pinker-twitter-can-hone-writing-skills", "https://www.theguardian.com/science/2014/nov/12/rosetta-mission-philae-historic-landing-comet", ] hubble_urls = [ "https://webbtelescope.org/contents/news-releases/2019/news-2019-41", "https://hubblesite.org/contents/news-releases/2018/news-2018-21.html", "https://hubblesite.org/contents/news-releases/2018/news-2018-03.html", "https://hubblesite.org/contents/news-releases/1990/news-1990-23.html", "https://hubblesite.org/contents/news-releases/1990/news-1990-17.html", ] space_urls = [ "https://www.space.com/7078-shuttle-astronauts-deploy-satellites-landing.html", "https://www.space.com/201-explore-colors-stars.html", "https://www.space.com/9429-cargo-ship-delivers-healthy-halloween-treats-space-station.html", "https://www.space.com/37172-trappist-1-planet-visualizations-explained.html", "https://www.space.com/15107-venus-pleiades-april-skywatching-tips.html", "https://www.space.com/11521-royal-wedding-space-station-astronauts-message.html", ] nytimes_urls = [ "https://www.nytimes.com/2019/11/09/us/politics/impeachment-state-department.html?action=click&module=Top%20Stories&pgtype=Homepage", "http://www.nytimes.com/2015/01/02/world/europe/turkey-police-thwart-attack-on-prime-ministers-office.html", "https://www.nytimes.com/2019/11/09/us/politics/michael-bloomberg-democrats.html?action=click&module=Top%20Stories&pgtype=Homepage", "https://www.nytimes.com/2019/11/04/science/space/nasa-boeing-starliner-tes.html", ] # <a id="scrape-articles"></a> # # ## 2. [Scrape articles](#scrape-articles) # + # # ========= GET SOUP FOR A SINGLE URL (DO NOT DELETE) ========= # link = # page_response = requests.get(link, timeout=5) # soup = BeautifulSoup(page_response.content, "lxml") # print(soup.prettify()) # text, date = get_space_text_from_soup(soup, page_response) # text # + # # ========= DATES FROM SPACE.com (DO NOT DELETE) ========= # dates = [] # for url in space_urls: # r = requests.get(url) # soup = BeautifulSoup(r.content, "lxml") # # print(soup.prettify()) # published_datetime = soup.find("meta", {"name": "pub_date"}).get("content") # dates.append(published_datetime) # # print(dates) # df = pd.DataFrame(pd.Series(dates), columns=["publication_date"]) # df = append_datetime_attrs(df, date_col="publication_date") # df # + # # ========= APPEND SINGLE ARTICLE SCRAPE TO HDF5 FILE (DO NOT DELETE) ========= # l = [] # for site, links in urls.items(): # # print(site) # for k, link in enumerate(links[:2]): # # print(f"{site}_{k+1}") # df_row = pd.DataFrame(np.random.rand(1, 9), columns=list("ABCDEFGHI")) # df_row["publication"] = site # df_row.to_hdf(h5_path, key=f"{site}_{k+1}", format="t", mode="a") # l.append(df_row) # print(f"Scraped {len(l)} articles") # pd.concat( # [ # pd.read_hdf(h5_path, key=f"{site}_{k+1}") # for site in urls.keys() # for k, link in enumerate(links[:2]) # ], # axis=0, # ignore_index=True, # ) # - from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry # + # # ========= TEST SET DATA FROM GUARDIAN (DO NOT DELETE) ========= # guardian_urls = [ # "https://www.theguardian.com/science/2019/dec/09/european-space-agency-to-launch-clearspace-1-space-debris-collector-in-2025", # "https://www.theguardian.com/science/2019/nov/04/nasa-voyager-2-sends-back-first-signal-from-interstellar-space", # "https://www.theguardian.com/science/2019/dec/12/spacewatch-esa-awards-first-junk-clean-up-contract-clearspace", # "https://www.theguardian.com/science/2019/nov/28/spacewatch-you-wait-ages-for-a-rocket-launch-then-", # "https://www.theguardian.com/science/2019/dec/26/scientists-attempt-to-recreate-overview-effect-from-earth", # "https://www.theguardian.com/science/2019/dec/15/exomars-race-against-time-to-launch-troubled-europe-mission-to-mars", # "https://www.theguardian.com/science/2019/nov/06/cosmic-cats-nuclear-interstellar-messages-extraterrestrial-intelligence", # "https://www.theguardian.com/science/2019/nov/14/spacewatch-boeing-proposes-direct-flights-moon-2024-nasa", # "https://www.theguardian.com/science/2019/nov/24/mars-robot-will-send-samples-to-earth", # "https://www.theguardian.com/science/2019/nov/06/daniel-lobb-obituary", # "https://www.theguardian.com/science/2019/dec/09/european-space-agency-to-launch-clearspace-1-space-debris-collector-in-2025", # "https://www.theguardian.com/science/2020/feb/27/biggest-cosmic-explosion-ever-detected-makes-huge-dent-in-space", # "https://www.theguardian.com/science/2020/feb/06/christina-koch-returns-to-earth-after-record-breaking-space-mission", # "https://www.theguardian.com/science/2020/jan/01/international-space-station-astronauts-play-with-fire-for-research", # "https://www.theguardian.com/science/2020/jan/05/space-race-moon-mars-asteroids-commercial-launches", # "https://www.theguardian.com/science/2019/oct/08/nobel-prizes-have-a-point-parking-space", # "https://www.theguardian.com/science/2019/oct/31/spacewatch-nasa-tests-new-imaging-technology-in-space", # "https://www.theguardian.com/science/blog/2020/feb/06/can-we-predict-the-weather-in-space", # "https://www.theguardian.com/science/2019/sep/08/salyut-1-beat-skylab-in-space-station-race", # "https://www.theguardian.com/science/2020/feb/13/not-just-a-space-potato-nasa-unveils-astonishing-details-of-most-distant-object-ever-visited-arrokoth", # ] # l_texts = {} # for k, link in enumerate([guardian_urls[0]]): # print(f"Scraping article number {k+1}, Link: {link}") # # print(site, link) # start_time = time() # r_session = requests.Session() # retries = Retry( # total=2, # backoff_factor=0.1, # status_forcelist=[500, 502, 503, 504], # ) # r_session.mount("http://", HTTPAdapter(max_retries=retries)) # try: # page_response = r_session.get(link, timeout=5) # except Exception as ex: # print(f"{ex} Error connecting to {link}") # else: # try: # soup = BeautifulSoup(page_response.content, "lxml") # # print(soup.prettify()) # except Exception as e: # print(f"Experienced error {str(e)} when scraping {link}") # text = np.nan # else: # text = get_guardian_text_from_soup(soup) # scrape_minutes, scrape_seconds = divmod(time() - start_time, 60) # print( # f"Scraping time: {int(scrape_minutes):d} minutes, {scrape_seconds:.2f} seconds" # ) # l_texts[link] = [text] # df = pd.DataFrame.from_dict(l_texts, orient="index").reset_index() # df.rename(columns={"index": "url", 0: "text"}, inplace=True) # display(df) # # df.to_csv("data/guardian_3.csv", index=False) # - # First, we will iterate over a Python dictionary of all the news publications and perform the following actions within each iteration # 1. Scrape page with `BeautifulSoup` and get soup # 2. Get text and (optionally) date from soup # 3. Store extracted contents from soup in a dictionary # 4. Convert dictionary into single row `DataFrame` # 5. Rename date column # 6. Apend publication name as column to `DataFrame` # 7. Export single-row `DataFrame` to HDF file # - this would be a single listing's details # 8. Convert dictionary with all rows into `DataFrame` # - this would be all listings' details # 9. Append datetime attributes to full `DataFrame` # 10. Append full `DataFrame` for publication to a list # + cell_st = time() # Main controller loop for scraping article text from url l = [] for site, links in urls.items(): (Path(data_dir) / site).mkdir(parents=True, exist_ok=True) # print(site, links) l_texts = {} date_published = np.nan for k, link in enumerate(links[30000:]): l_texts_single_listing = {} print(f"Scraping article number {k+1} from {site}, Link: {link}") # print(site, link) start_time = time() try: # 1. Get soup page_response = requests.get(link, timeout=5) soup = BeautifulSoup(page_response.content, "lxml") # print(soup.prettify()) # 2. Get text (and optionally date) if site == "guardian": text = get_guardian_text_from_soup(soup) elif site == "hubble": text = get_hubble_text_from_soup(soup) elif site == "space": text, date_published = get_space_text_from_soup(soup, page_response) elif site == "nytimes": text, date_published = get_nytimes_text_from_soup(soup) except Exception as e: print(f"Experienced error {str(e)} when scraping {link} from {site}") text = np.nan scrape_minutes, scrape_seconds = divmod(time() - start_time, 60) print( f"Scraping time: {int(scrape_minutes):d} minutes, {scrape_seconds:.2f} seconds" ) # 3. Store text and date in dictionary l_texts[link] = [text, date_published] l_texts_single_listing[link] = [text, date_published] # 4. Convert dictionary of text and date, for single listing, to DataFrame df_row = pd.DataFrame.from_dict( l_texts_single_listing, orient="index" ).reset_index() # 5. Rename publication date column of DataFrame df_row.rename( columns={"index": "url", 0: "text", 1: "publication_date"}, inplace=True ) # 6. Append publication name as column to DataFrame df_row["publication"] = site # print(Path(data_dir) / site / f"scrapes_{site}_{k+1}.h5") # 7. Store DataFrame in HDF file df_row.to_hdf( Path(data_dir) / site / f"scrapes_{site}_{k+1}.h5", key=f"{site}_{k+1}", format="t", mode="w", ) # print(text) # Delay between scraping urls delay_between_scrapes = randint( min_delay_between_scraped, max_delay_between_scraped ) if (k + 1) < len(links[30000:]): print(f"Pausing for {delay_between_scrapes} seconds\n") sleep(delay_between_scrapes) # 8. Convert dictionary of text and date, for all listings, to DataFrame df = pd.DataFrame.from_dict(l_texts, orient="index").reset_index() df.rename(columns={"index": "url", 0: "text", 1: "publication_date"}, inplace=True) df["publication"] = site # display(df) # 9. (Optional) Append datetime attributes for space.com and nytimes publications if site in ["space", "nytimes"]: df = append_datetime_attrs(df, date_col="publication_date", publication=site) else: for L in [ "year", "month", "day", "dayofweek", "dayofyear", "weekofyear", "quarter", ]: df[L] = np.nan # 10. Append DataFrame to list l.append(df) total_minutes, total_seconds = divmod(time() - cell_st, 60) print( f"Cell exection time: {int(total_minutes):d} minutes, {total_seconds:.2f} seconds" ) # + # # ========= LOAD SUBSET (NOT ALL) OF HDF5 FILES (DO NOT DELETE) ========= # pd.concat( # [ # pd.read_hdf(Path(data_dir) / site / f"scrapes_{site}_{k+1}.h5", key=f"{site}_{k+1}") # for site, links in urls.items() # for k, link in enumerate(links[0:10]) # ], # axis=0, # ignore_index=True, # ) # - # Finally, we'll concatenate the list of `DataFrame`s of scraped text data into a single `DataFrame` and export it to a separate `*.csv` file per publication dfs = pd.concat(l, axis=0, ignore_index=True).drop_duplicates() if site == "space": dfs = dfs[~pd.isnull(dfs["text"])] display(dfs) print(dfs.shape) dfs.to_csv(Path(data_dir) / f"{site}.csv", index=False)	15,649
/Source Code and Outputs/deprecated_old/mixup/mixup MNIST NN augmentation 0.75_weighted_perturb_loss.ipynb	e0dec2dbd6751effe91f9385f8e749813af739b6	[]	no_license	DHKLeung/UCL_MSc_CSML_Dissertation	https://github.com/DHKLeung/UCL_MSc_CSML_Dissertation	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	14,567	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import torch from torchvision import transforms, datasets, models # + """ Configuration and Hyperparameters """ torch.set_default_tensor_type(torch.cuda.FloatTensor) # default all in GPU transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) batch_size = 128 step_size = 0.01 random_seed = 0 epochs = 100 L2_decay = 1e-4 alpha = 1. perturb_loss_weight = 0.75 torch.manual_seed(random_seed) # - """ Data """ train_set = datasets.MNIST('./data', train=True, download=True, transform=transform) train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=8) test_set = datasets.MNIST('./data', train=False, download=True, transform=transform) test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=8) model = models.resnet18(pretrained=True) for param in model.parameters(): param.requires_grad = True model.conv1 = torch.nn.Conv2d(1, 64, 7, stride=2, padding=3, bias=False) model.fc = torch.nn.Linear(512, 10) criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=step_size, momentum=0.9, weight_decay=L2_decay) def mixup_MNIST(inputs, labels, alpha): lmbda = torch.distributions.beta.Beta(alpha, alpha).sample() batch_size = labels.size(0) idx = torch.randperm(batch_size) mixup_inputs = lmbda * inputs + (1 - lmbda) * inputs[idx] labels_b = labels[idx] return mixup_inputs, labels, labels_b, lmbda def mixup_criterion(criterion, predicts, labels, labels_b, lmbda): mixup_loss = lmbda * criterion(predicts, labels) + (1 - lmbda) * criterion(predicts, labels_b) return mixup_loss """ Training """ model.train() for epoch in range(epochs): epoch_loss = 0. epoch_mixup_loss = 0. epoch_org_loss = 0. for i, data in enumerate(train_loader, 0): inputs, labels = data inputs = inputs.to('cuda') labels = labels.to('cuda') mixup_inputs, labels, labels_b, lmbda = mixup_MNIST(inputs, labels, alpha) optimizer.zero_grad() outputs = model(mixup_inputs) mixup_loss = mixup_criterion(criterion, outputs, labels, labels_b, lmbda) ## outputs_org = model(inputs) loss_org = criterion(outputs_org, labels) weighted_total_loss = mixup_loss * perturb_loss_weight + loss_org * (1 - perturb_loss_weight) epoch_mixup_loss += mixup_loss.item() epoch_org_loss += loss_org.item() epoch_loss += (mixup_loss.item() + loss_org.item()) weighted_total_loss.backward() ## optimizer.step() print('{}: {} {} {}'.format(epoch, epoch_mixup_loss, epoch_org_loss, epoch_loss)) torch.save(model.state_dict(), './mixup_model_pytorch_mnist_augment') model = models.resnet18(pretrained=False) model.conv1 = torch.nn.Conv2d(1, 64, 7, stride=2, padding=3, bias=False) model.fc = torch.nn.Linear(512, 10) model.load_state_dict(torch.load('./mixup_model_pytorch_mnist_augment')) model.eval() correct = 0 total = 0 with torch.no_grad(): for data in test_loader: inputs, labels = data inputs = inputs.to('cuda') labels = labels.to('cuda') outputs = model(inputs) _, predicts = torch.max(outputs, 1) total += labels.size(0) correct += (predicts == labels).sum().item() print(correct / total) model.eval() correct = 0 total = 0 with torch.no_grad(): for data in train_loader: inputs, labels = data inputs = inputs.to('cuda') labels = labels.to('cuda') outputs = model(inputs) _, predicts = torch.max(outputs, 1) total += labels.size(0) correct += (predicts == labels).sum().item() print(correct / total)	4,077
/2.Math/4. EDA/raw_LiveCoding.ipynb	33ec8885feb2f34ffda08c662196fda229436b1d	[]	no_license	samil-web/AI	https://github.com/samil-web/AI	3	0	null	2021-09-10T07:58:35	2021-09-10T07:22:39	Jupyter Notebook	Jupyter Notebook	false	false	.py	182,805	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import numpy as np import matplotlib.pyplot as plt # https://github.com/Swetha14/LTFS---Loan-Default-Challenge/blob/master/train_sample.zip df = pd.read_csv("data/train_sample.csv") df.head(10) df.tail(10) df1 = pd.read_csv("data/train_sample.csv", index_col=0) df1 df.dtypes type(df["Date.of.Birth"][0]) df["Employment.Type"][0] df["Employment.Type"].unique() df["loan_default"].unique() # 1 is bad # # 0 is good df["loan_default"].value_counts() df["ltv"].hist(bins=25) df["ltv"].head(10) df["ltv"].head(100000).hist(bins=10) df.asset_cost[df["asset_cost"]<0.9100000].hist(bins=200) df["asset_cost"].hist(bins=200) df.asset_cost[df.asset_cost > 0.201000000] df.boxplot(column="asset_cost") df.boxplot(column="ltv") df.boxplot(column="asset_cost", by="Employment.Type") df["Date.of.Birth"] # + from datetime import datetime dateparse = lambda x: datetime.strptime(x, '%Y(-\|/)%m(-\|/)%d %H:%M:%S') df2 = pd.read_csv("data/train_sample.csv", parse_dates=['Date.of.Birth'], date_parser=try_parsing_date) # - def try_parsing_date(text): for fmt in ('%d-%m-%y', '%d.%m.%Y', '%d/%m/%Y'): try: return datetime.strptime(text, fmt) except ValueError: pass print(text) raise ValueError('no valid date format found') str(datetime.strptime("12-12-12", '%d-%m-%y')).split("-")[0] df2["Date.of.Birth"] df2["year_of_birth"] = df2["Date.of.Birth"].apply(lambda x: int(str(x).split("-")[0])) df2["year_of_birth"] df2[df2.year_of_birth < 1960].boxplot(column="asset_cost", by="year_of_birth") import seaborn as sns sns.boxplot(x=df2[df2.year_of_birth == 1960]["asset_cost"]) plt.scatter(df2.year_of_birth, df2.loan_default) a = df2[df2.year_of_birth<1960].groupby("year_of_birth")["loan_default"].value_counts() a.plot.bar(stacked=True) data = pd.DataFrame(a) data data.unstack().plot(kind='bar', stacked=True)	2,178
/Baby_Names_Births_exercise/.ipynb_checkpoints/Birth_example_1_TEST-checkpoint.ipynb	8c5f9158d4d83da90af91730403b7404efd2abca	[ "MIT" ]	permissive	FelipeChiriboga/Data_Science_Portfolio	https://github.com/FelipeChiriboga/Data_Science_Portfolio	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	32,605	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd # %matplotlib inline # + #df = names_2000 names_2000 = pd.read_csv('names/yob2000.txt', names=['name','gender', 'count']) #names_2000.set_index('name', inplace=True) # - df.head() names_2000.head(10) # + names_2000[names_2000['count'] > 1000].head() # - one_two = names_2000[names_2000['count'].between(1000, 2000)] one_two.shape freq_boys = names_2000[(names_2000['count'] > 1000) & (names_2000['gender'] == 1000)] freq_boys.shape b = names_2000['gender'] != 'F' b.head() def initial(s): returns[0] print(initial) my_functions = [initial, pd.DataFrame, print, zip] # + def get_initial(s): return s[0] names_2000['initial'] = names_2000['name'].apply(get_initial) names_2000.head() # - # %matplotlib inline names_2000.groupby('initial') ['count'].sum().plot.bar() ini = names_2000.groupby(['gender', 'initial'])['count'].sum() topchars = ini.sort_values(ascending=False).head(10) topchars.plot.bar() # + import pandas as pd birthnames = [] a = 1880 while a <= 2017: fl = str("names/yob" + str(a) + ".txt") bth = pd.read_csv(fl, names=['names', 'sex','birthcnt']) #probably need to add the name column bth['year'] = a birthnames.append(bth) a = a + 1 birthnames = pd.concat(birthnames) birthnames # + #Goal 3 # easy: create a bar plot with 5 names in one year. # Medium: plot a time series with one name over all years # hard: plot the number of distinct boy/girl names over time	1,729
/notebooks/Tutorial: YOLO/Convolutional Neural Networks.ipynb	4ddb6b3a31181fc847e6ba27c925020e2f988575	[ "Apache-2.0" ]	permissive	gabrielnieves18/tensorflow-tutorials	https://github.com/gabrielnieves18/tensorflow-tutorials	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	15,799	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" import numpy as np import pandas as pd import matplotlib.pyplot as plt # %matplotlib inline # + _cell_guid="79c7e3d0-c299-4dcb-8224-4455121ee9b0" _uuid="d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" meta_data = pd.read_csv('../input/training_set_metadata.csv') test_meta_data = pd.read_csv('../input/test_set_metadata.csv') # + _uuid="2eec377e2ae127bb44b7d0f98bf169f372b897fb" classes = np.unique(meta_data['target']) classes_all = np.hstack([classes, [99]]) # create a dictionary {class : index} to map class number with the index # (index will be used for submission columns like 0, 1, 2 ... 14) target_map = {j:i for i, j in enumerate(classes_all)} # create 'target_id' column to map with 'target' classes target_ids = [target_map[i] for i in meta_data['target']] meta_data['target_id'] = target_ids meta_data.head() #meta_data['hostgal_specz'] # + _uuid="eeb61301f53a373a6114389d785f00027ae5f523" # Build probability arrays for both the galactic and extragalactic groups galactic_cut = meta_data['hostgal_specz'] == 0 galactic_data = meta_data[galactic_cut] extragalactic_data = meta_data[~galactic_cut] galactic_classes = np.unique(galactic_data['target_id']) extragalactic_classes = np.unique(extragalactic_data['target_id']) # add class_99 (index = 14) galactic_classes = np.append(galactic_classes, 14) extragalactic_classes = np.append(extragalactic_classes, 14) # + [markdown] _uuid="49507175719b5752150907f36e4da3165ed90efe" # * # # EDA # + _uuid="8684abbef947f6f5b080152464ef3a6811ea9275" plt.figure(figsize=(20,20)) plt.subplot(221) plt.scatter(meta_data[~galactic_cut]['hostgal_specz'], meta_data[~galactic_cut]['target']) plt.xlabel('hostgal_specz') plt.ylabel('classes') plt.yticks(classes_all) plt.subplot(222) plt.scatter(meta_data['hostgal_specz'], meta_data['target']) plt.xlabel('hostgal_specz') plt.ylabel('classes') plt.yticks(classes_all) plt.show() # + _uuid="8684abbef947f6f5b080152464ef3a6811ea9275" plt.figure(figsize=(20,20)) plt.subplot(221) plt.scatter(meta_data[~galactic_cut]['hostgal_photoz'], meta_data[~galactic_cut]['target']) plt.xlabel('hostgal_photoz') plt.ylabel('classes') plt.yticks(classes_all) plt.subplot(222) plt.scatter(meta_data['hostgal_photoz'], meta_data['target']) plt.xlabel('hostgal_photoz') plt.ylabel('classes') plt.yticks(classes_all) plt.show() # + _uuid="8684abbef947f6f5b080152464ef3a6811ea9275" plt.figure(figsize=(20,20)) plt.subplot(221) plt.scatter(meta_data[~galactic_cut]['distmod'], meta_data[~galactic_cut]['target']) plt.xlabel('distmod') plt.ylabel('classes') plt.yticks(classes_all) plt.subplot(222) plt.scatter(meta_data['distmod'], meta_data['target']) plt.xlabel('distmod') plt.ylabel('classes') plt.yticks(classes_all) plt.show() # + _uuid="8684abbef947f6f5b080152464ef3a6811ea9275" plt.figure(figsize=(20,20)) plt.subplot(221) plt.scatter(meta_data[~galactic_cut]['mwebv'], meta_data[~galactic_cut]['target']) plt.xlabel('mwebv') plt.ylabel('classes') plt.yticks(classes_all) plt.subplot(222) plt.scatter(meta_data['mwebv'], meta_data['target']) plt.xlabel('mwebv') plt.ylabel('classes') plt.yticks(classes_all) plt.show() # + _uuid="0b4060b5605213dd9c156fa01f656fad77ba9505" #color = meta_data[meta_data['hostgal_photoz_err'] > 0.35] meta_data['photoz_big_err'] = 0 meta_data.loc[meta_data['hostgal_photoz_err'] > 0.35, 'photoz_big_err'] = 1 # + _uuid="60075eba967d045bec9450da52cc0ef2ffdfc3a2" meta_data.describe() # + _uuid="f7f16964eecb85fb1d3c7122a0213fcc1fb27987" plt.figure(figsize=(20,20)) color = meta_data['photoz_big_err'] color = meta_data['photoz_big_err'] plt.subplot(221) plt.scatter(meta_data['hostgal_specz'], meta_data['hostgal_photoz'], c = color) plt.xlabel('hostgal_specz') plt.ylabel('hostgal_photoz') plt.subplot(222) plt.scatter(meta_data['hostgal_specz'], meta_data['hostgal_photoz_err'], c = color) plt.xlabel('hostgal_specz') plt.ylabel('hostgal_photoz_err') plt.yticks(np.arange(0,2,0.1)) plt.subplot(223) plt.scatter(meta_data['hostgal_photoz'], meta_data['hostgal_photoz_err'], c = color) plt.xlabel('hostgal_photoz') plt.ylabel('hostgal_photoz_err') # + _uuid="5f66457490e2893dd58d0288f2646d1c1ad7a06c" #test_meta_data[test_meta_data['hostgal_specz'].isnull()] # + _uuid="919c17b35dd11b0970a4c4cb78ce4ee429b64e6f" plt.figure(figsize=(15,15)) plt.subplot(221) plt.scatter(meta_data['hostgal_photoz'], meta_data['target']) plt.xlabel('hostgal_photoz') plt.ylabel('classes') plt.yticks(classes_all) plt.subplot(222) plt.scatter(meta_data[meta_data['photoz_big_err'] == 0]['hostgal_photoz'], meta_data[meta_data['photoz_big_err'] == 0]['target']) plt.xlabel('hostgal_photoz') plt.ylabel('classes') plt.yticks(classes_all) plt.xticks(np.arange(0,3,0.1)) plt.show() # + [markdown] _uuid="95cb963b4de429b9e00cd760834b5bd14cac0d80" # first: (hostgal_specz >= 1.1) => class 88, 95, 99 (10, 13, 14) # # second: (hostgal_photoz >= 1.1 & photoz_big_err = 0) => class 88, 95, 99 (10, 13, 14) # # third: other even probabilities counted on the previous rounds # # # + [markdown] _uuid="8c0fd28998e6129404bdc622a1535755d7da5ab2" # * # + [markdown] _uuid="9230565e6601845f490801f3a96d1705186bf891" # # Weights # # Weights are based on this discussion: https://www.kaggle.com/c/PLAsTiCC-2018/discussion/67194 , but, apparently, we have different weights for Galactic and Extragalactic groups for the class_99! # # It is also good to check this kernel for more precise calculation of weights: https://www.kaggle.com/ganfear/calculate-exact-class-weights # + _uuid="c464a70b9c725867d0dbc70b8fb2b3d2ac96f85d" # Weighted probabilities for Milky Way galaxy galactic_probabilities = np.zeros(15) for x in galactic_classes: if(x == 14): galactic_probabilities[x] = 0.014845745 continue if(x == 5): galactic_probabilities[x] = 0.196867058 continue galactic_probabilities[x] = 0.197071799 # + _uuid="371292863929044eff3ae3602b710b9341a0312b" # Weighted probabilities for Extra Galaxies extragalactic_probabilities = np.zeros(15) for x in extragalactic_classes: if(x == 14): extragalactic_probabilities[x] = 0.147286644 continue if(x == 7): extragalactic_probabilities[x] = 0.15579259 continue if(x == 1): extragalactic_probabilities[x] = 0.155388186 continue if(x == 10 or x == 13): extragalactic_probabilities[x] = 0.076512622 continue extragalactic_probabilities[x] = 0.077701467 # + _uuid="7bf98a4fbd2fc24bcd92ff952bf48ab9ba8d06ed" # Weighted probabilities for Remote Classes bigz_probabilities = np.zeros(15) for x in extragalactic_classes: if(x == 14): bigz_probabilities[x] = 0.398923589 continue if(x == 10 or x == 13): bigz_probabilities[x] = 0.207233249 continue if(x == 7): extragalactic_probabilities[x] = 0.041550573 continue if(x == 1): extragalactic_probabilities[x] = 0.041442716 continue bigz_probabilities[x] = 0.020723325 #p = (1 - (50.077340579/2 + 0.154666479/2 + 0.155069005/2 + 0.148880461/2))/2 #p = 0.28867029 # + [markdown] _uuid="80a693faa4fa1bba7b05fb774dac4252b38ae4d1" # ** # + _uuid="aa9b04c735c6a13bcb48511747b66780a78abe68" #test_meta_data['object_id'].count() #test_meta_data[test_meta_data['hostgal_specz'] >= 1.1]['object_id'].count() #( np.isnan(row['hostgal_specz']) ) and (row['hostgal_photoz'] >= 1.2 and row['hostgal_photoz_err'] <= 0.3 #test_meta_data[(test_meta_data['hostgal_photoz'] >= 1.1) & (test_meta_data['hostgal_photoz_err'] <= 0.35)]['object_id'].count() #x = 84239 / 3492890 * 0.7037 #x = int(x) #y = 1 - x #print(x, y) #type(x) # + _uuid="04fa08c03cf8bb61d4811c316200b95c1d90df18" # Apply this prediction to test_meta_data table import tqdm def do_prediction(table): probs = [] for index, row in tqdm.tqdm(table.iterrows(), total=len(table)): if ( row['hostgal_specz'] >= 1.2 ): prob = bigz_probabilities elif ( ( np.isnan(row['hostgal_specz']) ) and (row['hostgal_photoz'] >= 1.2 and row['hostgal_photoz_err'] <= 0.3) ): prob = bigz_probabilities elif ( row['hostgal_photoz'] == 0 ): prob = galactic_probabilities else: prob = extragalactic_probabilities probs.append(prob) return np.array(probs) test_pred = do_prediction(test_meta_data) # + _uuid="554ef771214b68cd54053d3d7c5a1557f8e45d54" test_df = pd.DataFrame(index=test_meta_data['object_id'], data=test_pred, columns=['class_%d' % i for i in classes_all]) test_df.to_csv('./submission_eda.csv') x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size) x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size) feed_dict_tr = { x: x_batch, y_true: y_true_batch } feed_dict_val = { x: x_valid_batch, y_true: y_valid_batch } session.run(optimizer, feed_dict=feed_dict_tr) if i % int(data.train.num_examples/batch_size) == 0: val_loss = session.run(cost, feed_dict=feed_dict_val) epoch = int(i / int(data.train.num_examples/batch_size)) loss.append(val_loss) epochs.append(epoch) accuracy_array.append(session.run(accuracy, feed_dict=feed_dict_tr)) loss_map = { 'loss': loss, 'epochs': epochs, 'accu': accuracy_array } # if (epoch > 0) and (epoch % 5) == 0: # pd_loss = pd.DataFrame(loss_map) # pd_loss.plot(x="accu", y="loss", kind='line') # plt.show() show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss, session) saver.save(session, './dogs-cats-model') total_iterations += num_iteration # -	10,536
/preprocessing/.ipynb_checkpoints/prepro_1117-checkpoint.ipynb	13967b5842a5e8c00bbe57aab006ffe93c32f903	[]	no_license	wttttt-wang/kaggle_housePrices	https://github.com/wttttt-wang/kaggle_housePrices	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	60,924	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # PART ONE: Data Reading import pandas as pd import numpy as np from scipy.stats import skew import matplotlib.pyplot as plt # %matplotlib inline def read_data(): #step1:reading csv data train = pd.read_csv('../input/train.csv') test = pd.read_csv('../input/test.csv') #train.head() # take a brief look at training data all_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'], test.loc[:,'MSSubClass':'SaleCondition'])) # concat training&test data return train,test,all_data # + # PART TWO: Filling missing value from scipy.stats import mode # for numeric features, filling with mean(), for categorial features, filling with mode()[most common] def mean_fill(df,attr): if df[attr].dtype == 'object': df[attr].fillna(mode(df[attr]).mode[0],inplace=True) else: df[attr].fillna(df[attr].mean(),inplace=True) # #test for categorial feature # mean_fill(all_data,'GarageCond') from sklearn.ensemble import RandomForestRegressor def randomforest_fill(df,attr): # get all the numeric features to predict the missing value, because # that sklearn's random forest algo can only handle numeric features. # It could be better to transform the categorial features before calling this method numeric_df = all_data.loc[:,all_data.isnull().any().values==False].select_dtypes(include = ['float64','int64']) numeric_df[attr]=df[attr] numeric_df_notnull = numeric_df.loc[(df[attr].notnull())] numeric_df_isnull = numeric_df.loc[(df[attr].isnull())] X = numeric_df_notnull.drop(attr,axis=1) Y = numeric_df_notnull[attr] # use RandomForest to train data clf = RandomForestRegressor(n_estimators = 1000, n_jobs = -1) # n_jobs:The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores clf.fit(X,Y) predicted_attr = clf.predict(numeric_df_isnull.drop(attr,axis=1)) df.loc[(df[attr].isnull()),attr] = predicted_attr #randomforest_fill(all_data,'MasVnrArea') ## before filling the missing value #all_data.loc[234]['MasVnrArea'] ''' # output 234 NaN 234 0.0 Name: MasVnrArea, dtype: float64 ''' ## after filling the missing value #all_data.loc[234]['MasVnrArea'] ''' # output 234 82.882 234 0.000 Name: MasVnrArea, dtype: float64 ''' # treat the missing value as a different value def ano_value_for_miss(df,attr): df[attr].fillna('No_value',inplace=True) ## test # ano_value_for_miss(all_data, 'Alley') # + # PART THREE: Data Tranformation # data normalization for numeric feature # whether to use this method depends on the model used # if distance based model is used, then u need to call this # It seems that the popular model Xgbost doesnt need normalization def normalization(df,attr): # filled when used pass # get dummies for one categorial feature def dummy_one(df,attr): h_dummies = pd.get_dummies(df[attr],prefix=attr) df.drop([attr],axis=1,inplace=True) df = df.join(h_dummies) return df # all_data = dummy_one(all_data, 'Alley') # get dummies for all categorial features in df def dummy_all(df): df = pd.get_dummies(df) return df #all_data = dummy_all(all_data) #all_data.info(verbose=True,max_cols=1000) from scipy.stats import skew # Attetion here: for log transform to make features more normally distributed and # this makes linear regression preform better, since linear regression is sensitive to outliers. # But!! ! note that if a tree-based model is used, then this log transform is not necessary. # In a word, many preprecessing steps must be contacted to ur model used. # the predict object or features could be skewed, we can do log tansform for those variables # do log transform for one feature/target # threshold: determine whether to do log transform on this feature/target def log_skewed_one(df, attr, threshold): # calculate the skewness skewness = skew(df[attr].dropna()) if skewness > threshold: df[attr] = np.log1p(df[attr]) #log_skewed_one(train, 'SalePrice', 0.75) #train['SalePrice'].hist() def log_skewed_all(df,threshold): # get index of all numeric features numeric_feats = df.dtypes[df.dtypes != 'object'].index # then compute the skewness for each features skewed_feats = df[numeric_feats].apply(lambda x: skew(x.dropna())) # get the features whose skeness>0.75 skewed_feats = skewed_feats[skewed_feats>threshold] skewed_feats = skewed_feats.index df[skewed_feats] = np.log1p(df[skewed_feats]) #log_skewed_all(train,0.75) #train['SalePrice'].hist() # + # PART FOUR: Feature Decomposition from sklearn.decomposition import PCA # Note that the input df must not have categorial features or missing value, # do this after preprocessing fo filling missing value and feature transformation def pca_reduc(df, num_fea_toleave='mle'): # @return type: pd.DataFrame pca = PCA(n_components=num_fea_toleave) after_pca = pca.fit_transform(df) print 'Percentage of variance explained by each of the selected components:' print(pca.explained_variance_ratio_) #print pd.DataFrame(new).info() return pd.DataFrame(after_pca) #all_data = dummy_all(all_data) #all_data.fillna(all_data.mean(),inplace=True) #all_data = pca_reduc(all_data,30) #all_data.info(verbose=True, max_cols=1000) from sklearn.decomposition import KernelPCA # Kernel PCA ==> non-linear dimensionality reduction through the use of kernels # Somewhat like kernel in SVM # kernel = “linear” \| “poly” \| “rbf” \| “sigmoid” \| “cosine” \| “precomputed” def kernelpca_reduc(df, kernel='linear',num_fea_toleave=50): kpca = KernelPCA(n_components=num_fea_toleave,kernel = kernel,n_jobs=-1) after_kpca = kpca.fit_transform(df) print 'the selected features Eigenvalues in decreasing order:' print (kpca.lambdas_) return pd.DataFrame(after_kpca) #all_data = dummy_all(all_data) #all_data.fillna(all_data.mean(),inplace=True) #all_data = kernelpca_reduc(all_data,kernel='rbf',num_fea_toleave=50) #print all_data.shape from sklearn.decomposition import TruncatedSVD # Dimensionality reduction using truncated SVD def truncatedSVD_reduc(df,num_fea_toleave=50): # provide a random_state to get stable output svd = TruncatedSVD(n_components=num_fea_toleave, n_iter=7, random_state=42) after_trans = svd.fit_transform(df) print 'Percentage of variance explained by each of the selected components:' print(svd.explained_variance_ratio_) return pd.DataFrame(after_trans) #all_data = dummy_all(all_data) #all_data.fillna(all_data.mean(),inplace=True) #all_data = truncatedSVD_reduc(all_data,num_fea_toleave=50) #print all_data.shape # + # PART FIVE: Feature Selection from sklearn.feature_selection import RFECV # RFECV: Feature ranking with recursive feature elimination and cross-validated selection of the best number of features. def fea_sel_rfecv(train_x,train_y,test_x,estimator): rfecv = RFECV(estimator=estimator,scoring='neg_mean_squared_error',n_jobs=-1) after_d = rfecv.fit_transform(train_x,train_y) print("Optimal number of features : %d" % rfecv.n_features_) # Plot number of features VS. cross-validation scores plt.figure() plt.xlabel("Number of features selected") plt.ylabel("Cross validation score(neg_mean_squared_error)") plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_) return pd.DataFrame(after_d),pd.DataFrame(rfecv.transform(test_x)) #alldata_nomissing = pd.read_csv('../input/alldata_after_filling_missing.csv') #from sklearn import svm #clf = svm.LinearSVR() #after_d,after_d_test = (fea_sel_rfecv(alldata_nomissing.iloc[:1460],train['SalePrice'],alldata_nomissing.iloc[1460:],clf)) #print after_d.shape #print after_d_test.shape from sklearn.feature_selection import SelectFromModel # u can see from 'SelectFromModel' that this method use model result to select features, 'Wrapper' # estimator: a supervised model with fit() method def fea_sel_tree(train_x,train_y,estimator): estimator = estimator.fit(train_x,train_x) print 'feature importances in this model', print sorted(estimator.feature_importances_,reverse=True) model = SelectFromModel(estimator,prefit = True) after_sel = model.transform(train_x) return pd.DataFrame(after_sel) #train = dummy_all(train) #train.fillna(train.mean(),inplace=True) #from sklearn.ensemble import RandomForestRegressor #clf = RandomForestRegressor(random_state=0,n_estimators=50) #print fea_sel_tree(train.iloc[:,1:-1],train['SalePrice'],clf).shape # - train,test,all_data = read_data() print all_data.info() def missing_data_fill(): train,test,all_data = read_data() # step1: # for features that having many missing values(more than half): ano_value_for_missing value # ir for the features whose NA has special meaning, like BsmtQual # dummy_na = True means 'Add a column to indicate NaNs' all_data = pd.get_dummies(all_data,dummy_na=True,columns=['Alley','FireplaceQu','PoolQC','Fence', 'MiscFeature','BsmtQual','GarageFinish']) # filling missing value with mean()/ mode for attr in ['MSZoning','LotFrontage','Utilities','BsmtFinSF2','BsmtHalfBath', 'BsmtUnfSF','TotalBsmtSF','Electrical','BsmtFullBath','Functional','GarageType', 'GarageQual','GarageCond','SaleType','Exterior1st','Exterior2nd','MasVnrType','KitchenQual']: mean_fill(all_data,attr) # step2: # along with filling missing values, we do data transformation all_data = pd.get_dummies(all_data) # for important feature: randomforest_fill # could be better to do this after dummies all the object features for attr in ['MasVnrArea','BsmtFinSF1','GarageYrBlt','GarageCars','GarageArea']: randomforest_fill(all_data,attr) all_data.to_csv('../input/alldata_after_filling_missing.csv',index=False) #all_data.info(verbose=True,max_cols=1000) ## show the columns that containing null value #all_data.loc[:,all_data.isnull().any().values==True] ## show the columns whose type is 'object' #all_data.loc[:,all_data.dtypes=='object'] # + # step3: # we use feature selection to exclude some very unimportant features firstly. alldata_nomissing = pd.read_csv('../input/alldata_after_filling_missing.csv') #print alldata_nomissing.info(verbose=True,max_cols=1000) from sklearn import svm clf = svm.LinearSVR() after_d,after_d_test = (fea_sel_rfecv(alldata_nomissing.iloc[:1460],train['SalePrice'],alldata_nomissing.iloc[1460:],clf)) print after_d.shape print after_d_test.shape # - # lets skip feature transformation first, just fit the model directly. # xgboost import xgboost as xgb #dtrain = xgb.DMatrix(after_d,label=train['SalePrice']) model_xgb = xgb.XGBRegressor(n_estimators=360, max_depth=2, learning_rate=0.1) model_xgb.fit(alldata_nomissing.iloc[:1460],train['SalePrice']) pre_val = pd.DataFrame(model_xgb.predict(alldata_nomissing.iloc[1460:])) test_label = pd.read_csv('../input/test_id',header=None) result = pd.DataFrame() result['Id'] = test_label[0] result['SalePrice'] = pre_val[0] result.to_csv('../input/result_xgb_1119_nofeaselec.csv',index=False) # + active="" # # - from sklearn.pipeline import Pipeline import xgboost as xgb from sklearn.decomposition import PCA from sklearn.decomposition import PCA, NMF from sklearn.feature_selection import SelectKBest, chi2 from sklearn.model_selection import GridSearchCV # the key in the dict of Pipeline is the name u want give to the step pipe = Pipeline([ ('reduce_dim',PCA()), ('regression',xgb.XGBRegressor()) ]) # optional for feature nums N_FEATURES_OPTIONS = [i for i in range(30,250,10)] N_ESTIMATOR_OPTIONS = [i for i in range(300,500,20)] param_grid=[ { 'reduce_dim': [PCA(iterated_power=7), NMF()], 'reduce_dim__n_components': N_FEATURES_OPTIONS, 'regression__n_estimators': N_ESTIMATOR_OPTIONS }, { 'reduce_dim': [SelectKBest(chi2)], 'reduce_dim__k': N_FEATURES_OPTIONS, 'regression_n_estimators': N_ESTIMATOR_OPTIONS } ] grid = GridSearchCV(pipe, cv=3, n_jobs=-1, param_grid=param_grid) grid.fit(alldata_nomissing.iloc[:1460],train['SalePrice']) mean_scores = np.array(grid.cv_results_['mean_test_score']) # + # actually, we can do some operations on features, and get more features to select. # Its shown that results from features may also work. # for example, alpha = num_buy/num_click do means something in shopping website's analysis. # + # dummies之后删除所有值都相同的列 #实际上所有dummies的feature中全为0的column都不存在	12,877
/train_v1.ipynb	bc094210527a7be3397934e635255cbce04b0b09	[ "MIT" ]	permissive	abekoh/splatoon_game_winner	https://github.com/abekoh/splatoon_game_winner	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	29,075	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] _cell_guid="56fbfcbd-7cee-4054-9142-48ecc8f689c3" _uuid="78d4e02d62194c4b78f419ac6b332e02fd1bf6a7" # # Predicting sales with a nested KerasRegressor # + _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load in import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the "../input/" directory. # For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory import os print(os.listdir("../input")) # Any results you write to the current directory are saved as output. # + [markdown] _cell_guid="97ff7f5e-de74-4a7c-b034-bcf1432480f2" _uuid="2dd003abe546efb14933e446b6854697ac10dfca" # ## Create training and test sets # + _cell_guid="deb3ac11-c600-48dc-b9d7-2b31d802caab" _uuid="6ed50a5c0665dc88ac7a6d0c8f4f9c57187fd2e6" # First we create a dataframe with the raw sales data, which we'll reformat later DATA = '../input/' sales = pd.read_csv(DATA+'sales_train.csv', parse_dates=['date'], infer_datetime_format=True, dayfirst=True) sales.head() # + _cell_guid="e64d8618-78fd-41cf-8361-c12f35ee75db" _uuid="9069d629e7479bdae511d9f7303762b8fa85a2c1" # Let's also get the test data test = pd.read_csv(DATA+'test.csv') test.head() # + _cell_guid="d55fc349-4a95-4d4a-8aaf-827d57bce777" _uuid="6b3e74f88c77bf2e0eb76f00482fce1cf70b3b64" # Now we convert the raw sales data to monthly sales, broken out by item & shop # This placeholder dataframe will be used later to create the actual training set df = sales.groupby([sales.date.apply(lambda x: x.strftime('%Y-%m')),'item_id','shop_id']).sum().reset_index() df = df[['date','item_id','shop_id','item_cnt_day']] df = df.pivot_table(index=['item_id','shop_id'], columns='date',values='item_cnt_day',fill_value=0).reset_index() df.head() # + _cell_guid="910d25e3-8401-4815-8248-043d44139e13" _uuid="8983b18b147317811bd7d1ceeec0b1502caab8ce" # Merge the monthly sales data to the test data # This placeholder dataframe now looks similar in format to our training data df_test = pd.merge(test, df, on=['item_id','shop_id'], how='left') df_test = df_test.fillna(0) df_test.head() # + _cell_guid="2565c11e-6b3a-4268-becd-5cab11fee861" _uuid="303707b5b9d2c54fc771ecaae2117ce1b33d067c" # Remove the categorical data from our test data, we're not using it df_test = df_test.drop(labels=['ID', 'shop_id', 'item_id'], axis=1) df_test.head() # + _cell_guid="fde5595e-0cf6-4620-87e2-6031ba8ea68d" _uuid="005d1e880ce7bea66471a7fb0e67d051cc133550" # Now we finally create the actual training set # Let's use the '2015-10' sales column as the target to predict TARGET = '2015-10' y_train = df_test[TARGET] X_train = df_test.drop(labels=[TARGET], axis=1) print(y_train.shape) print(X_train.shape) X_train.head() # + _cell_guid="fe54e321-f3d2-4217-a396-4416285ff1ff" _uuid="846ff06038a2cf228b383d3c3c75989ae3a726be" # To make the training set friendly for keras, we convert it to a numpy matrix # X_train = X_train.as_matrix() # X_train = X_train.reshape((214200, 33, 1)) # y_train = y_train.as_matrix() # y_train = y_train.reshape(214200, 1) print(y_train.shape) print(X_train.shape) # X_train[:1] # + _cell_guid="d7e744b1-4707-4df7-b8b4-4c2b92b0856b" _uuid="2214c539e1f1348b9fb5e903df514434c0e4191e" # Lastly we create the test set by converting the test data to a numpy matrix # We drop the first month so that our trained LSTM can output predictions beyond the known time range X_test = df_test.drop(labels=['2013-01'],axis=1) # X_test = X_test.as_matrix() # X_test = X_test.reshape((214200, 33, 1)) print(X_test.shape) # + [markdown] _cell_guid="e8ac0b5c-09f3-4bf1-8ceb-8f54fb81f9a3" _uuid="2c9ac0c8f0f2ba3bf312c5fc6224facce415402e" # ## Build and Train the model # + _cell_guid="d59a768d-c77d-44b4-9d48-14a752962b91" _uuid="512b649b54d56cad4651e57083735121808bc459" from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasRegressor from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline # + _cell_guid="6c3898a6-e583-4426-a2bd-27b40e33f0c5" _uuid="37595bce4deffcd88c38c5eef6e0678a5eadbef8" # Create the model using the NestedLSTM class - two layers are a good starting point # Feel free to play around with the number of nodes & other model parameters model = Sequential() model.add(Dense(64, input_dim=33, init='normal', activation='relu')) model.add(Dense(32, init='normal', activation='relu')) model.add(Dense(16, init='normal', activation='relu')) model.add(Dense(8, init='normal', activation='relu')) model.add(Dense(4, init='normal', activation='relu')) model.add(Dense(1, init='normal')) model.compile(loss='mean_squared_error', optimizer = 'adam') # model = Sequential() # model.add(NestedLSTM(64, input_shape=(33, 1), depth=3, dropout=0.2, recurrent_dropout=0.2)) # model.add(Dense(1)) # # The adam optimizer works pretty well, although you might try RMSProp as well # model.compile(loss='mse', # optimizer='adam', # metrics=['mean_squared_error']) model.summary() # + _cell_guid="08d56185-5f4e-44d0-8802-8eddac643569" _uuid="51b1a7acdbd3ceb2fef16d24385778ed1d68e4ed" # It's training time! BATCH = 2000 print('Training time, it is...') model.fit(X_train, y_train, batch_size=BATCH, epochs=10 ) # + [markdown] _cell_guid="4e4c11ad-2a76-4ced-bfff-a24e8ba5ab5c" _uuid="272b8e352e7c606b1cc3da4f1b12dd2b1c0e4f62" # ## Get test set predictions and Create submission # + _cell_guid="71f93b2d-f548-4362-a04d-0f5923eabd06" _uuid="b801abe918b33a85f55f040235c94dbbadb8d045" # Get the test set predictions and clip values to the specified range y_pred = model.predict(X_test).clip(0., 20.) # Create the submission file and submit! preds = pd.DataFrame(y_pred, columns=['item_cnt_month']) preds.to_csv('submission.csv',index_label='ID') # + _uuid="6eeed9ca595753c75fee93a07e5fcd9ae000450a" 3)} => 6/36 # # 8 {(2,6),(6,2),(3,5),(5,3),(4,4)} => 5/36 # # 9 {(3,6),(6,3),(5,4),(4,5)} => 4/36 # # 10 {(4,6),(6,4),(5,5)} => 3/36 # # 11 {(5,6),(6,5)} => 2/36 # # 12 {(6,6)} = > 1/36 # + [markdown] id="QTPVMzwCZdb7" # ## Inference # + [markdown] id="aLnt8I-_Zmb-" # ### Sample Mean and population Mean # # # * Let's consider a sample of 500 houses at random from 1460 houses and plot it's mean # * But the mean of these 500 houses can be near or pretty far away from the mean of the 1460 houses calculated earlier. # + [markdown] id="SNfsZPebnjrI" # ## Central Limit Theorem # # The central limit theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population then the distribution of the sample means will be approximately normally distributed. # # ## Explanation # # Suppose we are interested in estimating average height of a population. We can not measure every person height in the given population. We take a sample from the given population. But when we take the sample 2 important things should be noted, 1. Sample size and 2. Distribution of sample. # # The central limit theorem says, if we take the sample in significantly large size, the mean of the sample will be normally distributed. For instance, let us assume the sample size as N to be 3, which means we take 3 data points randomly as groups from the given population data such as [155, 160, 171], [152, 168, 164], [172, 151, 154], and so on. Suppose we collect 1000 such groups from the population and calculate average of every group, we will have 1000 averages. When we plot this 1000 average data we will have a distribution as a normal distribution. # # ## Importance of Central Limit Theorem # # * Signifies the importance of Sample size # # * No matter what the distribution of population, the shape of sample distribution becomes normal when the sample size (N) increases. # # * Important for Inferential Statistical Analysis # # + [markdown] id="f_ODZu7rp3Cd" # Creating a varaible and storing sales price data. # + id="gU0W21uipDqg" df_SalePrice=df['SalePrice'] df_SalePrice.describe() # + id="c3kpxBl6eW97" df_SalePrice.mean() # + id="9J0ZaRDEgWyx" # plot all the observation in SalesPrice data plt.hist(df_SalePrice, bins=100) plt.xlabel('SalePrice') plt.ylabel('count') plt.title('Histogram of Sales frequency') plt.axvline(x=df_SalePrice.mean(),color='r') # + [markdown] id="VQNqJ-pB02tS" # Observation: # # * We can see the vertical red line, mean of data, almost at the centre of main distribution. # # * Most of the distribution is in normal but not 100%. # # * Here, the data point after the 500000 on x-axis is an outlier, and the points around 400000 maybe or maynot be an outlier since they are very close to out main distribution graph. # + [markdown] id="-BeAbD6v31ZB" # Note:- We can also see from the above plot that the population is not normal, Therefore, we need to draw sufficient samples of different sizes and compute their means (known as sample means). We will then plot those sample means to get a normal distribution. # + id="PxMhFgU8n6H0" #We will take sample size=20, 60 & 500 #Calculate the arithmetice mean and plot the mean of sample 500 times array1 = [] array2 = [] array3 = [] n = 500 for i in range(1,n): array1.append(df_SalePrice.sample(n=20,replace= True).mean()) array2.append(df_SalePrice.sample(n=60,replace= True).mean()) array3.append(df_SalePrice.sample(n=500,replace= True).mean()) #print(array) fig , (ax1,ax2,ax3) = plt.subplots(nrows=1, ncols=3,figsize=(25,8)) #plt.figure() #plt.subplot(311) ax1.hist(array1, bins=100,color='r') ax1.set_xlabel('SalePrice') ax1.set_ylabel('count') ax1.set_title('Sample size = 20') ax1.axvline(x=np.mean(array1),color='b') # for giving mean line #ax2.subplot(312) ax2.hist(array2, bins=100, color='g') ax2.set_xlabel('SalePrice') ax2.set_ylabel('count') ax2.set_title('Sample size = 60') ax2.axvline(x=np.mean(array2),color='r') # for giving mean line #ax3.subplot(313) ax3.hist(array3,bins=100) ax3.set_xlabel('SalePrice') ax3.set_ylabel('count') ax3.set_title('Sample size = 500') ax3.axvline(x=np.mean(array3),color='r') # for giving mean line # + [markdown] id="VTPvcy9kv8W7" # ## Confidence Interval # # Confidence Interval (CI) is a type of estimate computed from the statistics of the observed data. This proposes a range of plausible values for an unknown parameter (for example, the mean). The interval has an associated confidence level that the true parameter is in the proposed range. # + [markdown] id="elLp-G6pxTON" # The 95% confidence interval defines a range of values that you can be 95% certain contains the population mean. With large samples, you know that mean with much more precision than you do with a small sample, so the confidence interval is quite narrow when computed from a large sample. # + [markdown] id="bpJz5fxNxb7R" # ##Calculating the Confidence Interval # # Step 1: start with # # The number of observations n # Thhe mean X # The standard deviation s # # Note: we should use the standard deviation of the entire population, but in many cases we won't know it. # # We can use the standard deviation for the sample if we have enough observations (at least n=30, hopefully more) # # Step 2: # # Decide what Confidence Interval we want: 95% or 99% are common choices. Then find the "Z" value for that Confidence Interval here: # # ![](https://i.imgur.com/AjRb5aG.png) # # # # Step 3: Use that Z value in this formula for the Confidence Interval # # ![](https://i.imgur.com/jdrj6wC.png) # # Note:-The value after the ± is called the margin of error # # # + id="UmGo758awQJ1" # importing math library import math # lets seed the random values np.random.seed(10) # lets take a sample size sample_size = 1000 sample = np.random.choice(a= df['SalePrice'], size = sample_size) sample_mean = sample.mean() print("Sample Mean:",sample_mean) # Get the z-critical value* z_critical = stats.norm.ppf(q = 0.95) # Check the z-critical value print("z-critical value: ",z_critical) # Get the population standard deviation pop_stdev = df['SalePrice'].std() # checking the margin of error margin_of_error = z_critical * (pop_stdev/math.sqrt(sample_size)) # defining our confidence interval confidence_interval = (sample_mean - margin_of_error, sample_mean + margin_of_error) # lets print the results print("Confidence interval:",end=" ") print(confidence_interval) print("True mean: {}".format(df['SalePrice'].mean())) # + [markdown] id="TZilBKNeJ5HS" # ## Hypothesis Testing # # * $Statistical Hypothesis$, sometimes called confirmatory data analysis, is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. # # ### Null Hypothesis # # * In Inferential Statistics, The Null Hypothesis is a general statement or default position that there is no relationship between two measured phenomena or no association among groups. # # * Statistical hypothesis tests are based on a statement called the null hypothesis that assumes nothing interesting is going on between whatever variables you are testing. # # * Therefore, in our case the Null Hypothesis would be: # The Mean of House Prices in OldTown is not different from the houses of other neighborhoods # # ### Alternate Hypothesis # # * The alternate hypothesis is just an alternative to the null. For example, if your null is I'm going to win up to 1000 then your alternate is I'm going to win more than 1000. Basically, you're looking at whether there's enough change (with the alternate hypothesis) to be able to reject the null hypothesis # # ### The Null Hypothesis is assumed to be true and Statistical evidence is required to reject it in favor of an Alternative Hypothesis. # # # 1. Once you have the null and alternative hypothesis in hand, you choose a significance level (often denoted by the Greek letter α). The significance level is a probability threshold that determines when you reject the null hypothesis. # # 2. After carrying out a test, if the probability of getting a result as extreme as the one you observe due to chance is lower than the significance level, you reject the null hypothesis in favor of the alternative. # # 3. This probability of seeing a result as extreme or more extreme than the one observed is known as the p-value. # + [markdown] id="wnQ0lIjWJ9UE" # ### P Value # # * In statistical hypothesis testing, the p-value or probability value is the probability of obtaining test results at least as extreme as the results actually observed during the test, assuming that the null hypothesis is correct. # # * So now say that we have put a significance (α) = 0.05 # * This means that if we see a p-value of lesser than 0.05, we reject our Null and accept the Alternative to be true # # + [markdown] id="q9wfeeEtMG4L" # # # * The pvalue is the Probability that tells that result from sample data occured by chance. # * The pvalue tells the number of times null hypothesis is true # # * Low p-value is good; It indicates data did not occur by chance. # * For example, a p-value of .01 means there is only a 1% probability that the results from an experiment happened by chance. # * Usually it is accepted that if the p-value is lower than significance level α (equal to 0.05) , then we should reject the null hypothesis. # # + [markdown] id="3HdcFQalPyRe" # ###T-Test # # The T-test is a statistical test used to determine whether a numeric data sample differs significantly from the population or whether two samples differ from one another. # # Purpose: # # * It tells us - Is there a significant difference between two sets of data. # # * It lets us know if those differences could have happened by chance. # # * It doesn't look at only mean but spread of standard deviation to derive conclusion how significant two data sets are # # + [markdown] id="G_MEoYqw_974" # ## Type 1 and Type 2 Error # # * In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis. # # * Type II error is the rejection of a false null hypothesis. # # ### Type 1 and Type 2 Error Example # # For example, let's look at the trail of an accused criminal. The null hypothesis is that the person is innocent, while the alternative is guilty. # * A Type 1 error in this case would mean that the person is not found innocent and is sent to jail, despite actually being innocent. # * A Type 2 Erroe Example In this case would be, the person is found innocent and not sent to jail despite of him being guilty in real.	17,401
/Tuesday_Lesson.ipynb	5240dff8415e40c195bc9ffcd29031882058ecb6	[]	no_license	laurariv01/Hangman-Program	https://github.com/laurariv01/Hangman-Program	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	25,233	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import os import sys import random import math import numpy as np import skimage.io import matplotlib import matplotlib.pyplot as plt import cv2 # %matplotlib inline # Root directory of the project ROOT_DIR = os.getcwd() # + # Import Mask RCNN sys.path.append(ROOT_DIR) # To find local version of the library from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize # Import SUN config sys.path.append(os.path.join(ROOT_DIR, "samples/sun/")) # To find local version import sun # Directory to save logs and trained model MODEL_DIR = os.path.join(ROOT_DIR, "logs") # Local path to trained weights file SUN_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_sun.h5") # Directory of images to run detection on IMAGE_DIR = os.path.join(ROOT_DIR, "images") # - SUN_MODEL_PATH # + # #!wget https://github.com/hateful-kate/Mask_RCNN/releases/download/v3.0/mask_rcnn_sun.h5 # + class InferenceConfig(sun.SunConfig): # Set batch size to 1 since we'll be running inference on # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU GPU_COUNT = 1 IMAGES_PER_GPU = 1 config = InferenceConfig() config.display() # + # Create model object in inference mode. model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config) # Load weights trained on SUN RGB-D model.load_weights(SUN_MODEL_PATH, by_name=True) # - class_names = ['BG', 'bed', 'books', 'ceiling', 'chair', 'floor', 'furniture', 'objects', 'picture', 'sofa', 'table', 'tv', 'wall', 'window'] # !wget https://preview.ibb.co/cubifS/sh_expo.jpg -P ./images # + # Load a random image from the images folder file_names = next(os.walk(IMAGE_DIR))[2] image = skimage.io.imread(os.path.join(IMAGE_DIR, 'sh_expo.jpg')) # Run detection results = model.detect([image], verbose=1) # Visualize results r = results[0] visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']) # + # #!mkdir videos # #!wget https://github.com/hateful-kate/Mask_RCNN/releases/download/v3.0/Achievement.mp4 -P ./videos # - # !ls ./videos # + def random_colors(N): np.random.seed(1) colors = [tuple(255 * np.random.rand(3)) for _ in range(N)] return colors def apply_mask(image, mask, color, alpha=0.5): """apply mask to image""" for n, c in enumerate(color): image[:, :, n] = np.where( mask == 1, image[:, :, n] * (1 - alpha) + alpha * c, image[:, :, n] ) return image def display_instances(image, boxes, masks, ids, names, scores): """ take the image and results and apply the mask, box, and Label """ n_instances = boxes.shape[0] colors = random_colors(n_instances) if not n_instances: print('NO INSTANCES TO DISPLAY') else: assert boxes.shape[0] == masks.shape[-1] == ids.shape[0] for i, color in enumerate(colors): if not np.any(boxes[i]): continue y1, x1, y2, x2 = boxes[i] label = names[ids[i]] score = scores[i] if scores is not None else None caption = '{} {:.2f}'.format(label, score) if score else label mask = masks[:, :, i] image = apply_mask(image, mask, color) image = cv2.rectangle(image, (x1, y1), (x2, y2), color, 2) image = cv2.putText(image, caption, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.7, color, 2) return image # + batch_size = 1 VIDEO_DIR = os.path.join(ROOT_DIR, "videos") VIDEO_SAVE_DIR = os.path.join(VIDEO_DIR, "save") try: if not os.path.exists(VIDEO_SAVE_DIR): os.makedirs(VIDEO_SAVE_DIR) except OSError: print ('Error: Creating directory of data') frames = [] frame_count = 0 # - # !ls ./videos/save # + video = cv2.VideoCapture(os.path.join(VIDEO_DIR, 'Achievement.mp4')); # Find OpenCV version (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.') if int(major_ver) < 3 : fps = video.get(cv2.cv.CV_CAP_PROP_FPS) print("Frames per second using video.get(cv2.cv.CV_CAP_PROP_FPS): {0}".format(fps)) else : fps = video.get(cv2.CAP_PROP_FPS) print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps)) video.release(); # - frames = [] sec = 226 vidcap = cv2.VideoCapture(os.path.join(VIDEO_DIR, 'Achievement.mp4')) success,frame = vidcap.read() count = 226 while success: # Save each frame of the video to a list sec += 1 frames.append(frame) print('frame_count :{0}'.format(sec)) batch_size = 1 VIDEO_DIR = os.path.join(ROOT_DIR, "videos") VIDEO_SAVE_DIR = os.path.join(VIDEO_DIR, "save") if len(frames) == batch_size: results = model.detect(frames, verbose=1) print('Predicted') for i, item in enumerate(zip(frames, results)): frame = item[0] r = item[1] frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'], class_names, r['scores']) name = '{0}.jpg'.format(sec + i - batch_size) name = os.path.join(VIDEO_SAVE_DIR, name) cv2.imwrite(name, frame) print('writing to file:{0}'.format(name)) frames = [] # Clear the frames array to start the next batch #cv2.imwrite("frame%d.jpg" % count, frame) # save frame as JPEG file success,frame = vidcap.read() print('Read a new frame: ', success) count += 1 # + def make_video(outvid, images=None, fps=30, size=None, is_color=True, format="FMP4"): """ Create a video from a list of images. """ from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize fourcc = VideoWriter_fourcc(format) vid = None for image in images: if not os.path.exists(image): raise FileNotFoundError(image) img = imread(image) if vid is None: if size is None: size = img.shape[1], img.shape[0] vid = VideoWriter(outvid, fourcc, float(fps), size, is_color) if size[0] != img.shape[1] and size[1] != img.shape[0]: img = resize(img, size) vid.write(img) vid.release() return vid import glob import os # Directory of images to run detection on ROOT_DIR = os.getcwd() VIDEO_DIR = os.path.join(ROOT_DIR, "videos") VIDEO_SAVE_DIR = os.path.join(VIDEO_DIR, "save") images = list(glob.iglob(os.path.join(VIDEO_SAVE_DIR, '.'))) # Sort the images by integer index images = sorted(images, key=lambda x: float(os.path.split(x)[1][:-3])) outvid = os.path.join(VIDEO_DIR, "out.mp4") make_video(outvid, images, fps=30) # - # !ls -lah ./videos/ hould be [7, 9, 4.9] import statistics nums= [2,7,4.2,1.6,9,4.4,4.9] x=statistics.mean(nums) print("mean is:", x) new_nums= list(filter(lambda nums: True if nums <=4.5875 else False, nums)) print(new_nums) #Presentation Code # - # ## Reduce() <br> # <p>Be very careful when using this function, as of Python 3 it's been moved to the 'functools' library and no longer is a built-in function.<br>The creator of Python himself, says to just use a for loop instead.</p> # #### Syntax # + from functools import reduce # reduce(func, list) #Presentation Code # use for loop below instead to calculate sum of list, rather than reduce which may cause errors # reduce functions passed in must accept two parameters l_1= [1,2,3,4,5] def subtractNums (num1,num2): return num1 - num2 result = reduce(subtractNums, l_1) print(result) def addNums (num1,num2): return num1 + num2 result2=reduce(addNums, l_1) print(result2) #Presentation Code # - # #### Using Lambda's with Reduce() #Presentation Code result=reduce(lambda x,y: x+y, l_1) print(result) # #### In-Class Exercise #4 <br> # <p>Use the reduce function to multiply the numbers in the list below together with a lambda function.</p> # + #Presentation Code # output should be 24 mylist=[1,2,3,4] result=reduce(lambda x,y: xy, mylist) print(result) #Presentation Code # - # ## Recursion <br> # <p>Recursion means that a function is calling itself, so it contanstly executes until a base case is reached. It will then push the returning values back up the chain until the function is complete. A prime example of recursion is computing factorials... such that 5! (factorial) is 54321 which equals 120.</p> # #### Implementing a Base Case # + # must always have a base case, otherwise it will infinitely loop #Presentation Code def addNums(num): #set base case here if num <=1: print("addNums(1)=1") return num else: print("addNums({}) = {} + addNums({})".format(num,num,num-1)) return num + addNums(num-1) addNums(7) # addNums(3) = 3 + addNums(2) = 3 + 2 + addNums(1) = 3 + 2 + 1 = 6 # addNums(2) = 2 + addNums(1) = 2 + 1 = 3 # addNums(1) = 1 #Presentation Code # - # #### Writing a Factorial Function # + # 5! = 5 * 4 * 3 * 2 * 1 = 120 #Presentation Code def factorial(num): if num <= 1: return 1 else: return num * factorial (num-1) factorial(5) # - # #### In-Class Exercise #5 <br> # <p>Write a recursive function that subtracts all numbers to the argument given.</p> # + # result of passing in 3 should equal 2... we're not subtracting 3 - 2 - 1, we're getting the result of 3 - subNums(2) # subNums(2) = 2 - subNums(1) and subNums(1) = 1, so the result is subNums(3) = 3 - 1 which is 2 #result should be 3-2-1 def subtractNums(num): #set base case here if num <=1: print("subtractNums(1)=1") return num else: print("subtractNums({}) = {} - subtractsNums({})".format(num,num,num-1)) return num - subtractNums(num-1) print(subtractNums(5)) # - # ## Generators <br> # <p>Generators are a type of iterable, like lists or tuples. They do not allow indexing, but they can still be iterated through with for loops. They are created using functions and the yield statement.</p> # #### Yield Keyword <br> # <p>The yield keyword denotes a generator, it doesn't return so it won't leave the function and reset all variables in the function scope, instead it yields the number back to the caller.</p> # + # using a for loop #Presentation Code def my_range(stop,start = 0, step = 1): while start < stop: yield start start += step for i in my_range(13, start=1): print(i) # for i in my_range(10, start=2): # print(i) # - # #### Infinite Generator # + # bad, never create infinite loops # - # #### In-Class Exercise #6 <br> # <p>Create a generator that takes a number argument and yields that number squared, then prints each number squared until zero is reached.</p> # + # always create a base case def squared(num): while num > 0: yield num *2 num -= 1 for i in squared(10): print (i) # - # # Exercises # ### Exercise #1 <br> # <p>Filter out all of the empty strings from the list below</p> # + places = ["","Argentina", "", "San Diego","","","","Boston","New York"] list(filter(None, places)) # - # ### Exercise #2 <br> # <p>Write an anonymous function that sorts this list by the last name...<br><b>Hint: Use the ".sort()" method and access the key"</b></p> author = ["Joel Carter", "Victor aNisimov", "Andrew P. Garfield","David hassELHOFF","Gary A.J. Bernstein"] author.sort(key=lambda name: name.split(" ")[-1].lower()) print (author) # ### Exercise #3 <br> # <p>Convert the list below from Celsius to Farhenheit, using the map function with a lambda...</p> # + # F = (9/5)C + 32 places = [('Nashua',32),("Boston",12),("Los Angelos",44),("Miami",29)] celsius_to_farhenheit= lambda data: (data[0], (9/5)*data[1] + 32) list(map(celsius_to_farhenheit, places)) # - # ### Exercise #4 <br> # <p>Write a recursion function to perform the fibonacci sequence up to the number passed in.</p> # + #example: 0,1,1,2,3,5,8,13 def fibonacci(n): #this is my base case if n <= 1: return n else: return(fibonacci(n-1) + fibonacci(n-2)) nterms = 8 for i in range(nterms): print(fibonacci(i))	12,357
/tempConverter.ipynb	28eff434e9145ac7cad95c6727aa96d060eb29a5	[]	no_license	sealdakota/Python-Examples	https://github.com/sealdakota/Python-Examples	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,731	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + #Dakota Seal #takes input farenheit temperature and converts it to celsius. fahrenheit = input("input a temperature in farenheit that you would like converted to celsius: ") #While loop that checks if temperature is less than or equal to 0 kelvin. belowZero = True while belowZero == True: if float(fahrenheit) <= float(-459.67): fahrenheit = input("Sorry, but temperatures don't go that low, please enter something more reasonable: ") belowZero = True else: belowZero = False #converts fahrenheit to celsius celsius = (float(fahrenheit)-32) * 5/9 print(str("%.2f" % celsius)) # -	889
/Getting and Knowning your data/Chipotle/Excercise with solutions .ipynb	57add271fc88943784a453027c93f30a8c60b1dc	[]	no_license	Siddhesh-Nawale/Pandas-Excercise	https://github.com/Siddhesh-Nawale/Pandas-Excercise	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	27,683	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Algoritmos de Machine Learning # En este Notebook, usaremos los Dataframes creados en el Notebook de Features para entrenar distintos modelos de Machine Learning y predecir si los tweets son reales o no. # + import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report, confusion_matrix, accuracy_score import warnings warnings.filterwarnings('ignore') # - # ## Carga de dataframes train_df = pd.read_csv('../Data/train_features.csv', encoding='latin-1',dtype={'id': np.uint16,'target': np.bool}) test_df = pd.read_csv('../Data/test_features.csv', encoding='latin-1',dtype={'id': np.uint16}) sample_submission = pd.read_csv('../Data/sample_submission.csv') train_df.head(2) test_df.head(2) # ## Algoritmos # ### Random Forest # ###### Entrenamiento X = train_df.sort_values(by='id',ascending=True).iloc[:,2:] Y = train_df.sort_values(by='id',ascending=True).iloc[:,1] X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2) # + from sklearn.ensemble import RandomForestClassifier rf_classifier = RandomForestClassifier(n_estimators=10000,max_depth=15,min_samples_split=20) rf_classifier.fit(X_train, Y_train) Y_pred = rf_classifier.predict(X_test) # - print(classification_report(Y_test,Y_pred)) print(accuracy_score(Y_test,Y_pred)) # Analizamos la importancia de los features. # + plt.figure(figsize=(20,8)) plt.bar(X.columns, rf_classifier.feature_importances_) plt.xlabel('Features', fontsize=16, fontweight='bold') plt.ylabel('Importancia', fontsize=16, fontweight='bold') plt.title('Importancia de Features con RF', fontsize=20, fontweight='bold') plt.xticks(rotation=45,weight='bold') plt.show() # - # ###### Predicción registros_a_predecir = test_df.sort_values(by='id',ascending=True).iloc[:,1:] prediccion_RF = rf_classifier.predict(registros_a_predecir) prediccion_RF = prediccion_RF.astype(int) # Damos el formato para realizar el submit. resultado_RF = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion_RF}) resultado_RF.head() resultado_RF.to_csv('..\Predicciones\prediccion_RF.csv',index=False) # ### XGBoost # ###### Entrenamiento from xgboost import XGBClassifier import xgboost as xgb xgboost = XGBClassifier(max_depth=1, objective= 'binary:logistic', nthread=4,n_estimators=2000, learning_rate=0.02, colsample_bytree=0.75) xgboost.fit(X_train,Y_train) Y_pred = xgboost.predict(X_test) print(classification_report(Y_test,Y_pred)) print(accuracy_score(Y_test,Y_pred)) # Analizamos la importancia de los features con XGBOOST. # + plt.rcParams['figure.figsize'] = [20, 8] xgb.plot_importance(xgboost) plt.xlabel('F Score', fontsize=16, fontweight='bold') plt.ylabel('Features', fontsize=16, fontweight='bold') plt.title('Importancia de Features con XGB', fontsize=20, fontweight='bold') plt.yticks(weight='bold') plt.show() # - # ###### Predicción registros_a_predecir = test_df.sort_values(by='id',ascending=True).iloc[:,1:] prediccion_XGB = xgboost.predict(registros_a_predecir) prediccion_XGB = prediccion_XGB.astype(int) # Damos el formato para realizar el submit. resultado_XGB = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion_XGB}) resultado_XGB.head() resultado_XGB.to_csv('..\Predicciones\prediccion_XGB.csv',index=False) # ### Perceptrón Multicapa # ###### Entrenamiento X = train_df.iloc[:,2:] Y = train_df.iloc[:,1] X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2) # Normalizamos los campos. scaler = StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) # + from sklearn.neural_network import MLPClassifier mlp = MLPClassifier(hidden_layer_sizes=(10,10,10), max_iter=500, solver='adam',learning_rate_init=3e-4) mlp.fit(X_train, Y_train) Y_pred = mlp.predict(X_test) # - print(classification_report(Y_test,Y_pred)) print(accuracy_score(Y_test,Y_pred)) # ###### Predicción registros_a_predecir = test_df.iloc[:,1:] registros_a_predecir = scaler.transform(registros_a_predecir) prediccion_MLP = mlp.predict(registros_a_predecir) prediccion_MLP = prediccion_MLP.astype(int) # Damos el formato para realizar el submit. resultado_MLP = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion_MLP}) resultado_MLP.head() resultado_MLP.to_csv('..\Predicciones\prediccion_MLP.csv',index=False) # # ### Redes Neuronales usando Keras from keras.models import Sequential, Model from keras import layers from keras.callbacks import ModelCheckpoint from keras.initializers import Constant from keras.optimizers import Adam from keras.utils.vis_utils import plot_model # Importamos los archivos creados en el Notebook de Features para predecir usando Embeddings. tweets_padded = pd.read_csv('../Data/tweets_padded.csv', encoding='latin-1') matriz_de_embeddings = pd.read_csv('../Data/matriz_de_embeddings.csv', encoding='latin-1') tweets_padded = tweets_padded.to_numpy() matriz_de_embeddings = matriz_de_embeddings.to_numpy() train_embeddings = tweets_padded[:train_df.shape[0]] test_embeddings = tweets_padded[train_df.shape[0]:] # #### Primer modelo (Solo Embeddings) # ###### Entrenamiento # + model = Sequential() model.add(layers.Embedding(matriz_de_embeddings.shape[0],matriz_de_embeddings.shape[1],\ embeddings_initializer=Constant(matriz_de_embeddings),\ input_length = train_embeddings.shape[1],trainable = False)) model.add(layers.SpatialDropout1D(0.2)) model.add(layers.LSTM(100,dropout=0.2,recurrent_dropout=0.2)) model.add(layers.Dense(1,activation='sigmoid')) model.compile(loss='binary_crossentropy',optimizer=Adam(learning_rate=3e-4),metrics=['accuracy']) # - # Veamos el detalle del modelo que vamos a utilizar. model.summary() plot_model(model, to_file='modelo1_plot.png', show_shapes=True, show_layer_names=True) # Realizamos el split en train y tesr de los dataframes y entrenamos el modelo. X_train,X_test,Y_train,Y_test = train_test_split(train_embeddings,train_df['target'].values,test_size=0.2) model.fit(X_train,Y_train,batch_size=32,epochs=20,validation_data=(X_test,Y_test),verbose=2) score = model.evaluate(X_test,Y_test,verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) # ###### Predicción registros_a_predecir = test_embeddings prediccion = model.predict(registros_a_predecir) prediccion = np.round(prediccion).astype(int).reshape(3263) resultado_EM = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion}) resultado_EM.head() resultado_EM.to_csv('..\Predicciones\prediccion_EM.csv',index=False) # #### Segundo modelo (Embeddings + features) # ###### Entrenamiento train_features = train_df.iloc[:,2:] test_features = test_df.iloc[:,1:] def crear_modelo(): nlp_input = layers.Input(shape=(train_embeddings.shape[1],), name='nlp_input') x = layers.Embedding(matriz_de_embeddings.shape[0],matriz_de_embeddings.shape[1],\ embeddings_initializer=Constant(matriz_de_embeddings),trainable = False)(nlp_input) x = layers.SpatialDropout1D(0.2)(x) nlp_out = layers.LSTM(100,dropout=0.2,recurrent_dropout=0.2)(x) features_input = layers.Input(shape=(train_features.shape[1],), name='features_input') features_out = layers.Dense(4, activation="relu")(features_input) concat = layers.Concatenate(axis=1) x = concat([nlp_out,features_out]) x = layers.Dense(1, activation='sigmoid')(x) model = Model(inputs=[nlp_input,features_input], outputs=[x]) model.compile(loss='binary_crossentropy',optimizer=Adam(learning_rate=3e-4),metrics=['accuracy']) return model # Veamos el detalle del modelo que vamos a utilizar. modelo = crear_modelo() modelo.summary() plot_model(modelo, to_file='modelo2_plot.png', show_shapes=True, show_layer_names=True) # Realizamos el split en train y tesr de los dataframes y entrenamos el modelo. e_train,e_test,f_train,f_test,Y_train,Y_test = train_test_split(train_embeddings,train_features,train_df['target'].values,\ test_size=0.2) # Normalizamos los features. scaler = StandardScaler() scaler.fit(f_train) f_train = scaler.transform(f_train) f_test = scaler.transform(f_test) test_norm = scaler.transform(test_features) # Usamos un CheckPoint para guardar los pesos óptimos del entrenamiento. cp = ModelCheckpoint('pesos.h5', monitor='val_loss', save_best_only=True) modelo.fit([e_train,f_train],Y_train,\ batch_size=32,\ epochs=30,\ validation_data=([e_test,f_test],Y_test),\ callbacks=[cp],\ verbose=2) modelo.load_weights('pesos.h5') score = modelo.evaluate([e_test,f_test],Y_test,verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) # ###### Predicción registros_a_predecir = [test_embeddings,test_norm] prediccion = modelo.predict(registros_a_predecir) prediccion = np.round(prediccion).astype(int).reshape(3263) resultado_RN = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion}) resultado_RN.head() resultado_RN.to_csv('..\Predicciones\prediccion_RN.csv',index=False) # ###### Majority Voting # Viendo que los resultados de las predicciones con el modelo suelen ser variadas (no predicen siempre las mismas clases para los mismos ids), vamos a probar hacer un Majority Voting entre las predicciones. # + n_iteraciones = 11 prediccion_parcial = np.zeros(3263).astype(int) for i in range(1,n_iteraciones+1): modelo = crear_modelo() e_train,e_test,f_train,f_test,Y_train,Y_test = train_test_split(train_embeddings,train_features,train_df['target'].values,\ test_size=0.2) scaler = StandardScaler() scaler.fit(f_train) f_train = scaler.transform(f_train) f_test = scaler.transform(f_test) test_norm = scaler.transform(test_features) cp = ModelCheckpoint('pesos.h5', monitor='val_loss', save_best_only=True) history = modelo.fit([e_train,f_train],Y_train,\ batch_size=32,\ epochs=30,\ validation_data=([e_test,f_test],Y_test),\ callbacks=[cp],\ verbose=2) modelo.load_weights('pesos.h5') registros_a_predecir = [test_embeddings,test_norm] prediccion = modelo.predict(registros_a_predecir) prediccion_parcial = prediccion_parcial + np.round(prediccion).astype(int).reshape(3263) prediccion_individual = np.round(prediccion).astype(int).reshape(3263) prediccion_acumulativa = np.round(prediccion_parcial / i).astype(int).reshape(3263) resultado_individual = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion_individual}) resultado_individual.to_csv('..\Predicciones\prediccionRN'+str(i)+'.csv',index=False) if ((i % 2 == 1) & (i>= 3)): resultado_acumulativa = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion_acumulativa}) resultado_acumulativa.to_csv('..\Predicciones\prediccion_MV'+str(i)+'.csv',index=False) # - # ### GridSearch Embeddings # La idea es para nuestro mejor modelo buscar optimizar los hiper-parámetros buscando lograr un mejor score. # Primero vamos a crear un modelo "sencillo" para realizar una busqueda con GridSearch def create_model(lstm_p1,dropout_rate=0.2,activation='sigmoid'): model = Sequential() model.add(layers.Embedding(matriz_de_embeddings.shape[0],matriz_de_embeddings.shape[1],\ embeddings_initializer=Constant(matriz_de_embeddings),\ input_length = train_embeddings.shape[1],trainable = False)) model.add(layers.SpatialDropout1D(dropout_rate)) model.add(layers.LSTM(lstm_p1,dropout=dropout_rate,recurrent_dropout=dropout_rate)) model.add(layers.Dense(1,activation=activation)) model.compile(loss='binary_crossentropy',optimizer=Adam(learning_rate=3e-4),metrics=['accuracy']) return model batch_size = [32,64] epochs = [32,64] lstm_p1 = [64,100,128] param_grid = dict(lstm_p1=lstm_p1,\ batch_size=batch_size, epochs=epochs) # + from sklearn.model_selection import GridSearchCV from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasClassifier from keras.constraints import maxnorm model = KerasClassifier(build_fn=create_model,verbose=0) grid = GridSearchCV(estimator=model,param_grid=param_grid,n_jobs=1,cv=3) # - X_train,X_test,Y_train,Y_test = train_test_split(train_embeddings,train_df['target'].values,test_size=0.2) grid_result = grid.fit(X_train,Y_train,validation_data=(X_test,Y_test),verbose=2) best_model = grid.best_estimator_ grid.best_params_ grid.best_score_ # ## RandomizedSearchCV # Ahora veamos si podemos agregar algunos hiper-parámetros más y buscar la optimizaciónd e estos con RandomSearch (más eficiente que GridSearch porque no testea TODAS las combinaciones) def create_model(lstm_p1,dropout_rate=0.2,activation='sigmoid',learn_rate): model = Sequential() model.add(layers.Embedding(matriz_de_embeddings.shape[0],matriz_de_embeddings.shape[1],\ embeddings_initializer=Constant(matriz_de_embeddings),\ input_length = train_embeddings.shape[1],trainable = False)) model.add(layers.SpatialDropout1D(dropout_rate)) model.add(layers.LSTM(lstm_p1,dropout=dropout_rate,recurrent_dropout=dropout_rate)) model.add(layers.Dense(1,activation=activation)) model.compile(loss='binary_crossentropy',optimizer=Adam(learning_rate=learn_rate),metrics=['accuracy']) return model batch_size = [32,64] epochs = [32,64] lstm_p1 = [64,100] dropout_rate = [0.0, 0.1, 0.2, 0.3] activation = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid'] learn_rate = [3e-4,0.001, 0.01, 0.1, 0.2, 0.3] param_rndm = dict(lstm_p1=lstm_p1,dropout_rate=dropout_rate,\ batch_size=batch_size, epochs=epochs,\ activation=activation,learn_rate=learn_rate) # + from sklearn.model_selection import RandomizedSearchCV from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasClassifier from keras.constraints import maxnorm model = KerasClassifier(build_fn=create_model,verbose=0) rndm = RandomizedSearchCV(estimator=model,param_distributions=param_rndm,n_jobs=1,cv=3) # - X_train,X_test,Y_train,Y_test = train_test_split(train_embeddings,train_df['target'].values,test_size=0.2) rndm_result = rndm.fit(X_train,Y_train,validation_data=(X_test,Y_test),verbose=2) best_model = rndm.best_estimator_ rndm.best_params_ rndm.best_score_ # Probemos ahora con estos hiper-parámetros que optuvimos realizar predicciones. # + model = Sequential() model.add(layers.Embedding(matriz_de_embeddings.shape[0],matriz_de_embeddings.shape[1],\ embeddings_initializer=Constant(matriz_de_embeddings),\ input_length = train_embeddings.shape[1],trainable = False)) model.add(layers.SpatialDropout1D(0.2)) model.add(layers.LSTM(64,dropout=0.2,recurrent_dropout=0.2)) model.add(layers.Dense(1,activation='tanh')) model.compile(loss='binary_crossentropy',optimizer=Adam(learning_rate=3e-4),metrics=['accuracy']) # - X_train,X_test,Y_train,Y_test = train_test_split(train_embeddings,train_df['target'].values,test_size=0.2) model.fit(X_train,Y_train,batch_size=64,epochs=64,validation_data=(X_test,Y_test),verbose=2) # El score previo para este mismo modelo obtenido fue # <br> # Test loss: 0.4567945599555969 # Test accuracy: 0.810899555683136 score = model.evaluate(X_test,Y_test,verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) # Podemos observar que mejora el score obtenido para Embeddings con la optimizacion de hiper-parámetros. registros_a_predecir = test_embeddings prediccion = model.predict(registros_a_predecir) prediccion = np.round(prediccion).astype(int).reshape(3263) resultado_EM = pd.DataFrame({'id':sample_submission['id'].values.tolist(),'target':prediccion}) resultado_EM.head() resultado_EM.to_csv('..\Predicciones\prediccion_EM_op.csv',index=False)	16,777
