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

analisis/plots/20230804_RotationalDopplerShift_v2/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul  2 16:30:09 2020
+
+@author: oem
+"""
+
+
+import os
+import numpy as np
+import time
+import matplotlib.pyplot as plt
+from scipy.signal import argrelextrema
+from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
+import random
+from scipy.signal import savgol_filter as sf
+
+
+def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
+    
+    if titadoppler==0:
+        NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
+    elif titadoppler==90:
+        NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
+    else:
+        NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
+    
+    PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
+    
+    return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
+
+
+def GetClosestIndex(Vector, value, tolerance=1e-3):
+
+    i = 0
+    while i<len(Vector):
+        if abs(Vector[i] - value) < tolerance:
+            return i
+        else:
+            i = i + 1
+    return GetClosestIndex(Vector, value, tolerance=2*tolerance)
+    
+
+def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
+    """
+    Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
+    Si no encuentra, devuelve el valor teórico.
+    """
+    
+    IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
+    IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
+    IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
+    IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
+    IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
+    IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
+
+    EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
+    EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
+    EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
+
+    EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
+    EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
+    EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
+    
+    IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
+    IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
+    IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
+        
+    try:
+        FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
+        IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
+    except:
+        FreqDR1 = TeoDR[0]
+        IndiceDR1 = IndiceDRteo1
+    try:
+        FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
+        IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
+    except:
+        FreqDR2 = TeoDR[1]
+        IndiceDR2 = IndiceDRteo2
+    try:
+        FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
+        IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
+    except:
+        FreqDR3 = TeoDR[2]
+        IndiceDR3 = IndiceDRteo3
+    try:
+        FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
+        IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
+    except:
+        FreqDR4 = TeoDR[3]
+        IndiceDR4 = IndiceDRteo4
+    try:
+        FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
+        IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
+    except:
+        FreqDR5 = TeoDR[4]
+        IndiceDR5 = IndiceDRteo5
+    try:
+        FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
+        IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
+    except:
+        FreqDR6 = TeoDR[5]
+        IndiceDR6 = IndiceDRteo6
+                
+    return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
+
+
+def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
+    """
+    Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
+    cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
+    Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
+        1: Devuelve la fluorescencia absoluta de los minimos
+        2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no 
+        hubiera una resonancia oscura y hubiera una recta. Ese valor esta a  DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
+        3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
+        o el valor limite por izquierda de la curva
+        4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz        
+    
+    """
+    IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
+    FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
+    FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
+    DistanciaFrecuenciaCociente = 25
+
+    if NormalizationCriterium==0:
+        print('che')
+        return FrequencyOfMinimums, FluorescenceOfMinimums
+    
+    if NormalizationCriterium==1:      
+        Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
+        Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
+        return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
+        
+    if NormalizationCriterium==2:
+        k = 0
+        while k < len(Freq):
+            if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
+                FluoIzquierda = Fluo[k]
+                indiceizquierda = k
+                print('Izq:', Freq[k])
+                break
+            else:
+                k = k + 1
+        
+        l = 0
+        while l < len(Freq):
+            if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
+                FluoDerecha = Fluo[l]
+                indicederecha = l
+                print('Der: ', Freq[l])
+                break
+            else:
+                l = l + 1
+        FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
+        print(FluoNormDivisor)
+    
+    if NormalizationCriterium==3:
+        #asintotico
+        FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
+
+    if NormalizationCriterium==4:
+        #este te tira la fluorescencia de detuning 0
+        FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
+
+   
+    RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
+    print('Esto: ', RelativeFluorescenceOfMinimums)
+
+    if NormalizationCriterium==2 and getindices==True:
+        return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
+    
+    return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
+
+
+def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
+    """
+    Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
+    La estructura es:
+        MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
+    """
+    Mapa1 = MapasDR1[0]
+    
+    Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
+    Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
+    Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
+    
+    Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
+    Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
+    Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
+        
+    return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
+
+
+def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
+    Fluo1 = RelMinMedido1[0]
+    
+    Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
+    Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
+    Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
+    
+    Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
+    Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
+    Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
+    
+    return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
+
+
+
+def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
+    """
+    Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
+    Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
+    en blanco la zona donde el valor medido se puede hallar.
+    """
+    CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
+    
+    i = 0
+    while i<len(theoricalmap):
+        j = 0
+        while j<len(theoricalmap[0]):
+            if abs(theoricalmap[i][j]-target) < tolerance:
+                CoincidenceMatrix[i][j] = 1
+            j=j+1
+        i=i+1
+    return CoincidenceMatrix
+
+def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
+    
+    MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
+    AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
+
+    
+    MatrixMapsVectors = []
+    
+    for i in range(len(MapsVectors)):
+        MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
+
+    for i in range(len(MatrixMapsVectors)):
+        MatrixSum = MatrixSum + MatrixMapsVectors[i]
+        
+    MaxNumberOfCoincidences = np.max(MatrixSum)
+    
+    ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
+    
+    for i in range(len(ListMatrixSum)):
+        for j in range(len(ListMatrixSum[0])):
+            if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
+                AbsoluteCoincidencesMatrix[i][j] = 1
+    
+    return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
+
+
+def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
+    NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
+    
+    Xsum = 0
+    Xvec = []
+    Ysum = 0
+    Yvec = []
+    N = len(NonZeroIndices[0])
+    for i in range(N):
+        Xsum = Xsum + X[NonZeroIndices[1][i]]
+        Xvec.append(X[NonZeroIndices[1][i]])
+        Ysum = Ysum + Y[NonZeroIndices[0][i]]
+        Yvec.append(Y[NonZeroIndices[0][i]])
+    Xaverage = Xsum/N
+    Yaverage = Ysum/N
+    Xspread = np.std(Xvec)
+    Yspread = np.std(Yvec)
+    return Xaverage, Yaverage, N, Xspread, Yspread
+    
+
+def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
+    ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
+    IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
+    FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
+    FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
+    FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
+    FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
+    FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
+    print('hola')
+    print(RelativeFluorescenceOfMinimums0)
+    return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
+
+
+def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
+    Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
+    NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
+    return Frequencyvector, NoisyFluovector
+
+
+def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
+    Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
+    NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
+    return Frequencyvector, NoisyFluovector
+
+def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
+    Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
+    NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
+    return Frequencyvector, NoisyFluovector
+
+
+def AddNoiseToCPT(Fluo, noisefactor):
+    return [f+noisefactor*(2*random.random()-1) for f in Fluo]
+
+
+def SmoothNoisyCPT(Fluo, window=11, poly=3):
+    SmoothenFluo = sf(Fluo, window, poly)
+    return SmoothenFluo
+
+
+def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
+    """
