agrego analisis de lo que medi ese dia, branching ratio, curvas sp con un ion,...

agrego analisis de lo que medi ese dia, branching ratio, curvas sp con un ion, y eficiencia de deteccion

analisis/plots/20210703/BranchingFraction.py

+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+import re
+import ast
+from scipy.optimize import curve_fit
+import os
+
+# Solo levanto algunos experimentos
+ALL_FILES_SP = """000001523-SingleLine.h5"""
+
+ALL_FILES_DP = """000001525-SingleLine.h5
+000001526-SingleLine.h5
+000001527-SingleLine.h5
+000001528-SingleLine.h5
+000001529-SingleLine.h5
+000001530-SingleLine.h5"""
+
+def Background_Risetime_AOM(t, bineo, bkgr_in, bkgr_end, rise_time, rise_time_init):
+    """simulo un prendido de AOM"""
+    i = 0
+    j = 1
+    Background = []
+    while i < len(t):
+        if t[i] < rise_time_init:
+            Background.append(bkgr_in)
+        else:
+            rise_step = (bkgr_end-bkgr_in)*bineo/rise_time
+            if t[i] < rise_time_init + rise_time:
+                Background.append(bkgr_in + j*rise_step)
+                j = j + 1
+            else:
+                Background.append(bkgr_end)
+        i = i + 1
+    return np.array(Background)
+
+
+def expo(T, tau, N0, C):
+    global T0
+    return N0*np.exp(-(T-T0)/tau) + C
+
+def pow_from_amp(amp):
+    """Paso de amplitud urukul a potencia medida por Nico"""
+    # Forma altamente ineficiente de hacer esto, pero me salio asi
+    amplitudes_UV = np.array([0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30])
+    assert amp in amplitudes_UV
+    potencias_UV = np.array([5, 11, 20, 32, 47, 67, 86, 105, 120, 134, 144])
+    return potencias_UV[np.where(amplitudes_UV == amp)][0]
+
+def SubstractUniformBackground(counts, times, timetarget):
+    "a partir de time, deberia solo haber background"
+    i = min(range(len(times)), key=lambda i: abs(times[i]-timetarget))
+    countsbkg = counts[i:]
+    background = int(np.mean(countsbkg))
+    print(background)
+    return background, np.array([c-background for c in counts])
+
+def SubstractDynamicBackground(counts, times, rise_time_init, rise_time, time_initbkgr_target, time_endbkgr_target):
+    "a partir de time, deberia solo haber background"
+
+    i_initbkg = min(range(len(times)), key=lambda i: abs(times[i]-time_initbkgr_target))
+    i_endbkg = min(range(len(times)), key=lambda i: abs(times[i]-time_endbkgr_target))
+    countsbkg_init = counts[0:i_initbkg]
+    countsbkg_end = counts[i_endbkg:]
+    background_init = int(np.mean(countsbkg_init))
+    background_end = int(np.mean(countsbkg_end))
+    
+    DynamicBkg = Background_Risetime_AOM(times, times[1]-times[0], background_init, background_end, rise_time, rise_time_init)
+    
+    Counts_Substracted_Bkg = np.array([counts[i] - DynamicBkg[i] for i in range(len(counts))])
+    
+    return Counts_Substracted_Bkg, DynamicBkg
+
+amplitudes_UV = np.flip(np.array([0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30]))
+potencias_UV = np.flip(np.array([4, 10, 19, 32, 49, 71, 96, 125, 155, 183, 208, 229]))
+
+amplitudes_IR = np.array([0.35, 0.30, 0.25, 0.20, 0.15, 0.10])
+potencias_IR = np.array([255, 210, 151, 88, 37, 9])
+
+"""
+plt.figure()
+plt.plot(amplitudes_UV, potencias_UV, 'ko-', lw=0.2)
+plt.xlabel("Amplitud Urukul")
+plt.ylabel("Potencia UV/uW")
+plt.grid()
+
+plt.figure()
+plt.plot(amplitudes_IR, potencias_IR, 'bo-', lw=0.2)
+plt.xlabel("Amplitud Urukul")
+plt.ylabel("Potencia IR/uW")
+plt.grid()
+"""
+
+#%%
+
+## Mostrar corte de los histos:
+# fig, ax = plt.subplots()
+# ax.axvline(T0, color='k')
+os.chdir('/home/oem/Documentos/Doctorado/Artiq/Repositorio/artiq_experiments/artiq_master/results/2021-07-02/17')
+
+
+BINW = 25e-9
+T0 = 0.01e-6
+#T0 = 0.02e-6
+
+figSP, axSP = plt.subplots()
+#axSP = axSP.twinx()
+
+for i, fname in enumerate(ALL_FILES_SP.split()):
+    
+    if i==0:
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        
+        laser_UV_amp = data['datasets']['laser_UV_amp']