/ANN.ipynb	9241ead09a530df632f30e1ca49af20fe70d0ba8	[]	no_license	Ak-code15/Deep-learning-project	https://github.com/Ak-code15/Deep-learning-project	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	304,914	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import numpy as np import pandas as pd import tensorflow as tf import keras import matplotlib.pyplot as plt import seaborn as sn from keras.models import Sequential dataset=pd.read_excel('Features_2500RPM_60dB.xlsx') dataset.shape x = dataset.iloc[:,:-14].values y14 = dataset.iloc[:,-1].values y4 = dataset.iloc[:,-2].values x.shape labels=np.unique(y4) labels from sklearn.preprocessing import LabelEncoder lb=LabelEncoder() y4=lb.fit_transform(y4) y4 y4.shape from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(x,y4,test_size=0.2,random_state=0) from sklearn.preprocessing import StandardScaler sc = StandardScaler() x_train = sc.fit_transform(x_train) x_test = sc.transform(x_test) model=tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(units = 84, activation = 'relu')) model.add(tf.keras.layers.Dense(units = 4, activation = 'softmax')) model.compile(optimizer='adam', loss="SparseCategoricalCrossentropy", metrics=['accuracy']) y_test.shape x_train.shape y_train x_test.shape ann2=model.fit(x_train,y_train,validation_data=(x_test,y_test), batch_size = 32, epochs = 20) plt.plot(ann2.history['accuracy']) plt.plot(ann2.history['val_accuracy']) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.title('Model Accuracy') plt.legend(['train', 'val'], loc='upper left') plt.show() # + plt.plot(ann2.history['loss']) plt.plot(ann2.history['val_loss']) plt.ylabel('Loss') plt.xlabel('Epoch') plt.title('Model Loss') plt.legend(['train', 'val'], loc='upper left') plt.show() # - y_pred = model.predict(x_test) y_pred = (y_pred>0.5) y_pred = 1y_pred y_pred from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred.argmax(axis=1)) cm df_cm = pd.DataFrame(cm, columns=labels, index = labels) df_cm.index.name = 'Actual' df_cm.columns.name = 'Predicted' plt.figure(figsize = (10,7)) sn.set(font_scale=1.4) sn.heatmap(df_cm, cmap="Blues", annot=True,annot_kws={"size": 16}) from sklearn.metrics import classification_report print(classification_report(y_test, y_pred.argmax(axis=1))) # ### 14-CLASS labels2=np.unique(y14) labels2 y14=lb.fit_transform(y14) y14.shape from sklearn.model_selection import train_test_split x_train2,x_test2,y_train2,y_test2=train_test_split(x,y14,test_size=0.2,random_state=0) from sklearn.preprocessing import StandardScaler sc = StandardScaler() x_train2 = sc.fit_transform(x_train2) x_test2 = sc.transform(x_test2) model2=tf.keras.models.Sequential() model2.add(tf.keras.layers.Dense(units = 84, activation = 'relu')) model2.add(tf.keras.layers.Dense(units = 14, activation = 'softmax')) model2.compile(optimizer='adam', loss="SparseCategoricalCrossentropy", metrics=['accuracy']) ann3=model2.fit(x_train2,y_train2,validation_data=(x_test2,y_test2), batch_size = 32, epochs = 20) plt.plot(ann3.history['accuracy']) plt.plot(ann3.history['val_accuracy']) plt.ylabel('Accuracy') plt.xlabel('Epoch') plt.title('Model Accuracy') plt.legend(['train', 'val'], loc='lower right') plt.show() # + plt.plot(ann3.history['loss']) plt.plot(ann3.history['val_loss']) plt.ylabel('Loss') plt.xlabel('Epoch') plt.title('Model Loss') plt.legend(['train', 'val'], loc='upper left') plt.show() # - y_pred2 = model2.predict(x_test2) y_pred2=(y_pred2>0.5) y_pred2 = 1y_pred2 y_pred2 from sklearn.metrics import confusion_matrix cm2 = confusion_matrix(y_test2, y_pred2.argmax(axis=1)) cm2 df_cm = pd.DataFrame(cm2, columns=labels2, index =labels2) df_cm.index.name = 'Actual' df_cm.columns.name = 'Predicted' plt.figure(figsize = (10,7)) sn.set(font_scale=1.4)#for label size sn.heatmap(df_cm, cmap="Blues", annot=True,annot_kws={"size": 16})# font size # + from keras.wrappers.scikit_learn import KerasClassifier from sklearn.model_selection import cross_val_score from keras.models import Sequential from keras.layers import Dense def my_classifier(): classifier = Sequential() classifier.add(Dense( units = 84, activation="relu" )) classifier.add(Dense( units = 4, activation="softmax" )) classifier.compile( optimizer = "adam", loss="SparseCategoricalCrossentropy", metrics=['accuracy'] ) return classifier #this classifier will be use to the 10 different training fold #for k-cross validation on 1 test fold classifier = KerasClassifier(build_fn = my_classifier, batch_size = 32, nb_epoch = 20) accuracies = cross_val_score( estimator=classifier, X = x_train, y = y_train, cv=5 ) #the important variable is cv which mean the number of #fold in cross validation that we will use #after we got the accuracies, find the mean mean = accuracies.mean() variance = accuracies.std() # - std = np.sqrt(variance) std mean variance	5,136
/notebooks/my_rebel.ipynb	a7536a2da04c26384f4e5c20bc30403d495cf6f9	[ "MIT" ]	permissive	chapmanbe/isys90069_w2020_explore	https://github.com/chapmanbe/isys90069_w2020_explore	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	14,332	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Who Are Our Rebels # # In this notebook I'm going to use some simple NLP to try to explore who were our favorite rebels. In the process I hope to demonstrate some of the data-wrangling challenges that go along with NLP. # ### Get Data from Canvas # # Canvas has a RESTful API. I'm going to use it to pull down the responses to the homework assignments. # # By the way, you can also use the Canvas API to access your data. # # The cell below contains the code I used to get the data from Canvas. # ```Python # with open(os.path.join(os.path.expanduser("~"), ".canvaslms", "quiz_token")) as f: # token = f.read() # # from canvasapi import Canvas # from bs4 import BeautifulSoup # import unicodedata # # API_URL = "https://canvas.lms.unimelb.edu.au/" # canvas = Canvas(API_URL, token) # bec = canvas.get_user(canvas.get_current_user().id) # ehealth = canvas.get_course(110024) # # # This is the id number for the assignment # rebel_id = 139157 # # rebels = ehealth.get_assignment(rebel_id) # # rebel_submissions = rebels.get_submissions() # # responses = [(b.user_id, b.body) for b in rebel_submissions] # # # len(responses) # # len(set([r[0] for r in responses])) # # rebel_text = [unicodedata.normalize("NFKC", BeautifulSoup(r[1]).getText()) for r in responses if r[1]] # # with open("rebel_text.json", "w") as f: # json.dump(rebel_text, f) # ``` # + import os from collections import Counter import json # get token import random import matplotlib.pyplot as plt # - # ### with open("rebel_text.json", "r") as f: rebel_text = json.load(f) rebel_text # ### We are going to use the very popular [Spacy](https://spacy.io/) NLP package. # # If you are interested in learning more about Spacy, we have some notebooks [here](https://github.com/Melbourne-BMDS/md3nlp_20020) that you can run online with binder to learn more. import spacy from IPython.display import SVG, YouTubeVideo from spacy import displacy nlp = spacy.load("en_core_web_sm") # #### Entity Recognition # # Spacy will parse the sentences and then try to recognize different entitites that are named in the text, such as people or organizations or diseases. Let's see how it works. for txt in rebel_text: doc = nlp(txt) displacy.render(doc, style="ent") print('-'72) # ### Spacy seems to do OK # #### But there are some consistent failures # # Sometimes the solitary surnames are recognized as `ORG`s (organizations). This is not surprising because # # - Dyson is a vaccum cleaner # - Tesla is a car company # # The answer about Nikola Tesla is particularly problematic where we see Tesla as an organization, a piece of art, and a product---everything except a person. # # ![Tesla labels](tesla_edison.png) # ### Filtering Entities # # Let's reduce the number of the recognized entities by only keeping entities that might conceivably be one of our rebels, which in the Tesla case is a problem. Eventually my algorithm is going to count the number of times a name is mentioned to guess that the most frequently named person is the identified hero. rebels = [] labels = ['ORG', 'PERSON', 'WORK_OF_ART', 'PRODUCT'] for txt in rebel_text: doc = nlp(txt) rebels.append([ent for ent in doc.ents if ent.label_ in labels and ent.string != 'Freeman' and ent.string != 'Dyson']) rebels # ### Sort identified entities # # I want to sort the identified entities for each document from longest to shortest. This is so that I can combine entities such as "Albert Einstein" and "Einstein". for r in rebels: r.sort(key=lambda x:len(x.string), reverse=True) # ### With our sorted lists, we can try to replace partial names with full names def get_full_names(r): n = len(r) for i in range(n-1): for j in range(i,n): if r[j].string in r[i].string: r[j] = r[i] return None # Let's use `get_full_names` to replace all partial names (e.g. 'Albert' or 'Einstein' with the full name e.g. 'Albert Einstein'). for i in range(len(rebels)): r = rebels[i] print(i) print("Before") print(r) get_full_names(r) print("After") print(r) print('-'20) # ### How well did it work? # # Most of the substitutions worked reasonably well, but cases 5 (Venter) and 6 (Tesla) clearly failed. Let's examine those to see what is happening. # # We are comparing the `string` attributes (`r[j].string in r[i].string`), so let's look at the strings for ent in rebels[5]: print("'%s'"%ent.string) for ent in rebels[6]: print("'%s'"%ent.string) # ### Extra Spaces! # # We can see that the `Venter` and `Tesla` strings have an extra space after them so our comparison 'Venter ' in 'John Craig Venter' fails. Similarly with 'Tesla '. If we use the Python `strip` method, we can delete leading and trailing white spaces. def get_full_names2(r): n = len(r) for i in range(n-1): for j in range(i,n): if r[j].string.strip() in r[i].string.strip(): r[j] = r[i] return None # + with open("rebel_text.json", "r") as f: rebel_text = json.load(f) rebels = [] labels = ['ORG', 'PERSON', 'WORK_OF_ART', 'PRODUCT'] for txt in rebel_text: doc = nlp(txt) rebels.append([ent for ent in doc.ents if ent.label_ in labels and ent.string.strip() != 'Freeman' and ent.string.strip() != 'Dyson' and ent.string.strip() != 'Freeman Dyson']) for r in rebels: r.sort(key=lambda x:len(x.string), reverse=True) for i in range(len(rebels)): r = rebels[i] print(i) print("Before") print(r) get_full_names2(r) print("After") print(r) print('-'*20) # - # ### Count the identified Entities counted=[Counter(r) for r in rebels] for c in counted: print(c.most_common(5)) # ### How did our counting work? # # Again, pretty well, but sometimes we have a name that is counted with the same frequency as a non-name entity (e.g. `(Madame Curie, 2), (a Nobel Prize, 2)`. So let's start by selecting the entities that are counted at the top-frequency and then see if we can select entities that are a `PERSON'. # + def most_frequent(counted): count = counted[0][1] return [c for c in counted if c[1] == count] top_counted = [most_frequent(c.most_common(5)) for c in counted if c] top_counted # - # ### Return the top `PERSON` # # If there is more than one `PERSON`, we'll just return the first one. def get_top_person(counted): try: return [ent for ent in counted if ent[0].label_ == 'PERSON'][0] except: return None top_counted_persons = [c[0] if len(c) == 1 else get_top_person(c) for c in top_counted] top_counted_persons identified_rebels = [e[0] for e in top_counted_persons if e] identified_rebels identified_rebels.sort(key=lambda x:len(x.string), reverse=True) identified_rebels get_full_names2(identified_rebels) identified_rebels counted_identified_rebels = Counter(identified_rebels) counted_identified_rebels.most_common(60) f, axs = plt.subplots(1,figsize=(15,15)) pd.DataFrame([x.string.strip() for x in identified_rebels])[0].value_counts().head(60).plot.barh(axes=axs) axs.set_xlabel("Counts") f.savefig("identified_rebels.png") # ## Discussion # # I took a fairly simplistic approach to identifying the named rebels. The technique was not robust to several textual features, such as typos and misspellings possessive form. Because I was counting mentions of names, if someone used a lot of pronouns to refer to the rebel I might not have identified them properly. Identify the answer you submitted. Did I correctly find your rebel? If not, can you think of things in your writing that could be edited to make the identification task easier?	7,940
/3.K-Nearest Neighbors/2.Code - Using an API/KNN.ipynb	608eb89b191bcf5021578e086c4024963fbf93c6	[ "MIT" ]	permissive	ananth-repos/machine-learning	https://github.com/ananth-repos/machine-learning	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	50,760	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] hide_input=false id="bb021ec4" # # IMT 2021 # # # Ecrêtage de puissance sur une ligne de distribution triphasée BT* par batterie alimentée par une centrale photovoltaïque (élément de correction ) # # # # (BT Basse Tension <1000V) # + [markdown] hide_input=false id="227e4f19" # ### Problématique: # # Une ligne de distribution d'énergie électrique voit sa puissance à distribuer augmentée pendant les saisons d'affluence touristique (période mai-aout). Le choix économique permettant d'écrêter cette puissance porte sur un système de batterie électrochimique rechargé par une microcentrale photovoltaïque placé sur site. # + [markdown] id="7bf92883" # ### Sprint 1- Evaluation de la consommation du village pendant la période estivale # + id="eabbb239" # + [markdown] id="0f573274" # #### Données: # # P10_Olinda.csv: Courbe de charge triphasée (puissance absorbée par le village) toutes les 10 min sur la période mai-aout (les données temps sont en heure locale) # Puissance limite de la ligne 11kW/par phase. # # + [markdown] hide_input=true id="53c3a266" # #### Resultats attendues: # # a- Mise en forme de la puissance en fonction du temps (graphique). # # b- Tracer de la monotone de puissance (distribution de la puissance par ordre décroissant). # En déduire la puissance max absorbée par le village ainsi que les deux contraintes (puissance et énergie) que doit fournir le système de substitution au réseau (batterie). # # c- La consommation hebdomadaire (kWh) entre mai et aout en fonction des heures creuses (22 h à 6 H) et pleines(6h-22h) # # + [markdown] hide_input=false id="f27cb5b1" # ##### Résultats # + [markdown] id="be5865f5" # Mise en forme de la puissance en foncton du temps # + hide_input=false cellView="form" colab={"base_uri": "https://localhost:8080/", "height": 460} id="1f137698" executionInfo={"status": "error", "timestamp": 1631960959155, "user_tz": -120, "elapsed": 8, "user": {"displayName": "Stephane Reyes", "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64", "userId": "14852497390560645681"}} outputId="e8c51479-80af-4c36-b7ab-4abbb28a6473" #@title Mise en forme de la puissance en foncton du temps import pandas as pd source=pd.read_csv('/Users/srmac/Dropbox/Ecole des Mines Ales/Projet UE energie/Maui_olindaCourbe_charge_maijuinjuilletaoutV1.csv', sep=',' ) source.drop(['date', 'h'], axis = 1, inplace = True) serie=pd.Series(source.values.ravel('F')) per1 = pd.date_range(start ='2015-05-01', end ='2015-09-01', freq ='10min') Conso=pd.DataFrame(serie.values,index=per1[:-1], columns=["Puissance"]) #### graphique production consommation a=list(Conso['Puissance']) conso=a b=list(Conso.index) time_unix_conso=[(b[i].value)/1E6 for i in range(len(b))] array_conso=a dsp_conso=list(map(list, zip(time_unix_conso, array_conso))) from highcharts import Highstock, Highchart H = Highstock() H.add_data_set(dsp_conso,'line','Consommation',tooltip = { 'valueDecimals': 0}) options = { 'rangeSelector' : { 'selected' : 2 }, 'title' : { 'text' : 'Consommation(kW)' }, 'lang': { 'shortMonths': [ 'Janv', 'Fév', 'Mars', 'Avril', 'Mai', 'Juin', 'Juil', 'Août', 'Sept', 'Oct', 'Nov', 'Déc' ], 'weekdays': [ 'Dimanche', 'Lundi', 'Mardi', 'Mercredi', 'Jeudi', 'Vendredi', 'Samedi' ], }, } H.set_dict_options(options) H # + hide_input=true id="da0ff732" outputId="e8273855-8efb-4554-8add-4929bd357998" import numpy as np import matplotlib.pyplot as plt Plimit=113 Monotone_Conso=[x for x in sorted(Conso["Puissance"],reverse=True)] P_stock=Plimit-Conso[Conso['Puissance']>=Plimit] NRJ_stock=sum(P_stock['Puissance'])/6 plt.figure(figsize=(15,12)) plt.plot(np.arange(0,len(Monotone_Conso)),Monotone_Conso) plt.title("Monotone de Puissance absorbée ") plt.xlabel('Pas de temps 10 minutes') plt.ylabel('Puissance W') plt.plot(10, Monotone_Conso[0], 'ro') plt.vlines(x=10, ymin=0, ymax= Monotone_Conso[0], colors='gray', ls=':', lw=2, label='Puissance max abosrbée') plt.hlines(y=Plimit, xmin=10, xmax=len(conso), colors='red', ls=':', lw=2, label='Puissance max absorbée') plt.text(0,Monotone_Conso[0],'Puissance max '+str(round(Monotone_Conso[0],0))+" kW", fontsize=14, c='red') plt.text(len(Monotone_Conso)/2,Plimit+0.5,'Puissance limite max de la ligne '+str(Plimit)+' kW', fontsize=14, color='red') plt.plot #plt.fill_between(Monotone_Conso, 6, where=Monotone_Conso>6, color='#539ecd') # + hide_input=true id="746107cb" outputId="b7e25d1c-7a1e-4358-8bef-89d98d2cd89b" print("La puissance maximale absorbée :"+str(round(max(conso),0))+" kW") print('Puissance max system stockage= ' + str(round(min(P_stock['Puissance']),0))+" kW") print("Energie stockée à restituer= "+str(round(NRJ_stock,0))+ " kWh") # + hide_input=true id="88b27b59" outputId="a10a869a-61e4-4db5-85d2-289b957da420" H = Highchart(width=850, height=500) Conso_HP=Conso[(Conso.index.hour>=6)&(Conso.index.hour<22)] Conso_HC=Conso[(Conso.index.hour<6)\|(Conso.index.hour>21)] P_stock_average_weekly=P_stock.groupby(pd.Grouper(freq='W')).sum()/6 Conso_average_weekly_HP=Conso_HP.groupby(pd.Grouper(freq='W')).sum()/6 Conso_average_weekly_HC=Conso_HC.groupby(pd.Grouper(freq='W')).sum()/6 time=list(Conso.index.strftime("%d/%m/%Y")) data0=list(P_stock_average_weekly['Puissance']) data1=list(Conso_average_weekly_HP['Puissance']) data2=list(Conso_average_weekly_HC['Puissance']) options = { 'chart': {'type': 'column'}, 'title': {'text': 'Energie moyenne hebdomadaire'}, 'xAxis': {'categories':time}, 'yAxis': [{'min': -100,'tickInterval':50,'title': {'text': 'Energie'},'labels': {'format': '{value} kWh'}}], #{'title': {'text': 'Puissance en HP estimée', 'style': {'color':'#F62114' }},'labels': {'format': '{value} kW'},'opposite': True}], 'legend': {'shadow': False}, 'tooltip': {'shared': True}, 'plotOptions': {'column': {'stacking':'normal','grouping': False,'shadow': False,'borderWidth': 0,'pointWidth': 25}}, } H.set_dict_options(options) #H.add_data_set(data0, 'column', 'Energie stockage ', color='#ff7f00',pointPadding=0.2, pointPlacement=-0.2) H.add_data_set(data1, 'column', 'Consommation HP ', color='#F5130F',stack='conso',pointPadding=0.2, pointPlacement=-0.2) H.add_data_set(data2, 'column', 'Consommation HC', color='#4444C8',stack='conso',pointPadding=0.2, pointPlacement=-0.2) H # + [markdown] hide_input=true id="0d9c69ba" # ### Sprint 2- Evaluation de la ressource solaire pendant la période estivale # + [markdown] id="2bff4dcb" # #### Données: # # point GPS Olinda: 20.80892 (North);-156.28288 (West) # # Site pvgis:https://re.jrc.ec.europa.eu/pvg_tools/; # # données météo:TMY; Typical Meteorological Year(fichier .csv); Année de réference: 2006-2015 # données sortie de générateur photovoltaique:DONNÉES DU RAYONNEMENT HORAIRES; année ref 2015 lmontage fixe, inclinaison 0°, orientation 0°, 1 kWp,pertes 14%. # # Caractéristique module photovoltaïque Q.PEAK DUO-G9 350 W (orie # # # # + [markdown] hide_input=true id="1715af6a" # #### Resultats attendues: # # # a- Mise en forme et comparaison de l'énergie (par unité de surface) hebdomadaire solaire (Irradiation) et sortie de générateur photovoltaïque (électrique). # # b- Déduire en première approximation la surface au sol de module PV puis la puissance crête du generateur photovoltaïque. # # + hide_input=true id="0d230311" outputId="39a5dc4d-872a-4c2d-a267-d414ef6815ea" import pandas as pd from datetime import timedelta source=pd.read_csv('/Users/srmac/Dropbox/Ecole des Mines Ales/Projet UE energie/tmy_20.809_-156.283_2006_2015.csv', sep=',',skip_blank_lines=True,header=16, skipfooter=13) index=pd.to_datetime(source["time(UTC)"], format='%Y%m%d:%H%M') prod=pd.DataFrame(source["G(h)"].values, index=index,columns=["Irradiance_Globale"]) prod['Date']=prod.index prod['Date'] = prod['Date'].apply(lambda x: x.strftime('2015-%m-%d %H:%M')) prod.reset_index() prod.set_index(pd.to_datetime(prod['Date']), inplace=True) prod.index=prod.index-timedelta(hours=10) prod=prod.loc['2015-05-01':'2015-08-31'] source_elec=pd.read_csv('/Users/srmac/Dropbox/Ecole des Mines Ales/Projet UE energie/Timeseries_20.809_-156.283_NS_1kWp_crystSi_14_0deg_0deg_2015_2015.csv', sep=',',skip_blank_lines=True,header=10, skipfooter=13) index=pd.to_datetime(source_elec["time"], format='%Y%m%d:%H%M') prod_elec=pd.DataFrame(source_elec["P"].values, index=index,columns=["Puissance"]) prod_elec.index=prod_elec.index-timedelta(hours=10) prod_elec=prod_elec.loc['2015-05-01':'2015-08-31'] #### graphique production consommation a=list(prod['Irradiance_Globale']) b=list(prod.index) time_unix_prod=[b[i].value//1E6 for i in range(len(b))] array_prod=a dsp_prod=list(map(list, zip(time_unix_prod, array_prod))) a_elec=list(prod_elec['Puissance']) b_elec=list(prod.index) time_unix_prod_elec=[b_elec[i].value//1E6 for i in range(len(b_elec))] array_prod_elec=[x/((1.6371.031)/0.35) for x in a_elec] # + hide_input=true id="3b58d26e" outputId="3b97a61d-9934-4380-cf52-afdd10cd7f02" dsp_prod_elec=list(map(list, zip(time_unix_prod_elec, array_prod_elec))) from highcharts import Highstock H = Highstock() H.add_data_set(dsp_prod,'line','Irradiance',tooltip = { 'valueDecimals': 0}) H.add_data_set(dsp_prod_elec,'line','Production electrique',tooltip = { 'valueDecimals': 0}) options = { 'rangeSelector' : { 'selected' : 2 }, 'title' : { 'text' : 'Irradiance au sol vs Production électrique (W/m2)' }, 'lang': { 'shortMonths': [ 'Janv', 'Fév', 'Mars', 'Avril', 'Mai', 'Juin', 'Juil', 'Août', 'Sept', 'Oct', 'Nov', 'Déc' ], 'weekdays': [ 'Dimanche', 'Lundi', 'Mardi', 'Mercredi', 'Jeudi', 'Vendredi', 'Samedi' ], }, } H.set_dict_options(options) H # + hide_input=true id="47b37f04" outputId="829b9829-531b-4a5f-b226-0d530530426c" H = Highchart(width=850, height=500) prod_average_weekly=prod.groupby(pd.Grouper(freq='W')).sum()/6 prod_elec_average_weekly=prod_elec.groupby(pd.Grouper(freq='W')).sum()/6 P_stock_average_weekly=P_stock.groupby(pd.Grouper(freq='W')).sum()/6 time=list(prod_average_weekly.index.strftime("%d/%m/%Y")) data0=[x/1000 for x in list(prod_average_weekly['Irradiance_Globale'])] data1=[x(-1) for x in list(round(P_stock_average_weekly.Puissance,0))] data3=[x/1000 for x in list(prod_elec_average_weekly['Puissance'])] data2=[data1[i]/data3[i] for i in np.arange(len(data1))] options = { 'chart': {'type': 'column'}, 'title': {'text': 'Irradiation hebdomadaire'}, 'xAxis': {'categories':time}, 'yAxis': [{'min': 0,'tickInterval':1,'title': {'text': 'Irradiation'},'labels': {'format': '{value} kWh/m2'}}, {'min': 0,'tickInterval':10,'title': {'text': 'Energie à stocker', 'style': {'color':'#FFFFF' }},'labels': {'format': '{value} kWh'},'opposite': True}], 'legend': {'shadow': False}, 'tooltip': {'shared': True}, 'plotOptions': {'column': {'stacking':'normal','grouping': False,'shadow': False,'borderWidth': 0,'pointWidth': 25}}, } H.set_dict_options(options) H.add_data_set(data0,'column', 'Irradiation hebdomadaire ', color='#ff7f00',pointPadding=0.2, pointPlacement=0.2) H.add_data_set(data1,'column', 'Energie à stocker ', color='#8282DA',yAxis=1,pointPadding=0.2, pointPlacement=-0.2) H.add_data_set(data2,'scatter', 'Surface ', color='#E82F1C',yAxis=1,pointPadding=0.2, pointPlacement=-0.2) H.add_data_set(data3,'column', 'Energie surfacique PV hebdomadaire ', color='#f3d440 ',pointPadding=0.2, pointPlacement=0.2) H # + [markdown] id="cfa4bda9" # En première approximation et pour un module de type Q.PEAK DUO-G9 350W # + [markdown] hide_input=false id="82a77e4c" # ### Sprint 4- Problématique de transfert de puissance sur ligne électrique de distribution, application # # # + [markdown] id="173ff252" # #### Données: # # longueur de la ligne: 2km # # résistivité=3,32 10 -8 Ωm # # section: 180 mm2 # # réactance linéique = 3,0 10-4 Ω/m # # # courbe de charge du village: fichier csv # # Phi=0.52 rad # # tension du poste de distribution: 250V # # charge linéaire (Facteur de puissance = cos phi) # # chute de tension < 10% de la tension normalise 230 V # + [markdown] id="e1d40f1b" # #### Méthode: # # a-Modélisation monophasée de la ligne de distribution triphasée # # b-Bilan de puissance # # c-Tracer l'équation dans le plan V=f(P), agir sur le paramètre Phi et conclure. # # d- Mettre en évidence la problématique de puissance transmissible par cette ligne # + [markdown] id="55a16bb5" # a-Modélisation monophasé de la ligne de distribution triphasé # # On modélise la ligne sur une phase en tenant compte de ses caractéristiques. # + hide_input=true id="5cc3ce42" outputId="4b3b3457-a810-482a-a607-a4b8a5b6c70e" # commentaire ligne avec R+X # resolution equation seconde degre stav # version 3 import numpy as np R=float(input("Saisir la valeur de Rligne=")) X=float(input("Saisir la valeur de Xligne=")) E=float(input("Saisir la valeur de E=")) Phi=float(input("Saisir la valeur de Phi=")) dU=0.1 X1=[] X2=[] PB=[] for Pb in range(0,int(E2/(X2+R2)0.5),1): a=1 b=2Xnp.tan(Phi)Pb+2PbR-E2 c=((R2+X2)/np.cos(Phi)2)Pb2 #calcul de delta delta=b2-4ac #affichage #print("résolution de l'équation ",a," vb² + ",b," vb + ",c) # condition sur delta dans cet ordre >0 puis ==0 puis <0 if delta>0: x1=((-b-delta*0.5)/(2a))0.5 x2=((-b+delta0.5)/(2a))0.5 #print("Delta est positif donc il y a 2 solutions") #print("VB1 =",x1) #print("VB2 =",x2) #print("Pb =",Pb) X1.append(x1) X2.append(x2) PB.append(Pb) else: if delta==0: x0=(-b/(2a))*0.5 #print("Delta est nul donc il y a 1 solution unique") #print("x0 =",x0) else: #print("Pas de solution dans l'espace de réel") break #représentation graphique import numpy as np import matplotlib.pyplot as plt #encadrement pour le graphique dU=0.1 Rc=3230*2/(max(Conso['Puissance'])1000) V=list((RcPB[i])0.5 for i in range (len(PB))) plt.figure(figsize=(15,12)) plt.plot(PB,X1) plt.plot(PB,X2) plt.plot(PB,V) idx1 = np.argwhere(np.isclose(X2, V, atol=0.1)).reshape(-1) plt.plot(idx1[0], V[idx1[0]], 'ro') idx2 = np.argwhere(np.isclose(230(1-dU), X2, atol=0.1)).reshape(-1) plt.plot(idx2[0], X2[idx2[0]], 'ro') # single vline with specific ymin and ymax plt.vlines(x=idx1[0], ymin=0, ymax= V[idx1[0]], colors='gray', ls=':', lw=2, label='P') plt.text(idx1[0],V[idx1[0]]/2,str(idx1[0])+"W", rotation=90,fontsize=14) plt.vlines(x=idx2[0], ymin=0, ymax= X2[idx2[0]], colors='black', ls=':', lw=2, label='P maxi-chute de tension') plt.text(idx2[0],X2[idx2[0]]/2,str(idx2[0])+"W", rotation=90,fontsize=14) plt.vlines(x=max(PB), ymin=0, ymax= X2[max(PB)], colors='red', ls=':', lw=2, label='P maxi-hors chute de tension') plt.text(max(PB),X2[max(PB)]/2,str(max(PB))+"W", rotation=90,fontsize=14) # place legend outside plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left') plt.axhline(y=230(1-dU), color="red") plt.text(100, 230(1-dU)+2, 'tension mini='+ str(230(1-dU))+" V", fontsize=10, color='red') plt.plot plt.ylim(ymax = 280, ymin = 0) plt.title("V=f(P) par phase") plt.axhline(y=0,color='black') plt.axvline(x=0,color='black') plt.xlabel('Puissance active transmise en W') # Légende abscisse plt.ylabel('Tension en bout de ligne en V') plt.show() # affiche la figure a l'ecranplt.legend(bbox_to_anchor=(1.0, 1), loc='upper left') # + hide_input=true colab={"base_uri": "https://localhost:8080/", "height": 17} id="d6bc1eec" executionInfo={"status": "ok", "timestamp": 1631961085188, "user_tz": -120, "elapsed": 265, "user": {"displayName": "Stephane Reyes", "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64", "userId": "14852497390560645681"}} outputId="b51717ba-4027-4525-872f-822d15418c01" language="html" # <script src="https://cdn.rawgit.com/parente/4c3e6936d0d7a46fd071/raw/65b816fb9bdd3c28b4ddf3af602bfd6015486383/code_toggle.js"></script> # # + id="5f80ce7d" # + id="b0d4621d" annels=128, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, stride=2, padding=0, bias=True) self.dec1_2 = CBR2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.dec1_1 = CBR2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.score_fr = nn.Conv2d(in_channels=64, out_channels=num_classes, kernel_size=1, stride=1, padding=0, bias=True) # Output Segmentation map def forward(self, x): enc1_1 = self.enc1_1(x) enc1_2 = self.enc1_2(enc1_1) pool1 = self.pool1(enc1_2) enc2_1 = self.enc2_1(pool1) enc2_2 = self.enc2_2(enc2_1) pool2 = self.pool2(enc2_2) enc3_1 = self.enc3_1(pool2) enc3_2 = self.enc3_2(enc3_1) pool3 = self.pool3(enc3_2) enc4_1 = self.enc4_1(pool3) enc4_2 = self.enc4_2(enc4_1) pool4 = self.pool4(enc4_2) enc5_1 = self.enc5_1(pool4) enc5_2 = self.enc5_2(enc5_1) unpool4 = self.unpool4(enc5_2) cat4 = torch.cat((unpool4, enc4_2), dim=1) dec4_2 = self.dec4_2(cat4) dec4_1 = self.dec4_1(dec4_2) unpool3 = self.unpool3(dec4_1) cat3 = torch.cat((unpool3, enc3_2), dim=1) dec3_2 = self.dec3_2(cat3) dec3_1 = self.dec3_1(dec3_2) unpool2 = self.unpool2(dec3_1) cat2 = torch.cat((unpool2, enc2_2), dim=1) dec2_2 = self.dec2_2(cat2) dec2_1 = self.dec2_1(dec2_2) unpool1 = self.unpool1(dec2_1) cat1 = torch.cat((unpool1, enc1_2), dim=1) dec1_2 = self.dec1_2(cat1) dec1_1 = self.dec1_1(dec1_2) output = self.score_fr(dec1_1) return output # + # 구현된 model에 임의의 input을 넣어 output이 잘 나오는지 test model = UNet(num_classes=12) x = torch.randn([1, 3, 512, 512]) print("input shape : ", x.shape) out = model(x).to(device) print("output shape : ", out.size()) model = model.to(device) # - # ## train, validation, test 함수 정의 def train(num_epochs, model, data_loader, val_loader, criterion, optimizer, saved_dir, val_every, device): print('Start training..') best_loss = 9999999 for epoch in range(num_epochs): model.train() for step, (images, masks, _) in enumerate(data_loader): images = torch.stack(images) # (batch, channel, height, width) masks = torch.stack(masks).long() # (batch, channel, height, width) # gpu 연산을 위해 device 할당 images, masks = images.to(device), masks.to(device) # inference outputs = model(images) # loss 계산 (cross entropy loss) loss = criterion(outputs, masks) optimizer.zero_grad() loss.backward() optimizer.step() # step 주기에 따른 loss 출력 if (step + 1) % 25 == 0: print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format( epoch+1, num_epochs, step+1, len(train_loader), loss.item())) # validation 주기에 따른 loss 출력 및 best model 저장 if (epoch + 1) % val_every == 0: avrg_loss = validation(epoch + 1, model, val_loader, criterion, device) if avrg_loss < best_loss: print('Best performance at epoch: {}'.format(epoch + 1)) print('Save model in', saved_dir) best_loss = avrg_loss save_model(model, saved_dir) def validation(epoch, model, data_loader, criterion, device): print('Start validation #{}'.format(epoch)) model.eval() with torch.no_grad(): total_loss = 0 cnt = 0 mIoU_list = [] for step, (images, masks, _) in enumerate(data_loader): images = torch.stack(images) # (batch, channel, height, width) masks = torch.stack(masks).long() # (batch, channel, height, width) images, masks = images.to(device), masks.to(device) outputs = model(images) loss = criterion(outputs, masks) total_loss += loss cnt += 1 outputs = torch.argmax(outputs.squeeze(), dim=1).detach().cpu().numpy() mIoU = label_accuracy_score(masks.detach().cpu().numpy(), outputs, n_class=12)[2] mIoU_list.append(mIoU) avrg_loss = total_loss / cnt print('Validation #{} Average Loss: {:.4f}, mIoU: {:.4f}'.format(epoch, avrg_loss, np.mean(mIoU_list))) return avrg_loss # ## 모델 저장 함수 정의 # + # 모델 저장 함수 정의 val_every = 1 saved_dir = './saved' if not os.path.isdir(saved_dir): os.mkdir(saved_dir) def save_model(model, saved_dir, file_name='Unet_best_model.pt'): check_point = {'net': model.state_dict()} output_path = os.path.join(saved_dir, file_name) torch.save(model.state_dict(), output_path) # - # ## 모델 생성 및 Loss function, Optimizer 정의 # + # Loss function 정의 criterion = nn.CrossEntropyLoss() # Optimizer 정의 optimizer = torch.optim.Adam(params = model.parameters(), lr = learning_rate, weight_decay=1e-6) # - train(num_epochs, model, train_loader, val_loader, criterion, optimizer, saved_dir, val_every, device) # ## 저장된 model 불러오기 (학습된 이후) # + # best model 저장된 경로 model_path = './saved/Unet_best_model.pt' # best model 불러오기 checkpoint = torch.load(model_path, map_location=device) model.load_state_dict(checkpoint) # 추론을 실행하기 전에는 반드시 설정 (batch normalization, dropout 를 평가 모드로 설정) # model.eval() # + # 첫번째 batch의 추론 결과 확인 for imgs, image_infos in test_loader: image_infos = image_infos temp_images = imgs model.eval() # inference outs = model(torch.stack(temp_images).to(device)) oms = torch.argmax(outs.squeeze(), dim=1).detach().cpu().numpy() break i = 3 fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 16)) print('Shape of Original Image :', list(temp_images[i].shape)) print('Shape of Predicted : ', list(oms[i].shape)) print('Unique values, category of transformed mask : \n', [{int(i),category_names[int(i)]} for i in list(np.unique(oms[i]))]) # Original image ax1.imshow(temp_images[i].permute([1,2,0])) ax1.grid(False) ax1.set_title("Original image : {}".format(image_infos[i]['file_name']), fontsize = 15) # Predicted ax2.imshow(oms[i]) ax2.grid(False) ax2.set_title("Predicted : {}".format(image_infos[i]['file_name']), fontsize = 15) plt.show() # - # ## submission을 위한 test 함수 정의 def test(model, data_loader, device): size = 256 transform = A.Compose([A.Resize(256, 256)]) print('Start prediction.') model.eval() file_name_list = [] preds_array = np.empty((0, sizesize), dtype=np.long) with torch.no_grad(): for step, (imgs, image_infos) in enumerate(data_loader): # inference (512 x 512) outs = model(torch.stack(imgs).to(device)) oms = torch.argmax(outs, dim=1).detach().cpu().numpy() # resize (256 x 256) temp_mask = [] for img, mask in zip(np.stack(imgs), oms): transformed = transform(image=img, mask=mask) mask = transformed['mask'] temp_mask.append(mask) oms = np.array(temp_mask) oms = oms.reshape([oms.shape[0], sizesize]).astype(int) preds_array = np.vstack((preds_array, oms)) file_name_list.append([i['file_name'] for i in image_infos]) print("End prediction.") file_names = [y for x in file_name_list for y in x] return file_names, preds_array # ## submission.csv 생성 # + # sample_submisson.csv 열기 submission = pd.read_csv('./submission/sample_submission.csv', index_col=None) # test set에 대한 prediction file_names, preds = test(model, test_loader, device) # PredictionString 대입 for file_name, string in zip(file_names, preds): submission = submission.append({"image_id" : file_name, "PredictionString" : ' '.join(str(e) for e in string.tolist())}, ignore_index=True) # submission.csv로 저장 submission.to_csv("./submission/Baseline_UNet.csv", index=False) # - # ## Reference # # en(index_full[168+window-1:-24], OHC_GLORYS2V3_series_running_mean_sum + OHC_GLORYS2V3_white_running_mean_std, OHC_GLORYS2V3_series_running_mean_sum - OHC_GLORYS2V3_white_running_mean_std, alpha=0.3,edgecolor='tomato', facecolor='tomato') plt.plot(index_full[12+window-1:-12],OHC_SODA3_series_running_mean_sum,'g-',linewidth=2.0,label='SODA3') plt.fill_between(index_full[12+window-1:-12], OHC_SODA3_series_running_mean_sum + OHC_SODA3_white_running_mean_std, OHC_SODA3_series_running_mean_sum - OHC_SODA3_white_running_mean_std, alpha=0.3,edgecolor='lightgreen', facecolor='lightgreen') #plt.plot(index_full[window-1:-48],OHC_NEMO_series_running_mean_sum,color='darkorange',linestyle='-',linewidth=2.0,label='OGCM Hindcast') #plt.fill_between(index_full[window-1:-48], OHC_NEMO_series_running_mean_sum + OHC_NEMO_white_running_mean_std, # OHC_NEMO_series_running_mean_sum - OHC_NEMO_white_running_mean_std, # alpha=0.3,edgecolor='yellow', facecolor='yellow') #plt.title('{} ({}) from {}N to {}N with a running mean of {} months'.format(part_title,title_depth,lat_interest_list[i],lat_interest_list[i+1],window)) fig7.set_size_inches(12.5, 6) plt.xlabel("Time",fontsize=16) #plt.xticks(np.linspace(0, 444, 38), index_year_full) plt.xticks(np.arange(13,len(year_ORAS4)12+12+1,60),index_year,fontsize=16) #plt.xticks(rotation=60) plt.ylabel("Ocean Heat Content (1E+22 Joule)",fontsize=16) plt.yticks(np.arange(-2.0,2.0,0.5),fontsize=16) plt.legend(frameon=True, loc=2, prop={'size': 14}) props = dict(boxstyle='round', facecolor='white', alpha=0.8) ax = plt.gca() ax.text(0.52,0.08,text_content,transform=ax.transAxes,fontsize=14,verticalalignment='top',bbox=props) plt.show() fig7.savefig(os.path.join(output_path,'Comp_OHC_lowpss_window_60m_60N_90N.png'), dpi = 200)	26,945
/ML-model_FastAI.ipynb	e386e20b73207847815bfc2ec5cc8d93ca159fc5	[]	no_license	Jacobjeevan/NLP-Malayalam	https://github.com/Jacobjeevan/NLP-Malayalam	0	1	null	null	null	null	Jupyter Notebook	false	false	.py	33,063	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Week 12 Homework # # Due Wednesday December 8th. # # Try to take this as a practice test with a 3 hour limit. # ### Exercise 1 # # Create a function that takes a list of strings and outputs a dictionary where the key is the string and the value is the length of the corresponding string. # + def ex1(lst): return {item: len(item) for item in lst} try: assert(ex1(['']) == {'': 0}) assert(ex1(['a']) == {'a': 1}) assert(ex1(['hello', 'world']) == {'hello': 5, 'world': 5}) assert(ex1(['python', 'data', 'science']) == {'python': 6, 'data': 4, 'science': 7}) assert(ex1(['a']) == {'a': 1}) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 2 # # Create a function that outputs all the even numbers under 500 that are also divisible by 9. # + def ex2(): # lst2 = [] # for x in range(500): # if x % 2 == 0 and x % 9 == 0: # lst2.append(x) # return lst2 return [x for x in range(500) if x % 18 == 0] try: assert(ex2() == [0,18,36,54,72,90,108,126,144,162,180,198,216,234,252,270,288,306,324,342,360,378,396,414,432,450,468,486]) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 3 # # Create a class that implements a linked list. # + class Node: def __init__(self, data): self.data = data self.next = None def __repr__(self): return f"<Node data: {self.data}>" class LinkedList: def __init__(self, node=None): self.head = node self.tail = node def append(self, data): new_node = Node(data) if self.tail: self.tail.next = new_node self.tail = new_node else: self.head = new_node self.tail = new_node def output_list(self): current_node = self.head while current_node: print(current_node) current_node = current_node.next def __repr__(self): return f"<Linked List {self.head} {self.tail}>" node = Node(5) linked_list = LinkedList(node) linked_list.append(10) linked_list.append(15) linked_list.append(20) linked_list.append(25) linked_list.append(30) linked_list.append(35) linked_list.append(40) linked_list.output_list() # + # s = [0] # print(s[-1]) # print(s[0]) # + # if None: # print("Hello") # else: # print('none is falsy') # - # ### Exercise 4 # # Given the following two sets, assign to variable `m` the numbers that are contained by both sets (the numbers that are in both s and t). # + s = {0,2,4,6,8,10} t = {0,1,3,5,7,9} m = s.intersection(t) try: assert(m == {0}) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 5 # # Fix the following class definition so that the code runs without error. (Only change the class definition). # + class Exercise5: def __init__(self, num, lname, fname): self.num = num self.last_name = lname self.first_name = fname @property def get_attrs(self): return [self.num, self.last_name, self.first_name] instance = Exercise5('46', 'Biden', 'Joe') try: assert(instance.get_attrs == ['46', 'Biden', 'Joe']) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 6 # # Create a class called `Time` that takes two arguments (`hours` and `minutes`) and sets them as instance attributes. Then create a function that overloads the addition and equality operators so you can add the times together and then check if two instances are equal in time. # + class Time: def __init__(self, hrs, mins): # instance attributes self.hrs = hrs self.mins = mins # overload the methods, dunder add #def __add__() time1 = Time(0, 30) time2 = Time(1, 30) time3 = Time(2, 0) time1 + time2 try: assert(time1 + time2 == time3) print("solution is correct") except: print("solution is incorrect") # + class Time: def __init__(self, hrs, mins): # instance attributes self.hrs = hrs self.mins = mins # overload the methods, dunder add # will take self & other # what does other refer to ? -> refers to the other instance def __add__(self, other): f =self.hrs + other.hrs g =self.mins + other.mins if g >= 60: f += g // 60 #f = f + g // 60 g %= 60 # #g = g % 60 return f,g # we want the minutes, handle the overlap with the minutes, should not be getting (1,60) but (2,0) time1 = Time(0, 30) time2 = Time(1, 30) time1 + time2 # time3 = Time(2, 0) # time1 + time2 # try: # assert(time1 + time2 == time3) # print("solution is correct") # except: # print("solution is incorrect") # + # what can we do with hours and minutes, self.total in the __init__ # normalize two differnet numbers to make comparable, in this call normalized time in minutes to compare and add easily class Time: def __init__(self, hrs, mins): # instance attributes self.hrs = hrs self.mins = mins self.total_time = self.hrs * 60 + self.mins #print(self.total_time) #def __repr__(self): #return f"<Time h:{self.hrs} m:{self.mins}>" # overload the methods, dunder add # will take self & other # what does other refer to ? -> refers to the other instance def __add__(self, other): # if other is not instance of Time returns None. function that does not return anything returns NONE # difference with the addition dunder, if isinstance(other, Time): added_time = self.total_time + other.total_time return Time(added_time // 60, added_time % 60) # equality for the assert now easily, have to define whats eqaul to us, we could look at the total_time def __eq__(self, other): # when checking equality, return a Bool if isinstance(other, Time): return self.total_time == other.total_time #will give us True or False time1 = Time(0, 30) time2 = Time(1, 30) time1 + time2 # for the equality to override it time3 = Time(2, 0) # time1 + time2, time1 is self.total_time and time2 is other.total_time try: assert(time1 + time2 == time3) print("solution is correct") except: print("solution is incorrect") # + class Time: def __init__(self, hrs, mins): self.hrs = hrs self.mins = mins self.total_time = self.hrs * 60 + self.mins def __add__(self, other): if isinstance(other, Time): added_time = self.total_time + other.total_time return Time(added_time // 60, added_time % 60) def __eq__(self, other): # when checking equality, return a Bool if isinstance(other, Time): return self.total_time == other.total_time #will give us True or False elif isinstance(other, tuple): return other == () time1 = Time(0, 30) time2 = Time(1, 30) time1 + time2 # for the equality to override it time3 = Time(2, 0) # time1 + time2, time1 is self.total_time and time2 is other.total_time try: assert(time1 + time2 == time3) print("solution is correct") except: print("solution is incorrect") # - 5 // 2, 5 % 2 # + def func(): pass s=func() s == None # + def func(): pass s=func("hello") s == None # + class Num: def __init__(self, n): self.num = n def __add__(self, other): # when we add, want to return a val #pass if isinstance(other, Num): return self.num + other.num # can also do it like this #new_num = Num(self.num + other.num) #return new_num def __repr__(self): return f"<Num {self.num}>" n = Num(5) e = Num(3) n + e # + class Num: def __init__(self, n): self.num = n def __add__(self, other): # when we add, want to return a val #pass if isinstance(other, Num): #return self.num + other.num # can also do it like this new_num = Num(self.num + other.num) return new_num def __repr__(self): return f"<Num {self.num}>" n = Num(5) e = Num(3) n + e # + the __init__ making instance of the class # - # # student class, and instance of the student class, that will be object, when we want to use instances, will need a way to refer to instance of # class, may not know what we want to do, set functions, refer to self doesnt exist yet but will want to use later, __init__ constructor sets up the # instance, define hours, be able to creat instance of time and have attrs that exist in single instance later # # time1 creates instance of the class, create like a function and have # # class Space: # moon = "grey" # s = Space # f = Space # # s.moon == f.moon # # # ### Exercise 7 # # Create a function that creates an `m` by `n` matrix (`m` rows and `n` columns) filled with zeros. def ex7(m,n): #return [[0 for x in range(m)] for y in range(n)] return [ [0] * n for x in range(m) ] try: assert(ex7(0,0) == []) assert(ex7(1,1) == [[0]]) assert(ex7(2,2) == [[0,0],[0,0]]) assert(ex7(3,5) == [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]) print("solution is correct") except: print("solution is incorrect") # ### Exercise 8 # # Create a function that takes an arbitrary number of integer arguments and sums them. # + def ex8(args): #return sum(args) return sum(args) try: assert(ex8(3,5,7) == 15) assert(ex8(2,4) == 6) assert(ex8(range(10)) == 45) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 9 # # Create a decorator function that wraps the following function so that the string returned has anchor tags (e.g `<a>hello world</a>`) # + def decorator_func(func): def wrapper(): return f"<a>{func()}</a>" return wrapper @decorator_func def func(): return "hello world" try: assert(func() == '<a>hello world</a>') print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 10 # # Using list comprehension, create a list with two for loops that generate the following output: # # [('0', '0'), ('0', '1'), ('1', '0'), ('1', '1')] # + l = [ (str(x), str(y)) for x in range(2) for y in range(2) ] try: assert(l == [('0', '0'), ('0', '1'), ('1', '0'), ('1', '1')]) print("solution is correct") except: print("solution is incorrect") # - # %load_ext tutormagic # + # %%tutor --lang python3 l = [ (str(x), str(y)) for x in range(2) for y in range(2) ] try: assert(l == [('0', '0'), ('0', '1'), ('1', '0'), ('1', '1')]) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 11 # # Create a dictionary comprehension that generates the following dictionary: # ``` # {'a': [''], 'b': [''], 'c': ['']} # ``` # + #d = { key: [''] for key in ['a', 'b', 'c'] } # too verbose #d = { key:[val] for key in ['a', 'b', 'c'] for val in [''] } d = { key: [''] for key in ('a','b','c') } try: assert(d == {'a': [''], 'b': [''], 'c': ['']}) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 12 # # Create a lambda expression that takes `a` and `b` and computes `c`: # # $$ a^2 + b^2 = c^2 $$ # # To find c by itself # # $$ √ a^2 + b^2 = c^2 $$ # + #from math import sqrt #f = lambda a,b: (a 2 + b 2) f = lambda a,b: (a 2 + b 2)*0.5 try: assert(f(3,4) == 5.0) assert(f(5,12) == 13.0) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 13 # # Create a generator function that yields one even number at a time. # + # def generator(num): # return [num2 for num in range(num)] # generator(4) def generator(num): for x in range(num): if x % 2 == 0: yield x start = generator(2) # - next(start) next(start) # ### Exercise 14 # # Write a function that recursively calculates the following if `n` is greater than 0. If `n` is less than 0, return 0.5. # # $$ n = (n-1) + (n-1)$$ # $$ n -1 = (n - 1 -1) + (n-2)$$ # $$ n-2 = (n-2-1) + (n-1-1) =(n-3) + (n-3) $$ # We need a base case from recursion, to have terminating point # if we keep looping thru things, like while, if we don't put in comdition, same goes for the recursion # calls itself and will continue until reaching condition, condition no longer calling te func, will take # that result and return, cascade again, keep calling itslef until hit return statement and bubble back up # to the top. # # What would be the exit conditon: # if less than 0 - exit condition, if less than zero return 0.5 # $$ n! = n * (n-1)! $$ # $$ (n-1)! = (n-1) * (n-2)!$$ # # Will continue going until we reach 0 # + # %%timeit def ex14(n): if n < 0: return 0.5 else: #calculate, start the recursion return ex14(n-1) + ex14(n-1) #return 2 * ex14(n-1) # number of less recursion calls ex14(2) # try: # assert(ex14(1) == 21) # assert(ex14(2) == 22) # assert(ex14(3) == 23) # assert(ex14(4) == 24) # assert(ex14(10) == 2*10) # print("solution is correct") # except: # print("solution is incorrect") # + # %%timeit def ex14(n): if n < 0: return 0.5 else: #calculate, start the recursion #return ex14(n-1) + ex14(n-1) return 2 ex14(n-1) # number of less recursion calls ex14(2) # - # #### Exercise 15 # # Write a function that sends a get request to a url and returns the headers. Use the following url to test. # # https://www.httpbin.org # + import requests def headers(url): request = requests.get(url) return request.headers r = headers("https://www.httpbin.org") r # - # ### Exercise 16 # # Write a ternary operator that sets the variable `s` to an input if the length of the input is greater than 25 characters, otherwise set `s` to `None` # + m = input("use this input") s = m if len(m) > 25 else None print(s) # - # ### Exercise 17 # # Using the json library, output the value of the key `menuitem`. # + import json s = """{"menu": { "id": "file", "value": "File", "popup": { "menuitem": [ {"value": "New", "onclick": "CreateNewDoc()"}, {"value": "Open", "onclick": "OpenDoc()"}, {"value": "Close", "onclick": "CloseDoc()"} ] } }}""" d = json.loads(s) d["menu"] # + import json s = """{"menu": { "id": "file", "value": "File", "popup": { "menuitem": [ {"value": "New", "onclick": "CreateNewDoc()"}, {"value": "Open", "onclick": "OpenDoc()"}, {"value": "Close", "onclick": "CloseDoc()"} ] } }}""" d = json.loads(s) d["menu"]["popup"] # + import json s = """{"menu": { "id": "file", "value": "File", "popup": { "menuitem": [ {"value": "New", "onclick": "CreateNewDoc()"}, {"value": "Open", "onclick": "OpenDoc()"}, {"value": "Close", "onclick": "CloseDoc()"} ] } }}""" d = json.loads(s) d["menu"]["popup"]["menuitem"] # - # ### Exercise 18 # # Using a slice, output the even numbers backwards # + s = [1,2,3,4,5,6,7,8,9,10] s = s[::-2] try: assert(s == [10, 8, 6, 4, 2]) print("solution is correct") except: print("solution is incorrect") # - # ### Exercise 19 # # Create a class called `Square` that inherits from `Shape` and overrides the area method. # + class Shape: def __init__(self, color): self.color = color def area(self, side): return sum(side) def __repr__(self): return f"Shape" s = Shape("red") s.area(8,5,6,7) # + class Square(Shape): def __init__(self, length): self.length = length def area(self, length): return self.length2 # def __repr__(self): # pass sq = Square("black") sq.area sq.area(8) # - # ### Exercise 20 # # Create a function that at worst executes in linear time. # + def print_student(student_list): for student in student_list: print(student) print_student(['rakshanda', 'chioma', 'juan', 'bryant', 'anna']) # - # ### Topics for final assessment: # # ### Data Types # Strings # * Ints # * Floats # * Bools # # ### Python Collection Data Structures # * Lists # * Tuples # * Sets # * Dictionaries # # Related topics: # * related methods (string, list, dict, set) # * built in methods (`abs`, `all`, `any`, `dir`, `format`, `input`, `getattr`, `len`, `max`, `min`, `ord`, `pow`, `print`, `range`, `reversed`, `round`, `setattr`, `sum`, `type`) # * indexing/slicing # * casting (`bool`, `int`, `str`, `float`, `dict`, `set`) # * comprehensions # * mutability # * arithmetic operators (`+`, `-`, ``, `/`, `//`, `%`,``) # assignment operators (`=`, `+=`, `-=`, `/=`, `//=`, etc.) # * comparison operator (`==`, `!=`, `>`, `<`, `>=`, `<=`) # * identity operators (`is`, `is not`) # * membership operators (`in`, `not in`) # # ### Control Flow/Structures # * `if`/`elif`/`else` # * `for` # * `while` # * `with` # * `try`/`except` # * `pass`/`break`/`continue` # * `finally` # # Related topics: # * conditional statements # * logical operators (`and`/`or`/`not`) # * nested conditionals/loops # * boolean values of objects # * ternary operators # # ### Functions # * function definition/declaration # * scope of variables inside functions # * positional arguments # * keyword arguments # * arbitrary arguments (positional and keyword) # * unpacking arguments (`` and ``) # lambda functions # * currying functions # * decorator functions # * recursion # * iterators/generators (`yield`, `next`, `iter`) # * functional programming built ins (`map`, `filter`, `zip`) # # ### Classes # * class definition # * class objects # * instance objects # * class/instance attributes # * class methods # * inheritance # * multiple inheritance # * operator overloading # * decorators (`classmethod`, `staticmethod`, `property`) # # ### Modules # * modules # * packages # * importing # * scripts # # ### Algorithms and Data Structures # * sorting algorithms (no code) # * queues # * stacks # * linked lists # * Big O Notation # # ### Other Topics: # * errors (handling with `try`/`except` and raising with `raise` # * reading and writing files (`with` statement) # * file formats (json and csv) # * sending web requests (`requests`) # * parsing html (`BeautifulSoup`) # * crawling/scraping # * web requests (`GET`, `POST`, `PUT`, `DELETE`) # * CRUD (create, read, update, delete # * SQL (tables, columns, rows) # * git (adding files, committing, pushing)	19,474
/jupyter-python/data_analysis/.ipynb_checkpoints/2018.07.RogunHPP.AlignmentDXFtoNodesCSV-checkpoint.ipynb	b3519c82247d9d04691e6853ed783f31d018ebd7	[]	no_license	JJK-engineering/data-organizer	https://github.com/JJK-engineering/data-organizer	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	57,970	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Rogun HPP - Alignment DXF to Nodes in CSV # ToDo # all data (e.g. file names) as variables # markdown titles # comments with explanations # this example use grass script, do again with pygrass defining class & methods (to go into API later) # + # set up Python for GRASS GIS import os import sys import subprocess from IPython.display import Image # set up GRASS GIS runtime environment gisbase = subprocess.check_output(["grass", "--config", "path"]).strip() os.environ['GISBASE'] = gisbase os.environ['GRASS_FONT'] = 'sans' os.environ['GRASS_OVERWRITE'] = '1' #overwrite existing maps sys.path.append(os.path.join(gisbase, "etc", "python")) # set display modules to render into a file (named map.png by default) os.environ['GRASS_RENDER_IMMEDIATE'] = 'cairo' os.environ['GRASS_RENDER_FILE_READ'] = 'TRUE' os.environ['GRASS_LEGEND_FILE'] = 'legend.txt' # import GRASS GIS import grass.script as gs import grass.script.setup as gsetup from grass.script import core as grass # for pygrass from grass.pygrass.modules.shortcuts import raster as r, vector as v, general as g, display as d from subprocess import PIPE # further setup for GRASS GIS gs.set_raise_on_error(True) #gs.set_capture_stderr(True) #might be Python 2 vs 3 issue (unsure if Python 3 required for this Notebook) # + # https://grasswiki.osgeo.org/wiki/GRASS_Python_Scripting_Library # GRASS Python Scripting Library # How to retrieve error messages from read_command(): def read2_command(args, kwargs): #rename to e.g. read_grass kwargs['stdout'] = grass.PIPE kwargs['stderr'] = grass.PIPE ps = grass.start_command(args, **kwargs) return ps.communicate() # + # create a mapset (mapset does not already exist) # should only do once (but will report error and exit if already exists) # dir /home/kaelin_joseph/projects/RogunHEP/grassdata should already exist # !grass -c EPSG:3857 /home/kaelin_joseph/projects/RogunHPP/grassdata/RogunHPP -e # should use grass scipt ToDo JK !! # define all parameters separately ToDo JK !! #EPSG:3857 #WGS84 Pseudo Mercator # - # open mapset rcfile = gsetup.init(gisbase, "/home/kaelin_joseph/projects/RogunHPP/grassdata", "RogunHPP/", "PERMANENT") # check grass env print grass.gisenv() # check projection info read2_command('g.proj', flags = 'jf') #check mapsets grass.mapsets() # + # read dxf data # read2_command("v.in.dxf", input='/home/kaelin_joseph/projects/RogunHPP/data/testing/Aignment_DG4.dxf', # output='alignment_dg4', flags='e')[0] read2_command("v.in.dxf", input='/home/kaelin_joseph/projects/RogunHPP/data/in/AlignmentDG4.dxf', output='alignment_dg4', flags='e')[0] # output in 'RogunHPP/PERMANENT/vector/topography2m_r5_reduced' #read2_command("v.in.dxf") # pattern for 'printing grass output nicely # decode must be applied to each member of tuple # [0] -> stdout # [1] -> stderr # above are according to doc, however it seems that [1] is where all output is ToDo JK: ?? # - # set region from vector data bounds read2_command('g.region', vector='alignment_dg4') # check grass region print(g.region(flags='p',stdout_=PIPE).outputs.stdout.decode()) # view and check topography # !rm map.png #ToDo JK: pythonize read2_command("d.vect", map='alignment_dg4', color='green') Image(filename="map.png") points_out = read2_command("v.to.points", input='alignment_dg4', output='alignment_dg4_points') print(points_out[1].decode()) # view and check topography read2_command("d.vect", map='alignment_dg4_points', color='red') Image(filename="map.png") read2_command("v.out.ascii", input='alignment_dg4_points', type='point', separator=',', output='alignment_dg4_points.csv') # !head -5 alignment_dg4_points.csv #ToDo JK: pythonize # + # #!v.out.ascii --help # - read2_command("v.out.ascii", input='alignment_dg4', type='line', format='wkt', output='alignment_dg4_lines.csv') # !head -5 alignment_dg4_lines.csv #ToDo JK: pythonize # cut LINESTRING (first 10 char's) from each line of 'lines' output file	4,740
/mission_to_mars.ipynb	154f95ec69c72b397593e6518b39f24494f7d85e	[]	no_license	jwoh1323/Web-Scraping-and-Document-Databases-HW	https://github.com/jwoh1323/Web-Scraping-and-Document-Databases-HW	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	14,889	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- from splinter import Browser from bs4 import BeautifulSoup import pandas as pd executable_path = {'executable_path': 'chromedriver.exe'} browser = Browser('chrome', executable_path, headless=False) # # NASA Mars News # + # URL of page to be scraped url = 'https://mars.nasa.gov/news/' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # + # store latest news title title = soup.find_all('div', class_='content_title') news_title = title[0].text news_title # + # store paragraph of the latest news body = soup.find_all('div', class_='article_teaser_body') news_p = body[0].text news_p # - # # JPL Mars Space Images - Featured Image # + # store the current Featured Mars Image and assign the url string to a variable url = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # + image = soup.find_all('a', class_= 'button fancybox') image = image[0]['data-fancybox-href'] featured_image_url = "https://www.jpl.nasa.gov/" + image featured_image_url # - # # Mars Weather # + # scrape the latest Mars weather tweet url = 'https://twitter.com/marswxreport?lang=en' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # - weather = soup.find_all('p', class_='TweetTextSize TweetTextSize--normal js-tweet-text tweet-text') mars_weather = weather[0].text mars_weather # # Mars Facts # + url = 'https://space-facts.com/mars/' table = pd.read_html(url) table # + # scrape the table and covert it to panda dataframe df = table[0] df.columns = ['Description', 'Value'] df # + # covert to html html_table = df.to_html() html_table = html_table.replace('\n', '') # + # save the table directly to a file df.to_html('table.html') # - # # Mars Hemispheres # + url = 'https://astrogeology.usgs.gov/search/map/Mars/Viking/cerberus_enhanced' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # - title = soup.find_all('h2', class_ = 'title') title = title[0].text title img = soup.find_all('a', target = '_blank') img = img[0]['href'] img # + url = 'https://astrogeology.usgs.gov/search/map/Mars/Viking/schiaparelli_enhanced' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # - title2 = soup.find_all('h2', class_ = 'title') title2 = title2[0].text title2 img2 = soup.find_all('a', target = '_blank') img2 = img2[0]['href'] img2 # + url = 'https://astrogeology.usgs.gov/search/map/Mars/Viking/syrtis_major_enhanced' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # - title3 = soup.find_all('h2', class_ = 'title') title3 = title3[0].text title3 img3 = soup.find_all('a', target = '_blank') img3 = img3[0]['href'] img3 # + url = 'https://astrogeology.usgs.gov/search/map/Mars/Viking/syrtis_major_enhanced' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') # - title4 = soup.find_all('h2', class_ = 'title') title4 = title4[0].text title4 img4 = soup.find_all('a', target = '_blank') img4 = img4[0]['href'] img4 hemisphere_image_urls = [ {"title": title, "img_url": img}, {"title": title2, "img_url": img2}, {"title": title3, "img_url": img3}, {"title": title4, "img_url": img4}, ] this np.random.randint was set to (1, 7) so an output of every number from 1 to 12 would be possible. The number 1 was then removed from the final dictionary using del dict[1]. # # Solution 2 - For this function a default dict was used so it would not throw a key error. The dice values were set and np.random.randint was used to simulate the values. # ****************************************************************************************************************************** # <br/> # + # Solution 1 # Adapted from references [1, 2, 3, 4] def dicerolls(k,n): # Set variables roll = n noroll = 0 dicevalue = k * 6 # Use list to store dice dice = [] for i in range(k): dice.append(i+1) # Dictionary to store result dict = {} while dicevalue > 0: dict[dicevalue] = 0 dicevalue -= 1 # Roll the dice while roll > 0: diceroll = np.random.randint(1,7) for i in dice: result = (diceroll + noroll) dicevalue = dict.get(diceroll) dict[result] = dicevalue + 1 noroll += 6 roll -= 1 noroll = 0 # Remove 1 from dict output del dict[1] print(dict) dicerolls(2,1000) # + # Solution 2 # Adapted from stackoverflow[5] # Set variables # Number of dice rolls n = 1000 # Number of dice k = 2 # Create a defaultdict to store the results # Defaultdict is a sub-class of the dict class that returns a dictionary-like object. # The functionality of both dictionaries and defualtdict are almost same except for the # fact that defualtdict never raises a KeyError[5] dicerolls = defaultdict(int) # Loop through n times for _ in range(n): # Simulate random values for the dice rolled k = np.random.randint(2, 13) # Increase the result by 1 dicerolls[k]+=1 # Print results print(dicerolls) # - # # #### References: # # [1]. codegrepper.com https://www.codegrepper.com/code-examples/python/dice+rolling+function+python # # [2]. careerkarma.com https://careerkarma.com/blog/python-dictionary-get/ # # [3]. stackoverflow.com https://stackoverflow.com/questions/9001509/how-can-i-sort-a-dictionary-by-key # # [4]. stackoverflo.com https://stackoverflow.com/questions/5844672/delete-an-element-from-a- # # [5]. stackoverflow.com https://stackoverflow.com/questions/60343980/rolling-2-dice-1000-times-and-counting-the-number-of-times-the-sum-of-the-two-di # *************************************************************************************************************************** # <br/> # <br/> # # ## Task 3 - numpy.random.binomial # # # #### Task # The numpy.random.binomial function can be used to # simulate flipping a coin with a 50/50 chance of heads or tails. Interestingly, if a # coin is flipped many times then the number of heads is well approximated by a # bell-shaped curve. For instance, if we flip a coin 100 times in a row the chance of # getting 50 heads is relatively high, the chances of getting 0 or 100 heads is relatively # low, and the chances of getting any other number of heads decreases as you move # away from 50 in either direction towards 0 or 100. Write some python code that # simulates flipping a coin 100 times. Then run this code 1,000 times, keeping track # of the number of heads in each of the 1,000 simulations. Select an appropriate # plot to depict the resulting list of 1,000 numbers, showing that it roughly follows # a bell-shaped curve. You should explain your work in a Markdown cell above the # code. # # #### Solution # Binomial distribution is a distribution where only two outcomes are possible, such as success or failure, gain or loss or win or lose and the probability of success and failure is the same for all its trials <sup>1</sup>. Taking the example of a coin toss, there are only two possible outcomes, heads or tails. The probability of getting a heads (success) can be seen as p = 0.5 and the probability of getting a tails (failure) can be seen as q = 1 - p = 0.5. This function can also be used if outcomes are not equal, eg. if the probability of success is p = 0.2 then the probability of failure is q = 1 - 0.2 = 0.8. Each trial is independent since the outcome of the previous toss doesn’t determine or affect the outcome of the current toss<sup>1</sup>. The total number of trials can be set using n = 20 and by setting size = 1000 we can run the 20 trials 1000 times and view the outcome <sup>2</sup>. # ************************************************************************************************************************** # <br/> # + # Coin toss example which displays binomial distribution[2]. # Number of trials size = 1000 # Number of independent coin tosses in each trial n = 20 # Probability of success for each experiment p = 0.5 # Run the trials bd = np.random.binomial(n, p, size) # Plot the result to show distribution ax = sns.histplot(bd, kde=True, color='red', bins=12) ax.set_xlabel ('Binomial distribution') # + # Example which displays binomial distribution with unequal probability[2] # Run trials bd = np.random.binomial(20, 0.2, 1000) # Plot distribution ax = sns.histplot(bd, kde=True, color='red', bins=12) ax.set_xlabel ('Binomial distribution') # - # #### References: # # [1]. analyticsvidhya.com https://www.analyticsvidhya.com/blog/2017/09/6-probability-distributions-data-science/ # # [2]. towardsdatascience.com https://towardsdatascience.com/fun-with-the-binomial-distribution-96a5ecabf65b # ******************************************************************************************************************* # <br/> # <br/> # # ## Task 4 - Simpson's Paradox # #### Task # Simpson’s paradox is a well-known statistical paradox # where a trend evident in a number of groups reverses when the groups are combined # into one big data set. Use numpy to create four data sets, each with an x array # and a corresponding y array, to demonstrate Simpson’s paradox. You might # create your x arrays using numpy.linspace and create the y array for each # x using notation like y = a * x + b where you choose the a and b for each # x , y pair to demonstrate the paradox. You might see the Wikipedia page for # Simpson’s paradox for inspiration. # #### Solution # Simpson's paradox is a phenomenon in probability and statistics in which trends that appears in different groups of data disappear or reverse when these groups are combined. Simpson’s paradox happens because disaggregation of the data can cause certain subgroups to have an imbalanced representation compared to other subgroups. This might be due to the relationship between the variables, or simply due to the way that the data has been seperated into subgroups <sup>1</sup>. This result is particularly problematic when frequency data is given casual interpretations and has been used to illustrate the kind of misleading results misapplied statistics can generate. The paradox can be resolved when causal relations are appropriately addressed in the statistical modeling. It is also referred to as Simpson's reversal, Yule–Simpson effect, amalgamation paradox, or reversal paradox <sup>2</sup>. # # Below is a fictional example showing Simpson's Paradox in the context of a correlation reversal. We have created data on the number of hours of exercise per week versus the risk of developing a disease for two sets of patients, those below the age of 50 and those over the age of 50. Here are individual plots showing the relationship between exercise and probability of disease <sup>3</sup>. # ****************************************************************************************************************************** # <br/> # + # Example adapted from https://towardsdatascience.com/simpsons-paradox-how-to-prove-two-opposite-arguments-using-one-dataset-1c9c917f5ff9 [3]. # Create data samples for under_50 n_samples = 100 # Set the seed so the results stay the same np.random.seed(42) # Simulate age values ages = np.random.randint(20, 50, n_samples) # Simulate hours values hours = np.random.randint(1, 5, n_samples) + np.random.randn(n_samples) # Calculate probability p = 12 + 0.5 * ages + -2.1 * hours + np.random.randn(n_samples) * 2 # Create dataframe under_50 = pd.DataFrame({'age': ages, 'Hours Exercised': hours, 'probability': p}) # Create data samples for over_50 n_samples = 100 # Simulate age values ages = np.random.randint(50, 85, n_samples) # Simulate hours values hours = np.random.randint(3, 8, n_samples) + np.random.randn(n_samples) * 0.5 # p = 40 + 0.32 * ages + -3.2 * hours + np.random.randn(n_samples) over_50 = pd.DataFrame({'age': ages, 'Hours Exercised': hours, 'probability': p}) # Function used to create plots and show relationships def plot_relationship(data, c, color, ax): #Plot a scatter plot with linear fit# x, y = np.array(data[c]), np.array(data['probability']) # Linear fit (polynomial of degree 1) b, m = polyfit(x, y, 1) # Plot scatterplot data.plot(x = c, y = 'probability', c = color, style = 'o', legend = None, ax = ax, ms = 10) # Plot linear fit ax.plot(x, m * x + b, '-', color = 'k'); if color == '#d9d142': plt.title(f'Probability vs {c.capitalize()} over 50') elif color == '#04c5ff': plt.title(f'Probability vs {c.capitalize()} under 50') else: plt.title(f'Probability vs {c.capitalize()} Combined') corr_coef = np.corrcoef(x, y)[0][1] ax = plt.gca() plt.ylabel('Probability'); plt.text(0.2, 0.75, r'$\rho$ = ' + f'{round(corr_coef, 2)}', fontsize = 28, color = 'k', transform=ax.transAxes) plt.figure(figsize = (20, 8)) ax = plt.subplot(1, 2, 1) plot_relationship(under_50, 'Hours Exercised', '#04c5ff', ax) ax = plt.subplot(1, 2, 2) plot_relationship(over_50, 'Hours Exercised', '#d9d142', ax) # - # <br/> # The plots clearly show a negative correlation, indicating that increased levels of exercise per week are connected with a lower risk of developing a disease for both groups. However, when combined on a single plot the correlation has completely reversed <sup>3</sup>. # Create combined plot plt.figure(figsize = (10, 8)) combined = pd.concat([under_50, over_50], axis = 0) ax = plt.subplot(1, 1, 1) plot_relationship(combined, 'Hours Exercised', 'r', ax) # #### References: # # [1]. kdnuggets.com https://www.kdnuggets.com/2020/09/simpsons-paradox.html # # [2]. wikipedia.org # https://en.wikipedia.org/wiki/Simpson%27s_paradox#:~:text=Simpson's%20paradox%2C%20which%20also%20goes,when%20these%20groups%20are%20combined. # # [3]. towardsdatascience.com https://towardsdatascience.com/simpsons-paradox-how-to-prove-two-opposite-arguments-using-one-dataset-1c9c917f5ff9	14,360
