Skip to content

A
artiq_experiments
Commit 405c667a authored by Marcelo Luda's avatar Marcelo Luda
Browse files
Options

intentos de ajuste de 3 iones

parent 9f5a811c
Hide whitespace changes
Inline Side-by-side
Showing with 1164 additions and 18 deletions
analisis/plots/20231123_CPTconmicromocion3/lolo_CPT_plotter_20231123_multi.py 0 → 100644
View file @ 405c667a
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Ploteo de datos y ajustes
@author: lolo
"""
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
#%% Funciones auxiliares
from scipy.stats.distributions import t,chi2
def estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05):
if nombres is None:
nombres = [ f'{j}' for j in range(len(parametros)) ]
# Cantidad de parámetros
P = len(parametros)
# Número de datos
N = len(datos_x)
# Grados de libertas (Degrees Of Freedom)
dof = N-P-1
# Cauculamos coordenadas del modelo
# modelo_x = datos_x if modelo_x_arr is None else modelo_x_arr
# modelo_y = modelo( modelo_x, *parametros )
# Predicción del modelo para los datos_x medidos
prediccion_modelo = modelo( datos_x, *parametros )
# Calculos de cantidades estadísticas relevantes
COV = pcov # Matriz de Covarianza
SE = np.sqrt(np.diag( COV )) # Standar Error / Error estandar de los parámetros
residuos = datos_y - prediccion_modelo # diferencia enrte el modelo y los datos
SSE = sum(( residuos )**2 ) # Resitual Sum of Squares
SST = sum(( datos_y - np.mean(datos_y))**2) # Total Sum of Squares
# http://en.wikipedia.org/wiki/Coefficient_of_determination
# Expresa el porcentaje de la varianza que logra explicar el modelos propuesto
Rsq = 1 - SSE/SST # Coeficiente de determinación
Rsq_adj = 1-(1-Rsq) * (N-1)/(N-P-1) # Coeficiente de determinación Ajustado
# https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#In_least_squares_regression_analysis
# Expresa la correlación que hay entre los datos y la predicción del modelo
r_pearson = np.corrcoef( datos_y , prediccion_modelo )[0,1]
# Reduced chi squared
# https://en.wikipedia.org/wiki/Reduced_chi-squared_statistic
chi2_ = sum( residuos**2 )/N
chi2_red = sum( residuos**2 )/(N-P)
# Chi squared test
chi2_test = sum( residuos**2 / abs(prediccion_modelo) )
# p-value del ajuste
p_val = chi2(dof).cdf( chi2_test )
sT = t.ppf(1.0 - alpha/2.0, N - P ) # student T multiplier
CI = sT * SE # Confidence Interval
print('R-squared ',Rsq)
print('R-sq_adjusted',Rsq_adj)
print('chi2 ',chi2_)
print('chi2_reduced ',chi2_red)
print('chi2_test ',chi2_test)
print('r-pearson ',r_pearson)
print('p-value ',p_val)
print('')
print('Error Estandard (SE):')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , SE[i])
print('')
print('Intervalo de confianza al '+str((1-alpha)*100)+'%:')
for i in range(P):
print(f'parametro[{nombres[i]:>5s}]: ' , parametros[i], ' ± ' , CI[i])
return dict(R2=Rsq,R2_adj=Rsq_adj,chi2=chi2_,chi2_red=chi2_red,
chi2_test=chi2_test,r=r_pearson,pvalue=p_val,
SE=SE,CI=CI)
#%% Importaciones extra
# /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
PARAMETROS = np.load('PARAMETROS.npz',allow_pickle=True)
for var_name in PARAMETROS.keys():
globals()[var_name] = PARAMETROS[var_name]
print(f'loaded: {var_name}')
#%%
"""
Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
"""
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
# os.chdir('../20231123_CPTconmicromocion3/Data/')
folder = '../20231123_CPTconmicromocion3/Data/'
CPT_FILES = f"""
{folder}/000016262-IR_Scan_withcal_optimized
{folder}/000016239-IR_Scan_withcal_optimized
{folder}/000016240-IR_Scan_withcal_optimized
{folder}/000016241-IR_Scan_withcal_optimized
{folder}/000016244-IR_Scan_withcal_optimized
{folder}/000016255-IR_Scan_withcal_optimized
{folder}/000016256-IR_Scan_withcal_optimized
{folder}/000016257-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(CPT_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
Voltages = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['data_array']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures.append(np.array(data['datasets']['no_measures']))
Voltages.append(np.array(data['datasets']['scanning_voltages']))
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
CountsSplit = []
CountsSplit.append(Split(Counts[0],len(Freqs[0])))
CountsSplit_2ions = []
CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
#%%
"""
Ploteo la cpt de referencia / plotting the reference CPT
"""
jvec = [2] # de la 1 a la 9 vale la pena, despues no
drs = [390.5, 399.5, 406, 413.5]
drive=22.1
Frequencies = Freqs[0]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Frequencies], CountsSplit[0][j], yerr=np.sqrt(CountsSplit[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
#%%
"""
AHORA VAMOS A MEDICIONES CON MAS DE UN ION!!!
