meto archivos nuevos

analisis/plots/20220503_EspectrosUVnuevos/UV_spectrums.py

@@ -10,7 +10,14 @@ from scipy import interpolate
 
 # Solo levanto algunos experimentos
 Calib_Files = """000007155-UV_Scan_withcalib_Haeffner
-000007160-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""" 
 
 
 
@@ -28,6 +35,7 @@ def SeeKeys(files):
 Amps = []
 Freqs = []
 Counts = []
+T_readouts = []
 
 for i, fname in enumerate(Calib_Files.split()):
     print(SeeKeys(Calib_Files))
@@ -38,6 +46,7 @@ for i, fname in enumerate(Calib_Files.split()):
     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, )
@@ -45,30 +54,107 @@ for i, fname in enumerate(Calib_Files.split()):
 
 
 #%%
+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)) + 10 #40 es el piso de ruido estimado
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2))
+
+def MicromotionSpectra(det, A, beta, x0):
+    ftrap=22.1
+    gamma=23
+    P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+100
+    i = 1
+    #print(P)
+    while i <= 2:
+        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()
 
-j = 0 #UV_cooling en 90 MHz
+for j in jvec:
 
-FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
-CountsChosen = Counts[j]
+    FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+    CountsChosen = Counts[j]
     
-portion = 0.
+    portion = 0.
     
-popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):])
+    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)
+    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()
-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.xlabel('Frecuencia (MHz)')
-plt.ylabel('Cuentas')
-plt.legend()
 
-print(f'Ancho medido: {round(popt[1])} MHz') 
+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=[200,4,220], bounds=((-1000,-10,-300),(1000,10,300)))
+    
+    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=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') 
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