cosas de la compu de nico

analisis/plots/20250403_UVspectra_bladetrap/UV_spectrums_new.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 = """000012744-UV_Scan_withcalib_Haeffner
+000012745-UV_Scan_withcalib_Haeffner
+000012749-UV_Scan_withcalib_Haeffner
+000012750-UV_Scan_withcalib_Haeffner
+000012751-UV_Scan_withcalib_Haeffner
+000012752-UV_Scan_withcalib_Haeffner
+000012753-UV_Scan_withcalib_Haeffner
+000012754-UV_Scan_withcalib_Haeffner
+000012755-UV_Scan_withcalib_Haeffner""" 
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230616_newUVspectra//Data')
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\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 = []
+T_readouts = []
+
+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']))
+    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
+
+
+
+#primero muestro las meds 0 y 1 que son dos mediciones con potencia UV quizas un poco 
+#alta pero en la segunda el ion esta un poquito mejor compensado y se ve cómo se afina el espectro
+
+jvec = [6,7,8] 
+
+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 {round(popt[2],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[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, gamma):
+    ftrap=22.1
+    #gamma=28
+    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()
+
+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=[100000,0.1,215, 22], bounds=((0,0,0, 0),(10000000,10,300, 100)))
+    print(popt)
+    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'Beta: {round(popt[1],2)}') 
+    
+
+#%%
+from scipy.special import jv
+
+def Lorentzian(f, A, x0, gamma, offset):
+    offset = 50
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + offset #40 es el piso de ruido estimado
+
+#para tres potencias distintas del UV:
+jvec = [2,3,4,5] 
+
+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 {round(popt[2],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(popt)
+    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, gamma):
+    ftrap=22.1
+    #gamma=35
+    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 = [6, 7] #6, 7, 8: tres compensaciones distintas. la 8 en realidad el ion se fue un pcoo del pinhole entonces se ve mas chica
+
+   
+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=[100000,0.1,215, 30], bounds=((0,0,0,0),(10000000,10,300,100)))
+    
+    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, label=f'Beta: {round(popt[1],2)}, Gamma: {round(popt[3],1)}')
+    #plt.plot(freqslong, Lorentzian(freqslong, popt[0], popt[1], popt[2]), label=f'FWHM {round(popt[1])} MHz')
+    plt.plot(freqslong, MicromotionSpectra(freqslong, *popt))
+        
+
+
+    #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: {round(popt[1],2)}') 
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