Las mediciones estan buenas, habria que ver de ajustarlas bien, yo no lo logre.
"""
"""
Ploteo la cpt de referencia / plotting the reference CPT
1: 2 iones, -100 mV dcA
2: 2 iones, -150 mV dcA
3: 2 iones, -50 mV dcA
4: 2 iones, 5 voltajes (el ion se va en la 4ta medicion y en la 5ta ni esta)
5, 6 y 7: 3 iones en donde el scaneo esta centrado en distintos puntos
"""
jvec = [3] # desde la 1, pero la 4 no porque es un merge de curvitas
plt.figure()
i = 0
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)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
"""
Mergeo la 5, 6 y 7
"""
Freqs5 = [2*f*1e-6 for f in Freqs[5]]
Freqs6 = [2*f*1e-6 for f in Freqs[6]]
Freqs7 = [2*f*1e-6 for f in Freqs[7]]
Counts5 = Counts[5]
Counts6 = Counts[6]
Counts7 = Counts[7]
i_1_ini = 0
i_1 = 36
i_2_ini = 0
i_2 = 24
f_1 = 18
f_2 = 30
scale_1 = 0.92
scale_2 = 0.98
#Merged_freqs_test = [f-f_2 for f in Freqs6[i_2_ini:i_2]]+[f-f_1 for f in Freqs5[i_1_ini:i_1]]+Freqs7
#plt.plot(Merged_freqs_test,'o')
Merged_freqs = [f-f_2 for f in Freqs6[0:i_2]]+[f-f_1 for f in Freqs5[0:i_1]]+Freqs7
Merged_counts = [scale_2*c for c in Counts6[0:i_2]]+[scale_1*c for c in Counts5[0:i_1]]+list(Counts7)
Merged_freqs_rescaled = np.linspace(np.min(Merged_freqs),np.max(Merged_freqs),len(Merged_freqs))
#drs = [391.5, 399.5, 405.5, 414]
drs = [370,379,385,391.5]
plt.figure()
i = 0
for j in jvec:
plt.plot([f-f_1 for f in Freqs5[0:i_1]], [scale_1*c for c in Counts5[0:i_1]],'o')
plt.plot([f-f_2 for f in Freqs6[0:i_2]], [scale_2*c for c in Counts6[0:i_2]],'o')
plt.plot(Freqs7, Counts7,'o')
plt.errorbar(Merged_freqs, Merged_counts, yerr=np.sqrt(Merged_counts), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
for dr in drs:
plt.axvline(dr)
plt.axvline(dr+drive, color='red', linestyle='dashed', alpha=0.3)
plt.axvline(dr-drive, color='red', linestyle='dashed', alpha=0.3)
plt.legend()
#%%
"""
ajusto la mergeada de 3 iones
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = -20
offsetxpi = 438+correccion
DetDoppler = -35-correccion-22
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [f-offsetxpi for f in Merged_freqs]
CountsDR = Merged_counts
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
import numba
@numba.jit
def FitEIT_MM1(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
return ScaledFluo1+OFFSET
#return ScaledFluo1
@numba.jit
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
freqs = np.array(freqs)
#BETA1, BETA2, BETA3 = 0, 0, 2
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence3 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA3, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
# ScaledFluo1 = np.array([f*SCALE1 for f in Fluorescence1])
# ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
# ScaledFluo3 = np.array([f*SCALE3 for f in Fluorescence3])
# return ScaledFluo1+ScaledFluo2+ScaledFluo3+OFFSET
Fluorescence1 = np.array(Fluorescence1)
Fluorescence2 = np.array(Fluorescence2)
Fluorescence3 = np.array(Fluorescence3)
return SCALE1*Fluorescence1+SCALE2*Fluorescence2+SCALE3*Fluorescence3+OFFSET
if not 'popt_1ions' in globals().keys():
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
pp1 = estadistica(FreqsDR,CountsDR,FitEIT_MM1,pcov_1ions,popt_1ions,nombres=None,alpha=0.05)
print(f"time: {round(time.time()-t0,1)} seg")
if not 'popt_3ions' in globals().keys():
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
print(f"time: {round(time.time()-t0,1)} seg")
print("Arranamos FIT 3")
def fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3):
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