/Chatbot/4_seq2seq_part2B.ipynb	92192481644a83f20497d837160a13fa5a9d2db9	[]	no_license	goldin2008/Research_in_NLP	https://github.com/goldin2008/Research_in_NLP	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	50,456	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # 演示seq2seq lib中的beam search使用方法 # + import math import numpy as np import sys import tensorflow as tf # sys.path.append('C:\\Users\\reade\\Documents\\lecture4\\seq2seq') sys.path.append('/Users/yuleinku/Google Drive/BOOK/聊天机器人Chatbot/lecture4/seq2seq') from seq2seq.encoders import rnn_encoder from seq2seq.decoders import (basic_decoder, beam_search_decoder) # - # # 产生/demo 合成数据 # + PAD = 0 EOS = 1 vocab_size = 10 input_embedding_size = 16 encoder_hidden_units = 32 decoder_hidden_units = encoder_hidden_units import helpers as data_helpers batch_size = 10 # 一个generator，每次产生一个minibatch的随机样本 batches = data_helpers.random_sequences(length_from=3, length_to=8, vocab_lower=2, vocab_upper=10, batch_size=batch_size) print('产生%d个长度不一（最短3，最长8）的sequences, 其中前十个是:' % batch_size) for seq in next(batches)[:min(batch_size, 10)]: print(seq) # - # # 定义使用beamsearch decoder的seq2seq模型 # # ### 声明placholder和定义encoder部分，同part2A # + tf.reset_default_graph() sess = tf.InteractiveSession() mode = tf.contrib.learn.ModeKeys.TRAIN with tf.name_scope('minibatch'): encoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='encoder_inputs') encoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='encoder_inputs_length') decoder_targets = tf.placeholder(shape=(None, None), dtype=tf.int32, name='decoder_targets') decoder_targets_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='decoder_targets_length') decoder_inputs = tf.placeholder(shape=(None, None), dtype=tf.int32, name='decoder_inputs') decoder_inputs_length = tf.placeholder(shape=(None,), dtype=tf.int32, name='decoder_inputs_length') decoder_initial_state = tf.placeholder(shape=(None, None), dtype=tf.float32, name='decoder_initial_state') # 2-a. 定义encoder encoder_params = rnn_encoder.UnidirectionalRNNEncoder.default_params() encoder_params["rnn_cell"]["cell_params"]["num_units"] = encoder_hidden_units encoder_params["rnn_cell"]["cell_class"] = "BasicLSTMCell" encoder_params # 2-b. 定义encoding过程 # 输入数据转化为embedding格式 with tf.name_scope('embedding'): input_embeddings = tf.Variable( tf.random_uniform([vocab_size, input_embedding_size], -1.0, 1.0), dtype=tf.float32) output_embeddings = tf.Variable( tf.random_uniform([vocab_size, input_embedding_size], -1.0, 1.0), dtype=tf.float32) encoder_inputs_embedded = tf.nn.embedding_lookup(input_embeddings, encoder_inputs) # 使用UnidirectionalRNNEncoder编码 encode_fn = rnn_encoder.UnidirectionalRNNEncoder(encoder_params, mode) encoder_output = encode_fn(encoder_inputs_embedded, encoder_inputs_length) # - # ## 定义decoding模型，使用seq2seq.decoders.beam_search_decoder.BeamSearchDecoder # 1. input embedding # 2. helper <-- decoder_input, decoder_input_length # 3. basic_decoder.BasicDecoder # ### config decoder的选项，任何基于RNN的decoding操作都需要设定的超参数 decode_params = beam_search_decoder.BeamSearchDecoder.default_params() decode_params["rnn_cell"]["cell_params"]["num_units"] = decoder_hidden_units decode_params # ### config beam_search的选项，即beam_search操作的超参数 # # * beam_width # * length_penalty_weight # * choose_successors_fn # + from seq2seq.inference import beam_search config = beam_search.BeamSearchConfig( beam_width = 10, vocab_size = vocab_size, eos_token = EOS, length_penalty_weight = 0.6, choose_successors_fn = beam_search.choose_top_k) config # - from seq2seq.contrib.seq2seq import helper as decode_helper # + beam_helper = decode_helper.GreedyEmbeddingHelper( embedding=output_embeddings, start_tokens=[0] * config.beam_width, end_token=-1) decoder_fn = basic_decoder.BasicDecoder(params=decode_params, mode=mode, vocab_size=vocab_size) """ decoder_fn = create_decoder( helper=beam_helper, mode=tf.contrib.learn.ModeKeys.INFER) """ decoder_fn = beam_search_decoder.BeamSearchDecoder( decoder=decoder_fn, config=config) # - decoder_inputs_embedded = tf.nn.embedding_lookup(input_embeddings, decoder_inputs) with tf.name_scope('minibatch'): helper = decode_helper.TrainingHelper( inputs = decoder_inputs_embedded, sequence_length = decoder_inputs_length) decoder_fn = basic_decoder.BasicDecoder(params=decode_params, mode=mode, vocab_size=vocab_size) decoder_output, decoder_state = decoder_fn(encoder_output.final_state, helper) # + loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits( labels=tf.one_hot(decoder_targets, depth=vocab_size, dtype=tf.float32), logits=tf.transpose(decoder_output.logits, perm = [1, 0, 2])) ) """ # 通过阅读decoder_helper的定义， # 输入数据是batch-major # 而输出数据是time-major... # 所以需要对输出的logits做一次transpose # labels: [batch_size, max_length, vocab_size] # logits （tranpose之前）: [max_length, batch_size, vocab_size] loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits = tf.transpose(decoder_output.logits, perm=[1,0,2]), labels = decoder_targets)) """ train_op = tf.train.AdamOptimizer(learning_rate = 0.001).minimize(loss) # - sess.run(tf.global_variables_initializer()) def next_feed(): batch = next(batches) encoder_inputs_, encoder_inputs_length_ = data_helpers.batch(batch) decoder_targets_, decoder_targets_length_ = data_helpers.batch( [(sequence) + [EOS] for sequence in batch] ) decoder_inputs_, decoder_inputs_length_ = data_helpers.batch( [[EOS] + (sequence) for sequence in batch] ) # 在feedDict里面，key可以是一个Tensor return { encoder_inputs: encoder_inputs_.T, decoder_inputs: decoder_inputs_.T, decoder_targets: decoder_targets_.T, encoder_inputs_length: encoder_inputs_length_, decoder_inputs_length: decoder_inputs_length_, decoder_targets_length: decoder_targets_length_ } fd= next_feed() fd fd[encoder_inputs].shape fd[decoder_inputs].shape fd[decoder_targets].shape # ## 4. 我们已经定义了一个计算图 # * 图的输入端是encoder_inputs 和 encoder_inputs_length # * 图的输出端是encoder_output [encoder_out1, decoder_out1] = sess.run( [encoder_output, decoder_output], fd) encoder_out1.outputs.shape decoder_out1.cell_output.shape decoder_out1.logits.shape decoder_out1.predicted_ids.shape print('encoder output information:') print(encoder_out1.outputs.shape) print(encoder_out1.final_state.c.shape) print(encoder_out1.final_state.h.shape) print('decoder output information:') print(decoder_out1.predicted_ids.shape) decoder_out1.predicted_ids.shape x = next_feed() print('encoder_inputs:') print(x[encoder_inputs][0,:]) print('decoder_inputs:') print(x[decoder_inputs][0,:]) print('decoder_targets:') print(x[decoder_targets][0,:]) # + def next_feed(): batch = next(batches) encoder_inputs_, encoder_inputs_length_ = data_helpers.batch(batch) decoder_targets_, _ = data_helpers.batch( [(sequence) + [EOS] for sequence in batch] ) decoder_inputs_, decoder_inputs_length_ = data_helpers.batch( [[EOS] + (sequence) for sequence in batch] ) # 在feedDict里面，key可以是一个Tensor return { encoder_inputs: encoder_inputs_.T, decoder_inputs: decoder_inputs_.T, decoder_targets: decoder_targets_.T, encoder_inputs_length: encoder_inputs_length_, decoder_inputs_length: decoder_inputs_length_ } batch_size = 100 batches = data_helpers.random_sequences(length_from=3, length_to=8, vocab_lower=2, vocab_upper=10, batch_size=batch_size) print('产生100个长度不一的sequence') print('其中前十个是:') for seq in next(batches)[:10]: print(seq) # + loss_track = [] max_batches = 3001 batches_in_epoch = 100 try: # 一个epoch的learning for batch in range(max_batches): fd = next_feed() _, l = sess.run([train_op, loss], fd) loss_track.append(l) if batch == 0 or batch % batches_in_epoch == 0: print('batch {}'.format(batch)) print(' minibatch loss: {}'.format(sess.run(loss, fd))) predict_ = sess.run(decoder_output.predicted_ids, fd) for i, (inp, pred) in enumerate(zip(fd[encoder_inputs], predict_.T)): print(' sample {}:'.format(i + 1)) print(' input > {}'.format(inp)) print(' predicted > {}'.format(pred)) if i >= 2: break print() except KeyboardInterrupt: print('training interrupted') # - # %matplotlib inline import matplotlib.pyplot as plt plt.plot(loss_track) print('loss {:.4f} after {} examples (batch_size={})'.format(loss_track[-1], len(loss_track)*batch_size, batch_size))	9,738
/RayTest-Ray.ipynb	8267373aef78a5f772ae6d3d0f3deb7001a57181	[]	no_license	anthonysmc/LBL-Research-2021--atlas-group	https://github.com/anthonysmc/LBL-Research-2021--atlas-group	1	0	null	null	null	null	Jupyter Notebook	false	false	.py	11,565	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + """ #CURRENT VERSION A simple test of Ray This example uses placement_group API to spread work around """ import random import os import platform import ray import time ray.init(ignore_reinit_error=True) @ray.remote class Actor(): def __init__(self, actor_id) -> None: self.pid = os.getpid() self.hostname = platform.node() self.ip = ray._private.services.get_node_ip_address() self.actor_id = actor_id def ping(self): print(f"{self.actor_id} {self.pid} {self.hostname} {self.ip} {time.time()} - ping") time.sleep(random.randint(1,3)) return f"{self.actor_id}" @ray.remote def main(): # Get list of nodes to use print(f"Found {len(actors)} Worker nodes in the Ray Cluster:") # Setup one Actor per node print(f"Setting up {len(actors)} Actors...") time.sleep(1) # Ping-Pong test messages = [actors[a].ping.remote() for a in actors] time.sleep(1) for _ in range(10): new_messages, messages = ray.wait(messages, num_returns=1) for ray_message_id in new_messages: pong = ray.get(ray_message_id) print(pong, "- pong") check = actors[pong].ping.remote() time.sleep(1) messages.append(check) actors = { "actor1" : Actor.remote(actor_id="actor1"), "actor2" : Actor.remote(actor_id="actor2"), "actor3" : Actor.remote(actor_id="actor3"), "actor4" : Actor.remote(actor_id="actor4"), "actor5" : Actor.remote(actor_id="actor5") } print(actors) if __name__ == "__main__": main.remote() # + # VERSION TWO """ A simple test of Ray This example uses placement_group API to spread work around """ import os import platform import ray import time ray.init(ignore_reinit_error=True) @ray.remote class Actor(): def __init__(self) -> None: self.pid = os.getpid() self.hostname = platform.node() self.ip = ray._private.services.get_node_ip_address() def ping(self): print(f"{self.pid} {self.hostname} {self.ip} - ping") time.sleep(1) return f"{self.pid} {self.hostname} {self.ip} - pong" @ray.remote def main(): # Get list of nodes to use print(f"Found {len(actors)} Worker nodes in the Ray Cluster:") # Setup one Actor per node print(f"Setting up {len(actors)} Actors...") actor = [] for a in actors: node_ip_str = f"node:{ray._private.services.get_node_ip_address()}" actor.append(Actor.remote()) time.sleep(1) # Ping-Pong test for _ in range(2): for a in actors: time.sleep(1) messages = a.ping.remote() time.sleep(1) print(f"Received back message {ray.get(messages)} \n") actors = [Actor.remote() for _ in range(5)] print(actors) if __name__ == "__main__": main.remote() # + # VERSION THREE """ A simple test of Ray This example uses placement_group API to spread work around """ import os import platform import ray import time ray.init(ignore_reinit_error=True) @ray.remote class Actor(): def __init__(self) -> None: self.pid = os.getpid() self.hostname = platform.node() self.ip = ray._private.services.get_node_ip_address() def ping(self): print(f"{self.pid} {self.hostname} {self.ip} - ping") time.sleep(1) return f"{self.pid} {self.hostname} {self.ip} - pong" @ray.remote def main(): # Get list of nodes to use print(f"Found {len(actors)} Worker nodes in the Ray Cluster:") # Setup one Actor per node print(f"Setting up {len(actors)} Actors...") actor = [] for a in actors: node_ip_str = f"node:{ray._private.services.get_node_ip_address()}" actor.append(Actor.remote()) time.sleep(1) # Ping-Pong test for _ in range(2): messages = [a.ping.remote() for a in actors] for msg in ray.get(messages): time.sleep(1) print(f"Received back message {msg}") time.sleep(1) actors = [Actor.remote() for _ in range(5)] print(actors) if __name__ == "__main__": main.remote() 8ms sampling_rate = 44100 duration = 2 # sec hop_length = 3472 # to make time steps 128 fmin = 20 fmax = sampling_rate // 2 n_mels = 128 n_fft = n_mels 20 padmode = 'constant' samples = sampling_rate * duration def get_default_conf(): return conf def set_fastai_random_seed(seed=42): # https://docs.fast.ai/dev/test.html#getting-reproducible-results # python RNG random.seed(seed) # pytorch RNGs import torch torch.manual_seed(seed) torch.backends.cudnn.deterministic = True if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) # numpy RNG import numpy as np np.random.seed(seed) # + def mono_to_color(X, mean=None, std=None, norm_max=None, norm_min=None, eps=1e-6): # Stack X as [X,X,X] X = np.stack([X, X, X], axis=-1) # Standardize mean = mean or X.mean() X = X - mean std = std or X.std() Xstd = X / (std + eps) _min, _max = Xstd.min(), Xstd.max() norm_max = norm_max or _max norm_min = norm_min or _min if (_max - _min) > eps: # Normalize to [0, 255] V = Xstd V[V < norm_min] = norm_min V[V > norm_max] = norm_max V = 255 * (V - norm_min) / (norm_max - norm_min) V = V.astype(np.uint8) else: # Just zero V = np.zeros_like(Xstd, dtype=np.uint8) return V def convert_wav_to_image(df, source): X = [] for i, row in tqdm_notebook(df.iterrows()): x = read_as_melspectrogram(conf, source/str(row.fname), trim_long_data=False) x_color = mono_to_color(x) X.append(x_color) return X def save_as_pkl_binary(obj, filename): """Save object as pickle binary file. Thanks to https://stackoverflow.com/questions/19201290/how-to-save-a-dictionary-to-a-file/32216025 """ with open(filename, 'wb') as f: pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) def load_pkl(filename): """Load pickle object from file.""" with open(filename, 'rb') as f: return pickle.load(f) # + conf = get_default_conf() def convert_dataset(df, source_folder, filename): X = convert_wav_to_image(df, source=source_folder) save_as_pkl_binary(X, filename) print(f'Created {filename}') return X convert_dataset(trn_curated_df, TRN_CURATED, MELS_TRN_CURATED); convert_dataset(test_df, TEST, MELS_TEST); # - # ## Creating Best 50s # + df = trn_noisy_df.copy() df['singled'] = ~df['labels'].str.contains(',') singles_df = df[df.singled] cat_gp = (singles_df.groupby( ['labels']).agg({ 'fname':'count' }).reset_index()).set_index('labels') plot = cat_gp.plot( kind='barh', title="Number of samples per label", figsize=(15,20)) plot.set_xlabel("Noisy Set's Number of Samples", fontsize=20) plot.set_ylabel("Label", fontsize=20); # - labels = singles_df['labels'].unique() print(labels) print(len(labels)) # + idxes_best50s = np.array([random.choices(singles_df[(singles_df['labels'] == l)].index, k=50) for l in labels]).ravel() best50s_df = singles_df.loc[idxes_best50s] grp = (best50s_df.groupby( ['labels']).agg({ 'fname':'count' }).reset_index()).set_index('labels') grp.plot( kind='barh', title="Best 50s' Number of samples per label", figsize=(15,20)); # - best50s_df.to_csv(CSV_TRN_NOISY_BEST50S, index=False) # ### Now best 50s are selected # # Making preprocessed data is as follows, but you have to run locally. Kernel cannot hold all the noisy preprocessed data on memory. # + # Convert noisy set first X_trn_noisy = convert_dataset(trn_noisy_df, TRN_NOISY, MELS_TRN_NOISY) # Then choose preprocessed data for 50s, and save it X = [X_trn_noisy[i] for i in idxes_best50s] save_as_pkl_binary(X, MELS_TRN_NOISY_BEST50S) # -	8,223
/Manipulating Elements in a Data Frame.ipynb	72ac63bb3039b5a2df051d42e0ca28691e154917	[]	no_license	haider2122/Data-Analysis	https://github.com/haider2122/Data-Analysis	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	27,196	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda root] # language: python # name: conda-root-py # --- # + active="" # BEST Practices to Manipulate elements in a Data Frame # - import pandas as pd t=pd.read_csv('titanic.csv') # + active="" # Changing a single value # - t.loc[2,'age']=27 t.head(3) t.iloc[1,0]=0 t.head(2) # + active="" # Changing Multiple Values # - t.loc[[4,6,8],['survived']]=[1,1,1] t.head(9) # + #The above can also be done using iloc # + l=list(t.loc[t.age<=1].index)#Creating a list of index's of babies(age<1) l # - t.loc[l,'age']=1 t.iloc[l,::]#Therefore values Have been rounded t.iloc[4,0:3]=[1,1,'male']#Changing values in a row t.replace(0,'zero').head(2) t.replace(t.loc[2,'sex'],'male').head(2)#Hence value of row changes # + active="" # Views and Copy's # If its a view, a change on the slice will reflect in the orginal data frame. # - age=t.loc[::,'age'] age._is_view age.iloc[1:4]=4#Hence warning comes of value being changed in orginal data frame x=t.loc[t.age<1] x._is_view#Fence the following is a copy, won't reflect in the data frame # + active="" # Two Methods to perform definite and practical manipulation # # + active="" # 1) To Change Values in a Data Frame- # avoid chained indexing # - t.loc[8,'age']=10#directly changing value in data frame # + active="" # 2) Avoid Changing Values in a data frame,but only operate with a slice of data frame # - age=t.age.copy() age.iloc[2]=1#Changing value only in Series colab={"base_uri": "https://localhost:8080/"} outputId="9539decb-54d3-4c39-902b-eccc1917e5b8" # Dowload and extract CEFAR signature dataset URL = "http://www.cedar.buffalo.edu/NIJ/data/signatures.rar" filename = "signatures.rar" dataset_directory ="signatures" if os.path.exists(dataset_directory) == False: if os.path.exists(filename) == False: print("Dowloading dataset") # !wget --output-document=$filename $URL print("Extracting rar file") # !mkdir -p signatureDataset # !unrar x -y $filename $dataset_directory # + id="UHHKNIlbDTIH" # Create list that consist filename of genuine signatures # Assume all signature is genuine signature sign_list = [] sign_labels = [] sign_path = os.path.join(dataset_directory, "full_org") for sign in sorted(os.listdir(sign_path)): if sign.find("png") > 0: sign_list.append(sign) sign_labels.append(int(sign.split("_")[1])) # Convert list to numpy array sign_array = np.array(sign_list) sign_label_array = np.array(sign_labels) num_writers = 55 total_signature_for_each_writer = 24 total_size_signatures = sign_label_array.shape[0] # + colab={"base_uri": "https://localhost:8080/"} id="ExZbza31GQt8" outputId="d28a1ffa-f2db-406a-a451-674a66caaf42" # Show the full path of the signatures print("Filename is : ", sign_array[0]) print("Label is : ", sign_label_array[0]) # Size of signature images print("Total size of the signatures is : ", total_size_signatures) # Shape of a signature image im = cv2.imread(os.path.join(sign_path, sign_array[0])) print("Shape is : ", im.shape) # + id="UrTu6HwzHyV2" # Visualize the signatures def visualize_signature(isWriterSame = False, isSignatureSame = False): img_size = (224, 224) w1 = np.random.randint(num_writers - 1) + 1 w2 = np.random.randint(num_writers - 1) + 1 s1 = np.random.randint(total_signature_for_each_writer - 1) + 1 s2 = np.random.randint(total_signature_for_each_writer - 1) + 1 if isWriterSame == True: w2 = w1 if isSignatureSame == True: s2 = s1 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,10)) im1_name = "/original_" + str(w1) + "_" + str(s1) + ".png" im2_name = "/original_" + str(w2) + "_" + str(s2) + ".png" orig_im_1 = cv2.resize(cv2.imread(sign_path + im1_name), img_size) orig_im_2 = cv2.resize(cv2.imread(sign_path + im2_name), img_size) ax1.imshow(orig_im_1, cmap = "gray") ax2.imshow(orig_im_2, cmap = "gray") ax1.set_title("Signature 1") ax2.set_title("Signature 2") ax1.axis('off') ax2.axis('off') # + colab={"base_uri": "https://localhost:8080/", "height": 301} id="VFgrVpC-RZMv" outputId="664ed617-8823-4a43-ae0b-57246097e244" visualize_signature(isWriterSame = True, isSignatureSame = True) # + colab={"base_uri": "https://localhost:8080/", "height": 301} id="w_sUZ4yJXH57" outputId="a7ac970c-3be7-43f8-d35e-6e8af06d919a" visualize_signature() # + colab={"base_uri": "https://localhost:8080/", "height": 301} id="iVDNQY83IyIC" outputId="91a8d605-8303-45ce-a72a-cc4c68b845a7" visualize_signature(isWriterSame = True) # + id="egbYYl7sXKzQ" def create_pairs(x, digit_indices): '''Positive and negative pair creation. Alternates between positive and negative pairs. ''' pairs = [] labels = [] n = min([len(digit_indices[d]) for d in range(num_writers)]) - 1 n = min([len(digit_indices[d]) for d in range(num_writers)]) - 1 for d in range(num_writers): for i in range(n): for j in range(2): z1, z2 = digit_indices[d][i], digit_indices[d][i + 1] pairs += [[x[z1], x[z2]]] inc = random.randrange(1, num_writers + 1) dn = (d + inc) % num_writers z1, z2 = digit_indices[d][i], digit_indices[dn][i] pairs += [[x[z1], x[z2]]] labels += [1, 0] return np.array(pairs), np.array(labels) def create_pairs_on_set(images, labels): digit_indices = [np.where(labels == i)[0] for i in range(1, num_writers + 1)] pairs, y = create_pairs(images, digit_indices) y = y.astype('float32') return pairs, y def show_image(image): plt.figure() plt.imshow(image) plt.colorbar() plt.grid(False) plt.show() # + id="xCfo8U8BSZcu" # create pairs pairs, y = create_pairs_on_set(sign_array, sign_label_array) # preprocessing pairs_image = [] img_size = (224, 224, 1) for i in range(pairs.shape[0]): # resize images im1 = cv2.resize(cv2.imread(os.path.join(sign_path, pairs[i][0]), cv2.IMREAD_GRAYSCALE), img_size[0:2]) im2 = cv2.resize(cv2.imread(os.path.join(sign_path, pairs[i][1]), cv2.IMREAD_GRAYSCALE), img_size[0:2]) # normalize im1 = im1.astype('float32') im2 = im2.astype('float32') # normalize values im1 = im1 / 255.0 im2 = im2 / 255.0 pairs_image += [[im1, im2]] pairs_image_array = np.array(pairs_image) # + colab={"base_uri": "https://localhost:8080/", "height": 538} id="hF2HuOaNDb2_" outputId="841102a5-f24d-42e8-dd89-072ff7b8fe3f" # array index this_pair = random.randrange(len(pairs)-1) # show images at this index show_image(pairs_image_array[this_pair][0]) show_image(pairs_image_array[this_pair][1]) # print the label for this pair print(y[this_pair]) # + id="j_Jl5t-oK-dK" # shuffle data shuffled_x, shuffled_y = shuffle(pairs_image_array, y) # split data as train and test train_ratio = 0.8; train_size = int(train_ratio * shuffled_x.shape[0]) train_x = shuffled_x[:train_size] train_y = shuffled_y[:train_size] test_x = shuffled_x[train_size:] test_y = shuffled_y[train_size:] # + colab={"base_uri": "https://localhost:8080/"} id="750vPg0kLyfh" outputId="3c98c183-89e6-4542-c909-8ba50bc66ec7" # Lengh of train and test data print("Size of train image pairs is : ", train_x.shape[0]) print("Size of test image pairs is : ", test_x.shape[0]) # + [markdown] id="t-ZJnujnN0FJ" # ## Build the Model # # + id="o0o4--UWN4NB" def initialize_base_network(): input = Input(shape=img_size, name="base_input2") x = Conv2D(128, kernel_size=(7,7), activation="relu", name='conv1_1', strides=4)(input) x = BatchNormalization()(x) x = MaxPooling2D(pool_size=(3,3), strides=(2,2))(x) x = Conv2D(256, kernel_size=(5,5), activation="relu", name='conv2_1', strides=1)(x) x = BatchNormalization()(x) x = MaxPooling2D(pool_size=(3,3), strides=(2,2))(x) x = Conv2D(512, kernel_size=(3,3), activation="relu", name='conv3_1', strides=1)(x) x = Conv2D(1024, kernel_size=(3,3), activation="relu", name='conv5_1', strides=1)(x) x = GlobalAveragePooling2D()(x) x = Dense(1024, activat	8,192
/book/packt/Bioinformatics.with.Python.Cookbook/notebooks/Welcome.ipynb	6803d8c756c7d8a69152c8592abbe01d06c2fb82	[]	no_license	xenron/sandbox-python	https://github.com/xenron/sandbox-python	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	7,005	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # # Python for Bioinformatics # ###[Click here for the datasets used in the book](Datasets.ipynb) # ## Python 2 or 3? # Depending on your Python version, some content might not be available. Lets test that: import platform major, minor, patch = platform.python_version_tuple() if major == 3: print('Python 3: The Phylogenomics module will not work, but all the Big Data content will') else: print('Python 2: The Phylogenomics module will work, but some Big Data content will not') # ## Python and the surrounding software ecology # * [Interfacing with R](00_Intro/Interfacing_R.ipynb) # * [R Magic](00_Intro/R_magic.ipynb) # ## Next Generation Sequencing # * [Accessing Databases](01_NGS/Accessing_Databases.ipynb) # * [Basic Sequence Processing](01_NGS/Basic_Sequence_Processing.ipynb) # * [Working with FASTQ files](01_NGS/Working_with_FASTQ.ipynb) # * [Working with BAM files](01_NGS/Working_with_BAM.ipynb) # * [Working with VCF files](01_NGS/Working_with_VCF.ipynb) # * [Filtering SNPs](01_NGS/Filtering_SNPs.ipynb) # ## Genomics # * [Reference Genomes](02_Genomes/.ipynb) # * [Low Quality Reference Genomes](02_Genomes/.ipynb) # * [Annotations](02_Genomes/.ipynb) # * [Getting](02_Genomes/.ipynb) # * [Orthology](02_Genomes/.ipynb) # * [Gene Ontology](02_Genomes/.ipynb) # ## Population Genetics # * [Data Formats with PLINK](03_PopGen/Data_Formats.ipynb) # * [The Genepop Format](03_PopGen/Genepop_Format.ipynb) # * [Exploratory Analysis](03_PopGen/Exploratory_Analysis.ipynb) # * [F statistics](03_PopGen/F-stats.ipynb) # * [Principal Components Analysis (PCA)](03_PopGen/PCA.ipynb) # * [Admixture/Structure](03_PopGen/Admixture.ipynb) # # ## Simulation in Population Genetics # * [Introducing Forward-time simulations](04_PopSim/Basic_SimuPOP.ipynb) # * [Simulating selection](04_PopSim/Selection.ipynb) # * [Doing population structure with island and stepping-stone models](04_PopSim/Pop_Structure.ipynb) # * [Modeling complex demographic scenarios](04_PopSim/Complex.ipynb) # * [Simulating the coalescent with Biopython and fastsimcoal](04_PopSim/Coalescent.ipynb) # ## Phylogenetics # * [Preparing the Ebola dataset](05_Phylo/Exploration.ipynb) # * [Aligning genetic and genomic data](05_Phylo/Alignment.ipynb) # * [Comparing sequences](05_Phylo/Comparison.ipynb) # * [Playing recursively with trees](05_Phylo/Trees.ipynb) # * [Reconstructing Phylogenetic trees](05_Phylo/Reconstruction.ipynb) # * [Visualizing Phylogenetic data](05_Phylo/Visualization.ipynb) # # ## Proteomics # * [Finding a protein in multiple databases](06_Prot/Intro.ipynb) # * [Introducing Bio.PDB](06_Prot/PDB.ipynb) # * [Extracting more information from a PDB file](06_Prot/Stats.ipynb) # * [Computing distances on a PDB file](06_Prot/Distance.ipynb) # * [Doing geometric operations](06_Prot/Mass.ipynb) # * [Implementing a basic PDB parser](06_Prot/Parser.ipynb) # * [Parsing mmCIF files with Biopython](06_Prot/mmCIF.ipynb) # # The code for the PyMol recipe can be found on the pymol directory of the [github project](https://github.com/tiagoantao/bioinf-python) # ## Other topics # * [Accessing the Global Biodiversity Information Facility (GBIF)via REST](07_Other/GBIF.ipynb) # * [Geo-referencing GBIF datasets](07_Other/GBIF_Extra.ipynb) # * [Accessing molecular-interaction databases with PSIQUIC](07_Other/PSICQUIC.ipynb) # * [Plotting protein interactions with Cytoscape the hard way](07_Other/Cytoscape.ipynb) # ## Advanced Python for Bioinformatics # * [Setting the stage for high performance computing](08_Advanced/Intro.ipynb) # * [Designing a poor-human concurrent executor](08_Advanced/Multiprocessing.ipynb) # * [Doing parallel computing with IPython](08_Advanced/IPythonParallel.ipynb) # * [Approximating the median in a large dataset](08_Advanced/Median.ipynb) # * [Optimizing code with Cython and Numba](08_Advanced/Cython_Numba.ipynb) # * [Programming with lazyness](08_Advanced/Lazy.ipynb) # * [Thinking with generators](08_Advanced/Generators.ipynb) # pt_MC,pcov_MC = leastsq(ellip_moffat2D,x0=p,args=(xy,iteration_data,error),maxfev = 10000000) [amp_MC,x0_MC,y0_MC,A_MC,B_MC,C_MC,alpha_MC]= popt_MC theta_MC = 0.5np.arctan(C_MC/(A_MC - B_MC)) a_MC = np.sqrt(2/(A_MC + B_MC + np.sqrt(C_MC2 +(A_MC - B_MC)2))) b_MC = np.sqrt(2/(A_MC + B_MC - np.sqrt(C_MC2 +(A_MC - B_MC)2))) [fwhm1_MC,fwhm2_MC] = [2a_MCnp.sqrt(2(1/alpha_MC)-1),2b_MCnp.sqrt(2(1/alpha_MC)-1)] par_MC = [amp_MC,x0_MC,y0_MC,A_MC,B_MC,C_MC,alpha_MC,a_MC,b_MC,theta_MC,fwhm1_MC,fwhm2_MC] parameters_MC[:,l] = par_MC else: p= [amp,x0,y0] popt_MC,pcov_MC = leastsq(ellip_moffat2D_fixkin,x0=p,args=(xy,iteration_data,error,fix_par),maxfev = 10000000) [amp_MC,x0_out,y0_out]= popt_MC parameters_MC[:,l] = popt_MC parameters_err = np.std(parameters_MC,1) return par,parameters_err,model,res # + def moffat_table(full_data,full_error,D_A,D_L,muse_sampling_size,obj,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): final_data = np.append(full_data,[D_A,D_L,muse_sampling_size]) final_error = np.append(full_error,[0,0,0]) column_names={'amp_Hb_blr':0,'x0_Hb_Blr':1,'y0_Hb_Blr':2,'A':3,'B':4,'C':5,'alpha':6,'a':7,'b':8,'theta':9,'fwhm1':10,'fwhm2':11,'amp_OIII_br':12,'x0_OIII_br':13,'y0_OIII_br':14,'amp_OIII_nr':15,'x0_OIII_nr':16,'y0_OIII_nr':17,'D_A':18,'D_L':19,'sampling_size':20} columns=[] for key in column_names.keys(): columns.append(fits.Column(name=key,format='E',array=[final_data[column_names[key]]])) columns.append(fits.Column(name=key+'_err',format='E',array=[final_error[column_names[key]]])) coldefs = fits.ColDefs(columns) hdu = fits.BinTableHDU.from_columns(coldefs) hdu.writeto('%s/%s/9_arcsec_moffat_table_%s.fits'%(destination_path_cube,obj,obj),overwrite=True) def source_moffat_table(obj,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): t1 = Table.read('%s/%s/source_%s.fits'%(destination_path_cube,obj,obj),format='fits') t2 = Table.read('%s/%s/9_arcsec_moffat_table_%s.fits'%(destination_path_cube,obj,obj),format='fits') new = hstack([t1, t2]) new.write('%s/%s/%s_9_arcsec_moffat_table.fits'%(destination_path_cube,obj,obj),overwrite=True) def maps(Hb_blr_br_data,OIII_br_data,OIII_nr_data,Hb_model,OIII_br_model,OIII_nr_model,Hb_res,OIII_br_res,OIII_nr_res,obj,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): hdus=[] hdus.append(fits.PrimaryHDU()) hdus.append(fits.ImageHDU(Hb_blr_br_data,name='Hb_blr_br_data')) hdus.append(fits.ImageHDU(OIII_br_data,name='OIII_br_data')) hdus.append(fits.ImageHDU(OIII_nr_data,name='OIII_nr_data')) hdus.append(fits.ImageHDU(Hb_model,name='Hb_blr_br_model')) hdus.append(fits.ImageHDU(OIII_br_model,name='OIII_br_model')) hdus.append(fits.ImageHDU(OIII_nr_model,name='OIII_nr_model')) hdus.append(fits.ImageHDU(Hb_res,name='Hb_blr_br_res')) hdus.append(fits.ImageHDU(OIII_br_res,name='OIII_br_res')) hdus.append(fits.ImageHDU(OIII_nr_res,name='OIII_nr_res')) hdu = fits.HDUList(hdus) hdu.writeto('%s/%s/9_arcsec_maps_%s.fits'%(destination_path_cube,obj,obj),overwrite='True') def fluxden_compare(obj,Hb_blr_br_data,OIII_br_data,Hb_model,OIII_br_model,Hb_blr_br_err,OIII_br_err,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): f_blr_data = np.sum(Hb_blr_br_data) f_wing_data = np.sum(OIII_br_data) f_blr_model = np.sum(Hb_model) f_wing_model = np.sum(OIII_br_model) f_blr_err = np.sqrt(np.sum(Hb_blr_br_err2)) f_wing_err = np.sqrt(np.sum(OIII_br_err2)) tab_par = [f_blr_data,f_wing_data,f_blr_model,f_wing_model] tab_err = [f_blr_err,f_wing_err,0,0] column_names={'flux_blr_data':0,'flux_wing_data':1,'flux_blr_model':2,'flux_wing_model':3} columns=[] for key in column_names.keys(): columns.append(fits.Column(name=key,format='E',array=[tab_par[column_names[key]]])) columns.append(fits.Column(name=key+'_err',format='E',array=[tab_err[column_names[key]]])) coldefs = fits.ColDefs(columns) hdu = fits.BinTableHDU.from_columns(coldefs) hdu.writeto('%s/%s/%s_9_arcsec_fluxden_HbOIII.fits'%(destination_path_cube,obj,obj),overwrite=True) def fluxden_comp_table(obj,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): t1 = Table.read('%s/%s/source_%s.fits'%(destination_path_cube,obj,obj),format='fits') t2 = Table.read('%s/%s/%s_9_arcsec_fluxden_HbOIII.fits'%(destination_path_cube,obj,obj),format='fits') new = hstack([t1, t2]) new.write('%s/%s/%s_9_arcsec_fluxden_HbOIII.fits'%(destination_path_cube,obj,obj),overwrite=True) def emp_table(obj,emp_blr,emp_wing,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): popt = [emp_blr,emp_wing] column_names={'emp_fact_blr':0,'emp_fact_wing':1} columns=[] for key in column_names.keys(): columns.append(fits.Column(name=key,format='E',array=[popt[column_names[key]]])) coldefs = fits.ColDefs(columns) hdu = fits.BinTableHDU.from_columns(coldefs) hdu.writeto('%s/%s/%s_9_arcsec_scaling_subcube.fits'%(destination_path_cube,obj,obj),overwrite=True) def emp_fact_table(obj,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): t1 = Table.read('%s/%s/source_%s.fits'%(destination_path_cube,obj,obj),format='fits') t2 = Table.read('%s/%s/%s_9_arcsec_scaling_subcube.fits'%(destination_path_cube,obj,obj),format='fits') new = hstack([t1, t2]) new.write('%s/%s/%s_9_arcsec_scaling_subcube.fits'%(destination_path_cube,obj,obj),overwrite=True) # - def algorithm_script(obj,z,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE"): print ('%s'%(obj)) (Hb_blr_br_dat,OIII_br_dat,OIII_nr_dat,amp_Hb_blr_br,amp_OIII_br,amp_OIII_nr,Hb_blr_br_error,OIII_br_error,OIII_nr_error) = flux_data_err(obj) (Hb_blr_br_data,Hb_blr_br_err) = remove_bad_pixel(Hb_blr_br_dat,Hb_blr_br_error) (OIII_br_data,OIII_br_err) = remove_bad_pixel(OIII_br_dat,OIII_br_error) (OIII_nr_data,OIII_nr_err) = remove_bad_pixel(OIII_nr_dat,OIII_nr_error) box_size = np.shape(Hb_blr_br_data)[0] (brightest_pixel_Hb_blr_br_x,brightest_pixel_Hb_blr_br_y,brightest_pixel_OIII_br_x,brightest_pixel_OIII_br_y,brightest_pixel_OIII_nr_x,brightest_pixel_OIII_nr_y) = brightest_pixel_flux_map(Hb_blr_br_data,OIII_br_data,OIII_nr_data) print (brightest_pixel_OIII_nr_x,brightest_pixel_OIII_nr_y) if box_size ==45: muse_sampling_size = 0.2 else: muse_sampling_size = 0.4 print (muse_sampling_size) (Hb_par,Hb_error,Hb_model,Hb_res) = elliptical_moffat_fit(Hb_blr_br_data,Hb_blr_br_err,box_size,amp_Hb_blr_br,brightest_pixel_Hb_blr_br_x,brightest_pixel_Hb_blr_br_y,muse_sampling_size,None,100) print (Hb_par,Hb_error) #print (red_chi_sq_Hb) fixed_param = [Hb_par[3],Hb_par[4],Hb_par[5],Hb_par[6]] (OIII_br_par,OIII_br_error,OIII_br_model,OIII_br_res) = elliptical_moffat_fit(OIII_br_data,OIII_br_err,box_size,amp_OIII_br,brightest_pixel_OIII_br_x,brightest_pixel_OIII_br_y,muse_sampling_size,fixed_param,100) print (OIII_br_par,OIII_br_error) (OIII_nr_par,OIII_nr_error,OIII_nr_model,OIII_nr_res) = elliptical_moffat_fit(OIII_nr_data,OIII_nr_err,box_size,amp_OIII_nr,brightest_pixel_OIII_nr_x,brightest_pixel_OIII_nr_y,muse_sampling_size,fixed_param,100) print (OIII_nr_par,OIII_nr_error) (D_A,D_L) = dist(z, H0=70, WM=.286) (full_data,full_error) = (np.append(Hb_par,[OIII_br_par,OIII_nr_par]),np.append(Hb_error,[OIII_br_error,OIII_nr_error])) moffat_table(full_data,full_error,D_A,D_L,muse_sampling_size,obj,destination_path_cube="/home/mainak/Downloads/Outflow_paper1/MUSE") maps(Hb_blr_br_data,OIII_br_data,OIII_nr_data,Hb_model,OIII_br_model,OIII_nr_model,Hb_res,OIII_br_res,OIII_nr_res,obj) fluxden_compare(obj,Hb_blr_br_data,OIII_br_data,Hb_model,OIII_br_model,Hb_blr_br_err,OIII_br_err) source_moffat_table(obj) plt.imshow(Hb_blr_br_data,origin='lower') plt.plot(Hb_par[1],Hb_par[2],'kx') plt.show() plt.imshow(Hb_model,origin='lower') plt.plot(Hb_par[1],Hb_par[2],'kx') plt.show() plt.imshow(OIII_br_data,origin='lower') plt.plot(OIII_br_par[1],OIII_br_par[2],'bx') plt.show() plt.imshow(OIII_br_model,origin='lower') plt.plot(OIII_br_par[1],OIII_br_par[2],'bx') plt.show() plt.imshow(OIII_nr_data,origin='lower') plt.plot(OIII_nr_par[1],OIII_nr_par[2],'gx') plt.show() plt.imshow(OIII_nr_model,origin='lower') plt.plot(OIII_nr_par[1],OIII_nr_par[2],'gx') plt.show() # + z = {"HE0108-4743":0.02392} objs = z.keys() for obj in objs: (Hb_blr_br_data,OIII_br_data,OIII_nr_data,amp_Hb_blr_br,amp_OIII_br,amp_OIII_nr,Hb_blr_br_err,OIII_br_err,OIII_nr_err) = flux_data_err(obj) box_size = np.shape(Hb_blr_br_data)[1] y, x = np.mgrid[:box_size, :box_size] xy=(x,y) algorithm_script(obj,z[obj]) # + z = {"HE0021-1819":0.053197,"HE0040-1105":0.041692,"HE0108-4743":0.02392,"HE0114-0015":0.04560 ,"HE0119-0118":0.054341,"HE0212-0059":0.026385,"HE0224-2834":0.059800,"HE0227-0913":0.016451,"HE0232-0900":0.043143 ,"HE0253-1641":0.031588,"HE0345+0056":0.031,"HE0351+0240":0.036,"HE0412-0803":0.038160,"HE0429-0247":0.042009 ,"HE0433-1028":0.035550,"HE0853+0102":0.052,"HE0934+0119":0.050338,"HE1011-0403":0.058314,"HE1017-0305":0.049986 ,"HE1029-1831":0.040261,"HE1107-0813":0.058,"HE1108-2813":0.024013,"HE1126-0407":0.061960,"HE1237-0504":0.009 ,"HE1248-1356":0.01465,"HE1330-1013":0.022145,"HE1353-1917":0.035021,"HE1417-0909":0.044,"HE2128-0221":0.05248 ,"HE2211-3903":0.039714,"HE2222-0026":0.059114,"HE2233+0124":0.056482,"HE2302-0857":0.046860} objs = z.keys() for obj in objs: (Hb_blr_br_data,OIII_br_data,OIII_nr_data,amp_Hb_blr_br,amp_OIII_br,amp_OIII_nr,Hb_blr_br_err,OIII_br_err,OIII_nr_err) = flux_data_err(obj) box_size = np.shape(Hb_blr_br_data)[1] y, x = np.mgrid[:box_size, :box_size] xy=(x,y) algorithm_script(obj,z[obj]) # + z = {"HE0021-1810":0.05352} objs = z.keys() for obj in objs: (Hb_blr_br_data,OIII_br_data,OIII_nr_data,amp_Hb_blr_br,amp_OIII_br,amp_OIII_nr,Hb_blr_br_err,OIII_br_err,OIII_nr_err) = flux_data_err(obj) box_size = np.shape(Hb_blr_br_data)[1] y, x = np.mgrid[:box_size, :box_size] xy=(x,y) algorithm_script(obj,z[obj]) # - 21.63782717011039, 22.119457979406324 x,y 169.94.8480.2np.sqrt((21.65 - 22.18)2 + (21.68-21.81)2)	14,915
/Data Cleaning and Modelling/Modelling State Variables/Tidying up data.ipynb	091f6c51f2266b6640501d544dd65ee3d2b6519b	[ "MIT" ]	permissive	ashez2051/Metamodelling-of-Pandit-Hinch-Niederer-Model	https://github.com/ashez2051/Metamodelling-of-Pandit-Hinch-Niederer-Model	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,233,175	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # %pylab inline # + import os import urllib dataset = 'mnist.pkl.gz' def reporthook(a,b,c): print "\rdownloading: %5.1f%%"%(ab100.0/c), if not os.path.isfile(dataset): origin = "https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz" print('Downloading data from %s' % origin) urllib.urlretrieve(origin, dataset, reporthook=reporthook) # - import gzip import pickle with gzip.open(dataset, 'rb') as f: train_set, valid_set, test_set = pickle.load(f) print "train_set", train_set[0].shape, train_set[1].shape print "valid_set", valid_set[0].shape, valid_set[1].shape print "test_set", test_set[0].shape, test_set[1].shape imshow(train_set[0][0].reshape((28, 28)), cmap="gray") def show(x, i=[0]): plt.figure(i[0]) imshow(x.reshape((28,28)), cmap="gray") i[0]+=1 for i in range(5): print train_set[1][i] show(train_set[0][i]) W = np.random.uniform(low=-1, high=1, size=(2828,10)) b = np.random.uniform(low=-1, high=1, size=10) x = train_set[0][0] y = train_set[1][0] show(x) y Pr = exp(dot(x, W)+b) Pr.shape Pr = Pr/Pr.sum() print Pr Pr.argmax() loss = -log(Pr[y]) loss gradb = Pr.copy() gradb[y] -= 1 print gradb print Pr.shape, x.shape, W.shape gradW = dot(x.reshape(784,1), Pr.reshape(1,10), ) gradW[:, y] -= x W -= 0.1 gradW b -= 0.1 * gradb def compute_Pr(x): Pr = exp(dot(x, W)+b) return Pr/Pr.sum(axis=1, keepdims=True) def compute_accuracy(Pr, y): return mean(Pr.argmax(axis=1)==y) W = np.random.uniform(low=-1, high=1, size=(2828,10)) b = np.random.uniform(low=-1, high=1, size=10) score = 0 N=5000020 d = 0.001 learning_rate = 1e-2 for i in xrange(N): if i%50000==0: print i, "%5.3f%%"%(score100) x = train_set[0][i%50000] y = train_set[1][i%50000] Pr = exp(dot(x, W)+b) Pr = Pr/Pr.sum() loss = -log(Pr[y]) score =(1-d) if Pr.argmax() == y: score += d gradb = Pr.copy() gradb[y] -= 1 gradW = dot(x.reshape(784,1), Pr.reshape(1,10), ) gradW[:, y] -= x W -= learning_rate * gradW b -= learning_rate * gradb x = test_set[0][:10] y = test_set[1][:10] Pr = compute_Pr(x) print Pr.argmax(axis=1) print y for i in range(10): show(x[i]) nb.freesurfer.io.read_label((os.path.join( datadir,'fsaverage5/rh.cortex.label'))) surf_mesh = {} surf_mesh['coords'] = np.concatenate((Fs_Mesh_L[0], Fs_Mesh_R[0])) surf_mesh['tri'] = np.concatenate((Fs_Mesh_L[1], Fs_Mesh_R[1])) bg_map = np.concatenate((Fs_Bg_Map_L, Fs_Bg_Map_R)) medial_wall = np.concatenate((Mask_Left, 10242 + Mask_Right)) # - # # 3. plot mean thickness along the cortex fig02 = myvis.plot_surfstat(surf_mesh, bg_map, Mean_thickness, mask = medial_wall, cmap = 'viridis', vmin = 1.5, vmax = 4) # # 4. build the stats model # + term_intercept = FixedEffect(1, names="intercept") term_age = FixedEffect(age, "age") model = term_intercept + term_age slm = SLM(model, -age, surf=surf_mesh) slm.fit(thickness) tvals = slm.t.flatten() pvals = slm.fdr() print("t-values: ", tvals) # These are the t-values of the model. print("p-values: ", pvals) # These are the p-values of the model. fig03 = myvis.plot_surfstat(surf_mesh, bg_map, tvals, mask = medial_wall, cmap = 'gnuplot', vmin = tvals.min(), vmax = tvals.max()) fig04 = myvis.plot_surfstat(surf_mesh, bg_map, pvals, mask = medial_wall, cmap = 'YlOrRd', vmin = 0, vmax = 0.05) plt.show()	3,875
/Treemap.ipynb	5077fc53d3cc21737e557cfcb5ed7626c10a2c25	[]	no_license	yoxf/notebooks	https://github.com/yoxf/notebooks	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	5,370	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ## The Wonderful World of ML - Session 2 Assignment # Two of my favorite and most highly recommended references for machine learning are: # # - [An Introduction to Statistical Learning with Applications in R - James, Witten, Hastie, and Tibshirani](https://github.com/MichaelSzczepaniak/WonderfulML/raw/master/docs/ISLR%20Seventh%20Printing.pdf) # - [The Elements of Statistical Learning - Hastie, Tibshirani and Friedman](https://github.com/MichaelSzczepaniak/WonderfulML/raw/master/docs/TheElementsOfStatisticalLearning_Hastie_Tibshirani_Friedman_print10.pdf) # # Pdf versions of both of these books have been uploaded to the repo and can be downloaded using the links under each title. If you are relatively new to the area of machine learning, the first reference will be your friend. If you are an experienced pro, have great math skills and/or need more depth on a particular topic, the second reference is an excellent reference. I will refer to the first reference as the ISL and the second as the ESL throughout the rest of this series. # 1) If you haven't done so by now, install jupyter notebook and configure it with an R kernel if you are an R user. If you are Python user, your Anaconda install will have Python configured out of the box. # Python users - If you have installed the [latest version of Anaconda](https://www.continuum.io/downloads), you should have jupyter notebook as part of this install. If you have a distribution of Python which doesn't include jupyter, you can do a pip install as described [here](http://jupyter.readthedocs.io/en/latest/install.html). # # R users - Because jupyter runs on Python, you will also need to install a Python distribution if you don't have one installed on your system already. I recommend installing the [latest version of Anaconda](https://www.continuum.io/downloads) if you don't have a compelling reason not to use this distribution because it comes with jupyter as mentioned earlier. # # After Python and jupyter, I recommend that both R and Python users configure jupyter with an R kerenel. I followed the [instructions described in this video](https://www.youtube.com/watch?v=I9a9Jj2A95g) and used [this reference](https://irkernel.github.io/installation/) as I went through the process. # 2) What was the cost function Sondra mentioned that is used for linear regression? # Answer: The residual sum of squares: # # $$\sum_{i=1}^n(h_{\theta}(x_i) - y_i)^2$$ # 3) Equation (3.3) of the ISL defines the Residual Sum of Squares which can be written more generally as: # # $$ # RSS = \sum_{n=1}^N (\mathbf{t}_n - \mathbf{x}_n^T\mathbf{w})^2 # = \sum_{n=1}^N (\mathbf{t}_n - \mathbf{x}_n^T\mathbf{w})(\mathbf{t}_n - \mathbf{x}_n^T\mathbf{w})^T # $$ # # where $\mathbf{t}_n$ is referred to as the target vector for the the nth sample. Some texts refer to the target as $y$... # # For simple linear regression, we only have a single target $t$ and a single predictor $x$. If we substitute $y$ for $t$, $b$ for the intercept parameter $\hat{\beta_0}$ and $m$ for the slope parameter $\hat{\beta_1}$, the above equation simplifies to: # # $$ # RSS = \sum_{n=1}^N (y_n - (mx_n + b))^2 # $$ # # If I define the arrays $\mathbf{x}$ and $\mathbf{y}$ as: # # $$ # \mathbf{x} = # \begin{bmatrix} # 5 \\ 10 \\ 15 \\ 20 # \end{bmatrix}\quad # \mathbf{y} = # \begin{bmatrix} # 5.5 \\ 6.5 \\ 10.5 \\ 9.5 # \end{bmatrix} # $$ # # Create 3 plots on a single chart of $RSS$ on the y axis and the slope $m$ on the x axis for three values of b: 1, 3, and 5. The code in the next block will get you started. # + import numpy as np x = np.linspace(5, 20, num=4) y = np.array([5.5, 6.5, 10.5, 9.5]) m_vals = np.linspace(0, 1, num=6) b_vals = np.linspace(1, 5, num=3) def linearSquareResidual(targets, features, m, b): yhat = (m * features) + b sqr_res = (targets - yhat)2 return sqr_res def getLinRssVals(y_vec, x_vec, m_vec, b_vec): """ Returns an array of arrays. Each inner array is an array of RSS values for a particular intercept value (b) in b_vec. Each value in the inner array corresponds to a value of the slope in m_vec. """ rss_vals = [] # set the intercept for j in range(len(b_vec)): rss_m = [] # compute RSS for with set intecept over range of slope values for i in range(len(m_vec)): rss_m.append(sum(linearSquareResidual(y_vec, x_vec, m_vec[i], b_vec[j]))) rss_vals.append(np.array(rss_m)) return(np.array(rss_vals)) plot_data = getLinRssVals(y, x, m_vals, b_vals) # Plot 3 RSS curves import matplotlib as mp, matplotlib.pyplot as plt # %matplotlib inline # plot the points plt.figure(figsize=(12, 8)) plt.plot(m_vals, plot_data[0], 'ro', label='b = 1') plt.plot(m_vals, plot_data[1], 'go', label='b = 3') plt.plot(m_vals, plot_data[2], 'bo', label='b = 5') # plot smooth splines between the points m_interpolate_x = np.linspace(m_vals.min(), m_vals.max(), 300) # 300 interpolated points # We know RSS is quadratic in the parameters, so let's fit quadatics for # smooth looking curves - TODO: refactor next 2 lines as list comprehensions rss_quad_fits = (np.polyfit(m_vals, plot_data[0], 2), np.polyfit(m_vals, plot_data[1], 2), np.polyfit(m_vals, plot_data[2], 2)) f = np.poly1d(rss_quad_fits[0]), np.poly1d(rss_quad_fits[1]), np.poly1d(rss_quad_fits[2]) plt.plot(m_interpolate_x, f[0](m_interpolate_x), 'r') plt.plot(m_interpolate_x, f[1](m_interpolate_x), 'g') plt.plot(m_interpolate_x, f[2](m_interpolate_x), 'b') plt.ylim([0, 50]) # RSS get much larger, but need to zoom in to see minimas plt.legend(loc='upper right') plt.xlabel('m slope value') plt.ylabel('RSS') #x, y, m_vals, b_vals, linearSquareResidual(y, x, 0.4, 3), plot_data # - # Based on the plots you just built, what are the best values for m and b that fit this data? # Answer: green curve at m ~ 0.4, b = 3 and blue curve at m ~ 0.25, b = 5 have very similar minima # 4) You are thinking about using logistic regression to determine if your stock trading has a chance of making you some money. You design your own signal variable x which you derive from data that is readily available and use it to back-test your model on historical data. You simulate a trade for various values of x and assign a value of 1 if the trade made money and a 0 if it lost money. You plot your data, fit a sigmoid function through the data, and it looks like this: # # <img src="https://raw.githubusercontent.com/MichaelSzczepaniak/WonderfulML/master/docs/graphics/logistic_reg_stock_example.jpg"> # # What is the main assumption we are making in terms of how we are modeling this data? HINT: What quantity are we assuming can be modeled as a line? # Answer: From page 132 of the ISL, we are assuming that the probability of a winning trade can be modeled as a sigmoid function which implies that the natural log of the odds ratio $\ln{\bigg(\frac{p(x)}{1 - p(x)}\bigg)}$ is linear (see equation 4.4). # 5) You were excited to learn about K-Means clustering from Sondra's presentation and decided to give it a try. You first run an analyis in R and get one result which looks reasonable. You then run the same analysis in Python and again get results which look reasonable, but these results are substantially different from the results you obtained using R. # # Why do think you might have gotten different results on the same dataset? # Answer: The K-means algorithm is sensitive to the starting conditions your use (see ISL pages 388 and 389) # 6) The day after Sondra's presentation, you are having lunch with your colleague Chris who is working on helping a client who runs a large data center detect when servers may be at risk of failing. You are excited to learn that Chris is using anomaly detection to characterize the servers in the client's datacenter and ask her what her model looks like. # # Chris invites you over to her desk to show you two contour plots of probability density vs. two variables. The two variables in the first plot she calls x1 and x2 and the plot looks like this: # # <img src="https://raw.githubusercontent.com/MichaelSzczepaniak/WonderfulML/master/docs/graphics/circular_contours1.jpg"> # # She then shows you another contour plot of probability density vs. two different variable x3 and x4 which looks like this: # # <img src="https://raw.githubusercontent.com/MichaelSzczepaniak/WonderfulML/master/docs/graphics/eliptical_contours1.jpg"> # # What do these plots suggest about the relationship between x1 and x2 vs. the relationship between x3 and x4? # Answer: ** The variables x1 and x2 are independent which means the off-diagonal terms of the corvariance matrix $\sum_k$ in equation 4.8 in the ESL are all zero. The variables x3 and x4 appear to have some dependence which means the off-diagonal terms of the corvariance matrix $\sum_k$ in equation 4.8 in the ESL are non-zero. # # We'll see this equation again when we explore Linear and Quadratic Discriminant Analysis (LDA and QDA).	9,400
/ipynb/10-09/17. Sorting Techniques/.ipynb_checkpoints/156. Sorting Recursive Merge Sort-checkpoint.ipynb	d2bb584f720e1533256a39eef5c468f1412de119	[]	no_license	BSCdfdff/algorithmscpp	https://github.com/BSCdfdff/algorithmscpp	0	0	null	null	null	null	Jupyter Notebook	false	false	.cpp	8,921	// --- // jupyter: // jupytext: // text_representation: // extension: .cpp // format_name: light // format_version: '1.5' // jupytext_version: 1.15.2 // kernelspec: // display_name: C++17 // language: C++17 // name: xcpp17 // --- // # Merge Sort // ___ // // + Recurive (Top/Down) // // // // ## Recursive Merge Sort // // ___ // // Here we have the following unsorted array: // // // $$ // A= // \newcommand\T{\Rule{0pt}{1em}{.3em}} // \begin{array}{\|r\|r\|r\|r\|r\|r\|r\|r\|} // \hline // 8\T&2\T&9\T&6\T&5\T&3\T&7\T&4 \\\hline // _0\T&_1\T&_2\T&_3\T&_4\T&_5\T&_6\T&_7 \\\hline // \end{array} // $$ // // $$$$ // // Remember the idea with merge sort" // // 1. Break list into half // 2. Then break that into half // 3. And so on, until you end up with on element per list, and the list is itself. // 4. And we know that when we have 1 element, that element is sorted. // // Once it has a single list. // // 1. It will start merging into one single list. // // // // // // // ## Lets Code (Recursive) // ___ // // $$ // A= // \newcommand\T{\Rule{0pt}{1em}{.3em}} // \begin{array}{\|r\|r\|r\|r\|r\|r\|r\|r\|} // \hline // _l\T&_\T&_\T&_{mid}\T&_\T&_\T&_\T&_h\\\hline // 8\T&2\T&9\T&6\T&5\T&3\T&7\T&4 \\\hline // _0\T&_1\T&_2\T&_3\T&_4\T&_5\T&_6\T&_7 \\\hline // _i\T&\T&\T&\T&j\T&\T&\T& \\\hline // \end{array} // $$ // // // // // 1. Remember we divide the list into two halves, when there are more than one element. That is if l is less than h, get mid. // // ``` // if (l < h){ // mid=(l+h)/2; // ... // } // ``` // // 2. So the two halves must be merged with each other now. But merging can only happen, if the two halves are sorted. So how do we sort it. We sort the two halves RECURSIVELY, using merge sort. // // 3.So we perform merge sort for LHS, from l to mid: // // ``` // MergeSort(A,l,mid); // // ``` // 4.So we perform merge sort for RHS, from mid+1 to h: // // ``` // MergeSort(A,mid+1,h); // // ``` // // And the above (LHS and RHS), it will do(sort) recursively // // Then when LHS and RHS is sorted, it will need to sort it into single array. // // 5. And we merge the two list into single array: // // ``` // Merge(A, l, mid, h); // // ``` // // 6. And the above is last statement in recursive procedure. // 7. And its so small, nut its recursive, and we now how recursive function expands. // // // ``` // void MergeSort(int A[], int l, int h){ // // if (l < h){ // mid=(l+h)/2; // MergeSort(A,l,mid); // MergeSort(A,mid+1,h); // Merge(A, l, mid, h); // } // // } // ``` // // ## Lets trace the above // // ___ // // ``` // // +---+---+---+---+---+---+---+---+ // \| 8 \| 2 \| 9 \| 6 \| 5 \| 3 \| 7 \| 4 \| // +---+---+---+---+---+---+---+---+ // // // +---+---+ +---+---+ +---+---+ +---+---+ // \| 2 \| 8 \| \| 6 \| 9 \| \| 3 \| 5 \| \| 4 \| 7 \| // +---+---+ +---+---+ +---+---+ +---+---+ // // +---+---+---+---+ +---+---+---+---+ // \| 2 \| 6 \| 8 \| 9 \| \| 3 \| 4 \| 5 \| 7 \| // +---+---+---+---+ +---+---+---+---+ // // // +---+---+---+---+---+---+---+---+ // \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| // +---+---+---+---+---+---+---+---+ // // // ``` // // // // ``` // // // +-----------------------------+ // \| 0,7 \| // +-----------------------------+ // / \ // / \ // / \ // +----------+ +----------+ // \| 0,3 \| \| 4,7 \| // +----------+ +----------+ // / \ / \ // / \ / \ // +---+ +---+ +---+ +---+ // \|0,1\| \|2,3\| \|4,5\| \|6,7\| // /+---+\ /+---+\ /+---+\ /+---+\ // / \ / \ / \ / \ // / \ / \ / \ / \ // +---+ +---+ +---+ +---+ +---+ +---+ +---+ +---+ // \|0,0\| \|1,1\| \|2,2\| \|3,3\| \|4,4\| \|5,5\| \|6,0\| \|7,7\| // +---+ +---+ +---+ +---+ +---+ +---+ +---+ +---+ // ``` // // Merge sort is only sorting technique that requires an extra array or space (for comparison based soring) // // // #include <iostream> #include <climits> #include <math.h> #define INSERTION_OPERATOR operator<< #define EXTRACTION_OPERATOR operator>> #define ADDITION_OPERATOR operator+ using namespace std; // + void MergeSingleArray(int A[],int l, int mid, int h){ int i, j, k; i=l; j=mid+1; k=l; int B[100]; while (i <=mid && j <= h){ if (A[i] < A[j]) B[k++] = A[i++]; else B[k++] = A[j++]; } for (; i <= mid; i++) { B[k++] = A[i]; } for (; j <= h; j++) { B[k++] = A[j]; } //Copy B To A for (int i = l; i<=h;i++){ A[i]=B[i]; } } // + void MergeSortRecur(int A[], int l, int h){ int mid; if (l < h){ mid=(l+h)/2; MergeSortRecur(A,l,mid); MergeSortRecur(A,mid+1,h); MergeSingleArray(A, l, mid, h); } } // + int T[] = {2,5,8,12,3,6,7,10}; int l=0; int h=7; MergeSortRecur(T,l, h); for (int i = 0; i<=h;i++){ cout<<T[i]<<" "; } cout<<endl; // -	6,207
/21. Z-Test.ipynb	1f9de3d622fa0eb7b9c24dcb7e2953f7199aacc4	[]	no_license	shakirshakeelzargar/DATA-ANALYTICS-PYTHON-TRAINING	https://github.com/shakirshakeelzargar/DATA-ANALYTICS-PYTHON-TRAINING	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,538	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + # Demonstrating significant differences between a # vector of measurements and a single value # Using the statsmodels package for doing test from statsmodels.stats import weightstats as stests import numpy as np ls=range(1,100) data=np.asarray(ls) #data=np.random.normal(size=100) singleValue=3.3 # Assuming data are normally distributed, we can do z-test testResult=stests.ztest(data,value=singleValue) print(testResult) pValue=testResult[1] print("p-value is: "+str(pValue)) print("") # -	767
/catagory.ipynb	3caf25043ba3c81641a507cde946e80ca23a233e	[]	no_license	ckdrjs96/yogiyo2	https://github.com/ckdrjs96/yogiyo2	2	0	null	null	null	null	Jupyter Notebook	false	false	.py	6,492,841	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import pandas as pd import numpy as np import matplotlib.pyplot as plt from datetime import datetime countpop = pd.DataFrame(pd.read_csv("mass_county_pop.csv")) countpop countpop[countpop['County'] == 'Suffolk'].values[0][1] x = countpop.values.tolist() x[1][1] None sent_split=sent.split('\n') find=['사진','포토'] for sent in sent_split: for word in find: if re.search(word,sent): if extract==None: extract=sent else: extract=extract+'\n'+sent break return extract event['photo']=event.reviewevent.map(lambda x: extract_photo(x)) pd.set_option('display.max_colwidth', None) event[event.photo.notnull()] pd.set_option('display.max_rows', None) event.photo[event.photo.notnull()] def extract_star(sent): if sent != sent : return None extract=None sent_split=sent.split('\n') find=['별'] for sent in sent_split: for word in find: if re.search(word,sent): if extract==None: extract=sent else: extract=extract+'\n'+sent break return extract event['star']=event.reviewevent.map(lambda x: extract_star(x)) def extract_star2(sent): if sent != sent : return None extract=None sent_split=sent.split('\n') find=['별'] for sent in sent_split: token=okt.morphs(sent) for word in find: if word in token: if extract==None: extract=sent else: extract=extract+'\n'+sent break return extract event['star']=event.reviewevent.map(lambda x: extract_star2(x)) event.star[event.star.notnull()] len(event.star[event.star.notnull()]) def extract_nickname(sent): if sent != sent : return None extract=None sent_split=sent.split('\n') find=['닉네임','아이디'] for sent in sent_split: for word in find: if re.search(word,sent): if extract==None: extract=sent else: extract=extract+'\n'+sent break return extract event['nickname']=event.reviewevent.map(lambda x: extract_nickname(x)) len(event.nickname[event.nickname.notnull()]) def extract_jimm(sent): if sent != sent : return None extract=None sent_split=sent.split('\n') find=['찜'] findx=['계란찜','갈비찜','아구찜','해물찜','찜닭','찜탕','두찜','행찜','뼈다귀찜'] for sent in sent_split: if re.search(find[0],sent): cnt=0 for word in findx: if re.search(word,sent): break else: if extract==None: extract=sent else: extract=extract+'\n'+sent return extract event['jimm']=event.reviewevent.map(lambda x: extract_jimm(x)) len(event.jimm[event['jimm'].notnull()]) event.head() # + #event.to_csv(PATH+'category.csv') # - def change(x): if x==None : return False else: return True event['photo_t']=event.photo.map(lambda x: change(x)) event['star_t']=event.star.map(lambda x: change(x)) event['nickname_t']=event.nickname.map(lambda x: change(x)) event['jimm_t']=event.jimm.map(lambda x: change(x)) event.jimm[event['jimm'].notnull()] event_t=event.iloc[:,[0,1,2,3,8,9,10,11]] # + #event_t.to_csv(PATH+'category_t.csv') # - def extract_jimm2(sent): if sent != sent : return None extract=None sent_split=sent.split('\n') find=['찜'] for sent in sent_split: if re.search(find[0],sent): clean_sent=re.sub('[^가-힣]',' ',sent) if '찜'in okt.morphs(clean_sent): if extract==None: extract=sent else: extract=extract+'\n'+sent return extract event_t def delete(isservice,t): if isservice: return t else: return None def catagory(photo,star,nickname,jimm): cnt=0 if photo: cnt=cnt+1 if star: cnt=cnt+2 if nickname: cnt+=4 if jimm: cnt+=8 return cnt event_t['catagory']=event_t.apply(lambda x:catagory(x['photo_t'],x['star_t'],x['nickname_t'],x['jimm_t']),axis=1) event_t['catagory']=event_t.apply(lambda x: delete(x['isservice'],x['catagory']),axis=1) event_t event_t.groupby('catagory').count().iloc[:,1] event_t.catagory.value_counts(sort=True).plot(kind='bar') catagory2=event_t[['shop','catagory']] # + #catagory2.to_csv(PATH+'catagory2.csv',index=False) # - event_t.dropna(inplace=True) event_t.photo_t.value_counts() event_t.star_t.value_counts() event_t.nickname_t.value_counts() event_t.jimm_t.value_counts() event_t.to_csv(PATH+'iscatagory.csv', index=False) catagory2.catagory.map(lambda x: -1 if x !=x else x ) catagory2.catagory=catagory2.catagory.map(lambda x: -1 if x !=x else x ) # + #catagory2.to_csv(PATH+'catagory2.csv',index=False)	5,393
/MATPLOTLIB/1_Matplotlib.ipynb	a2b5589f99877c3d0a29331ab8016bf960250cc9	[]	no_license	JunaidMalik997/Data-Science	https://github.com/JunaidMalik997/Data-Science	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	394,149	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # 1. Matplotlib is the most popular plotting library for Python. # 2. It gives us control over every aspect of a figure # 3. It was designed to have a similar feel to Matlab's graphical plotting # 4. It works very well with Pandas and Numpy arrays # 5. conda install matplotlib # 6. matplotlib.org (official website of matplotlib) import matplotlib.pyplot as plt # %matplotlib inline #this command in Jupyter Notebook is going us to allow us to see the plots we create inside the jupyter notebook # + #plt.show() #if not using jupyter notebook # - import numpy as np x=np.linspace(0,5,11) y=x**2 x y #Functional Method of Matplotlib for creating the plots plt.plot(x,y) plt.xlabel('X Label') plt.ylabel('Y Label') plt.title('Title') # + #drawing multi plots on the same canvas plt.subplot(2,1,1) #subplot takes in the arguments no.of rows, no.of columns and the plot number we are referring to plt.plot(x,y) plt.subplot(2,1,2) plt.plot(y,x) # + plt.subplot(1,2,1) plt.plot(x,y,'r') #plot represented by red line plt.subplot(1,2,2) plt.plot(y,x,'b') #plot represented by blue line # + #Object Oriented Method in Matplotlib for creating plots #The main idea in using a more formal object oriented method is create figure objects and then call methods off of it. # - fig=plt.figure() #The purpose of using plt.figure() is to create a figure object. #The whole figure is regarded as the figure object. #a figure object has been created. It is just an imaginary blank canvas # + #we can also add a set of aixs to this canvas axes=fig.add_axes([0.1,0.1,0.8,0.8]) #add_axes takes 4 arguments -->left,bottom,width,height(in a list) -->range from 0 and 1(basically the %age of blank canvas #we want to take) axes.plot(x,y) # + #putting in 2 sets of figure on one canvas fig=plt.figure() #creating figure object axes1=fig.add_axes([0.1,0.1,0.8,0.8]) axes2=fig.add_axes([0.2,0.5,0.4,0.3]) axes1.plot(x,y) axes1.set_title('Larger Plot') axes2.plot(y,x) axes2.set_title('Smaller Plot') # - fig=plt.figure() axes1=fig.add_axes([0.1,0.1,0.8,0.8]) axes1.plot(x,y) #we get empty set of axes and then we can plot of that axes # # Matplotlib Part 2 # + #create subplots using object oriented programming # - import matplotlib.pyplot as plt import numpy as np fig,axes=plt.subplots(nrows=1,ncols=2) #tuple unpacking #the above statement is just the fancy way of calling [fig=plt.figure()] and [axes1=fig.add_axes([])] (combo of both) #axes.plot(x,y) axes #it is just an array of matplotlib axes(list of axes object) #since it is a list of matplotlib axes we can actually iterate over it (below cell) #these are the axes which we manually created when we said [fig.add_axes([])] # + fig,axes=plt.subplots(nrows=1,ncols=2) for current_ax in axes: current_ax.plot(x,y) # + #since we can iterate through the axes object which is a list we can also index it. fig,axes=plt.subplots(nrows=1,ncols=2) axes[0].plot(x,y) axes[0].set_title('First Plot') axes[1].plot(y,x) axes[1].set_title('Second Plot') plt.tight_layout() #takes care of any overlapping plots # - # # Figure Size and DPI # + #Figure Size, Aspect Ration and DPI(dots per inch/pixels per inch) #Matplotlib allows us to control each of the aspects, and we can specify them when we are calling the figure object # + fig=plt.figure(figsize=(3,2)) #figsize is the tuple which is the width and height of the figure in inches ax=fig.add_axes([0,0,1,1]) ax.plot(x,y) # + fig=plt.figure(figsize=(8,2)) #figsize is the tuple which is the width and height of the figure in inches ax=fig.add_axes([0,0,1,1]) ax.plot(x,y) # + fig,axes=plt.subplots(figsize=(8,2)) axes.plot(x,y) # + fig,axes=plt.subplots(nrows=2,ncols=1,figsize=(8,2)) axes[0].plot(x,y) axes[1].plot(y,x) plt.tight_layout() # + #how to save a figure. To save a figure we can use matplotlib to generate just high quality outputs in a number of formats #jpg,png,jpeg,pdf etc fig # - fig.savefig('My_picture.jpg',dpi=200) #we specify the name of the file and the file format we want #it saves the figure in the same location as this .ipynb file # + #basics fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y) ax.set_title('Title') ax.set_xlabel('X') ax.set_ylabel('Y') # + #legends, with legends we can use labeled text to actually clarify what plot is what plot fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y) ax.plot(y,x) #we will get two plots on the same graph # + ##we would need to add in a legend to reocgnize each plot like in above cell #place ax.legend() at the end of code and define labels as follows fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,label='Exponential Increase') ax.plot(y,x,label='Exponential Decrease') ax.legend() #looks at the plot calls and checks to see if there is a label # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,label='Exponential Increase') ax.plot(y,x,label='Exponential Decrease') ax.legend(loc=0) #chooses best legend location for our plot (always recommended) #check documentation of legend() command to look for other locations # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,label='Exponential Increase') ax.plot(y,x,label='Exponential Decrease') ax.legend(loc=10) #centre #ax.legend(loc=(0.1,0.1)) # - # # Matplotlib Part 3 # + #Plot Appearance # + #setting colors with matplotlib fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green') #we can pass in the string for very basic colors # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='#FF8C00') #RGB Hex Code--> We can make our own custom colors (search on google for more) # + #Line width and line style fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',linewidth=3) # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,alpha=0.5) #alpha helps us control how transparent the line is #lw represents Line Width--> and it will still work # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,linestyle='--') # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,linestyle='-.') # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,ls='steps') # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,ls='-') # + #let's talk about markers #Markers are going to be used when we have a few datapoints # - x len(x) #x is an array of 11 points. Let's say we want to mark those 11 points on the plot (below cell) # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,linestyle='-',marker='o',markersize=10) #Refer to jupyter notebook for more details # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,linestyle='-',marker='o',markersize=10, markerfacecolor='yellow') # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,linestyle='-',marker='o',markersize=10, markerfacecolor='yellow',markeredgewidth=3) # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,linestyle='-',marker='o',markersize=10, markerfacecolor='yellow',markeredgewidth=3,markeredgecolor='red') # - # # Control over axis appearance (Plot Range) # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=2,ls='--') # + #let's say we only want to show the plot between 0 and 1 on x axis fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=2,ls='--') ax.set_xlim([0,1]) #specify limits of lower bound and upper bound for x axis (pass a list) # + fig=plt.figure() ax=fig.add_axes([0,0,1,1]) ax.plot(x,y,color='green',lw=3,ls='--') ax.set_xlim([0,1]) ax.set_ylim([0,2]) # -	8,056
