agrego carpeta de nuevos plots, soy muri

analisis/plots/20220520_EspectrosUVyCPT_descompensacion/UV_CPT_spectrums.py

+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 = """000007155-UV_Scan_withcalib_Haeffner
+000007160-UV_Scan_withcalib_Haeffner
+000007161-UV_Scan_withcalib_Haeffner
+000007163-UV_Scan_withcalib_Haeffner
+000007165-UV_Scan_withcalib_Haeffner
+000007198-UV_Scan_withcalib_Haeffner
+000007209-UV_Scan_withcalib_Haeffner
+000007211-UV_Scan_withcalib_Haeffner
+000007212-UV_Scan_withcalib_Haeffner""" 
+
+directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220503_EspectrosUVnuevos/Data/'
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\Data
+
+def SeeKeys(files,directory = ''):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(directory + fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+#%%    
+
+Amps = []
+Freqs = []
+Counts = []
+T_readouts = []
+
+for i, fname in enumerate(Calib_Files.split()):
+    print(SeeKeys(Calib_Files,directory = directory))
+    print(i)
+    print(fname)
+    data = h5py.File(directory + 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']))
+    T_readouts.append(np.array(data['datasets']['t_readout']))    
+
+
+#def GetBackground(countsper100ms, )
+
+
+
+#%%
+from scipy.special import jv
+
+def Lorentzian(f, A, x0, gamma, offset):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + offset #40 es el piso de ruido estimado
+
+
+jvec = [8] #UV_cooling en 90 MHz
+
+plt.figure()
+
+for j in jvec:
+
+    FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+    CountsChosen = Counts[j]
+    
+    portion = 0.
+    
+    popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[12000, 208, 30, 30])
+    
+    freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+    
+    plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5)
+    #plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
+    plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2], popt[3]), label=f'FWHM 30 MHz')
+    #plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+    #plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+    plt.xlabel('Frecuencia (MHz)')
+    plt.ylabel('Cuentas')
+    plt.legend()
+    
+    print(f'Ancho medido: {round(popt[2])} MHz') 
+
+#%%
+
+
+
+
+#%%
+from scipy.special import jv
+from scipy.optimize import curve_fit
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2))
+
+def MicromotionSpectra(det, A, beta, x0, offset):
+    ftrap=22.1
+    gamma=23
+    P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+ offset
+    i = 1
+    #print(P)
+    while i <= 5:
+        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
+
+
+jvec = [7] #UV_cooling en 90 MHz
+
+"""
+plt.figure()
+
+for j in jvec:
+
+    FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+    CountsChosen = Counts[j]
+    
+    portion = 0.
+    
+    popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[12000, 25, 208, 30])
+    
+    freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+    
+    plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5)
+    #plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
+    plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2], popt[3]), label=f'FWHM 30 MHz')
+    plt.axvline(popt[2]+2*22.1, linestyle='--', linewidth=1)
+    plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+    plt.xlabel('Frecuencia (MHz)')
+    plt.ylabel('Cuentas')
+    plt.legend()
+    
+    print(f'Ancho medido: {round(popt[1])} MHz') 
+"""    
+   
+plt.figure()
+
+for j in jvec:
+
+    FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+    CountsChosen = Counts[j]
+    
+    portion = 0.
+    
+    popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[70000,0.5,215,200], bounds=((70000,0.1,200,-500),(100000,10,300,500)))
+    
+    freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+    
+    plt.errorbar(FreqsChosen, CountsChosen, yerr=np.sqrt(np.array(CountsChosen)), fmt='o', capsize=5, markersize=5)
+    #plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
+    plt.plot(freqslong, MicromotionSpectra(freqslong, *popt), label='Beta ={:0.2f}'.format(popt[1]))
+    #plt.plot(freqslong, MicromotionSpectra(freqslong, 70000,0.2,215,220), label=f'FWHM 30 MHz')
+    #plt.axvline(popt[2]+2*22.1, linestyle='--', linewidth=1)
+    #plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+    plt.xlabel('Frecuencia (MHz)')
+    plt.ylabel('Cuentas')
+    plt.legend()
+    
+    print(f'Beta medido: {popt[1]}')