Fluorescence3 = FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
return Fluorescence3/sum(Fluorescence3)
par_ini = popt_1ions.tolist()[:2]+[1/3]*2 + [0.1] +popt_1ions.tolist()[-1:]*3
bounds = ((0, 0, 0, 0, 0, 0, 0, 0),
(2, 20, 1, 1, 1, 10, 10, 10))
popt_3ions, pcov_3ions = curve_fit(fun, FreqsDR, CountsDR, p0=par_ini,bounds=bounds )
print(f"time: {round(time.time()-t0,1)} seg")
freqs = np.array(FreqsDR)
SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = popt_3ions
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
# Fluorescence= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
Fluorescence= fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqs, Fluorescence*sum(CountsDR))
plt.plot(freqs, fun(freqs, *par_ini )*sum(CountsDR))
#%% Acá hay un ajuste del de 3 iones que da razonable
FreqsDR, CountsDR = np.array(FreqsDR) , np.array(CountsDR)
t0 = time.time()
print("Arranamos FIT 1")
par_ini = [0.65, 7.06, 86070, 3917, 1.64]
popt_1ions, pcov_1ions = curve_fit(FitEIT_MM1, FreqsDR, CountsDR, p0=par_ini, bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 7e3, 10)))
print(f"time: {round(time.time()-t0,1)} seg")
par_ini = popt_1ions.tolist()[:2]+[1/3]*2 + [0.1] +popt_1ions.tolist()[-1:]*3
bounds = ((0, 0, 0, 0, 0, 0, 0, 0),
(2, 20, 1, 1, 1, 10, 10, 10))
# bounds = (-np.inf, np.inf)
plt.figure()
plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
l1, =plt.plot(freqs, fun(freqs, *par_ini )*sum(CountsDR))
plt.draw()
print("Arranamos FIT 3")
def fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3):
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
Fluorescence3 = FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
l1.set_ydata(Fluorescence3/sum(Fluorescence3)*sum(CountsDR))
print(SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
plt.pause(0.1)
return Fluorescence3/sum(Fluorescence3)
popt_3ions, pcov_3ions = curve_fit(fun, FreqsDR, CountsDR/CountsDR.sum(), p0=par_ini,bounds=bounds )
print(f"time: {round(time.time()-t0,1)} seg")
freqs = np.array(FreqsDR)
SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = popt_3ions
SCALE3 = max(1-SCALE2-SCALE1-OFFSET,0)
# Fluorescence= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
Fluorescence= fun(freqs, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
plt.plot(freqs, Fluorescence*sum(CountsDR))
if False:
popt_3ions = np.array([0.70790618, 7.41165226, 0.1707309 , 0.13974759, 0.02322333,
3.69298275, 3.68847693, 1.36216489])
#%%
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
if False:
# Ejemplo 3 iones
t0 = time.time()
freqs, SG, SP = np.array(FreqsDR), 0.65, 7.06
SCALE1, SCALE2, SCALE3, OFFSET = 1,1,1,1
BETA1, BETA2, BETA3 = 1.64, 2, 3
TEMP = 0.1e-3
Fluorescence3= FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, SCALE3, OFFSET, BETA1, BETA2, BETA3)
print(f"time: {round(time.time()-t0,1)} seg")
Detunings, Fluorescence = np.array(freqs), np.array(Fluorescence3)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings, Fluorescence*sum(CountsDR)/sum(Fluorescence))
if False:
# Ejemplo 1 ion
t0 = time.time()
freqs, SG, SP, BETA1 = FreqsDR, 0.65, 7.06, 1.64
TEMP = 0.1e-3
Detunings, Fluorescence1 =PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
print(f"time: {round(time.time()-t0,1)} seg")
Detunings, Fluorescence1 = np.array(Detunings), np.array(Fluorescence1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings, Fluorescence1*sum(CountsDR)/sum(Fluorescence1))
FittedEITpi_3ions = FitEIT_MM(freqslong, *popt_3ions)
#FittedEITpi_3ions = FitEIT_MM(freqslong, popt_3ions[0],popt_3ions[1],popt_3ions[2],popt_3ions[3],popt_3ions[4],popt_3ions[5],4,2,0)