+    FUNCION VIEJA
+    
+    Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
+    Minimumcriterion:
+        1: Saca los minimos con funcion argelextrema
+        2: Directamente con las frecuencias teoricas busca las fluorescencias
+    
+    Normalizationcriterium:
+        1: Devuelve la fluorescencia absoluta de los minimos
+        2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no 
+        hubiera una resonancia oscura y hubiera una recta. Ese valor esta a  DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
+        3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
+        o el valor limite por izquierda de la curva
+    
+    """
+    FluorescenceOfMaximum = max(Fluo)
+    FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
+
+
+    #criterio para encontrar los minimos
+    #criterio usando minimos de la fluorescencia calculados con la curva
+    if MinimumCriterium == 1:
+        LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
+        FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
+        FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
+
+
+    #criterio con las DR teoricas
+    if MinimumCriterium == 2:
+        FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
+        FrequencyOfMinimums = []
+        FluorescenceOfMinimums =[]
+        print(FrecuenciasDRTeoricas)
+        k=0
+        
+        ventanita = 0.001
+        while k < len(Freq):
+            if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
+                FrequencyOfMinimums.append(Freq[k])
+                FluorescenceOfMinimums.append(Fluo[k])
+            elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
+                FrequencyOfMinimums.append(Freq[k])
+                FluorescenceOfMinimums.append(Fluo[k])
+            elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
+                FrequencyOfMinimums.append(Freq[k])
+                FluorescenceOfMinimums.append(Fluo[k])
+            elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
+                FrequencyOfMinimums.append(Freq[k])
+                FluorescenceOfMinimums.append(Fluo[k])
+            elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
+                FrequencyOfMinimums.append(Freq[k])
+                FluorescenceOfMinimums.append(Fluo[k])
+            elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
+                FrequencyOfMinimums.append(Freq[k])
+                FluorescenceOfMinimums.append(Fluo[k])
+            k = k + 1
+        print(FrequencyOfMinimums)
+        if len(FrequencyOfMinimums) !=  len(FrecuenciasDRTeoricas):
+            print('NO ANDA BIEN ESTO PAPI, revisalo')
+        
+            
+    #esto es para establecer un criterio para la fluorescencia relativa
+    DistanciaFrecuenciaCociente = 15
+    
+    if NormalizationCriterium==1:      
+        FluoNormDivisor = 1 
+        
+    if NormalizationCriterium==2:
+        k = 0
+        while k < len(Freq):
+            if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
+                FluoIzquierda = Fluo[k]
+                print('Izq:', Freq[k])
+                break
+            else:
+                k = k + 1
+        
+        l = 0
+        while l < len(Freq):
+            if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
+                FluoDerecha = Fluo[l]
+                print('Der: ', Freq[l])
+                break
+            else:
+                l = l + 1
+        FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
+        print(FluoNormDivisor)
+    
+    if NormalizationCriterium==3:
+        FluoNormDivisor = Fluo[0]
+    
+    RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
+    
+    return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
+
+
+def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
+    
+    #primero buscamos las frecuencias referencia que se parezcan a las 6:
+    i = 0
+    FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
+    
+    FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
+    
+    while i < len(FrequencyOfMinimumsVector):
+        if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
+            FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
+            print('Cool! Taking the DR identified with any curve')
+            break
+        else:
+            i = i + 1
+            if i==len(FrequencyOfMinimumsVector):
+                print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
+                FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
+    
+    if taketheoricalDR:
+        FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
+        
+        
+            
+    Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
+    
+    print('Ventana = ', Ventana)
+    DarkResonance1Frequency = []
+    DarkResonance1Fluorescence = []
+    
+    DarkResonance2Frequency = []
+    DarkResonance2Fluorescence = []
+    
+    DarkResonance3Frequency = []
+    DarkResonance3Fluorescence = []
+   
+    DarkResonance4Frequency = []
+    DarkResonance4Fluorescence = []
+    
+    DarkResonance5Frequency = []
+    DarkResonance5Fluorescence = []
+    
+    DarkResonance6Frequency = []
+    DarkResonance6Fluorescence = []    
+    
+    i = 0
+    
+   
+    while i < len(FrequencyOfMinimumsVector):
+        j = 0
+        FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
+
+
+        while j < len(FrequencyOfMinimumsVector[i]):
+            
+            
+            if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
+                DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
+                DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
+                FrecuenciasReferencia[0] = 0
+            
+            elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
+                DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
+                DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
+                FrecuenciasReferencia[1] = 0
+
+            elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
+                DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
+                DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
+                FrecuenciasReferencia[2] = 0
+
+            elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
+                DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
+                DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
+                FrecuenciasReferencia[3] = 0
+
+            elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
+                DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
+                DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
+                FrecuenciasReferencia[4] = 0
+
+            elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
+                DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
+                DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
+                FrecuenciasReferencia[5] = 0
+                
+            else:
+                #print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
+                pass
+            j = j + 1
+        
+        if np.count_nonzero(FrecuenciasReferencia) > 0:
+            if FrecuenciasReferencia[0] != 0:
+                DarkResonance1Frequency.append(FrecuenciasReferencia[0])
+                DarkResonance1Fluorescence.append()
+                
+            if FrecuenciasReferencia[1] != 0:
+                DarkResonance2Frequency.append(FrecuenciasReferencia[1])
+                DarkResonance2Fluorescence.append(0)
+
+            if FrecuenciasReferencia[2] != 0:
+                DarkResonance3Frequency.append(FrecuenciasReferencia[2])
+                DarkResonance3Fluorescence.append(0)
+
+            if FrecuenciasReferencia[3] != 0:
+                DarkResonance4Frequency.append(FrecuenciasReferencia[3])
+                DarkResonance4Fluorescence.append(0)
+
+            if FrecuenciasReferencia[4] != 0:
+                DarkResonance5Frequency.append(FrecuenciasReferencia[4])
+                DarkResonance5Fluorescence.append(0)
+
+            if FrecuenciasReferencia[5] != 0:
+                DarkResonance6Frequency.append(FrecuenciasReferencia[5])
+                DarkResonance6Fluorescence.append(0)            
+            
+        i = i + 1
+
+    return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
+
+
+
+def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
+    
+    """
+    Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
+    
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+    """
+    
+    Fluovectors = []
+    
+    for titaprobe in titaprobeVec:
+        
+        tinicial = time.time()
+
+        ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
+
+        tfinal = time.time()
+        
+        print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")   
+        
+        if plot:
+            plt.figure()
+            plt.xlabel('Repump detuning (MHz')
+            plt.ylabel('Fluorescence (A.U.)')
+            plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
+            plt.legend()
+            
+        Fluovectors.append(Fluovector)
+        
+        if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
+            Fluovectors = Fluovector
+   
+    return ProbeDetuningVectorL, Fluovectors
+
+
+
+def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
+    
+    """
+    Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
+    
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+    """
+    
+    Fluovectors = []
+    
+    for titaprobe in titaprobeVec:
+        
+        tinicial = time.time()
+
+        ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
+
+        tfinal = time.time()
+        
+        print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")   
+        
+        if plot:
+            plt.figure()
+            plt.xlabel('Repump detuning (MHz')
+            plt.ylabel('Fluorescence (A.U.)')
+            plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
+            plt.legend()
+            
+        Fluovectors.append(Fluovector)
+        
+        if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
+            Fluovectors = Fluovector
+   
+    return ProbeDetuningVectorL, Fluovectors
+
+
+

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/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr  7 22:30:01 2020
+
+@author: nico
+"""
+
+
+import numpy as np
+import time
+import matplotlib.pyplot as plt
+from scipy.signal import argrelextrema
+
+
+"""
+Scripts para el calculo de la curva CPT
+"""
+
+
+def H0matrix(Detg, Detp, u): 
+    """
+    Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning del repump y u es el campo magnético en Hz/Gauss.
+    Para esto se toma la energía del nivel P como 0
+    """
+    
+    eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5, Detp-2*u/5, Detp+2*u/5, Detp+6*u/5) #pagina 26 de Oberst. los lande del calcio son iguales a Bario.