/01-DataJoint Basics - Interactive.ipynb	7905cee9d2f0016d2b8e386dce573d15cb2b38fd	[]	no_license	xibby/playground_tutorial	https://github.com/xibby/playground_tutorial	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	41,046	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Getting started with DataJoint # Now that you have successfully connected to DataJoint (if not, please visit [Connecting to DataBase](00-ConnectingToDatabase.ipynb) first), let's dive into using DataJoint! In this notebook, we will: # # 1. learn what a data pipeline is # 2. create our first simple data pipeline in DataJoint # 3. insert some data into the pipeline # 4. basic queries to flexibly explore the data pipeline # 5. fetch the data from the pipeline # 6. delete entries from tables # As always, let's start by importing the `datajoint` library. import datajoint as dj # # So... What is a data pipeline? # If you visit the [documentation for DataJoint](https://docs.datajoint.io/introduction/Data-pipelines.html), we define a data pipeline as follows: # > A data pipeline is a sequence of steps (more generally a directed acyclic graph) with integrated storage at each step. These steps may be thought of as nodes in a graph. # # While this is an accurate description, it may not be the most intuitive definition. Put succinctly, a data pipeline is a listing or a "map" of various "things" that you work with in a project, with line connecting things to each other to indicate their dependecies. The "things" in a data pipeline tends to be the nouns you find when describing a project. The "things" may include anything from mouse, experimenter, equipment, to experiment session, trial, two-photon scans, electric activities, to receptive fields, neuronal spikes, to figures for a publication! A data pipeline gives you a framework to: # # 1. define these "things" as tables in which you can store the information about them # 2. define the relationships (in particular the dependencies) between the "things" # # A data pipeline can then serve as a map that describes everything that goes on in your experiment, capturing what is collected, what is processed, and what is analyzed/computed. A well designed data pipeline not only let's you organize your data well, but can bring out logical clarity to your experiment, and may even bring about new insights by making how everything in your experiment relates together obvious. # # Let's go ahead and build together a pipeline from scratch to better understand what a data pipeline is all about. # # Building our first pipeline: # Let's build a pipeline to collect, store and process data and analysis for our hypothetical single electrode recording or calcium imaging recording in mice. To help us understand the project better, here is a brief description: # > * Your lab houses many mice, and each mouse is identified by a unique ID. You also want to keep track of information about each mouse such as their date of birth, and gender. # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on an any given day, a mouse undergoes at most one recording session. # > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. # # > * In a session of electrophysiology # >> * you record electrical activity from a single neuron. You use recording equipment that produces separate data files for each neuron you recorded. # >> * Neuron's activities are recorded as raw traces. Neuron's spikes needs to be detected for further analysis to be performed. # > * In a sesssion of calcium imaging # >> * you scan a brain region containing a number of neurons. You use recording equipment that produces separate data files for each scan you performed. # >> * you would like to segment the frames and get the regions of interest (ROIs), and save a mask for each ROI # >> * finally you would like to extract the trace from each segmented ROI # Pipeline design starts by identifying things or entities in your project. Common entities includes experimental subjects (e.g. mouse), recording sessions, and two-photon scans. # Let's revisit the project description, this time paying special attention to what (e.g. nouns) about your experiment. Here I have highlighted some nouns in particular. # > * Your lab houses many mice, and each mouse is identified by a unique ID. You also want to keep track of information about each mouse such as their date of birth, and gender. # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on an any given day, a mouse undergoes at most one recording session. # > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. # # > * In a session of electrophysiology # >> * you record electrical activity from a single neuron. You use recording equipment that produces separate data files for each neuron you recorded. # >> * Neuron's activities are recorded as raw traces. Neuron's spikes needs to be detected for further analysis to be performed. # > * In a sesssion of calcium imaging # >> * you scan a brain region containing a number of neurons. You use recording equipment that produces separate data files for each scan you performed. # >> * you would like to segment the frames and get the regions of interest (ROIs), and save a mask for each ROI # >> * finally you would like to extract the trace from each segmented ROI # Just by going though the description, we can start to identify things or entities that we might want to store and represent in our data pipeline: # # * mouse # * experimental session # # For ephys: # # >* neuron # >* spikes # # For calcium imaging: # # >* scan # >* regions of interest # >* trace # In the current notebook, we will design the tables for mouse and experimental sessions, the rest of the pipeline will be designed in the subdirectory `electrophysioloy` and `calcium_imaging` # In DataJoint data pipeline, we represent these entities as tables. Different kinds of entities become distinct tables, and each row of the table is a single example (instance) of the category of entity. # # For example, if we have a `Mouse` table, then each row in the mouse table represents a single mouse! # When constructing such table, we need to figure out what it would take to uniquely identify each entry. Let's take the example of the mouse and think about what it would take to uniquely identify a mouse. # After some thought, we might conclude that each mouse can be uniquely identified by knowing its mouse ID - a unique ID number assigned to each mouse in the lab. The mouse ID is then a column in the table or an attribute that can be used to uniquely identify each mouse. Such attribute is called the primary key of the table. # # \| mouse_id* \| # \|:--------:\| # \| 11234 \| # \| 11432 \| # Once we have successfully identified the primary key of the table, we can now think about what other columns, or non-primary key attributes that we would want to include in the table. These are additional information about each entry in the table that we want to store. # For the case of mouse, what other information about the mouse you might want to store? Based on the project description, we would probably want to store information such as the mouse's date of birth and gender. # \| mouse_id* \| dob \| sex \| # \|:--------:\|------------\|--------\| # \| 11234 \| 2017-11-17 \| M \| # \| 11432 \| 2018-03-04 \| F \| # Now we have an idea on how to represent information about mouse, let's create the table using DataJoint! # ## Create a schema - house for your tables # Every table lives inside a schema - a logical collection of one or more tables in your pipeline. Your final pipeline may consists of many tables spread across one or more schemas. Let's go ahead and create the first schema to house our table. # We create the schema using `dj.schema()` function, passing in the name of the schema. For this workshop, you are given the database privilege to create any schema starting with your username followed by a `_` charcter. So if your username is `john`, you can make any schema starting with `john_`, such as `john_tutorial`. # Let's create a schema called `pipeline`, prefixed by `username_`. schema = dj.schema('{YOUR_USERNAME}_tutorial') # Now that we have a schema to place our table into, let's go ahead and define our first table! # ## Creating your first table # In DataJoint, you define each table as a class, and provide the table definition (e.g. attribute definitions) as the `definition` static string property. The class will inherit from the `dj.Manual` class provided by DataJoint (more on this later). @schema class Mouse(dj.Manual): definition = """ # Experimental animals mouse_id : int # Unique animal ID --- dob=null : date # date of birth sex="unknown" : enum('M','F','unknown') # sex """ # Let's take a look at our brand new table Mouse() # ## Insert entries with `insert1` and `insert` methods # The table was successfully defined, but without any content, the table is not too interesting. Let's go ahead and insert some mouse into the table, one at a time using the `insert1` method. # Let's insert a mouse with the following information: # * mouse_id: 0 # * date of birth: 2017-03-01 # * sex: male Mouse.insert1((0, '2017-03-01', 'M')) Mouse() # You could also insert1 as a dictionary data = { 'mouse_id': 100, 'dob': '2017-05-12', 'sex': 'F' } Mouse.insert1(data) Mouse() # We can also insert multiple mice together using the `insert` method, passing in a list of data. data = [ (1, '2016-11-19', 'M'), (2, '2016-11-20', 'unknown'), (5, '2016-12-25', 'F') ] Mouse.insert(data) # Of course, you can insert a list of dictionaries # + data = [ {'mouse_id': 10, 'dob': '2017-01-01', 'sex': 'F'}, {'mouse_id': 11, 'dob': '2017-01-03', 'sex': 'F'}, ] # insert them all Mouse.insert(data) # - Mouse() # ## Data integrity # DataJoint checks for data integrity, and ensures that you don't insert a duplicate by mistake. Let's try inserting another mouse with `mouse_id: 0` and see what happens! Mouse.insert1( {'mouse_id': 0, 'dob': '2018-01-01', 'sex': 'M', }) # Go ahead and insert a few more mice into your table before moving on. # + data = [ {'mouse_id': 12, 'dob': '2017-03-21', 'sex': 'F'}, {'mouse_id': 18, 'dob': '2017-05-01', 'sex': 'F'}, {'mouse_id': 19, 'dob': '2018-07-21', 'sex': 'M'}, {'mouse_id': 22, 'dob': '2019-12-15', 'sex': 'F'}, {'mouse_id': 34, 'dob': '2018-09-22', 'sex': 'M'} ] # insert them all Mouse.insert(data) # - Mouse() # ENTER YOUR CODE - Insert more mice # ## Create tables with dependencies # Congratulations! We have successfully created your first table! We are now ready to tackle and include other entities in the project into our data pipeline. # # Let's now take a look at representing an experimental session. # As with mouse, we should think about what information (i.e. attributes) is needed to uniquely identify an experimental session. Here is the relevant section of the project description: # # > * As a hard working neuroscientist, you perform experiments every day, sometimes working with more than one mouse in a day! However, on an any given day, a mouse undergoes at most one recording session. # > * For each experimental session, you would like to record what mouse you worked with and when you performed the experiment. You would also like to keep track of other helpful information such as the experimental setup you worked on. # Based on the above, it appears that you need to know: # # * the date of the session # * the mouse you recorded from in that session # # to uniquely identify a single experimental session. # Note that, to uniquely identify an experimental session (or simply a session), we need to know the mouse that the session was about. In other words, a session cannot existing without a corresponding mouse! # # With mouse already represented as a table in our pipeline, we say that the session depends on the mouse! We would graphically represent this in an entity relationship diagram (ERD) by drawing the line between two tables, with the one below (session) dependeing on the one above (mouse). # Thus we will need both mouse and a new attribute session_date to uniquely identify a single session. # # Remember that a mouse is already uniquely identified by its primary key - mouse_id. In DataJoint, you can declare that session depends on the mouse, and DataJoint will automatically include the mouse's primary key (`mouse_id`) as part of the session's primary key, along side any additional attribute(s) you specificy. @schema class Session(dj.Manual): definition = """ # Experiment session -> Mouse session_date : date # date --- experiment_setup : int # experiment setup ID experimenter : varchar(100) # experimenter name data_path='' : varchar(255) # """ # You can actually generate the entity relationship diagram (ERD) on the fly by calling `dj.ERD` with the schema object dj.ERD(schema) # Let's try inserting a few sessions manually. # + data = { 'mouse_id': 0, 'session_date': '2017-05-15', 'experiment_setup': 0, 'experimenter': 'Edgar Y. Walker' } Session.insert1(data) # - Session() # Let's insert another session for `mouse_id = 0` but on a different date. # + data = { 'mouse_id': 0, 'session_date': '2018-01-15', 'experiment_setup': 100, 'experimenter': 'Jacob Reimer' } Session.insert1(data) Session() # - # And another session done on the same date but on a different mouse # + data = { 'mouse_id': 18, 'session_date': '2018-01-15', 'experiment_setup': 101, 'experimenter': 'Jacob Reimer' } # insert them all Session.insert1(data) # - Session() # What happens if we try to insert a session for a mouse that doesn't exist? bad_data = { 'mouse_id': 9999, # this mouse doesn't exist! 'session_date': '2017-05-15', 'experiment_setup': 0, 'experimenter': 'Edgar Y. Walker' } Session.insert1(bad_data) # # Querying data # Often times, you don't want all data but rather work with a subset of entities matching specific criteria. Rather than fetching the whole data and writing your own parser, it is far more efficient to narrow your data to the subset before fetching. # # For this, DataJoint offers very powerful yet intuitive querying syntax that let's you select exactly the data you want before you fetch it. # # It is also critical to note that the result of any DataJoint query represents a valid entity. # We will introduce three major types of queries used in DataJoint: # * restriction (`&`) and negative restriction (`-`): filter data # * join (``): bring fields from different tables together # projection (`.proj()`): focus on a subset of attributes # * aggregation (`.aggr()`): simple computation of one table against another table # ## Restrictions (`&`) - filter data with certain conditions # The restriction operation, `&`, let's you specify the criteria to narrow down the table on the left. # ### Exact match # Mouse with id 0 Mouse & 'mouse_id = 0' # All male mice (`'sex = "M"'`) Mouse & 'sex = "M"' # All female mice (`'sex="F"'`) Mouse & 'sex = "F"' # We can also use as a dictionary as a restrictor, with one field or multiple fields Mouse & dict(mouse_id=5) # ### Inequality # You can also use inequality in your query to match based on numerical values. # Mouse that is born after 2017-01-01 Mouse & 'dob > "2017-01-01"' # Mouse that is born within a range of dates Mouse & 'dob between "2017-03-01" and "2017-08-23"' # Mouse that is not male Mouse & 'sex != "M"' # You can easily combine multiple restrictions to narrow down the entities based on multiple attributes. # Let's find all mouse that is not male AND born after 2017-01-01. Mouse & 'sex != "M"' & 'dob > "2017-01-01"' Mouse & 'sex != "M" and dob > "2017-01-01"' # Result of one query can be used in another query! Let's first find all female mice and store the result. female_mice = Mouse & 'sex = "F"' female_mice # and among these mice, find ones with mouse_id > 10 # ENTER YOUR CODE # In computer science/math lingo, DataJoint operations are said to satisfy closure property. Practically speaking, this means that the result of a query can immediately be used in another query, allowing you to build more complex queries from simpler ones. # ### Restriction one table with another # All mice that has a session Mouse & Session # ### Combining restrictions # All the above queries could be combined # Male mice that had a session Mouse & Session & 'sex = "M"' # Give me all mice that have had an experimental session done on or before 2017-05-19 Mouse & (Session & 'session_date <= "2017-05-19"') # ### Negative restriction - with the `-` operator # All mice that do not have any session Mouse - Session # Male mice that do not have any session # ENTER YOUR CODE # ## Joining () - bring fields from different tables together # Sometimes you want to see information from multiple tables combined together to be viewed (and queried!) simultaneously. You can do this using the join `` operator. # Behavior of join: # # 1. match the common field(s) of the primary keys in the two tables # 2. do a combination of the non-matched part of the primary key # 3. listing out the secondary attributes for each combination # 4. if two tables have secondary attributes that share a same name, it will throw an error. To join, we need to rename that attribute for at least one of the tables. # looking at the combination of mouse and session Mouse * Session # Here each row represents a unique (and valid!) combination of a mouse and a session. # The combined table can be queried using any of the attributes (columns) present in the joined tables: # Find 'experimenter = "Jacob Reimer"' and 'sex = "M"' Mouse * Session & 'experimenter = "Jacob Reimer"' & 'sex = "M"' Mouse * Session & 'session_date > "2017-05-19"' # ## Projection .proj(): focus on attributes of interest # Beside restriction (`&`) and join (``) operations, DataJoint offers another type of operation: projection (`.proj()`). Projection is used to select attributes (columns) from a table, to rename them, or to create new calculated attributes. # From the Mouse* table, suppose we want to focus only on the `sex` attribute and ignore the others, this can be done as: Mouse.proj('sex') # Note that `.proj()` will always retain all attributes that are part of the primary key # ### Rename attribute with proj() # Say we want to rename the exisiting attribute `dob` of the `Mouse` table to `date_of_birth`, this can be done using `.proj()` Mouse.proj(date_of_birth='dob') # ### Perform simple computations with proj() # Projection is perhaps most useful to perform simple computations on the attributes, especially on attributes from multiple tables by using in conjunction with the join (``) operation (Mouse Session).proj(age='datediff(session_date, dob)') # Note: as you can see, the projection results keep the primary attributes from the `Mouse * Session` joinning operation, while removing all other non-primary attributes. To Keep all other attributes, you can use the `...` syntax (Mouse * Session).proj(..., age='datediff(session_date, dob)') # # Fetch data # Once you have successfully narrowed down to the entities you want, you can fetch the query results just by calling fetch on it! # ## Fetch one or multiple entries: `fetch()` # All male mouse male_mouse = Mouse & 'sex = "M"' male_mouse # Fetch it! male_mouse.fetch() # or all in one step (Mouse & 'sex = "M"').fetch() # Fetch as a list of dictionaries (Mouse & 'sex = "M"').fetch(as_dict=True) # Fetch as a pandas dataframe (Mouse & 'sex = "M"').fetch(format='frame') # ### Fetch the primary key (Mouse & 'sex = "M"').fetch('KEY') # ### Fetch specific fields sex, dob = Mouse.fetch('sex', 'dob') sex dob # Or fetch them together as a list of dictionaries info = Mouse.fetch('sex', 'dob', as_dict=True) info # ## Fetch data from only one entry: `fetch1()` # When knowing there's only 1 result to be fetched back, we can use `.fetch1()`. `fetch1` will always return the fetched result in a dictionary format mouse_0 = (Mouse & {'mouse_id': 0}).fetch1() # "fetch1()" because we know there's only one mouse_0 # `fetch1()` could also fetch the primary key (Mouse & {'mouse_id': 0}).fetch1('KEY') # or fetch specific fields: sex, dob = (Mouse & {'mouse_id': 0}).fetch1('sex', 'dob') sex dob # ## Deletion (`.delete()`) - deleting entries and their dependencies # Now we have a good idea on how to restrict table entries, this is a good time to introduce how to delete entries from a table. # To delete a specific entry, you restrict the table down to the target entry, and call `delete` method. (Mouse & 'mouse_id = 100').delete() # Calling `delete` method on an unrestricted table will attempt to delete the whole table! Mouse.delete() # # Summary # Congratulations! You have successfully created your first DatJoint pipeline, using dependencies to establish the link between the tables. You have also learned to query and fetch the data. # # In the next session, we are going to extend our data pipeline with tables to represent imported data and define new tables to compute and hold analysis results. # # We will use both ephys and calcium imaging as example pipelines: # + [02-electrophysiology](./electrophysiology/02-Imported%20Tables%20-%20Interactive.ipynb) # + [02-calcium imaging](./calcium_imaging/02-Imported%20Tables%20-%20Interactive.ipynb)	22,521
/Titanic_Submission.ipynb	3847c4da61df87956d5156ab11719ffa321b584a	[]	no_license	Homni/Data-Udacity	https://github.com/Homni/Data-Udacity	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	160,313	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # TITANIC # ## Data Analysis # The data was provided by Udacity as part of the Data Analysis Nanodegree. This data contains demographics and passenger information from 891 of the 2224 passengers and crew on board the Titanic. Extra detail on the data can be found on Kaggles website on the following [Link](https://www.kaggle.com/c/titanic/data). # ### Questions # # This Data Analysis will explore the hypothesis that woman and upper class passengers are the majority of survivors. # # 1. Are woman a majority of survivors? # 2. Does having an upper class ticket means surviving the titanic? # import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # %pylab inline #Add the data csv file to original var original = pd.read_csv('titanic_data.csv') #Use df as variable for data so original is available for backups df = original #look at the first 3 rows of the data df.head(3) #look at the first 3 rows of the data df.tail(3) # Both Age and Cabin show NaN's, that will be investigated later. # # Columns with Passenger ID, Name, Ticket, will be excluded due to being unique to each passenger and not indicating a relation to the questions being investigated. #exclude Passenger ID, Name and Ticket df = df.drop(['PassengerId', 'Name', 'Ticket'], axis = 1) #check how it is now df.head() print('We are left with',df.shape[0],'lines and',df.shape[1],'columns') # The columns contain the following description: # # - Survived : 1 = Yes, 0 = No # - Pclass 1 : Upper, 2 = Middle, 3 = Lower # - Sex # - Age # - SibSp : Number of Siblings/Spouses Aboard # - Parch : Number of Parents/Children Aboard # - Fare : Passenger Fare # - Cabin # - Embarked : Port of Embarkation # C = Cherbourg # Q = Queenstown # S = Southampton #check what kind of data df.dtypes #to better look at the data, change letter for cities emberked df['Embarked'] = df['Embarked'].replace(['S','Q','C'],['Southampton','Queenstown','Cherbourg']) df.head() # Dealing with NaN's #looking at the entire data set numbers df.describe() #checking how many nulls. df.isnull().sum() # Due to data sample being large, and age being expected to be a large influence on the survival rate and questions investigated, it was prefered to exclude NaN's from Age, which would still remain with a sample size of 714. # # Embarked NaN's are also excluded. # # Cabin seems to be the biggest concern, over 70% is NaN's. Thus, the Cabin column is excluded. However, if more information on cabin was available, their locations and emergency exits, it could be a good source of information. #excluding Cabin df = df.drop(['Cabin'], axis=1) #excluding rows with NaN's df = df.dropna() df.head() # We can now start looking at the data from a graphical point of view in order to look for patterns # + #create a variable for percenta number, and total of surviving females df_female_per = around(df[df.Sex=='female'].Survived.value_counts(normalize=True).sort_index(),2) df_female_num = df[df.Sex=='female'].Survived.value_counts().sort_index() df_female_sum = df[df.Sex=='female'].Survived.value_counts().sort_index().sum() #create a variable for percent, number, and total of surviving males df_male_per = around(df[df.Sex=='male'].Survived.value_counts(normalize=True).sort_index(),2) df_male_num = df[df.Sex=='male'].Survived.value_counts().sort_index() df_male_sum = df[df.Sex=='male'].Survived.value_counts().sort_index().sum() g = sns.FacetGrid(df, col="Sex") g.map(plt.hist, "Age"); print('The boat had', df_male_num.sum(),'males and', df_female_num.sum(),'females distributed the following way by age:') print('It is imaginable that males will be the majority of survival, since they are the majority on board') print("\n") # - # We can also see that both male and female are positively skewed and that the AGE range between 20 and 40 contains the higher number of individuals print("In summary we can see that females have a", (df_female_per[1]100),'%', "survival rate, with", (df_female_num[1]), "surviving from a total of",(df_female_sum),".") print("When comparing to man, the female numbers seem even more surprising. Man have a survival rate of", (df_male_per[1]100),'%', "with", (df_male_num[1]),"surviving from a total of ",(df_male_sum),".") # The plots below show how significant the diference is between male and female: # + #have a graph that shows the difference between male and fimale fig, ax = plt.subplots() #two graphs combined n_groups = 2 bar_width = 0.40 index = np.arange(n_groups) opacity = 0.9 error_config = {'ecolor': '0.3'} #graph for the male variable rects1 = plt.bar(index, df_male_num, 0.2, alpha=opacity, color='b', label='Men') #graph for the female variable rects2 = plt.bar(index + 0.15, df_female_num, 0.185, alpha=opacity, color='g', label='Woman') plt.xlabel('Survived (0=No, 1=Yes)') plt.ylabel('Scores') plt.title('Survival by Sex') plt.xticks(index + bar_width/2, ('0', '1')) plt.legend() plt.show() # - #graph that checks if class makes a difference in sex survival rate g = sns.factorplot(x="Sex", y="Survived", col="Pclass", data=df, saturation=0.9, kind="bar", ci=None, aspect=.6) (g.set_axis_labels("", "Survived") .set_xticklabels(["Men", "Women"]) .set_titles("{col_name} {col_var}") .set(ylim=(0, 1)) .despine(left=True)) #check if different embarking cities make a difference g = sns.factorplot(x="Sex", y="Survived", col="Embarked", data=df, saturation=0.9, kind="bar", ci=None, aspect=.6) (g.set_axis_labels("", "Survived") .set_xticklabels(["Men", "Women"]) .set_titles("{col_name} {col_var}") .set(ylim=(0, 1)) .despine(left=True)) print('The graph below shows that the embarking city has the same relation: most females surviving') print("\n") # ### Question 1 # # The series graphs above shows that despite man being the majority on the boat, woman survival rate is higher on all classes and has no distinction among where those people boarded the boat. # # Based on the different shown graphs above, it could be interpreted that being a woman on the titanic gives you a 75% chance of survival, no mather which class you are on the boat or which city you embarked. # ________________________ # # ### The same process from question 1 can be taken to analyse question 2 # + df_pclass1_per = round(df[df.Pclass==1].Survived.value_counts(normalize=True).sort_index(),2) df_pclass1_num = df[df.Pclass==1].Survived.value_counts().sort_index() df_pclass1_sum = df[df.Pclass==1].Survived.value_counts().sort_index().sum() df_pclass2_per = round(df[df.Pclass==2].Survived.value_counts(normalize=True).sort_index(),2) df_pclass2_num = df[df.Pclass==2].Survived.value_counts().sort_index() df_pclass2_sum = df[df.Pclass==2].Survived.value_counts().sort_index().sum() df_pclass3_per = round(df[df.Pclass==3].Survived.value_counts(normalize=True).sort_index(),2) df_pclass3_num = df[df.Pclass==3].Survived.value_counts().sort_index() df_pclass3_sum = df[df.Pclass==3].Survived.value_counts().sort_index().sum() # + #graph that shows all classes and its total passengers df_class = df['Pclass'].value_counts().sort_index() df_class.plot(kind='bar', figsize=(4,4), rot=0, grid=True) plt.title('Number of Passengers by Class') plt.xlabel('Class') plt.ylabel('Number of Passengers') print('We can see that the number of passengers on the Lower class is equal to the sum of both middle and upper class') print("\n") # - g_class_age = sns.FacetGrid(df, col="Pclass") g_class_age.map(plt.hist, "Age"); print('The graphs below shows Age distribution by class') print('We can see that Lower class, which is the majority of individuals on board, is mainly on the 20 to 40 age range') print("\n") # + n_groups = 2 fig, ax = plt.subplots() index = np.arange(n_groups) bar_width = 0.4 opacity = 0.9 error_config = {'ecolor': '0.3'} rects1 = plt.bar(index, df_pclass1_num, bar_width, alpha=opacity, color='b', label='Upper' ) rects2 = plt.bar(index + 0.15, df_pclass2_num, bar_width, alpha=opacity, color='g', label='Middle' ) rects3 = plt.bar(index + 0.30, df_pclass3_num, bar_width, alpha=opacity, color='r', label='Lower' ) plt.xlabel('Survived (0=No, 1=Yes)') plt.ylabel('Scores') plt.title('Survival by Class') plt.xticks(index + bar_width/2, ('0', '1')) plt.legend() plt.show() # + print("In summary we can see that the boat had", (df_class.sum()), "passengers, divided into 3 classes:") print("Upper Class, with 184 passengers, middle class with 173 passengers and lower class with 355 passengers") print("When comparing to each other, the lower class number is the biggest surprise. Lower class have a survival rate of", (df_pclass3_per[1]100),'%', "with", (df_pclass3_num[1]),"surviving from a total of ",(df_pclass3_sum),".") print("On the other end, upper class have a survival rate of", (df_pclass1_per[1]100),'%', "with", (df_pclass1_num[1]),"surviving from a total of ",(df_pclass1_sum),".") print("Lastly, middle class have a survival rate of", (df_pclass2_per[1]*100),'%', "with", (df_pclass2_num[1]),"surviving from a total of ",(df_pclass2_sum),".") # - # ### Question 2 # # The series graphs above show a similar trend from the sex comparison. The majority of passenger is composed of passengers in the lower class. However, it was the upper and middle classes that got the most survivors. # # Based on the different shown graphs above, it could be interpreted that being on upper class gives a better chance of surviving, when traveling on middle class that chance diminishes to a slightly higher chance of not surviving. For the lower class, the chance of surviving is minimal. # #### Project Limitations # # - Missing Values: This analysis is done on a sample of the total passanger number, so conclusion may be biased do due not having the entire dataset. When we first analyse the data we see that we have many missing values from the sample, which in turn reduces the sample from 891 to 714 # # - Other Variables: Basic variables are provided for this analysis. However other variables could also influence the survival rate. Such as: Being a crew member; where there lifeboats specifically for 1st class?; Cabin location related to lifeboats. These are a few variable that could influence our perception of who would have a higher survival rate. # # - Correlation does not imply Causation: Altough we may imply that being a female and not being on 3rd class increases the chances of surviving. It is not possible to conclude that there is a causation relationship from that, specially when we look at a sample with missing values. # # # ### References: # - http://seaborn.pydata.org/index.html # - http://matplotlib.org/examples/ # - https://www.kaggle.com/c/titanic/data # - http://stackoverflow.com/questions/14883339/two-bar-charts-in-matplotlib-overlapping-the-wrong-way # - http://pandas.pydata.org/ # - https://docs.scipy.org # - https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation	11,658
/Joy Ride/circular_track.ipynb	a051c559d4df0784c1612edd581efb5a845c9d6c	[]	no_license	lordtt13/self-driving-car-nanodegree	https://github.com/lordtt13/self-driving-car-nanodegree	1	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,994	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # goal: 用 mpl 的最新 API 画 k 线图。不再学习老 API。 # # 意外收获： # # 1. 多了成交量、均线图等常用指标。 # 2. 不开盘的日期，已从图中自动剔除，不再需要额外处理。 # 3. 相邻两天的矩形，不会相互覆盖。 # # 不愧是社区基础最好的库，新版本的改进很明显。 # # - 测试数据来源：https://raw.githubusercontent.com/matplotlib/mplfinance/master/examples/data/SP500_NOV2019_Hist.csv # - 本地路径：data/for-tutorial-only/SP500_NOV2019_Hist.csv import pandas as pd import mplfinance as mpf test_data_path = '../data/for-tutorial-only/SP500_NOV2019_Hist.csv' # Load data file. df = pd.read_csv(test_data_path, index_col=0, parse_dates=True) df.head() df.size # Plot candlestick. # Add volume. # Add moving averages: 3,6,9. mpf.plot(df, type='candle', style='charles', title='S&P 500, Nov 2019', ylabel='Price ($)', ylabel_lower='Shares \nTraded', volume=True, mav=(3, 6, 9), ) import matplotlib as mpl # 用于设置曲线参数 from cycler import cycler # 用于定制线条颜色 # + # 设置基本参数 # type:绘制图形的类型，有candle, renko, ohlc, line等 # 此处选择candle,即K线图 # mav(moving average):均线类型,此处设置7,30,60日线 # volume:布尔类型，设置是否显示成交量，默认False # title:设置标题 # y_label_lower:设置成交量图一栏的标题 # figratio:设置图形纵横比 # figscale:设置图形尺寸(数值越大图像质量越高) kwargs = dict( type='candle', mav=(3, 6, 9), volume=True, title='S&P 500, Nov 2019', ylabel='OHLC Candles', ylabel_lower='Shares\nTraded Volume', # figratio=(15, 10), # figscale=2, ) # 设置marketcolors # up:设置K线线柱颜色，up意为收盘价大于等于开盘价 # down:与up相反，这样设置与国内K线颜色标准相符 # edge:K线线柱边缘颜色(i代表继承自up和down的颜色)，下同。详见官方文档) # wick:灯芯(上下影线)颜色 # volume:成交量直方图的颜色 # inherit:是否继承，选填 mc = mpf.make_marketcolors( up='red', down='green', edge='i', wick='i', volume='in', inherit=True) # 设置图形风格 # gridaxis:设置网格线位置 # gridstyle:设置网格线线型 # y_on_right:设置y轴位置是否在右 s = mpf.make_mpf_style( gridaxis='both', gridstyle='-.', y_on_right=True, marketcolors=mc) # 设置均线颜色，配色表可见下图 # 建议设置较深的颜色且与红色、绿色形成对比 # 此处设置七条均线的颜色，也可应用默认设置 mpl.rcParams['axes.prop_cycle'] = cycler( color=['dodgerblue', 'deeppink', 'navy', 'teal', 'maroon', 'darkorange', 'indigo']) # # 设置线宽 # mpl.rcParams['lines.linewidth'] = .5 # 图形绘制 # show_nontrading:是否显示非交易日，默认False # savefig:导出图片，填写文件名及后缀 mpf.plot(df, **kwargs, style=s, show_nontrading=False, ) # -	2,509
/lectures/7.0 - Bagging.ipynb	bb371caa562c7222971536754789f3740b3a7a54	[]	no_license	vitaliyradchenko/projector_course	https://github.com/vitaliyradchenko/projector_course	9	6	null	2020-02-18T16:56:03	2020-02-18T07:29:10	Jupyter Notebook	Jupyter Notebook	false	false	.py	47,928	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="-4IC1hKgJQzo" # # <center> Bagging</center> # # From the previous lectures, you have already learned about different classification algorithms and also learned how to properly validate and evaluate the quality of the model. But what if you have already found the best model and can no longer improve the accuracy of the model? In this case, you need to apply more advanced machine learning techniques, which can be combined with the word “ensembles”. The ensemble is a kind of collection, the parts of which form a single whole. From everyday life, you know musical ensembles, where several musical instruments are combined, architectural ensembles with different buildings, etc. # # ### Ensemble # # A good example of ensembles is the Condorcet's jury theorem (1784). If each member of the jury has an independent opinion, and if the probability of a correct decision by a jury member is greater than 0.5, then the probability of a correct decision by the jury as a whole increases with the number of jurors and tends to one. If the probability of being right for each of the jury members is less than 0.5, then the probability of making the right decision by the jury as a whole monotonically decreases and tends to zero with an increase in the number of jurors. # # - $\large N $ — number of jurors # - $\large p $ — probability of correct jury decision # - $\large \mu $ — probability of correct decision of the entire jury # - $\large m $ — minimum majority of jury members, $ m = floor(N/2) + 1 $ # - $\large C_N^i$ — [combinations](https://en.wikipedia.org/wiki/Combination) $i$ elements, the number of $N$-combinations # $$ \large \mu = \sum_{i=m}^{N}C_N^ip^i(1-p)^{N-i} $$ # If $\large p > 0 $, then $\large \mu > p $ # If $\large N \rightarrow \infty $, then $\large \mu \rightarrow 1 $ # # # <img src="https://github.com/terpiljenya/machine_learning_projector/blob/main/pictures/bull.png?raw=1" align="right" width=15% height=15%> # # # Let's look at another example of ensembles - "The Wisdom of the Crowd". Francis Galton in 1906 visited the market, where a certain lottery was held for the peasants. # There were about 800 of them and they tried to guess the weight of the bull that stood in front of them. His weight was 1198 pounds. Not a single peasant guessed the exact weight of the bull, but if we calculate the average of their predictions, we get 1197 pounds. # This idea of error reduction has also been applied to machine learning. # # ## Bootstrap # # Bagging (or Bootstrap aggregation) is one of the first and simplest types of ensembles. It was invented by Leo Breiman in 1994. Bagging is based on the statistical method of bootstrapping, which allows estimation of many statistics of complex models. # # The bootstrap method is as follows. Let there be a sample $\large X$ of size $\large N$. Let's evenly take $\large N$ objects from the sample with return. This means that we will select an arbitrary sample object $\large N$ times (we assume that each object "gets" with the same probability $\large \frac{1}{N}$), and each time we choose from all initial $\large N$ objects. One can imagine a bag from which balls are taken out: the ball chosen at some step is returned back to the bag, and the next choice is again made equiprobably from the same number of balls. Note that due to the return, there will be repetitions among them. Denote the new sample by $\large X_1$. Repeating the procedure $\large M$ times, we generate $\large M$ subsamples $\large X_1, \dots, X_M$. Now we have a sufficiently large number of samples and can evaluate various statistics of the original distribution. # # Let's take a credit scoring dataset as an example. This is a binary classification task, where 0 is not overdue, 1 is overdue. One of the features in this dataset is the age of the client. Let's try to visualize the data and look at the distribution of this feature. # + id="4SZzD0KKJMa3" colab={"base_uri": "https://localhost:8080/", "height": 282} outputId="73c9cb7d-4921-4472-bdc5-4ab5693a17f3" import pandas as pd from matplotlib import pyplot as plt plt.style.use('ggplot') plt.rcParams['figure.figsize'] = 10, 6 import seaborn as sns # %matplotlib inline data = pd.read_csv("/content/drive/MyDrive/projector_course_data/credit_scoring_sample.csv", sep=";") fig = sns.kdeplot(data.loc[data["SeriousDlqin2yrs"] == 0, "age"], label = "Not late payment") fig = sns.kdeplot(data.loc[data["SeriousDlqin2yrs"] == 1, "age"], label = "Late payment") fig.set(xlabel="Age", ylabel="Density") plt.show() # + [markdown] id="4C_A-qFixXDJ" # As you may have noticed, the older the client of the bank, the better he repays the loan. Now it would be good to estimate the average age for each group. Since there is not enough data in our dataset, it is not entirely correct to look for the average, it is better to apply our new bootstrap knowledge. Let's generate 1000 new subsamples from our population and do an interval estimate of the mean. # + colab={"base_uri": "https://localhost:8080/"} id="cw-2qXPQwopc" outputId="483b6001-9b92-4bb7-fb22-b5b8225b8c7e" import numpy as np def get_bootstrap_samples(data, n_samples): # function to generate subsamples using bootstrap indices = np.random.randint(0, len(data), (n_samples, len(data))) samples = data[indices] return samples def stat_intervals(stat, alpha): # function for interval estimation boundaries = np.percentile(stat, [100 * alpha / 2., 100 * (1 - alpha / 2.)]) return boundaries # saving in separate numpy arrays data on loyal and already former customers good_credit = data.loc[data["SeriousDlqin2yrs"] == 0, "age"].values bad_credit = data.loc[data["SeriousDlqin2yrs"] == 1, "age"].values # set a seed for reproducible results np.random.seed(0) # generate samples using bootstrap and calculate the average for each group good_credit_mean_scores = [ np.mean(sample) for sample in get_bootstrap_samples(good_credit, 1000) ] bad_credit_mean_scores = [ np.mean(sample) for sample in get_bootstrap_samples(bad_credit, 1000) ] print("Age of good creditors: mean interval", stat_intervals(good_credit_mean_scores, 0.05)) print("Age of bad creditors: mean interval", stat_intervals(bad_credit_mean_scores, 0.05)) # + [markdown] id="fPylTMTbyU2-" # As a result, we got that with a 95% probability, the average age of loyal customers will be approximately 52-53 years old, while our bad customers are 6 years younger than them. # # ## Bagging # # Now you have an idea about bootstrapping, we can move on to bagging. Let there be a training sample $\large X$. Using the bootstrap, we will generate $\large X_1, \dots, X_M$ samples from it. Now, on each sample, we will train our classifier $\large a_i(x)$. The final classifier will average the answers of all these algorithms (in the case of classification, this corresponds to voting): $\large a(x) = \frac{1}{M}\sum_{i = 1}^M a_i(x)$. This scheme can be represented in the picture below. # # <img src="https://github.com/terpiljenya/machine_learning_projector/blob/main/pictures/bagging.png?raw=1" alt="image"/> # # Consider a regression problem with basic algorithms $\large b_1(x), \dots , b_n(x)$. Assume that there is a true response function for all $\large y(x)$ objects, and a distribution on $\large p(x)$ objects is given. In this case, we can write down the error of each regression function $$ \large \varepsilon_i(x) = b_i(x) − y(x), i = 1, \dots, n$$ # and write the mean squared error $$ \large E_x(b_i(x) − y(x))^{2} = E_x \varepsilon_i (x). $$ # # The average error of the constructed regression functions has the form $$ \large E_1 = \frac{1}{n}E_x \sum_{i=1}^n \varepsilon_i^{2}(x) $$ # # Assume that the errors are unbiased and uncorrelated: # # $$ \large \begin{array}{rcl} E_x\varepsilon_i(x) &=& 0, \\ # E_x\varepsilon_i(x)\varepsilon_j(x) &=& 0, i \neq j. \end{array}$$ # # Now let's build a new regression function that will average the answers of the functions we built: # $$ \large a(x) = \frac{1}{n}\sum_{i=1}^{n}b_i(x) $$ # # Find its root mean square error: # # $$ \large \begin{array}{rcl}E_n &=& E_x\Big(\frac{1}{n}\sum_{i=1}^{n}b_i(x)-y(x)\Big)^2 \\ # &=& E_x\Big(\frac{1}{n}\sum_{i=1}^{n}\varepsilon_i\Big)^2 \\ # &=& \frac{1}{n^2}E_x\Big(\sum_{i=1}^{n}\varepsilon_i^2(x) + \sum_{i \neq j}\varepsilon_i(x)\varepsilon_j(x)\Big) \\ # &=& \frac{1}{n}E_1\end{array}$$ # # Thus, averaging the answers made it possible to reduce the mean square of the error by n times! # # # Recall how the general error is decomposed: # $$\large \begin{array}{rcl} # \text{Err}\left(\vec{x}\right) &=& \mathbb{E}\left[\left(y - \hat{f}\left(\vec{x}\right)\right)^2\right] \\ # &=& \sigma^2 + f^2 + \text{Var}\left(\hat{f}\right) + \mathbb{E}\left[\hat{f}\right]^2 - 2f\mathbb{E}\left[\hat{f}\right] \\ # &=& \left(f - \mathbb{E}\left[\hat{f}\right]\right)^2 + \text{Var}\left(\hat{f}\right) + \sigma^2 \\ # &=& \text{Bias}\left(\hat{f}\right)^2 + \text{Var}\left(\hat{f}\right) + \sigma^2 # \end{array}$$ # # Bagging allows you to reduce the variance (variance) of the trained classifier, reducing the amount by which the error will differ if the model is trained on different data sets, or in other words, it prevents overfitting. The efficiency of bagging is achieved due to the fact that the basic algorithms trained on different subsamples turn out to be quite different, and their errors are mutually compensated during voting, and also due to the fact that outlier objects may not fall into some training subsamples. # # The `scikit-learn` library has an implementation of `BaggingRegressor` and `BaggingClassifier` that allows most other algorithms to be used "inside". Let's see how bagging works in practice and compare it with a decision tree using an example from [documentation](http://scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py). # # ![image](https://github.com/terpiljenya/machine_learning_projector/blob/main/pictures/tree_vs_bagging.png?raw=1) # # Decision tree error: # $$ \large 0.0255 (Err) = 0.0003 (Bias^2) + 0.0152 (Var) + 0.0098 (\sigma^2) $$ # Bagging error: # $$ \large 0.0196 (Err) = 0.0004 (Bias^2) + 0.0092 (Var) + 0.0098 (\sigma^2) $$ # # From the graph and results above, it can be seen that the error of variance is much smaller with bagging, as we proved theoretically above. # # Bagging is effective on small samples, when the exclusion of even a small part of training objects leads to the construction of significantly different base classifiers. In the case of large samples, subsamples of significantly smaller length are usually generated. # # It should be noted that the example we have considered is not very applicable in practice, since we made the assumption of uncorrelated errors, which is rarely true. If this assumption is wrong, then the error reduction is not so significant. In the following lectures, we will consider more complex methods for combining algorithms into a composition, which allow us to achieve high quality in real problems. # # ### Out-of-bag error # # Looking ahead, when using random forests, there is no need for cross-validation or a separate test set to get an unbiased estimate of the test set error. The internal evaluation during operation is obtained as follows: # # Each tree is built using different bootstrap samples from the original data. Approximately 37% of examples remain outside the bootstrap sample and are not used when constructing the k-th tree. # # This can be easily proved: let there be $\large \ell$ objects in the sample. At each step, all objects fall into the subsample with an equiprobable return, i.e. an individual object with probability $\large\frac{1}{\ell}.$ The probability that the object will NOT fall into the subsample (i.e. took $\large \ell$ times): $\large (1 - \frac{1}{\ell})^\ell$. For $\large \ell \rightarrow +\infty$ we get one of the "wonderfull" limits $\large \frac{1}{e}$. Then the probability of a particular object falling into the subsample is $\large \approx 1 - \frac{1}{e} \approx 63\%$. # # Let's see how this works in practice: # # ![image](https://github.com/terpiljenya/machine_learning_projector/blob/main/pictures/oob.png?raw=1) # # The figure shows that our classifier made a mistake in 4 observations that we did not use for training. So the accuracy of our classifier is: $\large \frac{11}{15}*100\% = 73.33\%$ # # It turns out that each basic algorithm is trained on ~63% of the original objects. This means that on the remaining ~37% can be checked immediately. The out-of-bag score is the average score of the underlying algorithms on those ~37% of the data on which they were not trained. # # + id="L4YyR49xyOgH" import warnings warnings.filterwarnings('ignore') scoring_label = data["SeriousDlqin2yrs"].astype(int) scoring_features = data.drop(["SeriousDlqin2yrs"], axis=1) # + colab={"base_uri": "https://localhost:8080/"} id="UhFBYABH8Ia3" outputId="f659a361-196e-43a3-d22c-382a7a4f8daf" scoring_features.isnull().mean() # + id="MOzF4ahw8K4N" scoring_features = scoring_features.fillna(scoring_features.median()) # + colab={"base_uri": "https://localhost:8080/"} id="f25Ebkgm8Ocz" outputId="63d8fd4a-007b-4184-fbd3-44cb87b672b9" scoring_features.isnull().mean() # + id="GmZ9Gcy98RWy" from sklearn.model_selection import StratifiedKFold, cross_val_score, RandomizedSearchCV from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import BaggingClassifier # + id="gg3oNu_W8WMl" skf = StratifiedKFold(shuffle=True, random_state=42) # + colab={"base_uri": "https://localhost:8080/"} id="PwQVqqOr8Xc1" outputId="1b731bec-d8ca-437d-d72b-618912e54c92" dt = DecisionTreeClassifier() parameters = {"max_depth": [None, 4, 8, 12, 15], "min_samples_leaf": [1, 2, 3, 5, 8]} r_grid_search = RandomizedSearchCV(dt, parameters, scoring ="roc_auc", cv=skf, random_state=42) r_grid_search = r_grid_search.fit(scoring_features, scoring_label) print(r_grid_search.best_score_) # + colab={"base_uri": "https://localhost:8080/"} id="3ZyATloC8e0_" outputId="51cba3f4-c8d5-48b7-9770-bd918877cba4" r_grid_search.best_estimator_ # + colab={"base_uri": "https://localhost:8080/"} id="3aTPRwYF8g-g" outputId="fe74f31d-db79-4a67-f05f-74925fed0ae7" parameters = { "max_features": [0.7, 0.8, 0.9], "max_samples": [0.7, 0.8, 0.9], "base_estimator__max_depth": [None, 4, 8, 12, 15], "base_estimator__min_samples_leaf": [1, 2, 3, 5, 8] } dt = DecisionTreeClassifier() bg = BaggingClassifier(dt, random_state=42, n_estimators=25) r_grid_search = RandomizedSearchCV(bg, parameters, scoring ='roc_auc', n_iter=20, cv=skf, random_state=42, n_jobs=10) r_grid_search = r_grid_search.fit(scoring_features, scoring_label) print(r_grid_search.best_score_) # + colab={"base_uri": "https://localhost:8080/"} id="Vatjmk4v8kov" outputId="52c39cd6-735a-4ee7-a4c4-040f43ba55b2" r_grid_search.best_estimator_ # + id="a3MYUelE8msX"	15,212
/course-content/Examples/matplotlib_inline.ipynb	5db6f82ea856e5785505dd443ce433d39ae7e728	[ "Apache-2.0" ]	permissive	ati-ozgur/course-python	https://github.com/ati-ozgur/course-python	2	1	null	null	null	null	Jupyter Notebook	false	false	.py	1,787	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import matplotlib.pylab as pl import pandas as pd csv_filename ="../data/c2k_data_comma.csv" df = pd.read_csv(csv_filename) ax = df[["i1_legid","i1_rcs_p"]].plot() pl.show() # %matplotlib inline df[["i1_legid","i1_rcs_p"]].plot() lts",engine) cr=county_results cr.head() # # Which 10 counties Bernie has the highest vote? bs=cr[cr.candidate=="Bernie Sanders"].sort_values(by=['votes'],ascending=False).head(10) dems=cr[(cr.candidate=="Bernie Sanders")\|(cr.candidate=="Hillary Clinton")] dems.head() compbh=pd.pivot_table(dems,values='votes',index=['state','county'],columns=['candidate']) compbh.columns compbh.head() compbh.sort_values("Bernie Sanders", ascending=False).head() # # Who won which states? compbh=pd.pivot_table(dems,values='votes',index='state',columns='candidate', aggfunc=sum) compbh.head() compbh['votediff']=compbh['Bernie Sanders']-compbh['Hillary Clinton'] compbh.sort_values('votediff',ascending=False) cf=pd.read_sql('county_facts',engine) cf.head() data=pd.merge(cr,cf,left_on='fips',right_on='fips') data.head() # # How does family size impact the voting decision? pd.pivot_table(data,values='votes',index="fips",columns='candidate',aggfunc=max).dropna() pt=pd.pivot_table(cr,values='votes',index=['state'],columns=['candidate'],aggfunc=max) pt.iloc[0:,1:].apply(np.max,axis=1) pt.iloc[1,2:][pt.iloc[1,2:]==440.0] def findmax(row): max_val=np.max(row) name=row[row==max_val].index[0] return name pt.iloc[0:,2:].apply(findmax,axis=1) np.max(pt.iloc[1,2:])	1,806
/homework/Day_033_HW.ipynb	39e3f63f606448697059bdf7486080e047f5e1ed	[]	no_license	ryanlu7374/ML100-Days	https://github.com/ryanlu7374/ML100-Days	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	21,964	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + language="javascript" # IPython.OutputArea.prototype._should_scroll = function(lines) { # return false; # } # + #load all the files for a user from matplotlib.pyplot import figure import random import glob import pandas as pd import numpy as np import matplotlib.pyplot as plt import datetime as dt import seaborn as sns import math import os import errno import matplotlib.patches as patches from copy import deepcopy from scipy.spatial.distance import cdist from matplotlib.patches import Ellipse import operator import pdb import calendar from sklearn.cluster import KMeans from sklearn import metrics from scipy.spatial.distance import cdist from matplotlib.patches import Ellipse, Circle # #%matplotlib nbagg pd.options.mode.chained_assignment = None staypts_filename = "C:/Users/12sha/Documents/thesislocation/Data/Final Example Results/User 001/200811/staypoints/staypoints.csv" raw_filename = "C:/Users/12sha/Documents/Geolife Trajectories 1.3/Data/001/Trajectory/200811.plt" destpng = "C:/Users/12sha/Documents/thesislocation/Writings/media/staypoint_test.png" # + filenames = glob.glob(raw_filename) list_of_files_dfs = [pd.read_csv(filename, skiprows=6, header = None) for filename in filenames] raw_df = pd.concat(list_of_files_dfs, ignore_index=True) raw_df.columns = ['Latitude', 'Longitude', '0', 'Altitude', 'NumDays', 'Date', 'Time'] staypts_df = pd.read_csv(staypts_filename, sep = '\t') # drop columns not needed raw_df = raw_df.drop(['0', 'Altitude', 'NumDays', 'Date', 'Time'], axis=1) staypts_df = staypts_df.drop(['Latitude', 'Longitude', 'Timestamp', 'Date', 'Time', 'Hour', 'Weekday', 'StayPoint', 'StayptId', 'StayMeanLat', 'StayMeanLon', 'State', 'StateId', 'Unnamed: 0'], axis=1) staypts_df.columns = ['Latitude', 'Longitude'] #raw_df = raw_df.drop_duplicates() raw_df = raw_df.reset_index(drop=True) #staypts_df = staypts_df.drop_duplicates() staypts_df = staypts_df.reset_index(drop=True) # + #assign the first lat and log as the base for the plot i.e. origin origin_lat = math.radians(raw_df["Latitude"][0]) origin_lon = math.radians(raw_df["Longitude"][0]) #convert each lat and lon into x and y for the plot w.r.t origin EARTH_RAD = 6378100 raw_df['X'] = 0.0 raw_df['Y'] = 0.0 for i in range(0, len(raw_df)): x = 0 y = 0 current_lat = math.radians(raw_df["Latitude"][i]) current_lon = math.radians(raw_df["Longitude"][i]) x = ((math.cos(current_lat) + math.cos(origin_lat))/2) EARTH_RAD * (current_lon - origin_lon) * math.pi / 180 y = (current_lat - origin_lat)* math.pi/180 * EARTH_RAD raw_df.at[i, 'X'] = x raw_df.at[i, 'Y'] = y fg = raw_df.plot(x='X', y='Y', color='g', label='Raw Coordinates') plt.title('User Movement Trend Vs Stay-points', fontsize = 30) min_x = min(raw_df['X']) max_x = max(raw_df['X']) min_y = min(raw_df['Y']) max_y = max(raw_df['Y']) xticks = np.arange(min_x,max_x,(max_x-min_x)/25) yticks = np.arange(min_y,max_y,(max_y-min_y)/50) fg.set(xticks=xticks, yticks=yticks) plt.xlabel('X') plt.ylabel('Y') #assign the first lat and log as the base for the plot i.e. origin origin_lat = math.radians(raw_df["Latitude"][0]) origin_lon = math.radians(raw_df["Longitude"][0]) #convert each lat and lon into x and y for the plot w.r.t origin EARTH_RAD = 6378100 staypts_df['X'] = 0.0 staypts_df['Y'] = 0.0 for i in range(0, len(staypts_df)): x = 0 y = 0 current_lat = math.radians(staypts_df["Latitude"][i]) current_lon = math.radians(staypts_df["Longitude"][i]) x = ((math.cos(current_lat) + math.cos(origin_lat))/2) * EARTH_RAD * (current_lon - origin_lon) * math.pi / 180 y = (current_lat - origin_lat)* math.pi/180 * EARTH_RAD staypts_df.at[i, 'X'] = x staypts_df.at[i, 'Y'] = y plt.plot(staypts_df['X'], staypts_df['Y'], 'r^', label='Stay-points', markersize=12) plt.rcParams["figure.figsize"] = [20,20] plt.legend(fontsize=30) plt.xlabel("X", fontsize=25) plt.ylabel("Y", fontsize=25) plt.savefig(destpng) plt.show() # - ttps://gis.stackexchange.com/questions/350771/earth-engine-simplest-way-to-move-from-ee-image-to-array-for-use-in-sklearn/351177#351177 # runoff_missing = -99999 # pot_runoff_data = np.array( # pot_runoff_img_scaled # .sampleRectangle(bbox, ['potential_sfc_runoff_mon_clim_cms'], smap_missing) # .get('potential_sfc_runoff_mon_clim_cms') # .getInfo()) # pot_runoff_data_mask = np.ma.masked_values(pot_runoff_data, smap_missing) # pot_runoff_data_drop = pot_runoff_data_mask[~pot_runoff_data_mask.mask] # + # print(pot_runoff_data) # + # pot_runoff_data_mask # pot_runoff_data_drop # + # _ = norm.normal_dist_plot(avail_porosity_data_drop) # - # ## MERIT Terrain Slope #include slope as a factor in scoring product 1 slope_img = ee.Image('users/jamesmcc/merit_slope/merit_terrain_slope') slope_mask = slope_img.lte(6) slope_img_scaled = norm.img_scale(slope_img, area_of_interest=common.bboxes()['conus']) # This dosent really need scaled, just a mask? # # Soil Types #incorporate soil types as a factor in scoring land parcels soil_types = ee.Image("OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02") # + #categorizing soil types and depths based on retaining plant-available water, grouping top soils and bottom soils together top_soils = [5,7,8,10] medium_soils = [2,4,6,9] low_soils = [1,3,11,12] soil_0 = soil_types.expression( "(b('b0') == 12) ? 1.0" + ": (b('b0') == 11) ? 1.0" + ": (b('b0') == 10) ? 1.0" + ": (b('b0') == 9) ? 1.0" + ": (b('b0') == 8) ? 0.7" + ": (b('b0') == 7) ? 0.7" + ": (b('b0') == 6) ? 0.7" + ": (b('b0') == 5) ? 0.7" + ": (b('b0') == 4) ? 0.4" + ": (b('b0') == 3) ? 0.4" + ": (b('b0') == 2) ? 0.4" + ": (b('b0') == 1) ? 0.4" + ": 0") soil_10 = soil_types.expression( "(b('b10') == 12) ? 1.0" + ": (b('b10') == 11) ? 1.0" + ": (b('b10') == 10) ? 1.0" + ": (b('b10') == 9) ? 1.0" + ": (b('b10') == 8) ? 0.7" + ": (b('b10') == 7) ? 0.7" + ": (b('b10') == 6) ? 0.7" + ": (b('b10') == 5) ? 0.7" + ": (b('b10') == 4) ? 0.4" + ": (b('b10') == 3) ? 0.4" + ": (b('b10') == 2) ? 0.4" + ": (b('b10') == 1) ? 0.4" + ": 0") soil_30 = soil_types.expression( "(b('b30') == 12) ? 1.0" + ": (b('b30') == 11) ? 1.0" + ": (b('b30') == 10) ? 1.0" + ": (b('b30') == 9) ? 1.0" + ": (b('b30') == 8) ? 0.7" + ": (b('b30') == 7) ? 0.7" + ": (b('b30') == 6) ? 0.7" + ": (b('b30') == 5) ? 0.7" + ": (b('b30') == 4) ? 0.4" + ": (b('b30') == 3) ? 0.4" + ": (b('b30') == 2) ? 0.4" + ": (b('b30') == 1) ? 0.4" + ": 0") soil_60 = soil_types.expression( "(b('b60') == 5) ? 1.0" + ": (b('b60') == 7) ? 1.0" + ": (b('b60') == 8) ? 1.0" + ": (b('b60') == 10) ? 1.0" + ": (b('b60') == 2) ? 0.7" + ": (b('b60') == 4) ? 0.7" + ": (b('b60') == 6) ? 0.7" + ": (b('b60') == 9) ? 0.7" + ": (b('b60') == 1) ? 0.3" + ": (b('b60') == 3) ? 0.3" + ": (b('b60') == 11) ? 0.3" + ": (b('b60') == 12) ? 0.3" + ": 0") soil_100 = soil_types.expression( "(b('b100') == 5) ? 1.0" + ": (b('b100') == 7) ? 1.0" + ": (b('b100') == 8) ? 1.0" + ": (b('b100') == 10) ? 1.0" + ": (b('b100') == 2) ? 0.7" + ": (b('b100') == 4) ? 0.7" + ": (b('b100') == 6) ? 0.7" + ": (b('b100') == 9) ? 0.7" + ": (b('b100') == 1) ? 0.3" + ": (b('b100') == 3) ? 0.3" + ": (b('b100') == 11) ? 0.3" + ": (b('b100') == 12) ? 0.3" + ": 0") soil_200 = soil_types.expression( "(b('b200') == 5) ? 1.0" + ": (b('b200') == 7) ? 1.0" + ": (b('b200') == 8) ? 1.0" + ": (b('b200') == 10) ? 1.0" + ": (b('b200') == 2) ? 0.7" + ": (b('b200') == 4) ? 0.7" + ": (b('b200') == 6) ? 0.7" + ": (b('b200') == 9) ? 0.7" + ": (b('b200') == 1) ? 0.3" + ": (b('b200') == 3) ? 0.3" + ": (b('b200') == 11) ? 0.3" + ": (b('b200') == 12) ? 0.3" + ": 0") top_soils = soil_0.expression('top_soil + soil_10 + soil_30', {'top_soil': soil_0.select('constant'), 'soil_10': soil_10.select('constant'), 'soil_30': soil_30.select('constant')}) bottom_soils = soil_60.expression('soil_60 + soil_100 + soil_200', {'soil_60': soil_60.select('constant'), 'soil_100': soil_100.select('constant'), 'soil_200': soil_200.select('constant')}) # - #scaling top soils and bottom soils top_soils_scaled = norm.img_scale(top_soils, area_of_interest=common.bboxes()['conus']) bottom_soils_scaled = norm.img_scale(bottom_soils, area_of_interest=common.bboxes()['conus']) # ## Product 1 # Simply give higher scores to places that have both high potential runoff and available porosity (and with low moisture), in only areas with acceptable slopes product_1 = avail_porosity_img_scaled.multiply(pot_runoff_img_scaled).multiply(top_soils_scaled).multiply(bottom_soils_scaled).divide(slope_img_scaled).updateMask(slope_mask) # + #understanding product_1 range product_1_range = norm.img_range(product_1, area_of_interest = bbox) palette_name = 'RdYlBu' palette_len = 11 palette = vis.brewer[palette_name][palette_len][::-1] vis.legend(palette=palette, minimum=product_1_range[0], maximum= product_1_range[1]) box_corners = bbox.toGeoJSON()['coordinates'][0] center_lon = mean([corner[0] for corner in box_corners]) center_lat = mean([corner[1] for corner in box_corners]) vis_params = { 'min': product_1_range[0], 'max': product_1_range[1], 'dimensions': 512, 'palette': palette} # + #sum up product_1 scores over douglas and coos county parcels to obtain a parcel score, obtain image, obtain image for bounding box we're interested in parcel_score_douglas = product_1.reduceRegions(collection= douglas_county_parcels, reducer= ee.Reducer.max(), scale = 90) parcel_score_douglas_2 = parcel_score_douglas.reduceToImage(properties = ['max'], reducer = ee.Reducer.firstNonNull()) # + parcel_score_coos = product_1.reduceRegions(collection= coos_county_parcels, reducer= ee.Reducer.max(), scale = 90) parcel_score_coos_2 = parcel_score_coos.reduceToImage(properties = ['max'], reducer = ee.Reducer.firstNonNull()) bound_box = ee.Feature(ee.Geometry.Polygon([[[-124.1, 43.38], [-124.1, 42.88], [-123.6, 42.88], [-123.6, 43.38]]])) bound_box_img = bound_box.getMapId()['image'] # + #calculate range of parcel score for douglas county #parcel_score_douglas_range = norm.img_range(parcel_score_douglas_2, area_of_interest = bbox) # + #creating the legend for parcel scores palette_name = 'RdYlBu' palette_len = 11 palette = vis.brewer[palette_name][palette_len][::-1] vis.legend(palette=palette, minimum = 0, maximum= 1) vis_params_2 = { 'min': 0, 'max': 1,'dimensions': 512, 'palette': palette} # - # + #creating the map, visualizing parcel score data for parcels in douglas and coos counties, option to visualize soil texture as well the_map = vis.folium_map(location=[43.25, -123.5], zoom_start=8, height=500) month_names = [ 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] month_name = month_names[month-1] palette_2 = ['black', 'yellow', 'brown', 'green'] douglas_img = douglas_county_parcels.getMapId()['image'] vis_params_3 = { 'min': 0, 'max': 1.0, 'palette': palette} vis_params_4 = { 'min': 0, 'max': 20.0, 'palette': palette} the_map.add_ee_layer(parcel_score_douglas_2, vis_params_2, name = 'Parcel Score_douglas') the_map.add_ee_layer(parcel_score_coos_2, vis_params_2, name = 'Parcel Score_coos') #the_map.add_ee_layer(top_soils_scaled, vis_params_3, name = 'Top Soils') #the_map.add_ee_layer(bottom_soils_scaled, vis_params_3, name = 'Bottom Soils') the_map.add_ee_layer(slope_img, vis_params_4, name = 'Slopes') #the_map.add_ee_layer(pot_runoff_img_scaled, vis_params_3, name = 'Runoff') #the_map.add_ee_layer(product_1, vis_params_3, name = 'Product_1') #the_map.add_ee_layer(avail_porosity_img_scaled, vis_params_3, name = 'Porosity') the_map.add_ee_layer(douglas_img, {}, name = 'Douglas_parcels') the_map.add_ee_layer(bound_box_img, {}, name = 'Bounding_Box') the_map.add_ee_layer(avail_porosity_img, vis_params_4, name = 'USDA Porosity') vis.folium_display(the_map) #douglas_img = douglas_county_parcels.draw(color = 'green', strokeWidth= 1) #coos_img = coos_county_parcels.draw(color = 'green', strokeWidth= 1) # - # + collection_name_d = ( 'users/amgadellaboudy/product_1_douglas') product_1_d_asset = ee.data.createAsset( {'type': 'ImageCollection'}, collection_name_d) description_d= 'Douglas_County_Water_Parcel_Score' collection_name_c = ( 'users/amgadellaboudy/product_1_coos') product_1_c_asset = ee.data.createAsset( {'type': 'ImageCollection'}, collection_name_c) description_c= 'Coos_County_Water_Parcel_Score' oregon_box = [[-124.1, 43.38], [-124.1, 42.88], [-123.6, 42.88], [-123.6, 43.38]] xx_d = ee.batch.Export.image.toAsset(parcel_score_douglas_2, description = description_d, assetId = product_1_d_asset['id'] + '/' + description_d, region=oregon_box, scale = 90 ) xx_c = ee.batch.Export.image.toAsset(parcel_score_coos_2, description = description_c, assetId = product_1_c_asset['id'] + '/' + description_c, region= oregon_box, scale = 90) xx_d.start() xx_c.start() # - # ## Product 2 # Ratio of potential_runoff:available_porosity? runoff_pixel_area_m2 = pot_runoff_img.pixelArea() pot_runoff_img_mm = pot_runoff_img.divide(runoff_pixel_area_m2).multiply(seconds_per_month[month-1] * 1000) # pot_runoff_mask = pot_runoff_img_mm.gte(1000) avail_porosity_mask = avail_porosity_img.gte(50) norm.img_range(pot_runoff_img_mm.updateMask(runoff_mask), area_of_interest=bbox) #norm.img_range(pot_runoff_img_mm, area_of_interest=bbox) norm.img_range(runoff_pixel_area_m2, area_of_interest=bbox) norm.img_range(avail_porosity_img, area_of_interest=bbox) product_2_raw = ( pot_runoff_img_mm .divide(avail_porosity_img) .updateMask(slope_mask) .updateMask(avail_porosity_mask) .updateMask(runoff_mask)) product_2_mask = product_2_raw.gte(1) product_2 = product_2_raw.updateMask(product_2_mask) # + product_2_range = norm.img_range(product_2, area_of_interest=bbox) palette_name = 'BrBG' palette_len = 11 palette = vis.brewer[palette_name][palette_len][::-1] vis.legend(palette=palette, minimum=product_2_range[0], maximum=product_2_range[1]) box_corners = bbox.toGeoJSON()['coordinates'][0] center_lon = mean([corner[0] for corner in box_corners]) center_lat = mean([corner[1] for corner in box_corners]) vis_params = { 'min': product_2_range[0], 'max': product_2_range[1], 'dimensions': 512, 'palette': palette} # - the_map = vis.folium_map(location=[center_lat, center_lon], zoom_start=4, height=500) the_map.add_ee_layer(product_2, vis_params, 'Product 2: ' + month_name) vis.folium_display(the_map)	15,523
/.ipynb_checkpoints/austin_info-checkpoint.ipynb	9eb7f3bda4e14ecc69ee3fa063e46a78eb7cbfde	[]	no_license	jschwan1282/ETL-Project	https://github.com/jschwan1282/ETL-Project	0	0	null	2019-08-24T20:38:23	2019-08-24T20:11:13	Jupyter Notebook	Jupyter Notebook	false	false	.py	14,058	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- import gspread from oauth2client.service_account import ServiceAccountCredentials import pandas as pd import datetime # import the datetime module from datetime import datetime as dt # import the datetime type from tqdm.notebook import tnrange, tqdm import pathlib import sys import os import shutil # # Read file containing the last updates def last_processed_date(file): """ read the file containing the last dates processed "processed_dates_update.csv" and calculate the next line to read in the worksheet. """ last_processed_dates = pd.read_csv(file) # calculate the starting line to read return last_processed_dates.shape[0] + 2 # # connect to google drive API and read the data in the file def set_google_drive_access_scope(): """ define the scope of the access to google drive and create credentials using the privatepprint.json file) """ scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive'] creds = ServiceAccountCredentials.from_json_keyfile_name('dataset_types_ODP.json', scope) client = gspread.authorize(creds) return client # ## get all the data between a row range in the google sheet def access_google_sheet(sheet_name, worksheet_name, client, file, first_column, last_column): """ getting access to the google sheet spreadsheet and the name of the "History" sheet we want to access """ sheet = client.open(sheet_name) # access the google sheet history = sheet.worksheet(worksheet_name) # access the worksheet "History" # extract all the raw data available next_row_to_read = last_processed_date(file) # returns the number of rows and columns in the worksheet by counting the number of non-empty cells in the first column. The first column corresponds to the date. # this solution should work for at least 5 years and until the number of retrieved rows will be large enough to load in memory. This because we read all the data in the first column row_total = len(history.col_values(1)) # set the batch of data to retrieve if row_total < next_row_to_read: # if there are new data to read compared to the last time the data was updated new_data = [0] else: cell_start = first_column + str(next_row_to_read) cell_end = last_column + str(row_total) new_data = history.batch_get([cell_start + ":" + cell_end]) return new_data # ## tranforming the data into tabular data and making some cleaning def transform_into_dataframe(data): """ transform the dict into a padas """ raw_data = pd.DataFrame(data) return raw_data # ## making some data cleaning def drop_not_used_columns(data): """ drop not required columns: openess indicator label, openess indicator value, total datasets label, total datasets """ return data.drop([1, 2, 3, 4], axis=1) # # equivalences table for removing duplicated datasets: same dataset type, different names def load_equivalences_table(): """ table creation by manually entry the possible datasets types writting: added as founded in the dataset """ data_types_equivalance = {"comma-separated-values":"csv", "sparql-query":"sparql", "tab-separated-values":"tsv", "pdf;type=pdf1x":"pdf", "rdf+xml":"rdf_xml"} return data_types_equivalance # # processing each row of the dataset def extract_row(row_to_process, data): """ extract the information correspondig to a specific row for reshape it properly: <date>, <data type>, <number of datasets> """ data_row = data.loc[row_to_process] #create a dataframe with the information extacter for each row. It will make further reshaping easier return pd.DataFrame(data_row).T.reset_index().drop(["index"], axis=1) def row_removing_empty_columns(data_row): """ remove the columns having no values. Here no value is represented by '' """ return data_row.drop([col for col in data_row if (data_row[col] == '').any()], axis=1) def columns_identification(data_row): """ extract the columns corresponding to the data types and the number of datasets per data type """ data_row_type_labels = data_row.columns[1:data_row.shape[1]:2] data_row_value_labels = data_row.columns[2:data_row.shape[1]:2] return data_row_type_labels, data_row_value_labels def columns_separation(data_row, data_row_type_labels, data_row_value_labels): """ create 2 dataframes: 1 containing the data set type and 1 containing the value of the corresponding dataset type and reshape as row-oriented by transposing it """ # dataset type columns extraction data_row_type = data_row[data_row_type_labels].T.reset_index() data_row_type = data_row_type.drop(["index"], axis=1).rename(columns={0:"dataset type"}) # rename the column name # number of dataset types columns extraction data_row_value = data_row[data_row_value_labels].T.reset_index() data_row_value = data_row_value.drop(["index"], axis=1).rename(columns={0:"number of datasets"}) # rename the column name return data_row_type, data_row_value def clean_dataset_types_description(data_row_type): """ clean the row_data_type by removing unwanted text: text before the "/" character. It also removes any rows with 'None' value """ data_row_type = data_row_type.dropna() #remove rows having 'None' values data_row_type_cleaned = data_row_type["dataset type"].apply(lambda x: x.lower()).apply(lambda x: x.split("/")) data_row_type_cleaned = pd.DataFrame(data_row_type_cleaned) data_row_type_cleaned = data_row_type_cleaned["dataset type"].apply(lambda x: x[-1]) return pd.DataFrame(data_row_type_cleaned) def merge_columns(data_row_type, data_row_value): """ concatenate the 2 created dataframes into 1 that will serve in the final datamodel for Qlik Sense """ data_row_transformed = pd.concat([data_row_type, data_row_value], axis=1, ignore_index=True) # rename the columns data_row_transformed.rename(columns={0:"dataset type", 1:"number of datasets"}, inplace=True) return data_row_transformed def find_and_replace_equivalent_dataset_name(data_row_transformed, data_types_equivalance): """ find and replace dataset types names to remove equivalent names for the same types of datasets """ data_row_transformed.replace(data_types_equivalance, inplace=True) return data_row_transformed def find_and_replace_equivalent_dataset_name(data_row_transformed, data_types_equivalance): """ find and replace dataset types names to remove equivalent names for the same types of datasets """ data_row_transformed.replace(data_types_equivalance, inplace=True) return data_row_transformed def data_grouping(data_row_transformed): """ removing duplicated data: group by dataset type and summing up the number of datasets """ return data_row_transformed.groupby(["dataset type"]).sum().reset_index().rename(columns={1:"number of datasets"}) # ## add the date column to finalise the datamodel def remove_hours(data_row): """ remove the hours minutes and seconds in the date dimention """ date_object = datetime.datetime.strptime(data_row[0][0], '%m/%d/%Y %H:%M:%S').date() # transform the datetime object into a string with format dd/mm/yyy date = date_object.strftime('%d/%m/%Y') return date def track_proccesed_dates(dates_proccesed, current_date): """ create a list of already processed dates from the google sheet file containing the data source. This list will be saved on a file for reuse when updating the processed data """ return dates_proccesed.append({"processed dates": current_date}, ignore_index=True) def month_change_detector(date_tracking, current_date, current_row): """ detect a month change in the dataset and add a flag to the rows of the last day of each month (flag = 1) otherwise, there is not flag (flag = 0). The current dataset exhibits a non-continuity of the dates, there are many days missing. """ # extract the previous date to check if the month has changed past_date = date_tracking.loc[current_row - 1][0] # transform the dates into a datetime object past_date_datetime = datetime.datetime.strptime(past_date, "%d/%m/%Y") current_date_datetime = datetime.datetime.strptime(current_date, "%d/%m/%Y") # extract the month of the date past_month = past_date_datetime.month current_month = current_date_datetime.month # detect month change if current_month != past_month: flag_month_change = 1 else: flag_month_change = 0 return flag_month_change, past_date, current_date def date_formatting(length_data_row, date): """ create a dataframe with the date having equal length that row_data_type_labels and row_data_value_labels """ date_list = [[date, 0] for i in range(length_data_row)] return pd.DataFrame(date_list) def row_data_merge(df_date, data_row_transformed): """ for the current processing row: add the date colum to the dataframe containing the cleaned version of the data types and the dataset number per type """ data_row_transformed = pd.concat([df_date, data_row_transformed], axis=1, ignore_index=True) # rename the columns, sort values by number of datasets type and remove a self-created "index" column data_row_transformed.rename(columns={0:"date", 1:"last day of the month", 2:"dataset type", 3:"number of datasets"}, inplace=True) data_row_transformed = data_row_transformed.sort_values(by=["number of datasets"], ascending=False).reset_index().drop(["index"], axis=1) return data_row_transformed def add_flag_for_month_change(data_processed, past_date): """ add a flag in case of month change detected - month_change_flag = 1 """ # add a flag=1 to a previous date if there is month change data_processed.loc[data_processed["date"] == past_date, "last day of the month"] = 1 return data_processed # ## update the data and the last update info def data_backup(directory_path, processed_dates_file, processed_data_file): """ created a copy of the previous files before append data to it renaming the file as <filename>_backup: * processed_dates_file {string}: filename containing the dates processed so far * data_file {string}: filename containing the data processed so far if the file doesn't exist it does anything """ # creates a backup of the file containing the dates processed so far dates_file = directory_path + processed_dates_file dates_file_backup = directory_path + str.split(processed_dates_file, ".")[0] + "_backup." + str.split(processed_dates_file, ".")[1] if pathlib.Path(dates_file).exists(): shutil.copyfile(dates_file, dates_file_backup) # creates a backup of the file containing the data processed so far data_file = directory_path + processed_data_file data_file_backup = directory_path + str.split(processed_data_file, ".")[0] + "_backup." + str.split(processed_data_file, ".")[1] if pathlib.Path(data_file).exists(): shutil.copyfile(data_file, data_file_backup) return def update_data_and_info_update(data_processed, date_processed, directory_path, processed_dates_file, data_file): """ update the processed data into a CSV file by appending the procesed data to existing one in the file: datasets_formats_processed.csv update the processed dates CSV file by appending the processed dates to the existing ones in the file: processed_dates_update.csv IMPORTANT: put attention to the date format in the "processed_dates_update.csv" file. The format doesn't match the format used in google sheets. You will need to change the date format in this file before going for updates """ data_backup(directory_path, processed_dates_file, data_file) # save the processed data into a CSV file with headers file_path_data = directory_path + data_file # my home laptop data_processed.to_csv(file_path_data, mode="a", index=False, header=False) # save the last row processed info (google sheet row number and last date present in this row) into a CSV file with headers file_path_update = directory_path + processed_dates_file # my home laptop date_processed.to_csv(file_path_update, mode="a", index=False, header=False) return def can_execute(data_cleaned): """ Enables the execution of the script depending of the dates in the google sheet file and the current date. It prevents the script runs when there isn't a change in the month making impossible to detect a month change. """ before_last_date = data_cleaned[0][len(data_cleaned[0]) - 2] before_last_date_month = int(before_last_date.split("/")[0]) last_date = data_cleaned[0][len(data_cleaned[0]) - 1] last_date_month = int(last_date.split("/")[0]) today_month = dt.today().month if (last_date_month >= before_last_date_month) & (today_month == last_date_month): print("script execution enabled...program continued") return 0 # continues the execution of the script else: print("script execution stopped. Not the right day for executing it...program terminated") return 1 # terminates the execution of the script # ## --> main function <-- def main(): # global variables SHEET = "ODP OPENNESS INDICATOR_local_4" WORKSHEET = "History" # DIRECTORY_PATH = "D:\\Dropbox\\Programming\\Python\\datasets files formats ODP\\" # my office laptop DIRECTORY_PATH = "D:\\Dropbox\\Programming\\Python\\datasets files formats ODP\\PROD\\" # my home laptop PROCESSED_DATES_FILE = "processed_dates_update.csv" PROCESSED_DATA_FILE = "datasets_formats_processed.csv" FIRST_COLUMN = "A" # first column in the google sheet file LAST_COLUMN = "EM" # last columnn in the google sheet file. To change when the data in the google sheet will go beyond the EM column # set connection to google drive google_client = set_google_drive_access_scope() # acquire the data data_types = access_google_sheet(SHEET, WORKSHEET, google_client, PROCESSED_DATES_FILE, FIRST_COLUMN, LAST_COLUMN)[0] if data_types: raw_data = transform_into_dataframe(data_types) else: print("No new data to read --> EXIT") sys.exit(0) # terminates the program with no errors total_rows = raw_data.shape[0] print(f'the size of the imported data is: {raw_data.shape}\n') # clean the data data_cleaned = drop_not_used_columns(raw_data) # enable or disable the execution of the script according to the current date and the data available in the google sheet if can_execute(data_cleaned): return # load the table containing equivalent names for the same dataset. It'll be used to have the same name for the same dataset type datasets_type_equivalences = load_equivalences_table() # process all the rows of the dataset #total_rows = 10 for current_row in tqdm(range(total_rows), desc="data rows processing"): #tqdm_notebook # process each row row_data = extract_row(current_row, data_cleaned) # remove empty columns in the extracted row row_data_clean = row_removing_empty_columns(row_data) # identification of the columns related to the dataset formats and the columns related to the number of dataset formats row_data_type_labels, row_data_value_labels = columns_identification(row_data_clean) # separation of the columns related to the dataset formats and the columns related to the number of dataset formats row_data_type, row_data_value = columns_separation(row_data_clean, row_data_type_labels, row_data_value_labels) # clean the dataset type desciption column by filtering out the dataset type row_data_type_clean = clean_dataset_types_description (row_data_type) # merge dataset types and dataset values into a single dataframe row_data_transformed = merge_columns(row_data_type_clean, row_data_value) # find and replace equivalent names for the same dataset type row_data_transformed_cleaned = find_and_replace_equivalent_dataset_name(row_data_transformed, datasets_type_equivalences) # combine together same dataset types and sum up the number of datasets per data type row_data_transformed_cleaned = data_grouping(row_data_transformed_cleaned) # remove hour info from the date date_clean = remove_hours(row_data) # add the current date to the list of already processed dates if current_row == 0: date_tracking = pd.DataFrame({"processed dates": [date_clean]}) else: date_tracking = track_proccesed_dates(date_tracking, date_clean) # detect a change of month month_change_flag, past_date, current_date = month_change_detector(date_tracking, date_clean, current_row) # generate a new date column with a month change flag column ready to add to the cleaned row dataset df_date = date_formatting(row_data_transformed_cleaned.shape[0], date_clean) # add the date info to the dataframe containing he dataset types and the number of dataset types and make some formatting row_data_final = row_data_merge(df_date, row_data_transformed_cleaned) # append the processed data if current_row == 0: data_processed = row_data_final else: data_processed = data_processed.append(row_data_final, ignore_index=True) # change the "last day of the month" column vaue to 0 --> 1 if month_change_flag: data_processed = add_flag_for_month_change(data_processed, past_date) # save the processed data into a CSV file update_data_and_info_update(data_processed, date_tracking, DIRECTORY_PATH, PROCESSED_DATES_FILE, PROCESSED_DATA_FILE) # # --> Execution of the data update starts here <-- if __name__ == '__main__': main()	18,722
