agrego todo lo de las mediciones

analisis/micromotionestimation.py

+# -*- coding: utf-8 -*-
+"""
+Created on Wed Oct  6 16:40:32 2021
+
+@author: Nico
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def GetTemp(Gamma, Det, kb=1.38e-23, hbar=1.03e-34):
+    T = (1/4)*(hbar/kb)*Gamma*((Gamma/(-2*Det)) + (-2*Det/Gamma))
+    return np.abs(T*1e3)
+
+GammaUV = 2*np.pi*22.4e6
+
+Det = 2*np.pi*np.arange(1e6, 50e6, 0.1e6)
+
+plt.figure()
+plt.plot(Det/GammaUV, GetTemp(GammaUV, Det))
+plt.axvline(0.5, linestyle='--', linewidth=1)
+plt.xlabel('Detuning/Gamma')
+plt.ylabel('Final Temp (mK)')
+
+MinTemp = np.min(GetTemp(GammaUV, Det))
+
+print(f'Min temp: {MinTemp} mK')
+
+#%%
+
+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, ftrap, gamma, beta, n):
+    P = (jv(0, beta)**2)/((det**2)+(0.5*gamma)**2)
+    i = 1
+    #print(P)
+    while i <= n:
+        P = P + ((jv(n, beta))**2)/(((det+n*ftrap)**2)+(0.5*gamma)**2) + ((jv(-n, beta))**2)/(((det-n*ftrap)**2)+(0.5*gamma)**2) 
+        i = i + 1
+        #print(P)
+    return P
+
+Det = np.arange(-50, 50, 0.1)
+
+ftrap = 21
+gamma = 22
+beta = np.arange(0, 2, 0.05)
+n = 1
+
+anchos = []
+
+for b in beta:
+    Micromotion = MicromotionSpectra(Det, ftrap, gamma, b, n)
+    popt, pcov = curve_fit(Lorentzian, Det, Micromotion)
+    anchos.append(popt[1])
+    
+plt.figure()
+plt.plot(beta, anchos, 'o')
+plt.xlabel('Micromotion index beta')
+plt.ylabel('Apparent linewidth(MHz)')
+plt.axhline(22, linewidth=1, linestyle='--')
+plt.grid()
+
+#%%
+    
+#chosenbeta = 1.25
+
+plt.figure()
+for chosenbeta in [0, 1, 1.25, 2]:
+    plt.plot(Det, MicromotionSpectra(Det, ftrap, gamma, chosenbeta, n), label=f'beta={chosenbeta}')
+#plt.ylim(0, 0.005)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Fluorescencia')
+plt.grid()
+plt.legend()
+
+
+
+
+
+
+
+
+
+
+
+

analisis/plots/20210824_SaturacionTransiciones/Saturation_Measurements.py

@@ -67,6 +67,8 @@ def SaturationCurve_detzero(p, F0, a):
 
 #%%    
 
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210824_SaturacionTransiciones\Data_IR
+
 IR_powers_vec = []
 IR_fluorescence_vec = []
 
@@ -99,6 +101,25 @@ plt.plot([popt[1]*p for p in IR_powers_vec_W], IR_fluorescence_vec[j], 'o')
 plt.plot([popt[1]*p for p in IR_powers_vec_W], [SaturationCurve_detzero(p, popt[0], popt[1]) for p in IR_powers_vec_W])
 plt.xlabel('Sat Parameter DP')
 plt.ylabel('counts')
+plt.title('Detuning = 0 MHz')
+plt.grid()
+
+
+plt.figure()
+plt.plot([1e6*p for p in IR_powers_vec_W], IR_fluorescence_vec[j], 'o')
+plt.plot([1e6*p for p in IR_powers_vec_W], [SaturationCurve_detzero(p, popt[0], popt[1]) for p in IR_powers_vec_W])
+plt.xlabel('Potencia (uW)')
+plt.ylabel('counts')
+plt.title('Detuning = 0 MHz')
+plt.grid()
+
+plt.figure()
+plt.plot([1e6*p for p in IR_powers_vec_W], IR_fluorescence_vec[j], 'o')
+plt.plot([1e6*p for p in IR_powers_vec_W], [SaturationCurve_detzero(p, popt[0], popt[1]) for p in IR_powers_vec_W])
+plt.xlabel('Potencia (uW)')
+plt.ylabel('counts')
+plt.title('Detuning = 0 MHz')
+plt.xlim(-30, 300)
 plt.grid()
 
 print(popt)
@@ -111,17 +132,20 @@ w_397 = 2*np.pi*755e12
 c=3e8
 hbar = 1.05e-34
 
-#g21, g23 = 2*np.pi*21.58e6, 2*np.pi*1.35e6
+g21, g23 = 2*np.pi*21.58e6, 2*np.pi*1.35e6
 
