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

analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/lolo_modelo_full_8niveles.py

@@ -7,6 +7,11 @@
 
 reingenieria del código que anda
 
+Surge de fusionar
+Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
+Data/EITfit/MM_eightLevel_2repumps_python_scripts.py
+
+
 MAPA de FUNCIONES
 
 CPTspectrum8levels_MM

analisis/plots/20231123_CPTconmicromocion3/lolo_analisis_superajuste.py

 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Ploteo de datos y ajustes
+Ploteo de datos y ajustes del barrido en voltaje del Endcap
+equivalente a pasear el ion en algun entorno del punto de compensación ideal
+y ver los efectos de micromoción (y tal vez temperatura)
+
+
+
 @author: lolo
 """
 
@@ -24,6 +29,7 @@ from time import time
 
 # /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
 
+
 from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
 
 
@@ -405,7 +411,7 @@ for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,sele
     ax.grid(True, ls=":")
 
     print(f'listo med {selectedcurve}')
-    print(popt_3_SA)
+    # print(popt_3_SA)
 
 
 for ax in axx[:,0]:
@@ -452,58 +458,86 @@ ax.set_xticks(num_med)
 ax.set_xlabel('Num. de medición')
 
 
-#%%
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Endcap hiperbola (con residuos)
 """
-Grafico distintas variables que salieron del SUper ajuste
+Veamos cómo varía el Beta con el voltaje del endcap
 """
 
 import seaborn as sns
 paleta = sns.color_palette("rocket")
 
-voltages_dcA = Voltages[0][1:10]
+
+I = slice(None,9)
+
+voltages_dcA  = Voltages[0][SelectedCurveVec]
+
+
 
 def lineal(x,a,b):
     return a*x+b
 
-def hiperbola(x,a,b,c,x0):
-    return a*np.sqrt(((x-x0)**2+c**2))+b
+def hiperbola(x,a,y0,b,x0):
+    """
+    Hiperbola de ecuación:
+        1 =(y-y0)²/a² - (x-x0)²/b² 
+    """
+    return a*np.sqrt(((x-x0)**2+b**2))+y0
 
-hiperbola_or_linear = True
+es_hiperbola = False
 
-if hiperbola_or_linear:
-    popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
+    
+# par_inicial = (100,0.1,1,-0.15)
+#               a   y0   b  x0
+par_inicial = (12,0.1,1,-0.13)
+
+
+popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
+
+xhip = np.linspace(-0.23,0.005,200)
 
-    xhip = np.linspace(-0.23,0.005,200)
+# plt.figure()
+# plt.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+# plt.plot(xhip,hiperbola(xhip,*popthip))
+# # plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
+# plt.xlabel('Endcap voltage (V)')
+# plt.ylabel('Modulation factor')
+# plt.grid()
+
+
+
+
+fig, axx = plt.subplots( 2, figsize=(10,7) ,  
+                        constrained_layout=True, sharex=True,
+                        gridspec_kw=dict(height_ratios=[10,2]))
+fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
 
-    plt.figure()
-    plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-    plt.plot(xhip,hiperbola(xhip,*popthip))
-    plt.xlabel('Endcap voltage (V)')
-    plt.ylabel('Modulation factor')
-    plt.grid()
+ax = axx[0]
 
-else:
-    poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],Betas_vec[0:3])
-    poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],Betas_vec[4:])
+ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+ax.plot(xhip,hiperbola(xhip,*popthip))
+# plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
 
-    minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
-    minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
+ax.set_ylabel('Modulation factor')
 
-    xini = np.linspace(-0.23,-0.13,100)
-    xfin = np.linspace(-0.15,0.005,100)
+ax = axx[1]
 
-    plt.figure()
-    plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-    plt.plot(xini,lineal(xini,*poptini))
-    plt.plot(xfin,lineal(xfin,*poptfin))
-    plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
-    plt.xlabel('Endcap voltage (V)')
-    plt.ylabel('Modulation factor')
-    plt.grid()
+ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
+            yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+
+ax.set_ylabel('Res.')
+ax.set_xlabel('Endcap voltage (V)')
+
+for ax in axx:
+    ax.grid(True, ls=":", color='lightgray')
 
 
 print([t*1e3 for t in Temp_vec])
 
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Este hay que armarlo aún
+
 plt.figure()
 plt.errorbar(voltages_dcA,[t*1e3 for t in Temp_vec],yerr=[t*1e3 for t in ErrorTemp_vec],fmt='o',capsize=5,markersize=5,color=paleta[3])
 # plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
@@ -514,30 +548,11 @@ plt.ylabel('Temperature (mK)')
 plt.grid()
 #plt.ylim(0,2)
 