/Kaggle_SQL/SQL exercise - 1.ipynb	79c5b2dd426912fec0131a630a98eeed72132a4f	[]	no_license	rk1489/Technocolabs-Internship-Project	https://github.com/rk1489/Technocolabs-Internship-Project	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	11,845	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import QUANTAXIS as QA import matplotlib.pyplot as plt import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei','KaiTi', 'FangSong'] # 汉字字体,优先使用楷体，如果找不到楷体，则使用黑体 mpl.rcParams['font.size'] = 14 # 字体大小 mpl.rcParams['axes.unicode_minus'] = False # 正常显示负号 import numpy as np import pandas as pd from base.JuUnits import excute_for_multidates from sklearn.neighbors.kde import KernelDensity # + def get_Q1_list(start, end): return [str(y)+'-03-31' for y in range(int(start), int(end)+1)] def drop_by_quantile_multidates(obj, floor=.00,upper=1., column=None): return excute_for_multidates(obj, drop_by_quantile, floor=floor,upper=upper, column=column).sort_index() def drop_by_quantile(obj, floor=.00,upper=1., column=None): if isinstance(obj, pd.Series): qt = obj.quantile([floor,upper]) return obj[(obj>=qt[floor]) & (obj<=qt[upper])] if isinstance(obj, pd.DataFrame): assert column, 'COLUMN CANT be NONE when obj is dataframe' qt = obj[column].quantile([floor,upper]) return obj[(obj[column]>=qt[floor]) & (obj[column]<=qt[upper])] raise TypeError('obj must be series or dataframe') stocks = QA.QA_fetch_stock_list() # code_all= stocks[stocks.code.map(lambda x:x[0] in condition)].code.unique().tolist() code_all= stocks.code.unique().tolist() finances = QA.QA_fetch_financial_report_adv(code_all,get_Q1_list('2017','2017'))#.data finances = finances.get_key(code_all, ['2017-03-31'], ['totalAssets','ROE']) finances_filted = drop_by_quantile_multidates(finances,.1,0.90,'ROE') # finances.describe(include = 'all') # finances['totalAssets'] = finances['totalAssets'].apply(lambda x: round(x/100000000,2)) # finances['ROE'] = finances['ROE'].apply(lambda x: round(x,1)) # finances.describe() # finances.quantile([.01,.05,.1,.9,.95,.99]) # finances # print(finances['ROE'].rank()) # - fig = plt.figure(figsize=(1120/72,420/72)) finances_filted['ROE'].plot(ax=fig.add_subplot(1,2,1)) # plt.scatter(finances_filted['ROE'].index.get_level_values('code'),finances_filted['ROE'].values) finances_filted['ROE'].plot(kind="kde",ax=fig.add_subplot(1,2,2)) # finances_filted['ROE'].describe() # finances_filted['totalAssets'].mean() # plt.show() # + X_plot = np.linspace((finances_filted['ROE']).min()-1, (finances_filted['ROE']).max()+1, 200)[:, np.newaxis] kde = KernelDensity(kernel='epanechnikov', bandwidth=0.5).fit((finances_filted['ROE']).values.reshape(-1, 1)) log_dens = kde.score_samples(X_plot) # 返回的是点x对应概率密度的log值，需要使用exp求指数还原 print(kde.get_params()) plt.figure(figsize = (10, 8)) # 设置画布大小 plt.plot(X_plot, np.exp(log_dens), marker='.', linewidth=1, c="b", label='kernel density') plt.xlabel('variable') plt.ylabel('pdf') kde = KernelDensity(kernel='gaussian', bandwidth=0.25).fit((finances_filted['ROE']).values.reshape(-1, 1)) log_dens = kde.score_samples(X_plot) # # kde = KernelDensity(kernel='epanechnikov', bandwidth=1).fit(((finances_filted['ROE']-finances_filted['ROE'].mean())/finances_filted['ROE'].std()).values.reshape(-1, 1)) # 高斯核密度估计 plt.plot(X_plot, np.exp(log_dens), marker='.', linewidth=1, c="r", label='kernel density') plt.show() # - plt.figure(figsize = (10, 8)) # 设置画布大小 plt.plot(X_plot, np.cumsum(np.exp(log_dens)*np.abs(X_plot[0]-X_plot[1])), marker='.', linewidth=1, c="b", label='kernel density') plt.xlabel('variable') plt.ylabel('cdf') plt.legend(fontsize = 15) # 显示图例,设置图例字体大小 plt.show() np.exp( kde.score_samples(np.array([1.3,1.3]).reshape(-1,1))) np.exp(kde.score(np.array([1.3,1.3]).reshape(-1,1)))	3,836
/Week1-2 All Basics/Lecture4 python data science stack/8. Other packages/Other packages.ipynb	0a9e3aeeb3fa8b67007616385883b68ef29936c3	[]	no_license	kwarodom/100DaysMLCodeChallenge	https://github.com/kwarodom/100DaysMLCodeChallenge	1	2	null	null	null	null	Jupyter Notebook	false	false	.py	32,545	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Go faster with Numba and Cython def polyn(n): total = 0 for i in range(n): total += (7nn) + (-3n) + 42 return total ntimes = 10000 # %timeit -n $ntimes polyn(1000) import numba @numba.jit def polyn(n): total = 0 for i in range(n): total += (7nn) + (-3n) + 42 return total # %timeit -n $ntimes polyn(1000) # %load_ext cython # + language="cython" # def ployn(int n): # cdef int total = 0 # cdef i # # for i in range(n): # total += (7nn) + (-3n) + 42 # return total # - # %timeit -n $ntimes polyn(1000) # + language="cython" # from libc.math cimport hypot # # def dist(double x1, double y1, double x2, double y2): # cdef double dx = abs(x1 - x2) # cdef double dy = abs(y1 - y2) # return hypot(dx, dy) # - dist(1,1, 2,2) from scipy.spatial.distance import euclidean euclidean([1,1], [2,2]) # # Understand deep learning from sklearn.datasets import load_digits digits = load_digits() # %matplotlib inline import matplotlib.pyplot as plt idx = 17 plt.imshow(digits['images'][idx], cmap=plt.cm.gray, interpolation='none') digits['target'][idx] digits['images'].shape digits['data'].shape from sklearn.model_selection import train_test_split from keras.utils import np_utils X = digits['data'] y = digits['target'] y = np_utils.to_categorical(y) y[0] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) in_dim = X.shape[1] out_dim = y.shape[1] from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential() model.add(Dense(128, input_shape=(in_dim,))) model.add(Activation('relu')) model.add(Dense(out_dim)) model.add(Activation('sigmoid')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X_train, y_train) loss, accuracy = model.evaluate(X_test, y_test) accuracy model.predict(X_test[:3]) model.predict(X_test[:3]).argmax(axis=1) y_test[:3].argmax(axis=1) model.save('digits.h5') from keras.models import load_model model1 = load_model('digits.h5') model1.predict(X_test[:3]).argmax(axis=1) # # Work with image processing import cv2 img = cv2.imread('coffee.jpg') type(img) img.shape # %matplotlib inline import matplotlib.pyplot as plt plt.imshow(img) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) plt.imshow(gray) plt.imshow(gray, cmap=plt.cm.gray) edges = cv2.Canny(gray, 200, 300) plt.imshow(edges, cmap=plt.cm.gray) img = cv2.imread('child.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) import sys fname = '{}/share/OpenCV/haarcascades/haarcascade_frontalcatface.xml'.format(sys.prefix) model = cv2.CascadeClassifier(fname) faces = model.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(500, 500)) faces img = cv2.imread('child.jpg') for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 255), 50) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # # Understand NLP: NLTK from os import path fname = path.expanduser('~/nltk_data/corpora/abc/science.txt') with open(fname, 'rb') as fp: data = fp.read().decode(errors='replace') articles = data.split('\r\n\r\n') article = articles[0] print(article) from nltk.tokenize import sent_tokenize sents = sent_tokenize(article) print(sents[0]) from nltk.corpus import stopwords import re stop = set(stopwords.words('english')) def is_ok(token): return re.match('^[a-z]+$', token) and token not in stop from nltk.tokenize import word_tokenize def tokenize(sent): return [word for word in word_tokenize(sent.lower()) if is_ok(word)] from collections import Counter def summarize(text, n=3): sents = sent_tokenize(text) bow = [tokenize(sent) for sent in sents] tf = Counter() for sent in bow: tf.update(sent) def score(i): return sum(tf[word] for word in bow[i]) idx = sorted(range(len(bow)), key=score, reverse=True)[:n] return [sents[i] for i in idx] summarize(articles[0]) # # Understand NLP: SpaCy from os import path fname = path.expanduser('~/nltk_data/corpora/abc/science.txt') with open(fname, 'rb') as fp: data = fp.read().decode(errors='replace') articles = data.split('\r\n\r\n') article = articles[0] print(article) from nltk.tokenize import sent_tokenize sents = sent_tokenize(article) print(sents[0]) from nltk.corpus import stopwords import re stop = set(stopwords.words('english')) def is_ok(token): return re.match('^[a-z]+$', token) and token not in stop from nltk.tokenize import word_tokenize def tokenize(sent): return [word for word in word_tokenize(sent.lower()) if is_ok(word)] from collections import Counter def summarize(text, n=3): sents = sent_tokenize(text) bow = [tokenize(sent) for sent in sents] tf = Counter() for sent in bow: tf.update(sent) def score(i): return sum(tf[word] for word in bow[i]) idx = sorted(range(len(bow)), key=score, reverse=True)[:n] return [sents[i] for i in idx] summarize(articles[0]) import spacy nlp = spacy.load('en') fname = path.expanduser('~/nltk_data/corpora/inaugural/1993-Clinton.txt') with open(fname) as fp: data = fp.read() doc = nlp(data) sent = next(doc.sents) print(sent) for tok in sent: print('{} -> {}'.format(tok, tok.ent_type_)) for ent in doc.ents[:10]: print('{} -> {}'.format(ent.string, ent.label_)) [ent for ent in doc.ents if ent.label == spacy.symbols.PERSON] # # Bigger data with HDF5 and dask import pandas as pd import numpy as np df = pd.read_csv('taxi.csv.bz2', usecols=np.arange(21), parse_dates=['lpep_pickup_datetime', 'Lpep_dropoff_datetime']) store = pd.HDFStore('taxi.h5') store.append('taxi', df, data_columns=['VendorID']) store df1 = store.select('taxi', 'VendorID==1') df1['VendorID'].unique() import dask.dataframe as dd df = dd.read_csv('taxi-split/.csv', usecols=np.arange(21), parse_dates=['lpep_pickup_datetime', 'Lpep_dropoff_datetime']) vc = df['VendorID'].value_counts() vc vc.compute() vnd = df.groupby('VendorID') ta = vnd['Total_amount'] m = ta.mean() m.compute() at is not math.inf: content_part = page[:cut_at] footnote_part = page[cut_at:] content_part_s.append(content_part) footnote_part_s.append(footnote_part) else: content_part_s.append(page) content_text = "".join(content_part_s) # 把各頁的內文部分結合成內文 footnote_text = "".join(footnote_part_s) # 把各頁的附註部分結合成附註 content = content_text footnote = footnote_text # - print(content) print("--------------------------------------------------") print(footnote) # 第一種附註小數字出現的場合 content = re.sub(name+' ?'+str(footnote_indices[0])+' ?（', "{}（".format(name),content , 1) # 第二種附註小數字出現的場合 for index in footnote_indices[1:]: content = re.sub("([。，])" + index, r'\g<1>', content, count=1) print(content) # + # 清掉所有不需要的空格 # 先把需要的空格轉成另一個字符記錄起來，清完空格再回復原狀 content = re.sub(r'([a-zA-Z,）（]) ([a-zA-Z,）（])', '\g<1>Ä\g<2>', content) content = re.sub(r'(\n\d+) ', '\g<1>Ä', content) content = content.replace(" ","") content = content.replace("Ä", " ") footnote = re.sub(r'([a-zA-Z,]) ([a-zA-Z,])', '\g<1>Ä\g<2>', footnote) footnote = re.sub(r'(\n\d+) ', '\g<1>Ä', footnote) footnote = footnote.replace(" ","") footnote = footnote.replace("Ä", " ") # + # 處理newline，內文分出段落 # 因為句號後面換行的通常是一段落的結尾(但也可能不是) content = content.replace("。\n", "Å") content = content.replace("\n", "") content = content.replace("Å", "。\n\n") footnote = footnote.replace("。\n", "Å") footnote = footnote.replace("\n", "") footnote = footnote.replace("Å", "。\n\n") # - print(content) print("--------------------------------------------------") print(footnote) footnote = footnote[:-2] # 去掉最後的兩個newline f_lines = footnote.split('\n\n') # 這樣最後就不會多一個空的split，各條附註分開 biography["Footnotes"] = list(map(lambda line: line.split(" "), f_lines)) # 把各條附註小數字和其註釋分開 biography["Footnotes"] # 從內文去掉傳記撰者，並保存在傳記資訊 match = re.search(r'（([\w、]+)撰寫?）', content, flags=re.MULTILINE) # $ author_line = match[0] biography["Authors"] = match[1].split("、") content = content.replace(author_line, "") print(biography["Authors"]) print("-------------------------------------------------") print(content_text) # + # 從內文去掉傳記標題，保存別名，生日日期，死亡日期 reg = name + "（(.+，)?([\d?.])-([\d?.])）" title = re.search(reg, content, flags=re.MULTILINE) if len(title.groups()) == 2: biography["Birth"] = title[1] # group1 biography["Death"] = title[2] # group2 else: biography["Alias_s"].append(title[1]) biography["Birth"] = title[2] biography["Death"] = title[3] content = content.replace(title[0], "") # replace Whole match with empty string # - print(content)	9,025
/Task6/Task6.ipynb	cf8e9d391ae44dfab05a8f57ed1e29a34b2cf6b1	[ "MIT" ]	permissive	Cynthyah/SparksFoundation	https://github.com/Cynthyah/SparksFoundation	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	572,837	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # The Sparks Foundation # ## Task6 - Prediction using Decision Tree Algorithm # - Create the Decision Tree classiﬁer and visualize it graphically. # - The purpose is if we feed any new data to this classiﬁer, it would be able to predict the right class accordingly. # - Data can be found at https://bit.ly/3kXTdox -> save as Iris.csv # # ![image.png](attachment:ffc4510a-65ba-4c5e-978d-c59c8aa4d7d8.png) # ### Importing the libraries # + import pandas as pd import missingno import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score,confusion_matrix from sklearn import tree # - # ### Loading the dataset df = pd.read_csv("Iris.csv", low_memory=False) # ### Checking the data df.head() # The column Id in this analysis is not relevant, so I will drop it df.drop(columns='Id',inplace=True) df.shape df.info() # % of columns without values df.isna().sum() / len(df) * 100 # - The dataset has NO Null values # ### Iris plant has 3 classes: # - Iris Setosa # - Iris Versicolour # - Iris Virginica # # With the following attributes: # - sepal length # - sepal width # - petal length # - petal width df.describe() sns.pairplot(df, hue="Species") plt.show() # ### Using Decision Tree Algorithm # Decision Tree algorithm belongs to the family of supervised learning algorithms and it can be used for solving regression and classification problems too. # ### Extracting the data attributes(X) and corresponding labels(y) # + # extract features/attributes X = df.drop('Species', axis=1) # extract classes y = df['Species'] # checking the shape print(X.shape, y.shape) # - # ### After extracted the data attributes and corresponding labels, we will split them to train and test datasets using the function train_test_split from the library: # - sklearn.model_selection le = LabelEncoder() y = le.fit_transform(y) # test_size -> 25% of total dataset will be split, where 75% will assign as train data # In this case we have 150 rows in our dataset, we will use 112 as train data X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=0, test_size=0.25) # ### Training the model # #### Now we use the classification importing DecisionTreeClassifier function from sklearn library # If we use the parameter min_samples_leaf=10 to create our leaves with at least 10 samples classifier = DecisionTreeClassifier() classifier.fit(X_train, y_train) # ### Prediction y_pred = classifier.predict(X_test) y_pred = le.inverse_transform(y_pred) y_test = le.inverse_transform(y_test) y_pred cm = confusion_matrix(y_test,y_pred) print("Confusion_Matrix is: \n", cm) ac = accuracy_score(y_test,y_pred) print("Accuracy_Score is:", ac) # ### Visualisation plt.figure(figsize=(15,10)) dot_data = tree.plot_tree(classifier, feature_names=['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm'], class_names=['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], filled=True) plt.title("Decision Tree Visualization-Iris Dataset",fontsize=30) plt.show() .fit(x_train, y_train) # + id="d39W8cxmEGG8" outputId="56c7d5bb-5061-4bc5-cce2-0fa6938cb2c0" colab={"base_uri": "https://localhost:8080/"} #printing the Ridge regressor arguments print("Ridge regression coefficients = ",ridge_regression_classifier.coef_) print("Ridge regression intercept = ",ridge_regression_classifier.intercept_) # + id="-GzA_5qrEHgw" outputId="4640e51f-60ba-4f01-8c80-8b3e3ba25549" colab={"base_uri": "https://localhost:8080/"} #printing the Linear regressor arguments print("Linear regression coefficients = ",linear_regression_classifier.coef_) print("Linear regression intercept = ",linear_regression_classifier.intercept_) # + id="ulRVWxsREKtS" outputId="3a8beefd-02bb-4383-c5c2-57ae4325430d" colab={"base_uri": "https://localhost:8080/"} #making the pridictions on the testing dataset of Ridge regression ridge_regression_predictions = ridge_regression_classifier.predict(x_test) print("The predictions of the Ridge regressor are: \n", ridge_regression_predictions,"\n") # + id="PaKwbWowENpC" outputId="a1f45376-d190-491f-c00f-9ba2fc520add" colab={"base_uri": "https://localhost:8080/"} #making the pridictions on the testing dataset of Linear regression linear_regression_predictions = linear_regression_classifier.predict(x_test) print("The predictions of the linear regressor are: \n", linear_regression_predictions,"\n") # + id="NAudsxqjERIr" outputId="33a88167-c801-4ad2-d198-731cc644663d" colab={"base_uri": "https://localhost:8080/"} #Analyzing the performance of the Ridge Regression ridge_regression_score = ridge_regression_classifier.score(x_test, y_test) print("Score Value = ",ridge_regression_score, "\n") print("Comparing the predictions with the gound-truth: \n", np.column_stack((ridge_regression_predictions,y_test)), "\n\n") # + id="cJoe8odhEUUn" outputId="498c357a-aad7-4a91-eb04-6c7a5ff1e445" colab={"base_uri": "https://localhost:8080/"} #Analyzing the performance of the linear Regression linear_regression_score = linear_regression_classifier.score(x_test, y_test) print("Score Value = ",linear_regression_score, "\n") print("Comparing the predictions with the gound-truth: \n", np.column_stack((linear_regression_predictions,y_test)), "\n\n") # + id="R6XKwFRMEZzH" outputId="70e9da8e-a17f-4156-8f58-48ea67f26132" colab={"base_uri": "https://localhost:8080/"} #printing the both the Values of the Effeciency print("Score Value of Ridge = ",ridge_regression_score, "\n") print("Score Value of Linear = ",linear_regression_score, "\n")	6,044
/module/intro_cython/3-Basics.ipynb	438abf50b59f8a3e4bde2406143dcfe3ad95ee90	[]	no_license	slash1221/cythonup	https://github.com/slash1221/cythonup	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	3,755	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # Задание 1. Даны 2 строки: long_phrase и short_phrase. # Напишите код, который проверяет действительно ли длинная фраза long_phrase длиннее короткой short_phrase. И выводит True или False в зависимости от результата сравнения. long_phrase = 'Насколько проще было бы писать программы, если бы не заказчики' short_phrase = '640Кб должно хватить для любых задач. Билл Гейтс (по легенде)' # + long_phrase = 'Насколько проще было бы писать программы, если бы не заказчики' short_phrase = '640Кб должно хватить для любых задач. Билл Гейтс (по легенде)' len(long_phrase) > len(short_phrase) # - # Задание 2. Дано значение объема файла в байтах (в мегабайте 2^20 байт). Напишите перевод этого значения в мегабайты в формате: "Объем файла равен 213.68Mb". # + size_byte = 250000000 size_mb = size_byte/(220) print ("Объем файла равен " + str( round(size_mb,2) ) + "Mb") # - # Задание 3. Разработать приложение для определения знака зодиака по дате рождения. Пример: # # Введите месяц: март # # Введите число: 6 # # Вывод: # Рыбы date = int(input('Введите число:')) month = (input ('Введите месяц:')).lower() if ((month == '1' or month == 'январь') and date < 21) or ((month == '12' or month == 'декабрь') and 22 <= date <= 31 ): print ('Козерог') elif ((month == '2' or month == 'февраль') and date < 19) or ((month == '1' or month == 'январь') and 21 <= date <= 31): print ('Водолей') elif ((month == '3' or month == 'март' ) and date < 21) or ((month == '2' or month == 'февраль') and 19 <= date <= 29): print ('Рыбы') elif ((month == '4' or month == 'апрель') and date < 21) or ((month == '3' or month == 'март' ) and 21 <= date <= 31): print ('Овен') elif ((month == '5' or month == 'май') and date < 21) or ((month == '4' or month == 'апрель') and 21 <= date <= 30): print ('Телец') elif ((month == '6' or month == 'июнь') and date < 21) or ((month == '5' or month == 'май') and 21 <= date <= 31): print ('Близнецы') elif ((month == '7' or month == 'июль') and date < 23) or ((month == '6' or month == 'июнь') and 21 <= date <= 30): print ('Рак') elif ((month == '8' or month == 'август') and date < 19) or ((month == '7' or month == 'июль') and 23 <= date <= 31): print ('Лев') elif ((month == '9' or month == 'сентябрь') and date < 24) or ((month == '8' or month == 'август') and 19 <= date <= 31): print ('Дева') elif ((month == '10' or month == 'октябрь') and date < 24) or ((month == '9' or month == 'сентябрь') and 24 <= date <= 30): print ('Весы') elif ((month == '11' or month == 'ноябрь') and date < 24) or ((month == '10' or month == 'октябрь') and 24 <= date <= 31): print ('Скорпион') elif ((month == '12' or month == 'декабрь') and date < 22) or ((month == '11' or month == 'ноябрь') and 24 <= date <= 30): print ('Стрелец') else: print ('Не соответствует дате календаря') # Задание 4*. Нужно разработать приложение для финансового планирования. # Приложение учитывает сколько уходит на ипотеку, "на жизнь" и сколько нужно отложить на пенсию. # Пользователь вводит: # - заработанную плату в месяц. # - сколько процентов от ЗП уходит на ипотеку. # - сколько процентов от ЗП уходит "на жизнь". # - сколько раз приходит премия в год. # # Остальная часть заработанной платы откладывается на пенсию. # # Также пользователю приходит премия в размере зарплаты, от которой половина уходит на отпуск, а вторая половина откладывается. # # Программа должна учитывать сколько премий было в год. # # Нужно вывести сколько денег тратит пользователь на ипотеку и сколько он накопит за год. # # Пример: # # Введите заработанную плату в месяц: 100000 # # Введите сколько процентов уходит на ипотеку: 30 # # Введите сколько процентов уходит на жизнь: 50 # # Введите количество премий за год: 2 # # Вывод: # На ипотеку было потрачено: 360000 рублей # Было накоплено: 340000 рублей # + wage = int(input ('Введите заработанную плату в месяц: ')) loan = int(input ('Введите сколько процентов уходит на ипотеку: ')) expences = int(input ('Введите сколько процентов уходит на жизнь: ')) n_bonus = int(input ('Введите количество премий за год: ')) loan_in_year = wage 12 * loan / 100 savings = 12 * wage * (1 - loan/100 - expences/100) + n_bonus * wage * 0.5 print ('На ипотеку было потрачено: ' , round(loan_in_year) , ' рублей Было накоплено: ' , round (savings) , ' рублей') # -	4,649
/.ipynb_checkpoints/Data Wrangling--Web Scraping-checkpoint.ipynb	6a1e2257cd4c8b91ccb85b9f87bda35b521aece8	[]	no_license	tunghoangt/DSE1020	https://github.com/tunghoangt/DSE1020	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	625,292	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] colab_type="text" id="kMccmZPoWd_h" # # Mixup augmentation for NLP # # Using IMDB sentiment classification dataset # + colab={"base_uri": "https://localhost:8080/", "height": 527} colab_type="code" id="YhKEHbrxWd_n" outputId="368747f0-47d5-439f-f4b3-d4db6d6a2d18" # Import libraries try: import textaugment except ModuleNotFoundError: # !pip install textaugment import textaugment import pandas as pd import tensorflow as tf from tensorflow.keras.preprocessing import sequence from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Activation from tensorflow.keras.layers import Embedding from tensorflow.keras.layers import Conv1D, GlobalMaxPooling1D from tensorflow.keras.datasets import imdb from textaugment import MIXUP # %matplotlib inline # + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="JeMsxayIWd_r" outputId="814596bf-e5ca-47f1-c2ce-257e761e96c4" tf.__version__ # + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="_FbvA0uwRdEZ" outputId="8e912f45-8b7e-4ee7-a3ad-f342c3f090c7" textaugment.__version__ # + [markdown] colab_type="text" id="Oz8O8tISRdEg" # ## Initialize constant variables # + colab={} colab_type="code" id="mg1AcYIWWd_w" # set parameters: max_features = 5000 maxlen = 400 batch_size = 32 embedding_dims = 50 filters = 250 kernel_size = 3 hidden_dims = 250 epochs = 10 runs = 1 # + colab={"base_uri": "https://localhost:8080/", "height": 153} colab_type="code" id="ZRuNNVstWd_0" outputId="bc4ce3b2-5a12-4600-d1a8-b466615018df" print('Loading data...') (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) print(len(x_train), 'train sequences') print(len(x_test), 'test sequences') print('Pad sequences (samples x time)') x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) print('x_train shape:', x_train.shape) print('x_test shape:', x_test.shape) # + [markdown] colab_type="text" id="Tx73Y-asRdEz" # ## Initialize mixup # + colab={} colab_type="code" id="xvuxODUxRdE1" mixup = MIXUP() generator, step = mixup.flow(x_train, y_train, batch_size=batch_size, runs=runs) # + colab={"base_uri": "https://localhost:8080/", "height": 476} colab_type="code" id="6cm1o_fAWd_4" outputId="ea793754-100c-4c12-8acf-7798c096c399" print('Build model...') model = Sequential() # we start off with an efficient embedding layer which maps # our vocab indices into embedding_dims dimensions model.add(Embedding(max_features, embedding_dims, input_length=maxlen)) model.add(Dropout(0.2)) # we add a Convolution1D, which will learn filters # word group filters of size filter_length: model.add(Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)) # we use max pooling: model.add(GlobalMaxPooling1D()) # We add a vanilla hidden layer: model.add(Dense(hidden_dims)) model.add(Dropout(0.2)) model.add(Activation('relu')) # We project onto a single unit output layer, and squash it with a sigmoid: model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() # + [markdown] colab_type="text" id="b5zRyuq8UKmR" # ## Train model using mixup augmentation # + colab={"base_uri": "https://localhost:8080/", "height": 357} colab_type="code" id="oGLSfzcUWeAB" outputId="81464964-8fd3-4249-b901-0e05cb664436" h1 = model.fit(generator, steps_per_epoch=step, epochs=epochs, validation_data=(x_test, y_test)) # + colab={"base_uri": "https://localhost:8080/", "height": 298} colab_type="code" id="XKrXdkt8XeYo" outputId="0d463439-1718-4f90-bc24-b32f6dae7eda" pd.DataFrame(h1.history)[['loss','val_loss']].plot(title="With mixup") # + colab={"base_uri": "https://localhost:8080/", "height": 476} colab_type="code" id="Iiv7ahP8WeAF" outputId="0ad04311-b497-4830-dd50-a832daf583ac" print('Build model...') model2 = Sequential() # we start off with an efficient embedding layer which maps # our vocab indices into embedding_dims dimensions model2.add(Embedding(max_features, embedding_dims, input_length=maxlen)) model2.add(Dropout(0.2)) # we add a Convolution1D, which will learn filters # word group filters of size filter_length: model2.add(Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)) # we use max pooling: model2.add(GlobalMaxPooling1D()) # We add a vanilla hidden layer: model2.add(Dense(hidden_dims)) model2.add(Dropout(0.2)) model2.add(Activation('relu')) # We project onto a single unit output layer, and squash it with a sigmoid: model2.add(Dense(1)) model2.add(Activation('sigmoid')) model2.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model2.summary() # + colab={"base_uri": "https://localhost:8080/", "height": 357} colab_type="code" id="ygNHmhGMWeAI" outputId="1592613d-52d2-409b-e210-cceddb7f5bbd" h2 = model2.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test)) # + colab={"base_uri": "https://localhost:8080/", "height": 298} colab_type="code" id="DzJEhaPrWeAM" outputId="aec6c655-c5f8-434b-bb16-d1e1056adc03" pd.DataFrame(h2.history)[['loss','val_loss']].plot(title="Without mixup") # + [markdown] colab_type="text" id="M2HDERJbGr2a" # # Comparison # See the loss curve with mixup does not overfit. # + [markdown] colab={} colab_type="code" id="hqteWafKRdF1" # ## Cite the paper # ``` # @article{marivate2019improving, # title={Improving short text classification through global augmentation methods}, # author={Marivate, Vukosi and Sefara, Tshephisho}, # journal={arXiv preprint arXiv:1907.03752}, # year={2019} # }``` # # https://arxiv.org/abs/1907.03752 # -	6,330
/visualizations/.ipynb_checkpoints/Model_output-checkpoint.ipynb	c188012586b1e854732f331c526d49967dc6f15e	[]	no_license	diodz/ml-covid	https://github.com/diodz/ml-covid	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	9,306,357	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ### Visualizing Model Outputs # + import pandas as pd import numpy as np from numpy import inf import os import matplotlib.style as style import matplotlib.pyplot as plt import seaborn as sns import datetime as dt from pandas.plotting import register_matplotlib_converters np.seterr(divide = 'ignore') pd.options.mode.chained_assignment = None register_matplotlib_converters() # %matplotlib inline # - all_data = pd.read_pickle('.\\..\\data\\covid_df.pkl') output = pd.read_pickle('.\\..\\data\\predictions_log_total.pkl') mlp = pd.read_pickle('.\\..\\data\\mlp_predictions.pkl') # + def predicted_vs_real(full_df, output_df, country, target, logged=False, net=False): ''' Plots one model's prediction vs real trend ''' style.use('seaborn') date = output_df.index.min() pre = full_df[(full_df['Country'] == country) & (full_df['Date'] <= date)][[target, 'Date']] real = country + ' real' predict = country + ' prediction' post = output_df[[real, predict]] day = pre['Date'].max() + dt.timedelta(days=-1) row = pre[pre['Date'] == day][[target, 'Date']] val = row[target] if logged: row[real], row[predict] = (np.log(val), np.log(val)) else: row[real], row[predict] = (val, val) row.set_index('Date', inplace=True) post = row.append(post[[real, predict]]) if logged: post = np.exp(post) fig, ax = plt.subplots(figsize=(12, 8)) title = 'Prediction: {} in {}'.format(target, country) if net: title = 'Neural Net ' + title else: title = 'Linear Regression ' + title plt.title(label=title, fontsize=15) ax.axvline(x=date, ls=':', c='gray', label = str(date)) g = sns.lineplot(x=post.index, y=post[real], ax=ax, marker='X', color='darkorange') g = sns.lineplot(x=post.index, y=post[predict], ax=ax, marker='X', color='g') g = sns.lineplot(x=pre['Date'], y=pre[target], ax=ax, color='royalblue') plt.legend(('Prediction frontier\n {}'.format(date), 'Real', 'Predicted', 'Trend'), prop={'size': 12}) plt.ylabel(target) plt.show() def side_by_side(full_df, country, target, models, predictions, save_output=False): ''' Plots two different plots of models' predictions vs real trends side by side. --first predictions arg must be the one to be logged (LinReg) Inputs: full_df: (Pandas df) The full cleaned dataset country: (string) Country to examine target: (string) the outcome variable of the model models: (list) model names as strings predictions: (tuple of Pandas df) collection of model prediction data save_output: (boolean) switch to save image output Output: File if save_output=True plots figure ''' np.seterr(all='ignore') style.use('seaborn') date = predictions[0].index.min() pre = full_df[(full_df['Country'] == country) & (full_df['Date'] <= date)][[target, 'Date']] real, predict = (country + ' real', country + ' prediction') day = pre['Date'].max() + dt.timedelta(days=-1) fig, axes = plt.subplots(1, 2, figsize=(28,11)) title = ' Prediction: {} in {}'.format(target, country) post_trends = [] y_max = 0 for pred, model in zip(predictions, models): post = pred[[real, predict]] row = pre[pre['Date'] == day][[target, 'Date']] row.set_index('Date', inplace=True) val = row[target] if model == 'Linear Regression': row[real], row[predict] = (np.log(val), np.log(val)) post = np.exp(row.append(post[[real, predict]])) else: row[real], row[predict] = (val, val) post = row.append(post[[real, predict]]) post_trends.append(post) iterable = zip(models, predictions, axes, post_trends) for model, output, axis, trend in iterable: sub_title = model + title axis.set_title(sub_title) axis.axvline(x=date, ls=':', c='gray', label = str(date)) g = sns.lineplot(x=trend.index, y=trend[real], ax=axis, marker='X', color='darkorange') g = sns.lineplot(x=trend.index, y=trend[predict], ax=axis, marker='X', color='g') g = sns.lineplot(x=pre['Date'], y=pre[target], ax=axis, color='royalblue') axis.legend(('Prediction frontier\n {}'.format(date), 'Real', 'Predicted', 'Trend'), prop={'size': 12}) plt.ylabel(target) if output[[real, predict]].dropna().values.max() > y_max: y_max = output[[real, predict]].dropna().values.max() plt.ylim(0, y_max + y_max.15) if save_output: file_name = '{} {} {} comparison.png'.format(country, *models) plt.savefig('.\\..\\visualizations\\' + file_name) plt.show() #predicted_vs_real(all_data, mlp, 'Spain', 'Confirmed Cases', logged=False, net=True) side_by_side(all_data, 'Spain', 'Confirmed Cases', ['Linear Regression', 'Neural Network'], output, mlp, save_output=True) # + side_by_side(all_data, 'Austria', 'Confirmed Cases', ['Linear Regression', 'Neural Network'],output, mlp, save_output=True) side_by_side(all_data, 'Kenya', 'Confirmed Cases', ['Linear Regression', 'Neural Network'],output, mlp, save_output=True) # - for country in all_data['Country'].unique(): try: side_by_side(all_data, country, 'Confirmed Cases', output, mlp, save_output=True) except: print('country missing:', country, '\n\n\n') predicted_vs_real(all_data, output, 'Lesotho', 'Confirmed Cases', logged=True) predicted_vs_real(all_data, mlp, 'Lesotho', 'Confirmed Cases', logged=False, net=True) best_models = ['Bahrain', 'Belgium','Benin', 'China', 'Ecuador', 'Finland', 'France', 'Hungary', 'Iran', 'Lebanon', 'Mali', 'Moldova', 'Nicaragua', 'Pakistan', 'Philippines',\ 'South Africa', 'Ukraine', 'United Kingdom'] for country in best_models: predicted_vs_real(all_data, output, country, 'Confirmed Cases', logged=True)	6,205
/2-Data Analysis/3-Data Sources/Archivos/Teoria/Lectura Escritura.ipynb	631747ab9b2b1907f912706cdf116859e07c0688	[ "MIT" ]	permissive	Suryayta/thebridgedsftjun21	https://github.com/Suryayta/thebridgedsftjun21	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	151,167	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 2 # language: python # name: python2 # --- # + [markdown] deletable=true editable=true # Import all libraries and path of files # + deletable=true editable=true import matplotlib.pyplot as plt import pandas as pd DataFolder="C:\Users\Manuel\Documents\Polimi\Building systems\Project\Phyton" DataSetName="energydata_complete.csv" # + [markdown] deletable=true editable=true # Read the dataframe and change the numerical index to a data index # + deletable=true editable=true completeDataPath=DataFolder+"/"+DataSetName completeDF=pd.read_csv(completeDataPath,sep = ",",index_col=0) previousIndex= completeDF.index NewparsedIndex = pd.to_datetime(previousIndex) completeDF.index= NewparsedIndex # + [markdown] deletable=true editable=true # Find the target variable (Appliances), and all the correlations with other features. corrAppliance is the correlation of appliance with alla feature. # + deletable=true editable=true DFtarget=completeDF[["Appliances"]] corrAppliance=completeDF.corr().head(1) corrAppliance>0.08 # + [markdown] deletable=true editable=true # Use the heat map in order to visualize the correlation # + deletable=true editable=true plt.figure() plt.matshow(completeDF.corr()) plt.colorbar() plt.show() # + [markdown] deletable=true editable=true # As we can see, there is low correlation for Appliances, our model will be with a low quality. # Most reliable variables are lights,RH1,T2,T3,T6,Tout,Windspeed. Let's find a correlation with shifted one # + [markdown] deletable=true editable=true # Define a function to find shifted variables # + deletable=true editable=true def lag_column(df,column_name,lag_period=1): for i in range(1,lag_period+1,1): new_column_name = column_name+"-"+str(i)+" day" df[new_column_name]=df[column_name].shift(624i) return df # + [markdown] deletable=true editable=true # Let's try with temperature # + deletable=true editable=true DF_temperature_out = completeDF[['T_out']] DF_tout_lagged=lag_column(DF_temperature_out,'T_out',3) DF_corr_Tout=DFtarget.join([DF_tout_lagged]) DF_corr_Tout.corr().head(1) # + [markdown] deletable=true editable=true # Try with lights # + deletable=true editable=true DF_lights = completeDF[['lights']] DF_lights_lagged=lag_column(DF_lights,"lights",1) DF_corr_lights=DFtarget.join([DF_lights_lagged]) DF_corr_lights.corr().head(1) # + [markdown] deletable=true editable=true # Try with RH1 # + deletable=true editable=true DF_RH1 = completeDF[['RH_1']] DF_RH1_lagged=lag_column(DF_RH1,"RH_1",0) DF_corr_RH1=DFtarget.join([DF_RH1_lagged]) DF_corr_RH1.corr().head(1) # + [markdown] deletable=true editable=true # Try with appliances # + deletable=true editable=true DF_Appliances = completeDF[['Appliances']] DF_Appliances_lagged=lag_column(DF_Appliances,"Appliances",6) DF_Appliances_lagged.corr().head(1) # + [markdown] deletable=true editable=true # Try with T2 # + deletable=true editable=true DF_T2 = completeDF[['T2']] DF_T2_lagged=lag_column(DF_T2,"T2",2) DF_corr_T2=DFtarget.join([DF_T2_lagged]) DF_corr_T2.corr().head(1) #DF_T2_lagged # + [markdown] deletable=true editable=true # Try with T3 # + deletable=true editable=true DF_T3 = completeDF[['T3']] DF_T3_lagged=lag_column(DF_T3,"T3",0) DF_corr_T3=DFtarget.join([DF_T3_lagged]) DF_corr_T3.corr().head(1) # + [markdown] deletable=true editable=true # Not use T3 correlation # + [markdown] deletable=true editable=true # Try with T6 # + deletable=true editable=true DF_T6 = completeDF[['T6']] DF_T6_lagged=lag_column(DF_T6,"T6",4) DF_corr_T6=DFtarget.join([DF_T6_lagged]) DF_corr_T6.corr().head(1) # + [markdown] deletable=true editable=true # Try with windspeed # + deletable=true editable=true DF_Windspeed = completeDF[['Windspeed']] DF_Windspeed_lagged=lag_column(DF_Windspeed,"Windspeed",2) DF_corr_Windspeed=DFtarget.join([DF_Windspeed_lagged]) DF_corr_Windspeed.corr().head(1) # + [markdown] deletable=true editable=true # Now try to find a correlation in shorted time(10 minutes) instead of days: # + deletable=true editable=true def lag_column_short(df,column_name,lag_period=1): for i in range(1,lag_period+1,1): new_column_name = column_name+"-"+str(i)+" times 10 minutes" df[new_column_name]=df[column_name].shift(i) return df # + [markdown] deletable=true editable=true # DF_temperature_out = completeDF[['T_out']] # DF_tout_lagged_short=lag_column_short(DF_temperature_out,'T_out',5) # DF_tout_lagged_short # + [markdown] deletable=true editable=true # Join all data # + deletable=true editable=true DF_FinalSet=DFtarget.join([DF_lights_lagged,completeDF[['RH_1']],DF_Appliances_lagged.drop(["Appliances"],axis=1),DF_T2_lagged,completeDF[['T3']],DF_T6_lagged,DF_Windspeed_lagged]) # + [markdown] deletable=true editable=true # Set variables for day and night, day of the week and month of the year # + deletable=true editable=true DF_FinalSet['hour']=DF_FinalSet.index.hour DF_FinalSet['day_of_week']=DF_FinalSet.index.dayofweek DF_FinalSet['month']=DF_FinalSet.index.month DF_FinalSet['week_of_the_year']=DF_FinalSet.index.week DF_FinalSet # + [markdown] deletable=true editable=true # Define dayDetector function and week-end function and put that value in final dataset # + deletable=true editable=true def weekendDetector(day): weekendLabel=0 if(day == 5 or day == 6): weekendLabel=1 else: weekendLabel=0 return weekendLabel def dayDetector(hour): dayLabel=1 if(hour<20 and hour>9): dayLabel=1 else: dayLabel=0 return dayLabel simpleVectorOfDays = [0,1,2,3,4,5,6] weekendOrNotVector = [weekendDetector(thisDay) for thisDay in simpleVectorOfDays] hoursOfDayVector= range(0,24,1) dayOrNotVEctor =[dayDetector(ThisHour) for ThisHour in hoursOfDayVector] DF_FinalSet["weekend"] = [weekendDetector(thisDay) for thisDay in DF_FinalSet.index.dayofweek] DF_FinalSet["day_nigth"] = [dayDetector(thisHour) for thisHour in DF_FinalSet.index.hour] DF_FinalSet.dropna(inplace=True) # + [markdown] deletable=true editable=true # Let's try to build a model # + deletable=true editable=true DF_FinalSet.corr().head(1) # + [markdown] deletable=true editable=true # Remove day of the week, month, week of the year and weekend predictors from features. # + deletable=true editable=true DF_FinalSet.corr().head(1) DF_features = DF_FinalSet.drop(["Appliances","day_of_week","month","week_of_the_year","weekend"],axis=1) DF_target=DF_FinalSet[["Appliances"]] # + deletable=true editable=true #import sklearn to define test and train, test size is the fraction that will be test from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split (DF_features,DF_target,test_size=0.2) # + deletable=true editable=true #First model is simple linear model from sklearn import linear_model linear_reg=linear_model.LinearRegression() #empty alghoritm that we fill with fit # + [markdown] deletable=true editable=true # Fit the algorithm with X train and Y train and predict Appliances # + deletable=true editable=true linear_reg.fit(X_train,Y_train) predict_linearAppliances=linear_reg.predict(X_test) # + [markdown] deletable=true editable=true # Fill a DF with predicted values and Y_test for a period and plot to show the results. # + deletable=true editable=true #How to extract index of Y_test---> Y_test.index predict_DF_linearReg_split=pd.DataFrame(predict_linearAppliances,index =Y_test.index,columns =["AppliancesEnergy_predic_linearReg_split"]) predict_DF_linearReg_split=predict_DF_linearReg_split.join(Y_test) #Now we have a DF in which we have predicted value and value of completeDF, let's see if the prediction is good: plot a period and see if hte curves match predict_DF_linearReg_split_period=predict_DF_linearReg_split["2016-03-01":"2016-03-03"] plt.figure() predict_DF_linearReg_split_period.plot() plt.xlabel("time") plt.ylabel("Appliances") plt.ylim([0,800]) plt.title("Linear regression") plt.show() # + [markdown] deletable=true editable=true # Let's find the metrics. # + deletable=true editable=true from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score mean_squared_error_linearREG=mean_squared_error(Y_test,predict_linearAppliances) mean_absolute_error_linearREG=mean_absolute_error(Y_test,predict_linearAppliances) R2_score_linearReg= r2_score(Y_test,predict_linearAppliances) print "R2 index is "+str(R2_score_linearReg) print "mean squared error is "+str(mean_squared_error_linearREG) print "mean absolute error is "+str(mean_absolute_error_linearREG) # + [markdown] deletable=true editable=true # Let's use cross validation # + deletable=true editable=true from sklearn.model_selection import cross_val_predict predict_linearReg_CV=cross_val_predict(linear_reg,DF_features,DF_target,cv =10) #Lets put in a DF predict_DF_linearReg_CV=pd.DataFrame(predict_linearReg_CV,index =DF_target.index,columns =["Appliances_predic_linearReg_CV"]) predict_DF_linearReg_CV=predict_DF_linearReg_CV.join(DF_target) #use DF target instead of Y_test predict_DF_linearReg_CV_period= predict_DF_linearReg_CV["2016-03-01":"2016-03-02"] plt.figure() predict_DF_linearReg_CV_period.plot() plt.xlabel("time") plt.ylabel("Appliances") plt.ylim([0,800]) plt.title("Crossing validation") plt.show() # + [markdown] deletable=true editable=true # Let's find the metrics # + deletable=true editable=true mean_squared_error_CV=mean_squared_error(DF_target,predict_linearReg_CV) mean_absolute_error_CV=mean_absolute_error(DF_target,predict_linearReg_CV) R2_score_CV= r2_score(DF_target,predict_linearReg_CV) print "R2 index is "+str(R2_score_CV) print "mean squared error is "+str(mean_squared_error_CV) print "mean absolute error is "+str(mean_absolute_error_CV) # + [markdown] deletable=true editable=true # Now use Random Forest regressor to find another model # + deletable=true editable=true from sklearn.ensemble import RandomForestRegressor reg_RF=RandomForestRegressor() predict_RF_CV=cross_val_predict(reg_RF,DF_features,DF_target,cv =10) #heavy procedure # + [markdown] deletable=true editable=true # Do the DF, plot and find the metrics # + deletable=true editable=true predict_DF_RF_CV=pd.DataFrame(predict_RF_CV,index =DF_target.index,columns =["Appliances_predic_RF_CV"]) predict_DF_RF_CV=predict_DF_RF_CV.join(DF_target) predict_DF_RF_CV_period= predict_DF_RF_CV["2016-03-01":"2016-03-01"] plt.figure() predict_DF_RF_CV_period.plot() plt.xlabel("time") plt.ylabel("Appliances") plt.ylim([0,800]) plt.title("Regression line with RandomForestRegressor") plt.show() mean_squared_error_RF=mean_squared_error(DF_target,predict_RF_CV) mean_absolute_error_RF=mean_absolute_error(DF_target,predict_RF_CV) R2_score_RF= r2_score(DF_target,predict_RF_CV) print "R2 index is "+str(R2_score_RF) print "mean squared error is "+str(mean_squared_error_RF) print "mean absolute error is "+str(mean_absolute_error_RF) # + [markdown] deletable=true editable=true # Random Forest has bad fitting model since R2 is negative. # # # Now try to use another model called Supported Vector Regression, that uses normalize data # + deletable=true editable=true from sklearn.svm import SVR reg_SVR = SVR(kernel='rbf',C=10,gamma=1) def normalize(df): return (df-df.min())/(df.max()-df.min()) DF_features_norm=normalize(DF_features) DF_target_norm=normalize(DF_target) # + [markdown] deletable=true editable=true # Predict the data with SVR # + deletable=true editable=true predict_SVR_CV = cross_val_predict(reg_SVR,DF_features_norm,DF_target_norm,cv=10) #very heavy procedure predict_DF_SVR_CV=pd.DataFrame(predict_SVR_CV, index = DF_target_norm.index,columns=["AC_ConsPred_SVR_CV"]) predict_DF_SVR_CV = predict_DF_SVR_CV.join(DF_target_norm).dropna() # + [markdown] deletable=true editable=true # Plot the results and compute the metrics # + deletable=true editable=true plt.figure() predict_DF_SVR_CV["2016-03-01":"2016-03-02"].plot() plt.xlabel("time") plt.ylabel("Appliances ratio") plt.ylim([0,0.65]) plt.title("Regression normalized line with SVR") plt.show() mean_squared_error_SVR=mean_squared_error(predict_DF_SVR_CV[["Appliances"]],predict_DF_SVR_CV[['AC_ConsPred_SVR_CV']]) mean_absolute_error_SVR=mean_absolute_error(predict_DF_SVR_CV[["Appliances"]],predict_DF_SVR_CV[['AC_ConsPred_SVR_CV']]) R2_score_SVR= r2_score(predict_DF_SVR_CV[["Appliances"]],predict_DF_SVR_CV[['AC_ConsPred_SVR_CV']]) print "R2 index is "+str(R2_score_SVR) print "mean squared error is "+str(mean_squared_error_SVR) print "mean absolute error is "+str(mean_absolute_error_SVR) # + [markdown] deletable=true editable=true # This model fits very badly the set, if you try to compute the regression with non-normalized features you would find a model with an horizontal line that predicts better your target since R2 coefficient is very negative. # + deletable=true editable=true reg_SVR.fit(X_train,Y_train) predict_SVR_Appliances=reg_SVR.predict(X_test) predict_DF_SVR_split=pd.DataFrame(predict_SVR_Appliances,index =Y_test.index,columns =["AppliancesEnergy_predic_SVR_split"]) predict_DF_SVR_split=predict_DF_SVR_split.join(Y_test) # + [markdown] deletable=true editable=true # Plot # + deletable=true editable=true predict_DF_SVR_split_period=predict_DF_SVR_split["2016-03-01":"2016-03-07"] plt.figure() predict_DF_SVR_split_period.plot() plt.xlabel("time") plt.ylabel("Appliances") plt.ylim([0,800]) plt.title("Regression line with SVR") plt.show() # + [markdown] deletable=true editable=true # In the plot you can see the horizontal line # # In conclusion with linear and regression you can find a model that badly fits the data (R2 sligthly less than 0.2). # With SVR and RF model is bad fitted. # To increase accurancy of model you should find other variables more correlated with Appliances or other models that fit better dataset # # + deletable=true editable=true Notation* es otro formato de texto plano que se utiliza para el itercambio de datos. Originalmente se utilizaba como notación literal de objetos en JavaScript, pero actualmente es un formato de datos independiente del lenguaje. JavaScript es un lenguaje de programción web, por lo que JSON se utiliza mucho en el intercambio de objetos entre cliente y servidor. # # ¿Qué diferencia hay con un CSV o un Excel? Ya no tenemos esa estructura de fila/columna, sino que ahora es un formato tipo clave/valor, como si fuese un diccionario. En una tabla en la fila 1, columna 1, tienes un valor. En un JSON no, en la clave "mi_calve" puedes tener almacenado un valor, una lista o incluso un objeto. Salimos del formato tabla al que estamos acostubrados para ganar en flexibilidad. # # Un JSON tiene la siguiente pinta: # # ![imagen](./img/json_image.png) # data = { "firstName": "Jane", "lastName": "Doe", "hobbies": ["running", "sky diving", "singing"], "age": 35, "children": [ { "firstName": "Alice", "age": 6 }, { "firstName": "Bob", "age": 8 } ] } # Puedo guardar el JSON en un archivo. Para ello, usamos la librería `json`*, que viene incluida en la instalación de Anaconda. # + import json with open("data/data_file.json", "w") as write_file: json.dump(data, write_file) # - # O también objetos de una clase # + class Persona: def __init__(self, firstName, lastName, hobbies): self.firstName = firstName self.lastName = lastName self.hobbies = hobbies pers1 = Persona("Pepe", "Carrasco", ["Bricolaje", "Tenis"]) pers2 = Persona("Jose", "Carrasco", ["Bricolaje", "Tenis"]) # - pers1.__dict__ # Lo puedo guardar en un archivo pepe.json* with open("data/pepe.json", "w") as write_file: json.dump(pers2.__dict__, write_file) # Luego lo puedo volver a cargar # + with open("data/pepe.json", "r") as json_file: data = json.load(json_file) print(data) print(data['firstName']) # - # Para el siguiente ejemplo, utilizamos `pandas` y leeremos el archivo JSON, de tal manera que nos transforme los datos en formato tabla, en un `DataFrame`. df = pd.read_json('data/Musical_Instruments_5.json', lines = True) df # ## 6. TXT # Son simplemente archivos donde hay texto. Hemos visto que los CSVs y los JSON tienen su propio formato y extension. En el caso del .txt no tienen ninguno específico aunque no quita para que sus elementos estén separados por comas, y se pueda leer igualmente como si fuese un CSV. # # Cuando almancenamos datos siempre tienen una estructura, por lo que aunque sea un `.txt`, llevará los datos en formato json, separados por comas, tabulaciones, puntos y comas... # # Por ejemplo, si tenemos los datos de la liga guardados en un `.txt`, separados por tabulaciones, lo podremos leer con el `pd.read_csv()`. # + jupyter={"outputs_hidden": true} df = pd.read_csv('data/laligaTXT.txt', sep='\t') df.head() # - # Recuerda que la separación por tabulaciones, también tiene su propia extensión: el `.tsv`, que igualmente lo podremos leer con `read_csv()`. # + jupyter={"outputs_hidden": true} df = pd.read_csv('data/laligaTSV.tsv', sep='\t') df.head() # - # El método `read_csv()` no se ciñe únicamente a leer CSVs, sino a prácticamente cualquier archivo que lleve un acarácter concreto en la separación de sus campos. Si conocemos ese caracter, sabremos leer el archivo con `pandas`. # ## 7. ZIP # En ocasiones los datos que recibimos en nuestros programas están comprimidos, ya sea en un formato `.zip`, `.rar`, `.7z`, u otro tipo de archivo. # # En este apartado verás un ejemplo de cómo descomprimir archivos `.zip`. Para ello empleamos la librería `zipfile` que viene incluida en la instalación de Anaconda. [Tienes el enlace a la documentación para más detalle](https://docs.python.org/3/library/zipfile.html#zipfile-objects). # # Para extraer todos los archivos: # + import zipfile with zipfile.ZipFile('data/laligaZIP.zip') as zip_ref: zip_ref.extractall('data') # - # Si quieres descomprimir un archivo `.rar` [tendrás que descargarte un paquete como por ejemplo `unrar`.](https://pypi.org/project/unrar/) # <table align="left"> # <tr><td width="80"><img src="./img/ejercicio.png" style="width:auto;height:auto"></td> # <td style="text-align:left"> # <h3>Ejercicio zip</h3> # # Consulta la documentación para extrar un único archivo, por nombre # # </td></tr> # </table> with zipfile.ZipFile('data/laligaZIP.zip') as zip_ref: zip_ref.extract('laligaZIP.csv') # ## 8. pickle # `pickle` es el módulo que nos permite serializar y deserializar un objeto de Pyhton. Esta operación lo que hace es traducirlo a un stream de bytes. # # A efectos prácticos, lo que nos permite es guardar objetos de Python, y recuperarlos más adelante. # + import pickle df = pd.read_csv("data/laliga.csv") with open('data/pepe.json') as json_file: data = json.load(json_file) with open('importante', 'wb') as f: pickle.dump(pers1, f) pickle.dump(df, f) pickle.dump(data, f) # + with open('importante', 'rb') as f: a = pickle.load(f) b = pickle.load(f) c = pickle.load(f) print(a) print(b) print(c) # - # ## 9. Encoding # Los strings se almacenan internamente en un conjunto de bytes, caracter a caracter. Esta operación es lo que se conoce como *encoding, mientras que pasar de bytes a string sería decoding. Bien, ¿y eso en qué nos afecta? Dependiendo del encoding, se suelen almacenar en un espacio de bits de 0 a 255, es decir, en esa combinación de bits tienen que entrar todos los caracteres del lenguaje. # # El problema es que en toda esa combinación de bits no entran todos los caracteres del planeta, por lo que dependiendo del encoding que usemos, una combinación de bits significará una cosa u otra. Por ejemplo, una A mayuscula será lo mismo en el encodig europeo que en el americano, pero los bits reservados para representar una Ñ, en el encodig americano se traduce en otro caracter. # # Por tanto, hay que tener claro en qué encoding está el archivo y con qué encoding lo vamos a leer*. [En la documentación](https://docs.python.org/3/library/codecs.html#encodings-and-unicode) puedes realizar esta comprobación. Hay algunos que te tienen que ir sonando: # # 1. 'utf-8': normalmente se trabaja con este encodig que engloba la mayor parte de caracteres. # 2. 'unicode': estándar universal con el que no deberiamos tener problemas. # 3. 'ascii': encoding americano. Solo tiene 128 caracteres. # 4. 'latin': para oeste de Europa, Oceanía y Latinoamérica # # ![imagen](./img/encoding.jpg) pd.read_csv('data/encoding.csv', encoding = 'utf-8') # + jupyter={"outputs_hidden": true} pd.read_csv('data/encoding.csv', encoding = 'ascii') # - pd.read_csv('data/encoding.csv', encoding='iso8859_10') # ## 10. Archivos y carpetas # Resulta de gran utilidad automatizar lecturas/escrituras/borrado/movimientos de archivos entre carpetas. Si tenemos un proceso definido en Python, podremos ejecutarlo tantas veces queramos y de este modo evitar dedicarle tiempo tareas tediosas y rutinarias. Para ello tendremos que apoyarnos en el módulo de Python `os`. # # Lo primero de todo es saber en qué directorio estamos trabajando. Esto es fundamental para elegir bien la ruta relativa. import os os.getcwd() # El directorio de trabajo lo podríamos cambiar si quisiéramos, por ejemplo, al escritorio. os.chdir('C:\\Users\\Daney\\Desktop') print(os.getcwd()) os.chdir('C:\\Users\\Daney\\Desktop\\Archivos\\Bootcamps\\thebridgedsftjun21\\2-Data Analysis\\3-Data Sources\\Archivos\\Teoria') print(os.getcwd()) # Podemos juntar rutas en un único path. Realiza un concatenado con barras entendibles por Windows. os.path.join("C:/path/to/directory", "some_file.txt") # Si quieres buscar algún tipo de archivo concreto, tienes varias opciones: # - Buscar por nombre # - Buscar por extensión # # En función de lo que encuentres, realizarás una operación u otra. Ahora bien, igualmente para buscar, tendrás que recorrer todos los archivos que estén en un directorio o en varios directorios. Para listar todos los ARCHIVOS y CARPETAS que hay en el directorio actual de trabajo, utilizamos `os.listdir()`. os.listdir() # Voy a quedarme con todos los notebooks del actual directorio de trabajo. for i in os.listdir(): if i.endswith('.ipynb'): print("Notebook", i) # Si quiero acceder sólo a los directorios for i in os.listdir(): if '.' not in i: print("directorio", i) # Otro método interesante para bucear en los archivos y carpetas de un directorio concreto es el `os.walk()`. Va a devoler un iterable que podremos recorrer en un for y obtener en formato tupla todos los archivos, subcarpetas y ficheros de subcarpetas. Para cada elemento de la tupla tenemos: # - El directorio donde está apuntando. # - Los directorios que hay ahí. # - Los archivos que hay ahí. # + result_generator = os.walk(os.getcwd()) files_result = [x for x in result_generator] files_result # - # ¿Qué podemos hacer dentro de un directorio, aparte de listar ficheros y subdirectorios? Las principales operaciones serían: # - Crear o eliminar directorios # - Crear o eliminar ficheros # - Mover ficheros os.mkdir('direct_prueba') os.rmdir('direct_prueba') # + f = open("fichero.txt", "w") for i in range(10): f.write("Line:" + str(i)) f.close() # - import shutil shutil.move("importante", "img")	23,647
/rl_fun/RL playground.ipynb	7abd8346e8ebec5ee580c5e191794ecfb2bf5302	[ "Apache-2.0" ]	permissive	jialing3/corner_cases	https://github.com/jialing3/corner_cases	1	0	null	null	null	null	Jupyter Notebook	false	false	.py	103,056	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- print("Helloworld") print("Today date is",29) random import matplotlib.pyplot as plt # %matplotlib inline n = 10 arms = np.random.rand(n) eps = 0.1 def reward(prob): reward = 0 for i in range(10): if random.random() < prob: reward += 1 return reward # + # action-value av = np.array([np.random.randint(0, (n + 1)), 0]).reshape(1, 2) def best_arm(a): best_arm = 0 best_mean = 0 for u in a: # mean reward for each action avg = np.mean(a[np.where(a[:, 0] == u[0])][:, 1]) if best_mean < avg: best_mean = avg best_arm = u[0] return best_arm # - plt.xlabel('Plays') plt.ylabel('Avg Reward') for i in range(500): if random.random() > eps: # greedy arm selection choice = best_arm(av) else: # random arm selection choice = np.where(arms == np.random.choice(arms))[0][0] this_av = np.array([[choice, reward(arms[choice])]]) av = np.concatenate((av, this_av), axis=0) # percentage the correct arm is chosen perc_correct = 100 * (len(av[np.where(av[:, 0] == np.argmax(arms))]) * 1. / len(av)) # mean reward running_mean = np.mean(av[:, 1]) plt.scatter(i, running_mean) # + # experiment with different numbers of arms and different values for ϵ # + # The problem we've considered here is a stationary problem # because the underlying reward probability distributions for each arm do not change over time. # + # We certainly could consider a variant of this problem where this is not true, # a non-stationary problem. In this case, a simple modification would be # to weight more recent action-value pairs greater than distant ones, # thus if things change over time, we will be able to track them. # + n = 10 arms = np.random.rand(n) eps = 0.1 av = np.ones(n) # action-value counts = np.zeros(n) # how many times we've taken a particular action def reward(prob): total = 0 for i in range(10): if random.random() < prob: total += 1 return total # simpler best_arm function def best_arm(a): return np.argmax(a) plt.xlabel('Plays') plt.ylabel('Mean Reward') for i in range(500): if random.random() > eps: choice = best_arm(av) else: choice = np.where(arms == np.random.choice(arms))[0][0] counts[choice] += 1 k = counts[choice] rwd = reward(arms[choice]) old_avg = av[choice] new_avg = old_avg + (1. / k) * (rwd - old_avg) av[choice] = new_avg # weighted average running_mean = np.average(av, weights=np.array([counts[j] * 1. / np.sum(counts) for j in range(len(counts))])) plt.scatter(i, running_mean) # + # τ is a parameter called temperature the scales the probability distribution of actions. # A high temperature will tend the probabilities to be very simmilar, whereas a low temperature # will exaggerate differences in probabilities between actions. Selecting this parameter requires # an educated guess and some trial and error. # - # + # softmax # + n = 10 arms = np.random.rand(n) av = np.ones(n) # action-value counts = np.zeros(n) # how many times we've taken a particular action av_softmax = np.zeros(n) av_softmax[:] = 0.1 # initial probability def reward(prob): total = 0 for i in range(10): if random.random() < prob: total += 1 return total tau = 1.12 def softmax(av): normalization_factor = np.sum(np.exp(av[:] / tau)) probs = np.zeros(n) for i in range(n): probs[i] = np.exp(av[i] / tau) / normalization_factor return probs plt.xlabel('Plays') plt.ylabel('Mean Reward') for i in range(500): choice = np.where(arms == np.random.choice(arms, p=av_softmax))[0][0] counts[choice] += 1 k = counts[choice] rwd = reward(arms[choice]) old_avg = av[choice] new_avg = old_avg + (1. / k) * (rwd - old_avg) av[choice] = new_avg av_softmax = softmax(av) running_mean = np.average(av, weights=np.array([counts[j] * 1. / np.sum(counts) for j in range(len(counts))])) plt.scatter(i, running_mean) # + # Softmax action selection seems to do at least as well as epsilon-greedy, # perhaps even better; it looks like it converges on an optimal policy faster. # The downside to softmax is having to manually select the τ parameter. # Softmax here was pretty sensitive to τ and it took awhile of playing with it # to find a good value for it. Obviously with epsilon-greedy we had the parameter # epsilon to set, but choosing that parameter was much more intuitive. # - # + # The state space for 21 is much much larger than the single state in n-armed bandit. # In RL, a state is all information available to the agent (the decision maker) at a particular time t. # + # So what are all the possible combinations of information available to the agent (the player) in blackjack? # Well, the player starts with two cards, so there is the combination of all 2 playing cards. # Additionally, the player knows one of the two cards that the dealer has. # Thus, there are a lot of possible states (around 200). # As with any RL problem, our ultimate goal is to find the best policy to maximize our rewards. # + # Our main computational effort, therefore, is in iteratively improving our estimates for the values # of states or state-action pairs. # For example, given the cards total to 20, what is the value of hitting vs staying? # + # Problems like the n-armed bandit problem and blackjack have a small enough state or state-action space # that we can record and average rewards in a lookup table, giving us the exact average rewards for # each state-action pair. Most interesting problems, however, have a state space that is continuous or # otherwise too large to use a lookup table. That's when we must use function approximation # (e.g. neural networks) methods to serve as our QQ function in determining the value of states or state-actions. # + # This is why DeepMind's implementation actually feeds in the last 4 frames of gameplay, # effectively changing a non-Markov decision process into an MDP. # + # Qk(s,a)Qk(s,a) is the function that accepts an action and state and returns the value of # taking that action in that state at time step kk. This is fundamental to RL. # We need to know the relative values of every state or state-action pair. # + # π is a policy, a stochastic strategy or rule to choose action a given a state s. # Think of it as a function, π(s), that accepts state, s and returns the action to be taken. # There is a distinction between the π(s) function and a specific policy π. Our implementation # of π(s) as a function is often to just choose the action a in state s that has the highest # average return based on historical results, argmaxQ(s,a). As we gather more data and # these average returns become more accurate, the actual policy π may change. We may # start out with a policy of "hit until total is 16 or more then stay" but this policy # may change as we gather more data. Our implemented π(s) function, however, # is programmed by us and does not change. # + # Gt, cumulative return starting from a given state until the end of an episode. # + # Episode: the full sequence of steps leading to a terminal state and receiving a return. # + # vπ, a function that determines the value of a state given a policy π. # - # + # Monte Carlo # We'll use random sampling of states and state-action pairs # and observe rewards and then iteratively revise our policy, # which will hopefully converge on the optimal policy # as we explore every possible state-action couple. # + # code is functional and stateless # + import math import random def random_card(): card = random.randint(1, 13) if card > 10: card = 10 return card def useable_ace(hand): val, ace = hand return ace and val + 10 <= 21 def total_value(hand): val, ace = hand if useable_ace(hand): return val + 10 else: return val def add_card(hand, card): val, ace = hand if card == 1: ace = True return (val + card, ace) def eval_dealer(dealer_hand): while total_value(dealer_hand) < 17: dealer_hand = add_card(dealer_hand, random_card()) return dealer_hand def play(state, dec): player_hand = state[0] dealer_hand = state[1] if dec == 0: # 1 hit, 0 stay dealer_hand = eval_dealer(dealer_hand) player_tot = total_value(player_hand) dealer_tot = total_value(dealer_hand) status = 1 # 1 game is on, 2 play won, 3 draw, 4 dealer won if dealer_tot > 21 or dealer_tot < player_tot: status = 2 elif dealer_tot == player_tot: status = 3 elif dealer_tot > player_tot: status = 4 elif dec == 1: player_hand = add_card(player_hand, random_card()) dealer_hand = eval_dealer(dealer_hand) player_tot = total_value(player_hand) dealer_tot = total_value(dealer_hand) status = 1 if player_tot == 21: if dealer_tot == 21: status = 3 else: status = 2 elif player_tot > 21: status = 4 elif player_tot < 21: pass # game continues state = (player_hand, dealer_hand, status) return state def init_game(): status = 1 player_hand = add_card((0, False), random_card()) player_hand = add_card(player_hand, random_card()) dealer_hand = add_card((0, False), random_card()) if total_value(player_hand) == 21: if total_value(dealer_hand) != 21: status = 2 else: status = 3 state = (player_hand, dealer_hand, status) return state # - state = init_game() print(state) state = play(state, 1) print(state) # + # We will compress the states a bit by ignoring the useable ace boolean # for the dealer's hand because the dealer only shows a single card and # if it's an ace the player has no idea if it's useable or not, so it # offers no additional information to us. # + # Monte Carlo Reinforcement Learning # use an epsilon-greedy policy function to ensure # we have a good balance of exploration versus exploitation # + # In essence, with Monte Carlo we are playing randomly initialized games, # sampling the state-action pair space and recording returns. In doing so, # we can iteratively update our policy π. # + import numpy as np def init_state_space(): states = [] for card in range(1, 11): for val in range(11, 22): states.append((val, False, card)) states.append((val, True, card)) return states def init_state_actions(states): av = {} for state in states: av[(state, 0)] = 0.0 av[(state, 1)] = 0.0 return av def init_SA_count(state_actions): counts = {} for sa in state_actions: counts[sa] = 0 return counts # reward = 1 for winning, 0 for draw, -1 for losing def calc_reward(outcome): return 3 - outcome def update_Q_table(av_table, av_count, returns): for key in returns: av_table[key] = av_table[key] + (1. / av_count[key]) * (returns[key] - av_table[key]) return av_table # avg rewards - Q-value for each action given a state def qsv(state, av_table): if (state, 0) not in av_table: av_table[(state, 0)] = 0 if (state, 1) not in av_table: av_table[(state, 1)] = 0 stay = av_table[(state, 0)] hit = av_table[(state, 1)] return np.array([stay, hit]) # compress the state def get_RL_state(state): player_hand, dealer_hand, status = state player_val, player_ace = player_hand return (player_val, player_ace, dealer_hand[0]) # + epochs = 5000000 epsilon = 0.1 state_space = init_state_space() av_table = init_state_actions(state_space) av_count = init_SA_count(av_table) for i in range(epochs): state = init_game() player_hand, dealer_hand, status = state while player_hand[0] < 11: player_hand = add_card(player_hand, random_card()) state = (player_hand, dealer_hand, status) rl_state = get_RL_state(state) returns = {} while state[2] == 1: act_probs = qsv(rl_state, av_table) if random.random() < epsilon: action = random.randint(0, 1) else: action = np.argmax(act_probs) sa = (rl_state, action) returns[sa] = 0 if sa not in av_count: av_count[sa] = 0 av_count[sa] += 1 state = play(state, action) rl_state = get_RL_state(state) for key in returns.keys(): returns[key] = calc_reward(state[2]) av_table = update_Q_table(av_table, av_count, returns) print('Done') # - import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm # %matplotlib inline # + #fig = plt.figure(figsize=(8, 6)) #ax = fig.add_subplot(111, projection='3d') #ax.set_xlabel('Dealer card') #ax.set_ylabel('Player sum') #ax.set_zlabel('State-Value') x,y,z,a = [],[],[],[] for key in state_space: if (not key[1] and key[0] > 11 and key[2] < 21): y.append(key[0]) x.append(key[2]) state_value = max([av_table[(key, 0)], av_table[(key, 1)]]) z.append(state_value) if av_table[(key, 0)] >= av_table[(key, 1)]: a.append(0) else: a.append(1) #ax.azim = 230 #ax.plot_trisurf(x,y,z, linewidth=.02, cmap=cm.jet) # + # Here we've covered Monte Carlo reinforcement learning methods that depending on stochastically # sampling the environment and iteratively improving a policy π after each episode. One # disadvantage of Monte Carlo methods is that we must wait until the end of an episode # to update our policy. For some types of problems (like blackjack), this is okay, but # in a lot of cases, it makes more sense to be able to learn at each time step (immediately # after each action is taken). # - import pandas as pd strategy = pd.DataFrame(zip(x, y, z, a), columns=['Dealer card', 'Player sum', 'State-Value', 'Policy']) strategy.pivot(index='Player sum', columns='Dealer card', values='Policy') # + # The most important thing to learn from all of this is that in # essentially any RL method, our goal is to find an optimal Q function. # + # In the next part, I will abandon tabular learning methods and cover # Q-learning (a type of temporal difference (TD) algorithm) using a neural # network as our Q function (what we've all been waiting for). # - # + # Neural nets provide a functional approximator. # + # Our Q function actually looks like this: Q(s,a,θ) where θ is # a vector of parameters. And instead of iteratively updating values # in a table, we will iteratively update the θ parameters of # our neural network so that it learns to provide us with better # estimates of state-action values. # - # target: r_t+1 + γ ∗ maxQ(s′, a′) for non-terminal states # r_t+1 for terminal states (last state in an episode) # + # γ is a parameter 0-→1 that is called the discount factor. # Basically it determines how much each future reward is taken # into consideration for updating our Q-value. # + # If γ is close to 0, we heavily discount future rewards and # thus mostly care about immediate rewards. # + # s′ refers to the new state after having taken action a # and a′ refers to the next actions possible in this new state. # + # So maxQ(s′, a′) means we calculate all the Q-values for each # state-action pair in the new state, and take the maximium value # to use in our new value update. # (Note I may use s′ and a′ interchangeably with s_t+1 and a_t+1.) # + # In on-policy methods we iteratively learn about state values # at the same time that we improve our policy. In other words, # the updates to our state values depend on the policy. # + # In contrast, off-policy methods do not depend on the policy # to update the value function. Q-learning is an off-policy method. # It's advantageous because with off-policy methods, we can follow # one policy while learning about __another__. # + # For example, with Q-learning, we could always take completely random # actions and yet we would still learn about another policy function # of taking the best actions in every state. If there's ever a π # referenced in the value update part of the algorithm then it's # an on-policy method. # + import numpy as np def rand_pair(s, e): return np.random.randint(s, e), np.random.randint(s, e) # finds an array in the "depth" dimension of the grid def find_loc(state, obj): for i in range(4): for j in range(4): if all(state[i, j] == obj): return i, j # initialize stationary grid, all items are placed deterministically def init_grid(): state = np.zeros((4, 4, 4)) # place player state[0, 1] = np.array([0, 0, 0, 1]) # place wall state[2, 2] = np.array([0, 0, 1, 0]) # place pit state[1, 1] = np.array([0, 1, 0, 0]) # place goal state[3, 3] = np.array([1, 0, 0, 0]) return state # initialize player in random location, but keep wall, goal and pit stationary def init_grid_player(): state = np.zeros((4, 4, 4)) # place player state[rand_pair(0, 4)] = np.array([0, 0, 0, 1]) # place wall state[2, 2] = np.array([0, 0, 1, 0]) # place pit state[1, 1] = np.array([0, 1, 0, 0]) # place goal state[1, 2] = np.array([1, 0, 0, 0]) # find grid position of player (agent) a = find_loc(state, np.array([0, 0, 0, 1])) # find wall w = find_loc(state, np.array([0, 0, 1, 0])) # find goal g = find_loc(state, np.array([1, 0, 0, 0])) # find pit p = find_loc(state, np.array([0, 1, 0, 0])) if not all([a, w, g, p]): print('Invalid grid. Rebuilding...') return init_grid_player() return state # initialize grid so that goal, pit, wall, player are all randomly placed def init_grid_rand(): state = np.zeros((4, 4, 4)) # place player state[rand_pair(0, 4)] = np.array([0, 0, 0, 1]) # place wall state[rand_pair(0, 4)] = np.array([0, 0, 1, 0]) # place pit state[rand_pair(0, 4)] = np.array([0, 1, 0, 0]) # place goal state[rand_pair(0, 4)] = np.array([1, 0, 0, 0]) a = find_loc(state, np.array([0, 0, 0, 1])) w = find_loc(state, np.array([0, 0, 1, 0])) g = find_loc(state, np.array([1, 0, 0, 0])) p = find_loc(state, np.array([0, 1, 0, 0])) # if any of the "objects" are superimposed, just call the function again to re-place if not all([a, w, g, p]): print('Invalid grid. Rebuilding...') return init_grid_rand() return state # - def make_move(state, action): player_loc = find_loc(state, np.array([0, 0, 0, 1])) wall_loc = find_loc(state, np.array([0, 0, 1, 0])) goal_loc = find_loc(state, np.array([1, 0, 0, 0])) pit_loc = find_loc(state, np.array([0, 1, 0, 0])) state = np.zeros((4, 4, 4)) # up --> row - 1 if action == 0: new_loc = (player_loc[0] - 1, player_loc[1]) # down --> row + 1 elif action == 1: new_loc = (player_loc[0] + 1, player_loc[1]) # left --> column - 1 elif action == 2: new_loc = (player_loc[0], player_loc[1] - 1) # right --> column + 1 elif action == 3: new_loc = (player_loc[0], player_loc[1] + 1) if new_loc != wall_loc: if (np.array(new_loc) <= (3, 3)).all() and (np.array(new_loc) >= (0, 0)).all(): state[new_loc][3] = 1 new_player_loc = find_loc(state, np.array([0, 0, 0, 1])) if not new_player_loc: state[player_loc] = np.array([0, 0, 0, 1]) state[pit_loc][1] = 1 state[wall_loc][2] = 1 state[goal_loc][0] = 1 return state # + def get_loc(state, level): for i in range(4): for j in range(4): if state[i, j][level] == 1: return i, j def get_reward(state): player_loc = get_loc(state, 3) pit_loc = get_loc(state, 1) goal_loc = get_loc(state, 0) if player_loc == pit_loc: return -10 elif player_loc == goal_loc: return 10 else: return -1 def disp_grid(state): grid = np.zeros((4, 4), dtype='<U2') player_loc = find_loc(state, np.array([0, 0, 0, 1])) wall_loc = find_loc(state, np.array([0, 0, 1, 0])) goal_loc = find_loc(state, np.array([1, 0, 0, 0])) pit_loc = find_loc(state, np.array([0, 1, 0, 0])) for i in range(4): for j in range(4): grid[i, j] = ' ' if player_loc: grid[player_loc] = 'P' if wall_loc: grid[wall_loc] = 'W' if goal_loc: grid[goal_loc] = '+' if pit_loc: grid[pit_loc] = '-' return grid # - state = init_grid_rand() disp_grid(state) state = make_move(state, 3) state = make_move(state, 3) state = make_move(state, 1) state = make_move(state, 1) print('Reward: %s' % get_reward(state)) disp_grid(state) from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import RMSprop # + # An input layer of 64 units (because our state has a total of # 64 elements, remember its a 4x4x4 numpy array), 2 hidden layers # of 164 and 150 units, and an output layer of 4, one for each of # our possible actions (up, down, left, right) [in that order]. model = Sequential() model.add(Dense(164, init='lecun_uniform', input_shape=(64,))) model.add(Activation('relu')) model.add(Dropout(0.2)) model.add(Dense(150, init='lecun_uniform')) model.add(Activation('relu')) model.add(Dropout(0.2)) model.add(Dense(4, init='lecun_uniform')) model.add(Activation('linear')) # real-valued outputs rms = RMSprop() model.compile(loss='mse', optimizer=rms) # - state = init_grid_rand() model.predict(state.reshape(1, 64), batch_size=1) # + from IPython.display import clear_output import random epochs = 1000 gamma = 0.9 # coz it may take several moves to reach goal epsilon = 1 for i in range(epochs): state = init_grid() status = 1 # game in progress while (status) == 1: # run Q function on S to get Q values for all possible actions qval = model.predict(state.reshape(1, 64), batch_size=1) if random.random() < epsilon: # explore action = np.random.randint(0, 4) else: # exploit action = np.argmax(qval) # take action, observe new state S' new_state = make_move(state, action) # observe reward reward = get_reward(new_state) # get max_Q(S', a) new_Q = model.predict(new_state.reshape(1, 64), batch_size=1) max_Q = np.max(new_Q) y = np.zeros((1, 4)) y[:] = qval[:] if reward == -1: # non-terminal update = reward + gamma * max_Q else: # terminal update = reward y[0][action] = update # target output print('Game #: %s' % i) model.fit(state.reshape(1, 64), y, batch_size=1, nb_epoch=1, verbose=1) state = new_state if reward != -1: status = 0 clear_output(wait=True) if epsilon > 0.1: epsilon -= (1. / epochs) # - def test_algo(init=0): i = 0 if init == 0: state = init_grid() elif init == 1: state = init_grid_player() elif init == 2: state = init_grid_rand() print('Initial State:') print(disp_grid(state)) status = 1 while status == 1: qval = model.predict(state.reshape(1, 64), batch_size=1) action = np.argmax(qval) print('Move #: %s; Taking action: %s' % (i, action)) state = make_move(state, action) print(disp_grid(state)) reward = get_reward(state) if reward != -1: status = 0 print('Reward: %s' % reward) i += 1 if i > 10: print('Game lost; too many moves.') break test_algo(init=0) # + # soooooooo magical... # - # + # catastrophic forgetting: # a push-pull between very similar state-actions # (but with divergent targets) that results in this # inability to properly learn anything. # experience replay: # basically gives us minibatch updating in an # online learning scheme. # + # Thus, in addition to learning the action-value for the action # we just took, we're also going to use a random sample of our # past experiences to train on to prevent catastrophic forgetting. # + model.compile(loss='mse', optimizer=rms) # reset weights epochs = 3000 gamma = 0.975 epsilon = 1 batch_size = 40 buffer_size = 80 replay = [] # (S, A, R, S') h = 0 for i in range(epochs): state = init_grid_player() status = 1 while status == 1: qval = model.predict(state.reshape(1, 64), batch_size=1) if random.random() < epsilon: action = np.random.randint(0, 4) else: action = np.argmax(qval) new_state = make_move(state, action) reward = get_reward(new_state) # experience replay if len(replay) < buffer_size: replay.append((state, action, reward, new_state)) else: if h < buffer_size - 1: h += 1 else: h = 0 # circular buffer replay[h] = (state, action, reward, new_state) # randomly sample our experience replay memory minibatch = random.sample(replay, batch_size) X_train = [] y_train = [] for memory in minibatch: old_state, action, reward, new_state = memory old_qval = model.predict(old_state.reshape(1, 64), batch_size=1) new_Q = model.predict(new_state.reshape(1, 64), batch_size=1) max_Q = np.max(new_Q) y = np.zeros((1, 4)) if reward == -1: update = reward + gamma * max_Q else: update = reward y[0][action] = update X_train.append(old_state.reshape(64)) y_train.append(y.reshape(4)) X_train = np.array(X_train) y_train = np.array(y_train) print('Game #: %s' % i) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=1, verbose=1) state = new_state if reward != -1: status = 0 clear_output(wait=True) if epsilon > 0.1: epsilon -= (1. / epochs) # - test_algo(1) # + # magical ! # - test_algo(1) # + # need GPU to train the hardest variant with more epochs (>50K) # + import random import numpy as np from IPython.display import clear_output model.compile(loss='mse', optimizer=rms) # reset weights epochs = 50000 gamma = 0.975 epsilon = 1 batch_size = 40 buffer_size = 80 replay = [] # (S, A, R, S') h = 0 for i in range(epochs): state = init_grid_rand() status = 1 while status == 1: qval = model.predict(state.reshape(1, 64), batch_size=1) if random.random() < epsilon: action = np.random.randint(0, 4) else: action = np.argmax(qval) new_state = make_move(state, action) reward = get_reward(new_state) # experience replay if len(replay) < buffer_size: replay.append((state, action, reward, new_state)) else: if h < buffer_size - 1: h += 1 else: h = 0 # circular buffer replay[h] = (state, action, reward, new_state) # randomly sample our experience replay memory minibatch = random.sample(replay, batch_size) X_train = [] y_train = [] for memory in minibatch: old_state, action, reward, new_state = memory old_qval = model.predict(old_state.reshape(1, 64), batch_size=1) new_Q = model.predict(new_state.reshape(1, 64), batch_size=1) max_Q = np.max(new_Q) y = np.zeros((1, 4)) if reward == -1: update = reward + gamma * max_Q else: update = reward y[0][action] = update X_train.append(old_state.reshape(64)) y_train.append(y.reshape(4)) X_train = np.array(X_train) y_train = np.array(y_train) print('Game #: %s' % i) model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=1, verbose=1) state = new_state if reward != -1: status = 0 clear_output(wait=True) if epsilon > 0.1: epsilon -= (1. / epochs) # - test_algo(2) test_algo(2)	29,363
