codigos

analisis/plots/20221006_transitoriosv2/Transitorios_02.py

@@ -214,8 +214,9 @@ popt_vec = []
 pcov_vec = []
 
 plt.figure()
-for j in range(len(SP_Heigths)):
+for j in range(len(SP_Heigths)-1):
 #for j in [12]:
+    print(j)
     BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths[j][0:50]),np.mean(SP_Heigths[j][-50:]),59,67,965)
     
     #CorrectedSP_Height = [SP_Heigths[j][k]-BackgroundVector[k] for k in range(len(BackgroundVector))]
@@ -234,10 +235,15 @@ for j in range(len(SP_Heigths)):
     
 #%%
 plt.figure()
-plt.plot(UVpotVec, Taus,'o')
-plt.xlabel('UV power (mW)')
-plt.ylabel('Characteristic time (us)')
+plt.plot(UVpotVec[:-5], Taus[:-6],'o', markersize=10, color='purple')
+#plt.errorbar(UVpotVec[:-5], Taus[:-6], yerr=1e1*np.array(ErrorTaus[:-6]), fmt='.', capsize=2, markersize=2)
+plt.xlabel('UV power (mW)', fontsize=15)
+plt.ylabel('Characteristic time (us)', fontsize=15)
+plt.ylim(-0.1,3)
+plt.grid()
 
+
+#%%
 plt.figure()
 plt.plot(UVpotVec, Amps,'o')
 plt.xlabel('UV power (mW)')
@@ -262,8 +268,8 @@ plt.rcParams.update({
     "text.usetex": False,
 })
 
-#plt.figure()
-#plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
+plt.figure()
+plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
 
 #plot figuras papers
 
@@ -309,30 +315,36 @@ pcov_vec_v2 = []
 
 ki = 80 
 
+selectedj = [2,5,10]
+t0 = -0.2
+
 plt.figure()
 for j in range(len(SP_Heigths_v2)):
 #for j in [1]:
-    #BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths_v2[j][0:50]),np.mean(SP_Heigths_v2[j][-50:]),59,67,965)
-    
-    #CorrectedSP_Height_v2 = [SP_Heigths_v2[j][k]-BackgroundVector[k] for k in range(len(BackgroundVector))]
-    
-    CorrectedSP_Height_v2 = SP_Heigths_v2[j]
-    
-    popt, pcov = curve_fit(expo, RefBins[ki:], CorrectedSP_Height_v2[ki:], p0=(5, 1000, 100), bounds=((0, 0, 0), (50, 1e5, 1e5)))
-    popt_vec_v2.append(popt)
-    pcov_vec_v2.append(pcov)
-    plt.plot(RefBins, CorrectedSP_Height_v2)
-    plt.plot(RefBins[ki:], [expo(r, *popt) for r in RefBins][ki:])
-    
-    print(popt[0])
-    
-    print(pcov[0][0])
-    
-    Taus_v2.append(popt[0])
-    Amps_v2.append(popt[1])
-    Offsets_v2.append(popt[2])
-    ErrorTaus_v2.append(np.sqrt(pcov)[0][0])
+    if j in selectedj:
+        #BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths_v2[j][0:50]),np.mean(SP_Heigths_v2[j][-50:]),59,67,965)
+        
+        #CorrectedSP_Height_v2 = [SP_Heigths_v2[j][k]-BackgroundVector[k] for k in range(len(BackgroundVector))]
+        
+        CorrectedSP_Height_v2 = SP_Heigths_v2[j]
+        
+        popt, pcov = curve_fit(expo, RefBins[ki:], CorrectedSP_Height_v2[ki:], p0=(5, 1000, 100), bounds=((0, 0, 0), (50, 1e5, 1e5)))
+        popt_vec_v2.append(popt)
+        pcov_vec_v2.append(pcov)
+        plt.plot([r+t0 for r in RefBins], CorrectedSP_Height_v2)
+        plt.plot([r+t0 for r in RefBins[ki:]], [expo(r, *popt) for r in RefBins][ki:])
+        
+        print(popt[0])
+        
+        print(pcov[0][0])
+        
+        Taus_v2.append(popt[0])
+        Amps_v2.append(popt[1])
+        Offsets_v2.append(popt[2])
+        ErrorTaus_v2.append(np.sqrt(pcov)[0][0])
     
+plt.xlim(-0.2,2)
+
 #%%
 
 plt.figure()
@@ -397,7 +409,7 @@ plt.savefig('fig3_01.pdf')
 plt.savefig('fig3_01.svg')
 
