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analisis/plots/20210716_MeasuredCalibrationsTests/Measured_Calibration_tests.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 = """000002231-LaserPowerCalibration.h5""" 
+
+Meas_Files = """000002239-LaserPowerCalibration.h5
+000002241-LaserPowerCalibration.h5"""
+
+Test_Files = """000002231-LaserPowerCalibration.h5
+000002232-LaserPowerCalibration.h5
+000002233-LaserPowerCalibration.h5
+000002237-LaserPowerCalibration.h5""" 
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210716_MeasuredCalibrationsTests\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()))
+
+
+def GetXToInterpolate(vec):
+    return np.arange(min(vec), max(vec), 1e-2*np.abs((vec[1]-vec[0])))
+
+def Test_Calibrate_amplitudes(PD_UV_counts, Calibration_freqs, Calibration_amps, Calibration_PD_Value, Experiment_frequencies):
+    
+    Interpolation_functions = []
+    Interpolation_functions_2 = []
+
+    Calibration_freqs = Calibration_freqs[0:int(len(PD_UV_counts)/len(Calibration_amps))]
+
+    n = len(Calibration_freqs)
+    PD_split = np.matrix([PD_UV_counts[i * n:(i + 1) * n] for i in range((len(PD_UV_counts) + n - 1) // n )])
+    PD_T = np.transpose(PD_split)
+
+    amps = Calibration_amps
+
+    for i in range(len(PD_T)):
+        #PD_values = np.array(PD_T[i][0])[0]
+        PD_values = np.array(PD_T[i])[0]
+        f = interpolate.interp1d(PD_values, amps, kind='cubic')
+        g = interpolate.interp1d(amps, PD_values, kind='cubic')
+
+        Interpolation_functions.append(f)
+        Interpolation_functions_2.append(g)
+        
+
+    Calibrated_Amplitudes = []
+    for fi in Interpolation_functions:
+        try:
+            Calibrated_Amplitudes.append(float(fi(Calibration_PD_Value)))
+        except:
+            Calibrated_Amplitudes.append(0)
+            print('no, rey')
+
+
+    Function_Calibs_vs_freq = interpolate.interp1d(Calibration_freqs, Calibrated_Amplitudes, kind='quadratic')    
+    
+    Experiment_amps = []
+    
+    for freq in Experiment_frequencies:
+        calibamp = Function_Calibs_vs_freq(freq)
+        Experiment_amps.append(calibamp)
+    
+    fs = GetXToInterpolate(Calibration_freqs)
+    plt.figure()
+    plt.plot(Calibration_freqs, Calibrated_Amplitudes, 'o')
+    plt.plot(fs, Function_Calibs_vs_freq(fs))
+    
+    ExpectedFluo = []
+    i = 0
+    while i < len(Experiment_amps):
+        if Experiment_amps[i] == 0:
+            ExpectedFluo.append(0)
+        else:
+            ExpectedFluo.append(Interpolation_functions_2[i](Experiment_amps[i]))
+           
+        i = i + 1
+        
+    
+    return Experiment_amps, ExpectedFluo
+        
+
+#%%    
+
+
+Calibration_amps_vec = []
+Calibration_freqs_vec = []
+
+Experiment_amps_vec = []
+Experiment_freqs_vec = []
+
+PD_UV_counts_vec = []
+
+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()))
+    Calibration_amps_vec.append(np.array(data['datasets']['Calibration_amps']))
+    Calibration_freqs_vec.append(np.array(data['datasets']['Calibration_freqs']))
+    Experiment_amps_vec.append(np.array(data['datasets']['Experiment_amps']))
+    Experiment_freqs_vec.append(np.array(data['datasets']['Experiment_freqs']))
+    PD_UV_counts_vec.append(np.array(data['datasets']['PD_UV_counts']))
+    
+
+
+Final_Experiment_amps_vec = []
+Final_Experiment_freqs_vec = []
+Measured_PD_UV_counts_vec = []
+
