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

analisis/plots/20220520_CPTvariandoB/CPT_plotter_20220520.py

@@ -138,14 +138,14 @@ freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-Freq
 
 #[1.71811842e+04 3.34325038e-17]
 
-def FitEITpi(freqs, SG, SP, TITADOPPLER, DETDOPPLER):
+def FitEITpi(freqs, SG, SP, TITADOPPLER, DETDOPPLER, uguess):
     TITAPROBE=TITADOPPLER
     temp = 2e-3
-    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DETDOPPLER, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, TITADOPPLER, phiprobe, [TITAPROBE], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DETDOPPLER, DetRepump, uguess, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, TITADOPPLER, phiprobe, [TITAPROBE], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
     FinalFluo = [f*1.16e5 + 1.18e3 for f in MeasuredFluo]
     return FinalFluo
 
-popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5, 60, -10], bounds=((0, 0, 0, -100), (2, 10, 90, 0)))
+popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5, 60, -10, 20e6], bounds=((0, 0, 0, -100, 0e6), (2, 20, 90, 0, 40e6)))
 
 print(popt)
 

analisis/plots/20220520_EspectrosUVyCPT_descompensacion/UV_CPT_spectrums.py

@@ -14,10 +14,12 @@ Calib_Files = """000007324-UV_Scan_withcalib_Haeffner
 000007326-UV_Scan_withcalib_Haeffner
 000007327-IR_Scan_withcal_optimized
 000007328-UV_Scan_withcalib_Haeffner
-000007327-IR_Scan_withcal_optimized""" 
-
-directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/'
+000007327-IR_Scan_withcal_optimized
+000007197-IR_Scan_withcal_optimized
+000007198-UV_Scan_withcalib_Haeffner""" 
 
+#directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/'
+directory = '/home/nico/Documents/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/'
 
 #carpeta pc nico labo escritorio:
 #C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\Data
@@ -115,7 +117,7 @@ def MicromotionSpectra(det, A, beta, x0, offset):
     return P
 
 
-jvec = [2] #UV_cooling en 90 MHz
+jvec = [7] #UV_cooling en 90 MHz
 
 """
 plt.figure()
@@ -152,7 +154,7 @@ for j in jvec:
     
     portion = 0.
     
-    popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,1.5,220,200], bounds=((70000,0.1,200,-500),(1000000,10,300,500)))
+    popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,1.5,220,200], bounds=((7000,0.1,200,-500),(1000000,10,300,500)))
     
     freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
     

analisis/plots/20220526_CPTvariandoB_v2/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/20220527_CPTvariandoB_barriendopotenciaIR/Bvsk_Saturation_Measurements.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
+
+# Solo levanto algunos experimentos
+Calib_Files_IR = """000007808-IR_Saturation
+000007809-IR_Saturation
+000007810-IR_Saturation
+000007811-IR_Saturation
+000007812-IR_Saturation
+000007813-IR_Saturation
+000007814-IR_Saturation
+000007815-IR_Saturation
+000007816-IR_Saturation
+000007817-IR_Saturation
+000007820-IR_Saturation
+000007828-IR_Saturation
+000007829-IR_Saturation
+000007830-IR_Saturation
+000007831-IR_Saturation""" 
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data')
+
+#carpeta pc nico labo escritorio:
+#/home/nico/Documents/artiq_experiments/analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/Data
+
+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()))
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210824_SaturacionTransiciones\Data_IR
+
+IR_meas_amps_vec = []
+IR_fluorescence_vec = []
+
+
+for i, fname in enumerate(Calib_Files_IR.split()):
+    print(i)
+    #print(fname)
+    data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+    #print(list(data['datasets'].keys()))
+    meas = np.array(data['datasets']['measurements_IR_sat'])
+    IR_fluorescence_vec.append(meas)
+    IR_meas_amps_vec.append(np.array(data['datasets']['IR_amps']))
+    
+
+PotenciasIR = [155, 4.8, 5.4, 6.2, 7.1, 8.0, 9.0, 9.9, 11., 12.1, 13.4, 14.6, 16.1, 17.6, 19.2, 20.9, 22.6, 24.5, 26.5, 28.4, 30.6, 32.7, 34.9, 37.1, 39.6, 41.9, 44.3, 46.9, 49.5, 52.1, 54.8, 57.4, 60.1, 63., 65.7, 68.8, 71.6, 74.5, 77., 80., 82.9]
+
+Ivec = [-2.3, -2.2, -2.1, -2, -1.9, -1.8, -1.7, -1.6]
+
+#%%
+#background:
+    
+Background = list(IR_fluorescence_vec[11])+list(IR_fluorescence_vec[12])+list(IR_fluorescence_vec[13])+list(IR_fluorescence_vec[14])
+bkgr = int(np.mean(Background))
+print(f'El background de la medicion es {bkgr} cuentas por segundo')
+
+#%%
+
+FluovsB = []
+jselected = [0, 1, 2, 4, 6, 7, 8, 9]
+
+for j in jselected:
+    FluovsB.append(IR_fluorescence_vec[j])
+
+plt.figure()
+for j in range(len(jselected)):
+    plt.plot(PotenciasIR, [f-bkgr for f in FluovsB[j]], 'o')
+    plt.xlim(0, 90)
+plt.xlabel('Potencia IR (uW)')
+plt.ylabel('Cuentas')
+
+#%%
+from scipy.signal import savgol_filter as sf
+
+plt.style.use('seaborn-ticks')
+
+colors=sns.color_palette("rocket", 10)
+
+colorsselected=[colors[8],colors[5],colors[3],colors[0]]
+
+FluovsBshort = []
+jselected = [0, 1, 4, 8]
+
+for j in jselected:
+    FluovsBshort.append(IR_fluorescence_vec[j])
+
+bkgr2 = np.min([FluovsBshort[0][1],FluovsBshort[1][1],FluovsBshort[2][1],FluovsBshort[3][1]])
+
+plt.figure(figsize=(3.5, 3))
+for j in range(len(jselected)):
+    rawcuentas = [f-bkgr for f in FluovsBshort[j]]
+    cuentas = np.array(rawcuentas[0:3]+list(sf(rawcuentas[3:],7,3)))
+    plt.plot(PotenciasIR[1:], cuentas[1:], 'o', color=colorsselected[j], markersize=3.5)
+    plt.fill_between(PotenciasIR[1:], cuentas[1:]-np.sqrt(cuentas)[1:], cuentas[1:]+np.sqrt(cuentas)[1:], color=colorsselected[j], alpha=0.4)
+    #plt.errorbar(PotenciasIR, cuentas, yerr=1*np.sqrt(cuentas), color='r', fmt='o', capsize=2, markersize=4)
+    plt.xlim(0, 90)    
+    #plt.ylim(-100,3500)
+plt.grid()