 
-#%%
+        #%%
 
 """
 VEMOS UNA DP CON POTENCIA ALTA A VER SI DA LO QUE TIENE QUE DAR EL ANCHO DE LINEA
@@ -449,10 +461,16 @@ initi=0.10
 popt_UV, pcov_UV = curve_fit(expo, BinsUVhigh[int(initi*len(BinsUVhigh)):], HeightsUVhigh[int(initi*len(BinsUVhigh)):], p0=(1, 1000, 1800, 1), bounds=((0,0,0,0),(20, 1e7, 1e5, 1e3)))
 
 plt.figure()
-plt.plot(BinsUVhigh, HeightsUVhigh)
-plt.plot(BinsUVhigh, [expo(r, *popt_UV) for r in BinsUVhigh])
-plt.plot(BinsUVhigh[int(initi*len(BinsUVhigh))], HeightsUVhigh[int(initi*len(BinsUVhigh))],'o',markersize=5)
+plt.plot(BinsUVhigh, HeightsUVhigh,linewidth=4)
+plt.plot(BinsUVhigh[80:], [expo(r, *popt_UV) for r in BinsUVhigh][80:], color='fuchsia', linewidth=3, label='Exponential fit')
+#plt.plot(BinsUVhigh[int(initi*len(BinsUVhigh))], HeightsUVhigh[int(initi*len(BinsUVhigh))],'o',markersize=5)
 #plt.ylim(-1000,20000)
+plt.grid()
+plt.xlabel(r'Time ($\mu$s)', fontsize=15)
+plt.ylabel('Counts', fontsize=15)
+plt.xlim(-0.5, 5)
+plt.legend(fontsize=15)
+
 tauUV = popt_UV[0]
 print(tauUV)
 

analisis/plots/20230321_heatingrate/CPT_plotter_20230321.py

@@ -12,6 +12,8 @@ from scipy import interpolate
 
 #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/20230321_heatingrate/Data/')
+
 CPT_FILES = """000010420-IR_Scan_withcal_optimized
 000010436-IR_Scan_withcal_optimized
 000010437-IR_Scan_withcal_optimized
@@ -19,6 +21,12 @@ CPT_FILES = """000010420-IR_Scan_withcal_optimized
 000010439-IR_Scan_withcal_optimized
 000010440-IR_Scan_withcal_optimized
 000010441-IR_Scan_withcal_optimized
+000010425-IR_Scan_withcal_optimized
+000010426-IR_Scan_withcal_optimized
+000010427-IR_Scan_withcal_optimized
+000010428-IR_Scan_withcal_optimized
+000010429-IR_Scan_withcal_optimized
+000010430-IR_Scan_withcal_optimized
 """ 
 
 HEATING_FILES = """000010422-HeatingRate"""
@@ -81,7 +89,7 @@ for i, fname in enumerate(HEATING_FILES.split()):
 #%%
 
 """
-Ploteo la cpt de referencia
+Ploteo la cpt de referencia / plotting the reference CPT
 """
 
 jvec = [0]
@@ -103,6 +111,7 @@ from scipy.optimize import curve_fit
 
 """
 Ajusto un cpt para obtener todos los parámetros relevantes primero.
+I fit a cpt curve to retrieve all the relevant parameters first.
 """
 
 phidoppler, titadoppler = 0, 90
@@ -160,9 +169,10 @@ def FitEITpi(freqs, SG, SP, scale, offset, temp):
 #esos valores anteriores dan mal la ordenada al origen, cambie correccion y ahora da
 #[4.71134671e-01, 7.63142299e+00, 7.30866544e+04, 1.80899906e+02, 1.20863371e-03]
 
+popt_fullcpt, pcov_fullcpt = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
+
 print(f'Temperatura: ({round(1e3*popt_fullcpt[-1],2)} +- {round(1e3*np.sqrt(pcov_fullcpt[-1][-1]),2)}) mK')
 
-popt_fullcpt, pcov_fullcpt = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
 
 print(popt_fullcpt)
 
@@ -171,26 +181,44 @@ Det = popt_fullcpt[1]
 
 FittedEITpi = FitEITpi(freqslongpi, *popt_fullcpt)
 