#FittedEITpi_3ions = FitEIT_MM(freqslong, *popt_3ions)
print(popt_3ions)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_3ions, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
Veo la medicion de varios voltajes uno atras de otro
Se va en medio de la medicion 4, y en la 5 ni esta
"""
jvec = [2] # desde la 1, pero la 4 no porque es un merge de curvitas
Freqs
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[4]], CountsSplit_2ions[0][j], yerr=np.sqrt(CountsSplit_2ions[0][j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
#for dr in drs:
# plt.axvline(dr)
#plt.axvline(dr+drive)
plt.legend()
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
AJUSTO LA CPT DE 2 IONES CON UN MODELO EN DONDE SUMO DOS ESPECTROS CON BETAS DISTINTOS
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 27
offsetxpi = 421+correccion
DetDoppler = -16-correccion+5
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[1]]
CountsDR = Counts[1]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
BETA1, BETA2 = 3, 0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if not 'popt_2ions_1' in globals().keys():
popt_2ions_1, pcov_2ions_1 = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e3, 2.9e3, 3e3], bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 8e3)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_2sp = FitEIT_MM(freqslong, *popt_2ions_1)
#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
# beta1_2ions = popt_2ions_1[5]
# beta2_2ions = popt_2ions_1[6]
# errbeta1_2ions = np.sqrt(pcov_2ions_1[5,5])
# errbeta2_2ions = np.sqrt(pcov_2ions_1[6,6])
"""
Estos params dan bien poniendo beta2=0 y correccion=0 y son SG, SP, SCALE1, SCALE2, OFFSET, BETA1
#array([9.03123248e-01, 6.25865542e+00, 3.47684055e+04, 2.92076804e+04, 1.34556420e+03, 3.55045904e+00])
"""
"""
Ahora considerando ambos betas, con los parametros iniciales dados por los que se obtuvieron con beta2=0
y correccion=0 dan estos parametros que son los de antes pero con BETA2 incluido:
array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
"""
#arreglito = np.array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
FittedEITpi_2ions_1 = FitEIT_MM(freqslong, *popt_2ions_1)
print(popt_2ions_1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2ions_1, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
SUPER AJUSTE PARA MED DE 2 IONES
"""
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
SelectedCurveVec = [3]
if not 'popt_SA_vec_2ions' in globals().keys():
popt_SA_vec_2ions = []
pcov_SA_vec_2ions = []
for selectedcurve in SelectedCurveVec:
FreqsDR = Freqs[selectedcurve]
CountsDR = Counts[selectedcurve]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, TEMP, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
#SG = 0.6
#SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 for f in Fluorescence2])
if plot:
return ScaledFluo1+ScaledFluo2, Detunings
else:
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if True:
popt_3_SA_2ions, pcov_3_SA_2ions = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[448, -42, 0.6, 8.1, 4e4, 4e4, 6e3, 1, 1.2, 0.5e-3], bounds=((0, -100,0, 0, 0,0,0,0,0, 0), (1000, 0, 2, 20,5e6, 5e6,5e4, 10, 10,10e-3)))
#popt_3_SA_2ions = [448, -42, 8e4, 6e3, 2, 0.5e-3]
popt_SA_vec_2ions.append(popt_3_SA_2ions)
pcov_SA_vec_2ions.append(pcov_3_SA_2ions)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA_2ions, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA_2ions, plot=True)
raise ValueError('Acá tenes que levantar de nuevo los valores que van')