-g23 = 2*np.pi*23.05*1e6
+#g23 = 2*np.pi*23.05*1e6
 
-radius = np.sqrt(6*c*c*1/(hbar*(w_866**3)*g23*factor))
+radius = np.sqrt(12*c*c*2/(hbar*(w_866**3)*g23*factor))
 print(f'Diametro: {2*radius*1e6} um')
 
 plt.figure()
 plt.plot([p*1e6 for p in IR_powers_vec_W], [popt[1]*p for p in IR_powers_vec_W], 'o')
-plt.xlim(-10, 100)
-plt.ylim(-1, 5)
+plt.xlim(-4, 100)
+plt.ylim(-0.5, 4)
+plt.xlabel('Potencia (uW)')
+plt.ylabel('Sat Parameter DP')
+plt.grid()
 #plt.xlabel()
 #%%
 #Calculo el coeficiente de proporcionalidad entre S y Pot

analisis/plots/20210906_SaturacionTransiciones_MejorAlineacion/Saturation_Measurements_FixedAlignment.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_IR = """000003547-IR_Saturation.h5""" 
+
+Calib_Files_UV = """000002865-UV_Saturation.h5
+000002866-UV_Saturation.h5
+000002867-UV_Saturation.h5
+000002868-UV_Saturation.h5
+000002869-UV_Saturation.h5
+000002870-UV_Saturation.h5
+000002871-UV_Saturation.h5
+000002872-UV_Saturation.h5
+000002873-UV_Saturation.h5
+000002874-UV_Saturation.h5
+000002875-UV_Saturation.h5"""
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210824_SaturacionTransiciones\Data_UV
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210824_SaturacionTransiciones\Data_IR
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+def ConvertIRampstoPower(amp):
+    amps = [0.01, 0.03, 0.05, 0.07, 0.09, 0.11, 0.13, 0.15, 0.17, 0.19, 0.21, 0.23, 0.25, 0.27, 0.29, 0.31, 0.33, 0.35]
+    powers = [0, 0.22, 1.42, 5.1, 13.13, 27, 48, 78, 116, 161, 211, 261, 310, 363, 412, 448, 486, 516]
+    f = interpolate.interp1d(amps, powers)
+    return f(amp)
+
+
+def SaturationCurve(p, F0, a, det):
+    return 0.5*F0*a*p*(1/(1+a*p+4*((det**2)/(g23**2))))
+
+def SaturationCurve_detzero(p, F0, a):
+    return 0.5*F0*a*p*(1/(1+a*p))
+
+#%%    
+
+IR_powers_vec = []
+IR_fluorescence_vec = []
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210906_SaturacionTransiciones_MejorAlineacion\Data_IR
+
+for i, fname in enumerate(Calib_Files_IR.split()):
+    print(i)
+    #print(fname)
+    data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+    #print(list(data['datasets'].keys()))
+    meas = np.array(data['datasets']['measurements_IR_sat'])
+    IR_fluorescence_vec.append(meas)
+    IR_meas_amps = np.array(data['datasets']['IR_amps'])
+    IR_meas_powers = [ConvertIRampstoPower(amp) for amp in IR_meas_amps]
+    IR_powers_vec.append(IR_meas_powers)
+    
+
+#%%
+#Calculo el coeficiente de proporcionalidad entre S y Pot
+j = 0
+
+def SaturationCurve_detzero(p, F0, a):
+    return 0.5*F0*a*p*(1/(1+a*p))
+
+IR_powers_vec_W = [p*1e-6 for p in IR_powers_vec[j]]
+
+popt, pcov = curve_fit(SaturationCurve_detzero, IR_powers_vec_W, IR_fluorescence_vec[j], p0=(2e3, 5e4))