-#%%
-"""
-Ahora hago un ajuste con una hiperbola porque tiene mas sentido, por el hecho
-de que en el punto optimo el ion no esta en el centro de la trampa
-sino que esta a una distancia d
-"""
-def hiperbola(x,a,b,c,x0):
-    return a*np.sqrt(((x-x0)**2+c**2))+b
-
-popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
-
-xhip = np.linspace(-0.23,0.005,200)
-
-plt.figure()
-plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-plt.plot(xhip,hiperbola(xhip,*popthip))
-plt.xlabel('Endcap voltage (V)')
-plt.ylabel('Modulation factor')
-plt.grid()
-
 
 
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Ajuste de los betas y la temperatura
 
-#%%
 from scipy.special import jv
 
 

analisis/plots/20231212_Bstability/lolo_CPT_plotter_20231212.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Acá hay un análisis de la estabilidad del campo magnético (presuntamente).
+Se midieron N veces el mismo espectro y se analiza la variación del spliting
+de picos CPT que se debe al efecto Zeeman.
+
+El gráfico final muestra el corrimiento en procentual
+
+
+@author: lolo
+"""
+
+
+
+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 una resonancia oscura DD multiples veces a lo largo de una noche para ver estabilidad de B
+"""
+
+#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/20231212_Bstability/Data/')
+
+# CPT_FILES = """000016432-IR_Scan_withcal_optimized
+# 000016433-IR_Scan_withcal_optimized
+# 000016434-IR_Scan_withcal_optimized
+# 000016435-IR_Scan_withcal_optimized
+# 000016436-IR_Scan_withcal_optimized
+# 000016437-IR_Scan_withcal_optimized
+# 000016438-IR_Scan_withcal_optimized
+# 000016439-IR_Scan_withcal_optimized
+# 000016440-IR_Scan_withcal_optimized
+# 000016441-IR_Scan_withcal_optimized
+# 000016442-IR_Scan_withcal_optimized
+# 000016443-IR_Scan_withcal_optimized
+# """ 
+
+folder = '../20231212_Bstability/Data/'
+CPT_FILES = f"""
+{folder}/000016434-IR_Scan_withcal_optimized
+{folder}/000016435-IR_Scan_withcal_optimized
+{folder}/000016436-IR_Scan_withcal_optimized
+{folder}/000016437-IR_Scan_withcal_optimized
+{folder}/000016438-IR_Scan_withcal_optimized
+{folder}/000016439-IR_Scan_withcal_optimized
+{folder}/000016440-IR_Scan_withcal_optimized
+{folder}/000016441-IR_Scan_withcal_optimized
+{folder}/000016442-IR_Scan_withcal_optimized
+{folder}/000016443-IR_Scan_withcal_optimized
+""".strip()
+
+
+CALIB_FILES = f"""{folder}/000016430-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(CPT_FILES))
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+Freqs = []
+
+CalibCounts = []
+CalibFreqs = []
+
+AmpTisa = []
+UVCPTAmp = []
+No_measures = []
+Voltages = []
+
+for i, fname in enumerate(CPT_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']['IR1_Frequencies']))
+    Counts.append(np.array(data['datasets']['data_array']))
+    #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']))
+    Voltages.append(np.array(data['datasets']['scanning_voltages']))
+
+
+for i, fname in enumerate(CALIB_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    CalibFreqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    CalibCounts.append(np.array(data['datasets']['counts_spectrum']))
+
+
+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
+
+
+CountsSplit = []
+k=0
+for k in range(len(Counts)):
+    CountsSplit.append(Split(Counts[k],len(Freqs[k])))
+
+#%%
+from scipy.optimize import curve_fit
+
+def lorentzian(x,A,B,x0,g,C):
+    return 2*(A/np.pi)*g/(g**2 + 4*(x-x0)**2)+B+C*(x-x0)
+
+Freqscal = [2*f*1e-6 for f in CalibFreqs[0]]
+Countscal = CalibCounts[0]
+
+popt_dr1, pcov_dr1 = curve_fit(lorentzian,Freqscal[37:47],Countscal[37:47],p0=(-1000,1000,436,1,1))
+popt_dr2, pcov_dr2 = curve_fit(lorentzian,Freqscal[90:120],Countscal[90:120],p0=(-1000,1000,443,1,1))
+
+DeltaFreqs = popt_dr2[2]-popt_dr1[2]
+ZeroFrequency = 0.5*(popt_dr2[2]+popt_dr1[2])
+
+plt.figure()
+plt.plot(Freqscal,Countscal,'o')
