version 0 codigo

analisis/plots/20220520_EspectrosUVyCPT_descompensacion/UV_CPT_spectrums.py

@@ -9,17 +9,14 @@ 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""" 
+Calib_Files = """000007324-UV_Scan_withcalib_Haeffner
+000007325-IR_Scan_withcal_optimized
+000007326-UV_Scan_withcalib_Haeffner
+000007327-IR_Scan_withcal_optimized
+000007328-UV_Scan_withcalib_Haeffner
+000007327-IR_Scan_withcal_optimized""" 
 
-directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220503_EspectrosUVnuevos/Data/'
+directory = '/home/liaf-murib/Documents/Artiq/artiq_experiments/analisis/plots/20220520_EspectrosUVyCPT_descompensacion/Data/'
 
 
 #carpeta pc nico labo escritorio:
@@ -44,8 +41,14 @@ for i, fname in enumerate(Calib_Files.split()):
     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']))
+    try:
+        Amps.append(np.array(data['datasets']['UV_Amplitudes']))
+    except KeyError:
+        Amps.append(np.array(data['datasets']['IR_Amplitudes']))
+    try:
+        Freqs.append(np.array(data['datasets']['UV_Frequencies']))
+    except KeyError:
+        Freqs.append(np.array(data['datasets']['IR_Frequencies']))
     Counts.append(np.array(data['datasets']['counts_spectrum']))
     T_readouts.append(np.array(data['datasets']['t_readout']))    
 
@@ -61,7 +64,7 @@ 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
+jvec = [4] #UV_cooling en 90 MHz
 
 plt.figure()
 
@@ -72,20 +75,20 @@ for j in jvec:
     
     portion = 0.
     
-    popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[12000, 208, 30, 30])
+    #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.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') 
+    #print(f'Ancho medido: {round(popt[2])} MHz') 
 
 #%%
 
@@ -101,7 +104,7 @@ def Lorentzian(f, A, gamma, x0):
 
 def MicromotionSpectra(det, A, beta, x0, offset):
     ftrap=22.1
-    gamma=23
+    gamma= 29
     P = A*(jv(0, beta)**2)/(((det-x0)**2)+(0.5*gamma)**2)+ offset
     i = 1
     #print(P)
@@ -112,7 +115,7 @@ def MicromotionSpectra(det, A, beta, x0, offset):
     return P
 
 
-jvec = [7] #UV_cooling en 90 MHz
+jvec = [2] #UV_cooling en 90 MHz
 
 """
 plt.figure()
@@ -149,7 +152,7 @@ for j in jvec:
     
     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)))
+    popt, pcov = curve_fit(MicromotionSpectra, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):],p0=[100000,1.5,220,200], bounds=((70000,0.1,200,-500),(1000000,10,300,500)))
     
     freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
     
@@ -164,3 +167,4 @@ for j in jvec:
     plt.legend()
     
     print(f'Beta medido: {popt[1]}') 
+    print(popt)