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Commit 5d2f9455 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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agrego archivos de analisis de plots de CPT para el paper

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analisis/plots/20211020_CPT_DosLaseres/CPT_plotter_20211020.py 0 → 100644
View file @ 5d2f9455
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
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211020_CPT_DosLaseres\Data
ALL_FILES = """000004585-IR_Scan_withcal_optimized
000004586-IR_Scan_withcal_optimized
000004587-IR_Scan_withcal_optimized
000004589-IR_Scan_withcal_optimized
000004590-IR_Scan_withcal_optimized
000004591-IR_Scan_withcal_optimized
000004594-IR_Scan_withcal_optimized
000004597-IR_Scan_withcal_optimized
000004639-IR_Scan_withcal_optimized
000004652-IR_Scan_withcal_optimized
000004653-IR_Scan_withcal_optimized
000004654-IR_Scan_withcal_optimized
000004655-IR_Scan_withcal_optimized
000004656-IR_Scan_withcal_optimized
000004667-IR_Scan_withcal_optimized
000004668-IR_Scan_withcal_optimized
000004669-IR_Scan_withcal_optimized
000004676-IR_Scan_withcal_optimized
000004680-IR_Scan_withcal_optimized
000004681-IR_Scan_withcal_optimized
000004690-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\artiq_master\results\2021-07-14\16
Counts = []
Freqs = []
AmpTisa = []
No_measures = []
for i, fname in enumerate(ALL_FILES.split()):
print(i)
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']))
No_measures.append(np.array(data['datasets']['no_measures']))
#%%
j = 4 #Amp tisa = 0
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Amp Tisa = 0')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 6 #Amp tisa = 0.05
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Amp Tisa = 0.05')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
plt.title(r'Pol ECDL: $\sigma_+ + \sigma_-$, pol TISA: $\pi$')
#%%
j = 4 #Amp tisa = 0
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Amp Tisa = 0')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 5 #Amp tisa = 0.1
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Amp Tisa = 0.10')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
plt.title(r'Pol ECDL: $\sigma_+ + \sigma_-$, pol TISA: $\pi$')
#%%
j = 4 #Amp tisa = 0
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Amp Tisa = 0')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 3 #Amp tisa = 0.15
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Amp Tisa = 0.16')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
plt.title(r'Pol ECDL: $\sigma_+ + \sigma_-$, pol TISA: $\pi$')
#%%
j = 3 #Amp tisa = 0.15, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=r'Pol ECDL: $\sigma_+ + \sigma_-$, pol TISA: $\pi$')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 7 #Amp tisa = 0.15, pol edcl=pi, pol tisa = smsm
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=r'Pol ECDL: $\pi$, pol TISA: $\sigma_+ + \sigma_-$')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
plt.ylim(0, 550)
plt.title('Amp Tisa = 0.16')
#%%
j = 3 #Amp tisa = 0.15, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=r'Pol ECDL: $\sigma_+ + \sigma_-$, pol TISA: $\pi$')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 7 #Amp tisa = 0.15, pol edcl=pi, pol tisa = smsm
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=r'Pol ECDL: $\pi$, pol TISA: $\sigma_+ + \sigma_-$')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 4 #Amp tisa = 0
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=r'Amp Tisa = 0, Pol ECDL: $\sigma_+ + \sigma_-$')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
plt.ylim(0, 650)
plt.title('Amp Tisa = 0.16')
#%%
### NUEVAS MEDICIONES ###
j = 8 #Amp tisa = 0.15, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=r'Pol ECDL: $\sigma_+ + \sigma_-$, pol TISA: $\pi$')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
#%% LA MEJOR DE TODAS CON 4 PICOS SIN TISA
j = 9 #Amp tisa = 0, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', markersize=3, linewidth=1, label=r'Amp Tisa = 0')
#plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], np.sqrt(np.array(Counts[j])))
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
BestF = [2*f*1e-6 - 436 for f in Freqs[j]]
BestC = Counts[j]
j = 11 #Amp tisa = 0.14, pol edcl=smsm, pol tisa = pi
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', markersize=3, linewidth=1, label=r'Amp Tisa = 0.14')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 13 #Amp tisa = 0.15, pol edcl=smsm, pol tisa = pi
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', markersize=3, linewidth=1, label=r'Amp Tisa = 0.06')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.legend()
#%% LA MEJOR DE TODAS CON 4 PICOS SIN TISA
j = 14 #Amp tisa = 0, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', markersize=3, linewidth=1, label=f'Amp Tisa = {AmpTisa[j]}')
#plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], np.sqrt(np.array(Counts[j])))
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 15 #Amp tisa = 0.14, pol edcl=smsm, pol tisa = pi
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', markersize=3, linewidth=1, label=f'Amp Tisa = {AmpTisa[j]}')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 16 #Amp tisa = 0.14, pol edcl=smsm, pol tisa = pi
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', markersize=3, linewidth=1, label=f'Amp Tisa = {AmpTisa[j]}')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.legend()
plt.title('Mediciones de noche del jueves')
#%%
j = 18 #Amp tisa = 0, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([f*1e-6 for f in Freqs[j]], Counts[j], 'o', markersize=3, linewidth=1, label=f'Amp Tisa = {AmpTisa[j]}')
#plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], np.sqrt(np.array(Counts[j])))
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 19
plt.plot([f*1e-6 for f in Freqs[j]], Counts[j], 'o', markersize=3, linewidth=1, label=f'Amp Tisa = {AmpTisa[j]}')
#plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], np.sqrt(np.array(Counts[j])))
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.title('Referencia para mediciones del viernes')
#%%
j = 9 #Amp tisa = 0, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o', markersize=3, linewidth=1)
#plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], np.sqrt(np.array(Counts[j])))