+plt.xlabel(r'Repump power ($\mu$W)', fontsize=11)
+plt.ylabel('Counts', fontsize=11)
+plt.tight_layout()
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/Barriendopotencia_exp.png',dpi=500)
+
+#%%
+# en funcion del campo magnetico ahora
+
+FluovsBtrans = np.transpose(FluovsB)
+
+potsselected = [5, 26, 31, 36]
+
+plt.figure()
+for p in potsselected:
+    plt.plot(np.flip(np.abs(np.array(Ivec))), np.flip(np.array(FluovsBtrans[p])), 'o')
+
+#%%
+#pruebo un suavizado para los datos
+
+from scipy.signal import savgol_filter as sf
+
+FluoSel = IR_fluorescence_vec[0][1:]
+PotsSel = PotenciasIR[1:]
+plt.plot(PotsSel, FluoSel, 'o')
+plt.plot(PotsSel, sf(FluoSel, 13, 3))
+
+#%%
+#lo aplico
+from scipy.optimize import curve_fit
+
+"""
+def LinearLarmortoCurrentCalibrated(I):
+    Larmor = 379871*I+1145919
+    return Larmor
+"""
+
+def LinearLarmortoCurrentCalibrated(I):
+    Larmor = 257550*I+667309
+    return Larmor
+
+
+def ConvertLarmortoBfield(u):
+    c = 1398190.0452488689
+    return u/(2*np.pi)/c
+
+c = 1398190
+
+FluovsB = []
+jselected = [0, 1, 2, 4, 6, 7, 8, 9]
+
+Bvec = [ConvertLarmortoBfield(u) for u in LinearLarmortoCurrentCalibrated(np.array(Ivec))]
+
+for j in jselected:
+    FluovsB.append(IR_fluorescence_vec[j])
+
+"""
+plt.figure()
+for j in range(len(jselected)):
+    plt.plot(PotenciasIR, sf([f-bkgr for f in FluovsB[j]], 13, 3), 'o')
+    plt.xlim(0, 90)
+plt.xlabel('Potencia IR (uW)')
+plt.ylabel('Cuentas')
+"""
+MaxsFluosExp = []
+MaxsPotsExp = []
+
+for k in range(len(jselected)):
+    filteredfluo = sf([f-bkgr for f in FluovsB[k]], 13, 3)
+    maxfluo = np.max(filteredfluo)
+    j=0
+    happened=0
+    while j<len(filteredfluo):
+        try:
+            if np.abs(filteredfluo[j]-maxfluo)<10:
+                    MaxsFluosExp.append(filteredfluo[j])
+                    MaxsPotsExp.append(PotenciasIR[j])
+                    break                    
+            else:
+                j=j+1
+        except:
+            MaxsFluosExp.append(0)
+            MaxsPotsExp.append(0)            
+
+"""
+plt.figure()
+plt.plot(Ivec, MaxsPotsExp,'rx')
+plt.xlabel('Corriente bobina (A)')
+plt.ylabel('Potencia umbral (mW)')
+plt.grid()  
+"""
+
+#longBvec = np.arange(np.min(Bvec), np.max(Bvec), 0.1*Bvec[1]-0.1*Bvec[0])
+longBvec = np.arange(0.005, 0.033, 0.01*Bvec[1]-0.01*Bvec[0])
+
+def LinearFitPotvsB(b, pendiente, ordenada):
+    return pendiente*b + ordenada
+
+#popt_expvspot, pcov_expvspot = curve_fit(LinearFitPotvsB, np.array([0]+Bvec), np.array([0]+MaxsPotsExp))
+popt_expvspot, pcov_expvspot = curve_fit(LinearFitPotvsB, np.array(Bvec), np.array(MaxsPotsExp))
+
+print(popt_expvspot)
+print(f'Ordenada al origen: {round(popt_expvspot[1],1)} +- {round(np.sqrt(pcov_expvspot[1,1]),1)} mW/G')
+
+#%%
+'''
+Figura paper. Umbral vs campo magnetico con la calibracion
+'''
+
+plt.figure(figsize=(3.5, 3))
+plt.errorbar([b*1e3 for b in Bvec], MaxsPotsExp, xerr=1e3*MeanError/(2*np.pi)/c, yerr=4, color='purple', fmt="o", markersize=5)
+#plt.plot([0],[0],'o', markersize=5)
+plt.plot([b*1e3 for b in longBvec], LinearFitPotvsB(longBvec, *popt_expvspot), linewidth=2, color='orangered')
+plt.xlabel('Magnetic field (mG)', fontsize=11)
+plt.ylabel('Threshold power (mW)', fontsize=11)
+plt.xlim(6,32)
+plt.ylim(17,81)
+plt.grid()
+plt.tight_layout()
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/umbralvsB_exp.png',dpi=500)
+
+
+
+
+
+
+
+
+
+
+    