+    H0 = np.diag(eigenEnergies)
+    
+    return H0
+
+
+def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
+    """
+    Calcula la matriz de interacción Hsp + Hpd, en donde rabR es la frecuencia de rabi de la transición Doppler SP, 
+    rabP es la frecuencia de rabi de la transición repump DP, y las componentes ei_r y ei_p son las componentes de la polarización
+    del campo eléctrico incidente de doppler y repump respectivamente. Deben estar normalizadas a 1
+    """
+    HI = np.zeros((8, 8), dtype=np.complex_)
+    
+    i, j = 1, 3
+    HI[i-1, j-1] = (rabG/np.sqrt(3)) * np.cos(titadoppler)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1]) 
+    
+    i, j = 1, 4
+    HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(1j*phidoppler)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    i, j = 2, 3
+    HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(-1j*phidoppler)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+     
+    i, j = 2, 4
+    HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.cos(titadoppler)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])   
+    
+    
+    i, j = 3, 5
+    HI[i-1, j-1] = -(rabP/2) * np.sin(titaprobe)*np.exp(-1j*phiprobe)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    i, j = 3, 6
+    HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    i, j = 3, 7
+    HI[i-1, j-1] = rabP/np.sqrt(12) * np.sin(titaprobe)*np.exp(1j*phiprobe)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    
+    i, j = 4, 6
+    HI[i-1, j-1] = -(rabP/np.sqrt(12)) * np.sin(titaprobe)*np.exp(-1j*phiprobe)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    i, j = 4, 7
+    HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe) 
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    i, j = 4, 8
+    HI[i-1, j-1] = (rabP/2) * np.sin(titaprobe)*np.exp(1j*phiprobe)
+    HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
+    
+    return HI
+
+
+def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
+    Hintplus = np.zeros((8, 8), dtype=np.complex_)
+    Hintminus = np.zeros((8, 8), dtype=np.complex_)
+
+
+    Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
+    Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
+    Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
+    
+    Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
+    Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
+    Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
+    
+
+    Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
+    Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
+    Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
+    
+    Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
+    Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
+    Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
+
+    if forma==1:
+        Lplus = np.zeros((64, 64), dtype=np.complex_)
+        Lminus = np.zeros((64, 64), dtype=np.complex_)
+        DeltaBar = np.zeros((64, 64), dtype=np.complex_)
+    
+        for r  in range(8):
+            for q in range(8):
+                for k in range(8):
+                    for j in range(8):
+                        if j==q:
+                            if (k==2 or k==3) and r > 3:
+                                Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k]) 
+                            if (r==2 or r==3) and k > 3:
+                                Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
+                        elif r==k:
+                            if (q==2 or q==3) and j > 3:
+                                Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q]) 
+                            if (j==2 or j==3) and q > 3:
+                                Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
+    if forma==2:
+        deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
+        Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
+        Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
+    
+    DeltaBar = np.zeros((64, 64), dtype=np.complex_)   
+    for i in range(64):
+        
+        DeltaBar[i, i] = (1j)*(detr - detp)
+
+    return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
+
+
+def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
+    """
+    Devuelve Splus0 y Sminus0
+    """
+    
+    Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
+    Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
+    
+    for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
+        Sp = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
+        Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
+    L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
+    
+    return L1
+
+
+def EffectiveL(gPS, gPD, lwg, lwr, lwp):
+
+    """
+    Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
+    """
+    Leff = np.zeros((8, 8), dtype=np.complex_)
+    
+    Leff[0, 0] = 2*lwg
+    Leff[1, 1] = 2*lwg
+    Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
+    Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
+    Leff[4, 4] = 2*(lwr + lwp)
+    Leff[5, 5] = 2*(lwr + lwp)
+    Leff[6, 6] = 2*(lwr + lwp)
+    Leff[7, 7] = 2*(lwr + lwp)
+    
+    return (-0.5j)*Leff
+
+
+def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp):
+    """
+    Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
+    esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
+    salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
+    ya que los componentes de Cm son reales.
+    Esta M es la suma de las 8 matrices M.
+    
+    """
+    
+    M = np.matrix(np.zeros((64, 64), dtype=np.complex_))
+    
+    M[0,27] = (2/3)*gPS
+    
+    M[9,18] = (2/3)*gPS
+    
+    M[0,18] = (1/3)*gPS
+    M[1,19] = -(1/3)*gPS
+    M[8,26] = -(1/3)*gPS
+    M[9,27] = (1/3)*gPS
+    
+    M[36,18] = (1/2)*gPD
+    M[37,19] = (1/np.sqrt(12))*gPD
+    M[44,26] = (1/np.sqrt(12))*gPD
+    M[45,27] = (1/6)*gPD
+    
+    M[54,18] = (1/6)*gPD
+    M[55,19] = (1/np.sqrt(12))*gPD
+    M[62,26] = (1/np.sqrt(12))*gPD
+    M[63,27] = (1/2)*gPD
+    
+    M[45,18] = (1/3)*gPD
+    M[46,19] = (1/3)*gPD
+    M[53,26] = (1/3)*gPD
+    M[54,27] = (1/3)*gPD
+    
+    M[0,0] = 2*lwg
+    M[1,1] = 2*lwg
+    M[8,8] = 2*lwg
+    M[9,9] = 2*lwg
+    
+    factor1 = 1
+    factor2 = 1
+    factor3 = 1
+    factor4 = 1
+    
+    
+    #M[36, 45] = lwp
+    
+    
+    M[36,36] = 2*(lwr + factor1*lwp)
+    M[37,37] = 2*(lwr + factor1*lwp)
+    M[38,38] = 2*(lwr + factor1*lwp)
+    M[39,39] = 2*(lwr + factor1*lwp)
+    M[44,44] = 2*(lwr + factor2*lwp)
+    M[45,45] = 2*(lwr + factor2*lwp)
+    M[46,46] = 2*(lwr + factor2*lwp)
+    M[47,47] = 2*(lwr + factor2*lwp)
+    M[52,52] = 2*(lwr + factor3*lwp)
+    M[53,53] = 2*(lwr + factor3*lwp)
+    M[54,54] = 2*(lwr + factor3*lwp)
+    M[55,55] = 2*(lwr + factor3*lwp)
+    M[60,60] = 2*(lwr + factor4*lwp)
+    M[61,61] = 2*(lwr + factor4*lwp)
+    M[62,62] = 2*(lwr + factor4*lwp)
+    M[63,63] = 2*(lwr + factor4*lwp)
+
+    return M
+
+def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
+    """
+    Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
+    wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
+    que forman ambos láseres.
+    """
+    
+    kboltzmann = 1.38e-23 #J/K
+    
+    gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
+
+    return gammaD
+    
+
+def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0, 
+          phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
+
+    """
+    Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
+    Es la más eficiente hasta ahora.
