Merge branch 'master' of https://code.df.uba.ar/nnunez/artiq_experiments

analisis/plots/20230804_RotationalDopplerShift_v2/Data/EITfit/threeLevel_2repumps_CPTPlotter.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep  1 17:58:39 2020
+
+@author: oem
+"""
+
+
+
+import os
+import numpy as np
+#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
+from threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
+import matplotlib.pyplot as plt
+import time
+from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
+
+#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
+
+ub = 9.27e-24
+h = 6.63e-34
+c = (ub/h)*1e-4 #en unidades de MHz/G
+
+#u = 1e6
+u = 33.5e6
+
+B = (u/(2*np.pi))/c
+
+
+#sg, sp = 0.6, 5  #parámetros de control, saturación del doppler y repump
+#rabG, rabP = sg*gPS, sp*gPD #frecuencias de rabi
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
+
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+DetDoppler = -36 #42
+DetRepumpVec = [DetDoppler+29.6]
+
+Tvec = [0.7] #temperatura en mK
+alpha = 0*(np.pi/180) #angulo entre los láseres
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+#Calculo las resonancias oscuras teóricas
+#ResonanciasTeoricas, DRPositivas = CalculoTeoricoDarkResonances_8levels(u/(2*np.pi*1e6), titadoppler, DetDoppler, DetRepump)
+
+#Parametros de la simulacion cpt
+center = -45
+span = 80
+freqMin = center-span*0.5
+freqMax = center+span*0.5
+
+""" parametros para tener espectros coherentes
+freqMin = -56
+freqMax = 14
+"""
+freqStep = 1e-1
+
+noiseamplitude = 0
+
+RelMinMedido0Vector = []
+RelMinMedido1Vector = []
+RelMinMedido2Vector = []
+RelMinMedido3Vector = []
+RelMinMedido4Vector = []
+
+#Sr = np.arange(0, 10, 0.2)
+
+#Sg = np.arange(0.01, 1, 0.05)
+#Sp = np.arange(0.1, 6.1, 1)
+
+#Sg = [0.6**2]
+#Sp = [2.3**2]
+
+
+Sg = [1.4]
+Sp = [6]
+
+Sr = [11]
+
+
+
+
+i = 0
+
+save = False
+
+
+showFigures = True
+
+if not showFigures:
+    plt.ioff()
+else:
+    plt.ion()
+    
+fig1, ax1 = plt.subplots()
+
+offsetx = 464
+
+ax1.plot([f-offsetx for f in FreqsDR], CountsDR, 'o')
+
+run = True
+
+Scale = 730
+Offset = 600 #600 para 20k cuentas aprox
+
+MaxCoherenceValue = []
+
+for sg in Sg:
+    for sp in Sp:
+        rabG, rabP = sg*gPS, sp*gPD
+        for Ti in Tvec:
+            T = Ti*1e-3
+            for DetRepump in DetRepumpVec:
+                print(T)
+                for sr in Sr:
+                
+                    rabR = sr*gPD
+                    
+                    #MeasuredFreq, MeasuredFluo = GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+                    if run:
+                        MeasuredFreq4, MeasuredFluo4 = GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+                    
+                    #SmoothFluo = SmoothNoisyCPT(MeasuredFluo, window=9, poly=2)
+                    SmoothFluo4 = MeasuredFluo4
+                    
+                    #Scale = max(BestC)/max([100*s for s in SmoothFluo4])
+                    ax1.plot(MeasuredFreq4, [Scale*100*f + Offset for f in SmoothFluo4], label=f'Sr = {sr}')
+                    ax1.axvline(DetDoppler, linestyle='--', linewidth=1)
+                    #if sr != 0:
+                        #ax1.axvline(DetRepump, linestyle='--', linewidth=1)
+
+                    MaxCoherenceValue.append(np.max(SmoothFluo4))
+
+                    #print(titaprobe)
+                    ax1.set_xlabel('Detuning Rebombeo (MHz)')
+                    ax1.set_ylabel('Fluorescencia (AU)')
+                    ax1.set_title(f'B: {round(B, 2)} G, Sdop: {round(sg, 2)}, Sp: {round(sp, 2)}, Sr: {round(sr, 2)}, lw: {lw} MHz, T: {Ti} mK')
+                    #ax1.set_ylim(0, 8)
+                    #ax1.axvline(DetDoppler, linestyle='dashed', color='red', linewidth=1)
+                    #ax1.axvline(DetRepump, linestyle='dashed', color='black', linewidth=1)
+                    #ax1.set_title('Pol Doppler y Repump: Sigma+ Sigma-, Pol Probe: PI')
+                    #ax1.legend()
+                    ax1.grid()
+                    print (f'{i+1}/{len(Sg)*len(Sp)}')
+                    i = i + 1
+                    if save:
+                        plt.savefig(f'Mapa_plots_100k_1mk/CPT_SMSM_sdop{round(sg, 2)}_sp{round(sp, 2)}_sr{round(sr, 2)}.jpg')
+                    ax1.legend()
+                    
+"""
+plt.figure()
+plt.plot(Sr, MaxCoherenceValue, 'o')
+plt.xlabel('Sr')
+plt.ylabel('Coherence')
+"""
+
+"""
+                        
+plt.figure()
+plt.plot(MeasuredFreq, [100*f for f in SmoothFluo], color='darkred')
+plt.xlabel('Desintonía 866 (MHz)')
+plt.ylabel('Fluorescencia (A.U.)')
+plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
+plt.yticks(np.arange(0.4, 1.8, 0.2))
+plt.ylim(0.5, 1.6)
+plt.grid()
+              
+
+
+plt.figure()
+plt.plot(MeasuredFreq4, [100*f for f in SmoothFluo4], color='darkred')
+plt.xlabel('Desintonía 866 (MHz)')
+plt.ylabel('Fluorescencia (A.U.)')
+plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
+plt.yticks(np.arange(0.8, 2.4, 0.4))
+plt.grid()
+              
+"""
+
+
+
+
+#%%
+from scipy.optimize import curve_fit
+
+T = 0.5e-3
+
+sg = 0.7
+sp = 6
+sr = 0
+DetDoppler = -14
+DetRepump = 0
+
+FitsSp = []
+FitsOffset = []
+
+Sg = [0.87]
+
+def FitEIT(freqs, SP, offset):
+    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(0.87, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+    FinalFluo = [f*43000 + 2685 for f in MeasuredFluo]
+    return FinalFluo
+
+freqs = [f-offsetx+32 for f in FreqsDR]
+freqslong = np.arange(min(freqs), max(freqs)+freqs[1]-freqs[0], 0.1*(freqs[1]-freqs[0]))
+
+popt, pcov = curve_fit(FitEIT, freqs, CountsDR, p0=[5, 700], bounds=(0, [10, 1e6]))
+
+FitsSp.append(popt[0])
+FitsOffset.append(popt[1])
+
+print(popt)
+
+FittedEIT = FitEIT(freqslong, *popt)
+
+
+plt.figure()
+plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
+plt.plot(freqslong, FitEIT(freqslong, *popt))
+plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {T*1e3} mK, detDop: {DetDoppler} MHz')
+
+np.savetxt('CPT_measured.txt', np.transpose([freqs, CountsDR]))
+np.savetxt('CPT_fitted.txt', np.transpose([freqslong, FittedEIT]))

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_compensationcont.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
+
+#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
+
+
+
+"""
+en este codigo ploteo espectros CPT de resonancias D-D para configuracion colineal (insensible a velocidad perpendicular)
+y configuracion desplazada (sensible).
+Primero una gaussiana variando la potencia (power_files).
+Luego, variando compensacion con electrodo DCA y con electrodo OVEN.
