Merge branch 'master' of https://code.df.uba.ar/nnunez/artiq_experiments

analisis/plots/20221006_transitoriosv2/Transitorios_02.py

@@ -355,14 +355,22 @@ FIGURA PAPER
 
 jselected = [1, 5, 12]
 
+t0=0.18
+
 plt.figure()
-plt.plot([f*1e6 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[0]])
-plt.plot(RefBins[90:], [expo(r, *popt_vec[jselected[0]]) for r in RefBins[90:]])
-plt.plot([f*1e6 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[1]])
-plt.plot(RefBins[90:], [expo(r, *popt_vec[jselected[1]]) for r in RefBins[90:]])
-plt.plot([f*1e6 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[2]])
-plt.plot(RefBins[90:], [expo(r, *popt_vec[jselected[2]]) for r in RefBins[90:]])
-plt.xlim(-0.5,4)
+plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[0]])
+plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[0]]) for r in RefBins[90:]])
+plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[1]])
+plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[1]]) for r in RefBins[90:]])
+plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[2]])
+plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[2]]) for r in RefBins[90:]])
+plt.xlim(-0.5,3)
+plt.xlabel('Time (us)')
+plt.ylabel('Counts')
+
+plt.savefig('fig3_01.pdf')
+plt.savefig('fig3_01.svg')
+
 
 #%%
 """

analisis/plots/20221017_IonStatistics/Simulaciones/plot_decaytimes_vs_intensity_detunings.py

@@ -29,12 +29,12 @@ for cambio in [0]: # esto es para shiftear la potencia medida
                     capsize=3)
     
     
-        #ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \
-        #             yerr = 1e6*errsSIM.stdval.values, \
-        #             fmt='.--', ms=6, lw=.5, \
-        #             color="#3949ab",
-        #             capsize=3,
-        #             label=f'r={rad}; cambio={cambio}')
+        ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \
+                    yerr = 1e6*errsSIM.stdval.values, \
+                    fmt='.--', ms=6, lw=.5, \
+                    color="#3949ab",
+                    capsize=3,
+                    label=f'r={rad}; cambio={cambio}')
 
 ax2.set_xlim([1.2e-4, 0.2e-1])
 ax2.set_ylim([2e-1, 0.8e1])
@@ -47,4 +47,13 @@ ax2.grid(which='minor', alpha=0.2)
 ax2.set_xscale("log")
 ax2.set_yscale("log")
 
+plt.savefig('fig3_02.pdf')
+plt.savefig('fig3_02.svg')
+
+
+
+
+
+
+
 

analisis/plots/20221024_BranchingFractionMeasurement/Transitorios_BF.py

@@ -11,6 +11,8 @@ os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221024_Branchi
 
 Long_files = ['DP_long', 'SP_long']
 Calib_files = ['Fondo_IR_50M', 'Fondo_UV_50M']
+Short_files = [9132, 9134, 9136, 9138, 9140, 9142]
+Fondo_files = [9133, 9135, 9137, 9139, 9141, 9143]
 
 def expo(T, tau, N0, C):
     global T0
@@ -89,6 +91,44 @@ for i, fname in enumerate(Calib_files):
     
     Calib_Heigths.append(heigs)
     Calib_Bins.append(binsf) 
+    
+    
+BINW_short =  5e-9
+T0_short = -0.4e-6
+
+Short_Heigths = []
+Short_Bins = []
+
+for i, fname in enumerate(Short_files):
+    #print(i)
+    #print(fname)
+    data = h5py.File('Data/Cortas/'+'00000'+str(fname)+'-SingleLine.h5', 'r')
+    counts = np.array(data['datasets']['counts'])
+    bines = np.arange(counts.min(), counts.max()+BINW_short, BINW_short)
+
+    heigs, binsf  = np.histogram(counts, bines[bines>T0_short])
+    
+    Short_Heigths.append(heigs)
+    Short_Bins.append(binsf) 
+
+
+BINW_fondo =  5e-9
+T0_fondo = -0.4e-6
+
+Fondo_Heigths = []
+Fondo_Bins = []
+
+for i, fname in enumerate(Fondo_files):
+    #print(i)
+    #print(fname)
+    data = h5py.File('Data/Fondos/00000'+str(fname)+'-SingleLine.h5', 'r')
+    counts = np.array(data['datasets']['counts'])
+    bines = np.arange(counts.min(), counts.max()+BINW_fondo, BINW_fondo)
+
+    heigs, binsf  = np.histogram(counts, bines[bines>T0_fondo])
+    
+    Fondo_Heigths.append(heigs)
+    Fondo_Bins.append(binsf)     
 
 
 
@@ -109,8 +149,8 @@ RefBins = [t*1e6 for t in Long_Bins[0][:-1]]
 
 plt.figure()
 
-#for i in range(len(Long_Heigths)):
-#    plt.plot(Long_Bins[i][:-1], Long_Heigths[i])
+for i in range(len(Long_Heigths)):
+    plt.plot(Long_Bins[i][:-1], Long_Heigths[i])
     
 for i in range(len(Calib_Heigths)):
     plt.plot(Calib_Bins[i][:-1], Calib_Heigths[i])
@@ -131,6 +171,109 @@ branch = TotalSPfinal/(TotalSPfinal+TotalDPfinal)
 
 print(branch)
 