/startMLWithPython/.ipynb_checkpoints/chapter1-checkpoint.ipynb	52a672e77ed51995046359329e1979fec2823659	[]	no_license	takahiro1127/DeepLearning	https://github.com/takahiro1127/DeepLearning	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	240,018	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Feuille de TP3 - Partie A : Manipulation de fonctions # # + pycharm={"is_executing": false} # Import des modules import numpy as np # - # Attention : # + On rappelle que les noms de fonctions ne doivent pas commencer par «_» ni contenir de caractères accentués. # + Les tableaux sont typées. Lorsque vous modifier un élément d'une matrice, si les types sont différents, une conversion a lieu qui peut parfois engendrer des bugs... Voici un exemple pour illustrer cela. # + pycharm={"is_executing": false} A = np.array([[1,2], [3,4]]) print(A) A[0,0] = 1.5 # 1.5 va être converti en entier ! print(A) print(type(A), type(A[0,0])) A = np.array([[1.,2], [3,4]]) print(A) A[0,0] = 1.5 # Pas de conversion print(A) print(type(A), type(A[0,0])) A = np.array([[1,2], [3,4]], dtype='float128') print(A) A[0,0] = 1.5 print(A) print(type(A), type(A[0,0])) # - # ## Exercice 1 : Cryptographie basique : code de César (ou Caesar cypher) # # Le principe de ce code est de "décaler" les lettres. Par exemple, si la clef choisie est 1, la lettre A sera remplacée par B, la lettre B par C, etc. Nous nous proposons ici de fabriquer une fonction de cryptage et de décryptage pour la clef 13. # # #### a. <font color=red>Importer</font> le module `codecs` et tester les commandes `print(codecs.encode("abc", "rot+13"))` et `print(codecs.encode("abc", "rot-13"))` # + pycharm={"is_executing": false} import codecs print(codecs.encode("abc", "rot+13")) print(codecs.encode("nop", "rot-13")) # - # Comparer avec les 14, 15 et $16^\text{ème}$ lettres de l'alphabet. # #### b. Proposer une fonction `crypter()` qui demande à l'utilisateur de saisir une chaîne de caractères et affiche sa version codée. # + pycharm={"is_executing": false} # Mettre votre fonction ici def crypter(): """ Demande de saisir un message puis affiche sa version cryptée. """ mes = input("Donner une phrase à coder: ") print(codecs.encode(mes, "rot-13")) # - # #### c. Ecrire une fonction `decrypter()`qui demande à l'utilisateur de saisir une chaîne de caractères encodée et affiche sa version décodée. # #### d.Tester la fonction \verb+crypter()+ avec un message déjà crypté. Expliquer le résultat (indice : il y a 26 lettres dans l'alphabet). # # ## Exercice 2 # ### Question 1 # A l'aide du module `numpy`, implémenter informatiquement la fonction $f : x \mapsto \sin(\pi x)$. # ### Question 2 # Ecrire une fonction qui convertit une vitesse depuis des $km/h$ vers des miles par heure puis des noeuds. On rappelle que 1 mile = 1609 m et 1 noeud $= 0.514 m.s^{-1}$. # ### Question 3 # # 1. Ecrire une fonction qui prend en argument un tableau et renvoie un tableau de même taille rempli par le nombre décimal $3.0$ . # 2. A l'aide de la fonction numpy`isscalar` écrire une fonction qui prend un argument \verb+x+ et renvoie 3.0 si `x` est un scalaire et un tableau de 3.0 s'il s'agit d'un tableau. # ### Question 4 # L'aire d'un triangle peut être calculée à partir des coordonnées de ses trois sommets par la formule : # \begin{equation} # \mathcal{A} = \frac{1}{2}\big\vert (x_1 - x_3)(y_2-y_1) - (x_1-x_2)(y_3 - y_1) \big \vert # \end{equation} # Ecrire une fonction qui prend comme argument un tableau bi-dimensionnel dont chaque ligne est la coordonnée $(x,y)$ d'un sommet et renvoie son aire. # ### Question 5 # Ecrire une fonction qui prend en argument un tableau et remplace ces coefficients strictement positif par $1$ et ses coefficients strictement négatifs par $-1$. # ## Exercice : comparaison de nombre flottants # # 1. Importez le module \verb!scipy.linalg! comme \verb!slin!, définissez une matrice $A$ (si possible inversible) et un vecteur $u$. Calculez le déterminant de A, inversez la matrice, calculez $x=A^{-1}u$, puis calculez la norme de $Ax-u$. Comparer $Ax$ et $u$. # 2. Testez également la commande \verb!allclose! pour comparer $Ax$ et $u$. # 3. Recommencez en résolvant le système linéaire par la commande \verb!solve!. # + # Mettre ici votre code pour la question 1. # + # Mettre ici votre code pour la question 2. # + # Mettre ici votre code pour la question 3. moonlight i was a spin the stars and so when i say i love it all the blues i wish a spark of my heart when you say i was a said a spring and the star" sent = sent.split(" ") for word in sent: prob = model.predict(toNumber(word).reshape(1,CHAR_COUNT)) print ("{}".format(indToLabel[np.argmax(prob)])) # + id="R8LvKXLKqsEq" colab_type="code" colab={}	4,757
/05. Merge/Auto_MPG/.ipynb_checkpoints/Auto_MPG-checkpoint.ipynb	004a9e2a89040e05f79ce25be135afcc1c2accf6	[]	no_license	joypark88/python_data_science-	https://github.com/joypark88/python_data_science-	0	0	null	2021-01-07T09:39:06	2021-01-07T08:32:33	Jupyter Notebook	Jupyter Notebook	false	false	.py	43,722	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # ## MPG Cars # # ### Step 1. Import the necessary libraries import pandas as pd import numpy as np import random # ### Step 2. Import the first dataset cars1 and cars2. # ### Step 3. Assign each to a variable called cars1 and cars2 cars1 = pd.read_csv("https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars1.csv") cars2 = pd.read_csv("https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/05_Merge/Auto_MPG/cars2.csv") cars1 cars2 # ### Step 4. Oops, it seems our first dataset has some unnamed blank columns, fix cars1 cars1 = cars1.iloc[:, 0:9] #or #cars1 = cars1.loc[:, "mpg":"car"] cars1 # ### Step 5. What is the number of observations in each dataset? cars1.info() #cars1.shape cars2.info() #cars2.shape # ### Step 6. Join cars1 and cars2 into a single DataFrame called cars # + cars = pd.concat([cars1, cars2]) cars #or #cars = cars1.append(cars2) # - # ### Step 7. Oops, there is a column missing, called owners. Create a random number Series from 15,000 to 73,000. # + res = np.array([random.randrange(15000, 73000, 1) for i in range(398)]) #array 안에 리스트[]로 묶여있어야 됨. #another answer #nr_owners = np.random.randint(15000, high=73001, size=398, dtype='l') # - # ### Step 8. Add the column owners to cars # + #cars.owners = res col이 추가가 안됨 cars['owners'] = res cars	1,620
/Additional dataset testing_VGG16.ipynb	d75066c9afdec73f2812bf2a14ff4b389fcd9409	[]	no_license	eatingyeh/Face-Recognition-with-LeNet-and-VGG16	https://github.com/eatingyeh/Face-Recognition-with-LeNet-and-VGG16	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	88,045	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 (Anaconda 5) # env: # LD_LIBRARY_PATH: /ext/anaconda5/lib # PROJ_LIB: /ext/anaconda-2019.03/share/proj # PYTHONHOME: /ext/anaconda5/lib/python3.5 # PYTHONPATH: /ext/anaconda5/lib/python3.5:/ext/anaconda5/lib/python3.5/site-packages # language: python # metadata: # cocalc: # description: Python/R distribution for data science # priority: -1 # url: https://www.anaconda.com/distribution/ # name: anaconda5 # --- # # SymPy # + # %matplotlib inline import sympy as sp import numpy as np import matplotlib.pyplot as plt # - # --- # ## Part I # # $$ \Large {\displaystyle f(x)=3e^{-{\frac {x^{2}}{8}}}} \sin(x/3)$$ # * Find the first four terms of the Taylor expansion of the above equation (at x = 0) # * Make a plot of the function # * Plot size 10 in x 4 in # * X limts -5, 5 # * Y limits -2, 2 # * On the same plot: # * Over-plot the 1-term Taylor expansion using a different color/linetype/linewidth # * Over-plot the 2-term Taylor expansion using a different color/linetype/linewidth # * Over-plot the 3-term Taylor expansion using a different color/linetype/linewidth # * Over-plot the 4-term Taylor expansion using a different color/linetype/linewidth # --- # ## Part II # # $$\Large {\displaystyle g(x)=x^{2} - 0.75}$$ # # #### What are the value(s) for `x` where f(x) = g(x) # --- # ### Due Mon Mar 02 - 1 pm # - `File -> Download as -> HTML (.html)` # - `upload your .html file to the class Canvas page` ft(shift)).cumprod() # strategy returns if plot: plotcols = ['signal', 'bnh_returns', 'strat_returns'] results[plotcols].plot(secondary_y=['signal'], figsize=(20, 10)) return data['bnh_returns'][-1], data['strat_returns'][-1] # ## Strategy I # I. Backtests a strategy using three moving averages on any indices such as Nifty50, SPY, # HSI and so on. # 1. Compute three moving averages of 20, 40, and 80. # 2. Go long when the price crosses above all three moving averages. # 3. Exit the long position when the price crosses below any of the three moving # averages. # 4. Go short when the price crosses below all three moving averages. # 5. Exit the short position when the price crosses above any of the three moving # averages. # 6. Optional: Optimise all three moving averages def backtest_3ma_crossover(data, malist, plot=True): """Backtests moving average crossover strategy described above @params: data = pandas dataframe with daily prices indexed by date malist = [ma1, ma2, ma3] => list of 3 moving average windows for backtesting plot = True if plotting required @returns: results = dataframe with close to close returns, daily returns, moving averages, signal """ # Create dataframe to store values results = data[['Close']].copy() results['ma1'] = results['Close'].rolling(window=malist[0]).mean() # moving average 1 of close price results['ma2'] = results['Close'].rolling(window=malist[1]).mean() # moving average 2 of close price results['ma3'] = results['Close'].rolling(window=malist[2]).mean() # moving average 3 of close price results['c2c_rets'] = results['Close'].pct_change() # daily returns of close price results['daily_rets'] = results['Close'].pct_change() # daily returns for strategy # Long signal results['signal'] = np.where( (results.Close > results.ma1) & (results.Close > results.ma2) & (results.Close > results.ma3), 1, 0) # Long exit signal long_exit = (results.Close < results.ma1) \| \ (results.Close < results.ma2) \| \ (results.Close < results.ma3) results['signal'] = np.where( long_exit & (results.signal.shift(1) == 1), 0, results.signal) # Short signal results['signal'] = np.where( (results.Close < results.ma1) & (results.Close < results.ma2) & (results.Close < results.ma3), -1, results.signal) # Short exit signal short_exit = (results.Close > results.ma1) \| \ (results.Close > results.ma2) \| \ (results.Close > results.ma3) results['signal'] = np.where( short_exit & (results.signal.shift(1) == -1), 0, results.signal) if plot: plotcols = ['Close', 'ma1', 'ma2', 'ma3', 'signal'] results[plotcols].plot(secondary_y=['signal'], figsize=(20, 10)) return results malist = [20, 40, 80] results = backtest_3ma_crossover(data, malist) results plotcols = ['Close', 'ma1', 'ma2', 'ma3', 'signal'] results.iloc[80:200][plotcols].plot(secondary_y=['signal'], figsize=(20, 10)) bnh_rets, strat_rets = calc_returns(results) bnh_rets, strat_rets print(f'Buy & Hold returns: {bnh_rets}, Strategy returns: {strat_rets}') def optimise_3ma_crossover(data, ma1list, ma2list, ma3list): df = pd.DataFrame(columns=['ma1', 'ma2', 'ma3', 'strat_rets']) for ma1 in ma1list: for ma2 in ma2list: for ma3 in ma3list: results = backtest_3ma_crossover(data, [ma1, ma2, ma3], plot=False) rets = calc_returns(results, plot=False) print(f'ma1: {ma1}, ma2: {ma2}, ma3: {ma3}, strat_rets: {rets[1]}') df.loc[df.shape[0]] = [ma1, ma2, ma3, rets[1]] return df.loc[df['strat_rets'].idxmax()] # + ma1list = [5, 10, 15, 20, 30] ma2list = [35, 40, 50, 60, 70] ma3list = [80, 90, 100, 120, 150] best_param = optimise_3ma_crossover(data, ma1list, ma2list, ma3list) # - print('!!! Best parameters !!!') print(f'MA1: {best_param.ma1}, MA2: {best_param.ma2}, MA3: {best_param.ma3}, Strategy Returns: {best_param.strat_rets}') # ## Strategy II # II. Buy and sell the next day # 1. Buy the stock on the fourth day open, if the stock closes down consecutively for # three days. # 2. Exit on the next day open. # 3. Optional: Optimise the strategy by exiting the long position on the same day close. # Also, you can optimise the number of down days. There are high chances that the # number of down days would be different for each stock. def backtest_buy_after_subsequent_downs(data, downdays, sellmode='next_open', plot=True): """Backtests moving average crossover strategy described above @params: data = pandas dataframe with daily prices indexed by date downdays = number of downdays to generate buy signal sellmode = 'next_open' or 'sameday_close' => when to exit long position plot = True if plotting required @returns: results = dataframe with daily returns, moving averages, signal """ assert sellmode in ('next_open', 'sameday_close'), "Sellmode must be either 'next_open' or 'sameday_close'" assert isinstance(downdays, int) and downdays > 0, "Downdays must be a positive integer" # Create dataframe to store values results = data[['Open', 'Close']].copy() # daily returns of close price results['c2c_rets'] = results['Close'].pct_change() # daily returns based on strategy sellmode results['daily_rets'] = results['Open'].pct_change() if sellmode == 'next_open' \ else (results['Close'] - results['Open']) / results['Open'] # Long signal results['signal'] = 1 for i in range(downdays): results['signal'] = np.where( results['signal'] & (results['Close'].shift(i-1) < results['Close'].shift(i)), 1, 0 ) if plot: plotcols = ['Close', 'signal'] results[plotcols].plot(secondary_y=['signal'], figsize=(20, 10)) return results def optimise_buy_after_subsequent_downs(data, sellmodes, downdays): df = pd.DataFrame(columns=['sellmode', 'downdays', 'strat_rets']) for mode in sellmodes: for dd in downdays: results = backtest_buy_after_subsequent_downs(data, downdays=dd, sellmode=mode, plot=False) rets = calc_returns(results, plot=False) print(f'Sellmode: {mode}, Downdays: {dd}, strat_rets: {rets[1]}') df.loc[df.shape[0]] = [mode, dd, rets[1]] return df.loc[df['strat_rets'].idxmax()] downdays = 3 sellmode = 'next_open' results = backtest_buy_after_subsequent_downs(data, downdays=downdays, sellmode=sellmode, plot=False) results.head(15) bnh_rets, strat_rets = calc_returns(results, shift=2) print(f'Buy & Hold returns: {bnh_rets}, Strategy returns: {strat_rets}') results.head(50) plotcols = ['Close', 'signal'] results.iloc[:100][plotcols].plot(secondary_y=['signal'], figsize=(20, 10)) sellmodes = ['next_open', 'sameday_close'] downdays = [2, 3, 4, 5, 6, 7, 8, 9] best_param = optimise_buy_after_subsequent_downs(data, sellmodes=sellmodes, downdays=downdays) print('!!! Best parameters !!!') print(f'Sellmode: {best_param.sellmode}, Downdays: {best_param.downdays}, Strategy Returns: {best_param.strat_rets}') # ## Strategy III # III. Strategy based on RSI indicator. # 1. Buy the instrument such as Nifty or SPY when the RSI is less than 15 # 2. Exit conditions: # a. Take profit of 5% or RSI > 75 # b. Stop loss of - 2% # © Copyright QuantInsti Quantitative Learning Private Limited. Page 2 # 3. Optional: Optimise the strategy by adjusting the RSI value. Also, take profit and stop # loss criteria can be different for each stock. # 4. Note: You can use TA-Lib in Python to compute the RSI value. # + active="" # def ma(data, n, ix, on='Close'): # """Calculates moving average of prices # @params: # data = pandas dataframeith daily prices indexed by date # n = moving average lookback period # ix = reference current index position for window period # on = column whose average needs to be calculated # @returns: # ma = moving average of last 'n' days of 'on' price # """ # assert ix >= n, f"Moving average for {n} days cannot be calculated" # return data.iloc[ix-n+1:ix+1][on].mean() # + active="" # def backtest(data, malist, cap): # """Backtests moving average crossover strategy described above # @params: # data = pandas dataframe with daily prices indexed by date # malist = [ma1, ma2, ma3] => list of 3 moving average windows for backtesting # cap = initial capital # @returns: # daily_portfolio = daily portfolio value for strategy # """ # days = data.shape[0] # Number of trading days # values = pd.DataFrame(columns=['cash', 'investment'], index=data.index) # Create empty dataframe to store portfolio values # # for i in range(days): # if i < malist[0] or i < malist[1] or i < malist[2]: # skip initial days when all moving averages cannot be caculated # continue # buy = data.Close[i] > ma(data, malist[0], i) and data.Close[i] > ma(data, malist[1], i) and \ # data.Close[i] > ma(data, malist[2], i) and data.Close[i-1] < ma(data, malist[0], i-1) and \ # data.Close[i-1] < ma(data, malist[1], i-1) and data.Close[i-1] < ma(data, malist[1], i-1) # # if buy: # print(data.index[i], data.Close[i]) # + active="" # initial_cap = 100000 # malist = [20, 40, 80] # backtest(df, malist, initial_cap) ers.Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model c.shape y_train # + from tensorflow.keras.callbacks import EarlyStopping es = EarlyStopping(patience = 10, monitor='val_loss', restore_best_weights=True) model = init_model() model.fit(c, y_train, batch_size = 32, epochs=1000, validation_split = 0.3, callbacks = [es]) # - model.evaluate(c_test, y_test) # ### Record à 16.36% avec ce modèle from tensorflow.keras.models import Sequential from tensorflow.keras import layers def init_model(): model = Sequential() model.add(layers.Masking(mask_value = 0)) model.add(layers.LSTM(100, dropout=0.2, recurrent_dropout=0.2, activation='tanh')) model.add(layers.Dense(128, activation='tanh')) model.add(layers.Dense(y_train.shape[1], activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model model_record = model # # Traitement de la base de données finale vectorisée en amont all_countries = pd.read_hdf('../../raw_data/full_mean_df_hdf.h5', 'h5').dropna() age_list = [47, 51, 42, 56, 27, 44, 59, 52, 34, 60, 41, 57, 63,\ 53, 45, 48, 47, 53, 30, 75, 57, 44, 47, 24, 51, 54, 68,\ 49, 42, 50, 49, 40, 34, 34, 38, 44, 37, 54, 56, 61, 83,\ 44, 45, 59, 45, 67, 53, 48, 33, 42, 32, 57, 34, 47, 40, 48,\ 39, 49, 43, 35, 52, 63, 61, 50, 27, 50, 45, 80, 45, 50, 46,\ 59, 41, 47, 65, 51, 44, 34, 68, 37, 31, 57, 35, 37, 65, 57,\ 48, 61, 55, 72, 49, 45, 36, 58, 32, 57, 30, 57, 62, 44, 36, 51,\ 49, 54, 48, 62, 43, 43, 41, 36, 57, 54, 44, 44, 32, 33, 51, 51,\ 51, 56, 56, 43, 41, 43, 61, 63, 44, 36, 49, 49, 54, 42, 55, 53,\ 41, 62, 36, 19, 43, 56, 51, 70, 48, 51, 27, 46, 44, 53, 35, 35,\ 31, 60, 50, 31, 47, 43, 76, 45, 45, 53, 66, 52, 45, 45, 51, 40, 44,\ 48, 56, 56, 65, 27, 48, 39, 55, 35, 44, 40, 43, 52, 31, 50, 53, 58,\ 38, 59, 34, 52, 53, 46, 49, 58, 43, 55, 58, 26, 62, 72, 41, 36, 48,\ 56, 53, 61, 63, 59, 45, 45, 59, 46, 31, 62, 62, 41,\ 67, 44, 40, 62, 40, 49, 53, 61, 60, 38, 68, 54, 57,\ 37, 33, 50, 40, 63, 30, 45, 67, 57, 52, 43, 63, 64,\ 48, 63, 35, 68, 34, 53, 44, 48, 41, 54, 64, 45, 40,\ 41, 55, 61, 42, 46, 68, 36, 69, 47, 71, 55, 42, 52,\ 55, 34, 59, 75, 50, 39, 53, 50, 49, 61, 47, 29, 45,\ 62, 30, 64, 41, 62, 68, 32, 66, 44, 52, 44, 58, 59,\ 47, 58, 34, 66, 39, 70, 54, 41, 64, 43, 65, 74, 38,\ 46, 56, 47, 57, 38, 40, 35, 36, 57, 54, 44, 61, 32,\ 65, 42, 67, 50, 58, 46, 38, 65, 62, 53, 51, 58, 34,\ 29, 22, 53, 39, 59, 57, 61, 36, 55, 41, 71, 40, 49,\ 63, 44, 57, 49, 41, 65, 52, 54, 46, 33, 30, 45, 61,\ 65, 73, 56, 62, 56, 31, 53, 50, 56, 54, 51, 46, 48,\ 72, 59, 47, 60, 46, 72, 37, 59, 45, 53, 51, 48, 66, 67,\ 37, 41, 47, 35, 36, 34, 57, 36, 41, 60, 34, 54, 56, 37,\ 43, 48, 55, 44, 54, 54, 58, 38, 58, 41, 57, 33, 67, 53,\ 57, 41, 41, 48, 62, 38, 66, 46, 42, 70, 62, 54, 53, 75,\ 45, 33, 64, 45, 40, 38, 68, 30, 48, 37, 55, 67, 40, 37,\ 42, 49, 48, 50, 59, 57, 63, 55, 57, 38, 64, 33, 57, 51,\ 63, 48, 37, 52, 56, 57, 58, 57, 34, 34, 46, 57, 34, 60,\ 70, 40, 56, 63, 61, 50, 58] key_femme = [0, 2, 3, 4, 7, 13, 16, 17, 18 , 19, 24, 29, 30, 31, 34, 35, 43, 45, 47, 49, 53, 55, 59, 61, 62, 64,\ 73, 74, 78, 79, 80, 81, 83, 84, 86, 87, 88, 94, 96, 99, 101, 106, 108, 109, 110, 113, 115, 116, 120, 121, 125, 127, 128, 129, 130,\ 136, 141, 146, 152, 153, 154, 157, 160, 161, 164, 166, 170, 171, 172, 177, 180, 186,\ 187, 188, 191, 195, 197, 198, 201, 205, 206, 208, 209, 210, 211, 213, 214, 216, 219, 220, 222,\ 223, 225, 226, 228, 231, 235, 240, 248, 251, 252, 253, 256, 258, 261, 265, 272, 275, 278, 279, 280,\ 286, 289, 294, 296, 297, 299, 300, 304, 305, 306, 307, 310, 312, 313, 316, 318, 322, 327, 331, 332, 334,\ 337, 338, 340, 343, 343, 344, 349, 351, 352, 355, 356, 360, 361, 363, 365, 367, 374, 376, 379, 381, 383, 384,\ 388, 393, 394, 396, 399, 401, 402, 406, 407, 408, 409, 414, 415, 417, 418, 419, 421, 422, 423, 424, 426, 427,\ 432, 433, 434, 435, 436, 438, 441, 442, 443, 446, 447, 448, 451, 452, 457, 458, 459, 460, 461, 463, 464, 468] dico = {name:age for name, age in zip(all_countries.name.unique(), age_list)} for name in dico.keys(): wonderful_df["age"] = wonderful_df.name.map(dico) dico = {name:sex for name, age in zip(all_countries.name.unique(), age_list)} for name in dico.keys(): all_countries["sex"] = all_countries.name.map(dico) real_dict_femme = {key:name for key,name in enumerate(tweet_df.name)} reel_list_femme = [] for i in key_femme: reel_list_femme.append(real_dict_femme[i]) ll = [1] * len(key_femme) dict_femme = {name:key for name, key in zip(reel_list_femme,ll)} for name in all_countries['name'].unique(): if name in dict_femme.keys(): all_countries["sex"] = all_countries['name'].map(dict_femme) all_countries['sex'] = all_countries['sex'].fillna(0) test = np.arange(0,300,1) herbe = all_countries[test] y = all_countries['age'] y # + X_train3, X_test, y_train, y_test = train_test_split(herbe, y, test_size = 0.3) # Création des données cibles. y_train = y_train.values y_test = y_test.values X_train3 = np.array(X_train3) X_test = np.array(X_test) # - from tensorflow.keras import backend backend.clear_session() from tensorflow.keras.models import Sequential from tensorflow.keras import layers def init_model(): model = Sequential() #model.add(layers.GRU(units=27, activation='tanh')) #model.add(layers.LSTM(100, dropout=0.2, recurrent_dropout=0.2, activation='tanh')) model.add(layers.Dense(512, activation='relu')) model.compile(loss='mse', optimizer='adam', metrics=['mae']) return model X_train3.shape y_train.shape # + from tensorflow.keras.callbacks import EarlyStopping es = EarlyStopping(patience = 20, monitor='val_loss', restore_best_weights=True) model = init_model() model.fit(X_train3, y_train, batch_size = 32, epochs=1000, validation_split = 0.3, callbacks = [es]) # - model.evaluate(X_test, y_test) X_test import matplotlib.pyplot as plt loss = model.history.history['loss'] acc = model.history.history['mean_absolute_error'] val_loss = model.history.history['val_loss'] val_acc = model.history.history['val_mean_absolute_error'] plt.plot(loss, label = 'loss') plt.plot(val_loss, label='val_loss') plt.xlabel('n_epochs') plt.legend() plt.plot(acc, label='mean_absolute_error') plt.plot(val_acc, label='val_mean_absolute_error') plt.xlabel('n_epochs') plt.legend() herbe.loc[0,:] # Renvoie le député le plus proche de votre tweet def predict_tweet(df, model, n_tweet, one_tweet = True): ''' La fonction prend la base de données originale (par député), un modèle entraîné et un numéro de tweet en entrée. Elle renvoie le député le plus proche du texte proposé. Quand one_tweet = False, on ressort le député le plus proche du tweet. Quand one_tweet = True, on sort le député le plus proche du compte twitter. ''' test = np.arange(0,300,1) herbe = df[test] X_example = herbe.loc[n_tweet,:] coucou = np.array(X_example) if one_tweet: lol = np.reshape(coucou, (1, 300)) prediction = model.predict(lol) if one_tweet: deputy = y.columns[prediction.argmax()] return deputy all_countries.loc[85200,:] for i in range(85000,85300): print(predict_tweet(all_countries.reset_index(), model, i)) test = np.arange(0,300,1) herbe = all_countries.reset_index()[test] X_example = herbe.loc[1,:] coucou = np.array(X_example) model.predict(lol).argmax() all_countries.loc[0:300,:] import io # + languages = ['english'] def load_vec(emb_path, nmax=50000): vectors = [] word2id = {} with io.open(emb_path, 'r', encoding='utf-8', newline='\n', errors='ignore') as f: next(f) for i, line in enumerate(f): word, vect = line.rstrip().split(' ', 1) vect = np.fromstring(vect, sep=' ') assert word not in word2id, 'word found twice' vectors.append(vect) word2id[word] = len(word2id) if len(word2id) == nmax: break id2word = {v: k for k, v in word2id.items()} embeddings = np.vstack(vectors) return embeddings, id2word, word2id nmax = 100000 # maximum number of word embeddings to load emb_dict = {} for lang in languages: path = f"../../raw_data/vectors_{lang}.txt" #Select here embeddings, id2word, word2id = load_vec(path, nmax) emb_dict[lang] = [embeddings, id2word, word2id] print("Dict created") def multilang_word_vector(word, emb_dict, lang): try: if word in emb_dict.get(lang)[2].keys(): return emb_dict[lang][0][emb_dict[lang][2][word]] return [] except: return [] # - phrase = 'brazil' phrase = phrase.lower() sentence = [] for word in phrase.split(): sentence.append(multilang_word_vector(word, emb_dict, 'english')) sentence = pd.DataFrame(sentence) sentence = sentence.mean() coucou = np.array(sentence) lol = np.reshape(coucou, (1, 300)) prediction = model.predict(lol) y.columns[prediction[0].argsort()[-30:][::-1]] new_test_df[new_test_df['name'] == 'Clara PONSATÍ OBIOLS']['content'].str.find('europe') # # Traitement de la base de données finale traduite # ## Prétraitement wonderful_df = pd.read_pickle('../../raw_data/tweets_en.csv') wonderful_df dico = {name:age for name, age in zip(wonderful_df.name.unique(), age_list)} for name in dico.keys(): wonderful_df["age"] = wonderful_df.name.map(dico) real_dict_femme = {key:name for key,name in enumerate(tweet_df.name)} reel_list_femme = [] for i in key_femme: reel_list_femme.append(real_dict_femme[i]) ll = [1] * len(key_femme) dict_femme = {name:key for name, key in zip(reel_list_femme,ll)} for name in all_countries['name'].unique(): if name in dict_femme.keys(): wonderful_df["sex"] = wonderful_df['name'].map(dict_femme) wonderful_df['sex'] = wonderful_df['sex'].fillna(0) wonderful_df.head(2) clean_df = rmurl_df(wonderful_df, 'content') clean_df = lower_df(clean_df, 'content') clean_df = rmnumbers_df(clean_df, 'content') clean_df = rmpunct_df(clean_df, 'content') clean_df = rmstopwords_df(clean_df, 'content') clean_df = lemmatize_df(clean_df, 'content') clean_df = erase_fewletter_df(clean_df, 'content') clean_df = rmemojis_df(clean_df) # Cette fonction retourne automatiquement X_train, X_test, y_train, y_test de notre base de données twitter. def get_train_test_objects(df, column): ''' Les étapes que cette fonction réalise sont en commentaires. ''' # Copie de la base de données pour éviter les problèmes d'assignation abusive. df = df.copy() # Récupération de tous les tweets et du nom du député qui les a posté. Création de la cible y. df = df[[column, 'content']] y = pd.get_dummies(df[column]) # Transformation des tweets en suite de mots (strings) dans une liste. sentences = df['content'] sentences_inter = [] for sentence in sentences: sentences_inter.append(sentence.split()) # Séparation des données d'entraînement et de test sentences_train, sentences_test, y_train, y_test = train_test_split(sentences_inter, y, test_size = 0.3) # Vectorisation des phrases word2vec = Word2Vec(sentences=sentences_train) # Création des données d'entrée. X_train = embedding(word2vec,sentences_train) X_test = embedding(word2vec,sentences_test) X_train_pad = pad_sequences(X_train, padding='post',value=-1000, dtype='float32') X_test_pad = pad_sequences(X_test, padding='post',value=-1000, dtype='float32') # Création des données cibles. y_train = y_train.values y_test = y_test.values # Sorties de la fonction return X_train_pad, y_train, X_test_pad, y_test, word2vec X_train, y_train, X_test, y_test, word2vec = get_train_test_objects(clean_df, 'name') # ## Entraînement et évaluation du modèle # + from tensorflow.keras.models import Sequential from tensorflow.keras import layers def init_model(): model = Sequential() model.add(layers.Masking(mask_value = -1000)) model.add(layers.LSTM(350, dropout=0.2, recurrent_dropout=0.2, activation='tanh')) model.add(layers.Dense(128, activation='tanh')) model.add(layers.Dense(472, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model # + from tensorflow.keras.callbacks import EarlyStopping es = EarlyStopping(patience = 3, monitor='val_loss', restore_best_weights=True) model = init_model() model.fit(X_train, y_train, batch_size = 32, epochs=1000, validation_split = 0.3, callbacks = [es]) # - model.evaluate(X_test, y_test) # ## Score et sauvegarde du modèle model.save('model_deputies') word2vec.save('word2vec_deputies') # ### Députés def format_input(input1): sentences_inter = [] for sentence in input1: sentences_inter.append(sentence.split()) X_train = embedding(word2vec,sentences_inter) formated_input = pad_sequences(X_train, padding='post',value=-1000, dtype='float32') return formated_input pond = np.sqrt(clean_df.groupby('country').count()['content'].values) result = (1000*np.mean(model.predict(format_input(['netherlands water', 'amsterdam sweden'])), axis=0)/pond).argmax() pd.get_dummies(clean_df['country']).columns[result] word2vec.save('word2vec_country') model.save('model_country') # Score maximal : 5.45% Baseline : 0.21% # ### Groupe européen model_group = model # Score maximal : 33.81% # Baseline : 12.5% # ### Sexe model_sex = model # Score maximal : 59.18% # ### Age # ### Pays model.save('model_country') # Score maximal : 25.02% # Baseline : 8.09% # ## Prédictions ! from fast_bert.data_cls import BertDataBunch train = clean_df[['content', 'name']] train.rename(columns={"content":"text", "name":"label"}, inplace = True) x_train, x_test = train_test_split(train, test_size = 0.25) x_train, x_val = train_test_split(x_train, test_size = 0.25) x_train.to_csv('train.csv') x_val.to_csv('val.csv') pd.DataFrame(train['label'].unique()).to_csv('labels.csv') databunch = BertDataBunch('.', '.', tokenizer='bert-base-uncased', train_file='train.csv', val_file='val.csv', label_file='labels.csv', text_col='text', label_col='label', batch_size_per_gpu=16, max_seq_length=100, multi_gpu=True, multi_label=True, model_type='bert') from fast_bert.learner_cls import BertLearner from fast_bert.metrics import accuracy import logging import torch logger = logging.getLogger() device_cuda = torch.device("cpu") metrics = [{'name': 'accuracy', 'function': accuracy}] learner = BertLearner.from_pretrained_model( databunch, pretrained_path='bert-base-uncased', metrics=metrics, device=device_cuda, logger=logger, output_dir='.', finetuned_wgts_path=None, warmup_steps=500, multi_gpu=True, is_fp16=True, multi_label=True, logging_steps=50) learner.lr_find(start_lr=1e-5,optimizer_type='lamb') learner.fit(epochs=6, lr=6e-5, validate=True, # Evaluate the model after each epoch schedule_type="warmup_cosine", optimizer_type="adamw") clean_df.to_pickle('clean_df')	27,419
/05_0_MovieLens_100k/MovieLens-100k.ipynb	918e5a1143ee4416da3a85b19f831157a0e9c95c	[]	no_license	HsiYang506/Machine_Learning_Demo	https://github.com/HsiYang506/Machine_Learning_Demo	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	117,316	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # Assignment 1 import nltk nltk.download('punkt') nltk.download('wordnet') from nltk import sent_tokenize, word_tokenize from nltk.stem.snowball import SnowballStemmer from nltk.stem.wordnet import WordNetLemmatizer from nltk.corpus import stopwords import pandas as pd import numpy as np import re import string data = pd.read_csv("tweets-dataset.csv",encoding = 'utf-8', header = None) #print(data.head) lista= data.values.tolist() from matplotlib import pyplot as plt # + from nltk.tokenize import TweetTokenizer tz = TweetTokenizer(strip_handles= True) # - #print(lista[:20]) for i in lista[:20]: # i = i.translate(None, string.punctuation) #,print(i) i = re.sub(r"http\S+", "", i[0]) i = re.sub(r"@\S+", "", i) i = re.sub(r"[0-9]+","",i) translate_table = dict((ord(char), None) for char in string.punctuation) i = i.translate(translate_table) tokens = tz.tokenize(i) print(tokens) len_tok = len(tokens) len_type = len(set(tokens)) #print(len_type / len_tok) # + listfull = [] X = [] Y = [] k = 0 main_dict = dict() t = 0 for i in lista: i = re.sub(r"http\S+", "", i[0]) i = re.sub(r"@\S+", "", i) i = re.sub(r"[0-9]+","",i) i = i.lower() translate_table = dict((ord(char), None) for char in string.punctuation) i = i.translate(translate_table) tokens = tz.tokenize(i) #tokens = i.split() listfull = listfull + tokens for i in tokens: if i in main_dict: main_dict[i]+=1 else: main_dict[i] = 1 if t<40: X.append(len(listfull)) Y.append(len(set(listfull))) t+=1 if t==40: t=0 print(k) k+=1 print(main_dict) # - # # TTR(type to token ratio) types = list(set(listfull)) ttr = len(types)/len(listfull) print('ttr',ttr) len(listfull) # # Heap's Law # + #print(main_dict) # - from scipy.optimize import curve_fit def test2(x,a,b): return(a(x)b) param, param_cov = curve_fit(test2, X, Y) ans = [] for i in X: ans.append(param[0](i)**param[1]) print(param[0],param[1]) # Y = KN^B plt.plot(X, Y, 'o', color ='yellow', label ="data") plt.plot(X, ans, '--', color ='blue', label ="optimized data") plt.xlabel("Size of Corpus") plt.ylabel("Vocabulary size") plt.legend() plt.show() # # Zipf's law of meaning from nltk.corpus import wordnet #Import wordnet from the NLTK # + import random import math words = random.choices(types, k=60) words.sort() #print(words) dict2 = dict() x_new=[] y_new=[] for i in words: dict2[i] = wordnet.synsets(i) sy = wordnet.synsets(i) if len(sy)>0: #if the word exists in wordnet x_new.append(main_dict[i]) y_new.append(len(sy[0].lemmas())) def sort_list(list1, list2): zipped_pairs = zip(list2, list1) z = [x for _, x in sorted(zipped_pairs)] return z y_new = sort_list(y_new,x_new) x_new.sort() plt.figure(figsize=(8,3)) plt.xlabel("Frequency") plt.ylabel("Meanings") plt.plot(x_new,y_new) plt.legend() plt.show() # - # # Zipf's law of length # + from scipy.optimize import curve_fit x_len = [] y_freq = [] for k in types: x_len.append(len(k)) y_freq.append(main_dict[k]) param, param_cov = curve_fit(test_1, x_len, y_freq) predicted = [] for i in x_len: predicted.append(param[0]/i) plt.figure(figsize=(8,3)) plt.scatter( y_freq,x_len, s = 5) #plt.plot(predicted, x_len, color = 'black') plt.xlabel("Frequency") plt.ylabel("Length ") plt.legend() plt.show()	3,851
/Numpy/Numpy의 활용.ipynb	bb19ca5bcd048b8525e82c2156c6bcb72945c6cd	[]	no_license	jjangsungwon/Python-Data-Analysis-Tutorial	https://github.com/jjangsungwon/Python-Data-Analysis-Tutorial	2	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,860	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="6Oe99uTcvFkH" outputId="af065726-144b-4fab-9780-be300856e532" import pandas as pd data = pd.read_csv('SMSSpamCollection', sep = '\t', names = ['label', 'message']) data.head() # + id="YVb4eBU0ve-e" text = data['message'] label = data['label'] # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="mmbV487qvFkR" outputId="a0b2588b-75b5-4ff8-f8a3-591dcb277c66" #Number of Words #x = lambda a : a + 10 #print(x(5)) data['word_count'] = data['message'].apply(lambda x: len(str(x).split(" "))) data[['message','word_count']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="79s4zzkevFkZ" outputId="492c688e-0615-48e9-b8f3-4adcb014d302" #Number of characters data['char_count'] = data['message'].str.len() # this also includes spaces data[['message','char_count']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="THqkLpBnvFkf" outputId="d4de3d90-ad5b-416d-9fca-111d05464c7e" #Average Word Length def avg_word(sentence): words = sentence.split() #print(words) return (sum(len(word) for word in words)/len(words)) data['avg_word'] = data['message'].apply(lambda x: avg_word(x)) data[['message','avg_word']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 292} id="GG5JheoKvFkk" outputId="08bca9d7-40bc-4253-f573-71a1b240cca1" #Number of stopwords import nltk nltk.download('stopwords') from nltk.corpus import stopwords stop = stopwords.words('english') data['stopwords'] = data['message'].apply(lambda x: len([x for x in x.split() if x in stop])) data[['message','stopwords']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="DgD7qdIdvFkp" outputId="664a0012-b9c3-4244-97ab-f03e5b033706" #Number of special characters data['hastags'] = data['message'].apply(lambda x: len([x for x in x.split() if x.startswith('#')])) data[['message','hastags']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="j24BtSTBvFkt" outputId="3f1151f2-2ef6-4073-c794-cc4195c89155" #Number of numerics data['numerics'] = data['message'].apply(lambda x: len([x for x in x.split() if x.isdigit()])) data[['message','numerics']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 204} id="DiO48lLcvFkx" outputId="eddc5e48-2654-4b39-a91c-7376f6985f4b" #Number of Uppercase words data['upper'] = data['message'].apply(lambda x: len([x for x in x.split() if x.isupper()])) data[['message','upper']].head() # + colab={"base_uri": "https://localhost:8080/", "height": 289} id="uckIEBbVvFk1" outputId="42bc144b-e78c-4937-a2f5-b06d27ddd074" # !pip install textblob pos_family = { 'noun' : ['NN','NNS','NNP','NNPS'], 'pron' : ['PRP','PRP$','WP','WP$'], 'verb' : ['VB','VBD','VBG','VBN','VBP','VBZ'], 'adj' : ['JJ','JJR','JJS'], 'adv' : ['RB','RBR','RBS','WRB'] } # function to check and get the part of speech tag count of a words in a given sentence from textblob import TextBlob, Word, Blobber import nltk nltk.download('punkt') nltk.download('averaged_perceptron_tagger') def check_pos_tag(x, flag): cnt = 0 try: wiki = TextBlob(x) for tup in wiki.tags: ppo = list(tup)[1] if ppo in pos_family[flag]: cnt += 1 except: pass return cnt data['noun_count'] = data['message'].apply(lambda x: check_pos_tag(x, 'noun')) data['verb_count'] = data['message'].apply(lambda x: check_pos_tag(x, 'verb')) data['adj_count'] = data['message'].apply(lambda x: check_pos_tag(x, 'adj')) data['adv_count'] = data['message'].apply(lambda x: check_pos_tag(x, 'adv')) data['pron_count'] = data['message'].apply(lambda x: check_pos_tag(x, 'pron')) data[['message','noun_count','verb_count','adj_count', 'adv_count', 'pron_count' ]].head() # + colab={"base_uri": "https://localhost:8080/", "height": 666} id="39_sSFrxvFk5" outputId="57ed9291-a318-4310-c55a-a737d16f3d3f" data[['message','word_count','char_count','avg_word','stopwords','hastags','numerics','upper','noun_count','verb_count','adj_count', 'adv_count', 'pron_count','label' ]].head() # + id="ZiGZJ7JIvFk_" features = data[['word_count','char_count','avg_word','stopwords','hastags','numerics','upper','noun_count','verb_count','adj_count', 'adv_count', 'pron_count']] # + id="wrNdsWIpxclO" import numpy as np classes_list = ["ham","spam"] label_index = data['label'].apply(classes_list.index) label = np.asarray(label_index) # + id="ygOiAYEFxfUL" import numpy as np features_array = np.asarray(features) # + colab={"base_uri": "https://localhost:8080/", "height": 34} id="7pAcZDl6xqVk" outputId="3cbb3157-378e-4220-cde5-0c84764e9aef" features_array.shape # + id="UwUWlcdyX3zy" # data split into train and text import numpy as np from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(features_array, label, test_size=0.33, random_state=42) # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="G8SkHpcrzZgu" outputId="867419fb-b14c-4680-b881-a0224845d0f7" from sklearn.metrics import accuracy_score from sklearn import metrics from sklearn.svm import SVC model_SVM = SVC() model_SVM.fit(x_train, y_train) y_pred_SVM = model_SVM.predict(x_test) print("SVM") print("Accuracy score =", accuracy_score(y_test, y_pred_SVM)) print(metrics.classification_report(y_test, y_pred_SVM)) from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(n_estimators=100,max_depth=None,min_samples_split=2, random_state=0) rf.fit(x_train,y_train) y_pred_rf = rf.predict(x_test) print("random") print("Accuracy score =", accuracy_score(y_test, y_pred_rf)) print(metrics.classification_report(y_test, y_pred_rf)) from sklearn.linear_model import LogisticRegression LR = LogisticRegression() LR.fit(x_train,y_train) y_pred_LR = LR.predict(x_test) print("Logistic Regression") print("Accuracy score =", accuracy_score(y_test, y_pred_LR)) print(metrics.classification_report(y_test, y_pred_LR )) from sklearn.neighbors import KNeighborsClassifier neigh = KNeighborsClassifier(n_neighbors = 5) neigh.fit(x_train,y_train) y_pred_KNN = neigh.predict(x_test) print("KNN") print("Accuracy score =", accuracy_score(y_test, y_pred_KNN)) print(metrics.classification_report(y_test, y_pred_KNN )) from sklearn.naive_bayes import GaussianNB naive = GaussianNB() naive.fit(x_train,y_train) y_pred_naive = naive.predict(x_test) print("Naive Bayes") print("Accuracy score =", accuracy_score(y_test, y_pred_naive)) print(metrics.classification_report(y_test, y_pred_naive )) from sklearn.ensemble import GradientBoostingClassifier gradient = GradientBoostingClassifier(n_estimators=100,max_depth=None,min_samples_split=2, random_state=0) gradient.fit(x_train,y_train) y_pred_gradient = gradient.predict(x_test) print("Gradient Boosting") print("Accuracy score =", accuracy_score(y_test, y_pred_gradient)) print(metrics.classification_report(y_test, y_pred_gradient )) from sklearn.tree import DecisionTreeClassifier decision = DecisionTreeClassifier() decision.fit(x_train,y_train) y_pred_decision = decision.predict(x_test) print("Decision Tree") print("Accuracy score =", accuracy_score(y_test, y_pred_decision)) print(metrics.classification_report(y_test, y_pred_decision )) # + id="yxy-WRo5yBVp" # data split into train and text import numpy as np from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(features_array, label, test_size=0.33, random_state=42) # + colab={"base_uri": "https://localhost:8080/", "height": 34} id="N-yPCb1PZR7D" outputId="e1f52694-169f-4aed-f64d-f016fda65066" x_train.shape # + id="NWaoQuHCzn62" data = pd.read_csv('SMSSpamCollection', sep = '\t', names = ['label','message']) # + id="56f1LdRYY4iq" text = data['message'] class_label = data['label'] # + id="CV5iAnlCdhZN" import numpy as np classes_list = ["ham","spam"] label_index = class_label.apply(classes_list.index) label = np.asarray(label_index) # + id="iAZpYtCJdkT9" import numpy as np from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(text, label, test_size=0.33, random_state=42) # + id="LVeLdGr9doe5" from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer(ngram_range = (1,1)) x_train = vectorizer.fit_transform(X_train) x_test = vectorizer.transform(X_test) # + colab={"base_uri": "https://localhost:8080/", "height": 34} id="d8B2uJXFehCH" outputId="29a3b673-dedf-4a4d-9122-a1a8d04bb2e3" x_train.shape # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="6tEIjnT4elyj" outputId="5e469c4d-0a03-4734-ef3d-199d959ef0f2" vectorizer.get_feature_names() # + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="I1Zk24vQenaM" outputId="8f046fce-ec96-432d-b8da-de91e6a101b2" from sklearn.metrics import accuracy_score from sklearn import metrics from sklearn.svm import SVC model_SVM = SVC() model_SVM.fit(x_train, y_train) y_pred_SVM = model_SVM.predict(x_test) print("SVM") print("Accuracy score =", accuracy_score(y_test, y_pred_SVM)) print(metrics.classification_report(y_test, y_pred_SVM)) from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(n_estimators=100,max_depth=None,min_samples_split=2, random_state=0) rf.fit(x_train,y_train) y_pred_rf = rf.predict(x_test) print("random") print("Accuracy score =", accuracy_score(y_test, y_pred_rf)) print(metrics.classification_report(y_test, y_pred_rf)) from sklearn.linear_model import LogisticRegression LR = LogisticRegression() LR.fit(x_train,y_train) y_pred_LR = LR.predict(x_test) print("Logistic Regression") print("Accuracy score =", accuracy_score(y_test, y_pred_LR)) print(metrics.classification_report(y_test, y_pred_LR )) from sklearn.neighbors import KNeighborsClassifier neigh = KNeighborsClassifier(n_neighbors = 5) neigh.fit(x_train,y_train) y_pred_KNN = neigh.predict(x_test) print("KNN") print("Accuracy score =", accuracy_score(y_test, y_pred_KNN)) print(metrics.classification_report(y_test, y_pred_KNN )) from sklearn.naive_bayes import GaussianNB naive = GaussianNB() naive.fit(x_train.toarray(),y_train) y_pred_naive = naive.predict(x_test.toarray()) print("Naive Bayes") print("Accuracy score =", accuracy_score(y_test, y_pred_naive)) print(metrics.classification_report(y_test, y_pred_naive )) from sklearn.ensemble import GradientBoostingClassifier gradient = GradientBoostingClassifier(n_estimators=100,max_depth=None,min_samples_split=2, random_state=0) gradient.fit(x_train,y_train) y_pred_gradient = gradient.predict(x_test) print("Gradient Boosting") print("Accuracy score =", accuracy_score(y_test, y_pred_gradient)) print(metrics.classification_report(y_test, y_pred_gradient )) from sklearn.tree import DecisionTreeClassifier decision = DecisionTreeClassifier() decision.fit(x_train,y_train) y_pred_decision = decision.predict(x_test) print("Decision Tree") print("Accuracy score =", accuracy_score(y_test, y_pred_decision)) print(metrics.classification_report(y_test, y_pred_decision )) # + id="sk5zs952fdM-"	11,402
/3_web-scraping-II/.ipynb_checkpoints/7_(로컬에서)Tweeter-post_contents-checkpoint.ipynb	84c3ca66d411e2beee746884fcba444d4db4ab67	[]	no_license	jupyterbook/notebooks	https://github.com/jupyterbook/notebooks	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	27,305	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [default] # language: python # name: python3 # --- import os import tensorflow as tf from keras.models import Sequential from keras.utils import to_categorical from keras.layers import Dense import numpy as np import pandas as pd file = './datasets/HCAHPS 15.csv' df = pd.read_csv(file) df.columns # + # remove unessecary columns df1 = df[['Provider_ID','HCAHPS_Measure_ID', 'HCAHPS_Answer_Description', 'Patient_Survey_Star_Rating', 'Patient_Survey_Star_Rating_Footnote', 'HCAHPS_Answer_Percent','HCAHPS_Answer_Percent_Footnote', 'HCAHPS_Linear_Mean_Value','Number_of_Completed_Surveys', 'Number_of_Completed_Surveys_Footnote', 'Survey_Response_Rate_Percent', 'Survey_Response_Rate_Percent_Footnote','Measure_Start_Date', 'Measure_End_Date']] # + # get rid of all non Linear Mean score lines df2 = df1.loc[(df1['HCAHPS_Measure_ID'] == 'H_CLEAN_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_1_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_2_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_3_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_4_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_5_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_6_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_COMP_7_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_HSP_RATING_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_QUIET_LINEAR_SCORE') \| (df1['HCAHPS_Measure_ID'] == 'H_RECMND_LINEAR_SCORE'),:] # + # get rid of all the 'Not Applicable' and 'Not Available' rows for 'HCAHPS_Linear_Mean_Value' df2 = df2.loc[df2.HCAHPS_Linear_Mean_Value != 'Not Applicable'] df2 = df2.loc[df2.HCAHPS_Linear_Mean_Value != 'Not Available'] # + # get rid of remaining footnotes that void survey df2 = df2[pd.isnull(df2.Survey_Response_Rate_Percent_Footnote)] # + #re-index from 1 df2.reset_index(drop=True, inplace=True) # - df2.to_csv('./datasets/3_2222.csv') # + # create lists that will be zipped up into a dataframe: Provider_ID = [] Care_transition_LMS = [] Cleanliness_LMS = [] Communication_about_medicines_LMS = [] Discharge_information_LMS = [] Doctor_communication_LMS = [] Nurse_communication_LMS = [] Overall_hospital_rating_LMS = [] Pain_management_LMS = [] Quietness_LMS = [] Recommend_hospital_LMS = [] Staff_responsiveness_LMS = [] Survey_Response_Rate_Percent_Footnote = [] Measure_Start_Date = [] Measure_End_Date = [] x = 0 # - for i in range(0,len(df2.index)): if df2.loc[i,'HCAHPS_Answer_Description'] == 'Care transition - linear mean score': Provider_ID.append(df2['Provider_ID'].iloc[i]) Measure_Start_Date.append(df2['Measure_Start_Date'].iloc[i]) Measure_End_Date.append(df2['Measure_Start_Date'].iloc[i]) Care_transition_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Cleanliness - linear mean score': Cleanliness_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Communication about medicines - linear mean score': Communication_about_medicines_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Discharge information - linear mean score': Discharge_information_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Doctor communication - linear mean score': Doctor_communication_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Nurse communication - linear mean score': Nurse_communication_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Overall hospital rating - linear mean score': Overall_hospital_rating_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Pain management - linear mean score': Pain_management_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Quietness - linear mean score': Quietness_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Recommend hospital - linear mean score': Recommend_hospital_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) elif df2.loc[i,'HCAHPS_Answer_Description'] == 'Staff responsiveness - linear mean score': Staff_responsiveness_LMS.append(df2.loc[i,'HCAHPS_Linear_Mean_Value']) else: x=2 zippedList = list(zip(Provider_ID,Care_transition_LMS,Cleanliness_LMS,Communication_about_medicines_LMS, Discharge_information_LMS,Doctor_communication_LMS,Nurse_communication_LMS, Overall_hospital_rating_LMS,Pain_management_LMS,Quietness_LMS,Recommend_hospital_LMS, Staff_responsiveness_LMS,Measure_Start_Date,Measure_End_Date)) # + # Create a dataframe from zipped list df8 = pd.DataFrame(zippedList, columns = ['Provider_ID', 'Care_transition_LMS', 'Cleanliness_LMS', 'Communication_about_medicines_LMS', 'Discharge_information_LMS','Doctor_communication_LMS', 'Nurse_communication_LMS', 'Overall_hospital_rating_LMS','Pain_management_LMS', 'Quietness_LMS', 'Recommend_hospital_LMS', 'Staff_responsiveness_LMS','Measure_Start_Date', 'Measure_End_Date']) # - df8.to_csv('./datasets/15_2222.csv') np.append(exlude_indexes ,bhk_df[bhk_df['price_per_sqft']< stats['mean']].index.values ) return data.drop(exlude_indexes ,axis='index') data = remove_bhk_outliers(data) scatter_plot(data ,'Sahakara Nagar') # + data['price_per_sqft'].hist() plt.xlabel('Price Per Sq') plt.ylabel('Count') # - # <h2 style='color:blue'>Outlier Removal Using Bathrooms Feature</h2> data['bath'].unique() data[data['bath']>10] # It is unusual to have 2 more bathrooms than number of bedrooms in a home # + data['bath'].hist() plt.xlabel('Number of Bathroom') plt.ylabel('Count') # - data[ data['bath']>data['bhk']+2 ] data = data[ data['bath']<data['bhk']+2 ] data.shape data = data.drop(['size','price_per_sqft'] ,axis=1) # ## Model Building dummies = pd.get_dummies(data['location'],drop_first=True) data = pd.concat([data,dummies],axis=1) data = data.drop(['location'] ,axis=1) X = data.drop(['price'],axis=1) y = data['price'] X_train ,X_test ,y_train ,y_test = train_test_split(X,y,test_size=0.2,random_state=0) # ### Model 1 # + lr_clf = LinearRegression() lr_clf.fit(X_train ,y_train) # - lr_clf.score(X_test ,y_test) # ### Model 2 # # <h2 style='color:blue'>Use K Fold cross validation to measure accuracy of our LinearRegression model</h2> cv = ShuffleSplit(n_splits=5 ,test_size=0.2 ,random_state=0) cross_val_score(LinearRegression(),X,y,cv=cv) # ### Model 3 # # <h2 style='color:blue'>Find best model using GridSearchCV</h2> def find_best_model(X,y) : algos = { 'linear_regression' : { 'model': LinearRegression(), 'params': { 'normalize': [True, False] } }, 'lasso' : { 'model': Lasso(), 'params': { 'alpha': [1,2], 'selection': ['random', 'cyclic'] } }, 'decision_tree' : { 'model': DecisionTreeRegressor(), 'params': { 'criterion' : ['mse','friedman_mse'], 'splitter': ['best','random'] } } } scores = [] cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0) for algo_name, config in algos.items(): gs = GridSearchCV( config['model'], config['params'], cv=cv, return_train_score=False) gs.fit(X,y) scores.append({ 'model': algo_name, 'best_score': gs.best_score_, 'best_params': gs.best_params_ }) return pd.DataFrame(scores,columns=['model','best_score','best_params']) find_best_model(X,y) X.columns np.where(X.columns=='bhk')[0][0] # <h2 style='color:blue'>Test the model for few properties</h2> def predict_price(location ,sqft ,bath ,bhk) : # getting column index loc_index = np.where(X.columns==location)[0][0] x = np.zeros( len(X.columns) ) x[0] = sqft x[1] = bath x[2] = bhk if loc_index>=0: x[loc_index] = 1 return lr_clf.predict([x])[0] predict_price('1st Phase JP Nagar' ,1000 ,2 ,2) predict_price('1st Phase JP Nagar' ,1000 ,2 ,3) predict_price('1st Phase JP Nagar' ,1000 ,3 ,3) predict_price('Indira Nagar' ,1000 ,2 ,2) predict_price('Indira Nagar' ,1000 ,3 ,3) predict_price('Indira Nagar' ,1000 ,2 ,3)	10,007
/KNN_Project.ipynb	a00e5b07cf1bf0bfb6cb90a4fd88bd7f2a206977	[]	no_license	rtlaceste/Machine_Learning_Models-Data_Science	https://github.com/rtlaceste/Machine_Learning_Models-Data_Science	2	0	null	null	null	null	Jupyter Notebook	false	false	.py	2,060,863	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda root] # language: python # name: conda-root-py # --- # # 8-Classes from scipy import * from matplotlib.pyplot import * # %matplotlib inline # ## Introduction class RationalNumber: pass a=RationalNumber() if isinstance(a, RationalNumber): print('Indeed it belongs to the class RationalNumber') # ### The `__init__` method class RationalNumber: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator q = RationalNumber(10, 20) # Defines a new object q.numerator # returns 10 q.denominator # returns 20 # ## Attributes q = RationalNumber(3, 5) # instantiation q.numerator # attribute access q.denominator a = array([1, 2]) # instantiation a.shape z = 5 + 4j # instantiation z.imag q = RationalNumber(3, 5) q.numerator r = RationalNumber(7, 3) q.numerator = 17 q.numerator del r.denominator class RationalNumber: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator def convert2float(self): return float(self.numerator) / float(self.denominator) q = RationalNumber(10, 20) # Defines a new object q.convert2float() # returns 0.5 RationalNumber.convert2float(q) q.convert2float(15) # returns error # ### Special Methods # # * The method `repr` class RationalNumber: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator def convert2float(self): return float(self.numerator) / float(self.denominator) def __repr__(self): return '{} / {}'.format(self.numerator,self.denominator) q = RationalNumber(10, 20) q # * The method `__add__` class RationalNumber: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator def convert2float(self): return float(self.numerator) / float(self.denominator) def __repr__(self): return '{} / {}'.format(self.numerator,self.denominator) def __add__(self, other): p1, q1 = self.numerator, self.denominator if isinstance(other, int): p2, q2 = other, 1 else: p2, q2 = other.numerator, other.denominator return RationalNumber(p1 * q2 + p2 * q1, q1 * q2) q = RationalNumber(1, 2) p = RationalNumber(1, 3) q + p # RationalNumber(5, 6) q.__add__(p) class RationalNumber: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator def convert2float(self): return float(self.numerator) / float(self.denominator) def __repr__(self): return '{} / {}'.format(self.numerator,self.denominator) def __add__(self, other): p1, q1 = self.numerator, self.denominator if isinstance(other, int): p2, q2 = other, 1 else: p2, q2 = other.numerator, other.denominator return RationalNumber(p1 * q2 + p2 * q1, q1 * q2) def __eq__(self, other): return self.denominator * other.numerator == \ self.numerator * other.denominator p = RationalNumber(1, 2) # instantiation q = RationalNumber(2, 4) # instantiation p == q # True p = RationalNumber(1, 2) # instantiation p + 5 # corresponds to p.__add__(5) 5 + p # returns an error # * The reverse method `__radd__` class RationalNumber: def __init__(self, numerator, denominator): self.numerator = numerator self.denominator = denominator def convert2float(self): return float(self.numerator) / float(self.denominator) def __repr__(self): return '{} / {}'.format(self.numerator,self.denominator) def __add__(self, other): p1, q1 = self.numerator, self.denominator if isinstance(other, int): p2, q2 = other, 1 else: p2, q2 = other.numerator, other.denominator return RationalNumber(p1 * q2 + p2 * q1, q1 * q2) def __eq__(self, other): return self.denominator * other.numerator == \ self.numerator * other.denominator def __radd__(self, other): return self p = RationalNumber(1, 2) 5 + p # no error message any more # + import itertools class Recursion3Term: def __init__(self, a0, a1, u0, u1): self.coeff = [a1, a0] self.initial = [u1, u0] def __iter__(self): u1, u0 = self.initial yield u0 # (see chapter on generators) yield u1 a1, a0 = self.coeff while True : u1, u0 = a1 * u1 + a0 * u0, u1 yield u1 def __getitem__(self, k): return list(itertools.islice(self, k, k + 1))[0] # - r3 = Recursion3Term(-0.35, 1.2, 1, 1) for i, r in enumerate(r3): if i == 7: print(r) # returns 0.194167 break r3[7] # ### Attributes that depend on each other class Triangle: def __init__(self, A, B, C): self.A = array(A) self.B = array(B) self.C = array(C) self.a = self.C - self.B self.b = self.C - self.A self.c = self.B - self.A def area(self): return abs(cross(self.b, self.c)) / 2 tr = Triangle([0., 0.], [1., 0.], [0., 1.]) tr.area() tr.B = [12., 0.] tr.area() # still returns 0.5, should be 6 instead. # #### The function `property` class Triangle: def __init__(self, A, B, C): self._A = array(A) self._B = array(B) self._C = array(C) self._a = self._C - self._B self._b = self._C - self._A self._c = self._B - self._A def area(self): return abs(cross(self._c, self._b)) / 2. def set_B(self, B): self._B = B self._a = self._C - self._B self._c = self._B - self._A def get_B(self): return self._B def del_Pt(self): raise Exception('A triangle point cannot be deleted') B = property(fget = get_B, fset = set_B, fdel = del_Pt) tr = Triangle([0., 0.], [1., 0.], [0., 1.]) tr.area() tr.B = [12., 0.] tr.area() # returns 6.0 del tr.B # raises an exception # ### Bound and unbound methods class A: def func(self, arg): pass A.func # <unbound method A.func> instA = A() # we create an instance instA.func # <bound method A.func of ... > A.func(1) instA.func(1) # ### Class attributes class Newton: tol = 1e-8 # this is a class attribute def __init__(self,f): self.f = f # this is not a class attribute ... N1 = Newton(sin) N2 = Newton(cos) N1.tol N2.tol Newton.tol = 1e-10 N1.tol N2.tol N2.tol = 1.e-4 N1.tol # still 1.e-10 Newton.tol = 1e-5 # now all instances of the Newton classes have 1e-5 N1.tol # 1.e-5 N2.tol # 1e-4 but not N2. # #### Class Methods class Polynomial: def __init__(self, coeff): self.coeff = array(coeff) @classmethod def by_points(cls, x, y): degree = x.shape[0] - 1 coeff = polyfit(x, y, degree) return cls(coeff) def __eq__(self, other): return allclose(self.coeff, other.coeff) # + p1 = Polynomial.by_points(array([0., 1.]), array([0., 1.])) p2 = Polynomial([1., 0.]) print(p1 == p2) # - # ## Subclassing and Inheritance class OneStepMethod: def __init__(self, f, x0, interval, N): self.f = f self.x0 = x0 self.interval = [t0, te] = interval self.grid = linspace(t0, te, N) self.h = (te - t0) / N def generate(self): ti, ui = self.grid[0], self.x0 yield ti, ui for t in self.grid[1:]: ui = ui + self.h * self.step(self.f, ui, ti) ti = t yield ti, ui def solve(self): self.solution = array(list(self.generate())) def plot(self): plot(self.solution[:, 0], self.solution[:, 1]) def step(self, f, u, t): raise NotImplementedError() class ExplicitEuler(OneStepMethod): def step(self, f, u, t): return f(u, t) class MidPointRule(OneStepMethod): def step(self, f, u, t): return f(u + self.h / 2 * f(u, t), t + self.h / 2) # + def f(x, t): return -0.5 * x euler = ExplicitEuler(f, 15., [0., 10.], 20) euler.solve() euler.plot() hold(True) midpoint = MidPointRule(f, 15., [0., 10.], 20) midpoint.solve() midpoint.plot() # - argument_list = [f, 15., [0., 10.], 20] euler = ExplicitEuler(argument_list) midpoint = MidPointRule(argument_list) class ExplicitEuler(OneStepMethod): def __init__(self,args, kwargs): self.name='Explicit Euler Method' super(ExplicitEuler, self).__init__(args,*kwargs) def step(self, f, u, t): return f(u, t) # ## Encapsulation class Function: def __init__(self, f): self.f = f def __call__(self, x): return self.f(x) def __add__(self, g): def sum(x): return self(x) + g(x) return type(self)(sum) def __mul__(self, g): def prod(x): return self.f(x) g(x) return type(self)(prod) def __radd__(self, g): return self + g def __rmul__(self, g): return self * g T5 = Function(lambda x: cos(5 * arccos(x))) T6 = Function(lambda x: cos(6 * arccos(x))) # + import scipy.integrate as sci weight = Function(lambda x: 1 / sqrt((1 - x ** 2))) [integral, errorestimate] = \ sci.quad(weight * T5 * T6, -1, 1) # [7.7e-16, 4.04e-14) integral, errorestimate # - # ## Classes as decorators class echo: text = 'Input parameters of {name}\n'+\ 'Positional parameters {args}\n'+\ 'Keyword parameters {kwargs}\n' def __init__(self, f): self.f = f def __call__(self, args, kwargs): print(self.text.format(name = self.f.__name__, args = args, kwargs = kwargs)) return self.f(args, *kwargs) @echo def line(m, b, x): return m x + b line(2., 5., 3.) line(2., 5., x=3.) class CountCalls: """Decorator that keeps track of the number of times a function is called.""" instances = {} def __init__(self, f): self.f = f self.numcalls = 0 self.instances[f] = self def __call__(self, args, kwargs): self.numcalls += 1 return self.f(args, *kwargs) @classmethod def counts(cls): """Return a dict of {function: # of calls} for all registered functions.""" return dict([(f.__name__, cls.instances[f].numcalls) for f in cls.instances]) @CountCalls def line(m, b, x): return m x + b @CountCalls def parabola(a, b, c, x): return a * x ** 2 + b * x + c line(3., -1., 1.) parabola(4., 5., -1., 2.) CountCalls.counts() # returns {'line': 1, 'parabola': 1} parabola.numcalls # returns 1	10,962
/Chapter06/.ipynb_checkpoints/6.7 Identifying Right AD Banner Using MAB-checkpoint.ipynb	8420e2297946fa94cd766b37b469fb694ae2095e	[ "MIT" ]	permissive	PacktPublishing/Hands-On-Reinforcement-Learning-with-Python	https://github.com/PacktPublishing/Hands-On-Reinforcement-Learning-with-Python	770	367	MIT	2022-06-27T20:08:13	2022-06-26T16:53:33	Jupyter Notebook	Jupyter Notebook	false	false	.py	20,436	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [conda env:universe] # language: python # name: conda-env-universe-py # --- # # Identifying Right AD Banner Using MAB # # # Let us say you are running a website and you have five different banners for the same ad and you want to know which banner attracts the user? We model this problem statement as a bandit problem. Let us say these five banners are five bandits and we assign reward 1 if the user clicks the ad and reward 0 if the user does not click the ad. # # In a normal A/B testing, we perform complete exploration of all these five banners alone before deciding which banner is the best. But that will cost us lot of regret. Instead, we will use good exploration strategy for deciding which banner will give us most rewards (most clicks) # First, let us import necessary libraries import gym_bandits import gym import numpy as np import pandas as pd import matplotlib.pyplot as plt # %matplotlib inline import seaborn as sns env = gym.make("BanditTenArmedGaussian-v0") # Let us simulate a dataset with 5*10000 as shape where the column is the ad banner type and rows are either 0 or 1 i.e whether the ad has been clicked or not clicked by the user respectively # # df = pd.DataFrame() df['Banner_type_0'] = np.random.randint(0,2,100000) df['Banner_type_1'] = np.random.randint(0,2,100000) df['Banner_type_2'] = np.random.randint(0,2,100000) df['Banner_type_3'] = np.random.randint(0,2,100000) df['Banner_type_4'] = np.random.randint(0,2,100000) df.head(10) # First, let us initialize necessary variables # + # number of banners num_banner = 5 # number of iterations no_of_iterations = 100000 # list for storing banners which are selected banner_selected = [] # count number of times the banner was selected count = np.zeros(num_banner) # Q value of the banner Q = np.zeros(num_banner) # sum of rewards obtained by the banner sum_rewards = np.zeros(num_banner) # - # Now we define the epsilon greedy policy def epsilon_greedy(epsilon): random_value = np.random.random() choose_random = random_value < epsilon if choose_random: action = np.random.choice(num_banner) else: action = np.argmax(Q) return action for i in range(no_of_iterations): # select the banner using epsilon greedy policy banner = epsilon_greedy(0.5) # get the reward reward = df.values[i, banner] # update the selected banner count count[banner] += 1 # sum the rewards obtained by that banner sum_rewards[banner]+=reward # calculate the Q value of the banner Q[banner] = sum_rewards[banner]/count[banner] banner_selected.append(banner) # We can plot and see which banner type gives us most clicks(rewards) sns.distplot(banner_selected)	2,990
/confoundedCuisiner/Untitled.ipynb	67254aa97ab0c41a16010f63a44a551fe766899a	[]	no_license	peterschnatz/SampleDataChallenges	https://github.com/peterschnatz/SampleDataChallenges	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	10,238	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + from joblib import register_parallel_backend, parallel_backend # register_parallel_backend('threading') # - import warnings warnings.simplefilter(action='ignore', category=FutureWarning) # + import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.model_selection import StratifiedKFold from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc # + # # Class balance def print_unique(values): unique, counts = np.unique(values, return_counts=True) for cls, cnt in zip(unique, counts): print("Class [%d] Count [%d]" % (cls, cnt)) # + import matplotlib.pyplot as plt def view_histo(row, data): # print(row) # return hname = row["hname"] ls_number = row["lumi"] run_number = row["run"] is_good = row["good"] is_good_pixel = row["good_pixel"] plt.figure(figsize=(10, 5)) plt.title("%s Run: %s LS: %s GLabel: %d PLabel %d" % (hname, run_number, ls_number, is_good, is_good_pixel)) plt.plot(range(len(data)), data, drawstyle='steps-pre', label=hname) plt.legend() # + class Clustering: def __init__(self): self.clustering = None self.df = None def load_data(self, filename): df = pd.read_csv(filename) print(df.shape) # Filter list of columns which will be used for training bin_cols = [col for col in df.columns if 'bin_' in col] # remove first and last values as those are over/under flows self.bin_cols = bin_cols[1:-1] # Drop empty rows df.drop(df[df.entries == 0].index, inplace=True) # Drop garbage df.drop(["Unnamed: 0", "Unnamed: 0.1", "fromrun.1", "fromlumi.1", "hname.1"], axis=1, inplace=True, errors="ignore") print(df.shape) self.df = pd.concat([self.df, df], ignore_index=True) if self.df is not None else df # Normalization, divide every bin value by total entries self.X = self.df.filter(self.bin_cols, axis=1).copy().div(self.df.entries, axis=0) self.y = self.df["good_pixel"] print("DF", self.df.shape) hname = "chargeInner_PXLayer_1" c = Clustering() c.load_data("/home/mantydze/data/ZeroBias2017B/massaged/{hname}.csv".format(hname=hname)) c.load_data("/home/mantydze/data/ZeroBias2017D/massaged/{hname}.csv".format(hname=hname)) # + from sklearn.decomposition import PCA import numpy as np import random get_colors = lambda n: list(map(lambda i: "#" + "%06x" % random.randint(0, 0xFFFFFF),range(n))) good_bad_colors=["red", "green", "orange"] # red is -1 cluster_colors = get_colors(100) def do_pca(df_, X_): # import matplotlib.pyplot as plt pca = PCA(n_components=3) pcomp = pca.fit_transform(X_) print(pca.explained_variance_ratio_) plt.figure() plt.grid() plt.plot(np.cumsum(pca.explained_variance_ratio_)) plt.xlabel('Number of Components') plt.ylabel('Variance (%)') #for each component plt.title('Explained Variance ratio') plt.show() df_["pcx"] = pcomp[:,0] df_["pcy"] = pcomp[:,1] plt.scatter(df_["pcx"], df_["pcy"], color=[good_bad_colors[i] for i in df_["good_pixel"]], label=df_["good_pixel"]) plt.show() # + from sklearn.manifold import MDS def do_mds(df_, X_, labels_=None): mds = MDS(n_components=2) X_t = mds.fit_transform(X_) print(X_t.shape) df_["mdsx"] = X_t[:,0] df_["mdsy"] = X_t[:,1] if labels_: plt.scatter(df_["mdsx"], df_["mdsy"], color=[cluster_colors[i] for i in labels_]) plt.show() plt.scatter(df_["mdsx"], df_["mdsy"], color=[good_bad_colors[i] for i in df_["good_pixel"]]) plt.show() # - do_pca(c.df, c.X) # + goods = [297308, 297425, 297293, 297050] bads = [297179, 297180] goods = [297050, 297056, 297057, 297099, 297100]#, 297101, 297113, 297114, 297175, 297176] #, 297177, 297178, 297215, 297218, 297219, 297224, 297225, 297227, 297292, 297293, 297296, 297308, 297359, 297411, 297424, 297425, 297426, 297429, 297430, 297431, 297432, 297433, 297434, 297435, 297467, 297468, 297469, 297483, 297484, 297485, 297486, 297487, 297488, 297503, 297504, 297505, 297557, 297558, 297562, 297563, 297599, 297603, 297604, 297605, 297606, 297620, 297656, 297665, 297666, 297670, 297674, 297675, 297722, 297723, 298996, 298997, 299000, 299042, 299061, 299062, 299064, 299065, 299067, 299096, 299149, 299178, 299180, 299184, 299185, 299327, 299329, 299368, 299369, 299370, 299380, 299381, 299394, 299395, 299396, 299420, 299443, 299450, 299477, 299478, 299479, 299480, 299481, 299593, 299594, 299595, 299597, 299649] bads = [297046, 297047, 297048, 297049, 297168, 297169, 297170, 297171, 297179, 297180, 297181, 297211, 297281, 297282, 297283, 297284, 297285, 297286, 297287, 297288, 297289, 297290, 297291, 297495, 297496, 297497, 297498, 297499, 297501, 297502, 297662, 297663, 297664, 297671, 297672, 299316, 299317, 299318, 299324, 299325, 299326, 301086, 301665, 301912, 302646, 302660, 303948, 303989, 305249, 305250] subdf = c.df.copy() subdf = subdf[subdf["run"].isin(goods + bads)] subX = subdf.filter(c.bin_cols, axis=1).copy().div(subdf.entries, axis=0) suby = subdf["good_pixel"] # - rs = np.random.RandomState() subdf = c.df.sample(frac =.10, random_state=rs) subX = subdf.filter(c.bin_cols, axis=1).copy().div(subdf.entries, axis=0) suby = subdf["good"] do_pca(subdf, subX) # + # DBSCAN from scipy.spatial.distance import jensenshannon from sklearn.cluster import DBSCAN with parallel_backend('loky'): c1 = DBSCAN(eps=0.03, min_samples=10, metric=jensenshannon, metric_params={"base":2}, n_jobs=4) c1.fit(subX) print_unique(c1.labels_) print() print_unique(suby) # - do_mds(subdf, subX, list(c1.labels_)) # + match = 0 for o, e in zip(c1.labels_, suby): if o == e: match += 1 # print(o, e) print(match) # + from scipy.spatial.distance import jensenshannon from scipy import stats pi = 2 qi = 28 # js_pq = jensenshannon(subX.iloc[pi], subX.iloc[qi], base=10) # view_histo(subdf.iloc[pi], subX.iloc[pi]) # view_histo(subdf.iloc[qi], subX.iloc[qi]) js_pq = jensenshannon(c.X.iloc[pi], c.X.iloc[qi], base=2) view_histo(c.df.iloc[pi], c.X.iloc[pi]) view_histo(c.df.iloc[qi], c.X.iloc[qi]) print(js_pq) # - pi = 1 qi = 2 js_pq = jensenshannon(c.X.iloc[pi], c.X.iloc[qi], base=2) view_histo(c.df.iloc[pi], c.X.iloc[pi]) view_histo(c.df.iloc[qi], c.X.iloc[qi]) print(js_pq) from sklearn.mixture import GaussianMixture # + gm = GaussianMixture(n_components=2) predy = gm.fit_predict(subX, suby) for o, e in zip(predy, suby): print(o, e) # - # + # KMeans from sklearn.cluster import KMeans, SpectralClustering from sklearn import metrics sils = [] chss = [] for i in range(2, 20): km = SpectralClustering(n_clusters=i, random_state=1, affinity='nearest_neighbors') km.fit(c.X) # print_unique(km.labels_) labels = km.labels_ sil = metrics.silhouette_score(c.X, labels, metric = 'euclidean') chs = metrics.calinski_harabasz_score(c.X, labels) sils.append(sil) chss.append(chs) print(i, sil, chs) # - km.inertia_ plt.plot(sils, label="sils") plt.plot(chss, label="chss") # + # Histogram names to be trained cipxl = ["chargeInner_PXLayer_1", "chargeInner_PXLayer_2", "chargeInner_PXLayer_3", "chargeInner_PXLayer_4"] copxl = ["chargeOuter_PXLayer_1", "chargeOuter_PXLayer_2", "chargeOuter_PXLayer_3", "chargeOuter_PXLayer_4"] spxl = ["size_PXLayer_1", "size_PXLayer_2", "size_PXLayer_3", "size_PXLayer_4"] spxd = ["size_PXDisk_-3", "size_PXDisk_-2", "size_PXDisk_-1", "size_PXDisk_+1", "size_PXDisk_+2", "size_PXDisk_+3"] cpxd = ["charge_PXDisk_-3", "charge_PXDisk_-2", "charge_PXDisk_-1", "charge_PXDisk_+1", "charge_PXDisk_+2", "charge_PXDisk_+3"] hnames = cipxl + copxl + spxl + spxd + cpxd hnames = ["chargeInner_PXLayer_1"] # + results = {} for index, hname in enumerate(hnames): print(index+1, "/", len(hnames), hname) filename = "/home/mantydze/data/ZeroBias2017B/massaged/{hname}.csv".format(hname=hname) rft = RandomForestTrain() rft.load_data(filename) rft.train_eval(verbose=False) # - df = pd.DataFrame.from_dict(results, orient='index') df results from sklearn.metrics.pairwise import paired_distances from scipy.spatial.distance import jensenshannon, pdist pd = pdist(c.X, metric=jensenshannon) len(pd) pd	8,759