analisis/plots/20220527_CPTvariandoB_barriendopotenciaIR/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/20220615_CPTvariandocompensacion/CPT_plotter_20220615.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 de CPT para campo magnetico bajo y terrestre
+
+#/home/nico/Documents/artiq_experiments/analisis/plots/20220615_CPTvariandocompensacion/Data
+
+ALL_FILES = """000007971-IR_Scan_withcal_optimized
+000007972-IR_Scan_withcal_optimized
+000007973-IR_Scan_withcal_optimized
+000007976-IR_Scan_withcal_optimized
+000007980-IR_Scan_withcal_optimized
+000007981-IR_Scan_withcal_optimized
+000007982-IR_Scan_withcal_optimized
+000007983-IR_Scan_withcal_optimized
+000007984-IR_Scan_withcal_optimized
+000007985-IR_Scan_withcal_optimized
+000008063-IR_Scan_withcal_optimized
+""" 
+
+
+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(ALL_FILES))
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+Freqs = []
+
+AmpTisa = []
+UVCPTAmp = []
+No_measures = []
+
+for i, fname in enumerate(ALL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    #print(fname)
+    data = h5py.File(fname+'.h5', '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.
+    Freqs.append(np.array(data['datasets']['IR_Frequencies']))
+    Counts.append(np.array(data['datasets']['counts_spectrum']))
+    AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
+    UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
+    No_measures.append(np.array(data['datasets']['no_measures']))
+
+Counts_B = []
+Freqs_B = []
+
+
+for i, fname in enumerate(ALL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    #print(fname)
+    data = h5py.File(fname+'.h5', '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.
+    Freqs_B.append(np.array(data['datasets']['IR_Frequencies']))
+    Counts_B.append(np.array(data['datasets']['counts_spectrum']))
+
+
+
+#%%
+
+#barriendo posicion radial del ion en la trampa
+jvec = [0, 1, 2, 3]
+
+plt.figure()
+i = 0
+for j in jvec:
+    plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
+    #plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb  arkersize=3)
+    i = i + 1
+plt.grid()
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+#plt.legend()
+
+
+#%%
+import seaborn as sns
+
+#Barriendo angulo del IR con tisa apagado
+palette = sns.color_palette("rocket", 8)
+
+jvec = [4,5,6]
+
+plt.figure()
+i = 0
+for j in jvec:
+    plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), color=palette[j-4], fmt='o', capsize=2, markersize=2)
+    #plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb  arkersize=3)
+    i = i + 1
+plt.grid()
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+plt.ylim(2000, 8000)
+#plt.legend()
+
+jvec = [7,8,9]
+
+plt.figure()
+i = 0
+for j in jvec:
+    plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), color=palette[j-4], fmt='o', capsize=2, markersize=2)
+    #plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb  arkersize=3)
+    i = i + 1
+plt.grid()
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+plt.ylim(2000, 8000)
+#plt.legend()
+
+#%%
+
+#CPT con dos iones
+jvec = [10]
+
+plt.figure()
+i = 0
+for j in jvec:
+    plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), color='purple',fmt='o', capsize=2, markersize=2)
+    #plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb  arkersize=3)
+    i = i + 1
+plt.grid()
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+
+DR = [403.5,412.5,418,426.5]
+
+for dr in DR:
+    plt.axvline(dr,color='blue',linestyle='--',alpha=0.3)
+    #plt.axvline(dr+22,color='crimson',linestyle='--',alpha=0.3)
+    plt.axvline(dr-22,color='red',linestyle='--')    
+    #plt.axvline(dr+44,color='red',linestyle='--')
+
+#plt.legend()
+
+
+#%%
+from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
+from scipy.optimize import curve_fit
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+Temp = 0.5e-3
+
+sg = 0.544
+sp = 4.5
+sr = 0
+DetRepump = 0
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+
+u = 32.5e6
+
+#B = (u/(2*np.pi))/c
+
+correccion = 6
+
+offsetxpi = 440+1+correccion
+DetDoppler = -11.5-correccion
+
+drivefreq = 2*np.pi*22.135*1e6
+
+FreqsDR = [2*f*1e-6-offsetxpi+14 for f in Freqs[10]]
+CountsDR = Counts[10]
+
+freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+
+def FitEIT_MM(freqs, SCALE, OFFSET, SG, SP, BETA, TEMP):
+    BETA = 1.8
+    Detunings, Fluorescence = PerformExperiment_8levels(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    ScaledFluo = [f*SCALE + OFFSET for f in Fluorescence]
+    return ScaledFluo
+
+
+popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[1e3, 1e4, 0.7, 5, 2, 5e-3], bounds=((0, 0, 0, 0, 0, 0), (1e5, 1e5, 1.5, 10, 3, 15e-3)))
+
+FittedEITpi = FitEIT_MM(freqslong, *popt)
+
+plt.figure()
+plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
+plt.plot(freqslong, FittedEITpi)
+#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+
+
+
+
+
+
+
+
+

analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul  2 16:30:09 2020
+
+@author: oem
+"""
+
+
+import os
+import numpy as np
+import time
+import matplotlib.pyplot as plt
+from scipy.signal import argrelextrema
+from EITfit.MM_eightLevel_2repumps_python_scripts import CPTspectrum8levels
+import random
+from scipy.signal import savgol_filter as sf
+
+
+def PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None):
+    """
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+    """
+    tinicial = time.time()
+    ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe, beta, drivefreq, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
+    tfinal = time.time()
+    
+    #print('Done, Total time: ', round((tfinal-tinicial), 2), "s")   
+          
+    return ProbeDetuningVectorL, Fluovector
+
+
+def GenerateNoisyCPT(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
+    Frequencyvector, Fluovector = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe, plot=False, solvemode=1, detpvec=None)
+    NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
+    return Frequencyvector, NoisyFluovector
+
+
+def SmoothNoisyCPT(Fluo, window=11, poly=3):
+    SmoothenFluo = sf(Fluo, window, poly)
+    return SmoothenFluo
+
+
+
+
+
+

analisis/plots/20220615_CPTvariandocompensacion/Data/EITfit/MM_eightLevel_2repumps_CPTPlotter.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep  1 17:58:39 2020
+
+@author: nico
+"""
+
+
+
+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 MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels, GenerateNoisyCPT, SmoothNoisyCPT
+import matplotlib.pyplot as plt
+import time
+#from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
+import seaborn as sns
+
+#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 #magneton de bohr
+h = 6.63e-34 #cte de planck
+c = (ub/h)*1e-4 #en unidades de MHz/G
+
+u = 32e6 #esto equivale aprox al campo que tenemos
+
+B = (u/(2*np.pi))/c
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
+
+lw = 0.1 #ancho de linea de los laseres en MHz
+DopplerLaserLinewidth, ProbeLaserLinewidth = lw, lw #ancho de linea de los laseres
+
+DetDoppler = -15 #detuning doppler en MHz
+
+T = 5e-3 #temperatura en K
+alpha = 0 #angulo entre los láseres
+
+#estos son los angulos de la polarizacion de los laseres respecto al campo magnetico
+phidoppler, titadoppler = 0, 90
+titaprobe = 90
+phiprobe = 0
+
+#este es el desfasaje exp(i.phi) de la componente de la polarizacion y respecto a la x. Con 1 la polarizacion es lineal
+CircPr = 1 
+
+#Parametros de la simulacion cpt todo en MHz
+center = -10
+span = 120
+freqMin = center-span*0.5
+freqMax = center+span*0.5
+freqStep = 2e-1
+
+noiseamplitude = 0
+
+#parametros de saturacion de los laseres. g: doppler. p: probe (un rebombeo que scanea), r: repump (otro rebombeo fijo)
+sg = 0.6
+sp = 9
+
+drivefreq=2*np.pi*22.135*1e6
+
+#betavec = np.arange(0,1.1,0.1)
+betavec=[0, 1, 2]
+
+fig1, ax1 = plt.subplots()
