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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
#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']))
#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']))
IR1_Freqs.append(np.array(data['datasets']['IR1_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]
#CountsRois = [CountsRoi2, CountsRoi3, CountsRoi4, CountsRoi5, CountsRoi6, CountsRoi7]
CountsRois = [CountsRoi1]
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()
#%%
#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')