+plt.plot(Freqscal,lorentzian(Freqscal,*popt_dr1))
+plt.plot(Freqscal,lorentzian(Freqscal,*popt_dr2))
+plt.axvline(ZeroFrequency)
+
+
+print(DeltaFreqs)
+
+"""
+Estas cuentas estan en el cuaderno SMILE MORE WORRY LESS pag 25.
+La resonancia de la izquierda esta a (-4/5)*u. La de la derecha esta a (4/5)*u. 
+Por ende la diferencia es (8/5)*u.
+Definimos u como 1.4 MHz/G * B. Entonces Despejamos B facilmente.
+"""
+
+ub = 9.27e-24
+h = 6.63e-34
+u = 1e-6*(ub/h)*1e-4 #en unidades de MHz/G
+
+MagneticField = DeltaFreqs/((8/5)*u)
+
+print(f'Magnetic field: {MagneticField}')
+
+
+
+#%%
+
+"""
+Ploteo la cpt de referencia / plotting the reference CPT
+"""
+
+freqs = [2*f*1e-6 for f in Freqs[0]]
+
+def lorentzian(x,A,B,x0,g,C):
+    return 2*(A/np.pi)*g/(g**2 + 4*(x-x0)**2)+B+C*(x-x0)
+
+# ii_plot = 11
+# jj_plot = 0
+
+ii_plot = 9
+jj_plot = 0
+
+ii_problematic = []
+jj_problematic = []
+
+Centers = []
+Widths = []
+test = []
+for ii in range(len(CountsSplit)):
+    for jj in range(len(CountsSplit[0])):    
+#        print(ii)
+#        print(jj)            
+        try:
+            if ii==2 and jj==11:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs[:-10], CountsSplit[ii][jj][:-10],p0=(-1000,1000,436,1,1))
+            
+            elif ii==2 and jj==12:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs[40:], CountsSplit[ii][jj][40:],p0=(-1000,1000,436,1,1))
+            
+            elif ii==4 and jj==1:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs[:-86], CountsSplit[ii][jj][:-86],p0=(-1000,1000,436,1,1))
+            
+            elif ii==4 and jj==2:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==4 and jj==7:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==4 and jj==12:
+                popt_lorentz = [0,0,0,0,0]
+    
+            elif ii==4 and jj==13:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==4 and jj==14:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==11 and jj==2:
+                popt_lorentz = [0,0,0,0,0]                
+
+            elif ii==11 and jj==3:
+                popt_lorentz = [0,0,0,0,0]                
+            
+            else:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs, CountsSplit[ii][jj],p0=(-1000,1000,436,1,1))
+                
+            if popt_lorentz[2]>435.95 or popt_lorentz[2]<435.8:
+                if popt_lorentz[2]==0:
+                    pass
+                else:
+                    ii_problematic.append(ii)
+                    jj_problematic.append(jj)
+                    
+        except:
+            popt_lorentz=[0,0,0,0]
+            print("except")
+        print(ii,jj)
+        if ii == ii_plot and jj == jj_plot:
+            print('MATCH')
+            test.append(popt_lorentz)
+        Centers.append(popt_lorentz[2])
+        Widths.append(popt_lorentz[3])
+
+prob = 4
+
+print(ii_problematic[prob])
+print(jj_problematic[prob])
+
+kk=-83
+
+plt.figure()
+plt.plot(freqs, CountsSplit[ii_problematic[prob]][jj_problematic[prob]])
+plt.plot(freqs[kk], CountsSplit[ii_problematic[prob]][jj_problematic[prob]][kk],'o',markersize=10)
+plt.plot(freqs,lorentzian(freqs,*test[0]))
+#%%
+
+"""
+Usando que la DR de la izquierda esta a (-4/5)u, donde u = 1.4 MHz/G * B, 
+despejo y convierto la posicion de esa resonancia a campo magnetico
+"""
+
+def ConvertFreqsToMagneticField(f,zerofreq,u):
+    return np.abs(f-zerofreq)*(5/4)/(1.4)
+
+
+lentotal = len(CountsSplit)*len(CountsSplit[0])
+medtime=4/60
+timevec = np.linspace(0,medtime*lentotal, lentotal)
+
+plt.figure()
+plt.plot(timevec[4:],ConvertFreqsToMagneticField(Centers,ZeroFrequency,u)[4:],'o')
+plt.ylim(3.670,3.730)
+plt.xlabel('Time (h)')
+plt.ylabel('Magnetic field (G)')
+
+plt.figure()
+plt.plot(timevec[4:],[100*c/3.718 for c in ConvertFreqsToMagneticField(Centers,ZeroFrequency,u)][4:],'o')
+plt.ylim(98.5,100.1)
+plt.xlabel('Time (h)')
+plt.ylabel('Magnetic field variation (percent)')
+
+

