CPT_plotter_20211115.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

#BUENAS MEDICIONES VARIANDO PARAMETROS

#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211115_CPT_DosLaseres_v04\Data

ALL_FILES = """000005717-IR_Scan_withcal_optimized
000005718-IR_Scan_withcal_optimized
000005719-IR_Scan_withcal_optimized
000005720-IR_Scan_withcal_optimized
000005721-IR_Scan_withcal_optimized
000005722-IR_Scan_withcal_optimized
000005723-IR_Scan_withcal_optimized
000005724-IR_Scan_withcal_optimized
000005725-IR_Scan_withcal_optimized
000005726-IR_Scan_withcal_optimized
000005910-IR_Scan_withcal_optimized
000005911-IR_Scan_withcal_optimized
000005912-IR_Scan_withcal_optimized
000005913-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']))

#%%
#VARIANDO POTENCIA TISA
#Poca estadistica, 15k mediciones

jvec = [2, 3, 4, 1, 0, 5, 6, 7, 8, 9]
Amps = [0, 0.05, 0.07, 0.1, 0.2, 0.09, 0.13, 0.12, 0.12, 0.15]


plt.figure()
i = 0
for j in jvec:
    plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'AmpTisa = {Amps[i]}', markersize=3)
    i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#plt.legend()
#%%
jsel = [2]
plt.figure()
i = 0
for j in jvec:
    if j in jsel:
        plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'AmpTisa = {Amps[i]}', markersize=3)
    i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()


#%%
#ACA ANALIZO LAS PROFUNDIDADES DE LAS RESONANCIAS OSCURAS VS LA POTENCIA DEL TISA
import seaborn as sns

sns.set_palette("tab10")

jvec = [2, 3, 4,  5, 1, 7, 8, 9, 0, 6]
Amps = [0, 0.05, 0.07, 0.09, 0.1, 0.12, 0.12, 0.15, 0.2, 0.3] #CHEQUEADO!!!
Pots = [0, 30, 70, 125, 147, 209, 209, 320, 535, 924]

Sats = [p*7.2/924 for p in Pots]

#jvec = [2, 3, 4,  5, 1, 7, 9]
#Amps = [0, 0.05, 0.07, 0.09, 0.1, 0.12, 0.15] #CHEQUEADO!!!
#Pots = [0, 30, 70, 125, 147, 209, 320]

jvec = [2, 3, 4,  5, 1, 7, 9, 0, 6]
Amps = [0, 0.05, 0.07, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3] #CHEQUEADO!!!
Pots = [0, 30, 70, 125, 147, 209, 320, 535, 924]

Sats = [p*7.2/924 for p in Pots]

CountsDR1 = []
CountsDR2 = []
CountsDR3 = []
CountsDR4 = []
CountsLeft = []
CountsRight = []
CountsMax = []

for j in jvec:
    CountsDR1.append(Counts[j][46])
    CountsDR2.append(Counts[j][58])
    CountsDR3.append(Counts[j][68])
    CountsDR4.append(Counts[j][80])
    CountsLeft.append(Counts[j][0])
    CountsRight.append(Counts[j][-1])
    CountsMax.append(max(Counts[j]))
    
plt.errorbar(Sats, [CountsDR1[j] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR1), fmt='o', capsize=2, markersize=5, label=r'$m=-3/2$')
plt.errorbar(Sats, [CountsDR2[j] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR2), fmt='o', capsize=2, markersize=5, label=r'$m=-1/2$')
plt.errorbar(Sats, [CountsDR3[j] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR3), fmt='o', capsize=2, markersize=5, label=r'$m=1/2$')
plt.errorbar(Sats, [CountsDR4[j] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR4), fmt='o', capsize=2, markersize=5, label=r'$m=3/2$')
plt.xlabel('Sat Parameter TISA')
plt.ylabel('Fluorescence (a.u.)')
plt.legend()

#%%

plt.errorbar(Sats, [CountsDR1[j]/CountsDR1[0] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR1)/CountsDR1[0], fmt='o', capsize=2, markersize=5, label=r'$m=-3/2$')
plt.errorbar(Sats, [CountsDR2[j]/CountsDR2[0] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR2)/CountsDR2[0], fmt='o', capsize=2, markersize=5, label=r'$m=-1/2$')
plt.errorbar(Sats, [CountsDR3[j]/CountsDR3[0] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR3)/CountsDR3[0], fmt='o', capsize=2, markersize=5, label=r'$m=1/2$')
plt.errorbar(Sats, [CountsDR4[j]/CountsDR4[0] for j in range(len(CountsDR1))], yerr=2*np.sqrt(CountsDR4)/CountsDR4[0], fmt='o', capsize=2, markersize=5, label=r'$m=3/2$')
plt.xlabel('Sat Parameter TISA')
plt.ylabel('Fluorescence (a.u.)')
plt.legend()


