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

analisis/plots/20230707_MotionalSpectrum_v2/MotionalOptic.py

@@ -47,6 +47,7 @@ for i, fname in enumerate(MOTIONAL_FILES.split()):
     Counts_roi2.append(np.array(data['datasets']['counts_roi2']))
     IR1_amp_vec.append(np.array(data['datasets']['IR1_amp']))
 
+Potencias_IR = [0,20,50]
 
 #%%
 
@@ -54,7 +55,7 @@ for i, fname in enumerate(MOTIONAL_FILES.split()):
 Ploteo una curva para buscar su minimo
 """
 
-jvec = [0,1,2]
+jvec = [2,1,0]
 
 plt.figure()
 i = 0
@@ -62,75 +63,15 @@ i = 0
 kmin = 106
 
 for j in jvec:
-    plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts_roi1[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label=f'IR1_amp: {IR1_amp[j]}')
+    plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts_roi1[j], yerr=0.1*np.sqrt(Counts_roi1[j]), fmt='o', capsize=2, markersize=2, label=f'IR1 power: {Potencias_IR[j]} uW')
     #plt.plot([1*f*1e-3 for f in RealFreqs[j]][kmin], Counts[j][kmin], 'o', markersize=15)
     i = i + 1
-plt.xlabel('Frecuencia (kHz)')
-plt.ylabel('counts')
-#plt.xlim(782,787)
-#plt.ylim(2000,5000)
+plt.xlabel('Frecuencia mod IR2 (kHz)')
+plt.ylabel('Cuentas/400 ms')
+plt.xlim(780,810)
+plt.ylim(18680,19650)
 plt.grid()
-plt.legend(loc='upper left')
-
-
-
-
-
-#%%
-
-"""
-Ploteo las curvas de referencia 
-"""
-
-jvec = [0, 1, 2, 3, 8]
-
-
-
-plt.figure()
-i = 0
-for j in jvec:
-    plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label=f"Pot: {Potencias[j]} uW")
-    i = i + 1
-plt.xlabel('Frecuencia (kHz)')
-plt.ylabel('counts')
-#plt.xlim(782,787)
-plt.ylim(2000,5000)
-plt.grid()
-plt.legend(loc='upper left')
-
-
-#%%
-"""
-Busco el cociente entre el minimo y el maximo
-"""
-kmins = [106, 106, 106, 106, 110, 110, 110, 112, 108]
-
-
-minabs = np.min(Counts[2])
-
-MinimosFluos = []
-MaximosFluos = []
-CocientesFluos = []
-ErrorCocientesFluos = []
-
-for m in range(9):
-    mini = Counts[m][kmins[m]]-minabs
-    maxi = Counts[m][0]-minabs
-    MinimosFluos.append(mini)
-    MaximosFluos.append(maxi)
-    CocientesFluos.append(mini/maxi)
-    ErrorCocientesFluos.append((mini/maxi)*(np.sqrt(mini)/mini + np.sqrt(maxi)/maxi))
-    
-    
-plt.figure()
-plt.errorbar(PotenciasUsadas, CocientesFluos, yerr=ErrorCocientesFluos, fmt='o', capsize=5, markersize=15)
-plt.axhline(1)
-plt.xlabel('Potencia IR (uW)')
-
-
-
-
-
+plt.legend(loc='lower left')
 
 
 