+
+plt.figure()
+plt.plot([popt[1]*p for p in IR_powers_vec_W], IR_fluorescence_vec[j], 'o')
+plt.plot([popt[1]*p for p in IR_powers_vec_W], [SaturationCurve_detzero(p, popt[0], popt[1]) for p in IR_powers_vec_W])
+plt.xlabel('Sat Parameter DP')
+plt.ylabel('counts')
+plt.title('Detuning = 0 MHz')
+plt.grid()
+
+
+plt.figure()
+plt.plot([1e6*p for p in IR_powers_vec_W], IR_fluorescence_vec[j], 'o')
+plt.plot([1e6*p for p in IR_powers_vec_W], [SaturationCurve_detzero(p, popt[0], popt[1]) for p in IR_powers_vec_W])
+plt.xlabel('Potencia (uW)')
+plt.ylabel('counts')
+plt.title('Detuning = 0 MHz')
+plt.grid()
+
+
+print(popt)
+
+factor = popt[1]
+
+w_866 = 2*np.pi*346e12
+w_397 = 2*np.pi*755e12
+
+c=3e8
+hbar = 1.05e-34
+
+g21, g23 = 2*np.pi*21.58e6, 2*np.pi*1.35e6
+
+g23b = 2*np.pi*23.05*1e6
+
+radius = np.sqrt(6*c*c*2/(hbar*(w_866**3)*g23*factor))
+print(f'Diametro: {2*radius*1e6} um')
+
+print(f'Isat: {(2/(factor*np.pi*(radius**2)))*1e3/1e4} mW/cm2')
+
+plt.figure()
+plt.plot([p*1e6 for p in IR_powers_vec_W], [popt[1]*p for p in IR_powers_vec_W], 'o')
+plt.xlim(-10, 100)
+plt.ylim(-1, 5)
+#plt.xlabel()
+#%%
+#Calculo el coeficiente de proporcionalidad entre S y Pot
+j = 16
+
+w_866 = 2*np.pi*346e12
+w_397 = 2*np.pi*755e12
+
+c=3e8
+hbar = 1.05e-34
+
+g21, g23 = 2*np.pi*21.58e6, 2*np.pi*1.35e6
+
+g23 = 2*np.pi*23.05*1e6
+
+def SaturationCurve(p, F0, det):
+    return 0.5*F0*factor*p*(1/(1+factor*p+4*((det**2)/(g23**2))))
+
+
+IR_powers_vec_W = [p*1e-6 for p in IR_powers_vec[j]]
+
+popt, pcov = curve_fit(SaturationCurve, IR_powers_vec_W, IR_fluorescence_vec[j], p0=(4e3, 2e7))
+
+plt.figure()
+plt.plot([factor*p for p in IR_powers_vec_W], IR_fluorescence_vec[j], 'o', label=f'Det: {round(1e-6*popt[1]/(2*np.pi), 1)} MHz')
+plt.plot([factor*p for p in IR_powers_vec_W], [SaturationCurve(p, popt[0], popt[1]) for p in IR_powers_vec_W])
+plt.xlabel('Sat Parameter DP')
+plt.ylabel('counts')
+plt.legend()
+plt.grid()
+
+#factor = popt[1]
+
+print(f'Detuning: {1e-6*popt[1]/(2*np.pi)} MHz')
+
+#radius = np.sqrt(12*c*c/(hbar*(w_866**3)*g23*factor))
+#print(f'Diametro: {2*radius*1e6} um')
+
+
+
+
+
+
+
+
+
+
+
+
+
+

analisis/plots/20210906_SaturacionTransiciones_MejorAlineacion/Theory_Transition_Saturation_Curves.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 25 10:32:58 2021
+
+@author: oem
+"""
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def SaturationCurve(S, gamma, det):
+    return 0.5*S*(1/(1+S+4*((det**2)/(gamma**2))))
+
+gamma = 2*np.pi*1e6*22
+det = 2*np.pi*1e6*10
+
+Svec = np.arange(0, 5, 0.01)
+
+SatCurve = np.array([SaturationCurve(s, gamma, det) for s in Svec])
+
+plt.plot(Svec, SatCurve)
\ No newline at end of file

