actualizado archivo de heating rate con mejor fiteo

analisis/plots/20230321_heatingrate/CPT_plotter_20230321.py

@@ -97,9 +97,9 @@ plt.grid()
 plt.legend()
 
 #%%
-#from EITfit.threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
-#from EITfit.threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
-#from scipy.optimize import curve_fit
+from EITfit.threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
+from EITfit.threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
+from scipy.optimize import curve_fit
 
 """
 Ajusto un cpt para obtener todos los parámetros relevantes primero.
@@ -110,6 +110,10 @@ phirepump, titarepump = 0,  0
 phiprobe = 0
 titaprobe = 90
 
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 
+alpha = 0
+noiseamplitude = 0
+
 T = 0.6e-3
 
 sg = 0.544
@@ -127,7 +131,7 @@ u = 33.5e6
 c=3e8
 B = (u/(2*np.pi))/c
 
-correccion = 3 #con 8 fitea bien
+correccion = 6 #con 3 fitea bien
 
 offsetxpi = 440+1+correccion
 DetDoppler = -10.5-correccion
@@ -153,6 +157,11 @@ def FitEITpi(freqs, SG, SP, scale, offset, temp):
 #con broadening en ambos laseres da
 #[4.65604861e-01 8.06781613e+00 6.65914036e+04 4.12611199e-14, 1.80590400e-03]
 
+#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]
+
+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)
@@ -172,7 +181,7 @@ plt.plot(freqslongpi, FittedEITpi)
 """
 Simulo CPTs con todos esos parámetros para distintas temperaturas
 """
-TempVecTeorico = np.arange(1, 31, 1)
+TempVecTeorico = list(np.arange(0, 31, 1))
 CurvasTeoricas = []
 
 for tempi in TempVecTeorico:
@@ -248,11 +257,14 @@ plt.figure()
 #plt.plot(Heating_tim_ms,Temperaturas_interpoladas,'o')
 plt.errorbar(Heating_tim_ms,Temperaturas_interpoladas, yerr=np.array(Error_Temperaturas_interpoladas), fmt='o', capsize=2, markersize=7, color='black')
 plt.plot(Heating_tim_ms, lineal(np.array(Heating_tim_ms), *p1), color='red')
-plt.xlabel('Heating time (ms)')
-plt.ylabel('Temperature (mK)')
+plt.xlabel('Heating time (ms)', fontname='STIXGeneral', fontsize=15)
+plt.ylabel('Temperature (mK)', fontname='STIXGeneral', fontsize=15)
 plt.grid()
-plt.title(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
-
+plt.xticks([0, 5, 10, 15, 20 ,25, 30, 35], fontname='STIXGeneral', fontsize=15)
+plt.yticks([0, 5, 10, 15], fontname='STIXGeneral', fontsize=15)
+plt.title(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms', fontname='STIXGeneral', fontsize=15)
+plt.tight_layout()
+plt.savefig('Fig_heatingrate.svg')
 print(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
 
 

analisis/plots/20230321_heatingrate/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py

@@ -12,7 +12,7 @@ import numpy as np
 import time
 import matplotlib.pyplot as plt
 from scipy.signal import argrelextrema
-from threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
+from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
 import random
 from scipy.signal import savgol_filter as sf