#%%
plt.plot(Sats, [CountsDR1[j]/CountsDR1[0] for j in range(len(CountsDR1))], 'o', markersize=5, label=r'$m=-3/2$')
plt.plot(Sats, [CountsDR2[j]/CountsDR2[0] for j in range(len(CountsDR1))], 'o', markersize=5, label=r'$m=-1/2$')
plt.plot(Sats, [CountsDR3[j]/CountsDR3[0] for j in range(len(CountsDR1))], 'o', markersize=5, label=r'$m=1/2$')
plt.plot(Sats, [CountsDR4[j]/CountsDR4[0] for j in range(len(CountsDR1))], 'o', markersize=5, label=r'$m=3/2$')
plt.xlabel('Sat Parameter TISA')
plt.ylabel('Proportional fluorescence (a.u.)')
plt.legend()



#%%

def Lorentz(x, gamma, x0, scale, offset):
    return (scale/(np.pi*gamma))*((gamma**2)/(((x-x0)**2)+(gamma**2)))+offset

jsel = 12

i = 0
    
FreqsS = [2*f*1e-6 for f in Freqs[jsel]]
CountsS = Counts[jsel]    

Freqslong = np.arange(min(FreqsS), max(FreqsS)+FreqsS[1]-FreqsS[0], FreqsS[1]-FreqsS[0])

FreqsLorentzLimitsLow = FreqsS[0:int((1/8)*len(FreqsS))]
FreqsLorentzLimitsHigh = FreqsS[int((7/8)*len(FreqsS)):]

CountsLorentzLimitsLow = list(CountsS[0:int((1/8)*len(CountsS))])
CountsLorentzLimitsHigh = list(CountsS[int((7/8)*len(CountsS)):])

popt, pcov = curve_fit(Lorentz, FreqsLorentzLimitsLow+FreqsLorentzLimitsHigh, CountsLorentzLimitsLow+CountsLorentzLimitsHigh, p0=[60, 420, 1000, 1000])
print(popt)

plt.figure()
plt.plot(FreqsS, CountsS, 'o-', label=f'AmpTisa = {Amps[i]}', markersize=3)
plt.plot(Freqslong, Lorentz(Freqslong, *popt))
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()


#%%
#VARIANDO POTENCIA TISA en pi
#Mucha estadistica, 30k mediciones

jvec = [10, 11, 12]
Amps = [0.3, 0.07, 0]
Pots = []

Countsfixed = [6585, 6569, 6809, 6869, 6898, 6822, 6930, 7115, 7062, 6975, 6942,
       7113, 7126, 6940, 7070, 7109, 6059, 7235, 7251, 7245, 7326, 7256,
       7463, 7326, 7151, 7292, 7312, 7228, 7225, 7276, 7265, 7070, 7276,
       7045, 7103, 6938, 6802, 6702, 6559, 6281, 6006, 5808, 5583, 5254,
       4715, 4490, 4474, 4460, 4674, 5011, 5321, 5775, 6095, 6494, 6594,
       6590, 6471, 6076, 5695, 5563, 5690, 5779, 5878, 6195, 6437, 6481,
       6408, 6038, 5724, 5616, 5531, 5927, 6138, 6498, 6946, 6901, 7019,
       7051, 6770, 6308, 5941, 5660, 5342, 5502, 5780, 6171, 6364, 6787,
       7030, 7497, 7782, 8161, 8337, 8645, 8744, 8691, 9160, 9131, 9021,
       9271, 9305, 9330, 9331, 9269, 9411, 9182, 9333, 9340, 9129, 9225,
       9034, 9071, 9140, 8961, 8945, 8916, 8863, 8799, 7934, 8688, 8914]

plt.figure()
i = 0
for j in jvec:
    if j == 11:
        plt.plot([2*f*1e-6 for f in Freqs[j]], Countsfixed, 'o-', label=f'AmpTisa = {Amps[i]}', markersize=3)
    else:
        plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'AmpTisa = {Amps[i]}', markersize=3)

    i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#plt.legend()


#%%
#VARIANDO POTENCIA TISA en sigma
#Mucha estadistica, 30k mediciones

jvec = [13, 14, 15, 16]
Amps = [0.12, 0.06, 0.03, 0.01]
Pots = []

plt.figure()
i = 0
for j in jvec:
    plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'AmpTisa = {Amps[i]}', markersize=3)

    i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#plt.legend()


#%%
"""
FIGURA PARA EL PAPER CPT 2 LASERES, 1 REPUMP
"""