+#%%
+"""
+Acondicionamiento señales
+"""
+
+SP_corrected = [Long_Heigths[1][k]-Calib_Heigths[1][k] for k in range(len(Long_Heigths[1]))]
+DP_corrected = [Long_Heigths[0][k]-Calib_Heigths[0][k] for k in range(len(Long_Heigths[0]))]
+
+plt.plot(Long_Bins[1][:-1], SP_corrected)
+plt.plot(Long_Bins[0][:-1], DP_corrected)
+
+#%%
+"""
+Figura branching
+"""
+import matplotlib
+import seaborn as sns
+
+matplotlib.rcParams['mathtext.fontset'] = 'stix'
+matplotlib.rcParams['font.family'] = 'STIXGeneral'
+plt.style.use('seaborn-bright')
+plt.rcParams.update({
+    "text.usetex": False,
+})
+
+#plt.figure()
+#plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
+
+#plot figuras papers
+
+colors1=sns.color_palette("rocket", 10)
+colors2=sns.color_palette("mako", 10)
+color2 = colors2[1]
+color3 = colors2[4]
+color1 = colors2[8]
+
+bins1 = np.arange(150,300, 1)
+bins2 = np.arange(0,50,1)
+bins3 = np.arange(30,100,1)
+
+
+plt.figure(figsize = (3.8,3))
+
+plt.plot([1e6*b-0.18 for b in Long_Bins[1][:-1]], SP_corrected, color=(0,0,128/255, 1),linewidth=2.5,label='SP transition')
+plt.plot([1e6*b+0.09 for b in Long_Bins[0][:-1]], DP_corrected, color=(212/255,0,0, 1),linewidth=2.5,label='DP transition')
+
+#plt.hist(Stat_Heigths[0], bins=bins1, histtype='step',density = True,color = color1, alpha = 0.6)#,label = 'BG')
+#plt.hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True,color = color2, alpha = 0.6)#,label = 'UV laser')
+#plt.hist(Stat_Heigths[2], bins=bins3, histtype='step',density = True,color = color3, alpha = 0.6)#,label = 'Ion')
+
+#plt.legend(loc=(0.15,0.65), prop={'size': 11})
+plt.legend()
+plt.grid()
+plt.tight_layout()
+plt.xlim(-0.3, 2)
+plt.xticks([0, 1, 2], fontname='STIXGeneral')
+plt.yticks([0,5000, 10000, 15000], fontname='STIXGeneral')
+plt.xlabel('Time (us)', fontname='STIXGeneral')
+plt.ylabel('Counts', fontname='STIXGeneral')
+
+#poissoneidad =  np.var(Stat_Heigths)/np.mean(Stat_Heigths)
+#plt.title('Varianza/media = {:.4f}'.format(poissoneidad))
+#plt.savefig('bg_laser_ion_stats.pdf',dpi = 600 )
+
+name='fig02a'
+
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.pdf')
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg')
+
+
+#%%
+"""
+Ahora mido de nuevo pero alternando medicion y fondo para que de mejor si hay derivas de potencias.
+Estas mediciones DAN MUY BIEN. Tanto branching fraction como detection efficiency.
+"""
+
+med = 3
+
+plt.plot(Short_Bins[med][:-1],Short_Heigths[med])
+plt.plot(Fondo_Bins[med][:-1],Fondo_Heigths[med])
+
+TotalSP = np.sum(Short_Heigths[2])-np.sum(Fondo_Heigths[2])+np.sum(Short_Heigths[4])-np.sum(Fondo_Heigths[4])
+TotalDP = np.sum(Short_Heigths[3])-np.sum(Fondo_Heigths[3])+np.sum(Short_Heigths[5])-np.sum(Fondo_Heigths[5])
+
+
+branch = TotalSP/(TotalSP+TotalDP)
+
+print(branch)
+
+ErrorSP = np.sqrt(np.sum(Short_Heigths[2])+np.sum(Fondo_Heigths[2])+np.sum(Short_Heigths[4])+np.sum(Fondo_Heigths[4]))
+ErrorDP = np.sqrt(np.sum(Short_Heigths[3])+np.sum(Fondo_Heigths[3])+np.sum(Short_Heigths[5])+np.sum(Fondo_Heigths[5]))
+
+Errorbranch =np.sqrt((1/(TotalSP + TotalDP) - TotalSP/(TotalSP+TotalDP)**2)**2 * ErrorSP**2 + (TotalSP/(TotalSP+TotalDP)**2)**2*ErrorDP**2)
+
+DetectionEfficiency = 100*TotalDP/10e6
+ErrorDetectionEfficiency = 100*np.sqrt(TotalDP)/10e6
+
+
+Ratio = TotalSP/TotalDP
+
+ErrorRatio = np.sqrt((1/(TotalDP**2))*(ErrorSP**2)+((TotalSP/(TotalDP**2))**2)*(ErrorDP**2))
+print(ErrorRatio)
+