+for i, fname in enumerate(Meas_Files.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()))
+    Final_Experiment_amps_vec.append(np.array(data['datasets']['Experiment_amps']))
+    Final_Experiment_freqs_vec.append(np.array(data['datasets']['Experiment_freqs']))
+    Measured_PD_UV_counts_vec.append(np.array(data['datasets']['Measured_PD_UV_counts']))
+    
+
+#%%
+
+i = 0
+
+Calibration_amps= Calibration_amps_vec[i]
+Calibration_freqs = Calibration_freqs_vec[i]
+PD_UV_counts = PD_UV_counts_vec[i]
+Measured_PD_UV_counts = Measured_PD_UV_counts_vec[i]
+
+
+PDvalue = 0.04
+
+Experiment_frequencies = Final_Experiment_freqs_vec[0]
+
+Test_Experiment_frequencies = Calibration_freqs[0:len(Calibration_amps)]
+#Test_Experiment_frequencies = np.arange(0.9e8, 1.3e8, 0.05e8)
+
+calibamps, expectedfluos = Test_Calibrate_amplitudes(PD_UV_counts, Calibration_freqs, Calibration_amps, PDvalue, Experiment_frequencies)
+
+plt.figure()
+plt.plot(Experiment_frequencies, calibamps, 'ro', label='amps')
+plt.plot(Experiment_frequencies, expectedfluos, 'gx', label='Fluos')
+#plt.plot(Experiment_frequencies, Measured_PD_UV_counts, 'b.', label='Measures')
+plt.axhline(PDvalue)
+plt.legend()
+
+#%%
+len_amps = len(Calibration_amps)
+len_freqs = int(len(Calibration_freqs)/len_amps)
+
+freqs = Calibration_freqs[0:len_freqs]
+
+n = len(freqs)
+PD_split = np.matrix([PD_UV_counts[i * n:(i + 1) * n] for i in range((len(PD_UV_counts) + n - 1) // n )])
+PD_T = np.transpose(PD_split)
+
+plt.figure()
+for i in range(len(PD_split)):
+    plt.plot([f*1e-6 for f in freqs], np.array(PD_split[i])[0], 'o')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Voltaje fotodiodo (V)')
+plt.title('Láser UV')
+
+
+i = 10
+plt.figure()
+plt.plot(np.array(PD_T[i][0])[0], Calibration_amps, 'o', label=f"Freq: {round(freqs[i]*1e-6, 3)} MHz")
+plt.ylabel('Amplitud Artiq')
+plt.xlabel("Voltaje fotodiodo (V)")
+plt.legend()
+
+#%%
+
+def RemoveZeros(calib, freqs):
+    i, j = 0, 1
+    new_calib = calib
+    while i < len(calib):
+        if new_calib[0] == 0:
+            new_calib = new_calib[1:]
+            i = i + 1
+        else:            
+            init_len = len(new_calib)
+            final_calib = new_calib
+            while j < init_len:
+                if final_calib[-1] == 0:
+                    final_calib = final_calib[:-1]
+                    j = j + 1
+                else:
+                    if j == 1:
+                        return (i, j), freqs[i:], final_calib
+                    else:
+                        return (i, j), freqs[i:-j+1], final_calib
+        
+lista = [0, 1, 0, 1, 0, 0, 1, 1, 0, 0]
+freqs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+tupla, new_freqs, calib = RemoveZeros(lista, freqs)
+
+print(new_freqs)
+print(calib)
+
+#%%
+i = 15
+
+interpolations = []
+
+interpolations_g = []
+
+for i in range(len(PD_T)):
+    PD = np.array(PD_T[i][0])[0]
+    amps = Calibration_amps
+    f = interpolate.interp1d(PD, amps, kind='quadratic')
+    g = interpolate.interp1d(amps, PD, kind='quadratic')
+    xs = np.arange(min(PD), max(PD), 1e-4)
+    
+    interpolations.append(f)
+    interpolations_g.append(g)
+    
+
+Calibration_PD_value = 0.2
+
+Calibs = []
+for fi in interpolations:
+    try:
+        Calibs.append(float(fi(Calibration_PD_value)))
+    except:
+        Calibs.append(0)
+
+
+fig, ax1 = plt.subplots()
+ax2 = ax1.twinx()
+
+ax1.plot([f*1e-6 for f in freqs], Calibs, 'o', label='Amplitudes a setear')
+
+