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.axvline(419, linestyle='--', linewidth=1, color='red', label='Detuning UV = -22 MHz')
plt.legend()
#%%
j = 20 #Amp tisa = 0, pol edcl=smsm, pol tisa = pi
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o', markersize=3, linewidth=1)
#plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], np.sqrt(np.array(Counts[j])))
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.axvline(423, linestyle='--', linewidth=1, color='red', label='Detuning UV = -17 MHz')
plt.legend()
\ No newline at end of file
analisis/plots/20211026_CPT_DosLaseres_v02/CPT_plotter_20211026.py 0 → 100644
View file @ 5d2f9455
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
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211026_CPT_DosLaseres_v02\Data
ALL_FILES = """000004758-IR_Scan_withcal_optimized
000004759-IR_Scan_withcal_optimized
000004760-IR_Scan_withcal_optimized
000004761-IR_Scan_withcal_optimized
000004762-IR_Scan_withcal_optimized
000004768-IR_Scan_withcal_optimized
000004769-IR_Scan_withcal_optimized
000004770-IR_Scan_withcal_optimized
000004771-IR_Scan_withcal_optimized
000004800-IR_Scan_withcal_optimized
000004801-IR_Scan_withcal_optimized
000004802-IR_Scan_withcal_optimized
000004805-IR_Scan_withcal_optimized
000004806-IR_Scan_withcal_optimized
000004807-IR_Scan_withcal_optimized
000004808-IR_Scan_withcal_optimized
000004809-IR_Scan_withcal_optimized
000004810-IR_Scan_withcal_optimized
000004811-IR_Scan_withcal_optimized_superfine
000004812-IR_Scan_withcal_optimized_superfine
000004813-IR_Scan_withcal_optimized_superfine
000004814-IR_Scan_withcal_optimized_superfine
000004815-IR_Scan_withcal_optimized_superfine
000004816-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\artiq_master\results\2021-07-14\16
Counts = []
Freqs = []
AmpTisa = []
No_measures = []
for i, fname in enumerate(ALL_FILES.split()):
print(i)
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']))
No_measures.append(np.array(data['datasets']['no_measures']))
#%%
j = 0 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.3
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 1 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.legend()
j = 2 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.3
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
#%%
j = 3 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 4
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.legend()
#%%
j = 5 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 6
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 7
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 8
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
#plt.legend()
#%%
#Variando pot repump de menos a mas
j = 9
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 10
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
#%%
#Variando pot repump de menos a mas
j = 2
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
\ No newline at end of file
analisis/plots/20211101_CPT_DosLaseres_v03/CPT_plotter_20211101.py 0 → 100644
View file @ 5d2f9455
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
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
ALL_FILES = """000004961-IR_Scan_withcal_optimized
000004962-IR_Scan_withcal_optimized
000004963-IR_Scan_withcal_optimized
000004964-IR_Scan_withcal_optimized
000004965-IR_Scan_withcal_optimized
000004972-IR_Scan_withcal_optimized
000004973-IR_Scan_withcal_optimized
000004974-IR_Scan_withcal_optimized
000004975-IR_Scan_withcal_optimized
000004976-IR_Scan_withcal_optimized
000004977-IR_Scan_withcal_optimized
000004978-IR_Scan_withcal_optimized
000004979-IR_Scan_withcal_optimized
000004988-IR_Scan_withcal_optimized
000004989-IR_Scan_withcal_optimized
000004990-IR_Scan_withcal_optimized
000004991-IR_Scan_withcal_optimized
000004992-IR_Scan_withcal_optimized
000004993-IR_Scan_withcal_optimized
000004994-IR_Scan_withcal_optimized
000004995-IR_Scan_withcal_optimized
000004996-IR_Scan_withcal_optimized
000004997-IR_Scan_withcal_optimized
000004998-IR_Scan_withcal_optimized
000005003-IR_Scan_withcal_optimized
000005004-IR_Scan_withcal_optimized
000005005-IR_Scan_withcal_optimized
000005006-IR_Scan_withcal_optimized
000005007-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 = []
No_measures = []
for i, fname in enumerate(ALL_FILES.split()):
print(i)
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']))
No_measures.append(np.array(data['datasets']['no_measures']))
#%%
j = 4 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.3
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.ylim(1400, 5000)
plt.grid()
#%%
j = 1 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.legend()
j = 2 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.3
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 3 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 4
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.legend()
#%%
j = 5 #Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 6
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
#%%
j = 8
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 9
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 10
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='Cooling amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
#%%
j = 5
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='CPT amp uv = 0.3')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 10
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
#plt.figure()
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='CPT amp uv = 0.26')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 11
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='CPT amp uv = 0.22')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
j = 12
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label='CPT amp uv = 0.18')
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
#%% MENOS ESTADISTICA PERO DAN BIEN
VecPots = [0.3, 0.29, 0.28, 0.27, 0.26, 0.25, 0.24, 0.23, 0.22, 0.21, 0.20, 0.19, 0.18, 0.17, 0.16, 0.15]
jvec = [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28]
#Amp tisa = 0, amp cpt uv = 0.3, cooling amp uv = 0.26
plt.figure()
i = 0
for j in jvec:
plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'CPT amp uv = {jvec[i]}')
i = i + 1
plt.xlabel('Frecuencia MHz)')
plt.ylabel('counts')
plt.grid()
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