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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/20230510_MotionalSpectrum/Data/')
Data = []
#for ii in range(10,20):
# Data.append(f"ss{ii}.dat")
#for ii in range(40,49):
# Data.append(f"ss{ii}.dat")
FrequencyVec = []
FluoVec = []
for dd in Data:
data = np.genfromtxt(dd,
skip_header=1,
names=True,
dtype=None,
delimiter=' ')
FrequencyVec.append([data[i][1] for i in range(len(data))])
FluoVec.append([data[i][2] for i in range(len(data))])
#%%
#los voltajes son 2, 2.5, 3, 5, 7 y 9
ivec = [1,2,3,4, 5, 6, 7,8]
plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
plt.xlim(0.78,0.8)
plt.figure()
for i in ivec:
plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
plt.xlim(0.87,0.89)
#%%
#veo la menor radial en funcion del voltaje pp del driving
ivec = [1,2,3,4, 5, 6, 7, 8]
Vpp = [0.5, 2, 2.5, 3, 5, 2.5, 7, 9, 1]
plt.figure()
for i in ivec:
plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o', label=f'V={Vpp[i]} v')
plt.xlim(0.78,0.8)
plt.legend()
#%%
#lo arreglo un poco
plt.figure()
plt.plot([f*1e-6+0.001700 for f in FrequencyVec[6]], FluoVec[6],'-o', label=f'V={Vpp[6]} v')
plt.plot([f*1e-6+0.0025 for f in FrequencyVec[7]], FluoVec[7],'-o', label=f'V={Vpp[7]} v')
plt.plot([f*1e-6+0.0013 for f in FrequencyVec[8]], FluoVec[8],'-o', label=f'V={Vpp[8]} v')
plt.plot([f*1e-6 for f in FrequencyVec[3]], FluoVec[3],'-o', label=f'V={Vpp[3]} v')
plt.plot([f*1e-6 for f in FrequencyVec[4]], FluoVec[4],'-o', label=f'V={Vpp[4]} v')
plt.xlim(0.78,0.8)
plt.legend()
#%%
import scipy
ivec = [9]
plt.figure()
for i in ivec:
pics = scipy.signal.find_peaks([-1*f for f in FluoVec[i]], height=-101.5, distance=300)
plt.plot([1*f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
plt.plot(np.array(FrequencyVec[i])[pics[0]]*1e-6, -pics[1]['peak_heights'],'o')
aspectratio = (np.array(FrequencyVec[i])[pics[0]][-1]*1e-6)/(np.array(FrequencyVec[i])[pics[0]][0]*1e-6)
print(aspectratio**(-1))
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#%%
"""
Nuevas mediciones: dan mejor
"""
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataRawElectric/')
Data = []
for ii in range(1,10):
Data.append(f"s0{ii}.dat")
for ii in range(10,14):
Data.append(f"s{ii}.dat")
#for ii in range(40,49):
# Data.append(f"ss{ii}.dat")
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataRawElectric2/')
for ii in range(1,10):
Data.append(f"s0{ii}.dat")
for ii in range(10,24):
Data.append(f"s{ii}.dat")
#for ii in range(40,49):
# Data.append(f"ss{ii}.dat")
FrequencyVec = []
FluoVec = []
for dd in Data:
data = np.genfromtxt(dd,
skip_header=1,
names=True,
dtype=None,
delimiter=' ')
FrequencyVec.append([data[i][1] for i in range(len(data))])
FluoVec.append([data[i][2] for i in range(len(data))])
#%%
"""
Miro modo radial con roi chiquita, una en el medio del ion y otra en donde el ion se estira en el costadito
"""
ivec = [33]
plt.figure()
for i in ivec:
plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'-o')
#plt.xlim(0.3,0.6)
#%%
"""
Modos radiales
"""
ivec = [23]
plt.figure()
for i in ivec:
plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
#plt.xlim(0.3,0.6)
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#%%
"""
Todos los modos de una
"""
ivec = [33]
ki=3500
kf=-1500
plt.figure()
plt.plot([f*1e-6 for f in FrequencyVec[23]], FluoVec[23],'ro')
plt.plot([f*1e-6 for f in FrequencyVec[33]][ki:kf], FluoVec[33][ki:kf],'ro')
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.xlim(1.5,1.6)
#%%
"""
Miro modo radial con roi chiquita, una en el medio del ion y otra en donde el ion se estira en el costadito
"""
ivec = [0,1]
plt.figure()
plt.plot([f*1e-6 for f in FrequencyVec[0]], FluoVec[0],'o')
plt.plot([f*1e-6+0.003 for f in FrequencyVec[1]], FluoVec[1],'o')
#plt.xlim(0.3,0.6)
#%%
"""
Encuentro el primer modo relativo que es el breathing por ser sqrt(3)*wx
"""
ivec = [7]
plt.figure()
for i in ivec:
plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
plt.xlim(1.32,1.42)
plt.ylim(104.5,108.5)