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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
# Solo levanto algunos experimentos
Calib_Files = """000012744-UV_Scan_withcalib_Haeffner
000012745-UV_Scan_withcalib_Haeffner
000012749-UV_Scan_withcalib_Haeffner
000012750-UV_Scan_withcalib_Haeffner
000012751-UV_Scan_withcalib_Haeffner
000012752-UV_Scan_withcalib_Haeffner
000012753-UV_Scan_withcalib_Haeffner
000012754-UV_Scan_withcalib_Haeffner
000012755-UV_Scan_withcalib_Haeffner"""
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\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()))
#%%
Amps = []
Freqs = []
Counts = []
T_readouts = []
for i, fname in enumerate(Calib_Files.split()):
print(SeeKeys(Calib_Files))
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()))
Amps.append(np.array(data['datasets']['UV_Amplitudes']))
Freqs.append(np.array(data['datasets']['UV_Frequencies']))
Counts.append(np.array(data['datasets']['counts_spectrum']))
T_readouts.append(np.array(data['datasets']['t_readout']))
#def GetBackground(countsper100ms, )
#%%
from scipy.special import jv
def Lorentzian(f, A, x0, gamma, offset):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + offset #40 es el piso de ruido estimado
#primero muestro las meds 0 y 1 que son dos mediciones con potencia UV quizas un poco
#alta pero en la segunda el ion esta un poquito mejor compensado y se ve cómo se afina el espectro
jvec = [0,1]
plt.figure()
for j in jvec:
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.
popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[12000, 208, 30, 30])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5)
#plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2], popt[3]), label=f'FWHM {round(popt[2],1)} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
print(f'Ancho medido: {round(popt[2])} MHz')
#%%
from scipy.special import jv
from scipy.optimize import curve_fit
def Lorentzian(f, A, gamma, x0):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2))
def MicromotionSpectra(det, A, beta, x0):
ftrap=22.1
gamma=37
P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+100
i = 1
#print(P)
while i <= 2:
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
jvec = [1] #UV_cooling en 90 MHz
plt.figure()
for j in jvec:
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.
popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,0.1,215], bounds=((0,0,0),(10000000,10,300)))
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5)
#plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
plt.plot(freqslong, MicromotionSpectra(freqslong, *popt), label=f'FWHM 30 MHz')
#plt.axvline(popt[2]+2*22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
print(f'Beta: {round(popt[1],2)}')
#%%
from scipy.special import jv
def Lorentzian(f, A, x0, gamma, offset):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + offset #40 es el piso de ruido estimado
#para tres potencias distintas del UV:
jvec = [2,3,4,5]
plt.figure()
for j in jvec:
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.
popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[12000, 208, 30, 30])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5)
#plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2], popt[3]), label=f'FWHM {round(popt[2],1)} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
print(f'Ancho medido: {round(popt[2])} MHz')
#%%
from scipy.special import jv
from scipy.optimize import curve_fit
def Lorentzian(f, A, gamma, x0):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2))
def MicromotionSpectra(det, A, beta, x0, gamma):
ftrap=22.1
#gamma=30
P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+100
i = 1
#print(P)
while i <= 2:
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
jvec = [6, 7] #6, 7, 8: tres compensaciones distintas. la 8 en realidad el ion se fue un pcoo del pinhole entonces se ve mas chica
plt.figure()
for j in jvec:
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.
popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,0.1,215, 30], bounds=((0,0,0,0),(10000000,10,300,100)))
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5, label=f'Beta: {round(popt[1],2)}, Gamma: {round(popt[3],1)}')
#plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
plt.plot(freqslong, MicromotionSpectra(freqslong, *popt))
#plt.axvline(popt[2]+2*22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
print(f'Beta: {round(popt[1],2)}')