+
+#%%
+"""
+Ploteo la CPT de referencia junto al ajuste y a la resonancia oscura de interes
+I plot the reference CPT along with the fit to the model and the dark resonance of interest
+"""
+
+
+i_DR = 955
+
 plt.figure()
 plt.errorbar(FreqsDRpi, CountsDRpi, yerr=2*np.sqrt(CountsDRpi), fmt='o', capsize=2, markersize=2)
 plt.plot(freqslongpi, FittedEITpi)
+plt.plot(freqslongpi[i_DR], FittedEITpi[i_DR],'o', color='red', markersize=12)
+plt.xlabel('Detuning (MHz)')
+plt.ylabel('Counts')
+
 #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
 
 
+
 #%%
 """
 Simulo CPTs con todos esos parámetros para distintas temperaturas
+I simulate CPT curves with all the previous parameters but with varying temperatures
 """
-TempVecTeorico = list(np.arange(0, 31, 1))
+TempVecTeorico = list(np.arange(0.3,1,0.1))+list(np.arange(1, 31, 1))
 CurvasTeoricas = []
 
 for tempi in TempVecTeorico:
     CurvasTeoricas.append(FitEITpi(freqslongpi, *popt_fullcpt[:-1], tempi*1e-3))
 
 
+
 #%%
 """
 Acá agarro la primera y busco el valor i_DR que corresponde a la resonancia oscura de interés
+With the first one, I look for the value i_DR which corresponds to the dark resonance of interest
 """
 curva_ref = CurvasTeoricas[0]
 
@@ -201,12 +229,33 @@ plt.figure()
 plt.plot(freqslongpi, curva_ref)
 plt.plot(freqslongpi[i_DR], curva_ref[i_DR],'o')
 
+#%%
+"""
+ploteo algunos CPTs teoricos para algunas temperaturas
+Plotting some theory cpt curves for some temperatures
+"""
+
+plt.plot(freqslongpi, CurvasTeoricas[0])
+plt.plot(freqslongpi[i_DR], CurvasTeoricas[0][i_DR],'o',markersize=10)
+plt.plot(freqslongpi, CurvasTeoricas[10])
+plt.plot(freqslongpi[i_DR], CurvasTeoricas[10][i_DR],'o',markersize=10)
+plt.plot(freqslongpi, CurvasTeoricas[20])
+plt.plot(freqslongpi[i_DR], CurvasTeoricas[20][i_DR],'o',markersize=10)
+plt.xlabel('Detuning (MHz)')
+plt.ylabel('Fluorescence')
+plt.grid()
+
+
 
 #%%
 """
 Ahora interpolo los valores teóricos de las profundidades de esas resonancias
 y aplico la interpolación a las mediciones para obtener temperaturas.
 Luego, grafico las temperaturas en función de los tiempos de calentamiento.
+
+Now I interpolate the theoretical values of the depths of those resonances
+and apply the interpolation to the measurements to obtain temperatures.
+After that, I plot the temperatures with respect to the heating times
 """
 from scipy.interpolate import interp1d
 
@@ -235,7 +284,7 @@ plt.ylabel('Temperatura (mK)')
 
 plt.figure()
 #plt.plot(Heating_med, Heating_tim, 'o', color='blue')
-plt.errorbar(Heating_tim, Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=2)
+plt.errorbar([t*1e3 for t in Heating_tim], Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=5)
 
 plt.ylabel('Cuentas de DR medidas')
 plt.xlabel('Heating time (s)')
@@ -250,6 +299,7 @@ p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas)
 #%%
 """
 Grafico finalmente el plot del heating rate de la trampa
+Finally I plot the heating rate of the trap
 """
 
 
@@ -271,6 +321,7 @@ print(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
 #%%
 """
 Ahora voy a ver CPT enteras con tiempos de calentamiento distintos.
+Now I see whole CPT curves with different heating times
 """
 
 jvec = [3, 4]
@@ -306,10 +357,14 @@ plt.ylim(1000,2900)
 plt.grid()
 plt.legend()
 
+#%%
 
 """
 La siguiente curva probablemente no este bien medida ya que inmediatamente
 despues, los laseres se deslockearon. La dejo por las dudas.
+
+This curve is probably not well measured...
+
 """
 
 jvec = [5, 6]
@@ -330,4 +385,27 @@ plt.legend()
 plt.title('Ojo: medicion condicionada por derivas')
 