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA_2ions)
#print(f'Detdop:{popt_3_SA[1]},popt_3_SA:{popt[0]}')
#%%
#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels
from scipy.optimize import curve_fit
import time
"""
AJUSTO LA CPT DE 2 IONES CON UN MODELO EN DONDE SUMO DOS ESPECTROS CON BETAS DISTINTOS
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 27
offsetxpi = 421+correccion
DetDoppler = -16-correccion+5
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
FreqsDR = [2*f*1e-6-offsetxpi for f in Freqs[1]]
CountsDR = Counts[1]
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM(freqs, SG, SP, SCALE1, SCALE2, OFFSET):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
TEMP = 0.1e-3
BETA1, BETA2 = 3, 0
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
Detunings, Fluorescence2 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA2, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
ScaledFluo2 = np.array([f*SCALE2 + OFFSET for f in Fluorescence2])
return ScaledFluo1+ScaledFluo2
#return ScaledFluo1
if not 'popt_2ions_1' in globals().keys():
popt_2ions_1, pcov_2ions_1 = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.9, 6.2, 3.5e3, 2.9e3, 3e3], bounds=((0, 0, 0, 0, 0), (2, 20, 5e8, 5e8, 8e3)))
#popt, pcov = curve_fit(FitEIT_MM, FreqsDR, CountsDR, p0=[0.8, 8, 4e4, 3.5e3, 0], bounds=((0, 0, 0, 0, 0), (2, 15, 1e5, 1e5, 10)))
#array([7.12876797e-01, 7.92474752e+00, 4.29735308e+04, 1.74240582e+04,
#1.53401696e+03, 1.17073206e-06, 2.53804151e+00])
FittedEITpi_2sp = FitEIT_MM(freqslong, *popt_2ions_1)
#FittedEITpi = FitEIT_MM(freqslong, 0.8, 8, 4e4, 3.5e3, 0)
# beta1_2ions = popt_2ions_1[5]
# beta2_2ions = popt_2ions_1[6]
# errbeta1_2ions = np.sqrt(pcov_2ions_1[5,5])
# errbeta2_2ions = np.sqrt(pcov_2ions_1[6,6])
"""
Estos params dan bien poniendo beta2=0 y correccion=0 y son SG, SP, SCALE1, SCALE2, OFFSET, BETA1
#array([9.03123248e-01, 6.25865542e+00, 3.47684055e+04, 2.92076804e+04, 1.34556420e+03, 3.55045904e+00])
"""
"""
Ahora considerando ambos betas, con los parametros iniciales dados por los que se obtuvieron con beta2=0
y correccion=0 dan estos parametros que son los de antes pero con BETA2 incluido:
array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
"""
#arreglito = np.array([8.52685426e-01, 7.42939084e+00, 3.61998310e+04, 3.40160472e+04, 8.62651715e+02, 3.89756335e+00, 7.64867601e-01])
FittedEITpi_2ions_1 = FitEIT_MM(freqslong, *popt_2ions_1)
print(popt_2ions_1)
plt.figure()
plt.errorbar(FreqsDR, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(freqslong, FittedEITpi_2ions_1, color='darkgreen', linewidth=3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.title(f'Corr:{correccion},DetD:{DetDoppler}')
plt.grid()
#%%
"""
AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
"""
#%%
"""
SUPER AJUSTE (SA)
"""
if False:
GUARDAR = {}
for var in [ kk for kk in globals().keys() if kk.startswith('pop') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('pcov') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.startswith('Fitted') ]:
print(var)
GUARDAR[var] = globals()[var]
print('')
for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
print(var)
GUARDAR[var] = globals()[var]
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
Temp = 0.5e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 32.5e6
#B = (u/(2*np.pi))/c
correccion = 13
#DetDoppler = -11.5-correccion
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
drivefreq = 2*np.pi*22.135*1e6
#SelectedCurveVec = [1,2,3,4,5,6,7,8,9]
SelectedCurveVec = [0]
