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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 = """000004263-UV_Scan_withcalib_Haeffner
000004265-UV_Scan_withcalib_Haeffner
000004270-UV_Scan_withcalib_Haeffner
000004273-UV_Scan_withcalib_Haeffner
000004275-UV_Scan_withcalib_Haeffner
000004282-UV_Scan_withcalib_Haeffner
000004301-UV_Scan_withcalib_Haeffner
000004303-UV_Scan_withcalib_Haeffner"""
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211013_EspectrosUV\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 = []
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']))
#def GetBackground(countsper100ms, )
#%%
def Lorentzian(f, A, gamma, x0):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 10 #40 es el piso de ruido estimado
j = 1 #UV_cooling en 90 MHz
UV_cooling = 90
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.05
popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.figure()
plt.plot(FreqsChosen, CountsChosen, 'o', label='4 uW')
plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
print(f'Ancho medido: {round(popt[1])} MHz')
#%%
def Lorentzian(f, A, gamma, x0, C):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + C #40 es el piso de ruido estimado
j = 4 #UV_cooling en 90 MHz
UV_cooling = 90
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.05
popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
freqslong = np.arange(min(FreqsChosen)-100, max(FreqsChosen)+100, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.figure()
plt.plot(FreqsChosen, CountsChosen, 'o', label='1 uW')
plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.axhline(popt[-1])
plt.ylim(50, 1100)
plt.legend()
#%% COMPARATIVA DE AMBOS DONDE SE VE QUE UNO ES MAS FINO
def Lorentzian(f, A, gamma, x0):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 10 #40 es el piso de ruido estimado
j = 1 #UV_cooling en 90 MHz
UV_cooling = 90
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
plt.figure()
plt.plot(FreqsChosen, [c/max(CountsChosen) for c in CountsChosen], 'o', label='4 uW')
#plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
print(f'Ancho medido: {round(popt[1])} MHz')
j = 4 #UV_cooling en 90 MHz
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
#plt.figure()
plt.plot(FreqsChosen, [c/max(CountsChosen) for c in CountsChosen], 'o', label='1 uW')
#plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.legend()
#%%
def Lorentzian(f, A, gamma, x0, C):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + C #40 es el piso de ruido estimado
j = 5 #UV_cooling en 90 MHz
UV_cooling = 90
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 0.6
#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
popt, pcov = curve_fit(Lorentzian, FreqsChosen[:int(portion*len(FreqsChosen))], CountsChosen[:int(portion*len(FreqsChosen))], p0=[1e3, 40, 210, 100])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.figure()
plt.plot([f-popt[2] for f in FreqsChosen], CountsChosen, 'o', label='1 uW')
plt.plot([f-popt[2] for f in freqslong], Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
plt.axhline(popt[-1])
plt.legend()
print(f'Ancho medido: {round(popt[1])} MHz')
#%%
def Lorentzian(f, A, gamma, x0, C):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + C #40 es el piso de ruido estimado
j = 6 #UV_cooling en 90 MHz
UV_cooling = 90
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 1
#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
popt, pcov = curve_fit(Lorentzian, FreqsChosen[:int(portion*len(FreqsChosen))], CountsChosen[:int(portion*len(FreqsChosen))], p0=[1e3, 40, 210, 100])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
plt.figure()
plt.plot([f-popt[2] for f in FreqsChosen], CountsChosen/max(CountsChosen), 'o', label='2.5 uW')
plt.plot([f-popt[2] for f in freqslong], Lorentzian(freqslong, *popt)/max(CountsChosen), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
#plt.axhline(popt[-1])
plt.legend()
print(f'Ancho medido: {round(popt[1])} MHz')
def Lorentzian(f, A, gamma, x0):
return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 30 #40 es el piso de ruido estimado
j = 7 #UV_cooling en 90 MHz
UV_cooling = 90
FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
CountsChosen = Counts[j]
portion = 1
#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
popt, pcov = curve_fit(Lorentzian, FreqsChosen[:int(portion*len(FreqsChosen))], CountsChosen[:int(portion*len(FreqsChosen))], p0=[1e3, 40, 210])
freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
#plt.figure()
plt.plot([f-popt[2] for f in FreqsChosen], CountsChosen/max(CountsChosen), 'o', label='2.5 uW')
plt.plot([f-popt[2] for f in freqslong], Lorentzian(freqslong, *popt)/max(CountsChosen), label=f'FWHM {round(popt[1])} MHz')
#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Cuentas')
#plt.axhline(popt[-1])
plt.legend()
print(f'Ancho medido: {round(popt[1])} MHz')