analisis/plots/20230713_EspectrosCristal6iones/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/20230713_EspectrosCristal6iones/Espectros_cristal.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/20230713_EspectrosCristal6iones/Data/')
+
+
+MOTIONAL_FILES = """000013259-UV_Scan_withcal_optimized_andor
+000013260-UV_Scan_withcal_optimized_andor
+000013261-UV_Scan_withcal_optimized_andor
+000013262-UV_Scan_withcal_optimized_andor
+000013263-UV_Scan_withcal_optimized_andor
+000013264-UV_Scan_withcal_optimized_andor
+000013266-UV_Scan_withcal_optimized_andor
+000013267-UV_Scan_withcal_optimized_andor
+000013268-UV_Scan_withcal_optimized_andor
+000013269-UV_Scan_withcal_optimized_andor
+000013270-UV_Scan_withcal_optimized_andor
+000013271-UV_Scan_withcal_optimized_andor
+000013272-UV_Scan_withcal_optimized_andor
+"""
+
+
+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(MOTIONAL_FILES))
+
+#%%
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+CountsRoi1 = []
+CountsRoi2 = []
+CountsRoi3 = []
+CountsRoi4 = []
+CountsRoi5 = []
+CountsRoi6 = []
+CountsRoi7 = []
+#Amplitudes = []
+UV_Freqs    = []
+#IR_amps    = []
+
+for i, fname in enumerate(MOTIONAL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CountsRoi1.append(np.array(data['datasets']['counts_roi1']))
+    CountsRoi2.append(np.array(data['datasets']['counts_roi2']))
+    CountsRoi3.append(np.array(data['datasets']['counts_roi3']))
+    CountsRoi4.append(np.array(data['datasets']['counts_roi4']))
+    CountsRoi5.append(np.array(data['datasets']['counts_roi5']))
+    CountsRoi6.append(np.array(data['datasets']['counts_roi6']))
+    CountsRoi7.append(np.array(data['datasets']['counts_roi7']))
+    UV_Freqs.append(np.array(data['datasets']['UV_Frequencies']))
+    #IR_amps.append(np.array(data['datasets']['IR1_measurement_amp']))
+
+
+
+#%%
+
+"""
+En cristal de 7 iones (uno de ellos oscuro) veo espectros. Primero espectros uv.
+La roi1 es la general. Las demas son de cada uno de los 6 ioens brillantes del cristal.
+"""
+
+i = 0
+
+jvec=[0,1,2,3,5]
+
+step=0.1e8
+
+Desplazamientos = [0, 0.8*step, 1*step, -1*step, -2*step]
+
+plt.figure()
+for j in jvec:
+    if i in [2,4]:
+        #plt.errorbar(Amplitudes[j], CountsRoi1[j], yerr=np.sqrt(CountsRoi1[j]), color='red', fmt='-o', capsize=2, markersize=2)
+        #plt.plot(Amplitudes[j][1:], CountsRoi1[j][1:], 'o',color='red', markersize=2,label=f'UVamp: {UV_amps[j]}')
+        plt.plot([Desplazamientos[i]+f for f in UV_Freqs[j][1:]], CountsRoi3[j][1:], '-o', markersize=2)
+    i = i + 1
+plt.xlabel('Frecuencia')
+plt.ylabel('Cuentas ROI')
+#plt.xlim(0.05,0.23)
+#plt.ylim(7800,8550)
+plt.grid()
+plt.legend()
+
+#%%
+
+#mergeo mediciones porque medi variando el piezoB para tener mas rango
+
+Frequencies_vector = []
+Counts_vector = []
+
+kfin1 = 37
+kin2 = 9
+
+for counts in [CountsRoi1, CountsRoi2, CountsRoi3, CountsRoi4, CountsRoi5, CountsRoi6, CountsRoi7]:
+    Frequencies_vector.append([1e-6*2*f for f in [Desplazamientos[4]+f for f in UV_Freqs[5][1:kfin1]]+list(UV_Freqs[2][kin2:])])
+    Counts_vector.append(list(counts[5][1:kfin1])+list(counts[2][kin2:]))
+
+ivecs = [3,4]
+
+#ivecs = [2, 5, 6]
+
+#ivecs = [1]
+
+plt.figure()
+for i in range(len(Frequencies_vector)):
+    if i in ivecs:
+        plt.plot(Frequencies_vector[i], Counts_vector[i],'-o')
+plt.grid()
+plt.xlabel('Frequency (MHz)')
+plt.ylabel('Counts')
+
+#%%
+ftrap=22.1
+#ahora intento ajustarlos con modelo con micromocion
+
+from scipy.special import jv
+from scipy.optimize import curve_fit
+
+def MicromotionSpectra(det, A, beta, x0, gamma, offset):
+    ftrap=22.1
+    #gamma=30
+    P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+offset
+    i = 1
+    #print(P)
+    while i <= 1:
+        P = P + A*((jv(i, beta))**2)/((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2) + A*((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2) 
+        i = i + 1
+        #print(P)
+    return P
+
+
+popt_vec = []
+pcov_vec = []
+
+
+#uso como refe k=3
+jref=3
+popt_ref, pcov_ref = curve_fit(MicromotionSpectra, Frequencies_vector[jref], Counts_vector[jref], p0=[1000, 2, 274, 90, 14000], bounds=((0,0,200,20,0),(1e7,100,600,1000,25650)))
+
+freqslong = np.arange(min(Frequencies_vector[jref]), max(Frequencies_vector[jref])+100, (Frequencies_vector[jref][1]-Frequencies_vector[jref][0])*0.01)
+
+print(popt_ref)
+plt.figure()
+for j in range(1,len(Frequencies_vector)):
+    plt.plot(Frequencies_vector[j], Counts_vector[j])
+    if j == jref:
+        plt.plot(freqslong, MicromotionSpectra(freqslong, *popt_ref))
+
+for i in range(5):
+    plt.axvline(popt_ref[2]-i*ftrap, linestyle='dashed', color='black', linewidth=1, zorder=0)
+plt.grid()
+#%%
+for i in range(len(Frequencies_vector)):
+    if i != jref:
+        popt, pcov = curve_fit(MicromotionSpectra, Frequencies_vector[i], Counts_vector[i], p0=[popt_ref[0], 5, popt_ref[2], 60, popt_ref[4]], bounds=((popt_ref[0]-0.001*popt_ref[0],0,popt_ref[2]-0.001*popt_ref[2],0,popt_ref[4]-0.001*popt_ref[4]),(popt_ref[0]+0.001*popt_ref[0],100,popt_ref[2]+0.001*popt_ref[2],300, popt_ref[4]+0.001*popt_ref[4])))
+        popt_vec.append(popt)
+        pcov_vec.append(pcov)
+    else:
+        popt_vec.append(popt_ref)
+        pcov_vec.append(pcov_ref)
+        
+ftrap=22.1
+
+jeval=1
+
+freqslong = np.arange(min(Frequencies_vector[jeval]), max(Frequencies_vector[jeval])+100, (Frequencies_vector[jeval][1]-Frequencies_vector[jeval][0])*0.01)
+
+print(popt_vec[jeval])
+plt.figure()
+plt.plot(Frequencies_vector[jeval], Counts_vector[jeval])
+plt.plot(freqslong, MicromotionSpectra(freqslong, *popt_ref))
+plt.axvline(popt_ref[2], linestyle='dashed')
+plt.axvline(popt_ref[2]-ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]+ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]-2*ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]+2*ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]-3*ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]+3*ftrap, linestyle='dashed')
+
+
+
+

