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

analisis/plots/20230817_RotationalDopplerShift_v4/Data/EITfit/threeLevel_2repumps_CPTPlotter.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep  1 17:58:39 2020
+
+@author: oem
+"""
+
+
+
+import os
+import numpy as np
+#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
+from threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
+import matplotlib.pyplot as plt
+import time
+from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
+
+#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
+
+ub = 9.27e-24
+h = 6.63e-34
+c = (ub/h)*1e-4 #en unidades de MHz/G
+
+#u = 1e6
+u = 33.5e6
+
+B = (u/(2*np.pi))/c
+
+
+#sg, sp = 0.6, 5  #parámetros de control, saturación del doppler y repump
+#rabG, rabP = sg*gPS, sp*gPD #frecuencias de rabi
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
+
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+DetDoppler = -36 #42
+DetRepumpVec = [DetDoppler+29.6]
+
+Tvec = [0.7] #temperatura en mK
+alpha = 0*(np.pi/180) #angulo entre los láseres
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+#Calculo las resonancias oscuras teóricas
+#ResonanciasTeoricas, DRPositivas = CalculoTeoricoDarkResonances_8levels(u/(2*np.pi*1e6), titadoppler, DetDoppler, DetRepump)
+
+#Parametros de la simulacion cpt
+center = -45
+span = 80
+freqMin = center-span*0.5
+freqMax = center+span*0.5
+
+""" parametros para tener espectros coherentes
+freqMin = -56
+freqMax = 14
+"""
+freqStep = 1e-1
+
+noiseamplitude = 0
+
+RelMinMedido0Vector = []
+RelMinMedido1Vector = []
+RelMinMedido2Vector = []
+RelMinMedido3Vector = []
+RelMinMedido4Vector = []
+
+#Sr = np.arange(0, 10, 0.2)
+
+#Sg = np.arange(0.01, 1, 0.05)
+#Sp = np.arange(0.1, 6.1, 1)
+
+#Sg = [0.6**2]
+#Sp = [2.3**2]
+
+
+Sg = [1.4]
+Sp = [6]
+
+Sr = [11]
+
+
+
+
+i = 0
+
+save = False
+
+
+showFigures = True
+
+if not showFigures:
+    plt.ioff()
+else:
+    plt.ion()
+    
+fig1, ax1 = plt.subplots()
+
+offsetx = 464
+
+ax1.plot([f-offsetx for f in FreqsDR], CountsDR, 'o')
+
+run = True
+
+Scale = 730
+Offset = 600 #600 para 20k cuentas aprox
+
+MaxCoherenceValue = []
+
+for sg in Sg:
+    for sp in Sp:
+        rabG, rabP = sg*gPS, sp*gPD
+        for Ti in Tvec:
+            T = Ti*1e-3
+            for DetRepump in DetRepumpVec:
+                print(T)
+                for sr in Sr:
+                
+                    rabR = sr*gPD
+                    
+                    #MeasuredFreq, MeasuredFluo = GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+                    if run:
+                        MeasuredFreq4, MeasuredFluo4 = GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+                    
+                    #SmoothFluo = SmoothNoisyCPT(MeasuredFluo, window=9, poly=2)
+                    SmoothFluo4 = MeasuredFluo4
+                    
+                    #Scale = max(BestC)/max([100*s for s in SmoothFluo4])
+                    ax1.plot(MeasuredFreq4, [Scale*100*f + Offset for f in SmoothFluo4], label=f'Sr = {sr}')
+                    ax1.axvline(DetDoppler, linestyle='--', linewidth=1)
+                    #if sr != 0:
+                        #ax1.axvline(DetRepump, linestyle='--', linewidth=1)
+
+                    MaxCoherenceValue.append(np.max(SmoothFluo4))
+
+                    #print(titaprobe)
+                    ax1.set_xlabel('Detuning Rebombeo (MHz)')
+                    ax1.set_ylabel('Fluorescencia (AU)')
+                    ax1.set_title(f'B: {round(B, 2)} G, Sdop: {round(sg, 2)}, Sp: {round(sp, 2)}, Sr: {round(sr, 2)}, lw: {lw} MHz, T: {Ti} mK')
+                    #ax1.set_ylim(0, 8)
+                    #ax1.axvline(DetDoppler, linestyle='dashed', color='red', linewidth=1)
+                    #ax1.axvline(DetRepump, linestyle='dashed', color='black', linewidth=1)
+                    #ax1.set_title('Pol Doppler y Repump: Sigma+ Sigma-, Pol Probe: PI')
+                    #ax1.legend()
+                    ax1.grid()
+                    print (f'{i+1}/{len(Sg)*len(Sp)}')
+                    i = i + 1
+                    if save:
+                        plt.savefig(f'Mapa_plots_100k_1mk/CPT_SMSM_sdop{round(sg, 2)}_sp{round(sp, 2)}_sr{round(sr, 2)}.jpg')
+                    ax1.legend()
+                    
+"""
+plt.figure()
+plt.plot(Sr, MaxCoherenceValue, 'o')
+plt.xlabel('Sr')
+plt.ylabel('Coherence')
+"""
+
+"""
+                        
+plt.figure()
+plt.plot(MeasuredFreq, [100*f for f in SmoothFluo], color='darkred')
+plt.xlabel('Desintonía 866 (MHz)')
+plt.ylabel('Fluorescencia (A.U.)')
+plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
+plt.yticks(np.arange(0.4, 1.8, 0.2))
+plt.ylim(0.5, 1.6)
+plt.grid()
+              
+
+
+plt.figure()
+plt.plot(MeasuredFreq4, [100*f for f in SmoothFluo4], color='darkred')
+plt.xlabel('Desintonía 866 (MHz)')
+plt.ylabel('Fluorescencia (A.U.)')
+plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
+plt.yticks(np.arange(0.8, 2.4, 0.4))
+plt.grid()
+              
+"""
+
+
+
+
+#%%
+from scipy.optimize import curve_fit
+
+T = 0.5e-3
+
+sg = 0.7
+sp = 6
+sr = 0
+DetDoppler = -14
+DetRepump = 0
+
+FitsSp = []
+FitsOffset = []
+
+Sg = [0.87]
+
+def FitEIT(freqs, SP, offset):
+    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(0.87, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+    FinalFluo = [f*43000 + 2685 for f in MeasuredFluo]
+    return FinalFluo
+
+freqs = [f-offsetx+32 for f in FreqsDR]
+freqslong = np.arange(min(freqs), max(freqs)+freqs[1]-freqs[0], 0.1*(freqs[1]-freqs[0]))
+
+popt, pcov = curve_fit(FitEIT, freqs, CountsDR, p0=[5, 700], bounds=(0, [10, 1e6]))
+
+FitsSp.append(popt[0])
+FitsOffset.append(popt[1])
+
+print(popt)
+
+FittedEIT = FitEIT(freqslong, *popt)
+
+
+plt.figure()
+plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
+plt.plot(freqslong, FitEIT(freqslong, *popt))
+plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {T*1e3} mK, detDop: {DetDoppler} MHz')
+
+np.savetxt('CPT_measured.txt', np.transpose([freqs, CountsDR]))
+np.savetxt('CPT_fitted.txt', np.transpose([freqslong, FittedEIT]))

analisis/plots/20230817_RotationalDopplerShift_v4/RDS_IR2power.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
+from scipy import interpolate
+
+#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230817_RotationalDopplerShift_v4/Data')
+
+
+
+"""
+en este codigo ploteo espectros CPT de resonancias D-D para configuracion +2/+2 y +2/-2 (usando pentaprisma)
+"""
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+
+IRPOWER_FILES = """VaryingPower/000014529-IR_Scan_withcal_optimized
+VaryingPower/000014530-IR_Scan_withcal_optimized
+VaryingPower/000014523-IR_Scan_withcal_optimized
+VaryingPower/000014528-IR_Scan_withcal_optimized
+VaryingPower/000014522-IR_Scan_withcal_optimized
+VaryingPower/000014526-IR_Scan_withcal_optimized
+VaryingPower/000014524-IR_Scan_withcal_optimized
+VaryingPower/000014527-IR_Scan_withcal_optimized
+VaryingPower/000014525-IR_Scan_withcal_optimized
+VaryingPower/000014531-IR_Scan_withcal_optimized
+"""
+
+
+UVPOWER_FILES = """VaryingUVPower/000014581-IR_Scan_withcal_optimized
+VaryingUVPower/000014582-IR_Scan_withcal_optimized
+VaryingUVPower/000014583-IR_Scan_withcal_optimized
+VaryingUVPower/000014584-IR_Scan_withcal_optimized
+VaryingUVPower/000014585-IR_Scan_withcal_optimized
+VaryingUVPower/000014586-IR_Scan_withcal_optimized
+VaryingUVPower/000014587-IR_Scan_withcal_optimized
+VaryingUVPower/000014588-IR_Scan_withcal_optimized
+VaryingUVPower/000014589-IR_Scan_withcal_optimized
+"""
+
+
+
+
+
+def Split(array,n):
+    length=len(array)/n
+    splitlist = []
+    jj = 0
+    while jj<length:
+        partial = []
+        ii = 0
+        while ii < n:
+            partial.append(array[jj*n+ii])
+            ii = ii + 1
+        splitlist.append(partial)
+        jj = jj + 1
+    return splitlist
+
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+print(SeeKeys(IRPOWER_FILES))