+"""
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+CONTCOMP_FILES = """VaryingCompContinuously/000014219-IR_Scan_withcal_optimized
+VaryingCompContinuously/000014223-IR_Scan_withcal_optimized
+VaryingCompContinuously/000014232-IR_Scan_withcal_optimized
+"""
+
+def Split(array,n):
+    length=len(array)/n
+    splitlist = []
+    jj = 0
+    while jj<length:
+        partial = []
+        ii = 0
+        while ii < n:
+            partial.append(array[jj*n+ii])
+            ii = ii + 1
+        splitlist.append(partial)
+        jj = jj + 1
+    return splitlist
+
+
+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()))
+
+print(SeeKeys(CONTCOMP_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+ContcompCounts_merged = []
+ContcompVoltages = []
+ContcompFrequencies = []
+
+for i, fname in enumerate(CONTCOMP_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    ContcompCounts_merged.append(np.array(data['datasets']['data_array']))
+    ContcompVoltages.append(np.array(data['datasets']['scanning_voltages']))
+    ContcompFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+
+ContcompCounts = []
+for k in range(len(ContcompFrequencies)):
+    ContcompCounts.append(Split(ContcompCounts_merged[k],len(ContcompFrequencies[k])))
+
+#%%
+#gaussiano
+from scipy.signal import savgol_filter as sf
+bkgr = 40*5
+#bkgr = 0
+
+def arraytreatment(array):
+    #return (array[0]-array[1])/(array[0]-bkgr)
+    return array[1]
+
+Gaussian_measurement = ContcompCounts[0]
+Gaussian_voltages = ContcompVoltages[0]
+Gaussian_dr1depths = []
+for kk in range(len(Gaussian_voltages)):
+    Gaussian_dr1depths.append(arraytreatment(Gaussian_measurement[kk]))
+
+
+OAMcol_measurement = ContcompCounts[1]
+OAMcol_voltages = ContcompVoltages[1]
+OAMcol_dr1depths = []
+for kk in range(len(OAMcol_voltages)):
+    OAMcol_dr1depths.append(arraytreatment(OAMcol_measurement[kk]))
+
+OAMdesp_measurement = ContcompCounts[2]
+OAMdesp_voltages = ContcompVoltages[2]
+OAMdesp_dr1depths = []
+for kk in range(len(OAMdesp_voltages)):
+    OAMdesp_dr1depths.append(arraytreatment(OAMdesp_measurement[kk]))
+
+
+OAMcol = sf(OAMcol_dr1depths, 15, 2)
+OAMdesp = sf(OAMdesp_dr1depths, 15, 2)
+gauss = sf(Gaussian_dr1depths, 15, 2)
+
+plt.figure()
+# plt.plot(Gaussian_voltages, gauss, color='red', label='gaussian', alpha=0.8, zorder=0)
+# plt.plot(OAMcol_voltages, OAMcol, color='blue', label='oam colineal', alpha=0.8, zorder=0)
+# plt.plot(OAMdesp_voltages, OAMdesp, color='purple', label='oam desplaz',alpha = 0.8, zorder=0)
+plt.plot(Gaussian_voltages, Gaussian_dr1depths,'o', color='red', label='gaussian')
+plt.plot(OAMcol_voltages, OAMcol_dr1depths,'o', color='blue', label='oam colineal')
+plt.plot(OAMdesp_voltages, OAMdesp_dr1depths,'o', color='purple', label='oam desplaz')
+plt.title('Profundidad de resonancia %')
+plt.grid()
+plt.legend()
+
+#%%
+
+#indexcomp = 20
+#end = 30
+
+indexcomp=30
+
+end = len(Gaussian_voltages)
+
+plt.figure()
+plt.plot(Gaussian_voltages[:end], [g/gauss[indexcomp] for g in gauss][:end], 'o', color='red', label='gaussian', alpha=0.8, zorder=0)
+plt.plot(OAMcol_voltages[:end], [g/OAMcol[indexcomp] for g in OAMcol][:end], 'o', color='blue', label='oam colineal', alpha=0.8, zorder=0)
+plt.plot(OAMdesp_voltages[:end], [g/OAMdesp[indexcomp] for g in OAMdesp][:end], 'o', color='purple', label='oam desplaz',alpha = 0.8, zorder=0)
+# plt.plot(Gaussian_voltages, Gaussian_dr1depths,'o', color='red', label='gaussian')
+# plt.plot(OAMcol_voltages, OAMcol_dr1depths,'o', color='blue', label='oam colineal')
+# plt.plot(OAMdesp_voltages, OAMdesp_dr1depths,'o', color='purple', label='oam desplaz')