 
+#%%
+"""
+Ahora ploteo 6 curvas cpt para distintos valores de potencia del UV
+
+This is a plot of 6 different cpt curves for 6 different UV powers. I should fit them
+to obtain saturation parameters
+"""
+
+
+jvec = [7,8,9,10,11,12]
+
+plt.figure()
+for j in jvec:
+    plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+#plt.ylim(1000,2900)
+plt.grid()
+#plt.legend()
+
+
+
+
 

analisis/plots/20230421_CPTconmicromocion/Data/EITfit/MM_eightLevel_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.MM_eightLevel_2repumps_python_scripts import CPTspectrum8levels_MM
+import random
+from scipy.signal import savgol_filter as sf
+
+
+def PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None):
+    """
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+    """
+    tinicial = time.time()
+    ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, circularityprobe, beta, drivefreq, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
+    tfinal = time.time()
+    
+    #print('Done, Total time: ', round((tfinal-tinicial), 2), "s")   
+          
+    return ProbeDetuningVectorL, Fluovector
+
+
+def GenerateNoisyCPT_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
+    Frequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe, plot=False, solvemode=1, detpvec=None)
+    NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
+    return Frequencyvector, NoisyFluovector
+
+def GenerateNoisyCPT_MM_fit(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
+    
+    Frequencyvector, Fluovector = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, beta, drivefreq, freqs[0], freqs[-1], freqs[1]-freqs[0], circularityprobe, plot=False, solvemode=1, detpvec=None)
+    NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
+    return Frequencyvector, NoisyFluovector
+
+
+def SmoothNoisyCPT(Fluo, window=11, poly=3):
+    SmoothenFluo = sf(Fluo, window, poly)
+    return SmoothenFluo
+
+
+
+
+
+

analisis/plots/20230421_CPTconmicromocion/Data/EITfit/MM_eightLevel_2repumps_CPTPlotter.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep  1 17:58:39 2020
+
+@author: nico
+"""
+
+
+
+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 MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels, GenerateNoisyCPT, SmoothNoisyCPT
+import matplotlib.pyplot as plt
+import time
+#from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
+import seaborn as sns
+
+#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 #magneton de bohr
+h = 6.63e-34 #cte de planck
+c = (ub/h)*1e-4 #en unidades de MHz/G
+
+u = 32e6 #proportional to the magnetic field of around 5 G
+
+B = (u/(2*np.pi))/c
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
+
+lw = 0.1 #linewidth of the lasers, 0.1 MHz are the actual linewidths of both lasers
+DopplerLaserLinewidth, ProbeLaserLinewidth = lw, lw #ancho de linea de los laseres
+
+
+TempVec = [1e-3, 10e-3] #Temperature vector
+alpha = 0 #angle between lasers, which is zero
+
+#Polarization angles (we can keep it fixed in 90)
+phidoppler, titadoppler = 0, 90
+titaprobe = 90
+phiprobe = 0
+
+#este es el desfasaje exp(i.phi) de la componente de la polarizacion y respecto a la x. Con 1 la polarizacion es lineal
+CircPr = 1  #this has to do with the circularity of the polarizations and since both are linear it is one
+
+#Simulation parameters
+center = -10
+span = 120
+freqMin = center-span*0.5
+freqMax = center+span*0.5
+freqStep = 2e-1
+
+noiseamplitude = 0 #i dont know what it is
+
+#parametros de saturacion de los laseres. g: doppler. p: probe (un rebombeo que scanea), r: repump (otro rebombeo fijo)
+
+"""
+Good case: sg=0.6, sp=9, DetDoppler=-15
+"""
+
+DetDoppler = -15 #nice range: -30 to 0 
+sgvec = [0.8] #nice range: 0.1 to 10 #g is for green but is the doppler
+sp = 9 #nice range: 0.1 to 20 #p is for probe but is the repump
+
+
+
+drivefreq=2*np.pi*22.135*1e6 #ignore it
+#betavec = np.arange(0,1.1,0.1) #ignore it
+betavec=[0] #ignore it
+alphavec = [0] #ignore it
+
+
+
+fig1, ax1 = plt.subplots()
+
+FrequenciesVec = []
+FluorescencesVec = []
+
+for sg in sgvec:
+    for T in TempVec:
+        for alpha in alphavec:
+            for beta in betavec:
+                Frequencies, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+                
+                FrequenciesVec.append(Frequencies)
+                FluorescencesVec.append(Fluorescence)
+                
+                ax1.plot(Frequencies, [100*f for f in Fluorescence], label=fr'$\alpha={int(alpha*180/np.pi)}°$')  
+                ax1.set_xlabel('Detuning Rebombeo (MHz)')
+                ax1.set_ylabel('Fluorescencia (AU)')
+                ax1.set_title(f'Sdop: {sg}, Spr: {sp}, Temp: {int(T*1e3)} mK')
+                #ax1.legend()
+ax1.grid()
+
+
+#%%
+#Este bloque ajusta a las curvas con un beta de micromocion de 0
+
+from scipy.optimize import curve_fit
+
+def FitEIT_MM(freqs, Temp):
+    BETA = 0
+    scale=1
+    offset=0
+    Detunings, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    ScaledFluo = [f*scale + offset for f in Fluorescence]
+    return ScaledFluo
+
+TempMedidas = []
+FittedEIT_fluosVec = []
+
+for j in range(len(betavec)):
+    SelectedFluo = FluorescencesVec[j]
+    SelectedFreqs = FrequenciesVec[j]
+    
+    popt_mm, pcov_mm = curve_fit(FitEIT_MM, SelectedFreqs, SelectedFluo, p0=[1e-3], bounds=((0), (10e-3)))
+    
+    TempMedidas.append(1e3*popt_mm[2])
+    
+    print(popt_mm)
+    
+    FittedEIT_fluo = FitEIT_MM(SelectedFreqs, *popt_mm)
+    
+    FittedEIT_fluosVec.append(FittedEIT_fluo)
+    
+    plt.figure()
+    plt.plot(SelectedFreqs, SelectedFluo, 'o')
+    plt.plot(SelectedFreqs, FittedEIT_fluo)
+
+plt.figure()
+for i in range(len(FluorescencesVec)):
+    plt.plot(SelectedFreqs, FluorescencesVec[i], 'o', markersize=3)
+    plt.plot(SelectedFreqs, FittedEIT_fluosVec[i])    
+
+
+plt.figure()
+plt.plot(betavec, TempMedidas, 'o', markersize=10)
+plt.xlabel('Beta')
+plt.ylabel('Temperatura medida (mK)')
+plt.axhline(T*1e3, label='Temperatura real', linestyle='--', color='red')
+plt.legend()
+plt.grid()
\ No newline at end of file