# popt_SA_vec = []
# pcov_SA_vec = []
# Detuningsshort_vec = []
# Counts_vec = []
# Detuningslong_vec = []
# FittedCounts_vec = []
# Betas_vec = []
# ErrorBetas_vec = []
# Temp_vec = []
# ErrorTemp_vec = []
# DetuningsUV_vec = []
# ErrorDetuningsUV_vec = []
for selectedcurve in SelectedCurveVec:
#selectedcurve = 2 #IMPORTANTE: SELECCIONA LA MEDICION
FreqsDR = Freqs[0]
CountsDR = CountsSplit[0][selectedcurve]
if selectedcurve==1:
CountsDR[100]=0.5*(CountsDR[99]+CountsDR[101])
CountsDR[105]=0.5*(CountsDR[104]+CountsDR[106])
if selectedcurve==2:
CountsDR[67]=0.5*(CountsDR[66]+CountsDR[68])
CountsDR[71]=0.5*(CountsDR[70]+CountsDR[72])
if selectedcurve==6:
CountsDR[1]=0.5*(CountsDR[0]+CountsDR[2])
CountsDR[76]=0.5*(CountsDR[75]+CountsDR[77])
if selectedcurve==7:
CountsDR[117]=0.5*(CountsDR[116]+CountsDR[118])
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
CircPr = 1
alpha = 0
def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
#BETA = 1.8
# SG = 0.6
# SP = 8.1
# TEMP = 0.2e-3
freqs = [2*f*1e-6-offset for f in Freqs]
Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe, BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
if plot:
return ScaledFluo1, Detunings
else:
return ScaledFluo1
#return ScaledFluo1
do_fit = True
if do_fit:
popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
# popt_SA_vec.append(popt_3_SA)
# pcov_SA_vec.append(pcov_3_SA)
FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *popt_3_SA, plot=True)
freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *popt_3_SA, plot=True)
# DetuningsUV_vec.append(popt_3_SA[1])
# ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
# Betas_vec.append(popt_3_SA[6])
# ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
# Temp_vec.append(popt_3_SA[7])
# ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
# Detuningsshort_vec.append(Detunings_3_SA_short)
# Counts_vec.append(CountsDR)
# Detuningslong_vec.append(Detunings_3_SA_long)
# FittedCounts_vec.append(FittedEITpi_3_SA_long)
plt.figure()
plt.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
plt.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
plt.xlabel('Detuning (MHz)')
plt.ylabel('Counts')
plt.legend(loc='upper left', fontsize=20)
plt.grid()
print(f'listo med {selectedcurve}')
print(popt_3_SA)
analisis/plots/20231123_CPTconmicromocion3/lolo_graficos_pub.py
View file @ 405c667a
......@@ -300,6 +300,9 @@ alpha = 0
voltages_dcA = Voltages[0][SelectedCurveVec]
def hiperbola(x,a,y0,b,x0):
......@@ -441,14 +444,14 @@ x_hip = np.linspace(-0.23,0.005,200)
ax = ax_central
ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',
capsize=4,markersize=3,color='C3', label=r'fitted $\beta$')
ax.plot(x_hip,hiperbola(xhip,*popthip),color='C0', label=r'hyperbola model')
ax.plot(x_hip,hiperbola(x_hip,*param),color='C0', label=r'hyperbola model')
ax.set_ylabel(r'Modulation factor $\beta$', labelpad=-5)
ax.set_ylim(-0.05,3)
ax.set_xlim(-0.22,0)
ax.set_title(f'(g)', x=0.95, y=0.006, color='gray')
ax = ax_res
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*param),
yerr=ErrorBetas_vec[I],fmt='o',capsize=4,markersize=3,color='C3')
ax.axhline( 0 , color='C0')
ax.set_ylabel('Res.', labelpad=-5)
......@@ -479,9 +482,9 @@ for jj,ax in zip(selection,axes_vec):
# Leyenda ##############################
h1, l1 = ax_central.get_legend_handles_labels()
h2, l2 = axx[0,0].get_legend_handles_labels()
ax_central.legend(h1+h2, l1+l2, loc='upper left')
# h1, l1 = ax_central.get_legend_handles_labels()
# h2, l2 = axx[0,0].get_legend_handles_labels()
# ax_central.legend(h1+h2, l1+l2, loc='upper left')
......@@ -495,7 +498,7 @@ fig.savefig('grafico_central_opcion_A.pdf')