+EffectivePD = []
+for i in range(len(Calibs)):
+    try:
+        EffectivePD.append(interpolations_g[i](Calibs[i]))
+    except:
+        EffectivePD.append(0)
+
+ax2.plot([f*1e-6 for f in freqs], EffectivePD, 'ro', label='Potencia')
+
+ax1.set_xlabel('Frecuencia (MHz)')
+ax1.set_ylabel('Amplitudes')
+ax2.set_ylabel('Potencia PD (V)')
+fig.legend()
+
+
+h = interpolate.interp1d(freqs, Calibs, kind='quadratic')
+
+xs2 = np.arange(min(freqs), max(freqs), 1e5)
+
+plt.figure()
+plt.plot(freqs, Calibs, 'o')
+plt.plot(xs2, h(xs2))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

analisis/plots/20210723_UVCalibrationCheck/UV_Measured_Calibration_tests.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 = """000002552-UV_Scan.h5""" 
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20210723_UVCalibrationCheck\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()))
+
+
+def GetXToInterpolate(vec):
+    return np.arange(min(vec), max(vec), 1e-2*np.abs((vec[1]-vec[0])))
+
+def Test_Calibrate_amplitudes(PD_UV_counts, Calibration_freqs, Calibration_amps, Calibration_PD_Value, Experiment_frequencies):
+    
+    Interpolation_functions = []
+    Interpolation_functions_2 = []
+
+    Calibration_freqs = Calibration_freqs[0:int(len(PD_UV_counts)/len(Calibration_amps))]
+
+    n = len(Calibration_freqs)
+    PD_split = np.matrix([PD_UV_counts[i * n:(i + 1) * n] for i in range((len(PD_UV_counts) + n - 1) // n )])
+    PD_T = np.transpose(PD_split)
+
+    amps = Calibration_amps
+
+    for i in range(len(PD_T)):
+        #PD_values = np.array(PD_T[i][0])[0]
+        PD_values = np.array(PD_T[i])[0]
+        f = interpolate.interp1d(PD_values, amps, kind='cubic')
+        g = interpolate.interp1d(amps, PD_values, kind='cubic')
+
+        Interpolation_functions.append(f)
+        Interpolation_functions_2.append(g)
+        
+
+    Calibrated_Amplitudes = []
+    for fi in Interpolation_functions:
+        try:
+            Calibrated_Amplitudes.append(float(fi(Calibration_PD_Value)))
+        except:
+            Calibrated_Amplitudes.append(0)
+            print('no, rey')
+
+
+    Function_Calibs_vs_freq = interpolate.interp1d(Calibration_freqs, Calibrated_Amplitudes, kind='quadratic')    
+    
+    Experiment_amps = []
+    
+    for freq in Experiment_frequencies:
+        calibamp = Function_Calibs_vs_freq(freq)
+        Experiment_amps.append(calibamp)
+    
+    fs = GetXToInterpolate(Calibration_freqs)
+    plt.figure()
+    plt.plot(Calibration_freqs, Calibrated_Amplitudes, 'o')
+    plt.plot(fs, Function_Calibs_vs_freq(fs))
+    
+    ExpectedFluo = []
+    i = 0
+    while i < len(Experiment_amps):
+        if Experiment_amps[i] == 0:
+            ExpectedFluo.append(0)
+        else:
+            ExpectedFluo.append(Interpolation_functions_2[i](Experiment_amps[i]))
+           
+        i = i + 1
+        
+    
+    return Experiment_amps, ExpectedFluo
+        
+
+#%%    
+
+
+
+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()))
+    meas = np.array(data['datasets']['measurements'])
+    freqs = np.array(data['datasets']['UV_Frequencies'])
+    
+meas_eff = np.zeros(len(freqs))
+for i in range(len(meas)):
+    
+    meas_eff[i%len(freqs)] = meas_eff[i%len(freqs)] + meas[i]
+
+
+plt.plot(freqs, meas_eff, 'o')
+
+
+

analisis/plots/20210824_SaturacionTransiciones/Saturation_Measurements.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 = """000002843-IR_Saturation.h5