analisis/plots/20231218_CPT_DosLaseres_Reflotoajustes/lolo_CPT_plotter_20231218.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+??
+
+
+@author: lolo
+"""
+
+
+
+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
+
+"""
+CPT con tres laseres pero lso dos IR son el mismo entonces las DD son mas finas
+"""
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211223_CPT_DosLaseres_v07_ChristmasSpecial\Data
+
+folder = '../20231218_CPT_DosLaseres_Reflotoajustes/Data'
+
+ALL_FILES = f"""
+{folder}/000016420-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']['IR1_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']))
+
+
+
+#%%
+
+#Barriendo angulo del IR con tisa apagado
+
+jvec = [0]
+
+jselected = jvec
+
+plt.figure()
+i = 0
+for j in jvec:
+    if j in jselected:
+        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.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+plt.grid()
+plt.legend()
+
+#%%
+from scipy.optimize import curve_fit
+import time
+
+cwd = os.getcwd()
+os.chdir('../20231123_CPTconmicromocion3')
+from Data.EITfit.lolo_modelo_full_3niveles import GenerateNoisyCPT_fit
+os.chdir(cwd)
+
+# from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  90
+phiprobe = 0
+titaprobe = 0.1
+
+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
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 
+alpha = 0
+
+
+drivefreq = 2*np.pi*22.135*1e6
+noiseamplitude = 0
+
+selectedcurve=0
+
+FreqsDR = Freqs[selectedcurve]
+CountsDR = Counts[selectedcurve]
+   
+freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+CircPr = 1
+alpha = 0
+
+def FitEIT_MM_1ion(Freqs, offset, DetDoppler, DetRepump, SG, SP, SR, SCALE1, OFFSET, TEMP, U, plot=False):
+#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
+    # BETA1 = 0
+    # SG = 0.6
+    # SP = 8.1
+    # TEMP = 0.2e-3
+    # U = 32.5e6
+    freqs = [2*f*1e-6-offset for f in Freqs]
+  
+    #Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    Detunings, Fluorescence1 = 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)
+
+    ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
+    if plot:
+        return ScaledFluo1, Detunings
+    else:
+        return ScaledFluo1
+    #return ScaledFluo1
+
+do_fit = True
+if do_fit:
+    popt_1, pcov_1 = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[430, -25, 12, 0.9, 6.2, 3, 3e4, 2e3, 0.5e-3, 32e6], bounds=((0, -100, -20, 0, 0, 0, 0, 0, 0,20e6), (1000, 0, 50, 2, 20, 20, 5e6, 5e4, 15e-3,40e6)))
+
+    FittedEITpi_1_short, Detunings_1_short = FitEIT_MM_1ion(FreqsDR, *popt_1, plot=True)
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    FittedEITpi_1_long, Detunings_1_long = FitEIT_MM_1ion(freqslong, *popt_1, plot=True)
+
+#%%
+
+plt.figure()
+plt.errorbar(Detunings_1_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='red', alpha=0.5, capsize=2, markersize=2)
+plt.plot(Detunings_1_long, FittedEITpi_1_long, color='darkolivegreen', linewidth=3, label='med 1')
+#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+plt.xlabel('Detuning (MHz)')
+plt.ylabel('Counts')
+#plt.xlim(-20,0)
+plt.legend(loc='upper left', fontsize=20)
+plt.grid()
+
+#%%
+# u = 32.5e6
+
+# B = (u/(2*np.pi))/c
+
+# correccion = 8 #con 8 fitea bien
+
+# offsetxpi = 440+1+correccion
+# DetDoppler = -5.0-correccion
+
+# FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[5]]
+# CountsDRpi_3 = Counts_B[5]
+
+# freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
+
+# #[1.71811842e+04 3.34325038e-17]
+
+# def FitEITpi(freqs, SG, SP):
+#     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)
+#     FinalFluo = [f*6.554e4 + 1.863e3 for f in MeasuredFluo]
+#     return FinalFluo
+
+# popt_tisaoff, pcov_tisaoff = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5], bounds=((0, 0), (2, 10)))
+
+# print(popt_tisaoff)
+
+# Sat_3 = popt_tisaoff[0]
+# Det_3 = popt_tisaoff[1]
+
+# FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt_tisaoff)
+
+# plt.figure()
+# plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
+# plt.plot(freqslongpi_3, FittedEITpi_3)
+# #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+
+# FreqsCalibradas_B = FreqsDRpi_3
+
+
+
+
+
+
+
+
+
+