+plt.title(f'Tasa de variacion respecto a valor de referencia {Gaussian_voltages[indexcomp]} mV')
+plt.axvline(Gaussian_voltages[indexcomp])
+
+plt.grid()
+plt.legend()
+
+
+
+#%%
+
+"""
+Resonancias DD variando la potencia del IR2
+"""
+
+powermedvec = [0,1,2]
+
+AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+jj=0
+for med in powermedvec:
+    plt.plot([2*f*1e-6 for f in PowerIR1_Freqs[med][1:]], [c for c in PowerCounts[med][1:]], '-o', markersize=2, label=f'amp:{AmpsVecs[jj]}')
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.grid()
+plt.legend()
+plt.title('Variando potencia de IR2 para potencia de IR1 fija')
+
+

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_location.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
+
+#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
+
+
+
+"""
+en este codigo ploteo espectros CPT de resonancias D-D para configuracion +2/+2 y +2/-2 (usando pentaprisma)
+"""
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+LOC_FILES = """VaryingBeamlocation/000014331-IR_Scan_withcal_optimized
+VaryingBeamlocation/000014332-IR_Scan_withcal_optimized
+VaryingBeamlocation/000014333-IR_Scan_withcal_optimized
+VaryingBeamlocation/000014334-IR_Scan_withcal_optimized
+VaryingBeamlocation/000014357-IR_Scan_withcal_optimized
+VaryingBeamlocation/000014358-IR_Scan_withcal_optimized
+"""
+
+def Split(array,n):
+    length=len(array)/n
+    splitlist = []
+    jj = 0
+    while jj<length:
+        partial = []
+        ii = 0
+        while ii < n:
+            partial.append(array[jj*n+ii])
+            ii = ii + 1
+        splitlist.append(partial)
+        jj = jj + 1
+    return splitlist
+
+
+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()))
+
+print(SeeKeys(LOC_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+LocCounts = []
+LocFrequencies = []
+
+for i, fname in enumerate(LOC_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    LocCounts.append(np.array(data['datasets']['counts_spectrum']))
+    LocFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+
+
+#%%
+
+"""
+Resonancias DD configuracion +2/+2
+"""
+
+powermedvec = [0,1,2,3]
+
+AmpsVecs = ['Colineal', 'Desplazada', 'Colineal', 'Desplazada']
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+jj=0
+for med in powermedvec:
+    plt.plot([2*f*1e-6 for f in LocFrequencies[med][1:]], [c for c in LocCounts[med][1:]], '-o', markersize=2, label=f'{AmpsVecs[jj]}')
+    jj=jj+1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+plt.grid()
+plt.legend()
+plt.title('Espectros para distintas geometrías')
+
+#%%
+
+"""
+Resonancias DD configuracion +2/-2 (usando un pentaprisma)
+"""
+
+powermedvec = [4,5]
+
+AmpsVecs = ['Colineal', 'Desplazada']
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+jj=0
+for med in powermedvec:
+    plt.plot([2*f*1e-6 for f in LocFrequencies[med][1:]], [c for c in LocCounts[med][1:]], '-o', markersize=2, label=f'{AmpsVecs[jj]}')
+    jj=jj+1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+plt.grid()
+plt.legend()
+plt.title('Espectros para distintas geometrías')
+

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_temperature.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
+
+#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
+
+
+
+"""
+en este codigo ploteo espectros CPT de resonancias D-D para configuracion colineal (insensible a velocidad perpendicular)
+y configuracion desplazada (sensible).
+Primero una gaussiana variando la potencia (power_files).
+Luego, variando compensacion con electrodo DCA y con electrodo OVEN.
