import numpy as np import matplotlib.pyplot as plt import argparse bluefile = 'blue_2017_04_20_bin' redfile = 'red_2017_04_20_bin' postfile = 'bi_2017_04_20_bin' bg_blue = 0.31 bg_red = 0.39 data_fromfile = np.genfromtxt(bluefile) blue_single_atom = data_fromfile[:,0] blue_single_bg = data_fromfile[:,2] data_fromfile = np.genfromtxt(redfile) red_single_atom = data_fromfile[:,1] red_single_bg = data_fromfile[:,3] data_fromfile = np.genfromtxt(postfile) blue_atom_1 = data_fromfile[:,0] - bg_blue red_atom_1 = data_fromfile[:,1] - bg_red blue_bg_1 = data_fromfile[:,2] - bg_blue red_bg_1 = data_fromfile[:,3] - bg_red blue_atom_2= data_fromfile[:,4] - bg_blue red_atom_2 = data_fromfile[:,5] - bg_red blue_bg_2 = data_fromfile[:,6] - bg_blue red_bg_2 = data_fromfile[:,7] - bg_red binwidth = 2 binrange= np.arange(0,300,binwidth) hist_blue_single_atom , bins_blue_single_atom = np.histogram(blue_single_atom , binrange) hist_blue_single_bg, bins_blue_single_bg = np.histogram(blue_single_bg, binrange) width_blue = binwidth*0.7 center_blue = (bins_blue_single_atom[:-1] + bins_blue_single_atom[1:]) / 2 hist_red_single_atom , bins_red_single_atom = np.histogram(red_single_atom , binrange) hist_red_single_bg, bins_red_single_bg = np.histogram(red_single_bg, binrange) width_red = binwidth*0.7 center_red = (bins_red_single_atom[:-1] + bins_red_single_atom[1:]) / 2 # save post selected data for histo analysis --------- post_blue = [] post_blue_bg = [] post_red = [] post_red_bg =[] th_atom = blue_atom_2 + red_atom_2 #take data in phase 1 if phase 2 is good for i in range(0,len(blue_atom_1)-1): #if th_atom[i] < threshold[best_th_sum_idx]: if th_atom[i] < 69.09: post_blue.append(blue_atom_1[i]) post_blue_bg.append(blue_bg_1[i]) post_red.append(red_atom_1[i]) post_red_bg.append(red_bg_1[i]) #evaluate tx post_blue = np.array(post_blue) post_blue_bg = np.array(post_blue_bg) post_red = np.array(post_red) post_red_bg = np.array(post_red_bg) hist_blue_bi_atom , bins_blue_single_atom = np.histogram(post_blue , binrange) hist_red_bi_atom , bins_red_single_atom = np.histogram(post_red , binrange) hist_sum_bi_atom , bins_red_single_atom = np.histogram(post_blue+post_red, binrange) hist_sum_bi_bg, bins_red_single_atom = np.histogram(post_blue_bg + post_red_bg , binrange) print("mean blue atom "+'%.3f'% np.mean(blue_single_atom)+", mean blue bg "+'%.3f'% np.mean(blue_single_bg), ", tx "+'%.3f'% (np.mean(blue_single_atom)/np.mean(blue_single_bg)) ) print("mean red atom "+'%.3f'% np.mean(red_single_atom)+", mean red bg "+'%.3f'% np.mean(red_single_bg), ", tx "+'%.3f'% (np.mean(red_single_atom)/np.mean(red_single_bg)) ) print("mean sum atom "+'%.3f'% np.mean(post_blue+post_red )+", mean sum bg "+'%.3f'% np.mean(post_blue_bg + post_red_bg ), ", tx "+'%.3f'% (np.mean(post_blue+post_red)/np.mean(post_blue_bg + post_red_bg )) ) print("bins in bi run "+'%.0f'% len(blue_atom_1 )+", bins after post selection "+'%.0f'% len(post_blue ), ", ratio "+'%.4f'% (len(post_blue)/len(blue_atom_1 )) ) savedata=np.zeros((len(bins_blue_single_atom )-1,9)) savedata[:,0] = bins_blue_single_atom[:-1] savedata[:,1] = hist_blue_bi_atom savedata[:,2] = hist_blue_single_atom savedata[:,3] = hist_blue_single_bg savedata[:,4] = hist_red_bi_atom savedata[:,5] = hist_red_single_atom savedata[:,6] = hist_red_single_bg savedata[:,7] = hist_sum_bi_atom savedata[:,8] = hist_sum_bi_bg np.savetxt('histo.dat', savedata,header='#bin bi_blue sg_blue bg_blue bi_red sg_red bg_red bi_sum bi_sum_bg') #np.savetxt('histo.dat', np.transpose(np.array([bins_blue_single_atom, hist_blue_bi_atom , hist_blue_single_atom, hist_blue_single_bg, hist_red_bi_atom , hist_red_single_atom, hist_red_single_bg, hist_sum_bi_atom , hist_sum_bi_bg ])),header='#bin bi_blue sg_blue bg_blue bi_red sg_red bg_red bi_sum bi_sum_bg') # Three subplots sharing both x/y axes f, (blue_fig, red_fig, sum_fig) = plt.subplots(3, sharex=True, sharey=True) #blue_fig.set_title('Sharing both axes') # Fine-tune figure; make subplots close to each other and hide x ticks for # all but bottom plot. f.subplots_adjust(hspace=0) plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) blue_fig.tick_params( labelsize=16) red_fig.tick_params( labelsize=16) sum_fig.tick_params( labelsize=16) #blue_fig = plt.figure(1) #plt.bar(center_blue, hist_blue_single_atom/sum(hist_blue_single_atom), align='center', width=width_blue,alpha = 0.5) #plt.bar(center_blue, hist_blue_single_bg/sum(hist_blue_single_bg), align='center', width=width_blue, color='grey', alpha = 0.5) #plt.bar(center_blue, hist_blue_bi_atom/sum(hist_blue_bi_atom), align='center', width=width_blue, color='green', alpha = 0.5) blue_fig.hist(blue_single_bg , 100, (0,200), normed=True, histtype='step', color='grey', linewidth=1.5, label='reference') blue_fig.hist(blue_single_atom , 100, (0,200), normed=True, histtype='step', color='blue', linewidth=1.5, label='one side CW') blue_fig.hist(post_blue, 100, (0,200), normed=True, histtype='step', color='green', linewidth=1.5, label='both sides') blue_fig.legend() blue_fig.annotate('detector 1', xy=(50, 0.07), size='14' ) #blue_fig.xlabel('counts') #blue_fig.ylabel('rel. freq. of occurances') #blue_fig.title('BLUE detector') #blue_fig.xlim([0,140]) #blue_fig.ylim([0,0.1]) #blue_fig.savefig('blue_bin_hist'+'.pdf', bbox_inches='tight') #red_fig = plt.figure(2) plt.xlabel('counts', size =18) red_fig.set_ylabel(r'relative frequency of occurances', size =18) red_fig.get_yaxis().set_label_coords(-0.1,0.5) #plt.title('RED detector') #plt.bar(center_red, hist_red_single_atom/sum(hist_red_single_atom), align='center', width=width_red,alpha = 0.5) #plt.bar(center_red, hist_red_single_bg/sum(hist_red_single_bg), align='center', width=width_red, color='grey',alpha = 0.5) #plt.bar(center_blue, hist_red_bi_atom/sum(hist_red_bi_atom), align='center', width=width_blue, color='green', alpha = 0.5) red_fig.hist(red_single_bg , 100, (0,200), normed=True, histtype='step', color='grey',linewidth=1.5, label='reference') red_fig.hist(red_single_atom , 100, (0,200), normed=True, histtype='step', color='blue', linewidth=1.5, label='one side ACW') red_fig.hist(post_red, 100, (0,200), normed=True, histtype='step', color='green', linewidth=1.5,label='both sides') red_fig.legend() red_fig.annotate('detector 2', xy=(50, 0.07), size='14' ) #plt.savefig('red_bin_hist'+'.pdf', bbox_inches='tight') #plt.xlim([0,80]) #sum_fig = plt.figure(3) #plt.title('Blue+Red detector') #plt.bar(center_red, hist_sum_bi_atom/sum(hist_sum_bi_atom) , align='center', width=width_red, color='green',alpha = 0.5) #plt.bar(center_red, hist_sum_bi_bg/sum(hist_sum_bi_bg), align='center', width=width_red, color='grey',alpha = 0.5) sum_fig.hist(post_blue_bg + post_red_bg , 100, (0,200), normed=True, histtype='step', color='grey', linewidth=1.5, label='reference') sum_fig.hist(post_blue+post_red , 100, (0,200), normed=True, histtype='step', color='green', linewidth=1.5, label='both sides') sum_fig.legend() sum_fig.annotate('detector 1+2', xy=(50, 0.07), size='14' ) plt.xlim([0,200]) plt.ylim([0,0.09]) plt.yticks([0,0.05]) plt.savefig('hist'+'.pdf', bbox_inches='tight') #blue_fig.show() #red_fig.show() #sum_fig.show() plt.show()