+    """
+    
+    
+    db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
+    
+    #lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
+    lwg = np.sqrt(lwg**2 + db**2)
+    lwr = np.sqrt(lwr**2 + db**2)    
+    
+    CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
+    
+    Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
+    
+    Heffdaga = np.matrix(Heff).getH()
+    
+    Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
+    
+    for r  in range(8):
+        for q in range(8):
+            for k in range(8):
+                for j in range(8):
+                    if j!=q and r!=k:
+                        pass
+                    elif j==q and r!=k:
+                        if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2): #todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
+                            pass
+                        else:
+                            Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
+                    elif j!=q and r==k:
+                        if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
+                            pass
+                        else:
+                            Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
+                    else:
+                        if Heff[r,k] == Heffdaga[j,q]:
+                            pass
+                        else:
+                            Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
+
+    M = CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp) 
+    L0 = np.array(np.matrix(Lfullpartial) + M)
+    
+    nmax = 1
+    Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
+    factor1 = np.exp(1j*0.2*np.pi)
+    factor2 = np.exp(-1j*0.2*np.pi)
+    #print(factor)
+    L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
+    Lfull = L0 + L1    
+    
+    
+    #NORMALIZACION DE RHO
+    i = 0 
+    while i < 64:
+        if i%9 == 0:
+            Lfull[0, i] = 1
+        else:
+            Lfull[0, i] = 0
+        i = i + 1    
+    
+    return Lfull 
+
+
+"""
+Scripts para correr un experimento y hacer el análisis de los datos
+"""
+
+def CalculoTeoricoDarkResonances(u, titadoppler):
+    if titadoppler==0:
+        NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
+    elif titadoppler==90:
+        NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
+    
+    PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
+    
+    return NegativeDR, PositiveDR
+
+
+def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, 
+                      freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
+    """
+    Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
+    
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+    """
+    
+    phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180) 
+    phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
+    phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
+    
+    DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
+    Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
+    lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
+    
+    #u = 2*np.pi*u*1e6
+    
+    Fluovector = []
+    
+    tinicial = time.time()
+    
+    for Detp in DetProbeVector:
+        L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
+        
+        if solvemode == 1:
+            rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
+            Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
+            Fluovector.append(Fluo)                
+
+        if solvemode == 2:
+            Linv = np.linalg.inv(L) 
+            rhovectorized = [Linv[j][0] for j in range(len(Linv))]
+            Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
+            Fluovector.append(Fluo)
+        
+    tfinal = time.time()
+        
+    print('Done, Total time: ', round((tfinal-tinicial), 2), "s")   
+    
+    DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
+    
+    if plot:
+        plt.xlabel('Probe detuning (MHz)')
+        plt.ylabel('Fluorescence (A.U.)')
+        plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
+        plt.legend()
+        
+    return DetProbeVectorMHz, Fluovector
+
+
+def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, 
+                      freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
+    """
+    Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
+    
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+    """
+
+
+
+    
+    phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180) 
+    phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
+    phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
+    
+    DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
+    Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
+    #lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
+    lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
+
+    rabG = sg*gPS
+    rabR = sr*gPD
+    rabP = sp*gPD
+    #u = 2*np.pi*u*1e6
+    
+    Fluovector = []
+    
+    tinicial = time.time()
+    
+    for Detp in DetProbeVector:
+
+        L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
+        
+        if solvemode == 1:
+            coh = 5
+            rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
+            #Fluo = np.abs(rhovectorized[coh])
+            Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
+            Fluovector.append(Fluo)                
+
+        if solvemode == 2:
+            Linv = np.linalg.inv(L) 
+            rhovectorized = [Linv[j][0] for j in range(len(Linv))]
+            Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
+            Fluovector.append(Fluo)
+        
+    tfinal = time.time()
+        
+    print('Done, Total time: ', round((tfinal-tinicial), 2), "s")   
+    
+    DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
+    
+    if plot:
+        plt.xlabel('Probe detuning (MHz)')
+        plt.ylabel('Fluorescence (A.U.)')
+        plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
+        plt.legend()
+        
+    return DetProbeVectorMHz, Fluovector
+#%%
+
+if __name__ == "__main__":
+    ub = 9.27e-24
+    h = 6.63e-34
+    c = (ub/h)*1e-4 #en unidades de MHz/G
+    
+    B = 25 #campo magnetico en gauss
+    
+    u = c*B
+    
+    
+    sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
+    gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
+    rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
+    
+    lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
+    
+    Detg = -25
+    Detr = 20 #detuning del doppler y repump
+    
+    Temp = 0.0e-3 #temperatura en K
+    alpha = 0*(np.pi/180) #angulo entre los láseres
+    
+    phidoppler, titadoppler = 0, 90
+    phirepump, titarepump = 0, 90
+    
+    phiprobe, titaprobe = 0, 90
+    
+    
+    plotCPT = False
+    
+    freqMin = -50
+    freqMax = 50
+    freqStep = 5e-2
+    
+    Frequencyvector, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
+    
+    NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
+
+    plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
+    plt.xlabel('Probe detuning (MHz)')
+    plt.ylabel('Fluorescence (A.U.)')
+    
+    for PDR in PositiveDR:
+        plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
+    for NDR in NegativeDR:
+        plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
+
+
+
+    
+    #parametros que andan piola:
+        
+    
+    """
+    ub = 9.27e-24
+    h = 6.63e-34
+    c = (ub/h)*1e-4 #en unidades de MHz/G
+    
+    B = 17 #campo magnetico en gauss
+    
+    u = c*B
+    #u = 80e6
+    
+    sr, sp = 0.53, 4.2
+    gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
+    rabR, rabP = sr*gPS, sp*gPD
+    lw = 2*np.pi * 0.33e6
+    lwr, lwp = lw, lw #ancho de linea de los laseres
+    dr_spec = - 2*np.pi* 26e6
+    
+    
+    freqSteps = 500
+    freqMin = -100e6
+    freqMax = 100e6
+    
+    dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
+    
+    #dps = [-30e6]
+    
+    alfar = 90*(np.pi/180)
+    ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
+    
+    
+    alfap = 90*(np.pi/180)
+    ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
+    
+    """

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_CPT2.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
+
+POWER_FILES = """VaryingPower/000014020-IR_Scan_withcal_optimized
+VaryingPower/000014021-IR_Scan_withcal_optimized
+VaryingPower/000014024-IR_Scan_withcal_optimized
+VaryingPower/000014025-IR_Scan_withcal_optimized
+VaryingPower/000014026-IR_Scan_withcal_optimized
+"""
+
+
+EXTRA_FILES = """Extra/000014032-IR_Scan_withcal_optimized
+Extra/000014060-IR_Scan_withcal_optimized
+Extra/000014061-IR_Scan_withcal_optimized"""
+
+COMP_COL_FILES = """VaryingComp/Col/000014071-IR_Scan_withcal_optimized
+VaryingComp/Col/000014072-IR_Scan_withcal_optimized
+VaryingComp/Col/000014073-IR_Scan_withcal_optimized
+VaryingComp/Col/000014074-IR_Scan_withcal_optimized
+VaryingComp/Col/000014075-IR_Scan_withcal_optimized
+VaryingComp/Col/000014076-IR_Scan_withcal_optimized
+VaryingComp/Col/000014107-IR_Scan_withcal_optimized
+VaryingComp/Col/000014108-IR_Scan_withcal_optimized
+VaryingComp/Col/000014109-IR_Scan_withcal_optimized
+VaryingComp/Col/000014110-IR_Scan_withcal_optimized
+VaryingComp/Col/000014111-IR_Scan_withcal_optimized
+VaryingComp/Col/000014112-IR_Scan_withcal_optimized
+VaryingComp/Col/000014113-IR_Scan_withcal_optimized
+VaryingComp/Col/000014114-IR_Scan_withcal_optimized
+VaryingComp/Col/000014115-IR_Scan_withcal_optimized
+VaryingComp/Col/000014118-IR_Scan_withcal_optimized
+VaryingComp/Col/000014119-IR_Scan_withcal_optimized"""
+
+COMP_DESP_FILES = """VaryingComp/Desp/000014077-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014083-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014078-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014079-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014080-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014081-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014082-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014083-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014084-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014085-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014086-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014087-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014088-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014089-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014090-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014093-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014094-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014095-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014096-IR_Scan_withcal_optimized