+000002844-IR_Saturation.h5
+000002845-IR_Saturation.h5
+000002846-IR_Saturation.h5
+000002847-IR_Saturation.h5
+000002848-IR_Saturation.h5
+000002849-IR_Saturation.h5
+000002850-IR_Saturation.h5
+000002851-IR_Saturation.h5
+000002852-IR_Saturation.h5
+000002853-IR_Saturation.h5
+000002854-IR_Saturation.h5
+000002855-IR_Saturation.h5
+000002856-IR_Saturation.h5
+000002857-IR_Saturation.h5
+000002858-IR_Saturation.h5
+000002859-IR_Saturation.h5
+000002860-IR_Saturation.h5
+000002861-IR_Saturation.h5
+000002862-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, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.34, 0.35]
+    powers = [0, 0.08, 1.7, 8.4, 25.5, 58.5, 114, 194, 297, 416, 550, 705, 867, 1016, 1148, 1285, 1385, 1463, 1501]
+    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 = []
+
+
+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 = 8
+
+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=(4e3, 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.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
+
+g23 = 2*np.pi*23.05*1e6
+
+radius = np.sqrt(6*c*c*1/(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.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/20210824_SaturacionTransiciones/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/20210829_CPT_intentos/CPT_plotter.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
+ALL_FILES = """000003130-IR_Scan.h5""" 
+
+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()))
+
+print(SeeKeys(ALL_FILES))
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\artiq_master\results\2021-07-14\16
+
+for i, fname in enumerate(ALL_FILES.split()):
+    print(i)
+    print(fname)
+    data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    # Aca hago algo repugnante para poder levantar los strings que dejamos
+    # que además tenian un error de tipeo al final. Esto no deberá ser necesario
+    # cuando se solucione el error este del guardado.
+    freqs = np.array(data['datasets']['IR_frequencies'])
+    measurements = np.array(data['datasets']['measurements'])
+
+    
+y = np.reshape(measurements, (len(freqs),-1)).sum(axis=1)
+
+plt.figure()
+plt.plot([f*1e-6 for f in freqs], y, 'o-')
+plt.xlabel('Frecuencia AOM (MHz)')
+plt.ylabel('counts')
+plt.grid()
+
+
+
+
+
+
+
+
+
+
+
+
+
+

analisis/plots/20210831_PowerStabilization/ScanPlotter.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
+ALL_FILES = """000003316-LaserPowerCalibration.h5""" 
+
+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()))
+
+print(SeeKeys(ALL_FILES))
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\artiq_master\results\2021-07-14\16
+
+for i, fname in enumerate(ALL_FILES.split()):
+    print(i)
+    print(fname)
+    data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    # Aca hago algo repugnante para poder levantar los strings que dejamos
+    # que además tenian un error de tipeo al final. Esto no deberá ser necesario
+    # cuando se solucione el error este del guardado.
+    Calibration_amps = np.array(data['datasets']['Test_Experiment_amps'])
+    Calibration_freqs = np.array(data['datasets']['Test_Experiment_freqs'])
+    Measurements = np.array(data['datasets']['Test_Measured_PD_IR_counts'])
+    
+
+#%%
+plt.figure()
+plt.hist(Measurements)
+
+plt.figure()
+plt.plot(Calibration_freqs, Calibration_amps, 'o')
+
+plt.figure()
+plt.plot(Calibration_freqs, Measurements, 'o')
+
+
+
+
+
+