#FITEOSSSSS

#Variando la polarizacion del UV


phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0,  0
phiprobe = 0
titaprobe = 90

T = 0.6e-3

sg = 0.49
sp = 10
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 #con 8 fitea bien

offsetxpi = 440+1+correccion
DetDoppler = -4-correccion

FreqsDRpi_1 = [2*f*1e-6-offsetxpi+14 for f in Freqs[12]]
CountsDRpi_1 = Counts[12]

freqslongpi_1 = np.arange(min(FreqsDRpi_1), max(FreqsDRpi_1)+FreqsDRpi_1[1]-FreqsDRpi_1[0], 0.1*(FreqsDRpi_1[1]-FreqsDRpi_1[0]))


def FitEITpi(freqs, SG, SP, T):
    #temp = 4.7e-3
    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, 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*5.986e4 + 2.0905e3 for f in MeasuredFluo]
    return FinalFluo

popt, pcov = curve_fit(FitEITpi, FreqsDRpi_1, CountsDRpi_1, p0=[1, 10, 4e-3], bounds=(0, [2, 30, 1e-2]))

print(popt)

#scale = popt[2]
#offset = popt[3]

scale = 5.986e4
offset = 2.0905e3

FittedEITpi_1 = FitEITpi(freqslongpi_1, *popt)



plt.figure()
plt.errorbar(FreqsDRpi_1, CountsDRpi_1, yerr=2*np.sqrt(CountsDRpi_1), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_1, FittedEITpi_1)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')


from scipy.signal import savgol_filter as sf
import seaborn as sns

colors=sns.color_palette("rocket", 3)
colorscomp = sns.color_palette("viridis", 10)
plt.style.use('seaborn-ticks')


winl = 9
po = 3

FiltCounts = np.array([(c-offset)*100/scale for c in sf(CountsDRpi_1, winl, po)])
ErrorCounts = np.array([1*np.sqrt(c/scale) for c in CountsDRpi_1])

plt.figure()
plt.plot(FreqsDRpi_1, FiltCounts, 'o', markersize=3, color='navy')
plt.fill_between(FreqsDRpi_1, FiltCounts+ErrorCounts, FiltCounts-ErrorCounts, color='lightblue')
plt.plot(freqslongpi_1, [(c-offset)*100/scale for c in FittedEITpi_1], label='Fitted spectrum', color='darkviolet')
plt.legend()
plt.xlim(-54, 28)
plt.xlabel('Detuning probe (MHz)')
plt.ylabel('Fluorescence (a.u.)')
plt.grid()

#%%
#Esto es un ajuste de lorenziana por las afueras de la curva y la resta con el ajuste CPT para ver la profundidad de los picos


def Lorentz(x, gamma, x0, scale, offset):
    return (scale/(np.pi*gamma))*((gamma**2)/(((x-x0)**2)+(gamma**2)))+offset

jsel = 12

i = 0
    
FreqsLorentzLimitsLow = FreqsDRpi_1[0:int((1/8)*len(FreqsDRpi_1))]
FreqsLorentzLimitsHigh = FreqsDRpi_1[int((7/8)*len(FreqsDRpi_1)):]

CountsLorentzLimitsLow = list(CountsDRpi_1[0:int((1/8)*len(CountsDRpi_1))])
CountsLorentzLimitsHigh = list(CountsDRpi_1[int((7/8)*len(CountsDRpi_1)):])

popt, pcov = curve_fit(Lorentz, FreqsLorentzLimitsLow+FreqsLorentzLimitsHigh, CountsLorentzLimitsLow+CountsLorentzLimitsHigh, p0=[-6.64722445e+1,  4, -1.69385850e6,  9.24288619e2])
print(popt)

plt.figure()
plt.errorbar(FreqsDRpi_1, CountsDRpi_1, yerr=2*np.sqrt(CountsDRpi_1), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_1, Lorentz(freqslongpi_1, *popt))
plt.plot(freqslongpi_1, FittedEITpi_1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()

plt.figure()
plt.plot(freqslongpi_1, np.array(Lorentz(freqslongpi_1, *popt))-np.array(FittedEITpi_1))
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()



#%%
#Ajuste con el repump prendido muy fuerte, 

phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0,  0
phiprobe = 0
titaprobe = 90