+VaryingComp/Desp/000014097-IR_Scan_withcal_optimized"""
+
+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(POWER_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+PowerCounts = []
+PowerIR1_Freqs = []
+
+for i, fname in enumerate(POWER_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    PowerCounts.append(np.array(data['datasets']['counts_spectrum']))
+    PowerIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+ExtraCounts = []
+ExtraIR1_Freqs = []
+
+for i, fname in enumerate(EXTRA_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    ExtraCounts.append(np.array(data['datasets']['counts_spectrum']))
+    ExtraIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    
+CompColCounts = []
+CompColIR1_Freqs = []
+
+for i, fname in enumerate(COMP_COL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CompColCounts.append(np.array(data['datasets']['counts_spectrum']))
+    CompColIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    
+CompDespCounts = []
+CompDespIR1_Freqs = []
+
+for i, fname in enumerate(COMP_DESP_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CompDespCounts.append(np.array(data['datasets']['counts_spectrum']))
+    CompDespIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+#%%
+
+"""
+Resonancias DD variando la potencia del IR2
+"""
+
+powermedvec = [3,4,0,1,2]
+
+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')
+
+
+#%%
+
+"""
+Resonancias DD en config colineal, haces OAM, lindas con algunos parametros (ver cuaderno)
+"""
+
+extramedvec = [1]
+
+#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in extramedvec:
+    plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[med][1:]], [c for c in ExtraCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(433.5, 438)
+plt.ylim(1100,3200)
+plt.axvline(freqDR)
+plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Variando potencia de IR2 para potencia de IR1 fija')
+
+
+#%%
+
+"""
+Comparo resonancias DD con haces OAM en config colineal con desplazada
+"""
+
+extramedvec = [1,2]
+
+#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+secular=False    
+
+
+plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[1][1:]], [c for c in ExtraCounts[1][1:]], '-o', markersize=2, label='Colineales')
+plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[2][1:]], [c for c in ExtraCounts[2][1:]], '-o', markersize=2, label='Desplazadas')
+
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(425, 455)
+plt.ylim(1100,3200)
+
+if secular:
+    plt.axvline(freqDR)
+    plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Comparacion de config colineal (insensible) con desplazada (sensible)')
+
+
+#%%
+
+"""
+Resonancias DD config colineal OAM variando compensacion dcA
+"""
+
+import seaborn as sns
+paleta = sns.color_palette("tab10")
+
+compcolmedvec = [5,0,1,2,3,4]
+
+CompsDCA = [-100, -150, -200, -250, -300, -350]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+MinimosFluos_Col = []
+ErrorMinimosFluos_Col = []
+
+MinimosFluos_Col_Relativos = []
+
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
+    MinimosFluos_Col_Relativos.append([1-(c-30)/(np.mean(CompColCounts[med][0:20])-30) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(433.5, 438)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+#%%
+
+"""
+Resonancias DD config desplazada OAM variando compensacion dcA
+"""
+import seaborn as sns
+paleta = sns.color_palette("tab10")
+
+compdespmedvec = [0,2,3,4,5,6]
+
+CompsDCA = [-100, -100, -150, -200, -250, -300, -350]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR2)
+
+
+freqDR=435.8
+
+MinimosFluos_Desp = []
+ErrorMinimosFluos_Desp = []
+
+MinimosFluos_Desp_Relativos = []
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Desp.append([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])])
+    MinimosFluos_Desp_Relativos.append([1-(c-30)/(np.mean(CompDespCounts[med][0:20])-30) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+
+
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.ylim(400, 1280)
+#plt.ylim(0,100)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config desplazada OAM')
+
+#%%
+
+"""
+Ahora grafico el valor de los minimos en funcion de la descompensacion dcA
+"""
+
+CompsDCAcol = [-100, -150, -200, -250, -300, -350]
+CompsDCAdesp = [-100, -150, -200, -250, -300, -350]
+
+MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
+MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
+
+MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
+MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
+
+ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
+ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
+
+ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
+ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
+
+#DR1
+plt.figure()
+plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR1')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR1 counts - mean')
+#DR2
+plt.figure()
+plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR2')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR2 counts - mean')
+
+#%%
+
+"""
+Ahora veo los valores relativos a la fluorescencia al principio del espectro
+"""
+
+MinimosFluos_Col_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Col_Relativos))[0]
+MinimosFluos_Col_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Col_Relativos))[1]
+
+MinimosFluos_Desp_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[0]
+MinimosFluos_Desp_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[1]
+
+plt.figure()
+plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR1, 'o', markersize=12, label='Colineal')
+plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR1, 'o', markersize=12, label='Desplazada')
+plt.title('DR1')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR1 counts relatives')
+plt.legend()
+
+plt.figure()
+plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR2, 'o', markersize=12, label='Colineal')
+plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR2, 'o', markersize=12, label='Desplazada')
+plt.title('DR2')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR2 counts relatives')
+plt.legend()
+
+#hasta aca
+
+#%%
+"""
+Resonancias DD config colineal OAM variando compensacion OVEN
+"""
+
+import seaborn as sns
+
+compcolmedvec = [6,7,8,9,10,11,12,13,14]
+
+CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
+
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(433.5, 438)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+
+""" Le resto el fondo a ver como se ven"""
+
+paleta = sns.color_palette("tab10")
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.25
+
+idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+MinimosFluos_Col = []
+ErrorMinimosFluos_Col = []
+
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c-np.mean(CompColCounts[med][1:20]) for c in CompColCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c-np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(443, 445)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+
+""" Estas son dos curvas con mas estadistica """
+compcolmedvec = [15, 16]
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config colineal OAM mas estadistica')
+
+
+
+#%%
+import seaborn as sns
+
+"""
+Resonancias DD config desplazada OAM variando compensacion OVEN
+"""
+
+compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
+
+CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM')
+
+
+""" Le resto el fondo a ver como se ven y ploteo sus valores de DR"""
+
+paleta = sns.color_palette("tab10")
+
+compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+freqDR=435.8
+
+MinimosFluos_Desp = []
+ErrorMinimosFluos_Desp = []
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c-np.mean(CompDespCounts[med][1:20]) for c in CompDespCounts[med][1:]], '-o', color=paleta[jj], markersize=2, zorder=0)
+    plt.plot([DR1, DR2], [c-np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Desp.append(([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])]))
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM')
+
+
+
+""" Estas son dos curvas con mas estadistica """
+compdespmedvec = [17, 18]
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
+
+#%%
+""" Agarro la desplazada compensada para ver si se ven bandas laterales de movimiento (no)"""
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+#freqDR=435.8
+freqDR=DR2
+jj=0
+
+plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[18][1:]], [c for c in CompDespCounts[18][1:]], '-o', markersize=2)
+
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(442, 446)
+plt.axvline(freqDR)
+plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
+
+#%%
+
+"""
+Ahora grafico el valor de los minimos en funcion de la descompensacion OVEN
+"""
+
+CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
+CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
+
+MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
+MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
+
+MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
+MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
+
+ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
+ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
+
+ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
+ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
+
+#DR1
+plt.figure()
+plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR1[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR1[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:-2]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR1')
+plt.grid()
+plt.xlabel('CompOVEN voltage')
+plt.ylabel('DR1 counts - mean')
+#DR2
+plt.figure()
+plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR2[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR2[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:-2]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR2')
+plt.grid()
+plt.xlabel('CompOVEN voltage')
+plt.ylabel('DR2 counts - mean')
\ No newline at end of file

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_CPT2_lolo.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
+
+
+
+from glob import glob
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Carga de datos experimentales 
+
+
+#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')
+
+
+os.chdir('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
+
+# Levanto carpetas / archivos de datos por experimento
+POWER_FILES     = sorted(glob('VaryingPower/*'))
+EXTRA_FILES     = sorted(glob('Extra/*')) 
+COMP_COL_FILES  = sorted(glob('VaryingComp/Col/*')) 
+COMP_DESP_FILES = sorted(glob('VaryingComp/Desp/*')) 
+
+
+# Función para verificar lectura de archivos
+def SeeKeys(files):
+    for i, fname in enumerate(files):
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+# Veamos si está bien...