analisis/plots/20210908_fiteodecalibracion/IR_Fitting_calib.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 = """000003673-IR_LaserPowerCalibration_BS.h5""" 
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210908_fiteodecalibracion\Data
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+#%%    
+
+for i, fname in enumerate(Calib_Files.split()):
+    print(SeeKeys(Calib_Files))
+    print(i)
+    print(fname)
+    data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+    print(list(data['datasets'].keys()))
+    amps = np.array(data['datasets']['Experiment_amps_IR'])
+    freqs = np.array(data['datasets']['Experiment_freqs_IR'])
+
+
+#%%
+def Poly(f, a, b, c, d):
+    return a*(f**3) + b*(f**2) + c*f + d
+
+popt, pcov = curve_fit(Poly, freqs, amps)
+
+plt.figure()

analisis/plots/20211004_EspectrosUV/UV_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 = """000004035-UV_Scan_withcalib_Haeffner
+000004036-UV_Scan_withcalib_Haeffner
+000004062-UV_Scan_withcalib_Haeffner
+000004063-UV_Scan_withcalib_Haeffner
+000004064-UV_Scan_withcalib_Haeffner
+000004065-UV_Scan_withcalib_Haeffner
+000004066-UV_Scan_withcalib_Haeffner
+000004067-UV_Scan_withcalib_Haeffner
+000004068-UV_Scan_withcalib_Haeffner
+000004069-UV_Scan_withcalib_Haeffner
+000004071-UV_Scan_withcalib_Haeffner
+000004072-UV_Scan_withcalib_Haeffner
+000004073-UV_Scan_withcalib_Haeffner
+000004074-UV_Scan_withcalib_Haeffner
+000004075-UV_Scan_withcalib_Haeffner
+000004077-UV_Scan_withcalib_Haeffner
+000004078-UV_Scan_withcalib_Haeffner
+000004114-UV_Scan_withcalib_Haeffner
+000004115-UV_Scan_withcalib_Haeffner
+000004116-UV_Scan_withcalib_Haeffner
+000004119-UV_Scan_withcalib_Haeffner
+000004120-UV_Scan_withcalib_Haeffner
+000004129-UV_Scan_withcalib_Haeffner
+000004130-UV_Scan_withcalib_Haeffner
+000004131-UV_Scan_withcalib_Haeffner
+000004132-UV_Scan_withcalib_Haeffner
+000004133-UV_Scan_withcalib_Haeffner
+000004134-UV_Scan_withcalib_Haeffner""" 
+
+
+Calib_Files_1005 = """000004114-UV_Scan_withcalib_Haeffner
+"""
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211004_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 Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 5
+
+j = 17 #el 15 es lindo tambien
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+#plt.plot([f - 225 for f in FreqsChosen], CountsChosen, 'o', label='2.4 uW')
+plt.plot([f - 225 for f in FreqsChosen], CountsChosen, 'o')
+plt.plot([f - 225 for f in freqslong], Lorentzian(freqslong, *popt), label='Lorentzian fit')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*95-225, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+
+j = 18 #el 15 es lindo tambien
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+
+plt.plot(FreqsChosen, CountsChosen, 'o', label='1.1 uW')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*95, linestyle='--', linewidth=1, label='UV Cooling Freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+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)) + 0
+
+j = 22
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*100, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+
+j = 23
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*105, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+
+j = 24
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*110, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+#%%
+j = 25
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*110, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW, otra polarizacion')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+j = 26
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*110, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW, otra polarizacion')
+#plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+j = 27
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*110, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW, otra polarizacion')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
+
+#%% SIMULACION DE DOS CAMPANAS
+
+dif = 20
+amprand = 0.001
+Suma = Lorentzian(np.array(FreqsChosen), 1, 23, 215) + amprand*(np.random.rand()-0.5) + Lorentzian(np.array(FreqsChosen), 1, 23, 215+dif) + amprand*(np.random.rand()-0.5)
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, Suma)
+
+plt.figure()
+plt.plot(FreqsChosen, Suma, 'o')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+plt.axvline(2*105, linestyle='--', linewidth=1, label='UV cooling freq')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.title('Pot UV: 2.8 uW')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz')
\ No newline at end of file