analisis/plots/20230714_EspectrosCristal4iones/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/20230714_EspectrosCristal4iones/Espectros_cristal.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/20230714_EspectrosCristal4iones/Data/')
+
+
+MOTIONAL_FILES = """000013292-UV_Scan_withcal_optimized_andor
+000013293-UV_Scan_withcal_optimized_andor
+000013295-UV_Scan_withcal_optimized_andor"""
+
+
+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(MOTIONAL_FILES))
+
+#%%
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+CountsRoi1 = []
+CountsRoi2 = []
+CountsRoi3 = []
+CountsRoi4 = []
+CountsRoi5 = []
+
+#Amplitudes = []
+UV_Freqs    = []
+#IR_amps    = []
+
+for i, fname in enumerate(MOTIONAL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CountsRoi1.append(np.array(data['datasets']['counts_roi1']))
+    CountsRoi2.append(np.array(data['datasets']['counts_roi2']))
+    CountsRoi3.append(np.array(data['datasets']['counts_roi3']))
+    CountsRoi4.append(np.array(data['datasets']['counts_roi4']))
+    CountsRoi5.append(np.array(data['datasets']['counts_roi5']))
+    UV_Freqs.append(np.array(data['datasets']['UV_Frequencies']))
+    #IR_amps.append(np.array(data['datasets']['IR1_measurement_amp']))
+
+
+
+#%%
+
+"""
+En cristal de 4 iones veo espectros. Primero espectros uv.
+La roi1 es la general. Las demas son de cada uno de los 4 iones brillantes del cristal.
+"""
+
+i = 0
+
+jvec=[0,2]
+
+step=0.1e8
+
+Desplazamientos = [0, -2.5*step, 1.2*step]
+
+plt.figure()
+for j in jvec:
+        #plt.errorbar(Amplitudes[j], CountsRoi1[j], yerr=np.sqrt(CountsRoi1[j]), color='red', fmt='-o', capsize=2, markersize=2)
+        #plt.plot(Amplitudes[j][1:], CountsRoi1[j][1:], 'o',color='red', markersize=2,label=f'UVamp: {UV_amps[j]}')
+    plt.plot([Desplazamientos[j]+f for f in UV_Freqs[j][1:]], CountsRoi2[j][1:], '-o', markersize=2)
+    i = i + 1
+plt.xlabel('Frecuencia')
+plt.ylabel('Cuentas ROI')
+#plt.xlim(0.05,0.23)
+#plt.ylim(7800,8550)
+plt.grid()
+plt.legend()
+
+#%%
+
+#mergeo mediciones porque medi variando el piezoB para tener mas rango
+
+Frequencies_vector = []
+Counts_vector = []
+
+kk = 2
+
+for counts in [CountsRoi1, CountsRoi2, CountsRoi3, CountsRoi4, CountsRoi5]:
+    Frequencies_vector.append([1e-6*2*f for f in [Desplazamientos[kk]+f for f in UV_Freqs[kk][1:]]])
+    Counts_vector.append(list(counts[kk][1:]))
+
+ivecs = [1,2,3,4]
+
+#ivecs = [2, 5, 6]
+
+#ivecs = [1]
+
+plt.figure()
+for i in range(len(Frequencies_vector)):
+    if i in ivecs:
+        plt.plot(Frequencies_vector[i], Counts_vector[i],'-o')
+plt.grid()
+plt.xlabel('Frequency (MHz)')
+plt.ylabel('Counts')
+
+#%%
+ftrap=22.1
+#ahora intento ajustarlos con modelo con micromocion
+
+from scipy.special import jv
+from scipy.optimize import curve_fit
+
+def MicromotionSpectra(det, A, beta, x0, gamma, offset):
+    ftrap=22.1
+    #gamma=30
+    P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+offset
+    i = 1
+    #print(P)
+    while i <= 1:
+        P = P + A*((jv(i, beta))**2)/((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2) + A*((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2) 
+        i = i + 1
+        #print(P)
+    return P
+
+
+popt_vec = []
+pcov_vec = []
+
+
+#uso como refe k=3
+jref=3
+popt_ref, pcov_ref = curve_fit(MicromotionSpectra, Frequencies_vector[jref], Counts_vector[jref], p0=[1000, 2, 274, 90, 14000], bounds=((0,0,200,20,0),(1e7,100,600,1000,25650)))
+
+freqslong = np.arange(min(Frequencies_vector[jref]), max(Frequencies_vector[jref])+100, (Frequencies_vector[jref][1]-Frequencies_vector[jref][0])*0.01)
+
+print(popt_ref)
+plt.figure()
+for j in range(1,len(Frequencies_vector)):
+    plt.plot(Frequencies_vector[j], Counts_vector[j])
+    if j == jref:
+        plt.plot(freqslong, MicromotionSpectra(freqslong, *popt_ref))
+
+for i in range(5):
+    plt.axvline(popt_ref[2]-i*ftrap, linestyle='dashed', color='black', linewidth=1, zorder=0)
+plt.grid()
+#%%
+for i in range(len(Frequencies_vector)):
+    if i != jref:
+        popt, pcov = curve_fit(MicromotionSpectra, Frequencies_vector[i], Counts_vector[i], p0=[popt_ref[0], 5, popt_ref[2], 60, popt_ref[4]], bounds=((popt_ref[0]-0.001*popt_ref[0],0,popt_ref[2]-0.001*popt_ref[2],0,popt_ref[4]-0.001*popt_ref[4]),(popt_ref[0]+0.001*popt_ref[0],100,popt_ref[2]+0.001*popt_ref[2],300, popt_ref[4]+0.001*popt_ref[4])))
+        popt_vec.append(popt)
+        pcov_vec.append(pcov)
+    else:
+        popt_vec.append(popt_ref)
+        pcov_vec.append(pcov_ref)
+        
+ftrap=22.1
+
+jeval=1
+
+freqslong = np.arange(min(Frequencies_vector[jeval]), max(Frequencies_vector[jeval])+100, (Frequencies_vector[jeval][1]-Frequencies_vector[jeval][0])*0.01)
+
+print(popt_vec[jeval])
+plt.figure()
+plt.plot(Frequencies_vector[jeval], Counts_vector[jeval])
+plt.plot(freqslong, MicromotionSpectra(freqslong, *popt_ref))
+plt.axvline(popt_ref[2], linestyle='dashed')
+plt.axvline(popt_ref[2]-ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]+ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]-2*ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]+2*ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]-3*ftrap, linestyle='dashed')
+plt.axvline(popt_ref[2]+3*ftrap, linestyle='dashed')
+
+
+
+