+"""
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+TEMP_FILES = """VaryingTemp/000014316-IR_Scan_withcal_optimized
+"""
+
+def Split(array,n):
+    length=len(array)/n
+    splitlist = []
+    jj = 0
+    while jj<length:
+        partial = []
+        ii = 0
+        while ii < n:
+            partial.append(array[jj*n+ii])
+            ii = ii + 1
+        splitlist.append(partial)
+        jj = jj + 1
+    return splitlist
+
+
+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()))
+
+print(SeeKeys(TEMP_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+TempCounts_merged = []
+TempTimes = []
+TempFrequencies = []
+
+for i, fname in enumerate(TEMP_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    TempCounts_merged.append(np.array(data['datasets']['data_array']))
+    TempTimes.append(np.array(data['datasets']['scanning_heattimes']))
+    TempFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+
+TempCounts = []
+for k in range(len(TempFrequencies)):
+    TempCounts.append(Split(TempCounts_merged[k],len(TempFrequencies[k])))
+
+#%%
+#gaussiano
+from scipy.signal import savgol_filter as sf
+bkgr = 40*5
+#bkgr = 0
+
+def arraytreatment0(array):
+    #return (array[0]-array[2])/(array[0]-bkgr)
+    return array[0]
+
+def arraytreatment1(array):
+    #return (array[0]-array[1])/(array[0]-bkgr)
+    return array[1]
+
+def arraytreatment2(array):
+    #return (array[0]-array[2])/(array[0]-bkgr)
+    return array[2]
+
+# def errorarraytreatment1(array):
+#     return (array[0]-array[1])/(array[0]-bkgr)
+#     #return array[1]
+
+# def errorarraytreatment2(array):
+#     return (array[0]-array[2])/(array[0]-bkgr)
+#     #return array[1]
+
+
+Gaussian_measurement = TempCounts[0]
+Gaussian_times = TempTimes[0]
+Gaussian_dr0depths = []
+Gaussian_dr1depths = []
+Gaussian_dr2depths = []
+
+ErrorGaussian_dr1depths = []
+ErrorGaussian_dr2depths = []
+
+
+for kk in range(len(Gaussian_times)):
+    Gaussian_dr0depths.append(arraytreatment0(Gaussian_measurement[kk]))
+    Gaussian_dr1depths.append(arraytreatment1(Gaussian_measurement[kk]))
+    Gaussian_dr2depths.append(arraytreatment2(Gaussian_measurement[kk]))
+
+lim = 9
+
+plt.figure()
+# plt.plot(Gaussian_voltages, gauss, color='red', label='gaussian', alpha=0.8, zorder=0)
+# plt.plot(OAMcol_voltages, OAMcol, color='blue', label='oam colineal', alpha=0.8, zorder=0)
+# plt.plot(OAMdesp_voltages, OAMdesp, color='purple', label='oam desplaz',alpha = 0.8, zorder=0)
+plt.plot(Gaussian_times[:lim], Gaussian_dr0depths[:lim],'o', color='black', label='gaussian')
+plt.plot(Gaussian_times[:lim], Gaussian_dr1depths[:lim],'o', color='red', label='gaussian')
+plt.plot(Gaussian_times[:lim], Gaussian_dr2depths[:lim],'o', color='blue', label='gaussian')
+#plt.xlim(-0.002, 0.022)
+# plt.ylim(0,0.5)
+plt.title('Profundidad de resonancia %')
+plt.grid()
+plt.legend()
+
+#%%
+
+#indexcomp = 20
+#end = 30
+
+indexcomp=30
+
+end = len(Gaussian_voltages)
+
+plt.figure()
+plt.plot(Gaussian_voltages[:end], [g/gauss[indexcomp] for g in gauss][:end], 'o', color='red', label='gaussian', alpha=0.8, zorder=0)
+plt.plot(OAMcol_voltages[:end], [g/OAMcol[indexcomp] for g in OAMcol][:end], 'o', color='blue', label='oam colineal', alpha=0.8, zorder=0)
+plt.plot(OAMdesp_voltages[:end], [g/OAMdesp[indexcomp] for g in OAMdesp][:end], 'o', color='purple', label='oam desplaz',alpha = 0.8, zorder=0)
+# plt.plot(Gaussian_voltages, Gaussian_dr1depths,'o', color='red', label='gaussian')
+# plt.plot(OAMcol_voltages, OAMcol_dr1depths,'o', color='blue', label='oam colineal')
+# plt.plot(OAMdesp_voltages, OAMdesp_dr1depths,'o', color='purple', label='oam desplaz')
+plt.title(f'Tasa de variacion respecto a valor de referencia {Gaussian_voltages[indexcomp]} mV')
+plt.axvline(Gaussian_voltages[indexcomp])
+
+plt.grid()
+plt.legend()
+
+
+
+#%%
+
+"""
+Resonancias DD variando la potencia del IR2
+"""
+
+powermedvec = [0,1,2]
+
+AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+jj=0
+for med in powermedvec:
+    plt.plot([2*f*1e-6 for f in PowerIR1_Freqs[med][1:]], [c for c in PowerCounts[med][1:]], '-o', markersize=2, label=f'amp:{AmpsVecs[jj]}')
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.grid()
+plt.legend()
+plt.title('Variando potencia de IR2 para potencia de IR1 fija')
+
+