/YOLO V3.ipynb	ae5fb725d8d87692d64043901a7b1fe0991df86c	[ "MIT" ]	permissive	aboerzel/YOLO3-keras	https://github.com/aboerzel/YOLO3-keras	3	2	null	null	null	null	Jupyter Notebook	false	false	.py	2,108	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python (cv) # language: python # name: cv # --- # # Create and add dataset # ## Dataset Aufbereitung # 1. Verzeichnisstruktur Dataset (geeignet für lokale Entwicklung mit PyCharm und für das Training mit der FloydHub Cloud) # 2. Bilder für Training mit Kamera erstellen oder von Google ect. laden # 3. Bilder annotieren mit LabelImg (https://github.com/tzutalin/labelImg) => Annotationen im Pascal VOC Format # 1. Class-Names # # ![LabelImg](notebook\images\labelimg.png) # # # 4. Path der Annotationen von absolutem Pfad in relativen Pfad korrigieren mit yolo3_one_file_to_detect_them_all.py # - FloydHub Dataset erstellen... # # # # Grab the pretrained weights of yolo3 from https://pjreddie.com/media/files/yolov3.weights. # # ```python yolo3_one_file_to_detect_them_all.py -w yolo3.weights -i dog.jpg``` # # This weights must be put in the root folder of the repository. They are the pretrained weights for the backend only and will be loaded during model creation. The code does not work without this weights. # #	1,261
/_ll_nth_node_LinkedList.ipynb	3b3389db92dbfcb082fa45ed06605f257141191a	[]	no_license	samuelvinodh/udemy_python_ipynb	https://github.com/samuelvinodh/udemy_python_ipynb	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	2,496	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- class Node(object): def __init__(self,value): self.value = value self.nextnode = None a = Node(1) b = Node(2) c = Node(3) d = Node(4) e = Node(5) a.nextnode = b b.nextnode = c c.nextnode = d d.nextnode = e def nth_to_last_node(n,head): left_pointer = head right_pointer = head for i in range(n-1): if not right_pointer.nextnode: #raise LookupError('Error: n is larger than the Linked List') return 'Error: n is larger than the Linked List' right_pointer = right_pointer.nextnode while right_pointer.nextnode: left_pointer = left_pointer.nextnode right_pointer = right_pointer.nextnode return left_pointer.value target_node = nth_to_last_node(2,a) from nose.tools import assert_equal class TestNLast(object): def test(self,sol): assert_equal(sol(2,a),4) assert_equal(sol(3,a),3) assert_equal(sol(10,a),'Error: n is larger than the Linked List') print("All Test Cases Passed") t = TestNLast() t.test(nth_to_last_node)	1,323
/cervical_classification.ipynb	530c50401fa214f712647caa2d851050c8d5217e	[]	no_license	sunggeunkim/kaggle-cervical_cancer_screening	https://github.com/sunggeunkim/kaggle-cervical_cancer_screening	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	499,267	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] deletable=true editable=true # # Intel Cervical Cancer Screening # ### April 21, 2017 # ## Satchel Grant # # ### Overview # The goal of this notebook is to classify a woman's cervical type into 1 of 3 classes from medical images. This assists in determination of cancer diagnoses and treatments. # # The images are graphic, so I used a different coloring display style when viewing any images. # # ### Initial Imports # + deletable=true editable=true import numpy as np import matplotlib.pyplot as plt import matplotlib.image as mpimg import os from sklearn.utils import shuffle import scipy.misc as sci import time from PIL import Image import random import scipy.ndimage.interpolation as scizoom # %matplotlib inline def show(img): plt.imshow(img) plt.show() # + [markdown] deletable=true editable=true # ### Reading in the Data # The images are stored as jpg files, stored in folders corresponding to their classification. I read in the image os paths to be converted to images later in batches. I store their classification in a parallel array. # + deletable=true editable=true root_path = './train/' def read_paths(path, no_labels=False): # Takes a root path and returns all of the file # paths within the root directory. It uses the # subdirectories to create a corresponding label array # path - the path to the root directory # no_labels - optional argument to use file # names as labels instead of subdirectory file_paths = [] labels = [] labels_to_nums = dict() for dir_name, subdir_list, file_list in os.walk(path): if len(subdir_list) > 0: n_labels = len(subdir_list) for i,subdir in enumerate(subdir_list): labels_to_nums[subdir] = i else: type_ = dir_name.split('/')[-1] for img_file in file_list: if '.jpg' in img_file.lower(): file_paths.append(os.path.join(dir_name,img_file)) if no_labels: labels.append(img_file) else: labels.append(labels_to_nums[type_]) return file_paths, labels, n_labels image_paths, labels, n_labels = read_paths(root_path) image_paths, labels = shuffle(image_paths, labels) print("Number of data samples: " + str(len(image_paths))) print("Number of Classes: " + str(n_labels)) # + [markdown] deletable=true editable=true # This is a relatively small number of samples to use for deep learning... Luckily Kaggle provided more samples than just those in the train set. I will read those in as well after initial prototyping is finished. # + [markdown] deletable=true editable=true # ### Data Augmentation # The following cells add rotations and translations to the dataset. This increases the number of samples for training which helps the model generalize better. This prevents overfitting the training set. # + deletable=true editable=true def rotate(image, angle, ones=None, random_fill=True, color_range=255): # Rotates an image by the specified angle amount # and fills in resulting space with random values # image - the image as numpy array to be rotated # angle - the desired amount of rotation in degrees # ones - an numpy array of ones like the image with the same rotation # (used for broadcasting random filling into black space from rotation) # no_random - optional boolean to remove random filling in black space # color_range - the range of color values for the random filling if not random_fill: return sci.imrotate(image, angle).astype(np.float32) elif ones == None: ones = sci.imrotate(np.ones_like(image),angle) rot_image = sci.imrotate(image, angle).astype(np.float32) edge_filler = np.random.random(rot_image.shape).astype(np.float32)color_range rot_image[ones[:,:,:]!=1] = edge_filler[ones[:,:,:]!=1] return rot_image def translate(img, row_amt, col_amt, color_range=255): # * Returns a translated copy of an image by the specified row and column amount # and fills in the empty space with random values ** # image - the source image as numpy array to be translated # row_shift - the maximum vertical translation in both directions in pixels # col_shift - the maximum horizontal translation in both directions in pixels # color_range - the range of color values for the random filling translation = np.random.random(img.shape).astype(img.dtype)color_range if row_amt > 0: if col_amt > 0: translation[row_amt:,col_amt:] = img[:-row_amt,:-col_amt] elif col_amt < 0: translation[row_amt:,:col_amt] = img[:-row_amt,-col_amt:] else: translation[row_amt:,:] = img[:-row_amt,:] elif row_amt < 0: if col_amt > 0: translation[:row_amt,col_amt:] = img[-row_amt:,:-col_amt] elif col_amt < 0: translation[:row_amt,:col_amt] = img[-row_amt:,-col_amt:] else: translation[:row_amt,:] = img[-row_amt:,:] else: if col_amt > 0: translation[:,col_amt:] = img[:,:-col_amt] elif col_amt < 0: translation[:,:col_amt] = img[:,-col_amt:] else: return img.copy() return translation def random_zoom(image, max_zoom=1/3.): # * Returns a randomly zoomed (scaled) copy of an image within the scaling amount. # if the scaling zooms outward, the empty space is filled with random values ** # image - the source image as numpy array to be scaled # max_zoom - the maximum scaling amount in either direction color_range = 255 zoom_factor = 1 + (random.random()-0.5)max_zoom while zoom_factor == 1: zoom_factor = 1 + (random.random()-0.5)max_zoom # scipy's zoom function returns different size array # The following code ensures the zoomed image has same pixel size as initial image img_height, img_width = image.shape[:2] zoomed_h = round(img_heightzoom_factor) zoomed_w = round(img_widthzoom_factor) diff_h = abs(zoomed_h-img_height) diff_w = abs(zoomed_w-img_width) start_row = round(diff_h/2) start_col = round(diff_w/2) # Zoom in on image if zoom_factor > 1: end_row = start_row+img_height end_col = start_col+img_width zoom_img = scizoom.zoom(image,(zoom_factor,zoom_factor,1),output=np.uint8)[start_row:end_row, start_col:end_col] # Zoom out on image elif zoom_factor < 1: temp = scizoom.zoom(image,(zoom_factor,zoom_factor,1),output=np.uint8) temp_height, temp_width = temp.shape[:2] zoom_img = np.random.random(image.shape)color_range # Random pixels instead of black space for out zoom zoom_img[start_row:start_row+temp_height, start_col:start_col+temp_width] = temp[:,:] else: return image.copy() return zoom_img.astype(np.float32) def random_augment(image, rotation_limit=180, shift_limit=10, zoom_limit=1/3.): # * Returns a randomly rotated, translated, or scaled copy of an image. # image - source image as numpy array to be randomly augmented # rotation_limit - maximum rotation degree in either direction # shift_limit - maximum translation amount in either direction # zoom_limit - maximum scaling amount in either direction augmentation_type = random.randint(0,2) # Rotation if augmentation_type == 0: random_angle = random.randint(-rotation_limit,rotation_limit) while random_angle == 0: random_angle = random.randint(-rotation_limit,rotation_limit) aug_image = rotate(image,random_angle,random_fill=False) elif augmentation_type == 1: # Translation row_shift = random.randint(-shift_limit, shift_limit) col_shift = random.randint(-shift_limit, shift_limit) aug_image = translate(image,row_shift,col_shift) else: # Scale aug_image = random_zoom(image,max_zoom=zoom_limit) return aug_image def one_hot_encode(labels, n_classes): # Takes labels as values and converts them into one_hot labels. # Returns numpy array of one_hot encodings ** # labels - array or numpy array of single valued labels # n_classes - number of potential classes in labels one_hots = [] for label in labels: one_hot = [0]n_classes if label >= len(one_hot): print("Labels out of bounds\nCheck your n_classes parameter") return one_hot[label] = 1 one_hots.append(one_hot) return np.array(one_hots,dtype=np.float32) # + [markdown] deletable=true editable=true # ### Split into Training and Validation Sets # It is important to set aside images for validation. This is how you can determine if your model is overfitting or underfitting during training. # # + deletable=true editable=true training_percentage = .75 total_samples = len(image_paths) split_index = int(training_percentagetotal_samples) X_train_paths, y_train = image_paths[:split_index], labels[:split_index] X_valid_paths, y_valid = image_paths[split_index:], labels[split_index:] # + deletable=true editable=true print("Number of Training Samples: " + str(len(y_train))) print("Number of Validation Samples: " + str(len(y_valid))) # + [markdown] deletable=true editable=true # Since I am completing this notebook over the course of multiple days, I save the training paths and validation paths into seperate csv files along with their classification. This is essentially a checkpoint step so that it is easy to repeatedly save and restore the weights of the model later in the process. # + deletable=true editable=true def save_paths(file_name, paths, labels): with open(file_name, 'w') as csv_file: for path,label in zip(paths,labels): csv_file.write(path + ',' + str(label) + '\n') save_paths('train_set.csv', X_train_paths, y_train) save_paths('valid_set.csv', X_valid_paths, y_valid) # + [markdown] deletable=true editable=true # ### Previously Split Data # Here I read in the training and validation image paths from the csv files. This ensures that the two sets remain seperate throughout the notebook. # + deletable=true editable=true def get_split_data(file_name): paths = [] labels = [] with open(file_name, 'r') as f: for line in f: split_line = line.strip().split(',') paths.append(split_line[0]) labels.append(int(split_line[1])) return paths,labels # + deletable=true editable=true X_train_paths, y_train = get_split_data('train_set.csv') X_valid_paths, y_valid = get_split_data('valid_set.csv') n_labels = max(y_train)+1 print("Number of Training Samples: " + str(len(y_train))) print("Number of Validation Samples: " + str(len(y_valid))) # + deletable=true editable=true y_train = one_hot_encode(y_train, n_labels) y_valid = one_hot_encode(y_valid, n_labels) # + [markdown] deletable=true editable=true # ### Generator and Image Reader # To maximize memory, the images for both testing and training can be read in in batches. This increases the amount of images that can be trained on in a single epoch which helps the model generalize. In most cases, more training data is better for deep learning. # + deletable=true editable=true def convert_images(paths, labels, resize_dims=(256,256), randomly_augment=False): # Reads in images from their paths, resizes the images and returns # the images with their corresponding labels. # paths - the file paths to the images # labels - a numpy array of the corresponding labels to the images # resize_dims - the resizing dimensions for the image # add_zooms - optional parameter to add randomly scaled copies of the images to the output # randomly_augment - optional parameter to add randomly rotated, # translated, and scaled images to the output images = [] new_labels = [] for i,path in enumerate(paths): label = labels[i] try: img = mpimg.imread(path) resized_img = sci.imresize(img, resize_dims) except OSError: if i == 0: img = mpimg.imread(paths[i+1]) resized_img = sci.imresize(img, resize_dims) resized_img = random_augment(resized_img) elif i > 0: sub_index = -1 if randomly_augment: sub_index = -2 resized_img = random_augment(images[sub_index]) labels[i] = labels[i-1] label = labels[i] images.append(resized_img) if randomly_augment: images.append(random_augment(resized_img)) new_labels.append(label) new_labels.append(label) if randomly_augment: return np.array(images,dtype=np.float32), np.array(new_labels,dtype=np.float32) return np.array(images,dtype=np.float32), labels def image_generator(file_paths, labels, batch_size, resize_dims=(256,256), randomly_augment=False): # Generator function to convert image file paths to images with labels in batches. # file_paths - an array of the image file paths as strings # labels - a numpy array of labels for the corresponding images # batch_size - the desired size of the batch to be returned at each yield # resize_dims - the desired x and y dimensions of the images to be read in # add_zooms - optional parameter add an additional randomly zoomed image to the batch for each file path # randomly_augment - optional parameter add an additional randomly rotated, translated, # and zoomed image to the batch for each file path if randomly_augment: batch_size = int(batch_size/2) # the other half of the batch is filled with augmentations while 1: file_paths,labels = shuffle(file_paths,labels) for batch in range(0, len(file_paths), batch_size): images, batch_labels = convert_images(file_paths[batch:batch+batch_size], labels[batch:batch+batch_size], resize_dims=resize_dims, randomly_augment=randomly_augment) yield images, batch_labels # + [markdown] deletable=true editable=true # #### Note on Image Generation # The cervical images come in different sizes. Because I may use transfer learning, the images need to be resized anyway. # # If the classification results are too poor, I will try resizing without distorting the image. If I am still getting poor results, I will try using RNNs to find the key elements of the picture and crop the image. I may use this last technique regardless of my initial results simply to practice the method. It has proved very successful for other people on other projects in the past. # + deletable=true editable=true batch_size = 96 add_random_augmentations = True resize_dims = (256,256) n_train_samples = len(X_train_paths) if add_random_augmentations: n_train_samples = 2len(X_train_paths) train_steps_per_epoch = n_train_samples//batch_size + 1 if n_train_samples % batch_size == 0: train_steps_per_epoch = n_train_samples//batch_size valid_steps_per_epoch = len(X_valid_paths)//batch_size train_generator = image_generator(X_train_paths, y_train, batch_size, resize_dims=resize_dims, randomly_augment=add_random_augmentations) valid_generator = image_generator(X_valid_paths, y_valid, batch_size, resize_dims=resize_dims) # + [markdown] deletable=true editable=true # ### Viewing the Images # The following cell shows what some of the images look like. Due to the graphic nature of the images, I left the format in float32 instead of uint8 so that the coloring is less intrusive. # + deletable=true editable=true image_gen = image_generator(X_train_paths[:10], y_train[:10], 2,randomly_augment=True) for i in range(5): imgs, labels = next(image_gen) imgs = imgs show(imgs[0]) print(labels[0]) # + deletable=true editable=true plt.hist(np.array(y_train,dtype='float32'),3) plt.show() # + [markdown] deletable=true editable=true # ## Keras Section # # ### Keras Imports # # + deletable=true editable=true from keras.models import Sequential, Model from keras.layers import Conv2D, MaxPooling2D, Dense, Input, Flatten, Dropout, concatenate from keras.layers.normalization import BatchNormalization from keras import optimizers # + [markdown] deletable=true editable=true # ### Keras Model # I'm going to try using a personal model that has given me good success in the past. If it seems to produce bad results, I will likely try to use transfer learning instead and use a model like the Inception net. Cervixes, however, only have so many features that are important to notice. And these features seem more esoteric than the imagenet features that most pretrained models are trained on. My personal model trains quicker and easier than the larger nets and uses newer methods like batchnormalization and parallel convolutions. Thus I'm going to try it first and reevaluate if results are poor. # + deletable=true editable=true stacks = [] conv_shapes = [(1,1),(3,3),(5,5)] conv_depths = [12,12,10,10,8] pooling_filter = (2,2) pooling_stride = (2,2) dense_shapes = [150,64,20,n_labels] inputs = Input(shape=(resize_dims[0],resize_dims[1],3)) zen_layer = BatchNormalization()(inputs) for shape in conv_shapes: stacks.append(Conv2D(conv_depths[0], shape, padding='same', activation='elu')(inputs)) layer = concatenate(stacks,axis=-1) layer = BatchNormalization()(layer) layer = MaxPooling2D(pooling_filter,strides=pooling_stride,padding='same')(layer) # layer = Dropout(0.05)(layer) for i in range(1,len(conv_depths)): stacks = [] for shape in conv_shapes: stacks.append(Conv2D(conv_depths[i],shape,padding='same',activation='elu')(layer)) layer = concatenate(stacks,axis=-1) layer = BatchNormalization()(layer) # layer = Dropout(i10**-2+.05)(layer) layer = MaxPooling2D(pooling_filter,strides=pooling_stride, padding='same')(layer) layer = Flatten()(layer) fclayer = Dropout(0.5)(layer) for i in range(len(dense_shapes)-1): fclayer = Dense(dense_shapes[i], activation='elu')(fclayer) # if i == 0: # fclayer = Dropout(0.5)(fclayer) fclayer = BatchNormalization()(fclayer) outs = Dense(dense_shapes[-1], activation='softmax')(fclayer) # + [markdown] deletable=true editable=true # ### Keras Training # I read in a pretrained model that was trained on classifying statefarm drivers. Hopefully this will speed up the training process. # + deletable=true editable=true model = Model(inputs=inputs,outputs=outs) model.load_weights('model.h5', by_name=True) for i in range(10): adam_opt = optimizers.Adam(lr=0.0001) model.compile(loss='categorical_crossentropy', optimizer=adam_opt, metrics=['accuracy']) history = model.fit_generator(train_generator, train_steps_per_epoch, epochs=1, validation_data=valid_generator,validation_steps=valid_steps_per_epoch) model.save('model.h5') # + deletable=true editable=true	19,786
/notebooks/PRO-GAN.ipynb	42016578c1d7316c02c9e6e59b31e57502c561ee	[ "MIT" ]	permissive	jiwidi/BAM-DCGAN	https://github.com/jiwidi/BAM-DCGAN	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	9,126	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + import torch as th import torchvision as tv import pro_gan_pytorch.PRO_GAN as pg # select the device to be used for training device = th.device("cuda" if th.cuda.is_available() else "cpu") data_path = "cifar-10/" def setup_data(download=False): """ setup the CIFAR-10 dataset for training the CNN :param batch_size: batch_size for sgd :param num_workers: num_readers for data reading :param download: Boolean for whether to download the data :return: classes, trainloader, testloader => training and testing data loaders """ # data setup: classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') transforms = tv.transforms.ToTensor() trainset = tv.datasets.CIFAR10(root=data_path, transform=transforms, download=download) testset = tv.datasets.CIFAR10(root=data_path, transform=transforms, train=False, download=False) return classes, trainset, testset if __name__ == '__main__': # some parameters: depth = 6 # hyper-parameters per depth (resolution) num_epochs = [10, 20, 20, 20] fade_ins = [50, 50, 50, 50] batch_sizes = [128, 128, 128, 128] latent_size = 128 # get the data. Ignore the test data and their classes _, dataset, _ = setup_data(download=True) # ====================================================================== # This line creates the PRO-GAN # ====================================================================== pro_gan = pg.ConditionalProGAN(num_classes=10, depth=depth, latent_size=latent_size, device=device) # ====================================================================== # ====================================================================== # This line trains the PRO-GAN # ====================================================================== pro_gan.train( dataset=dataset, epochs=num_epochs, fade_in_percentage=fade_ins, batch_sizes=batch_sizes ) # ====================================================================== # - _, dataset, _ = setup_data(download=True) from torchvision import transforms import torchvision TRANSFORM_IMG = transforms.Compose([ transforms.Resize(128), #transforms.CenterCrop(256), transforms.ToTensor(), #transforms.ToPILImage(mode='RGB'), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) ]) TRAIN_DATA_PATH = '/home/jovyan/github/models/imagesProcessed/' train_data = torchvision.datasets.ImageFolder(root=TRAIN_DATA_PATH, transform=TRANSFORM_IMG) train_data.classes trainset.classes # + import torch as th import torchvision as tv import pro_gan_pytorch.PRO_GAN as pg from torchvision import transforms import torchvision TRAIN_DATA_PATH = '/home/jovyan/github/models/BAM-DCGAN/data/dataset_updated/training_set/' # select the device to be used for training device = th.device("cuda" if th.cuda.is_available() else "cpu") def setup_data(download=False): """ setup the CIFAR-10 dataset for training the CNN :param batch_size: batch_size for sgd :param num_workers: num_readers for data reading :param download: Boolean for whether to download the data :return: classes, trainloader, testloader => training and testing data loaders """ # data setup: TRANSFORM_IMG = transforms.Compose([ transforms.Resize((32,32)), #transforms.CenterCrop(256), transforms.ToTensor(), #transforms.ToPILImage(mode='RGB'), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) trainset = torchvision.datasets.ImageFolder(root=TRAIN_DATA_PATH, transform=TRANSFORM_IMG) testset = torchvision.datasets.ImageFolder(root=TRAIN_DATA_PATH, transform=TRANSFORM_IMG) classes = trainset.classes return classes, trainset, testset if __name__ == '__main__': # some parameters: depth = 4 # hyper-parameters per depth (resolution) num_epochs = [10, 20, 20, 20] fade_ins = [50, 50, 50, 50] batch_sizes = [32, 32, 32, 32] latent_size = 128 # get the data. Ignore the test data and their classes _, dataset, _ = setup_data(download=True) # ====================================================================== # This line creates the PRO-GAN # ====================================================================== pro_gan = pg.ConditionalProGAN(num_classes=len(dataset.classes), depth=depth, latent_size=latent_size, device=device) # ====================================================================== # ====================================================================== # This line trains the PRO-GAN # ====================================================================== pro_gan.train( dataset=dataset, epochs=num_epochs, fade_in_percentage=fade_ins, batch_sizes=batch_sizes, feedback_factor=1 ) # ====================================================================== # -	5,559
/bag-of-words/bag-of-words.ipynb	f9dd08802d5268b6f886cd4026a8d61dce4ecee1	[]	no_license	pharic/ipython-notebooks	https://github.com/pharic/ipython-notebooks	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	12,610	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python [py35] # language: python # name: Python [py35] # --- import pandas as pd df = pd.read_csv("labeledTrainData.tsv", header=0, delimiter="\t", quoting=3) # header=0 indicates that the first line contains column names. # # delimiter="\t" indicates that the file is tab separated. # # quoting=3 tells Python to ignore double quotes. df.describe() df.shape df.columns.values # The three columns are labelled id, sentiment and review. print(df["review"]) # We can see that some of the reviews have HTML tags like <br>. We can use BeautifulSoup to extract just the text. from bs4 import BeautifulSoup print("Before cleanup: ", df["review"][9]) print("After cleanup: ", BeautifulSoup(df["review"][9], "lxml").get_text()) import nltk nltk.download() from nltk.corpus import stopwords print(stopwords.words("english")) # Filtering out non-letter characters from the text import re letters = re.sub("[^a-zA-Z]", " ", BeautifulSoup(df["review"][9], "lxml").get_text()) bag_of_words = letters.lower().split() # Remove stop words from the bag of words bag_of_words = [w for w in bag_of_words if not w in stopwords.words("english")] print(" ".join(bag_of_words)) bins = bin_cut) print(age_data['YEARS_BINNED'].value_counts()) age_data.head() # - """ Your Code Here """ year_group_sorted = age_data['YEARS_BINNED'].value_counts().keys().sort_values() plt.figure(figsize=(8,6)) for i in range(len(year_group_sorted)): sns.distplot(age_data.loc[(age_data['YEARS_BINNED'] == year_group_sorted[i]) & \ (age_data['TARGET'] == 0), 'YEARS_BIRTH'], label = str(year_group_sorted[i])) sns.distplot(age_data.loc[(age_data['YEARS_BINNED'] == year_group_sorted[i]) & \ (age_data['TARGET'] == 1), 'YEARS_BIRTH'], label = str(year_group_sorted[i])) plt.title('KDE with Age groups') plt.show() # 計算每個年齡區間的 Target、DAYS_BIRTH與 YEARS_BIRTH 的平均值 age_groups = age_data.groupby('YEARS_BINNED').mean() age_groups # + plt.figure(figsize = (8, 8)) # 以年齡區間為 x, target 為 y 繪製 barplot """ Your Code Here """ px = 'YEARS_BINNED' py = 'TARGET' sns.barplot(px, py, data=age_data) # Plot labeling plt.xticks(rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)') plt.title('Failure to Repay by Age Group');	2,549
/1_Linear_Algebra.ipynb	bb45cef928b899e770a2c0599d2f551b5c8cc5d6	[]	no_license	dg5921096/Books-solutions	https://github.com/dg5921096/Books-solutions	0	0	null	2020-04-13T21:39:02	2020-04-13T21:38:39	null	Jupyter Notebook	false	false	.py	26,320	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # name: python3 # --- # + [markdown] id="view-in-github" colab_type="text" # <a href="https://colab.research.google.com/github/LCASouza/colab-teste/blob/main/ConsultaCEP.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # + id="xmeyW2ribdoC" import requests # + id="WFN7XXa_I_nH" def consultaCEP(cepInput): cepInput = str(cepInput) if len(cepInput) != 8: print('CEP {} inválido.'.format(cepInput)) return; retorno = requests.get('https://viacep.com.br/ws/{}/json/'.format(cepInput)); retorno = retorno.json() print('Estado: {}\n' 'Cidade: {}\n' 'Bairro: {}\n' 'Rua: {}\n' 'CEP: {}\n'.format(retorno['uf'], retorno['localidade'], retorno['bairro'], retorno['logradouro'], retorno['cep'], retorno['ddd'])) # + colab={"base_uri": "https://localhost:8080/"} id="9GeGbRKxBCSg" outputId="1d712daa-c416-4d1e-8604-22e8abb9c4be" consultaCEP(74958143) d "get_data_model". We also define a new function plot_history, which visualizes as a graph the training loss and if required even the validation loss. # + id="2aGBJBVmXd8b" colab_type="code" outputId="5d0fadb6-9f53-4c34-e292-f2f2cf1360a9" colab={"base_uri": "https://localhost:8080/", "height": 238} import numpy as np import keras np.random.seed(123) # for reproducibility from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.utils import np_utils from keras.utils import to_categorical import matplotlib.pyplot as plt from keras.datasets import mnist def plot_history(history, metric = None): # Plots the loss history of training and validation (if existing) # and a given metric if metric != None: fig, axes = plt.subplots(2,1) axes[0].plot(history.history[metric]) try: axes[0].plot(history.history['val_'+metric]) axes[0].legend(['Train', 'Val']) except: pass axes[0].set_title('{:s}'.format(metric)) axes[0].set_ylabel('{:s}'.format(metric)) axes[0].set_xlabel('Epoch') fig.subplots_adjust(hspace=0.5) axes[1].plot(history.history['loss']) try: axes[1].plot(history.history['val_loss']) axes[1].legend(['Train', 'Val']) except: pass axes[1].set_title('Model Loss') axes[1].set_ylabel('Loss') axes[1].set_xlabel('Epoch') else: plt.plot(history.history['loss']) try: plt.plot(history.history['val_loss']) plt.legend(['Train', 'Val']) except: pass plt.title('Model Loss') plt.ylabel('Loss') plt.xlabel('Epoch') def get_data_model(args = {}): # Returns simple model, flattened MNIST data and categorical labels num_classes=10 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(x_train.shape[0], x_train.shape[1] * x_train.shape[2]) x_test = x_test.reshape(x_test.shape[0], x_test.shape[1] * x_test.shape[2]) x_train= x_train.astype('float32') x_test= x_test.astype('float32') x_train /= 255 x_test /= 255 y_train=to_categorical(y_train,num_classes) y_test=to_categorical(y_test,num_classes) # Load simple model model = Sequential() model.add(Dense(512, activation='relu', input_shape=(784,), args)) model.add(Dense(512, activation='relu', args)) model.add(Dense(10, activation='softmax', *args)) return model, x_train, y_train, x_test, y_test model, x_train, y_train, x_test, y_test = get_data_model() model.summary() # + [markdown] id="mRx68ymWLCT7" colab_type="text" # ## Optimizers # # Once the model has been created, you need to define an optimizer to make it effectively working on your own particular problem, for example, in this tutorial we are addressing the digit classification problem. # Since there are methods more prone to get stuck in local minima and other methods converging faster, the choice of the optimizer can potentially affect both the final performance and the speed of convergence. A nice visualization of such behavior is the following animation, where you can notice some methods, i.e., Adadelta in yellow, and Rmsprop in black converge significantly faster than SGD in red failing in local minima. # # ![](http://ruder.io/content/images/2016/09/saddle_point_evaluation_optimizers.gif) # # The animation example above is from [Sebastian Ruder's blog](http://ruder.io/optimizing-gradient-descent/), who wrote an interesting article about the math formulation and properties of different optimizers. In this tutorial, we will follow a hands-on approach and will mainly focus on how to use them in Keras. # # To handle the optimization, Keras provides "the compile method" requiring two arguments in string format: a loss function and an optimizer. For example, we define as parameters Adam and Categorical Cross-entropy. # # As a rule of thumb, Adam is usually easier to tune due to the adaptive learning rate, whereas SGD with momentum [has been shown](https://arxiv.org/pdf/1712.07628.pdf) to reach better results when tuned correctly. Of course, the best way to learn the different optimizers is using them - please consult the official documentation [Optimizers in Keras](https://keras.io/optimizers/) to see the available options - and finally, report below in the table the training and validation losses you are able to get. # # + id="iwvy89V2hd7i" colab_type="code" outputId="486faaa5-5745-4332-e520-c7630d440ac9" colab={"base_uri": "https://localhost:8080/", "height": 166} from IPython.display import HTML, display import tabulate table = [["Optimizer","loss","val_loss","hyper-parameters"], ["Adam","-","-","-"], ["Sgd","-","-","lr=0.01, momentum=0.0, decay=0.0"], ["RMSprop","-" ,"-","lr=0.001, rho=0.9, epsilon=None, decay=0.0"], ["Adagrad","-","-","-"], ["Adadelta","-","-","-"], ["Adam","-","-","-"]] display(HTML(tabulate.tabulate(table, tablefmt='html'))) # + id="9od5lUWMWu5X" colab_type="code" outputId="7cad988a-4b1f-43d4-c992-c72250a8ae60" colab={"base_uri": "https://localhost:8080/", "height": 391} model.compile(optimizer='adam',loss='categorical_crossentropy') model.fit(x_train,y_train,batch_size=100, epochs=10,verbose=1,validation_data=(x_test,y_test)) # + [markdown] id="-yDSecajTyc6" colab_type="text" # ## Initializers # # # Weight initialization is a crucial step in tuning neural networks as different weights initializations may lead the model to reach different local minima. The weights are usually randomly initialized by different algorithms, e.g.. Xavier, He_normal, initialization. # In Keras, you can set the particular initialization strategy you want to use as an argument when declaring a layer. For example, you can give as input to the function "linear_layer" defining the mapping $y= Ax + b$, a kernel ($A$ in the equation) with a normal distribution (by default the `stddev` is 0.05) and zero bias ($b$ in the equation). # + id="A6lzySByXcMB" colab_type="code" colab={} linear_layer = Dense(64, kernel_initializer='random_normal', bias_initializer='zeros') # + [markdown] id="oF_EVmyooqYL" colab_type="text" # Now let's check the weights of the layers and see if they follow the distributions we set. # + id="YgHC0CSmowIK" colab_type="code" outputId="cb38e86b-13c0-43fe-bd6e-3ce8f0aa9df1" colab={"base_uri": "https://localhost:8080/", "height": 238} from keras.layers import Input import numpy as np from keras import backend as K input_x = K.variable(np.random.random((1, 64))) y = linear_layer(input_x) weights = linear_layer.get_weights() # Weights return an array with [kernel, bias] # Let's see the kernel weights print(weights[0]) # + id="kLDzNuqUp2M8" colab_type="code" outputId="4d57acc3-fcc8-4afc-c03b-646148a8210d" colab={"base_uri": "https://localhost:8080/", "height": 34} # Now let's check that the mean is 0 and stddev is 0.05 print(weights[0].mean(), weights[0].std()) # + id="Uohj6D4rqTfT" colab_type="code" outputId="04c2963c-f3f0-4607-b562-ac209862d8fa" colab={"base_uri": "https://localhost:8080/", "height": 68} # Let's print the bias now print(weights[1]) # + [markdown] id="69NkDCipqbmg" colab_type="text" # The number of initializations available in Keras is listed [in the documentation](https://keras.io/initializers/). By default in Keras the kernel weights are initialized as `'glorot_uniform'` and the bias to `'zeros'`. Glorot uniform, which is also called Xavier initialization was defined [here](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf). It samples the weights from a uniform distribution, whose range depends on the number of input and output units. Another initializer quite used is the `he_normal`, which draws the weights from a truncated normal distribution. # + [markdown] id="1-8ittW2L1DI" colab_type="text" # ## Losses # # Another important step in training deep neural networks is the choice of the loss function, which strictly depends on your problem. In this tutorial, we will introduce two typical losses i.e., sigmoid and mean squared error, for two standard problems in machine learning: classification and regression. In the following weeks, we will introduce further losses. The full list of standard losses in Keras is available [here](https://keras.io/losses/). # + [markdown] id="0T8aSdjCtSSO" colab_type="text" # ### Classification # # For classification problems, the standard loss used is the cross-entropy loss. For the binary case, the formula is $\mathcal{L} = y\log(p) + (1-y)\log(1-p)$, where $p$ is a probability value ranging in $[0, 1]$. # To constrain the activations to assume such values, typically it is applied a [Sigmoid activation](https://en.wikipedia.org/wiki/Sigmoid_function). In Keras, the loss is called `binary_crossentropy`, and it accepts as target a vector with an element in the range $[0, 1]$ (usually either 0 or 1) per input element. # + id="LUQhLNxr5VA7" colab_type="code" outputId="d948c22d-edcd-446c-d475-820b9dba1443" colab={"base_uri": "https://localhost:8080/", "height": 376} model, x_train, y_train, x_test, y_test = get_data_model() model.pop() model.add(Dense(1, activation='sigmoid')) # This three lines transform the problem in a binary one # We want to know if the number is bigger than 5 (label 1) or smaller (label 0) y_train = np.argmax(y_train, axis = 1) y_train[y_train < 5] = 0 y_train[y_train >= 5] = 1 model.compile(optimizer='adam',loss='binary_crossentropy', metrics=['binary_accuracy']) history = model.fit(x_train, y_train, epochs=3, batch_size=32, validation_split=0.2, verbose = 0) plot_history(history, 'binary_accuracy') # + [markdown] id="6jvNHqg--XYy" colab_type="text" # In case the number of classes is higher than 2, we use the cross-entropy loss, which has the form of $\mathcal{L} = -\sum_i y_i\log(p_i)$. The loss is called `categorical_crossentropy` in Keras, and accepts a one-hot encoded vector. A one-hot encoded vector has dimensionality $C$, where $C$ is the number of classes. All of the elements are set to 0, minus the corresponding class $c$, which is set to 1. If we have a vector of labels with a scalar from $[0, C)$ per training example, we can transform it into a one-hot encoding form by using the function `to_categorical`. # # Let's see an example using mnist data # + id="v_YBOLN77S1I" colab_type="code" outputId="136faa56-0d3a-43ad-b72a-c219bc23ca8d" colab={"base_uri": "https://localhost:8080/", "height": 34} (x_train, y_train), (x_test, y_test) = mnist.load_data() # The labels are an scalar from 0 to 9 per example print(y_train[:5]) # + id="Rgm-W7sa5U-P" colab_type="code" outputId="ba8e681b-4123-46d0-86a1-c8a87fc947e9" colab={"base_uri": "https://localhost:8080/", "height": 102} keras.utils.to_categorical(y_train[:5]) # + [markdown] id="OR_lJgB6CfFd" colab_type="text" # The output vector needs to be $\sum_i p_i = 1$, and we achieve that by applying the softmax activation function to the output vector. Keras accepts as input for the loss also vectors which are not scaled as $\sum_i p_i = 1$, but resulting in degraded performances of the network. For example, remove the `softmax` activation in the following example and re-run the code. You will see that the accuracy achieved is quite lower. # + id="BVTAPlc_B99R" colab_type="code" outputId="af059294-c530-439c-ab4a-336c0f7f48b1" colab={"base_uri": "https://localhost:8080/", "height": 376} # We use the function get_data_model, which already applies to_categorical _, x_train, y_train, x_test, y_test = get_data_model() ### Model defined with softmax model = Sequential() model.add(Dense(512, activation='relu', input_shape=(784,))) model.add(Dense(512, activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['categorical_accuracy']) history = model.fit(x_train, y_train, epochs=3, batch_size=32, validation_split=0.2, verbose = 0) plot_history(history, 'categorical_accuracy') # + [markdown] id="iFuOCwNftQgH" colab_type="text" # ### Regression # For regression problems, it is quite standard to use Mean Squared Error or Mean Absolute Error, depending on the problem. # # To give an example of regression problem, let's load the boston housing dataset. The problem involves in estimating the median value of certain hause in the area of Boston, Mass. The given set of features is defined [here](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html), amounting to a total of 13. Some of the features are "per capita crime rate by town", "average number of rooms per dwelling" or "pupil-teacher ratio by town". # + id="Li20u1NjrwS5" colab_type="code" outputId="89fcbf04-c1ee-4ad3-c04c-b81ebcf70633" colab={"base_uri": "https://localhost:8080/", "height": 85} from keras.datasets import boston_housing (x_train, y_train), (x_test, y_test) = boston_housing.load_data() print(y_train[:10]) # + [markdown] id="0JO5_oiCyPzR" colab_type="text" # We see that the labels are float, and we need to predict them. To do so, we need a network that has one output. Now we will train the network using both mean absolute error (MAE), and mean squared error (MSE). As a quick evaluation, we use the validation MAE metric to compare them. # + id="RSJH6_Mfsjp-" colab_type="code" colab={} model_mae = Sequential() model_mae.add(Dense(100, activation='relu', input_shape=(13,))) model_mae.add(Dense(1)) model_mae.compile(optimizer='adam',loss='mean_absolute_error', metrics=['mean_absolute_error', 'mean_squared_error']) history = model_mae.fit(x_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose = 0) # + id="J2xZRys8zNsD" colab_type="code" outputId="cb224d2f-592d-48c9-b861-8df984848ae5" colab={"base_uri": "https://localhost:8080/", "height": 376} plot_history(history, 'mean_absolute_error') # + id="2XYKtV1GzMQg" colab_type="code" colab={} model_mse = Sequential() model_mse.add(Dense(100, activation='relu', input_shape=(13,))) model_mse.add(Dense(1)) model_mse.compile(optimizer='adam',loss='mean_squared_error', metrics=['mean_absolute_error', 'mean_squared_error']) history = model_mse.fit(x_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose = 0) # + id="SBZB_MDCw1Os" colab_type="code" outputId="63ad6eab-dcbe-4b68-b0df-65b0d1cb6e48" colab={"base_uri": "https://localhost:8080/", "height": 376} plot_history(history, 'mean_squared_error') # + id="aMHRntQU0mrM" colab_type="code" outputId="f72c18b0-d5f7-4167-d79f-9dcfced70f12" colab={"base_uri": "https://localhost:8080/", "height": 51} results = model_mae.evaluate(x_test, y_test) print('MAE trained model achieves MAE: {:.4f} and MSE: {:.4f}'.format(results[1], results[2])) # + id="df2igCnI030F" colab_type="code" outputId="3a1dc163-8ef7-4728-89ae-0970caddf946" colab={"base_uri": "https://localhost:8080/", "height": 51} results = model_mse.evaluate(x_test, y_test) print('MSE trained model achieves MAE: {:.4f} and MSE: {:.4f}'.format(results[1], results[2])) # + [markdown] id="hWr2tc7l0IdG" colab_type="text" # Looking at the graphs, we can see that training using MSE as loss achieves a better MSE and worse MAE in the test set compared to the model training with MAE loss. It makes sense (though it is not always the case). # + [markdown] id="vnEITU4iy6Q5" colab_type="text" # Now let's print some predicted prices from the test set, along with the actual price, just to have an intuition of the output values. # + id="uTPGyWHxydF_" colab_type="code" outputId="ab9e7095-1abf-4ed6-8a40-f13fa57aaeec" colab={"base_uri": "https://localhost:8080/", "height": 68} pred_prices = model_mae.predict(x_test) print(pred_prices[:10, 0]) print(y_test[:10]) # + [markdown] id="miKutjvgtXuS" colab_type="text" # ## Regularization # + [markdown] id="F-VrqhSbWJ9t" colab_type="text" # ### Loss regularizers # # [Regularizers](https://keras.io/regularizers/) put some penalties to the optimization process. In practice by penalizing large values, weights are constrained to be small and as a result, overfitting is prevented. # # In Keras regularization works on a per-layer basis. It means you can define a regularization function for each layer. In particular, you can specify three regularization parameters each one related to a different type of penalty: # # `kernel_regularizer`: a penalty depending on the value of the kernel weights, e.g, larger kernel weights result in larger penalization. # * `bias_regularizer`: a penalty depending on the loss function depending on the value of the bias. # * `activity_regularizer`: a penalty applied to the loss function depending on the output. It results in smaller outputs in value when this regularizer is applied. # # Standard regularizers that can be applied are $l_1$, $l_2$ and $l_1+l_2$. # In the example below, we check the difference in training and validation accuracy by varying regularization strategy. # # + id="zv4c80XD_Yzr" colab_type="code" outputId="2a14a1f6-b85c-470a-eb3d-0dd7e39287b5" colab={"base_uri": "https://localhost:8080/", "height": 415} from keras import regularizers test_accuracy = [] train_accuracy = [] reg_values = [0.1, 0.00001] for reg_val in reg_values: print('Training with regularization value of {:f}'.format(reg_val)) args_dict = {'kernel_regularizer': regularizers.l2(reg_val)} model, x_train, y_train, x_test, y_test = get_data_model(args_dict) model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy']) history = model.fit(x_train, y_train, epochs=10, batch_size=32, verbose=0) train_accuracy.append(history.history['acc'][-1]) test_accuracy.append(model.evaluate(x_test, y_test)[-1]) import matplotlib.pyplot as plt plt.figure() plt.plot(reg_values, train_accuracy) plt.plot(reg_values, test_accuracy) plt.legend(['Training acc.', 'Test acc.']) plt.show() # + [markdown] id="FJ-J7tWRTQU7" colab_type="text" # ### Dropout # # Dropout is a layer that deactivates during training either some neurons or input data depending on the specific layer where it is applied by setting elements to with a certain probability .`In the evaluation phase, all the neurons are activated and dropout has no effect. The drop out value, i.e., the probability of disabling the input units can be set as a parameter when defining a layer. # # For example, the following layer the drop-out value is 0.3, that means $30%$ of the input data is switched off during training. # + id="HEqZN0LyTv1F" colab_type="code" colab={} prob_drop = 0.3 drop = keras.layers.Dropout(prob_drop) # + id="Pqcb3TWCWe4e" colab_type="code" outputId="3d4ba659-4428-4231-a1a8-3f0efaeff0ff" colab={"base_uri": "https://localhost:8080/", "height": 119} from keras.layers import Input import numpy as np from keras import backend as K x = np.random.random((1, 512)) input_x = K.variable(x) # Set learning phase is used to manually setting # the phase (0 evaluation, 1 training) # Dropout only affects training, so we set it to 1 K.set_learning_phase(1) y = K.eval(drop(input_x)) print('Input (10 elements)') print(x[0,0:10]) print('Output (10 elements)') print(y[0,0:10]) # + [markdown] id="iXhWs8c1Xw3h" colab_type="text" # We now check what percentage of elements have been set to 0, and what is the scaling value the other elements have. # + id="8njS0-XRXVsc" colab_type="code" outputId="72e4fcd6-c44c-4444-e332-23a3d000da2a" colab={"base_uri": "https://localhost:8080/", "height": 85} print('Drop percentage, should be close to {:f}'.format(prob_drop)) print(((y==0).sum())/(1.0y.shape[1])) print('Scaling value, should be {:f}'.format(1/(1-prob_drop))) print(((y[y!=0]).sum())/(1.0x[y!=0].sum())) # + [markdown] id="C98HL1rgVACR" colab_type="text" # ### Batch Normalization # # Batch Normalization is a layer that subtracts the mean of the batch for each input dimension and divides it by the standard deviation of the batch. The goal is to standardize all of the dimensions of the input feature to have mean 0 and variance 1. # The layer is defined in Keras by using: # + id="mt2E2uTqWGBB" colab_type="code" colab={} batch_norm = keras.layers.BatchNormalization() # + [markdown] id="ax8OE0cbZA7X" colab_type="text" # Now we will generate a batch of 512x1 (a feature vector of 512 components) using `np.random.random`, which is a uniform distribution under the $[0, 1)$ interval, resulting in mean 0.5 and variance 1/12. Finally, the batch normalization layer scales the distribution to have mean 0 and variance 1. # # # + id="8IS_u-HKY1WR" colab_type="code" outputId="50c7eb90-51fc-4872-b2f0-24e10e7dd1ae" colab={"base_uri": "https://localhost:8080/", "height": 119} from keras.layers import Input import numpy as np from keras import backend as K x = np.random.random((512, 1)) input_x = K.variable(x) y = K.eval(batch_norm(input_x)) print('Input') print(x[:10,0]) print('Output') print(y[:10,0]) # + id="ILdlLzAtZ3sp" colab_type="code" outputId="a66f2a6e-8cae-4c53-f293-9d4a867a1342" colab={"base_uri": "https://localhost:8080/", "height": 51} # Input mean should be ~0.5 and var ~1/12=0.0833 print(x.mean(), x.var()) # Output mean should be ~0 and var ~1 print(y.mean(), y.var()) # + [markdown] id="iDOne7XbrEhL" colab_type="text" # Batch normalization changes behaviour during evaluation: it computes the moving average of both mean and variance to normalize the testing data. # + [markdown] id="NuF3Y5DoKylT" colab_type="text" # ## HyperParameters Tuning # # There are several parameters in the training process that we can modify. Note that it is not good practice looking at the performance in the test set to tweak the hyperparameters. Hence, we first need to define a validation split, which we use to test the different models trained. The method `fit` has two relevant arguments: `validation_split` and `validation_data`. The argument passed to `validation_split` (0 by default) determines the ratio of the training set for validation purposes. For example, # ``` # model.fit(x_train, y_train, ..., validation_split=0.2) # ``` # uses 20% of `x_train` as validation data. # # Unfortunately, the validation data is randomly sampled and we can not fix the same splits during evaluations, so results are not directly comparable. An option is using the `validation_data` argument, where we can pass directly the split of data we want to use as validation in the form of a tuple `(data, labels)`. # # Let's see how we can do the split. First, we load the data: # + id="WWL_GXrnT_xS" colab_type="code" colab={} model, x_train, y_train, x_test, y_test = get_data_model() # + [markdown] id="XKS6nnKAUBJT" colab_type="text" # Now, we want to split `x_train` in training and validation, but we also need to follow the same partition for `y_train`. We can do so by using `numpy` functions: # + id="dVMgy4n7UOdL" colab_type="code" outputId="9149c644-cc52-4850-bc11-c2bbe48558ba" colab={"base_uri": "https://localhost:8080/", "height": 34} import numpy # We shuffle the indices in case the dataset follows an ordering # If we do not shuffle we may take only a subset of classes if the dataset is # ordered indices = numpy.random.permutation(x_train.shape[0]) val_ratio = 0.2 n_indices_train = int((1-val_ratio) * x_train.shape[0]) train_idx, val_idx = indices[:n_indices_train], indices[n_indices_train:] x_train, x_val = x_train[train_idx,:], x_train[val_idx,:] y_train, y_val = y_train[train_idx], y_train[val_idx] print(x_train.shape[0], x_val.shape[0]) # + [markdown] id="yBf2hHC_WF-U" colab_type="text" # Another way is to use a package called `sklearn`, which contains a function called `train_test_split` that performs the split. # + id="Hy2DPVA9WFjk" colab_type="code" outputId="eb918acc-f3ea-4845-80d3-5722b1dadfad" colab={"base_uri": "https://localhost:8080/", "height": 34} # Let's reload the data first model, x_train, y_train, x_test, y_test = get_data_model() from sklearn.model_selection import train_test_split x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.20) print(x_train.shape[0], x_val.shape[0]) # + id="TulgsLVmWE6t" colab_type="code" outputId="c0efe960-e5f4-482f-f770-f6d1b5f23b9b" colab={"base_uri": "https://localhost:8080/", "height": 376} model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['categorical_accuracy']) history = model.fit(x_train, y_train, epochs=2, batch_size=32, verbose=0, validation_data=(x_val, y_val)) plot_history(history, 'categorical_accuracy') # + [markdown] id="rvWaSNdZV6e_" colab_type="text" # Now, let's check if the accuracy in the test set is similar to the accuracy in the training set. # + id="UMRAQsCYVrcg" colab_type="code" outputId="81ec3253-4e1c-43c7-be08-3dc5eccbab72" colab={"base_uri": "https://localhost:8080/", "height": 51} print('Accuracy in the test set is {:.2f}'.format(model.evaluate(x_test, y_test)[-1])) # + [markdown] id="NMd2DVJCGmE7" colab_type="text" # One of the most important parameters to tweak is the training rate, which controls the update step performed during the backpropagation. Keras provides two callbacks that allow us to modify the learning rate during training. One is LearningRateScheduler, which allows us to define a rule to vary the learning rate depending on the epoch. For example, using the `lr_scheduler` function (found [here](https://stackoverflow.com/questions/39779710/setting-up-a-learningratescheduler-in-keras)), we can modify the loss function so that every 3 epochs is multiplied by 0.1. # + id="AC9in_Zhfnly" colab_type="code" outputId="ea760fa3-2e91-4528-bbd4-bd879c9cdb70" colab={"base_uri": "https://localhost:8080/", "height": 733} def lr_scheduler(epoch, lr): decay_rate = 0.1 decay_step = 3 if epoch % decay_step == 0 and epoch: return lr * decay_rate return lr lrate = keras.callbacks.LearningRateScheduler(lr_scheduler) model, x_train, y_train, x_test, y_test = get_data_model() model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['categorical_accuracy']) history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.2, callbacks=[lrate]) plot_history(history, 'categorical_accuracy') # + [markdown] id="jAF6VK2gkdRU" colab_type="text" # Now let's plot the learning rate in each epoch to check how the learning rate is decreased every three epochs as we defined in `lr_scheduler` # + id="jtugFeDVj8HD" colab_type="code" outputId="0295d8ba-7650-4d5f-aacd-473d88ea777f" colab={"base_uri": "https://localhost:8080/", "height": 361} learning_rate = history.history['lr'] plt.plot(range(1, len(learning_rate)+1), learning_rate) plt.ylabel('Learning Rate') plt.xlabel('Epochs') plt.show() # + [markdown] id="XAPrOzm0iDTs" colab_type="text" # Another callback provided is ReduceLROnPlateau, which reduces the learning rate whenever a given metric has stopped improving. There are 5 important arguments: # # * `monitor`: we specify the metric we want to track # * `patience`: number of epochs without improvement before reducing lr # * `factor`: the new learning rate will be `new_lr = lr * factor` # * `min_lr`: sets the minimum lr # * `min_delta`: margin to define when the metric has stopped improving # # + id="os7b7Oh0TO7m" colab_type="code" outputId="af3014f6-984b-424e-fa5d-f1d8f2ecbd0e" colab={"base_uri": "https://localhost:8080/", "height": 733} from keras.callbacks import ReduceLROnPlateau reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=1, min_lr=0.00001, min_delta = 0.01) model, x_train, y_train, x_test, y_test = get_data_model() model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['categorical_accuracy']) history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.2, callbacks=[reduce_lr]) plot_history(history, 'categorical_accuracy') # + [markdown] id="FX9W-7yNoyvL" colab_type="text" # Again, we check how the learning rate has changed. You can check that it has indeed decreased when the `val_loss` has not decreased by more than 0.01 until it reached the `min_lr` # + id="BVNFkE-qnKVk" colab_type="code" outputId="a1a7b622-6ebe-4513-8245-b2b5bccccbf4" colab={"base_uri": "https://localhost:8080/", "height": 361} learning_rate = history.history['lr'] plt.plot(range(1, len(learning_rate)+1), learning_rate) plt.ylabel('Learning Rate') plt.xlabel('Epochs') plt.show() # + [markdown] id="ugiRVHj-j8UX" colab_type="text" # ### Searching for the right set of parameters # # Apart from the learning rate, there are several hyperparameters we can tune: the optimizer parameters (momentum, beta, rho, decay), the dropout rate, the number of neurons/feature maps, batch size, regularization weights, etc.. After some time working with the models, you gain an intuition of what set of parameters work better. However, performing a proper search of hyperparameters could improve the results. A way to do this (among several others) is performing a grid search of parameters. There are several packages that help you to do hyperparameter optimization in Keras, the one we will use is called [`talos`](https://github.com/autonomio/talos). # + id="Az5-RBT1zFX8" colab_type="code" outputId="89a1a9a0-7d49-4318-f2be-bb3e8096614c" colab={"base_uri": "https://localhost:8080/", "height": 119} # !pip install talos # + [markdown] id="a2w5UNq0_I0L" colab_type="text" # Now we show a quick example of how to do it. We set only 2 epoch of training and `grid_downsampling=0.03`, which controls the number of sets of hyperparameters tested. # + id="Pm2XxaG_zPT-" colab_type="code" outputId="2b130b50-3438-44f3-c96a-b2d5eb1f7327" colab={"base_uri": "https://localhost:8080/", "height": 122} import talos as ta _, x_train, y_train, x_test, y_test = get_data_model() def model_scan(x_train, y_train, x_val, y_val, params): model = Sequential() model.add(Dense(params['first_neuron'], input_shape=(784,), activation=params['activation'])) model.add(Dropout(params['dropout'])) model.add(Dense(10, activation=params['last_activation'])) from talos.model.normalizers import lr_normalizer model.compile(optimizer=params['optimizer'](lr_normalizer(params['lr'], params['optimizer'])), loss=params['losses'], metrics=['categorical_accuracy']) out = model.fit(x_train, y_train, batch_size=params['batch_size'], epochs=params['epochs'], verbose=0, validation_data=[x_val, y_val]) return out, model from talos import live p = {'lr': (1, 10, 0.1), 'first_neuron':[4, 8, 16, 32, 64, 128], 'batch_size': [20, 30, 40], 'epochs': [2], 'dropout': (0, 0.40, 0.7), 'weight_regulizer':[None], 'emb_output_dims': [None], 'optimizer': [keras.optimizers.Adam, keras.optimizers.SGD], 'losses': ['categorical_crossentropy', 'logcosh'], 'activation':['relu', 'elu'], 'last_activation': ['softmax']} h = ta.Scan(x_train, y_train, params=p, dataset_name='first_test', experiment_no='2', model=model_scan, grid_downsample=0.03, print_params=True, disable_progress_bar=True) # + [markdown] id="5FCxXrdxCXRC" colab_type="text" # Now we check the results of the experiment (saved by default in a CSV file with the same name as `dataset_name + _ + experiment_no`) # + id="xARWdCT0_-Ig" colab_type="code" outputId="b89ecfba-1261-4bf9-bfaa-a14a76c818b2" colab={"base_uri": "https://localhost:8080/", "height": 261} from talos import Reporting r = Reporting('/content/first_test_2.csv') # returns the results dataframe r.data # + [markdown] id="cBBz_MMS_ezr" colab_type="text" # [Talos' documentation](https://autonomio.github.io/docs_talos/#introduction) provides more information about the package. There are also other packages that serve the same purpose, with several examples online, in case you want to do grid search, random search or other types of hyperparemeter search. # + [markdown] id="xm0oVnMQWLZt" colab_type="text" # ## Data augmentation # We will show some examples of data augmentation for images. # ### Images # Data augmentation techniques such as rotation, color jittering, scale or cropping are usually applied in deep learning pipelines. The main pipeline: we take as input an image, apply a transformation to it, and then use it for training. # # Keras includes a preprocessing module, with all [these transformations](https://keras.io/preprocessing/image/) implemented. The preprocessing module can be imported by doing # # # # + id="n4glvOHvh8qy" colab_type="code" colab={} from keras.preprocessing.image import ImageDataGenerator # + [markdown] id="2vftBzsolQcL" colab_type="text" # Then we need to fit it to the input data, and use `flow` to apply the transformations to the input data. # + id="yTLpcR43IHpX" colab_type="code" colab={} def plot_data_augmentation(augmentation_gen = ImageDataGenerator()): (x_train, y_train), (x_test, y_test) = mnist.load_data() augmentation_gen.fit(np.expand_dims(x_train, -1)) for X_batch, y_batch in augmentation_gen.flow(np.expand_dims(x_train, -1), y_train, batch_size=5, shuffle=False): for i in range(0, 5): plt.subplot(150 + 1 + i) plt.imshow(X_batch[i, :].reshape(28, 28), cmap=plt.get_cmap('gray')) # show the plot plt.show() break # + [markdown] id="OoJYjjapiISI" colab_type="text" # We will now visualize some of the transformations available to use. # # First, we plot some images without any transformations applied for comparison. # + id="MyoZHyOTheyU" colab_type="code" outputId="3051ede1-f6c1-4983-e084-ab6e45e7962c" colab={"base_uri": "https://localhost:8080/", "height": 127} plot_data_augmentation() # + [markdown] id="eFXEC5EejxWG" colab_type="text" # ### Rotation # A standard transformation is to rotate the image. We can do so by initializing ImageDataGenerator with `rotation_range=rot_val`. # + id="hjgfZ1z3j46X" colab_type="code" outputId="09bce4aa-a8ef-4425-f316-d719ea36c98d" colab={"base_uri": "https://localhost:8080/", "height": 127} # We first define the transformation we want to apply augmentation_gen = ImageDataGenerator(rotation_range=90) plot_data_augmentation(augmentation_gen) # + [markdown] id="Schj43v4ioTY" colab_type="text" # ### Shift # # We can define a maximum range of both horizontal (`width_shift_range`) and vertical (`height_shift_range`) shift. # + id="dbzq69FfjLd9" colab_type="code" outputId="e3620ed9-3b38-492b-96af-4170cef8200f" colab={"base_uri": "https://localhost:8080/", "height": 127} augmentation_gen = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3) plot_data_augmentation(augmentation_gen) # + [markdown] id="7uhYKLIPI1LA" colab_type="text" # ### Zooming # Zooming into the image can be done with `zoom_range` # + id="NGvZzyL0HQjF" colab_type="code" outputId="a52c43a5-9caa-4d20-fcbe-b5417dc2fa5f" colab={"base_uri": "https://localhost:8080/", "height": 127} augmentation_gen = ImageDataGenerator(zoom_range=0.4) plot_data_augmentation(augmentation_gen) # + [markdown] id="845GvrICI9IX" colab_type="text" # ### Flip # # We can define either horizontal flip (`horizontal_flip`) or vertical (`vertical_flip`). # + id="m6emZPbsHXOV" colab_type="code" outputId="0de687f1-d480-495d-e9bb-c370a98cf775" colab={"base_uri": "https://localhost:8080/", "height": 127} augmentation_gen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True) plot_data_augmentation(augmentation_gen) # + [markdown] id="GOE33AOLJdPK" colab_type="text" # ### Combining transformations # We can combine all the transformations and train a model. The ImageDataGenerator is a generator, so we need to use the method `fit_generator`, which is explained [in the documentation](https://keras.io/models/sequential/). # + id="Xqb15Ga0EsC9" colab_type="code" outputId="fcacf86e-ef60-4f25-9d94-39844376d44e" colab={"base_uri": "https://localhost:8080/", "height": 374} augmentation_gen = ImageDataGenerator( rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') (x_train, y_train), (x_test, y_test) = mnist.load_data() augmentation_gen.fit(np.expand_dims(x_train, -1)) train_gen = augmentation_gen.flow(np.expand_dims(x_train, -1), keras.utils.to_categorical(y_train)) model = Sequential() model.add(keras.layers.Flatten(input_shape=(28, 28, 1))) model.add(Dense(512, activation='relu')) model.add(Dense(512, activation='relu')) model.add(Dense(10, activation='softmax')) model.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['categorical_accuracy']) model.fit_generator(train_gen, samples_per_epoch=len(x_train), epochs=10) # + [markdown] id="njqA6ve8WHB1" colab_type="text" # ## Performance metrics # # [Available metrics in Keras](https://keras.io/metrics/) # # In order to evaluate the performance of the model, we use evaluation metrics. As we have seen in previous examples, some of the standard evaluation metrics are cross entropy and mean squared error for classification and regression problems, respectively. These metrics are passed at the run time, so when `model.fit` is called, it automatically keeps track of the training (and validation if given) performance per epoch. # # ``` # model.compile(metrics=['categorical_accuracy'...]) # ``` # # + [markdown] id="YokEU3S0Esby" colab_type="text" # If we call `model.evaluate` in any test data, we can also obtain the values of the metrics in the test data. # + [markdown] id="BPk96eQomRwQ" colab_type="text" # ### Custom metrics # # There are cases where we want to use a non-standard metric for evaluating our models. We can define any metrics in a function and pass it to the `compile` method. As in the example given in the [documentation](https://keras.io/metrics/), let's say that we want to keep track of the mean predicted value in the Boston housing dataset, we can do so and plot it by doing this: # + id="AjlLjPXSPcRJ" colab_type="code" outputId="71ead5db-6b7d-4a45-d05e-d15a7afd8737" colab={"base_uri": "https://localhost:8080/", "height": 376} import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) from keras.datasets import boston_housing (x_train, y_train), (x_test, y_test) = boston_housing.load_data() model = Sequential() model.add(Dense(100, activation='relu', input_shape=(13,))) model.add(Dense(1)) model.compile(optimizer='adam',loss='mean_absolute_error', metrics=[mean_pred]) history = model.fit(x_train, y_train, epochs=20, batch_size=32, validation_split=0.2, verbose = 0) plot_history(history, 'mean_pred') # + [markdown] id="9Q8t9ZSK2nmg" colab_type="text" # ### Tensorboard # Tensorboard is quite useful to monitor the different metrics in real time. Both Tensorflow and Pytorch users (using the TensorboardX module) use it. Tensorboard can be used in Keras by using the Tensorboard callback available ([documentation here](https://keras.io/callbacks/)). # # However, to make it working in a Colab environment, we need to follow a different process, which is explained [here](https://medium.com/@tommytao_54597/use-tensorboard-in-google-colab-16b4bb9812a6). When you run the following piece of code, a Tensorboard link will be displayed. If you click on it, and you will be redirected to the Tensorboard site. # + id="IMK1K_sd24ee" colab_type="code" outputId="744d88a3-167d-4c76-d3d9-baffd7b73bff" colab={"base_uri": "https://localhost:8080/", "height": 255} # !pip install tensorboardcolab # !wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip # !unzip ngrok-stable-linux-amd64.zip # + id="uwlns-E_6PSw" colab_type="code" outputId="bff4f554-23ea-4c01-9f00-48a46cf3f694" colab={"base_uri": "https://localhost:8080/", "height": 68} import tensorboardcolab as tbc K.clear_session() tboard = tbc.TensorBoardColab() from tensorboardcolab import TensorBoardColabCallback # + [markdown] id="PYSa2XXG4Cb8" colab_type="text" # Now, we can use a callback function to show the training progress in the given link. # + id="zFh9bJyJ4f7W" colab_type="code" outputId="f1180212-2d63-480d-b8ff-bb17b7df038a" colab={"base_uri": "https://localhost:8080/", "height": 391} model, x_train, y_train, x_test, y_test = get_data_model() model.compile(optimizer='adam',loss='categorical_crossentropy') model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.2,callbacks=[TensorBoardColabCallback(tboard)]) # + [markdown] id="N0x9hnGhUW9u" colab_type="text" # In the Tensorboard web site you should see two sections: Scalars and Graph. In Scalars there is the plot with the training and validation accuracy per epoch, and in Graph you should have the graph of your model. You can also plot images, histograms, distributions and other things in Tensorboard, which makes it quite useful to keep track of the training progress. # + [markdown] id="nUpwc1fHxF4N" colab_type="text" # Exercises # # 1) Please, import CIFAR-10 dataset and repeat the training by selecting the right metric among those we have seen so far for classification and regression problem. # # 2) Once you have done with exercise 1, move to HyperParameters Tuning and try different loss functions, regularizers, and establish the right set of parameters for your model on CIFAR-10 reporting results on a table. # # 3) Add some data augmentation trying the different strategies and observe reporting results in a table if it leads to any improvement. #	42,671