analisis/plots/20230720_EspectrosCristal2iones/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/20230720_EspectrosCristal2iones/Espectroscpt_cristal.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/20230713_EspectrosCristal6iones/Data/')
+
+
+MOTIONAL_FILES = """000013216-IR_Scan_withcal_optimized_andor
+"""
+
+
+
+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(MOTIONAL_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+CountsRoi1 = []
+CountsRoi2 = []
+CountsRoi3 = []
+CountsRoi4 = []
+CountsRoi5 = []
+CountsRoi6 = []
+CountsRoi7 = []
+#Amplitudes = []
+IR1_Freqs    = []
+#IR_amps    = []
+
+for i, fname in enumerate(MOTIONAL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CountsRoi1.append(np.array(data['datasets']['counts_roi1']))
+    CountsRoi2.append(np.array(data['datasets']['counts_roi2']))
+    IR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+
+#%%
+
+"""
+En cristal de 2 iones veo espectros cpt. 
+"""
+
+i = 0
+
+CountsRois = [CountsRoi1, CountsRoi2]
+
+plt.figure()
+
+f=[1]
+for counts in CountsRois:
+    plt.plot(IR1_Freqs[0][1:], [c for c in counts[0][1:]], '-o', markersize=2)
+    i=i+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Cuentas ROI')
+#plt.xlim(0.05,0.23)
+#plt.ylim(15550,16400)
+plt.grid()
+plt.legend()
+
+
+
+