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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
ALL_FILES = """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
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