+        laser_UV_freq = data['datasets']['laser_UV_freq']
+           
+        #print(laser_UV)
+        counts = np.array(data['datasets']['counts'])
+        bines = np.arange(counts.min(), counts.max()+BINW, BINW)
+        
+        ## Mostrar corte de los histos
+        # ax.hist(counts, bines[bines<3e-6], histtype='step', align='mid', color=f"C{i}")
+        
+        heigs_SP, binsf_SP  = np.histogram(counts, bines[bines>T0])
+        
+        backgroundtime = 4e-6
+        
+        
+        
+        rise_time_init = 1.02
+        rise_time = 0.11
+        time_initbkgr_target = 0.6
+        time_endbkgr_target = 4
+        
+        CountsSubstSP, DynamicBkg = SubstractDynamicBackground(heigs_SP, binsf_SP, rise_time_init*1e-6, rise_time*1e-6, time_initbkgr_target*1e-6, time_endbkgr_target*1e-6)
+        #bckg, CountsSubstSP = SubstractUniformBackground(heigs_SP, binsf_SP[:-1], backgroundtime)
+
+        #print('bckg DP:' + str(bckg))
+        
+        #axSP.plot([b*1e6 for b in binsf_SP], DynamicBkg, label='Rising AOM')            
+       
+        axSP.plot([b*1e6 for b in binsf_SP[:-1]], CountsSubstSP, label='Background substracted')
+           
+        axSP.plot([b*1e6 for b in binsf_SP[:-1]], heigs_SP, zorder=1)    
+        axSP.set_title('SP Transition')
+        axSP.set_xlabel('Time (us)')
+        axSP.set_ylabel('Counts')
+        #axSP.hlines(bckg, 4, 6, color='red', zorder=2, label='Background')
+        axSP.axvline(0.97, color='blue', linestyle='--', linewidth=0.5, label='UV turns on')
+        axSP.axvspan(0, 0.97, color='lightcyan')
+        axSP.legend()
+        
+        TotalCountsDetected_SP = np.sum(CountsSubstSP[int(0.*len(heigs_SP)):int(0.6*len(heigs_SP))])
+
+#%%
+figDP, axDP = plt.subplots()
+
+
+BINW = 20e-9
+T0 = 0.5e-6
+
+os.chdir('/home/oem/Documentos/Doctorado/Artiq/Repositorio/artiq_experiments/artiq_master/results/2021-07-02/18')        
+for i, fname in enumerate(ALL_FILES_DP.split()):
+    
+    if i==0:
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        
+        laser_IR_amp = data['datasets']['laser_IR_amp']
+        laser_IR_freq = data['datasets']['laser_IR_freq']
+           
+        #print(laser_UV)
+        counts = np.array(data['datasets']['counts'])
+        bines = np.arange(counts.min(), counts.max()+BINW, BINW)
+        
+        ## Mostrar corte de los histos
+        # ax.hist(counts, bines[bines<3e-6], histtype='step', align='mid', color=f"C{i}")
+        
+        heigs_DP, binsf_DP  = np.histogram(counts, bines[bines>T0])
+        
+        backgroundtime = 3e-6
+
+        bckg, CountsSubstDP = SubstractUniformBackground(heigs_DP, binsf_SP[:-1], backgroundtime)
+        
+        print('bckg DP:' + str(bckg))
+        
+        axDP.plot([b*1e6 for b in binsf_DP[:-1]], heigs_DP, zorder=1)    
+        axDP.plot([b*1e6 for b in binsf_DP[:-1]], CountsSubstDP, label='Background substracted')
+        axDP.set_title('DP Transition')
+        axDP.set_xlabel('Time (us)')
+        axDP.set_ylabel('Counts')
+        axDP.hlines(bckg, 4, 6, color='red', zorder=2, label='Background')
+        axDP.axvline(0.64, color='red', linestyle='--', linewidth=0.5, label='IR turns on')
+        axDP.axvspan(0.4, 0.64, color='papayawhip')
+        axDP.legend()
+        
+        TotalCountsDetected_DP= np.sum(CountsSubstDP[0:int(0.2*len(heigs_DP))])
+        
+print(TotalCountsDetected_DP)        
+print('BR: ', TotalCountsDetected_SP/TotalCountsDetected_DP)
+#%%
+figBOTH, axBOTH = plt.subplots()
+axBOTH.plot([1e6*t+0.32 - 1 for t in binsf_DP[:-1]], SubstractUniformBackground(heigs_DP, binsf_DP[:-1], backgroundtime), label='DP transition')        
+axBOTH.plot([1e6*t - 1 for t in binsf_SP[:-1]], SubstractUniformBackground(heigs_SP, binsf_SP[:-1], backgroundtime), label='SP transition')        
+axBOTH.set_xlim(-0.1, 3)
+axBOTH.set_xlabel('Time from pulse (us)')
+axBOTH.set_ylabel('Counts')
+axBOTH.set_title('PotUV: 229 uW, PotIR: 255 uW. Branching Fraction: 14.1')
+axBOTH.legend()
\ No newline at end of file