+print(SeeKeys(UVPOWER_FILES))
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+IrPowerCounts = []
+IrPowerFrequencies = []
+
+for i, fname in enumerate(IRPOWER_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    IrPowerCounts.append(np.array(data['datasets']['counts_spectrum']))
+    IrPowerFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+
+
+UvPowerCounts = []
+UvPowerFrequencies = []
+UvPowerAmps = []
+
+for i, fname in enumerate(UVPOWER_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    UvPowerCounts.append(np.array(data['datasets']['counts_spectrum']))
+    UvPowerFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+    UvPowerAmps.append(np.array(data['datasets']['UV_CPT_amp']))
+
+   
+def ErrorDRdepth(p, f, b):
+    ep = np.sqrt(p)
+    ef = np.sqrt(f)
+    eb = np.sqrt(b)
+    derivadap = 1/((f-b)**2)
+    derivadaf = ((p-b)/((f-b)**2))**2
+    derivadab = ((p-f)/((f-b)**2))**2
+    return 2*np.sqrt(derivadap*ep*ep + derivadaf*ef*ef + derivadab*eb*eb)
+
+#%%
+import seaborn as sns
+"""
+Resonancias DD configuracion +2/-2 colineal variando la potencia del laser IR2
+"""
+
+palette = sns.color_palette("tab10")
+
+# pmlocmedvec = [0, 4, 3, 5, 1, 7, 6, 2]
+# idxvec = [95, 95, 95, 94, 93, 98, 97, 97]
+
+irpowermedvec = [0,1,2,3,4,5,6,7,8,9]
+
+idxvecdr1 = [159, 159, 159, 159, 159, 159, 159, 159, 160, 159]
+idxvecdr2 = [394, 394, 394, 394, 394, 394, 394, 394, 394, 393]
+
+#irpowermedvec = [0]
+
+IrAmpVec = [0.03, 0.04, 0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.24, 0.33]
+PotIr2Vec = [0.05, 0.08, 0.48, 2.5, 7.5, 16.8, 31.2, 50.8, 73.6, 143]
+
+PotIr1 = 3.6
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+bkg = 130
+
+powdepthsdr1=[]
+errorpowdepthsdr1=[]
+
+powdepthsdr2=[]
+errorpowdepthsdr2=[]
+
+idxtest = 393
+
+jj=0
+for med in irpowermedvec:
+    if med == 0:
+        powdepthsdr1.append(1-(IrPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][50:150])-bkg))
+        powdepthsdr2.append(1-(IrPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][-100:-1])-bkg))
+        errorpowdepthsdr1.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr1[jj]],np.mean(IrPowerCounts[med][1:][50:150]), bkg))
+        errorpowdepthsdr2.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr2[jj]],np.mean(IrPowerCounts[med][1:][-100:-1]), bkg))
+
+    
+    else:    
+        powdepthsdr1.append(1-(IrPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][0:20])-bkg))
+        powdepthsdr2.append(1-(IrPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][0:20])-bkg))
+        errorpowdepthsdr1.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr1[jj]],np.mean(IrPowerCounts[med][1:][0:20]), bkg))
+        errorpowdepthsdr2.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr2[jj]],np.mean(IrPowerCounts[med][1:][0:20]), bkg))
+    plt.plot([2*f*1e-6 for f in IrPowerFrequencies[med][1:]], [c for c in IrPowerCounts[med][1:]], '-o', color=palette[med],markersize=2, alpha=0.7, label=f'{IrAmpVec[jj]}')
+    plt.plot([2*f*1e-6 for f in IrPowerFrequencies[med][1:]][idxtest], [c for c in IrPowerCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14)
+    jj=jj+1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+#plt.xlim(442,445)
+plt.grid()
+#plt.legend()
+#plt.title('Espectros para distintas geometrías')
+