/hw4/YounghoonSVMRecognizer.ipynb	72848954a163224fae15012beb918d530c9dbec1	[]	no_license	yhoonkim/CSE599H	https://github.com/yhoonkim/CSE599H	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	1,313,539	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # <a id="plotly_bubble_charts"></a> # ## [Bubble Charts](https://plot.ly/python/bubble-charts/) # __Bubble charts are good to use:__ # - When you want to display three diminsions of data. # - When your third varaibles represents the magnitude of the xy variables. # <a id="plotly_bubble_charts_basic"></a> # ### Basic Bubble Chart # + import plotly.graph_objects as go import pandas as pd from plotly.offline import init_notebook_mode, plot from IPython.core.display import display, HTML init_notebook_mode(connected=True) fig = go.Figure(data=[go.Scatter( x=[1, 2, 3, 4], y=[10, 11, 12, 13], mode='markers', marker_size=[40, 60, 80, 100]) ]) plot(fig, filename = 'figure8-6-1.html') display(HTML('figure8-6-1.html')) # - # <a id="plotly_bubble_charts_basic_customizing"></a> # ### Customizing Bubble Chart # + import plotly.graph_objects as go fig = go.Figure(data=[go.Scatter( x=[1, 3.2, 5.4, 7.6, 9.8, 12.5], y=[1, 3.2, 5.4, 7.6, 9.8, 12.5], mode='markers', marker=dict( color=[120, 125, 130, 135, 140, 145], size=[15, 30, 55, 70, 90, 110], #you can also define marker size in the market dictionary showscale=True #This adds a color scale to the chart ) )]) plot(fig, filename = 'figure8-6-2.html') display(HTML('figure8-6-2.html')) def __init__(self, sensorType, currentTimeMs, sensorTimestampMs, x, y, z): self.sensorType = sensorType # On my mac, I could cast as straight-up int but on Windows, this failed # This is because on Windows, a long is 32 bit but on Unix, a long is 64bit # So, forcing to int64 to be safe. See: https://stackoverflow.com/q/38314118 self.currentTimeMs = currentTimeMs.astype(np.int64) # sensorTimestampMs comes from the Arduino function # https://www.arduino.cc/reference/en/language/functions/time/millis/ # which returns the number of milliseconds passed since the Arduino board began running the current program. self.sensorTimestampMs = sensorTimestampMs.astype(np.int64) self.x = x.astype(float) self.y = y.astype(float) self.z = z.astype(float) # Calculate the magnitude of the signal self.mag = np.sqrt(self.x2 + self.y2 + self.z*2) self.sampleLengthInSecs = (self.currentTimeMs[-1] - self.currentTimeMs[0]) / 1000.0 self.samplesPerSecond = len(self.currentTimeMs) / self.sampleLengthInSecs def pad_with_mean(self, headPadLength, tailPadLength): self.signalLengthBeforePadding = len(self.x) self.x_padded = np.pad(self.x, (headPadLength, tailPadLength), 'mean') self.y_padded = np.pad(self.y, (headPadLength, tailPadLength), 'mean') self.z_padded = np.pad(self.z, (headPadLength, tailPadLength), 'mean') self.mag_padded = np.pad(self.mag, (headPadLength, tailPadLength), 'mean') # Returns a dict of numpy arrays for each axis of the accel + magnitude def get_data(self): return {"x":self.x, "y":self.y, "z":self.z, "mag":self.mag} # Returns a dict of numpy arrays for each axis of the accel + magnitude def get_processed_data(self): return {"x_p":self.x_p, "y_p":self.y_p, "z_p":self.z_p, "mag_p":self.mag_p} # A trial is one gesture recording and includes an accel SensorData object # In the future, this could be expanded to include other recorded sensors (e.g., a gyro) # that may be recorded simultaneously class Trial: # We actually parse the sensor log files in the constructor--this is probably bad practice # But offers a relatively clean solution def __init__(self, gestureName, endTimeMs, trialNum, accelLogFilenameWithPath): self.gestureName = gestureName self.trialNum = trialNum self.endTimeMs = endTimeMs self.accelLogFilenameWithPath = accelLogFilenameWithPath self.accelLogFilename = os.path.basename(accelLogFilenameWithPath) # unpack=True puts each column in its own array, see https://stackoverflow.com/a/20245874 # I had to force all types to strings because auto-type inferencing failed parsedAccelLogData = np.genfromtxt(accelLogFilenameWithPath, delimiter=',', dtype=str, encoding=None, skip_header=1, unpack=True) # The asterisk is really cool in Python. It allows us to "unpack" this variable # into arguments needed for the SensorData constructor. Google for "tuple unpacking" self.accel = SensorData("Accelerometer", parsedAccelLogData) # Utility function that returns the end time as a nice string def getEndTimeMsAsString(self): return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.endTimeMs / 1000)) def __str__(self): return "'{}' : Trial {} from {}".format(self.gestureName, self.trialNum, self.accelLogFilename) # Container for a single set of gestures and trials class GestureSet: def __init__(self, gesture_log_path, map_gestures_to_trials): self.path = gesture_log_path self.map_gestures_to_trials = map_gestures_to_trials # returns the longest trial (based on num rows recorded and not clock time) def get_longest_trial(self): longest_trial_length = -1 longest_trial = None for gesture_name, trial_list in self.map_gestures_to_trials.items(): for trial in trial_list: if longest_trial_length < len(trial.accel.x): longest_trial_length = len(trial.accel.x) longest_trial = trial return longest_trial # returns the base path def get_base_path(self): return os.path.basename(os.path.normpath(self.path)) # returns the number of gestures def get_num_gestures(self): return len(self.map_gestures_to_trials) # returns trials for a gesture name def get_trials_for_gesture(self, gesture_name): return self.map_gestures_to_trials[gesture_name] # creates an aggregate signal based on all trials for this gesture # TODO: in future could add in an argument, which takes a list of trial nums # to use to produce aggregate signal def create_aggregate_signal(self, gesture_name, signal_var_name): trials = self.get_trials_for_gesture(gesture_name) aggregate_signal = None trial_signals = [] trial_signals_original = [] first_trial = None first_trial_signal = None max_length = -1 for trial in trials: trial_signal = getattr(trial.accel, signal_var_name) if max_length < len(trial_signal): max_length = len(trial_signal) for i in range(len(trials)): if i == 0: first_trial = trials[i] trial_signal = getattr(first_trial.accel, signal_var_name) trial_signal_mod = np.copy(trial_signal) trial_signals.append(trial_signal_mod) trial_signals_original.append(trial_signal) array_length_diff = max_length - len(trial_signal_mod) trial_signal_mod = np.pad(trial_signal_mod, (0, array_length_diff), 'mean') aggregate_signal = trial_signal_mod first_trial_signal = trial_signal_mod else: cur_trial = trials[i] cur_trial_signal = getattr(trial.accel, signal_var_name) trial_signals_original.append(cur_trial_signal) array_length_diff = max_length - len(cur_trial_signal) cur_trial_signal_mod = np.pad(cur_trial_signal, (0, array_length_diff), 'mean') cur_trial_signal_mod = get_aligned_signal_cutoff_and_pad(cur_trial_signal_mod, first_trial_signal) trial_signals.append(cur_trial_signal_mod) aggregate_signal += cur_trial_signal_mod mean_signal = aggregate_signal / len(trial_signals) return mean_signal # Returns the minimum number of trials across all gestures (just in case we accidentally recorded a # different number. We should have the same number of trials across all gestures) def get_min_num_of_trials(self): minNumTrials = -1 for gestureName, trialSet in self.map_gestures_to_trials.items(): if minNumTrials == -1 or minNumTrials > len(trialSet): minNumTrials = len(trialSet) return minNumTrials # returns the total number of trials def get_total_num_of_trials(self): numTrials = 0 for gestureName, trialSet in self.map_gestures_to_trials.items(): numTrials = numTrials + len(trialSet) return numTrials # get random gesture name def get_random_gesture_name(self): gesture_names = list(self.map_gestures_to_trials.keys()) rand_gesture_name = gesture_names[random.randint(0, len(gesture_names) - 1)] return rand_gesture_name # get random trial def get_random_trial(self): rand_gesture_name = self.get_random_gesture_name() print("rand_gesture_name", rand_gesture_name) trials_for_gesture = self.map_gestures_to_trials[rand_gesture_name] return trials_for_gesture[random.randint(0, len(trials_for_gesture) - 1)] # returns a sorted list of gesture names def get_gesture_names_sorted(self): return sorted(self.map_gestures_to_trials.keys()) # prettify the str() def __str__(self): return "'{}' : {} gestures and {} total trials".format(self.path, self.get_num_gestures(), self.get_total_num_of_trials()) # + # This cell contains our file handling and parsing related functions from os import listdir import ntpath import os # Returns all csv filenames in the given directory # Currently excludes any filenames with 'fulldatastream' in the title def find_csv_filenames( path_to_dir, suffix=".csv" ): filenames = listdir(path_to_dir) return [ filename for filename in filenames if filename.endswith( suffix ) and "fulldatastream" not in filename ] # Parses and creates Trial objects for all csv files in the given dir # Returns a dict() mapping (str: gestureName) to (list: Trial objects) def parse_and_create_gesture_trials( path_to_dir ): csvFilenames = find_csv_filenames(path_to_dir) print("Found {} csv files in {}".format(len(csvFilenames), path_to_dir)) mapGestureNameToTrialList = dict() mapGestureNameToMapEndTimeMsToMapSensorToFile = dict() for csvFilename in csvFilenames: # parse filename into meaningful parts # print(csvFilename) filenameNoExt = os.path.splitext(csvFilename)[0]; filenameParts = filenameNoExt.split("_") gestureName = None timeMs = None numRows = None sensorName = "Accelerometer" # currently only one sensor but could expand to more # Added this conditional on May 15, 2019 because Windows machines created differently formatted # filenames from Macs. Windows machines automatically replaced the character "'" # with "_", which affects filenames like "Midair Zorro 'Z'_1556730840228_206.csv" # which come out like "Midair Zorro _Z__1557937136974_211.csv" instead if '__' in filenameNoExt: filename_parts1 = filenameNoExt.split("__") gestureName = filename_parts1[0] gestureName = gestureName.replace('_',"'") gestureName += "'" filename_parts2 = filename_parts1[1].split("_") timeMs = filename_parts2[0] numRows = filename_parts2[1] else: filenameParts = filenameNoExt.split("_") gestureName = filenameParts[0] timeMs = filenameParts[1] numRows = int(filenameParts[2]) # print("gestureName={} timeMs={} numRows={}".format(gestureName, timeMs, numRows)) if gestureName not in mapGestureNameToMapEndTimeMsToMapSensorToFile: mapGestureNameToMapEndTimeMsToMapSensorToFile[gestureName] = dict() if timeMs not in mapGestureNameToMapEndTimeMsToMapSensorToFile[gestureName]: mapGestureNameToMapEndTimeMsToMapSensorToFile[gestureName][timeMs] = dict() mapGestureNameToMapEndTimeMsToMapSensorToFile[gestureName][timeMs][sensorName] = csvFilename # print (mapGestureNameToMapEndTimeMsToMapSensorToFile) print("Found {} gestures".format(len(mapGestureNameToMapEndTimeMsToMapSensorToFile))) # track the longest array so we can resize accordingly (by padding with zeros currently) maxArrayLength = -1 trialWithMostSensorEvents = None # Now we need to loop through the data and sort each gesture set by timems values # (so that we have trial 1, 2, 3, etc. in order) for gestureName, mapEndTimeMsToMapSensorToFile in mapGestureNameToMapEndTimeMsToMapSensorToFile.items(): gestureTrialNum = 0 mapGestureNameToTrialList[gestureName] = list() for endTimeMs in sorted(mapEndTimeMsToMapSensorToFile.keys()): mapSensorToFile = mapEndTimeMsToMapSensorToFile[endTimeMs] accelFilenameWithPath = os.path.join(path_to_dir, mapSensorToFile["Accelerometer"]) gestureTrial = Trial(gestureName, endTimeMs, gestureTrialNum, accelFilenameWithPath) mapGestureNameToTrialList[gestureName].append(gestureTrial) if maxArrayLength < len(gestureTrial.accel.x): maxArrayLength = len(gestureTrial.accel.x) trialWithMostSensorEvents = gestureTrial gestureTrialNum = gestureTrialNum + 1 print("Found {} trials for '{}'".format(len(mapGestureNameToTrialList[gestureName]), gestureName)) # Perform some preprocessing listSamplesPerSec = list() listTotalSampleTime = list() print("Max trial length across all gesture is '{}' Trial {} with {} sensor events. Padding all arrays to match". format(trialWithMostSensorEvents.gestureName, trialWithMostSensorEvents.trialNum, maxArrayLength)) for gestureName, trialList in mapGestureNameToTrialList.items(): for trial in trialList: listSamplesPerSec.append(trial.accel.samplesPerSecond) listTotalSampleTime.append(trial.accel.sampleLengthInSecs) # preprocess signal before classification and store in new arrays trial.accel.x_p = preprocess(trial.accel.x, maxArrayLength) trial.accel.y_p = preprocess(trial.accel.y, maxArrayLength) trial.accel.z_p = preprocess(trial.accel.z, maxArrayLength) trial.accel.mag_p = preprocess(trial.accel.mag, maxArrayLength) print("Avg samples/sec across {} sensor files: {:0.1f}".format(len(listSamplesPerSec), sum(listSamplesPerSec)/len(listSamplesPerSec))) print("Avg sample length across {} sensor files: {:0.1f}s".format(len(listTotalSampleTime), sum(listTotalSampleTime)/len(listTotalSampleTime))) print() return mapGestureNameToTrialList # Performs some basic preprocesing on rawSignal and returns the preprocessed signal in a new array def preprocess(rawSignal, maxArrayLength): meanFilterWindowSize = 10 arrayLengthDiff = maxArrayLength - len(rawSignal) # CSE599 TODO: add in your own preprocessing here # Just smoothing the signal for now with a mean filter smoothed = np.convolve(rawSignal, np.ones((meanFilterWindowSize,))/meanFilterWindowSize, mode='valid') return smoothed # Returns the leafs in a path # From: https://stackoverflow.com/a/8384788 def path_leaf(path): head, tail = ntpath.split(path) return tail or ntpath.basename(head) # From: https://stackoverflow.com/questions/800197/how-to-get-all-of-the-immediate-subdirectories-in-python def get_immediate_subdirectories(a_dir): return [name for name in os.listdir(a_dir) if os.path.isdir(os.path.join(a_dir, name))] # Utility function to extract gesture name from filename def extract_gesture_name( filename ): # leaf = path_leaf(filename) tokenSplitPos = filename.index('_') gestureName = filename[:tokenSplitPos] return gestureName # Returns the minimum number of trials across all gestures (just in case we accidentally recorded a # different number. We should have 5 or 10 each for the A2 assignment) def get_min_num_of_trials( mapGestureToTrials ): minNumTrials = -1 for gestureName, trialSet in mapGestureToTrials.items(): if minNumTrials == -1 or minNumTrials > len(trialSet): minNumTrials = len(trialSet) return minNumTrials # returns the total number of trials def get_total_num_of_trials (mapGestureToTrials): numTrials = 0 for gestureName, trialSet in mapGestureToTrials.items(): numTrials = numTrials + len(trialSet) return numTrials # Helper function to align signals. # Returns a shifted signal of a based on cross correlation and a roll function def get_aligned_signal(a, b): corr = signal.correlate(a, b, mode='full') index_shift = len(a) - np.argmax(corr) a_shifted = np.roll(a, index_shift - 1) return a_shifted # Returns a shifted signal of a based on cross correlation and padding def get_aligned_signal_cutoff_and_pad(a, b): corr = signal.correlate(a, b, mode='full') index_shift = len(a) - np.argmax(corr) index_shift_abs = abs(index_shift - 1) a_shifted_cutoff = None if (index_shift - 1) < 0: a_shifted_cutoff = a[index_shift_abs:] a_shifted_cutoff = np.pad(a_shifted_cutoff, (0, index_shift_abs), 'mean') else: a_shifted_cutoff = np.pad(a, (index_shift_abs,), 'mean') a_shifted_cutoff = a_shifted_cutoff[:len(a)] return a_shifted_cutoff # calculate zero crossings # See: https://stackoverflow.com/questions/3843017/efficiently-detect-sign-changes-in-python # TODO: in future, could have a min_width detection threshold that ignores # any changes < min_width samples after an initial zero crossing was detected # TODO: could also have a mininum height after the zero crossing (withing some window) # to eliminate noise def calc_zero_crossings(s): # I could not get the speedier solutions to work reliably so here's a # custom non-Pythony solution cur_pt = s[0] zero_crossings = [] for ind in range(1, len(s)): next_pt = s[ind] if ((next_pt < 0 and cur_pt > 0) or (next_pt > 0 and cur_pt < 0)): zero_crossings.append(ind) elif cur_pt == 0 and next_pt > 0: # check for previous points less than 0 # as soon as tmp_pt is not zero, we are done tmp_pt = cur_pt walk_back_idx = ind while(tmp_pt == 0 and walk_back_idx > 0): walk_back_idx -= 1 tmp_pt = s[walk_back_idx] if tmp_pt < 0: zero_crossings.append(ind) elif cur_pt == 0 and next_pt < 0: # check for previous points greater than 0 # as soon as tmp_pt is not zero, we are done tmp_pt = cur_pt walk_back_idx = ind while(tmp_pt == 0 and walk_back_idx > 0): walk_back_idx -= 1 tmp_pt = s[walk_back_idx] if tmp_pt > 0: zero_crossings.append(ind) cur_pt = s[ind] return zero_crossings # - # ## Load the Data # + # Load the data root_gesture_log_path = './GestureLogs' # this dir should have a set of gesture sub-directories print(get_immediate_subdirectories(root_gesture_log_path)) gesture_log_paths = get_immediate_subdirectories(root_gesture_log_path) map_gesture_sets = dict() selected_gesture_set = None for gesture_log_path in gesture_log_paths: path_to_gesture_log = os.path.join(root_gesture_log_path, gesture_log_path) print("Reading in:", path_to_gesture_log) map_gestures_to_trials = parse_and_create_gesture_trials(path_to_gesture_log) gesture_set = GestureSet(gesture_log_path, map_gestures_to_trials) map_gesture_sets[gesture_set.get_base_path()] = gesture_set if setName in gesture_log_path: selected_gesture_set = gesture_set if selected_gesture_set is not None: print("The selected gesture set:", selected_gesture_set) def get_gesture_set_with_str(str): for base_path, gesture_set in map_gesture_sets.items(): if str in base_path: return gesture_set return None # - # ## Signal Exploration # + def topN_freqs(s, topN): sampling_rate = 77 n = len(s) fft = np.fft.fft(s) freqs = np.fft.fftfreq(n) freqs = freqs * sampling_rate # convert normalized freq bins to our freq bins freqs = freqs[range(n//2)] # one side freq range fft = np.abs(fft)[range(n//2)] # one side freq range # Exclude the freq = 0 freqs = freqs[1:len(freqs)] fft = fft[1:len(fft)] # find the max frequency # you could modify this to find the top ~3-5 max frequencies topN_freqs = [freqs[idx] for idx in np.argsort(fft)[::-1][:topN]] topN_ffts = [fft[idx] for idx in np.argsort(fft)[::-1][:topN]] return {"freqs": topN_freqs, "ffts":topN_ffts} def topN_peaks(s, topN): peaks, _ = signal.find_peaks(s, ) prominences = signal.peak_prominences(s, peaks)[0] widths = signal.peak_widths(s, peaks, rel_height=1)[0] topN_prominences = [prominences[idx] for idx in np.argsort(prominences)[::-1][:topN]] topN_widths = [widths[idx] for idx in np.argsort(prominences)[::-1][:topN]] if len(topN_widths) < topN: topN_widths += list(np.zeros(topN - len(topN_widths))) topN_prominences += list(np.zeros(topN - len(topN_prominences))) return {"prominences": topN_prominences, "widths": topN_widths} # + # first, let's graph some features along one dimension # Brainstorm features # - Length of signal # - Max accel magnitude # - Fundamental frequency # - Top frequency # - Intensity of top frequency # - Top 5 frequency intensities (just plot which bins) # - Average of values in the signal # - Std deviation # - Count of points above some threshold # - Counting the number of peaks (above a threshold) # - Zero crossings # - Distance between zero crossings # - Area under the curve # - Max frequency of signal # - Diff between max and mins # - ... other things... read some papers, brainstorm, visualize! import itertools mapMarkerToDesc = { ".":"point", ",":"pixel", "o":"circle", "v":"triangle_down", "^":"triangle_up", "<":"triangle_left", ">":"triangle_right", "1":"tri_down", "2":"tri_up", "3":"tri_left", "4":"tri_right", "8":"octagon", "s":"square", "p":"pentagon", "":"star", "h":"hexagon1", "H":"hexagon2", "+":"plus", "D":"diamond", "d":"thin_diamond", "\|":"vline", "_":"hline" } # # Plots the length of each trial's acceleration signal # markers = list(mapMarkerToDesc.keys()) # marker = itertools.cycle(markers) # plt.figure(figsize=(12, 4)) # for gesture_name in selected_gesture_set.get_gesture_names_sorted(): # trials = selected_gesture_set.map_gestures_to_trials[gesture_name] # x = list(len(trial.accel.mag) for trial in trials) # y = np.random.rand(len(x)) # s = [200] len(x) # # s is the marker size # plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) # plt.ylim((0,5)) # plt.legend(loc='upper left', bbox_to_anchor=(1,1)) # plt.title("1D Plot of Accel Mag") # plt.show() # # Plots the length of each trial's acceleration signal # markers = list(mapMarkerToDesc.keys()) # marker = itertools.cycle(markers) # plt.figure(figsize=(12, 4)) # for gesture_name in selected_gesture_set.get_gesture_names_sorted(): # trials = selected_gesture_set.map_gestures_to_trials[gesture_name] # x = list(max(trial.accel.mag) for trial in trials) # y = np.random.rand(len(x)) # s = [200] * len(x) # # s is the marker size # plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) # plt.ylim((0,5)) # plt.legend(loc='upper left', bbox_to_anchor=(1,1)) # plt.title("1D Plot of Accel Mag") # plt.show() # # Plots the maximum magnitude of each trial's processed acceleration signal # markers = list(mapMarkerToDesc.keys()) # marker = itertools.cycle(markers) # plt.figure(figsize=(12, 4)) # for gesture_name in selected_gesture_set.get_gesture_names_sorted(): # trials = selected_gesture_set.map_gestures_to_trials[gesture_name] # x = list(trial.accel.mag_p.max() for trial in trials) # y = np.random.rand(len(x)) # s = [200] * len(x) # plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) # plt.ylim((0,5)) # plt.legend(loc='upper left', bbox_to_anchor=(1,1)) # plt.title("1D Plot of Max Accel Mag") # plt.show() # # std deviation # markers = list(mapMarkerToDesc.keys()) # marker = itertools.cycle(markers) # plt.figure(figsize=(12, 4)) # for gesture_name in selected_gesture_set.get_gesture_names_sorted(): # trials = selected_gesture_set.map_gestures_to_trials[gesture_name] # x = list(np.std(trial.accel.mag_p) for trial in trials) # y = np.random.rand(len(x)) # s = [200] * len(x) # plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) # plt.ylim((0,5)) # plt.legend(loc='upper left', bbox_to_anchor=(1,1)) # plt.title("1D Plot of Accel Mag Stdev") # plt.show() # CSE599 TODO: Come up with ~5 more features to plot below # Max Freq markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 1)['freqs'][0] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of Max Freq") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 1)['ffts'][0] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of Amp of Max Freq") plt.show() # Top2 Freq markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['freqs'][1] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of 2nd Freq") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['ffts'][1] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of Amp of 2nd Freq") plt.show() # Top3 Freq markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['freqs'][2] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of 3rd Freq") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['ffts'][2] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of Amp of 3rd Freq") plt.show() # Max Peak markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['prominences'][0] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of prominence of the 1st Peak") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['widths'][0] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of width of the 1st Peak") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['prominences'][1] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of prominence of the 2nd Peak") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['widths'][1] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of width of the 2nd Peak") plt.show() # Max Peak markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['prominences'][2] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of prominence of the 3rd Peak") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['widths'][2] for trial in trials) y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.ylim((0,5)) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("1D Plot of width of the 3rd Peak") plt.show() # + # Now, let's explore the discriminability of 2 dimensions. We should begin to see # some clusters and visual groupings based on gesture type--that's good! # And rememember that the SVM is going to be in far more than 2 dimensions... but # it's harder to visualize anything > 2, so this just gives us some intuition about separation markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(trial.accel.mag_p.max() for trial in trials) y = list(len(trial.accel.mag) for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("2D Plot of Max Accel Mag vs. Signal Length") plt.show() # num peaks vs. std dev markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 4)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = [] for trial in trials: s = trial.accel.mag_p min_distance_between_peaks = 77 / 3.0 peak_indices, peak_properties = sp.signal.find_peaks(s, height=30, distance=min_distance_between_peaks) x.append(len(peak_indices)) y = list(np.std(trial.accel.mag_p) for trial in trials) #print(gesture_name, x, y) # y = np.random.rand(len(x)) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("2D Plot of Num Peaks vs. Std Dev") plt.show() # strongest freq intensity vs. std dev markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(np.abs(np.fft.fft(trial.accel.mag_p)).max() for trial in trials) y = list(np.std(trial.accel.mag_p) for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of Strongest Frequency Intensity vs Standard Deviation") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['prominences'][0] for trial in trials) y = list(topN_peaks(trial.accel.mag_p, 3)['widths'][0] for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of Max Peack Prominence vs Max Peak Width") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['prominences'][1] for trial in trials) y = list(topN_peaks(trial.accel.mag_p, 3)['widths'][1] for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of the 2nd Peack Prominence vs the Width of the Peak") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_peaks(trial.accel.mag_p, 3)['prominences'][2] for trial in trials) y = list(topN_peaks(trial.accel.mag_p, 3)['widths'][2] for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of the 3rd Peack Prominence vs the Width of the Peak") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['freqs'][0] for trial in trials) y = list(topN_freqs(trial.accel.mag_p, 3)['ffts'][0] for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of Max Freq. vs Amp of the Max Freq.") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['freqs'][1] for trial in trials) y = list(topN_freqs(trial.accel.mag_p, 3)['ffts'][1] for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of the 2nd Strongest Freq. vs Amp of the Freq.") plt.show() markers = list(mapMarkerToDesc.keys()) marker = itertools.cycle(markers) plt.figure(figsize=(12, 6)) for gesture_name in selected_gesture_set.get_gesture_names_sorted(): trials = selected_gesture_set.map_gestures_to_trials[gesture_name] x = list(topN_freqs(trial.accel.mag_p, 3)['freqs'][2] for trial in trials) y = list(topN_freqs(trial.accel.mag_p, 3)['ffts'][2] for trial in trials) s = [200] * len(x) plt.scatter(x, y, alpha=0.75, marker=next(marker), s=s, label=gesture_name) plt.legend(loc='upper left', bbox_to_anchor=(1,1)) plt.title("Scatter Plot of the 3rd Strongest Freq. vs Amp. of the Freq.") plt.show() # CSE599 In-Class Exercises TODO: Come up with ~5 more feature relationships to plot below # Consult gesture recognition research papers for ideas. For example, # Wu, J., et al. Gesture recognition with a 3-d accelerometer. UbiComp 2009 # - # ## SVM Experiment Infrastructure # + # This cell contains some helper methods like my kfold generation function # and confusion matrix plotting from sklearn import svm # needed for svm from sklearn.metrics import confusion_matrix import itertools # Returns a list of folds where each list item is a dict() with key=gesture name and value=selected trial # for that fold. To generate the same fold structure, pass in the same seed value (this is useful for # setting up experiments) def generate_kfolds(numFolds, map_gestures_to_trials, seed=None): # Quick check to make sure that there are numFolds of gesture trials for each gesture for gestureName, trials in map_gestures_to_trials.items(): if numFolds != len(trials): raise ValueError("For the purposes of this assignment, the number of folds={} must equal the number of trials for each gesture. Gesture '{}' has {} trials" .format(numFolds, gestureName, len(trials))) numGestures = len(map_gestures_to_trials) tmpMapGestureToTrials = dict() for gestureName, trials in map_gestures_to_trials.items(): tmpMapGestureToTrials[gestureName] = list(trials) gestureNames = list(map_gestures_to_trials.keys()) # create folds foldToMapGestureToTrial = list() random.seed(seed) for i in range(0, numFolds): curFoldMapGestureToTrial = dict() foldToMapGestureToTrial.append(curFoldMapGestureToTrial) for j in range(0, numGestures): curGestureName = gestureNames[j] trialList = tmpMapGestureToTrials[curGestureName] randTrialIndex = 0 if (len(trialList) > 0): randTrialIndex = random.randint(0, len(trialList) - 1) randTrial = trialList[randTrialIndex] curFoldMapGestureToTrial[curGestureName] = randTrial del trialList[randTrialIndex] return foldToMapGestureToTrial def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. """ if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=90) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') # - # ## Features # Returns a feature vectof for the given trial def extract_features_example(trial): # Play around with features to extract and use in your model # Brainstorm features, visualize ideas, try them, and iterate # This is likely where you will spend most of your time :) # This is the "feature engineering" component of working in ML features = [] features.append(trial.accel.mag.max()) features.append(np.std(trial.accel.mag)) # Younghoon's feature topN_peaks_X_info = topN_peaks(trial.accel.x_p, 3) topN_peaks_Y_info = topN_peaks(trial.accel.x_p, 3) topN_peaks_Z_info = topN_peaks(trial.accel.x_p, 3) topN_peaks_mag_info = topN_peaks(trial.accel.x_p, 3) features.append(topN_peaks_X_info['prominences'][0]) features.append(topN_peaks_X_info['prominences'][1]) features.append(topN_peaks_X_info['prominences'][2]) features.append(topN_peaks_X_info['widths'][0]) features.append(topN_peaks_X_info['widths'][1]) features.append(topN_peaks_X_info['widths'][2]) features.append(topN_peaks_Y_info['prominences'][0]) features.append(topN_peaks_Y_info['prominences'][1]) features.append(topN_peaks_Y_info['prominences'][2]) features.append(topN_peaks_Y_info['widths'][0]) features.append(topN_peaks_Y_info['widths'][1]) features.append(topN_peaks_Y_info['widths'][2]) features.append(topN_peaks_Z_info['prominences'][0]) features.append(topN_peaks_Z_info['prominences'][1]) features.append(topN_peaks_Z_info['prominences'][2]) features.append(topN_peaks_Z_info['widths'][0]) features.append(topN_peaks_Z_info['widths'][1]) features.append(topN_peaks_Z_info['widths'][2]) topN_freqs_info = topN_freqs(trial.accel.mag_p, 3) features.append(topN_freqs_info['freqs'][0]) features.append(topN_freqs_info['freqs'][1]) features.append(topN_freqs_info['freqs'][2]) features.append(topN_freqs_info['ffts'][0]) features.append(topN_freqs_info['ffts'][1]) features.append(topN_freqs_info['ffts'][2]) return features # ## Shape Matching # + # Calculate the maxLength maxLength = 0 selected_gesture_set = get_gesture_set_with_str(setName) gestureNamesSorted = sorted(selected_gesture_set.get_gesture_names_sorted()) for gestureName in gestureNamesSorted: gestureTrials = selected_gesture_set.map_gestures_to_trials[gestureName] for trial in gestureTrials: maxLength = max(maxLength, len(trial.accel.x)) # Take a signal (SensorData) and compare to the others and classify the gesture of the signal. def shape_matchin_clf(signal, otherFolds): minD = np.inf guess = '??' for fold in otherFolds: for gestureName in gestureNamesSorted: signalB = fold[gestureName].accel d = dist(signal.accel, signalB) if d < minD: guess = gestureName minD = d return guess # More efficient implementation is possible by reducing the length of the padded signals to 2 * maxLength. def dist(sigA, sigB): sigA_N = len(sigA.currentTimeMs) sigB_N = len(sigB.currentTimeMs) sigB.pad_with_mean(maxLength, maxLength2 - sigB_N) minD = np.inf argMinOffset = 0 for offset in range(0, maxLength + sigB_N): sigA.pad_with_mean(offset, 3maxLength - offset - sigA_N) dX = np.linalg.norm(sigA.x_padded - sigB.x_padded) dY = np.linalg.norm(sigA.y_padded - sigB.y_padded) dZ = np.linalg.norm(sigA.z_padded - sigB.z_padded) d = dX + dY + dZ if d < minD : argMinOffset = offset minD = d return minD # + numFolds = 5 numGestures = selected_gesture_set.get_num_gestures() numTrialsTotal = selected_gesture_set.get_total_num_of_trials() foldToMapGestureToTrial = generate_kfolds(numFolds, selected_gesture_set.map_gestures_to_trials, seed=5) mapGestureToCorrectMatches = dict() y_true = [] y_pred = [] gestureNamesSorted = selected_gesture_set.get_gesture_names_sorted() for gestureName in gestureNamesSorted: mapGestureToCorrectMatches[gestureName] = 0 for i in range(0, len(foldToMapGestureToTrial)): trainingFolds = foldToMapGestureToTrial.copy() testFold = trainingFolds.pop(i) trainingData = [] classLabels = np.array([]) # make predictions for this test set for testGestureName, testTrial in testFold.items(): y_true.append(testGestureName) prediction = shape_matchin_clf(testTrial, trainingFolds) y_pred.append(prediction) if testGestureName == prediction: mapGestureToCorrectMatches[testGestureName] += 1 totalCorrectMatches = 0 print("Shape-matching Results:\n") for gesture in mapGestureToCorrectMatches: c = mapGestureToCorrectMatches[gesture] print("{}: {}/{} ({}%)".format(gesture, c, numFolds, c / numFolds * 100)) totalCorrectMatches += mapGestureToCorrectMatches[gesture] print("\nTotal Shape-matching classifier accuracy {:0.2f}%\n".format(totalCorrectMatches / numTrialsTotal * 100)) cm = confusion_matrix(y_true, y_pred, gestureNamesSorted) plt.figure(figsize=(10,10)) plot_confusion_matrix(cm, classes=gestureNamesSorted, title='Confusion Matrix') plt.show() # - # ## The SVM # + # This is the simplest possible SVM using only a few features but gives you a sense of the overall approach # Some nice resources: # - A very simple classification example using scikit: # https://dbaumgartel.wordpress.com/2014/03/10/a-scikit-learn-example-in-10-lines/ # - A nice video overview of SVM: https://youtu.be/N1vOgolbjSc # - Official sci-kit learn: http://scikit-learn.org/stable/modules/svm.html from sklearn import svm from sklearn.preprocessing import StandardScaler import itertools numFolds = 5 selected_gesture_set = get_gesture_set_with_str(setName) numGestures = selected_gesture_set.get_num_gestures() numTrialsTotal = selected_gesture_set.get_total_num_of_trials() # Setting a seed here keeps producing the same folds set each time. Take that out # if you want to randomly produce a fold set on every execution foldToMapGestureToTrial = generate_kfolds(numFolds, selected_gesture_set.map_gestures_to_trials, seed=5) mapGestureToCorrectMatches = dict() y_true = [] y_pred = [] gestureNamesSorted = selected_gesture_set.get_gesture_names_sorted() for gestureName in gestureNamesSorted: mapGestureToCorrectMatches[gestureName] = 0 for i in range(0, len(foldToMapGestureToTrial)): trainingFolds = foldToMapGestureToTrial.copy() testFold = trainingFolds.pop(i) trainingData = [] classLabels = np.array([]) # build training data for this set of folds for trainingFold in trainingFolds: for trainingGestureName, trainingTrial in trainingFold.items(): features = extract_features_example(trainingTrial) trainingData.append(features) classLabels = np.append(classLabels, trainingGestureName) # Here, we train SVM, the 'rbf' kernal is default # if you use rbf, need to set gamma and C parameters # play around with different kernels, read about them, and try them. What happens? # see: # - https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py # - https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html clf = svm.SVC(kernel='rbf', gamma='scale') # kernel='linear' # clf = svm.SVC(kernel='linear') # kernel='linear' # Scale np_train = np.array(trainingData) scaler = StandardScaler() scaler.fit(np_train) np_train_scaled = scaler.transform(np_train) # clf.fit(np_train, classLabels) clf.fit(np_train_scaled, classLabels) # make predictions for this test set for testGestureName, testTrial in testFold.items(): features = np.array(extract_features_example(testTrial)) features_scaled = scaler.transform([features]) svmPrediction = clf.predict(features_scaled) # svmPrediction = clf.predict([features]) y_true.append(testGestureName) y_pred.append(svmPrediction) if testGestureName == svmPrediction[0]: mapGestureToCorrectMatches[testGestureName] += 1 totalCorrectMatches = 0 print("SVM Results:\n") for gesture in mapGestureToCorrectMatches: c = mapGestureToCorrectMatches[gesture] print("{}: {}/{} ({}%)".format(gesture, c, numFolds, c / numFolds * 100)) totalCorrectMatches += mapGestureToCorrectMatches[gesture] print("\nTotal SVM classifier accuracy {:0.2f}%\n".format(totalCorrectMatches / numTrialsTotal * 100)) cm = confusion_matrix(y_true, y_pred, gestureNamesSorted) plt.figure(figsize=(10,10)) plot_confusion_matrix(cm, classes=gestureNamesSorted, title='Confusion Matrix') plt.show() # - # ## The Decision Tree # + from sklearn import tree numFolds = 5 selected_gesture_set = get_gesture_set_with_str(setName) numGestures = selected_gesture_set.get_num_gestures() numTrialsTotal = selected_gesture_set.get_total_num_of_trials() foldToMapGestureToTrial = generate_kfolds(numFolds, selected_gesture_set.map_gestures_to_trials, seed=5) mapGestureToCorrectMatches = dict() y_true = [] y_pred = [] gestureNamesSorted = selected_gesture_set.get_gesture_names_sorted() for gestureName in gestureNamesSorted: mapGestureToCorrectMatches[gestureName] = 0 for i in range(0, len(foldToMapGestureToTrial)): trainingFolds = foldToMapGestureToTrial.copy() testFold = trainingFolds.pop(i) trainingData = [] classLabels = np.array([]) # build training data for this set of folds for trainingFold in trainingFolds: for trainingGestureName, trainingTrial in trainingFold.items(): features = extract_features_example(trainingTrial) trainingData.append(features) classLabels = np.append(classLabels, trainingGestureName) # Here, we train SVM, the 'rbf' kernal is default # if you use rbf, need to set gamma and C parameters # play around with different kernels, read about them, and try them. What happens? # see: # - https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py # - https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html # clf = svm.SVC(kernel='linear') # kernel='rbf' clf = tree.DecisionTreeClassifier() clf.fit(np.array(trainingData), classLabels) # make predictions for this test set for testGestureName, testTrial in testFold.items(): features = extract_features_example(testTrial) svmPrediction = clf.predict([features]) y_true.append(testGestureName) y_pred.append(svmPrediction) if testGestureName == svmPrediction[0]: mapGestureToCorrectMatches[testGestureName] += 1 totalCorrectMatches = 0 print("Decision Tree Results:\n") for gesture in mapGestureToCorrectMatches: c = mapGestureToCorrectMatches[gesture] print("{}: {}/{} ({}%)".format(gesture, c, numFolds, c / numFolds * 100)) totalCorrectMatches += mapGestureToCorrectMatches[gesture] print("\nTotal Decision Tree classifier accuracy {:0.2f}%\n".format(totalCorrectMatches / numTrialsTotal * 100)) cm = confusion_matrix(y_true, y_pred, gestureNamesSorted) plt.figure(figsize=(10,10)) plot_confusion_matrix(cm, classes=gestureNamesSorted, title='Confusion Matrix') plt.show() # - # ## The MLPClassifier # + from sklearn.neural_network import MLPClassifier numFolds = 5 selected_gesture_set = get_gesture_set_with_str(setName) numGestures = selected_gesture_set.get_num_gestures() numTrialsTotal = selected_gesture_set.get_total_num_of_trials() # Setting a seed here keeps producing the same folds set each time. Take that out # if you want to randomly produce a fold set on every execution foldToMapGestureToTrial = generate_kfolds(numFolds, selected_gesture_set.map_gestures_to_trials, seed=5) mapGestureToCorrectMatches = dict() y_true = [] y_pred = [] gestureNamesSorted = selected_gesture_set.get_gesture_names_sorted() for gestureName in gestureNamesSorted: mapGestureToCorrectMatches[gestureName] = 0 for i in range(0, len(foldToMapGestureToTrial)): trainingFolds = foldToMapGestureToTrial.copy() testFold = trainingFolds.pop(i) trainingData = [] classLabels = np.array([]) # build training data for this set of folds for trainingFold in trainingFolds: for trainingGestureName, trainingTrial in trainingFold.items(): features = extract_features_example(trainingTrial) trainingData.append(features) classLabels = np.append(classLabels, trainingGestureName) np_train = np.array(trainingData) scaler = StandardScaler() scaler.fit(np_train) np_train_scaled = scaler.transform(np_train) clf = MLPClassifier(solver='lbfgs', hidden_layer_sizes=[1000]) clf.fit(np_train_scaled, classLabels) # make predictions for this test set for testGestureName, testTrial in testFold.items(): features = np.array(extract_features_example(testTrial)) features_scaled = scaler.transform([features]) svmPrediction = clf.predict(features_scaled) # svmPrediction = clf.predict([features]) y_true.append(testGestureName) y_pred.append(svmPrediction) if testGestureName == svmPrediction[0]: mapGestureToCorrectMatches[testGestureName] += 1 totalCorrectMatches = 0 print("MLPClassifier Results:\n") for gesture in mapGestureToCorrectMatches: c = mapGestureToCorrectMatches[gesture] print("{}: {}/{} ({}%)".format(gesture, c, numFolds, c / numFolds * 100)) totalCorrectMatches += mapGestureToCorrectMatches[gesture] print("\nTotal MLPClassifier classifier accuracy {:0.2f}%\n".format(totalCorrectMatches / numTrialsTotal * 100)) cm = confusion_matrix(y_true, y_pred, gestureNamesSorted) plt.figure(figsize=(10,10)) plot_confusion_matrix(cm, classes=gestureNamesSorted, title='Confusion Matrix') plt.show() # -	56,978
/InterpretableClassification/ECG200_FS.ipynb	2092d35508967b9931fce2e328d697d447922bf0	[]	no_license	zhaoli064/ML_Projects	https://github.com/zhaoli064/ML_Projects	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	49,773	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- #installing seleniumlibrary # ! pip install selenium #importing required libraries import selenium import pandas as pd from selenium import webdriver #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") # + active="" # # + active="" # Q1: Write a python program to scrape data for “Data Analyst” Job position in # “Bangalore” location. You have to scrape the job-title, job-location, company_name, # experience_required. You have to scrape first 10 jobs data. # + active="" # # - driver.get(' https://www.naukri.com/') #finding element for job search bar search_job=driver.find_element_by_id('qsb-keyword-sugg') search_job.send_keys('Data Analyst') search_loc=driver.find_element_by_xpath("//input[@id='qsb-location-sugg']") search_loc.send_keys("Banglore") search_btn=driver.find_element_by_xpath("//div[@class='search-btn']/button") search_btn.click() #specifying the url of webpage to be scraped url="https://www.naukri.com/data-analyst-data-analyst-jobs-in-banglore?k=data%20analyst%2C%20data%20analyst&l=banglore" #let's open the webpage through our webdriver driver.get(url) # + active="" # 1.job-title 2.job-location 3.company_name 4.experience_required # - #creating the empty lists to which the final scraped data will be stored job_titles=[] company_names=[] locations_list=[] experience_list=[] #let's extract all the tags having the job titles title_tags=driver.find_elements_by_xpath("//a[@class='title fw500 ellipsis']") title_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in title_tags: title=i.text job_titles.append(title) job_titles[0:9] #let's extract all the tags having the company names companies_tags=driver.find_elements_by_xpath("//a[@class='subTitle ellipsis fleft']") companies_tags[0:9] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in companies_tags: company_name=i.text company_names.append(company_name) company_names[0:9] #Now we will extract all the html tags having the experience required experience_tags=driver.find_elements_by_xpath("//li[@class='fleft grey-text br2 placeHolderLi experience']/span[1]") experience_tags[0:9] #let's extract the text inside the tags using a for loop to iterate over all the tags for i in experience_tags: experience=i.text experience_list.append(experience) experience_list[0:9] #Let's extract all the html tags having the experience required locations_tags=driver.find_elements_by_xpath("//li[@class='fleft grey-text br2 placeHolderLi location']/span[1]") locations_tags[0:9] #now extract the text inside the tags using a for loop to iterate over all the tags for i in locations_tags: locations=i.text locations_list.append(locations) locations_list[0:9] # + active="" # So, now we have extracted the data required from the webpage and stored them in the 4 lists mentioned above. Now before craeting a dataframe from these lists, let's first check the length of each of the list. Because if the length of all of the lists are not equal, then a dataframe cannot be formed. # - print(len(job_titles),len(company_names),len(experience_list),len(locations_list)) jobs=pd.DataFrame({}) jobs['job_title']=job_titles jobs['company_name']=company_names jobs['experience_required']=experience_list jobs['job_location']=locations_list jobs # + active="" # # + active="" # Q2: Write a python program to scrape data for “Data Scientist” Job position in # “Bangalore” location. You have to scrape the job-title, job-location, # company_name, full job-description. You have to scrape first 10 jobs data. # + active="" # # - #importing required libraries import selenium import pandas as pd from selenium import webdriver #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get(' https://www.naukri.com/') #finding element for job search bar search_job=driver.find_element_by_id('qsb-keyword-sugg') search_job.send_keys('Data Scientist') search_loc=driver.find_element_by_xpath("//input[@id='qsb-location-sugg']") search_loc.send_keys("Banglore") search_btn=driver.find_element_by_xpath("//div[@class='search-btn']/button") search_btn.click() #specifying the url of webpage to be scraped url="https://www.naukri.com/data-scientist-jobs-in-banglore?k=data%20scientist&l=banglore" #let's open the webpage through our webdriver driver.get(url) # + active="" # 1.job-title 2.job-location 3.company_name 4.job_description # - #creating the empty lists to which the final scraped data will be stored job_titles=[] company_names=[] locations_list=[] job_description_list=[] #let's extract all the tags having the job titles title_tags=driver.find_elements_by_xpath("//a[@class='title fw500 ellipsis']") title_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in title_tags: title=i.text job_titles.append(title) job_titles[0:10] #let's extract all the tags having the company names companies_tags=driver.find_elements_by_xpath("//a[@class='subTitle ellipsis fleft']") companies_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in companies_tags: company_name=i.text company_names.append(company_name) company_names[0:10] #Let's extract all the html tags having the experience required locations_tags=driver.find_elements_by_xpath("//li[@class='fleft grey-text br2 placeHolderLi location']/span[1]") locations_tags[0:10] #now extract the text inside the tags using a for loop to iterate over all the tags for i in locations_tags: locations=i.text locations_list.append(locations) locations_list[0:10] urls=driver.find_elements_by_xpath("//a[@class='title fw500 ellipsis']") urls[0:10] #now extract the text inside the tags using a for loop to iterate over all the tags for i in urls[0:10]: print(i.get_attribute('href')) driver.get(i.get_attribute('href')) from selenium.common.exceptions import StaleElementReferenceException from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.common.by import By driver=webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get("https://www.naukri.com/") try: element=driver.find_element_by_xpath("//a[@class='title fw500 ellipsis']") print(element) except StaleElementReferenceException as e: print("Exception Raised: ",e) print("Refreshing the page!") driver.get("https://www.naukri.com/") WebDriverWait(driver,delay).until(EC.presence_of_element_located((By.CLASS_NAME,'title fw500 ellipsis'))) element=driver.find_element_by_xpath("//a[@class='title fw500 ellipsis']") print(element) #Let's extract all the html tags having the job description job_description_tags=driver.find_elements_by_xpath("//a[starts-with(@href='https')]") job_description_tags[0:9] #Let's extract all the html tags having the job description job_description_tags=driver.find_elements_by_xpath("//a[text()='https://www.naukri.com/job-listings']") job_description_tags[0:9] #now extract the text inside the tags using a for loop to iterate over all the tags for i in job_description_tags[0:9]: print(i.get_attribute('href')) driver.get(i.get_attribute('href')) #Let's extract all the html tags having the job description job_description_tags=driver.find_elements_by_xpath("//div[@class='leftSec']/section[2]/div[1]/p[2]") job_description_tags[0:9] # + active="" # # + active="" # Q3: In this question you have to scrape data using the filters available on the # webpage as shown below: # + active="" # # - #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get('https://www.naukri.com/') #finding element for job search bar search_job=driver.find_element_by_id('qsb-keyword-sugg') search_job.send_keys('Data Scientist') search_btn=driver.find_element_by_xpath("//div[@class='search-btn']/button") search_btn.click() location_checkbox=driver.find_element_by_xpath("//div[@data-filter-id='cities']/div[2]") location_checkbox.click() salary_checkbox=driver.find_element_by_xpath("//div[@data-filter-id='salaryRange']/div[2]") salary_checkbox.click() #creating the empty lists to which the final scraped data will be stored job_titles=[] company_names=[] locations_list=[] experience_list=[] #let's extract all the tags having the job titles title_tags=driver.find_elements_by_xpath("//a[@class='title fw500 ellipsis']") title_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in title_tags: title=i.text job_titles.append(title) job_titles[0:10] #let's extract all the tags having the company names companies_tags=driver.find_elements_by_xpath("//a[@class='subTitle ellipsis fleft']") companies_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in companies_tags: company_name=i.text company_names.append(company_name) company_names[0:10] #Now we will extract all the html tags having the experience required experience_tags=driver.find_elements_by_xpath("//li[@class='fleft grey-text br2 placeHolderLi experience']/span[1]") experience_tags[0:10] experience_list=[] #let's extract the text inside the tags using a for loop to iterate over all the tags for i in experience_tags: experience=i.text experience_list.append(experience) experience_list[0:10] #Let's extract all the html tags having the experience required locations_tags=driver.find_elements_by_xpath("//li[@class='fleft grey-text br2 placeHolderLi location']/span[1]") locations_tags[0:10] #now extract the text inside the tags using a for loop to iterate over all the tags for i in locations_tags: locations=i.text locations_list.append(locations) locations_list[0:10] print(len(job_titles),len(company_names),len(experience_list),len(locations_list)) jobs=pd.DataFrame({}) jobs['job_title']=job_titles[0:10] jobs['company_name']=company_names[0:10] jobs['experience_required']=experience_list[0:10] jobs['job_location']=locations_list[0:10] jobs # + active="" # # + active="" # Q4: Write a python program to scrape data for first 10 job results for Data scientist # Designation in Noida location. You have to scrape company_name, No. of days # ago when job was posted, Rating of the company. # + active="" # # - #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get('https://www.glassdoor.co.in/index.htm') #finding element for job search bar search_job=driver.find_element_by_id('scKeyword') search_job.send_keys('Data Scientist') search_loc=driver.find_element_by_xpath("//input[@id='scLocation']") search_loc.send_keys("Noida") search_btn=driver.find_element_by_xpath("//button[@class='pl-0 pr-xsm SearchStyles__searchKeywordSubmit']") search_btn.click() #creating the empty lists to which the final scraped data will be stored job_titles=[] company_names=[] daysago_jobposted=[] company_ratings=[] #let's extract all the tags having the job titles title_tags=driver.find_elements_by_xpath("//a[@class='jobLink css-1rd3saf eigr9kq2']") title_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in title_tags: title=i.text job_titles.append(title) job_titles[0:10] #let's extract all the tags having the company names companies_tags=driver.find_elements_by_xpath("//div[@class='d-flex justify-content-between align-items-start']") companies_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in companies_tags: company_name=i.text company_names.append(company_name) company_names[0:10] #let's extract all the tags having the no of days ago when job was posted days_jobposted_tags=driver.find_elements_by_xpath("//div[@class='d-flex align-items-end pl-std css-mi55ob']") days_jobposted_tags[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in days_jobposted_tags: days_jobposted=i.text daysago_jobposted.append(days_jobposted) daysago_jobposted[0:10] #let's extract all the tags having the rating of the company company_rating=driver.find_elements_by_xpath("//span[@class='css-19pjha7 e1cjmv6j1']") company_rating[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in company_rating: ratings=i.text company_ratings.append(ratings) company_ratings[0:10] print(len(job_titles),len(company_names),len(daysago_jobposted),len(company_ratings)) jobs=pd.DataFrame({}) jobs['job_title']=job_titles[0:10] jobs['company_name']=company_names[0:10] jobs['jobposted_days']=daysago_jobposted[0:10] jobs['company_rating']=company_ratings[0:10] jobs # + active="" # # + active="" # Q5: Write a python program to scrape the salary data for Data Scientist designation # in Noida location. # + active="" # # - #importing required libraries import selenium import pandas as pd from selenium import webdriver #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get('https://www.glassdoor.co.in/Salaries/index.htm') #finding element for job search bar search_job=driver.find_element_by_id('KeywordSearch') search_job.send_keys('Data Scientist') search_btn=driver.find_element_by_xpath("//button[@class='gd-btn-mkt']") search_btn.click() search_loc=driver.find_element_by_xpath("//input[@id='scLocation']") search_loc.send_keys("Noida") search_btn=driver.find_element_by_xpath("//button[@class='pl-0 pr-xsm SearchStyles__searchKeywordSubmit']") search_btn.click() #creating the empty lists to which the final scraped data will be stored company_names=[] range_salary=[] avg_salary=[] company_ratings=[] #let's extract all the tags having the average salary comp_name=driver.find_elements_by_xpath("//div[@class='d-flex']/div[2]/p[2]") comp_name[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in comp_name: names=i.text company_names.append(names) company_names[0:10] #let's extract all the tags having the average salary average_salary=driver.find_elements_by_xpath("//p[@class='d-block d-md-none m-0']") average_salary[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in average_salary: avegage_sal=i.text avg_salary.append(avegage_sal) avg_salary[0:10] #let's extract all the tags having the minimum salary salary_range=driver.find_elements_by_xpath("//p[@class='d-block d-md-none m-0 css-1kuy7z7']") salary_range[0:10] for i in salary_range: sal_range=i.text range_salary.append(sal_range) range_salary[0:10] print(len(company_names),len(average_salary),len(salary_range),len(company_ratings)) companyinfo=pd.DataFrame({}) companyinfo['comp_name']=company_names[0:10] companyinfo['comp_avg_sal']=average_salary[0:10] companyinfo['sal_range']=salary_range[0:10] companyinfo['company_rating']=company_ratings[0:10] # + active="" # # + active="" # Q6 : Scrape data of first 100 sunglasses listings on flipkart.com. You have to # scrape four attributes: # + active="" # # - #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get('https://www.flipkart.com/') #finding element for job search bar search_job=driver.find_element_by_xpath("//input[@type='text']") search_job.send_keys('sunglasses') search_btn=driver.find_element_by_xpath("//button[@class='L0Z3Pu']") search_btn.click() #creating the empty lists to which the final scraped data from page 1 will be stored brand_name=[] product_description=[] product_price=[] discount_perc=[] #let's extract all the tags having the brand name brand=driver.find_elements_by_xpath("//div[@class='_2WkVRV']") brand[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in brand: brands=i.text brand_name.append(brands) brand_name[0:40] #let's extract all the tags having the product description description=driver.find_elements_by_xpath("//a[@class='IRpwTa']") description[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in description: descriptions=i.text product_description.append(descriptions) product_description[0:40] #let's extract all the tags having the product price price=driver.find_elements_by_xpath("//div[@class='_30jeq3']") price[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in price: prices=i.text product_price.append(prices) product_price[0:40] #let's extract all the tags having the discount percentage discount=driver.find_elements_by_xpath("//div[@class='_3Ay6Sb']") discount[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in discount: discounts=i.text discount_perc.append(discounts) discount_perc[0:40] #scraping the elements of the next page(page2) driver.find_element_by_xpath("//a[@class='_1LKTO3']").click() #creating the empty lists to which the final scraped data from page 2 will be stored brand_name2=[] product_description2=[] product_price2=[] discount_perc2=[] #let's extract all the tags having the brand name brand2=driver.find_elements_by_xpath("//div[@class='_2WkVRV']") brand2[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in brand2: brands=i.text brand_name2.append(brands) brand_name2[0:40] #let's extract all the tags having the product description description2=driver.find_elements_by_xpath("//a[@class='IRpwTa']") description2[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in description2: descriptions=i.text product_description2.append(descriptions) product_description2[0:40] #let's extract all the tags having the product price price2=driver.find_elements_by_xpath("//div[@class='_30jeq3']") price2[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in price2: prices=i.text product_price2.append(prices) product_price2[0:40] #let's extract all the tags having the discount percentage discount2=driver.find_elements_by_xpath("//div[@class='_3Ay6Sb']") discount2[0:40] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in discount2: discounts=i.text discount_perc2.append(discounts) discount_perc2[0:40] #scraping the elements of the next page(page3) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 3 will be stored brand_name3=[] product_description3=[] product_price3=[] discount_perc3=[] #let's extract all the tags having the brand name brand3=driver.find_elements_by_xpath("//div[@class='_2WkVRV']") brand3[0:20] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in brand3: brands=i.text brand_name3.append(brands) brand_name3[0:20] #let's extract all the tags having the product description description3=driver.find_elements_by_xpath("//a[@class='IRpwTa']") description3[0:20] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in description3: descriptions=i.text product_description3.append(descriptions) product_description3[0:20] #let's extract all the tags having the product price price3=driver.find_elements_by_xpath("//div[@class='_30jeq3']") price3[0:20] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in price3: prices=i.text product_price3.append(prices) product_price3[0:20] #let's extract all the tags having the discount percentage discount3=driver.find_elements_by_xpath("//div[@class='_3Ay6Sb']") discount3[0:20] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in discount3: discounts=i.text discount_perc3.append(discounts) discount_perc3[0:40] # + #Adding lists having same attribute type from all the 3 pages # - brand_name_final=brand_name+brand_name2+brand_name3 product_description_final=product_description+product_description2+product_description3 product_price_final=product_price+product_price2+product_price3 discount_perc_final=discount_perc+discount_perc2+discount_perc3 print(len(brand_name_final),len(product_description_final),len(product_price_final),len(discount_perc_final)) sunglassesinfoflipkart=pd.DataFrame({}) sunglassesinfoflipkart['brand_name']=brand_name_final[0:100] sunglassesinfoflipkart['product_description']=product_description_final[0:100] sunglassesinfoflipkart['product_price']=product_price_final[0:100] sunglassesinfoflipkart['discount_perc']=discount_perc_final[0:100] sunglassesinfoflipkart # + active="" # # + active="" # Q7: Scrape 100 reviews data from flipkart.com for iphone11 phone. You have to # go the link: https://www.flipkart.com/apple-iphone-11-black-64-gb-includesearpods-poweradapter/p/itm0f37c2240b217?pid=MOBFKCTSVZAXUHGR&lid=LSTMOBFKC # TSVZAXUHGREPBFGI&marketplace. # + active="" # # - #let's connect to the webdriver driver = webdriver.Chrome("C:/Users/key/AppData/Roaming/Microsoft/Windows/Start Menu/Programs/chromedriver_win32/chromedriver.exe") driver.get('https://www.flipkart.com/apple-iphone-11-black-64-gb-includesearpods-poweradapter/p/itm0f37c2240b217?pid=MOBFKCTSVZAXUHGR&lid=LSTMOBFKCTSVZAXUHGREPBFGI&marketplace') #creating the empty lists to which the final scraped data from page 1 will be stored iphone11_rating=[] iphone11_reviewsum=[] iphone11_reviewfull=[] #scraping the elements of the first page(page1) driver.find_element_by_xpath("//div[@class='_3UAT2v _16PBlm']").click() #let's extract all the tags having the rating rating=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating: ratings=i.text iphone11_rating.append(ratings) iphone11_rating[0:10] #let's extract all the tags having the review summary reviwsum=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum: reviwsums=i.text iphone11_reviewsum.append(reviwsums) iphone11_reviewsum[0:10] #let's extract all the tags having the review full reviwfull=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull: reviwsfull=i.text iphone11_reviewfull.append(reviwsfull) iphone11_reviewfull[0:10] for page in range(0,10): rating_tags=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") for i in rating_tags: rating=i.text iphone_ratings.append(rating) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() iphone_ratings #scraping the elements of the next page(page2) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[11]").click() #creating the empty lists to which the final scraped data from page 2 will be stored iphone11_rating2=[] iphone11_reviewsum2=[] iphone11_reviewfull2=[] #let's extract all the tags having the rating rating2=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating2[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating2: ratings=i.text iphone11_rating2.append(ratings) iphone11_rating2[0:10] #let's extract all the tags having the review summary reviwsum2=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum2[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum2: reviwsums=i.text iphone11_reviewsum2.append(reviwsums) iphone11_reviewsum2[0:10] #let's extract all the tags having the review full reviwfull2=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull2[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull2: reviwsfull=i.text iphone11_reviewfull2.append(reviwsfull) iphone11_reviewfull2[0:10] #scraping the elements of the next page(page3) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 3 will be stored iphone11_rating3=[] iphone11_reviewsum3=[] iphone11_reviewfull3=[] #let's extract all the tags having the rating rating3=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating3[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating3: ratings=i.text iphone11_rating3.append(ratings) iphone11_rating3[0:10] #let's extract all the tags having the review summary reviwsum3=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum3[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum3: reviwsums=i.text iphone11_reviewsum3.append(reviwsums) iphone11_reviewsum3[0:10] #let's extract all the tags having the review full reviwfull3=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull3[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull3: reviwsfull=i.text iphone11_reviewfull3.append(reviwsfull) iphone11_reviewfull3[0:10] #scraping the elements of the next page(page4) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 4 will be stored iphone11_rating4=[] iphone11_reviewsum4=[] iphone11_reviewfull4=[] #let's extract all the tags having the rating rating4=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating4[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating4: ratings=i.text iphone11_rating4.append(ratings) iphone11_rating4[0:10] #let's extract all the tags having the review summary reviwsum4=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum4[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum4: reviwsums=i.text iphone11_reviewsum4.append(reviwsums) iphone11_reviewsum4[0:10] #let's extract all the tags having the review full reviwfull4=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull4[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull4: reviwsfull=i.text iphone11_reviewfull4.append(reviwsfull) iphone11_reviewfull4[0:10] #scraping the elements of the next page(page5) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 5 will be stored iphone11_rating5=[] iphone11_reviewsum5=[] iphone11_reviewfull5=[] #let's extract all the tags having the rating rating5=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating5[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating5: ratings=i.text iphone11_rating5.append(ratings) iphone11_rating5[0:10] #let's extract all the tags having the review summary reviwsum5=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum5[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum5: reviwsums=i.text iphone11_reviewsum5.append(reviwsums) iphone11_reviewsum5[0:10] #let's extract all the tags having the review full reviwfull5=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull5[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull5: reviwsfull=i.text iphone11_reviewfull5.append(reviwsfull) iphone11_reviewfull5[0:10] #scraping the elements of the next page(page6) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 6 will be stored iphone11_rating6=[] iphone11_reviewsum6=[] iphone11_reviewfull6=[] #let's extract all the tags having the rating rating6=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating6[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating6: ratings=i.text iphone11_rating6.append(ratings) iphone11_rating6[0:10] #let's extract all the tags having the review summary reviwsum6=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum6[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum6: reviwsums=i.text iphone11_reviewsum6.append(reviwsums) iphone11_reviewsum6[0:10] #let's extract all the tags having the review full reviwfull6=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull6[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull6: reviwsfull=i.text iphone11_reviewfull6.append(reviwsfull) iphone11_reviewfull6[0:10] #scraping the elements of the next page(page7) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 7 will be stored iphone11_rating7=[] iphone11_reviewsum7=[] iphone11_reviewfull7=[] #let's extract all the tags having the rating rating7=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating7[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating7: ratings=i.text iphone11_rating7.append(ratings) iphone11_rating7[0:10] #let's extract all the tags having the review summary reviwsum7=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum7[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum7: reviwsums=i.text iphone11_reviewsum7.append(reviwsums) iphone11_reviewsum7[0:10] #let's extract all the tags having the review full reviwfull7=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull7[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull7: reviwsfull=i.text iphone11_reviewfull7.append(reviwsfull) iphone11_reviewfull7[0:10] #scraping the elements of the next page(page8) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 8 will be stored iphone11_rating8=[] iphone11_reviewsum8=[] iphone11_reviewfull8=[] #let's extract all the tags having the rating rating8=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating8[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating8: ratings=i.text iphone11_rating8.append(ratings) iphone11_rating8[0:10] #let's extract all the tags having the review summary reviwsum8=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum8[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum8: reviwsums=i.text iphone11_reviewsum8.append(reviwsums) iphone11_reviewsum8[0:10] #let's extract all the tags having the review full reviwfull8=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull8[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull8: reviwsfull=i.text iphone11_reviewfull8.append(reviwsfull) iphone11_reviewfull8[0:10] #scraping the elements of the next page(page9) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 9 will be stored iphone11_rating9=[] iphone11_reviewsum9=[] iphone11_reviewfull9=[] #let's extract all the tags having the rating rating9=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating9[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating9: ratings=i.text iphone11_rating9.append(ratings) iphone11_rating9[0:10] #let's extract all the tags having the review summary reviwsum9=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum9[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum9: reviwsums=i.text iphone11_reviewsum9.append(reviwsums) iphone11_reviewsum9[0:10] #let's extract all the tags having the review full reviwfull9=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull9[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull9: reviwsfull=i.text iphone11_reviewfull9.append(reviwsfull) iphone11_reviewfull9[0:10] #scraping the elements of the next page(page10) driver.find_element_by_xpath("//nav[@class='yFHi8N']/a[12]").click() #creating the empty lists to which the final scraped data from page 10 will be stored iphone11_rating10=[] iphone11_reviewsum10=[] iphone11_reviewfull10=[] #let's extract all the tags having the rating rating10=driver.find_elements_by_xpath("//div[@class='_3LWZlK _1BLPMq']") rating10[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in rating10: ratings=i.text iphone11_rating10.append(ratings) iphone11_rating10[0:10] #let's extract all the tags having the review summary reviwsum10=driver.find_elements_by_xpath("//p[@class='_2-N8zT']") reviwsum10[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwsum10: reviwsums=i.text iphone11_reviewsum10.append(reviwsums) iphone11_reviewsum10[0:10] #let's extract all the tags having the review full reviwfull10=driver.find_elements_by_xpath("//div[@class='t-ZTKy']") reviwfull10[0:10] #Now extract the text inside the tags using a for loop to iterate over all the tags for i in reviwfull10: reviwsfull=i.text iphone11_reviewfull10.append(reviwsfull) iphone11_reviewfull10[0:10] # + #Adding lists having same attribute type from all the 10 pages # - iphone11_rating_final=iphone11_rating+iphone11_rating2+iphone11_rating3+iphone11_rating4+iphone11_rating5+iphone11_rating6+iphone11_rating7+iphone11_rating8+iphone11_rating9+iphone11_rating10 iphone11_reviewsum_final=iphone11_reviewsum+iphone11_reviewsum2+iphone11_reviewsum3+iphone11_reviewsum4+iphone11_reviewsum5+iphone11_reviewsum6+iphone11_reviewsum7+iphone11_reviewsum8+iphone11_reviewsum9+iphone11_reviewsum10 iphone11_reviewfull_fianl=iphone11_reviewfull+iphone11_reviewfull2+iphone11_reviewfull3+iphone11_reviewfull4+iphone11_reviewfull5+iphone11_reviewfull6+iphone11_reviewfull7+iphone11_reviewfull8+iphone11_reviewfull9+iphone11_reviewfull10 print(len(iphone11_rating_final),len(iphone11_reviewsum_final),len(iphone11_reviewfull_fianl)) iphone11reviewflipkart=pd.DataFrame({}) iphone11reviewflipkart['iphone11_rating']=iphone11_rating_final[0:79] iphone11reviewflipkart['review_summary']=iphone11_reviewsum_final[0:79] iphone11reviewflipkart['review_full']=iphone11_reviewfull_fianl[0:79] iphone11reviewflipkart # + active="" #	36,340
/Image Captioning/Untitled.ipynb	bdfccd369a1ff54f86e1d51f4c7f0dca7a36f871	[]	no_license	Genises/H-BRS_NeuralNetworks	https://github.com/Genises/H-BRS_NeuralNetworks	0	0	null	null	null	null	Jupyter Notebook	false	false	.py	39,395	# --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.15.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + from os import listdir, path import xml.etree.ElementTree as ET class data_manager(): def __init__(self, image_filepath : str, annotation_filepath : str): self.image_filepath = image_filepath self.annotation_filepath = annotation_filepath def create_data_dict(self): # For each dict data_dict = {} for folder in listdir(self.image_filepath): folder_path = self.image_filepath + '/' + folder for pciture in listdir(folder_path): image_path = folder_path + '/' + pciture # Get annotations image_number = pciture.split(".")[0] if not path.isfile(self.annotation_filepath + "/" + folder + "/" + image_number + ".eng"): continue # Makes special character beeing parsed parser = ET.XMLParser(encoding="ansi") tree = ET.parse(self.annotation_filepath + "/" + folder + "/" + image_number + ".eng", parser=parser) root = tree.getroot() tmp_dict = {} for child in root: tmp_dict[child.tag] = child.text data_dict[image_number] = tmp_dict print("Parsed " + str(len(data_dict)) + " entries!") return data_dict # + from keras.preprocessing.text import Tokenizer from keras.preprocessing.image import load_img from keras.preprocessing.image import img_to_array from keras.applications.resnet50 import preprocess_input # import sys # from PIL import Image # sys.modules['Image'] = Image class data_generator(): def train_tokenizer(self, data_dict : dict): texts = [] for key in data_dict: texts.append(data_dict[key]["DESCRIPTION"]) self.tokenizer = Tokenizer() self.tokenizer.fit_on_texts(texts) def picture_data(self, data_dict : dict, batch_size=32): if not self.tokenizer: self.train_tokenizer(data_dict) samples_per_epoch = len(data_dict) number_of_batches = samples_per_epoch/batch_size counter=0 while 1: for image_number in data_dict.keys(): #print(data_dict[image_number]["IMAGE"]) image = load_img("iaprtc12/" + data_dict[image_number]["IMAGE"], target_size=(224, 224)) # convert the image pixels to a numpy array image = img_to_array(image) # reshape data for the model image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) # prepare the image for the VGG model image = preprocess_input(image) caption = self.tokenizer.texts_to_sequences(data_dict[image_number]["DESCRIPTION"]) counter += 1 yield image, caption #restart counter to yeild data in the next epoch as well if counter >= number_of_batches: counter = 0 # - dm = data_manager("iaprtc12/images", "iaprtc12/annotations_complete_eng") data_dict = dm.create_data_dict() generator = data_generator() generator.train_tokenizer(data_dict) # + #Inspired by https://machinelearningmastery.com/develop-a-deep-learning-caption-generation-model-in-python/ from keras.models import Model from keras.layers import Input from keras.layers import Dense from keras.layers import LSTM from keras.layers import Embedding from keras.layers import Dropout from keras.layers.merge import add # define the captioning model def define_model(vocab_size, max_length): # feature extractor model inputs1 = Input(shape=(4096,)) fe1 = Dropout(0.5)(inputs1) fe2 = Dense(256, activation='relu')(fe1) # sequence model inputs2 = Input(shape=(max_length,)) se1 = Embedding(vocab_size, 256, mask_zero=True)(inputs2) se2 = Dropout(0.5)(se1) se3 = LSTM(256)(se2) # decoder model decoder1 = add([fe2, se3]) decoder2 = Dense(256, activation='relu')(decoder1) outputs = Dense(vocab_size, activation='softmax')(decoder2) # tie it together [image, seq] [word] model = Model(inputs=[inputs1, inputs2], outputs=outputs) # compile model model.compile(loss='categorical_crossentropy', optimizer='adam') # summarize model model.summary() #plot_model(model, to_file='model.png', show_shapes=True) return model # calculate the length of the description with the most words def max_length(data_dict): #lines = to_lines(descriptions) max_length = 0 for value in data_dict.values(): max_length = max(max_length, len(value["DESCRIPTION"])) return max_length vocab_size = len(generator.tokenizer.word_index) + 1 print('Vocabulary Size: %d' % vocab_size) # determine the maximum sequence length max_length = max_length(data_dict) print('Description Length: %d' % max_length) # define the model model = define_model(vocab_size, max_length) # train the model, run epochs manually and save after each epoch epochs = 20 steps = len(data_dict) for i in range(epochs): # create the data generator # fit for one epoch model.fit_generator(generator.picture_data(data_dict), epochs=1, steps_per_epoch=steps, verbose=1) # save model model.save('model_' + str(i) + '.h5') # - # ## ARCHIVE!!! # + from os import listdir from pickle import dump from keras.applications.vgg16 import VGG16 from keras.preprocessing.image import load_img from keras.preprocessing.image import img_to_array from keras.applications.vgg16 import preprocess_input from keras.models import Model # extract features from each photo in the directory def extract_features(directory): # load the model model = VGG16() # re-structure the model model.layers.pop() model = Model(inputs=model.inputs, outputs=model.layers[-1].output) # summarize print(model.summary()) # extract features from each photo features = dict() for supdic in listdir(directory): path = directory + '/' + supdic for name in listdir(path): # load an image from file filename = path + '/' + name image = load_img(filename, target_size=(224, 224)) # convert the image pixels to a numpy array image = img_to_array(image) # reshape data for the model image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2])) # prepare the image for the VGG model image = preprocess_input(image) # get features feature = model.predict(image, verbose=0) # get image id image_id = name.split('.')[0] # store feature features[image_id] = feature print('>%s' % name) return features # extract features from all images directory = 'iaprtc12/images' features = extract_features(directory) print('Extracted Features: %d' % len(features)) # save to file dump(features, open('features.pkl', 'wb')) # -	7,404

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.