analisis/plots/20210703/apagado_vs_potencia.py

+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+import re
+import ast
+from scipy.optimize import curve_fit
+import os
+
+# Solo levanto algunos experimentos
+ALL_FILES_SP = """000001503-SingleLine.h5
+000001504-SingleLine.h5
+000001505-SingleLine.h5
+000001506-SingleLine.h5
+000001507-SingleLine.h5
+000001508-SingleLine.h5
+000001509-SingleLine.h5
+000001510-SingleLine.h5
+000001511-SingleLine.h5
+000001512-SingleLine.h5
+000001513-SingleLine.h5"""
+
+#000001514-SingleLine.h5 #este tiene amplitud 0.08 que es muy poquito
+
+BINW = 20e-9
+T0 = 1.15e-6
+
+def expo(T, tau, N0, C):
+    global T0
+    return N0*np.exp(-(T-T0)/tau) + C
+
+def pow_from_amp(amp):
+    """Paso de amplitud urukul a potencia medida por Nico"""
+    # Forma altamente ineficiente de hacer esto, pero me salio asi
+    amplitudes_UV = np.array([0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30])
+    assert amp in amplitudes_UV
+    potencias_UV = np.array([5, 11, 20, 32, 47, 67, 86, 105, 120, 134, 144])
+    return potencias_UV[np.where(amplitudes_UV == amp)][0]
+
+amplitudes_UV = np.flip(np.array([0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30]))
+potencias_UV = np.flip(np.array([4, 10, 19, 32, 49, 71, 96, 125, 155, 183, 208, 229]))
+plt.plot(amplitudes_UV, potencias_UV, 'ko-', lw=0.2)
+plt.xlabel("Amplitud Urukul")
+plt.ylabel("Potencia /uW")
+plt.grid()
+#%%
+
+## Mostrar corte de los histos:
+# fig, ax = plt.subplots()
+# ax.axvline(T0, color='k')
+os.chdir('/home/oem/Documentos/Doctorado/Artiq/Repositorio/artiq_experiments/artiq_master/results/2021-07-02/17')
+
+fig0, [ax0, ax1_a] = plt.subplots(1, 2)
+ax1_b = ax1_a.twinx()
+
+allamps = np.array([])
+allpows = np.array([])
+alltaus = np.array([])
+allN0 = np.array([])
+
+for i, fname in enumerate(ALL_FILES_SP.split()):
+    print(i)
+    print(fname)
+    data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    # Aca hago algo repugnante para poder levantar los strings que dejamos
+    # que además tenian un error de tipeo al final. Esto no deberá ser necesario
+    # cuando se solucione el error este del guardado.
+    laser_UV_amp = data['datasets']['laser_UV_amp']
+    laser_UV_freq = data['datasets']['laser_UV_freq']
+    
+    
+    
+    #print(laser_UV)
+    counts = np.array(data['datasets']['counts'])
+    bines = np.arange(counts.min(), counts.max()+BINW, BINW)
+
+    ## Mostrar corte de los histos
+    # ax.hist(counts, bines[bines<3e-6], histtype='step', align='mid', color=f"C{i}")
+
+    heigs, binsf  = np.histogram(counts, bines[bines>T0])
+    ax0.step(binsf[:-1], heigs, label=f"AMP: {laser_UV_amp}", where='mid',
+            color=f"C{i}", lw=0.5, alpha=0.4)
+
+    popt, pcov = curve_fit(expo, binsf[:-1], heigs, p0=(2e-6, 400, 300))
+    print(popt) # tau, N0, C
+    ax0.plot(binsf, expo(binsf, *popt), label=f"tau: {popt[0]}",
+            color=f"C{i}", ls='-', lw=1, zorder=99)
+
+    allamps = np.append(allamps, laser_UV_amp)
+    laser_pow = pow_from_amp(laser_UV_amp)
+    allpows = np.append(allpows, laser_pow)
+    alltaus = np.append(alltaus, popt[0])
+    allN0 = np.append(allN0, popt[1])
+
+    ax1_a.plot(laser_pow , popt[0], 'o', color=f"C{i}", ms=5, )
+    ax1_b.plot(laser_pow, popt[1], '^', color=f"C{i}", ms=7, )
+
+ax1_a.plot(allpows, alltaus, 'k-', lw=0.2, zorder=0)
+ax1_b.plot(allpows, allN0, 'k-', lw=0.2, zorder=0)
+
+
+# plt.annotate(f"bin: {BINW}", (0,5e-5, 700), fontsize=14)
+ax0.set_xlabel("Tiempo")
+ax0.set_ylabel("Cuentas")
+
+ax1_a.set_xlabel("Potencia [uW]")
+ax1_a.set_ylabel("Tau (circulo)")
+ax1_b.set_ylabel("Alturas (triang)")
+
+ax1_a.grid(alpha=0.3)
+# plt.show()
+
+plt.figure()
+plt.plot(allpows, allN0/alltaus, 'ko-', lw=0.2)
+plt.grid(alpha=0.3)
+plt.xlabel("Potencia [uW]")
+plt.ylabel("Alturas/Tau")
+
+plt.show()
+input()