T = 0.6e-3

sg = 0.544
sp = 9.537
sr = 0
DetRepump = 0


lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres


u = 33.5e6

B = (u/(2*np.pi))/c

correccion = 4 #con 8 fitea bien

offsetxpi = 440+1+correccion
DetDoppler = -4-correccion

FreqsDRpi_2 = [2*f*1e-6-offsetxpi+14 for f in Freqs[10]]
CountsDRpi_2 = Counts[10]

freqslongpi_2 = np.arange(min(FreqsDRpi_2), max(FreqsDRpi_2)+FreqsDRpi_2[1]-FreqsDRpi_2[0], 0.1*(FreqsDRpi_2[1]-FreqsDRpi_2[0]))


def FitEITpi(freqs, SR, DETREPUMP, scale, offset):
    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*scale + offset for f in MeasuredFluo]
    return FinalFluo

popt, pcov = curve_fit(FitEITpi, FreqsDRpi_2, CountsDRpi_2, p0=[15, 71, 1e4, 1e5], bounds=((10, 50, 0, 0), (100, 300, 1e6, 1e6)))

print(popt)


Sat_2 = popt[0]
Det_2 = popt[1]

FittedEITpi_2 = FitEITpi(freqslongpi_2, *popt)

plt.figure()
plt.errorbar(FreqsDRpi_2, CountsDRpi_2, yerr=2*np.sqrt(CountsDRpi_2), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_2, FittedEITpi_2)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')



#%%
#Ajuste con el repump prendido muy fuerte, 

phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0,  0
phiprobe = 0
titaprobe = 90

T = 0.6e-3

sg = 0.544
sp = 9.537
sr = 0
DetRepump = 0


lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres


u = 33.5e6

B = (u/(2*np.pi))/c

correccion = 4 #con 8 fitea bien

offsetxpi = 440+1+correccion
DetDoppler = -4-correccion

FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[11]]
CountsDRpi_3 = Counts[11]

freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))


def FitEITpi(freqs, SR, DETREPUMP, scale, offset):
    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*scale + offset for f in MeasuredFluo]
    return FinalFluo

popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[15, 71, 1e4, 1e5], bounds=((0, 20, 0, 0), (100, 300, 1e6, 1e6)))

print(popt)

Sat_3 = popt[0]
Det_3 = popt[1]

FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)

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')

#%%
import seaborn as sns

#sns.set_theme(style="whitegrid")
sns.set_palette("rocket")

#NormCountsDRpi_2 = [(c-popt[3])/popt[2] for c in CountsDRpi_2]
#NormCountsDRpi_3 = [(c-popt[3])/popt[2] for c in CountsDRpi_3]
#NormCountsDRpi_1 = [(c-popt[3])/popt[2] for c in CountsDRpi_1]

#NormFittedEITpi_2 = [(c-popt[3])/popt[2] for c in FittedEITpi_2]
#NormFittedEITpi_3 = [(c-popt[3])/popt[2] for c in FittedEITpi_3]
#NormFittedEITpi_1 = [(c-popt[3])/popt[2] for c in FittedEITpi_1]




NormCountsDRpi_2 = [(c-0)/popt[2] for c in CountsDRpi_2]
NormCountsDRpi_3 = [(c-0)/popt[2] for c in CountsDRpi_3]
NormCountsDRpi_1 = [(c-0)/popt[2] for c in CountsDRpi_1]

NormFittedEITpi_2 = [(c-0)/popt[2] for c in FittedEITpi_2]
NormFittedEITpi_3 = [(c-0)/popt[2] for c in FittedEITpi_3]
NormFittedEITpi_1 = [(c-0)/popt[2] for c in FittedEITpi_1]



plt.figure()
plt.plot(freqslongpi_2, NormFittedEITpi_2, zorder=2)
plt.errorbar(FreqsDRpi_2, NormCountsDRpi_2, yerr=(np.sqrt(CountsDRpi_2))/popt[2], fmt='o', capsize=2, markersize=2, label=f'Sat Rep: {round(np.sqrt(Sat_2), 1)}', zorder=1)
plt.plot(freqslongpi_3, NormFittedEITpi_3, zorder=2)
plt.errorbar(FreqsDRpi_3, NormCountsDRpi_3, yerr=(np.sqrt(CountsDRpi_3))/popt[2], fmt='o', capsize=2, markersize=2, label=f'Sat Rep: {round(np.sqrt(Sat_3), 1)}', zorder=1)
plt.plot(freqslongpi_2, NormFittedEITpi_1, zorder=2)
plt.errorbar(FreqsDRpi_2, NormCountsDRpi_1, yerr=(np.sqrt(CountsDRpi_1))/popt[2], fmt='o', capsize=2, markersize=2, label=f'Sat Rep: 0', zorder=1)
plt.legend(markerscale=3, loc='lower right')
#plt.ylim(0.018, 0.14)
plt.xlim(-55, 35)
plt.xlabel('Repump Detuning [MHz]')
plt.ylabel('Norm. Fluorescence [a.u.]')

plt.savefig('CPT_3lasers.svg')