analisis/plots/20230510_MotionalSpectrum/CPT_plotter_20230510.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/20230510_MotionalSpectrum/Data/')
+
+
+MOTIONAL_FILES = """000011490-AD9910RAM
+000011491-AD9910RAM
+000011492-AD9910RAM
+000011493-AD9910RAM
+000011496-AD9910RAM
+000011499-AD9910RAM
+000011500-AD9910RAM
+000011501-AD9910RAM
+000011502-AD9910RAM
+000011503-AD9910RAM
+000011504-AD9910RAM
+000011505-AD9910RAM
+000011506-AD9910RAM
+000011507-AD9910RAM
+000011511-AD9910RAM
+000011512-AD9910RAM
+000011513-AD9910RAM
+000011514-AD9910RAM
+000011515-AD9910RAM
+000011516-AD9910RAM
+000011517-AD9910RAM
+000011518-AD9910RAM
+000011519-AD9910RAM
+000011521-AD9910RAM
+000011522-AD9910RAM
+000011560-AD9910RAM
+000011561-AD9910RAM
+000011562-AD9910RAM
+000011563-AD9910RAM
+000011564-AD9910RAM
+000011566-AD9910RAM
+000011567-AD9910RAM
+000011569-AD9910RAM
+000011571-AD9910RAM
+000011572-AD9910RAM
+000011574-AD9910RAM
+000011575-AD9910RAM
+000011576-AD9910RAM
+000011577-AD9910RAM
+000011580-AD9910RAM
+000011581-AD9910RAM
+000011582-AD9910RAM
+"""
+
+
+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(MOTIONAL_FILES))
+
+#%%
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+RealFreqs = []
+
+for i, fname in enumerate(MOTIONAL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    RealFreqs.append(np.array(data['datasets']['real_freq']))
+    Counts.append(np.array(data['datasets']['counts']))
+
+
+
+#%%
+
+"""
+Ploteo las curvas de referencia 
+"""
+
+jvec = [29,30]
+#jvec = [16]
+
+plt.figure()
+i = 0
+for j in jvec:
+    if i==0:
+        plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), color='red', fmt='-o', capsize=2, markersize=2)
+    else:
+        plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='-o', capsize=2, markersize=2)
+    i = i + 1
+plt.xlabel('Frecuencia (kHz)')
+plt.ylabel('counts')
+plt.xlim(851,856)
+plt.ylim(600,1700)
+plt.grid()
+plt.legend()
+
+
+