+print(SeeKeys(POWER_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+PowerCounts = []
+PowerIR1_Freqs = []
+
+for i, fname in enumerate(POWER_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    PowerCounts.append(np.array(data['datasets']['counts_spectrum']))
+    PowerIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+ExtraCounts = []
+ExtraIR1_Freqs = []
+
+for i, fname in enumerate(EXTRA_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    ExtraCounts.append(np.array(data['datasets']['counts_spectrum']))
+    ExtraIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    
+CompColCounts = []
+CompColIR1_Freqs = []
+
+for i, fname in enumerate(COMP_COL_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CompColCounts.append(np.array(data['datasets']['counts_spectrum']))
+    CompColIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    
+CompDespCounts = []
+CompDespIR1_Freqs = []
+
+for i, fname in enumerate(COMP_DESP_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CompDespCounts.append(np.array(data['datasets']['counts_spectrum']))
+    CompDespIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+#%%
+
+"""
+Resonancias DD variando la potencia del IR2
+"""
+
+powermedvec = [3,4,0,1,2]
+
+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')
+
+
+#%%
+
+"""
+Resonancias DD en config colineal, haces OAM, lindas con algunos parametros (ver cuaderno)
+"""
+
+extramedvec = [1]
+
+#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in extramedvec:
+    plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[med][1:]], [c for c in ExtraCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(433.5, 438)
+plt.ylim(1100,3200)
+plt.axvline(freqDR)
+plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Variando potencia de IR2 para potencia de IR1 fija')
+
+
+#%%
+
+"""
+Comparo resonancias DD con haces OAM en config colineal con desplazada
+"""
+
+extramedvec = [1,2]
+
+#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+secular=False    
+
+
+plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[1][1:]], [c for c in ExtraCounts[1][1:]], '-o', markersize=2, label='Colineales')
+plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[2][1:]], [c for c in ExtraCounts[2][1:]], '-o', markersize=2, label='Desplazadas')
+
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(425, 455)
+plt.ylim(1100,3200)
+
+if secular:
+    plt.axvline(freqDR)
+    plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Comparacion de config colineal (insensible) con desplazada (sensible)')
+
+
+#%%
+
+"""
+Resonancias DD config colineal OAM variando compensacion dcA
+"""
+
+import seaborn as sns
+paleta = sns.color_palette("tab10")
+
+compcolmedvec = [5,0,1,2,3,4]
+
+CompsDCA = [-100, -150, -200, -250, -300, -350]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+MinimosFluos_Col = []
+ErrorMinimosFluos_Col = []
+
+MinimosFluos_Col_Relativos = []
+
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
+    MinimosFluos_Col_Relativos.append([1-(c-30)/(np.mean(CompColCounts[med][0:20])-30) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(433.5, 438)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+#%%
+
+"""
+Resonancias DD config desplazada OAM variando compensacion dcA
+"""
+import seaborn as sns
+paleta = sns.color_palette("tab10")
+
+compdespmedvec = [0,2,3,4,5,6]
+
+CompsDCA = [-100, -100, -150, -200, -250, -300, -350]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR2)
+
+
+freqDR=435.8
+
+MinimosFluos_Desp = []
+ErrorMinimosFluos_Desp = []
+
+MinimosFluos_Desp_Relativos = []
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Desp.append([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])])
+    MinimosFluos_Desp_Relativos.append([1-(c-30)/(np.mean(CompDespCounts[med][0:20])-30) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+
+
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.ylim(400, 1280)
+#plt.ylim(0,100)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config desplazada OAM')
+
+
+
+
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Version Luda de los expectros al descompenzar  ###############################
+from scipy.signal import savgol_filter as savgol
+
+# Grafico con subplots
+fig,axx = plt.subplots(2,2,figsize=(12,8), constrained_layout=True , sharey='row', sharex='row' )
+fig.set_constrained_layout_pads(w_pad=1/72, h_pad=0, hspace=0, wspace=0)
+
+
+# En la fila de arriba los espectros ..........................................
+
+####################################
+print("Colineal")
+ax = axx[0][0]
+
+compcolmedvec = [5,0,1,2,3,4]
+CompsDCA_col = [-100, -150, -200, -250, -300, -350]
+
+ftrap = 22.1
+DR1 = 435.8
+DR2 = 444.2
+freqDR=435.8
+
+rango_dr1 = np.array([435,437])
+rango_dr2 = np.array([443,445])
+
+I_base_fit = np.arange(0,50).tolist()+np.arange(230,280).tolist()+np.arange(450,490).tolist()
+I_base_fit = np.array(I_base_fit)
+
+picos = {0:[], 1:[]}
+
+for med in compcolmedvec:
+
+    frecuencia = CompColIR1_Freqs[med][1:]*2e-6
+    cuentas    = CompColCounts[med][1:]
+    
+    if med==4:
+        # FIX OUTLIER
+        cuentas = cuentas.tolist()
+        cuentas.pop(291); cuentas.pop(291); cuentas.pop(291); 
+        cuentas = np.array(cuentas)
+        
+        frecuencia = frecuencia.tolist()
+        frecuencia.pop(291); frecuencia.pop(291); frecuencia.pop(291); 
+        frecuencia = np.array(frecuencia)
+    
+    
+    cuentas_suave    = savgol(cuentas,13,2)
+    
+    ax.plot(frecuencia,cuentas, '.', alpha=0.5)
+    ax.plot(frecuencia,cuentas_suave, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+    
+    mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+    ax.plot( [420,460], np.polyval(mm,[420,460]) , '-',  color=ax.get_lines()[-1].get_color(), alpha=0.5)
+    
+    for jj,rango in enumerate([rango_dr1,rango_dr2]):
+        I = np.arange(len(frecuencia))[(frecuencia>rango.min())&(frecuencia<rango.max())]
+        idx = cuentas_suave[I].argmin() + I[0]
+        
+        ax.plot(frecuencia[idx],cuentas_suave[idx], 'o', ms=12, color=ax.get_lines()[-1].get_color(),zorder=10)
+        
+        base = np.polyval(mm,rango.mean())
+        val_pico = cuentas_suave[I].min()
+        picos[jj].append( (base-val_pico)/base )
+        print( (base-val_pico)/base , end="\t")
+    print('')
+
+        
+    
+    # ax.plot(cuentas)
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel(r'frecuencia [kHz]')
+ax.set_ylabel(r'Cuentas')
+ax.set_title("Colineal")
+
+picos_col_dr1 = np.array(picos[0])
+picos_col_dr2 = np.array(picos[1])
+
+
+
+print("\n")
+
+
+
+
+
+
+
+####################################
+# Desplazado
+print("Despazado")
+ax = axx[0][1]
+
+# compdespmedvec = [0,2,3,4,5,6]
+compdespmedvec = [0,1,2,3,4,5]
+CompsDCA_desp = [-100, -150, -200, -250, -300, -350]
+ftrap = 22.1
+# DR1 = 435.8
+# DR2 = 444.2
+
+rango_dr1 = np.array([435,437])
+rango_dr2 = np.array([443,445])
+
+picos = {0:[], 1:[]}
+
+
+for med in compdespmedvec:
+
+    frecuencia = CompDespIR1_Freqs[med][1:]*2e-6
+    cuentas    = CompDespCounts[med][1:]
+    
+    if med==0:
+        # FIX OUTLIER
+        cuentas[207] = cuentas[206:208:2].mean()
+    
+    if med==1:
+        # raise ValueError('lolo')
+        cuentas = cuentas.tolist()
+        frecuencia = frecuencia.tolist()
+        for _ in range(4):
+            cuentas.pop(27)
+            frecuencia.pop(27)
+        cuentas = np.array(cuentas)
+        frecuencia = np.array(frecuencia)
+        
+    cuentas_suave    = savgol(cuentas,13,2)
+    
+    
+    ax.plot(frecuencia,cuentas, '.', alpha=0.5)
+    ax.plot(frecuencia,cuentas_suave, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+    
+    mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+    ax.plot( [420,460], np.polyval(mm,[420,460]) , '-',  color=ax.get_lines()[-1].get_color(), alpha=0.5)
+
+    for jj,rango in enumerate([rango_dr1,rango_dr2]):
+        I = np.arange(len(frecuencia))[(frecuencia>rango.min())&(frecuencia<rango.max())]
+        idx = cuentas_suave[I].argmin() + I[0]
+        
+        ax.plot(frecuencia[idx],cuentas_suave[idx], 'o', ms=12, color=ax.get_lines()[-1].get_color(),zorder=10)
+        
+        base = np.polyval(mm,rango.mean())
+        val_pico = cuentas_suave[I].min()
+        picos[jj].append( (base-val_pico)/base )    
+        print( (base-val_pico)/base , end="\t")
+    print('')
+    
+    # ax.plot(cuentas)
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel(r'frecuencia [kHz]')
+# ax.set_ylabel(r'Cuentas')
+ax.set_title("Desplazado")
+
+picos_desp_dr1 = np.array(picos[0])
+picos_desp_dr2 = np.array(picos[1])
+
+ax.set_xlim(427,453)
+
+
+
+
+# En la fila de abajo las profundidades .......................................
