update

analisis/plots/20231123_CPTconmicromocion3/lolo_graficos_pub.py

@@ -312,45 +312,31 @@ def hiperbola(x,a,y0,b,x0):
 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 #%% Graficamos todos los fiteos
 
-# tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec)
 
-# for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve in zip(*tmp_datos):
-#     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)
 
+# fig, axx = plt.subplots( 3,4, figsize=(13,8) ,  constrained_layout=True, sharex=True , sharey=True )
+# fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
 
-fig, axx = plt.subplots( 3,4, figsize=(13,8) ,  constrained_layout=True, sharex=True , sharey=True )
-fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
-
-tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec,axx.flatten())
+# tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec,axx.flatten())
 
-for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax in zip(*tmp_datos):
-    ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.3, capsize=2, markersize=2)
-    ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='black', linewidth=2, label=f'med {selectedcurve}', alpha=0.7)
-    #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
-    # ax.set_xlabel('Detuning (MHz)')
-    # ax.set_ylabel('Counts')
-    ax.legend(loc='upper left', fontsize=12)
-    ax.grid(True, ls=":")
+# for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,selectedcurve,ax in zip(*tmp_datos):
+#     ax.errorbar(Detunings_3_SA_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.3, capsize=2, markersize=2)
+#     ax.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, color='black', linewidth=2, label=f'med {selectedcurve}', alpha=0.7)
+#     #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+#     # ax.set_xlabel('Detuning (MHz)')
+#     # ax.set_ylabel('Counts')
+#     ax.legend(loc='upper left', fontsize=12)
+#     ax.grid(True, ls=":")
 
-    print(f'listo med {selectedcurve}')
-    # print(popt_3_SA)
+#     print(f'listo med {selectedcurve}')
+#     # print(popt_3_SA)
 
 
-for ax in axx[:,0]:
-    ax.set_ylabel('Counts')
+# for ax in axx[:,0]:
+#     ax.set_ylabel('Counts')
 
-for ax in axx[-1,:]:
-    ax.set_xlabel('Detuning (MHz)')
+# for ax in axx[-1,:]:
+#     ax.set_xlabel('Detuning (MHz)')
 
 
 
@@ -360,7 +346,7 @@ for ax in axx[-1,:]:
 
 
 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#%% Gráfico central 
+#%% Gráfico central OPCION A
 
 import matplotlib.pyplot as plt
 plt.rcParams.update({'font.size': 8})
@@ -508,284 +494,168 @@ fig.savefig('grafico_central_opcion_A.pdf')
 
 
 
-
-
-
-
-# import matplotlib.pyplot as plt
-# from matplotlib import gridspec
-
-# fig = plt.figure()
-# gs = gridspec.GridSpec(1,2)
-# ax1 = fig.add_subplot(gs[0])
-# ax2 = fig.add_subplot(gs[1], sharey=ax1)
-# plt.setp(ax2.get_yticklabels(), visible=False)
-
-# plt.setp([ax1, ax2], title='Test')
-# fig.suptitle('An overall title', size=20)
-# gs.tight_layout(fig, rect=[0, 0, 1, 0.97])
-
-# plt.show()
-
 #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#%% Endcap hiperbola (con residuos)
-"""
-Veamos cómo varía el Beta con el voltaje del endcap
-"""
-
-import seaborn as sns
-paleta = sns.color_palette("rocket")
+#%% Gráfico central OPCION A
 
+import matplotlib.pyplot as plt
+plt.rcParams.update({'font.size': 8})
 
-I = slice(None,9)
+# Para esto hace falta:
+# sudo apt install dvipng
 
-voltages_dcA  = Voltages[0][SelectedCurveVec]
+plt.rcParams['text.usetex']=True
+# plt.rcParams['text.latex.unicode']=True
+# params= {'text.latex.preamble' : [r'\usepackage{amsmath}']}
+# plt.rcParams.update(params)
 
 
+# fig, axx = plt.subplots( 3,4, figsize=(8.6/2.54*2,4) ,  constrained_layout=True, sharex=True , sharey=True )
+# fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
 