+
+FrequenciesVec = []
+FluorescencesVec = []
+
+for beta in betavec:
+    Frequencies, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    
+    FrequenciesVec.append(Frequencies)
+    FluorescencesVec.append(Fluorescence)
+    
+    ax1.plot(Frequencies, [100*f for f in Fluorescence], label=fr'$\beta={beta}$')  
+    ax1.set_xlabel('Detuning Rebombeo (MHz)')
+    ax1.set_ylabel('Fluorescencia (AU)')
+    ax1.set_title(f'Sdop: {sg}, Spr: {sp}, Temp: {int(T*1e3)} mK')
+    ax1.legend()
+ax1.grid()
+
+#%%
+beta=1
+Frequencies, Fluorescence1 = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+
+beta=2
+Frequencies, Fluorescence2 = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+
+
+plt.plot(Frequencies, [100*(Fluorescence1[j]+Fluorescence2[j]) for j in range(len(Fluorescence1))], label=fr'$\beta={beta}$')  
+plt.plot(Frequencies, [100*(Fluorescence1[j]+0*Fluorescence2[j]) for j in range(len(Fluorescence1))], label=fr'$\beta={beta}$')  
+plt.plot(Frequencies, [100*(0*Fluorescence1[j]+Fluorescence2[j]) for j in range(len(Fluorescence1))], label=fr'$\beta={beta}$')  
+plt.xlabel('Detuning Rebombeo (MHz)')
+plt.ylabel('Fluorescencia (AU)')
+
+#%%
+Betas = [0,0.1,0.2]
+FluosMM = []
+
+for beta in Betas:
+    Frequencies, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    FluosMM.append(np.array(Fluorescence))
+    
+plt.plot(Frequencies, 100*sum(FluosMM), label=fr'$\beta={beta}$')  
+plt.xlabel('Detuning Rebombeo (MHz)')
+plt.ylabel('Fluorescencia (AU)')
+
+
+
+
+
+#%%
+#Este bloque ajusta a las curvas con un beta de micromocion de 0
+
+from scipy.optimize import curve_fit
+
+def FitEIT_MM(freqs, Temp):
+    BETA = 0
+    scale=1
+    offset=0
+    Detunings, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    ScaledFluo = [f*scale + offset for f in Fluorescence]
+    return ScaledFluo
+
+TempMedidas = []
+FittedEIT_fluosVec = []
+
+for j in range(len(betavec)):
+    SelectedFluo = FluorescencesVec[j]
+    SelectedFreqs = FrequenciesVec[j]
+    
+    popt_mm, pcov_mm = curve_fit(FitEIT_MM, SelectedFreqs, SelectedFluo, p0=[1e-3], bounds=((0), (10e-3)))
+    
+    TempMedidas.append(1e3*popt_mm[2])
+    
+    print(popt_mm)
+    
+    FittedEIT_fluo = FitEIT_MM(SelectedFreqs, *popt_mm)
+    
+    FittedEIT_fluosVec.append(FittedEIT_fluo)
+    
+    plt.figure()
+    plt.plot(SelectedFreqs, SelectedFluo, 'o')
+    plt.plot(SelectedFreqs, FittedEIT_fluo)
+
+plt.figure()
+for i in range(len(FluorescencesVec)):
+    plt.plot(SelectedFreqs, FluorescencesVec[i], 'o', markersize=3)
+    plt.plot(SelectedFreqs, FittedEIT_fluosVec[i])    
+
+
+plt.figure()
+plt.plot(betavec, TempMedidas, 'o', markersize=10)
+plt.xlabel('Beta')
+plt.ylabel('Temperatura medida (mK)')
+plt.axhline(T*1e3, label='Temperatura real', linestyle='--', color='red')
+plt.legend()
+plt.grid()
\ No newline at end of file

analisis/plots/20220615_CPTvariandocompensacion/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]))