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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
#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')