analisis/plots/20211004_fiteodecalibracion2/IR_Fitting_calib.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 = """000004016-UV_LaserPowerCalibration_BS_rpi.h5""" 
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211004_fiteodecalibracion2\Data
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+#%%    
+
+for i, fname in enumerate(Calib_Files.split()):
+    print(SeeKeys(Calib_Files))
+    print(i)
+    print(fname)
+    data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+    print(list(data['datasets'].keys()))
+    amps = np.array(data['datasets']['Experiment_amps_UV'])
+    freqs = np.array(data['datasets']['Experiment_freqs_UV'])
+
+
+#%%
+def Poly(f, a3, a2, a1, a, b, c, d):
+    return a3*(f**6) + a2*(f**5) + a1*(f**4) + a*(f**3) + b*(f**2) + c*f + d
+
+popt, pcov = curve_fit(Poly, freqs, amps)
+
+freqslong = np.arange(min(freqs), max(freqs), (freqs[1]-freqs[0])*0.01)
+
+plt.figure()
+plt.plot(freqs, amps, 'o')
+plt.plot(freqslong, Poly(freqslong, *popt))
+
+plt.figure()
+plt.plot(freqs, np.array(amps)-Poly(freqs, *popt), 'o')

analisis/plots/20211006_EspectrosUV_FixedScript/UV_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 = """000004167-UV_Scan_withcalib_Haeffner
+000004168-UV_Scan_withcalib_Haeffner
+000004186-UV_Scan_withcalib_Haeffner
+000004187-UV_Scan_withcalib_Haeffner
+000004188-UV_Scan_withcalib_Haeffner
+000004228-UV_Scan_withcalib_Haeffner
+000004229-UV_Scan_withcalib_Haeffner
+000004230-UV_Scan_withcalib_Haeffner
+000004231-UV_Scan_withcalib_Haeffner
+000004232-UV_Scan_withcalib_Haeffner
+000004233-UV_Scan_withcalib_Haeffner""" 
+
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211006_EspectrosUV_FixedScript\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 Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 5
+
+j = 0 #UV_cooling en 100 MHz
+
+UV_cooling = 100
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='2.4 uW')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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')
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 5
+
+j = 1 #UV_cooling en 100 MHz
+
+UV_cooling = 110
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='2.4 uW')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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')
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+#%%
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 5
+
+j = 2
+
+UV_cooling = 100
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='Cool_every 20')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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')
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 5
+
+j = 3
+
+UV_cooling = 100
+
+Cool_every = 5
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='Cool_every 5')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+
+j = 4
+
+UV_cooling = 100
+
+Cool_every = 50
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='Cool_every 50')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+#%%
+
+m = 6.6e-26
+c = 3e8
+T = 100e-6
+kb=1.38e-23
+hbar=1.03e-34
+f0 = 755e12
+
+fL = 22
+fG = np.sqrt(8*kb*T*np.log(2)/(m*c*c))*f0*1e-6
+
+print(fG)
+
+
+"""
+wl = 589e-9
+T = 300
+m2 = 3.5e-23
+fG2 = 2*np.pi*(4*np.pi/wl)*np.sqrt(kb*T*2*np.log(2)/(m2))*1e-6
+print(fG2)
+"""
+
+fV = 0.5*fL + np.sqrt((fL**2)/4 + fG**2)
+print(fV)
+
+#%% en teoria esto esta "bien compensado" con -1.21 V en dcB
+
+
+j = 6
+
+UV_cooling = 90
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+lim = len(FreqsChosen)
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[0:lim], CountsChosen[0:lim])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.1 V')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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.xlim(170, 270)
+plt.grid()
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+j = 7
+
+UV_cooling = 90
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+lim = len(FreqsChosen)
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[0:lim], CountsChosen[0:lim])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.16 V')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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.xlim(170, 270)
+plt.grid()
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+j = 8
+
+UV_cooling = 90
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+lim = len(FreqsChosen)
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[0:lim], CountsChosen[0:lim])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.20 V')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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.xlim(170, 270)
+plt.grid()
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+"""
+j = 5
+
+UV_cooling = 90
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.22 V')
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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.xlim(170, 270)
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+#en teoria esto esta mal compensado poniendo -1.1 V en dcB
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 100
+
+"""
+j = 9
+
+UV_cooling = 90
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+lim = len(FreqsChosen)
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[0:lim], CountsChosen[0:lim])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.24 V')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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.xlim(170, 270)
+plt.grid()
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+
+j = 10
+
+UV_cooling = 90
+
+Cool_every = 20
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+lim = len(FreqsChosen)
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[0:lim], CountsChosen[0:lim])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.26 V')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+#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.xlim(170, 270)
+plt.grid()
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz, cooling en {(popt[2]-2*UV_cooling)/23} det/gamma')
+
+
+