+fig, ax = plt.subplots()
+ax.errorbar(PotIr2Vec, powdepthsdr1, yerr=errorpowdepthsdr1, fmt='o',capsize=4, markersize=10, label='DR left')
+ax.errorbar(PotIr2Vec, powdepthsdr2, yerr=errorpowdepthsdr2, fmt='o',capsize=4, markersize=10, label='DR right')
+ax.set_xlabel('IR1 amp')
+ax.set_ylabel('DR Relative depth')
+ax.set_xlim(-5,80)
+ax.set_xscale('linear')
+ax.set_ylim(0,1)
+ax.axvline(PotIr1, label='IR1 power', linestyle='--', color='firebrick', zorder=0, alpha=0.7)
+plt.legend()
+plt.grid()
+
+#%%
+import seaborn as sns
+"""
+Resonancias DD configuracion +2/-2 colineal variando la potencia del laser UV
+"""
+
+palette = sns.color_palette("tab10")
+
+# pmlocmedvec = [0, 4, 3, 5, 1, 7, 6, 2]
+# idxvec = [95, 95, 95, 94, 93, 98, 97, 97]
+
+uvpowermedvec = [0,1,2,3,4,5,6,7,8]
+
+idxvecdr1 = [95, 95, 95, 95, 95, 95,95,95,95]
+idxvecdr2 = [236, 236, 236, 236, 236, 236, 236, 236, 236, 236]
+
+#uvpowermedvec = [2]
+
+PotUvVec = [4, 0.76, 11, 22.4, 36, 50, 61, 65, 1.5]
+
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+bkg = 120
+
+powdepthsdr1=[]
+errorpowdepthsdr1=[]
+
+powdepthsdr2=[]
+errorpowdepthsdr2=[]
+
+idxtest = 236
+
+jj=0
+for med in uvpowermedvec:
+    
+    if med == 1:
+        powdepthsdr1.append(1-(UvPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][6:26])-bkg))
+        powdepthsdr2.append(1-(UvPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][6:26])-bkg))
+        
+
+    powdepthsdr1.append(1-(UvPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][2:20])-bkg))
+    powdepthsdr2.append(1-(UvPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][2:20])-bkg))
+    errorpowdepthsdr1.append(ErrorDRdepth(UvPowerCounts[med][1:][idxvecdr1[jj]],np.mean(UvPowerCounts[med][1:][0:20]), bkg))
+    errorpowdepthsdr2.append(ErrorDRdepth(UvPowerCounts[med][1:][idxvecdr2[jj]],np.mean(UvPowerCounts[med][1:][0:20]), bkg))
+    plt.plot([2*f*1e-6 for f in UvPowerFrequencies[med][1:]], [c for c in UvPowerCounts[med][1:]], '-o', color=palette[med],markersize=2, alpha=0.7, label=f'{UvPowerAmps[jj]}')
+    #plt.plot([2*f*1e-6 for f in UvPowerFrequencies[med][1:]][idxtest], [c for c in UvPowerCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14)
+    jj=jj+1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+#plt.xlim(442,445)
+plt.grid()
+#plt.legend()
+#plt.title('Espectros para distintas geometrías')
+
+fig, ax = plt.subplots()
+ax.errorbar(PotUvVec, powdepthsdr1, yerr=errorpowdepthsdr1, fmt='o',capsize=4, markersize=10, label='DR left')
+ax.errorbar(PotUvVec, powdepthsdr2, yerr=errorpowdepthsdr2, fmt='o',capsize=4, markersize=10, label='DR right')
+ax.set_xlabel('Uv power (uW)')
+ax.set_ylabel('DR Relative depth')
+#ax.set_xlim(-5,80)
+ax.set_xscale('linear')
+ax.set_ylim(0,1)
+plt.legend()
+plt.grid()
+
+#%%
+"""
+EXTRA: sensibilidad de CPT D-D con OAM +2/-2 a movimiento térmico del ion
+comparado con CPT tradicional S-D
+"""
+
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def sens(r,l=2):
+    return 2*l/r
+
+
+radius = np.arange(0.1e-6,10e-6,0.01e-6)
+
+plt.figure()
+plt.semilogy([r*1e6 for r in radius],sens(radius), linewidth=3, color='coral', label='D-D CPT +2/-2 OAM ')
+plt.xlabel('OAM radius (um)')
+plt.ylabel('Thermometry sensitivity')
+plt.axhline(8.6e6, color='indigo', linestyle='--', label='S-P CPT gaussian sensitivity')
+plt.grid()
+plt.legend()
+