+
+
+
+####################################
+ax = axx[1][0]
+ax.plot( CompsDCA_col , picos_col_dr1 , 'o--' , color='gray')
+for comp,pic in zip(CompsDCA_col , picos_col_dr1):
+    ax.plot(comp,pic,'o')
+ax.set_prop_cycle(None)
+ax.plot( CompsDCA_col , picos_col_dr2 , 'o-' , color='gray')
+for comp,pic in zip(CompsDCA_col , picos_col_dr2):
+    ax.plot(comp,pic,'o')
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+
+ax.set_xlabel('Descompensación [mV]')
+ax.set_ylabel('Profundidad relativa')
+
+####################################
+ax = axx[1][1]
+ax.plot( CompsDCA_desp , picos_desp_dr1 , 'o--' , color='gray' )
+for comp,pic in zip(CompsDCA_desp , picos_desp_dr1):
+    ax.plot(comp,pic,'o')
+ax.set_prop_cycle(None)
+ax.plot( CompsDCA_desp , picos_desp_dr2 , 'o-'  , color='gray')
+for comp,pic in zip(CompsDCA_desp , picos_desp_dr2):
+    ax.plot(comp,pic,'o')
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel('Descompensación [mV]')
+
+ax.semilogy()
+ax.set_ylim(0.09,1.05)
+
+
+for ax in axx[1]:
+    ax.grid(b=True,linestyle=':',color='lightgray', which='minor', axis='y')
+
+from matplotlib.ticker import FormatStrFormatter
+ax.yaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
+
+for ax in axx[0]:
+    ax.axvline(DR1, ls='--', color='gray')
+    ax.axvline(DR2, ls='-', color='gray')
+
+
+
+
+# fig.savefig('profundidad_vs_descompensacion_abs.png')
+
+
+
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Rehago pero en picos normalizados  ###############################
+from scipy.signal import savgol_filter as savgol
+
+# Grafico con subplots
+fig,axx = plt.subplots(2,2,figsize=(12,8), constrained_layout=True , sharey='row', sharex='row' )
+fig.set_constrained_layout_pads(w_pad=1/72, h_pad=0, hspace=0, wspace=0)
+
+
+# En la fila de arriba los espectros ..........................................
+
+####################################
+print("Colineal")
+ax = axx[0][0]
+
+compcolmedvec = [5,0,1,2,3,4]
+CompsDCA_col = [-100, -150, -200, -250, -300, -350]
+
+ftrap = 22.1
+DR1 = 435.8
+DR2 = 444.2
+freqDR=435.8
+
+rango_dr1 = np.array([435,437])
+rango_dr2 = np.array([443,445])
+
+I_base_fit = np.arange(0,50).tolist()+np.arange(230,280).tolist()+np.arange(450,490).tolist()
+I_base_fit = np.array(I_base_fit)
+
+picos = {0:[], 1:[]}
+
+for med in compcolmedvec:
+
+    frecuencia = CompColIR1_Freqs[med][1:]*2e-6
+    cuentas    = CompColCounts[med][1:]
+    
+    if med==4:
+        # FIX OUTLIER
+        cuentas = cuentas.tolist()
+        cuentas.pop(291); cuentas.pop(291); cuentas.pop(291); 
+        cuentas = np.array(cuentas)
+        
+        frecuencia = frecuencia.tolist()
+        frecuencia.pop(291); frecuencia.pop(291); frecuencia.pop(291); 
+        frecuencia = np.array(frecuencia)
+    
+    mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+    # ax.plot( [420,460], np.polyval(mm,[420,460]) , '-',  color=ax.get_lines()[-1].get_color(), alpha=0.5)
+    cuentas = cuentas / np.polyval(mm,frecuencia)
+    
+    cuentas_suave    = savgol(cuentas,13,2)
+    
+    
+    ax.plot(frecuencia,cuentas, '.', alpha=0.1)
+    ax.plot(frecuencia,cuentas_suave, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+    
+    
+    for jj,rango in enumerate([rango_dr1,rango_dr2]):
+        I = np.arange(len(frecuencia))[(frecuencia>rango.min())&(frecuencia<rango.max())]
+        idx = cuentas_suave[I].argmin() + I[0]
+        
+        ax.plot(frecuencia[idx],cuentas_suave[idx], 'o', ms=12, color=ax.get_lines()[-1].get_color(),zorder=10)
+        
+        base = 1
+        val_pico = cuentas_suave[I].min()
+        picos[jj].append( (base-val_pico)/base )
+        print( (base-val_pico)/base , end="\t")
+    print('')
+
+        
+    
+    # ax.plot(cuentas)
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel(r'frecuencia [kHz]')
+ax.set_ylabel(r'Cuentas normalizadas')
+ax.set_title("Colineal")
+
+picos_col_dr1 = np.array(picos[0])
+picos_col_dr2 = np.array(picos[1])
+
+
+
+print("\n")
+
+
+
+
+
+
+
+####################################
+# Desplazado
+print("Despazado")
+ax = axx[0][1]
+
+# compdespmedvec = [0,2,3,4,5,6]
+compdespmedvec = [0,1,2,3,4,5]
+CompsDCA_desp = [-100, -150, -200, -250, -300, -350]
+ftrap = 22.1
+# DR1 = 435.8
+# DR2 = 444.2
+
+rango_dr1 = np.array([435,437])
+rango_dr2 = np.array([443,445])
+
+picos = {0:[], 1:[]}
+
+
+for med in compdespmedvec:
+
+    frecuencia = CompDespIR1_Freqs[med][1:]*2e-6
+    cuentas    = CompDespCounts[med][1:]
+    
+    if med==0:
+        # FIX OUTLIER
+        cuentas[207] = cuentas[206:208:2].mean()
+    
+    if med==1:
+        # raise ValueError('lolo')
+        cuentas = cuentas.tolist()
+        frecuencia = frecuencia.tolist()
+        for _ in range(4):
+            cuentas.pop(27)
+            frecuencia.pop(27)
+        cuentas = np.array(cuentas)
+        frecuencia = np.array(frecuencia)
+    
+    mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+    # ax.plot( [420,460], np.polyval(mm,[420,460]) , '-',  color=ax.get_lines()[-1].get_color(), alpha=0.5)
+    cuentas = cuentas / np.polyval(mm,frecuencia)
+    
+    
+    cuentas_suave    = savgol(cuentas,13,2)
+    
+    
+    ax.plot(frecuencia,cuentas, '.', alpha=0.1)
+    ax.plot(frecuencia,cuentas_suave, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+
+    for jj,rango in enumerate([rango_dr1,rango_dr2]):
+        I = np.arange(len(frecuencia))[(frecuencia>rango.min())&(frecuencia<rango.max())]
+        idx = cuentas_suave[I].argmin() + I[0]
+        
+        ax.plot(frecuencia[idx],cuentas_suave[idx], 'o', ms=12, color=ax.get_lines()[-1].get_color(),zorder=10)
+        
+        base = 1
+        val_pico = cuentas_suave[I].min()
+        picos[jj].append( (base-val_pico)/base )    
+        print( (base-val_pico)/base , end="\t")
+    print('')
+    
+    # ax.plot(cuentas)
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel(r'frecuencia [kHz]')
+# ax.set_ylabel(r'Cuentas')
+ax.set_title("Desplazado")
+
+picos_desp_dr1 = np.array(picos[0])
+picos_desp_dr2 = np.array(picos[1])
+
+ax.set_xlim(427,453)
+
+
+
+
+# En la fila de abajo las profundidades .......................................