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Gráfico central OPCION A
#%% Gráfico central OPCION B
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 8})
......@@ -533,7 +536,7 @@ gs2 = gridspec.GridSpec(4,3, width_ratios=width_ratios,left=0.16,right=0.9,botto
ax_central = fig.add_subplot(gs2[1:-1, 1])
ax_dib = fig.add_subplot(gs2[0, 1])
ax_dib = fig.add_subplot(gs2[0, 1], sharex=ax_res)
ax_dib.spines[:].set_visible(False)
ax_dib.xaxis.set_tick_params(labelbottom=False)
ax_dib.yaxis.set_tick_params(labelleft=False)
......@@ -560,12 +563,29 @@ tmp_datos=(Detuningsshort_vec[selection,:],
np.array(selection)+1,
axes_vec,'abcfed')
from scipy.signal import savgol_filter as savgol
for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax,le in zip(*tmp_datos):
ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR),
fmt='o', color='darkgreen', alpha=0.2, capsize=2,
markersize=2, label='measured data')
# ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR),
# fmt='o', color='darkgreen', alpha=0.2, capsize=2,
# markersize=2, label='measured data')
yerr_max = CountsDR+2*np.sqrt(CountsDR)
yerr_min = CountsDR-2*np.sqrt(CountsDR)
yerr_max = savgol(yerr_max,3,2)
yerr_min = savgol(yerr_min,3,2)
ax.fill_between(Detunings_3_SA_short, yerr_max, yerr_min,
color='darkgreen', alpha=0.2)
ax.plot(Detunings_3_SA_short, CountsDR, '.', ms=2,
color='darkgreen', alpha=0.9)
ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long,
color='black', linewidth=2, label=f'micromotion model', alpha=0.7)
color='black', linewidth=1, label=f'micromotion model', alpha=0.7)
ax.grid(True, ls=":", color='lightgray')
......@@ -597,14 +617,14 @@ x_hip = np.linspace(-0.23,0.005,200)
ax = ax_central
ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',
capsize=4,markersize=3,color='C3', label=r'fitted $\beta$')
ax.plot(x_hip,hiperbola(xhip,*popthip),color='C0', label=r'hyperbola model')
ax.plot(x_hip,hiperbola(x_hip,*param),color='C0', label=r'hyperbola model')
ax.set_ylabel(r'Modulation factor $\beta$', labelpad=-5)
ax.set_ylim(-0.05,3)
ax.set_xlim(-0.22,0)
ax.set_title(f'(g)', x=0.95, y=0.006, color='gray')
ax = ax_res
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*param),
yerr=ErrorBetas_vec[I],fmt='o',capsize=4,markersize=3,color='C3')
ax.axhline( 0 , color='C0')
ax.set_ylabel('Res.', labelpad=-5)
......@@ -638,14 +658,29 @@ for jj,ax in zip(selection,axes_vec):
a, y0, b, x0 = param
ax_dib.plot( x_hip , x_hip*0+y0 , color='gray)
ax_dib.set_ylim(0,y0*1.1')
ax_dib.plot( x_hip , x_hip*0+y0 , color='gray')
ax_dib.set_ylim(-y0*0.1,y0*1.2)
ax_dib.plot( [x0], [0], 'x' )
ax_dib.text(x0+0.01, 0, 'trap center', fontsize = 8, ha='left',va='center', color='C0')
ax_dib.plot( voltages_dcA[I], voltages_dcA[I]*0+y0, '.' )
for Vx in voltages_dcA[I]:
ax_dib.plot( [Vx,x0], [y0,0], ':', color='lightgray', lw=1)
import matplotlib.patches as mpatches
arr = mpatches.FancyArrowPatch((x0, y0*1.1), (x0/2, y0*1.1),
arrowstyle='->,head_width=.15', mutation_scale=10, color='C1')
ax_dib.add_patch(arr)
ax_dib.annotate("ion position", (.5, .5), xycoords=arr, ha='center', va='bottom', color='C1')
# Leyenda ##############################
h1, l1 = ax_central.get_legend_handles_labels()
h2, l2 = axx[0,0].get_legend_handles_labels()
ax_central.legend(h1+h2, l1+l2, loc='upper left')
# h1, l1 = ax_central.get_legend_handles_labels()
# h2, l2 = axx[0,0].get_legend_handles_labels()
# ax_central.legend(h1+h2, l1+l2, loc='upper left')
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment