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Commit 5d2ee8a5 authored by Marcelo Luda's avatar Marcelo Luda
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analisis/plots/20231123_CPTconmicromocion3/lolo_ajuste_3iones_A.py 0 → 100644
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Acá hacemos el ajuste de 3 iones del archivo
artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/CPT_plotter_20231123.py
sacado de la parte que dice:
"ajusto la mergeada de 3 iones"
@author: lolo
"""
import h5py
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
import time
#%% Funciones auxiliares para información estadística
from scipy.stats.distributions import t,chi2
import inspect
def estadistica(datos_x,datos_y,modelo,pcov,parametros,nombres=None,alpha=0.05):
if nombres is None:
nombres = inspect.getfullargspec(modelo).args[1:]
# 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
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,parametros=parametros,nombres=nombres,alpha=alpha,N=N)
def print_estadistica(dic):
pars = 'N,pvalue,R2,R2_adj,chi2,chi2_red,chi2_test,r,SE,CI,parametros,nombres,alpha'.split(',')
N,pvalue,R2,R2_adj,chi2,chi2_red,chi2_test,r,SE,CI,parametros,nombres,alpha = [ dic[k] for k in pars ]
P = len(parametros)
print('R-squared ',R2)
print('R-sq_adjusted',R2_adj)
print('chi2 ',chi2)
print('chi2_reduced ',chi2_red)
print('chi2_test ',chi2_test)
print('r-pearson ',r)
print('p-value ',pvalue)
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])
def fit(model,x_data, y_data, fixed=None, plot=False, **kwargs):
"""Usa los mismos argumentos que curve_fit pero permite fixear parámetros y
visualizar el ajuste
fixed: nombres de los parametros fijos, con espacios
plot: si plotea o no el ajuste
"""
# Extraemos los parámetros del modelo
param_names = inspect.getfullargspec(model).args[1:]
if not 'p0' in kwargs.keys():
kwargs['p0'] = np.ones(len(param_names))
params_initial = kwargs['p0']
# Nos fijamos si hay que fijar algo
if isinstance(fixed, str):
fixed = fixed.split()
if fixed is None:
fixed = []
# Armoa lista de True/False de los parametros fijos
params_fixed = [ True if p in fixed else False for p in param_names ]
if True in params_fixed and 'bounds' in kwargs.keys():
try:
b0 = [ bb for fi,bb in zip(params_fixed,kwargs['bounds'][0]) if fi==False ]
except:
b0 = kwargs['bounds'][0]
try:
b1 = [ bb for fi,bb in zip(params_fixed,kwargs['bounds'][1]) if fi==False ]
except:
b1 =kwargs['bounds'][0]
kwargs['bounds'] = (b0,b1)
print("Parámetros:", param_names)
# print(locals())
print('\n')
if plot:
plt.figure()
plt.plot(x_data, y_data,'.')
ylim=plt.gca().get_ylim()
l1, = plt.plot(x_data, model(x_data,*params_initial),'r-')
plt.ylim(ylim)
# Función auxiliar para el ajuste
def aux_func(x_data,*args):
all_params = np.ones(len(param_names))
jj=0
for ii in range(len(all_params)):
if params_fixed[ii]:
all_params[ii] = params_initial[ii]
else:
all_params[ii] = args[jj]
jj += 1
# print(params_fixed,all_params)
rta = model(x_data,*all_params)
if plot:
l1.set_ydata(rta)
plt.draw()
plt.pause(0.01)
return rta
# Hacemos el curve_fit
t0 = time.time()
print("Arancamos el fit")
kwargs['p0'] = [ p for p,ff in zip(params_initial,params_fixed) if ff is False ]
params, pcov = curve_fit(aux_func, x_data, y_data, **kwargs )
print(f"time: {round(time.time()-t0,1)} seg")
# Rearmamos los parámetros para incluir los fijos
params2 = np.ones(len(param_names))
jj=0
for ii in range(len(params2)):
if params_fixed[ii]:
params2[ii] = params_initial[ii]
else:
params2[ii] = params[jj]
jj += 1
# Rearmo la pcov
ii, a = 0, []
for fixed in params_fixed:
if fixed:
a.append( [0]*len(pcov[0]) )
else:
a.append( pcov[ii].tolist() )
ii+=1
a = np.array(a)
ii, b = 0, []
for fixed in params_fixed:
if fixed:
b.append( [0]*len(a.T[0]) )
else:
b.append( a.T[ii].tolist() )
ii+=1
pcov2 = np.array(b)
# Caluclo información estadística
info = estadistica(x_data,y_data,model,pcov2,params2,nombres=None,alpha=0.05)
print_estadistica(info)
return params2, pcov2 , info
# fit(modelo, FreqsDR, CountsDR/CountsDR.sum(), p0=(1,1,1),bounds=(-1e6,1e6) )
# fit(modelo, FreqsDR, CountsDR/CountsDR.sum(), fixed="", plot=True, p0=(1,2,3),bounds=(-1e6,1e6) )
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%% Levantamos los datos %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
"""
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])))
###############################################################################
"""
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
"""
"""
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]
if False:
plt.figure()
jvec = [2]
i = 0
drive=22.1
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()
#%% Funciones para hacer ajustes
"""
ajusto la mergeada de 3 iones
"""
import numba
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
# @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
#%% Acá hay un ajuste del de 3 iones que da razonable
# FreqsDR, CountsDR = np.array(FreqsDR) , np.array(CountsDR)
# freqs = np.array(FreqsDR)
# 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)
# 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)
# try:
# l1.set_ydata(Fluorescence3/sum(Fluorescence3)*sum(CountsDR))
# except:
# print("Primer corrida")
# finally:
# print(SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3)
# plt.pause(0.1)
# return Fluorescence3/sum(Fluorescence3)
# 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()
# 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")
# 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])
#%% Armo algo para meter parametros fijos
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, info1 = 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))
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 = np.array([0.64872536, 7.06495239, 0.9985873 , 0.9985873 , 0.09090475,
1.63941009, 1.63941006, 1.80228481])
SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3 = par_ini
SCALE1, SCALE2 = 0.3, 0.3
par_ini = SG, SP, SCALE1, SCALE2, OFFSET, BETA1, BETA2, BETA3
t0 = time.time()
print("Arranamos FIT 1")
popt_3ions, pcov_3ions, info2 = fit(fun, FreqsDR, CountsDR/sum(CountsDR), fixed="SG SP", plot=True, p0=par_ini, bounds=bounds)
print(f"time: {round(time.time()-t0,1)} seg")
par_ini = popt_3ions
t0 = time.time()
print("Arranamos FIT 1")
popt_3ions, pcov_3ions, info2 = fit(fun, FreqsDR, CountsDR/sum(CountsDR), fixed="", plot=True, p0=par_ini, bounds=bounds)
print(f"time: {round(time.time()-t0,1)} seg")
analisis/plots/20231123_CPTconmicromocion3/lolo_graficos_pub.py
View file @ 5d2ee8a5
...@@ -677,6 +677,22 @@ arr = mpatches.FancyArrowPatch((x0, y0*1.1), (x0/2, y0*1.1), ...@@ -677,6 +677,22 @@ arr = mpatches.FancyArrowPatch((x0, y0*1.1), (x0/2, y0*1.1),
ax_dib.add_patch(arr) ax_dib.add_patch(arr)
ax_dib.annotate("ion position", (.5, .5), xycoords=arr, ha='center', va='bottom', color='C1') ax_dib.annotate("ion position", (.5, .5), xycoords=arr, ha='center', va='bottom', color='C1')
bbox = ax_dib.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
aspect_ratio = bbox.width / bbox.height
ax_dib.plot(0.1, 0, ">k", transform=ax_dib.transAxes, clip_on=False, ms=3, lw=1)
ax_dib.plot([0,0.1], [0,0], "-k", transform=ax_dib.transAxes, clip_on=False, lw=1)
ax_dib.plot(0, 0.1*aspect_ratio, "^k", transform=ax_dib.transAxes, clip_on=False, ms=3, lw=1)
ax_dib.plot([0,0], [0,0.1*aspect_ratio], "-k", transform=ax_dib.transAxes, clip_on=False, lw=1)
ax_dib.text(0.1, 0.13, "x", transform=ax_dib.transAxes,
fontsize=8, va='top')
ax_dib.text(0.03, 0.13*aspect_ratio, "y", transform=ax_dib.transAxes,
fontsize=8, va='top')
# Leyenda ############################## # Leyenda ##############################
# h1, l1 = ax_central.get_legend_handles_labels() # h1, l1 = ax_central.get_legend_handles_labels()
# h2, l2 = axx[0,0].get_legend_handles_labels() # h2, l2 = axx[0,0].get_legend_handles_labels()
...@@ -688,8 +704,8 @@ fig.align_ylabels([ax_central,ax_res]) ...@@ -688,8 +704,8 @@ fig.align_ylabels([ax_central,ax_res])
fig.tight_layout() fig.tight_layout()
# fig.savefig('grafico_central_opcion_B.png', dpi=300) fig.savefig('grafico_central_opcion_B.png', dpi=300)
# fig.savefig('grafico_central_opcion_B.pdf') fig.savefig('grafico_central_opcion_B.pdf')
......
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