+
+
+
+####################################
+ax = axx[1][0]
+ax.plot( CompsDCA_col , picos_col_dr1 , 'o--' , color='gray')
+for comp,pic in zip(CompsDCA_col , picos_col_dr1):
+    ax.plot(comp,pic,'o')
+ax.set_prop_cycle(None)
+ax.plot( CompsDCA_col , picos_col_dr2 , 'o-' , color='gray')
+for comp,pic in zip(CompsDCA_col , picos_col_dr2):
+    ax.plot(comp,pic,'o')
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+
+ax.set_xlabel('Descompensación [mV]')
+ax.set_ylabel('Profundidad relativa')
+
+####################################
+ax = axx[1][1]
+ax.plot( CompsDCA_desp , picos_desp_dr1 , 'o--' , color='gray' )
+for comp,pic in zip(CompsDCA_desp , picos_desp_dr1):
+    ax.plot(comp,pic,'o')
+ax.set_prop_cycle(None)
+ax.plot( CompsDCA_desp , picos_desp_dr2 , 'o-'  , color='gray')
+for comp,pic in zip(CompsDCA_desp , picos_desp_dr2):
+    ax.plot(comp,pic,'o')
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel('Descompensación [mV]')
+
+ax.semilogy()
+ax.set_ylim(0.09,1.05)
+
+
+for ax in axx[1]:
+    ax.grid(b=True,linestyle=':',color='lightgray', which='minor', axis='y')
+
+from matplotlib.ticker import FormatStrFormatter
+ax.yaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
+
+for ax in axx[0]:
+    ax.axvline(DR1, ls='--', color='gray')
+    ax.axvline(DR2, ls='-', color='gray')
+
+
+
+
+# fig.savefig('profundidad_vs_descompensacion_normalizado.png')
+
+
+
+
+#%%
+
+"""
+Ahora grafico el valor de los minimos en funcion de la descompensacion dcA
+"""
+
+CompsDCAcol = [-100, -150, -200, -250, -300, -350]
+CompsDCAdesp = [-100, -150, -200, -250, -300, -350]
+
+MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
+MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
+
+MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
+MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
+
+ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
+ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
+
+ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
+ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
+
+#DR1
+plt.figure()
+plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR1')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR1 counts - mean')
+#DR2
+plt.figure()
+plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR2')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR2 counts - mean')
+
+#%%
+
+"""
+Ahora veo los valores relativos a la fluorescencia al principio del espectro
+"""
+
+MinimosFluos_Col_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Col_Relativos))[0]
+MinimosFluos_Col_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Col_Relativos))[1]
+
+MinimosFluos_Desp_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[0]
+MinimosFluos_Desp_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[1]
+
+plt.figure()
+plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR1, 'o', markersize=12, label='Colineal')
+plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR1, 'o', markersize=12, label='Desplazada')
+plt.title('DR1')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR1 counts relatives')
+plt.legend()
+
+plt.figure()
+plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR2, 'o', markersize=12, label='Colineal')
+plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR2, 'o', markersize=12, label='Desplazada')
+plt.title('DR2')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR2 counts relatives')
+plt.legend()
+
+#hasta aca
+
+#%%
+"""
+Resonancias DD config colineal OAM variando compensacion OVEN
+"""
+
+import seaborn as sns
+
+compcolmedvec = [6,7,8,9,10,11,12,13,14]
+
+CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
+
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(433.5, 438)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+
+""" Le resto el fondo a ver como se ven"""
+
+paleta = sns.color_palette("tab10")
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.25
+
+idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+MinimosFluos_Col = []
+ErrorMinimosFluos_Col = []
+
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c-np.mean(CompColCounts[med][1:20]) for c in CompColCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c-np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(443, 445)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+
+""" Estas son dos curvas con mas estadistica """
+compcolmedvec = [15, 16]
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config colineal OAM mas estadistica')
+
+
+
+#%%
+import seaborn as sns
+
+"""
+Resonancias DD config desplazada OAM variando compensacion OVEN
+"""
+
+compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
+
+CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM')
+
+
+""" Le resto el fondo a ver como se ven y ploteo sus valores de DR"""
+
+paleta = sns.color_palette("tab10")
+
+compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+freqDR=435.8
+
+MinimosFluos_Desp = []
+ErrorMinimosFluos_Desp = []
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c-np.mean(CompDespCounts[med][1:20]) for c in CompDespCounts[med][1:]], '-o', color=paleta[jj], markersize=2, zorder=0)
+    plt.plot([DR1, DR2], [c-np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Desp.append(([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])]))
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM')
+
+
+
+""" Estas son dos curvas con mas estadistica """
+compdespmedvec = [17, 18]
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
+
+#%%
+""" Agarro la desplazada compensada para ver si se ven bandas laterales de movimiento (no)"""
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+#freqDR=435.8
+freqDR=DR2
+jj=0
+
+plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[18][1:]], [c for c in CompDespCounts[18][1:]], '-o', markersize=2)
+
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(442, 446)
+plt.axvline(freqDR)
+plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
+
+#%%
+
+"""
+Ahora grafico el valor de los minimos en funcion de la descompensacion OVEN
+"""
+
+CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
+CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
+
+MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
+MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
+
+MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
+MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
+
+ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
+ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
+
+ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
+ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
+
+#DR1
+plt.figure()
+plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR1[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR1[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:-2]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR1')
+plt.grid()
+plt.xlabel('CompOVEN voltage')
+plt.ylabel('DR1 counts - mean')
+#DR2
+plt.figure()
+plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR2[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR2[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:-2]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR2')
+plt.grid()
+plt.xlabel('CompOVEN voltage')
+plt.ylabel('DR2 counts - mean')
\ No newline at end of file

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')
+
+