-def lineal(x,a,b):
-    return a*x+b
 
-def hiperbola(x,a,y0,b,x0):
-    """
-    Hiperbola de ecuación:
-        1 =(y-y0)²/a² - (x-x0)²/b² 
-    """
-    return a*np.sqrt(((x-x0)**2+b**2))+y0
+## Layout ###########################
+from matplotlib import gridspec
+figsize=(8.6/2.54*2,4)
+width_ratios=[1,2,1]
 
-es_hiperbola = False
+fig, axx = plt.subplots(ncols=3, nrows=3, sharex=True, sharey=False,   constrained_layout=True,
+                        figsize=figsize, gridspec_kw=dict(width_ratios=width_ratios))
+fig.set_constrained_layout_pads(w_pad=1/72, h_pad=1/72, hspace=0, wspace=0)
+fig.set_constrained_layout_pads(w_pad=0, h_pad=0, hspace=0, wspace=0)
 
+gs = axx[0, 0].get_gridspec()
+# remove the underlying axes
+for ax in axx[:,1]:
+    ax.remove()
     
-# par_inicial = (100,0.1,1,-0.15)
-#               a   y0   b  x0
-par_inicial = (12,0.1,1,-0.13)
-
-
-popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
-
-xhip = np.linspace(-0.23,0.005,200)
-
-# plt.figure()
-# plt.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
-# plt.plot(xhip,hiperbola(xhip,*popthip))
-# # plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
-# plt.xlabel('Endcap voltage (V)')
-# plt.ylabel('Modulation factor')
-# plt.grid()
-
 
+gs2 = gridspec.GridSpec(4,3, width_ratios=width_ratios,left=0.16,right=0.9,bottom=0.12)
 
+ax_central = fig.add_subplot(gs2[1:-1, 1])
 
-fig, axx = plt.subplots( 2, figsize=(10,7) ,  
-                        constrained_layout=True, sharex=True,
-                        gridspec_kw=dict(height_ratios=[10,2]))
-fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
+ax_dib = fig.add_subplot(gs2[0, 1])
+ax_dib.spines[:].set_visible(False)
+ax_dib.xaxis.set_tick_params(labelbottom=False)
+ax_dib.yaxis.set_tick_params(labelleft=False)
+ax_dib.set_xticks([])
+ax_dib.set_yticks([])
 
-ax = axx[0]
+ax_res = fig.add_subplot(gs2[-1, 1], sharex=ax_central,zorder=-5)
+plt.setp(ax_central.get_xticklabels(), visible=False)
 
-ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
-ax.plot(xhip,hiperbola(xhip,*popthip))
-# plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
+for ax in axx[:,2]:
+    ax.yaxis.tick_right()
+    ax.yaxis.set_label_position("right")
 
-ax.set_ylabel('Modulation factor')
 
-ax = axx[1]
 
-ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
-            yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+## CPTs ########################################
+selection= (0,1,3,4,7,8)
+axes_vec = (axx[0,0],axx[1,0],axx[2,0],axx[2,2],axx[1,2],axx[0,2])
 
-ax.set_ylabel('Res.')
-ax.set_xlabel('Endcap voltage (V)')
+tmp_datos=(Detuningsshort_vec[selection,:],
+           Counts_vec[selection,:],
+           Detuningslong_vec[selection,:],
+           FittedCounts_vec[selection,:],
+           np.array(selection)+1,
+           axes_vec,'abcfed')
 
-for ax in axx:
+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.plot(Detunings_3_SA_long, FittedEITpi_3_SA_long, 
+            color='black', linewidth=2, label=f'micromotion model', alpha=0.7)
+    
     ax.grid(True, ls=":", color='lightgray')
+    
+    ax.set_yticklabels([ f"{int(y/1000)}k" for y in ax.get_yticks() ])
+    
+    ax.set_title(f'({le})', x=0.1, y=0.78, color='gray')
+    # ax.legend()
+    # ax.legend(ax.get_legend_handles_labels()[0], f'{selectedcurve}')
+    print(f'listo med {selectedcurve}')
 
 
-print([t*1e3 for t in Temp_vec])
-
-
-#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#%% Este hay que armarlo aún
-
-plt.figure()
-plt.errorbar(voltages_dcA,[t*1e3 for t in Temp_vec],yerr=[t*1e3 for t in ErrorTemp_vec],fmt='o',capsize=5,markersize=5,color=paleta[3])
-# plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
-print(f'\n\nTE FALTA DEFINIR LA VARIABLE minimum_voltage\n\n')
-plt.axhline(0.538)
-plt.xlabel('Endcap voltage (V)')
-plt.ylabel('Temperature (mK)')
-plt.grid()
-#plt.ylim(0,2)
-
-
-
-#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-#%% Ajuste de los betas y la temperatura
-
-from scipy.special import jv
-
-
-def expo(x,tau,A,B):
-    return A*np.exp(x/tau)+B
-
-def cuadratica(x,a,c):
-    return a*(x**2)+c
-
-def InverseMicromotionSpectra(beta, A, det, x0, gamma, B):
-    ftrap=22.1
-    #gamma=30
-    P = ((jv(0, beta)**2)/((((det-x0)**2)+(0.5*gamma)**2)**2))*(-2*(det-x0))
-    i = 1
-    #print(P)
-    while i <= 5:
-        P = P + (-2*(det-x0))*((jv(i, beta))**2)/(((((det-x0)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det-x0))*(((jv(-i, beta))**2)/((((det-x0)-i*ftrap)**2)+(0.5*gamma)**2)**2)
-        i = i + 1
-        #print(P)
-    #return 1/(A*P+B)
-    return 1/(A*P+B)
-
-
-def InverseMicromotionSpectra_raw(beta, A, det, B):
-    ftrap=22.1
-    gamma=21
-    P = ((jv(0, beta)**2)/((((det)**2)+(0.5*gamma)**2)**2))*(-2*(det))
-    i = 1
-    #print(P)
-    while i <= 3:
-        P = P + (-2*(det))*((jv(i, beta))**2)/(((((det)+i*ftrap)**2)+(0.5*gamma)**2)**2) + (-2*(det))*(((jv(-i, beta))**2)/((((det)-i*ftrap)**2)+(0.5*gamma)**2)**2)
-        i = i + 1
-        #print(P)
-    return A/P+B
-
-
-"""
-Temperatura vs beta con un ajuste exponencial 
-"""
+axx[2,0].set_xlabel('Detuning [MHz]')
+axx[2,2].set_xlabel('Detuning [MHz]')
 
-popt_exp, pcov_exp = curve_fit(expo,Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]])
-popt_quad, pcov_quad = curve_fit(cuadratica,Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]],p0=(1,10))
-#popt_rho22, pcov_rho22 = curve_fit(InverseMicromotionSpectra,Betas_vec,[t*1e3 for t in Temp_vec],p0=(10,10,-10,1,20)) #esto ajusta muy bien
-#popt_rho22, pcov_rho22 = curve_fit(InverseMicromotionSpectra,Betas_vec, [t*1e3 for t in Temp_vec],p0=(-10,-10,10,1,20)) #esto ajusta muy bien
-popt_rho22_raw, pcov_rho22_raw = curve_fit(InverseMicromotionSpectra_raw,Betas_vec[:10], [t*1e3 for t in Temp_vec[:10]],p0=(-10, -10, 1)) #esto ajusta muy bien
+axx[1,0].set_ylabel('Counts')
+axx[1,2].set_ylabel('Counts')
 
 
-print(popt_rho22_raw)
 
-betaslong = np.arange(0,2*2.7,0.01)
 
-print(f'Min temp predicted: {InverseMicromotionSpectra_raw(betaslong,*popt_rho22_raw)[100]}')
 
-plt.figure()
-plt.errorbar(Betas_vec[:10],[t*1e3 for t in Temp_vec[:10]],xerr=ErrorBetas_vec[:10], yerr=[t*1e3 for t in ErrorTemp_vec[:10]],fmt='o',capsize=5,markersize=5,color=paleta[3])
-#plt.plot(betaslong,expo(betaslong,*popt_exp),label='Ajuste exponencial')
-#plt.plot(betaslong,cuadratica(betaslong,*popt_quad),label='Ajuste cuadratico')
-#plt.plot(betaslong,InverseMicromotionSpectra(betaslong,*popt_rho22),label='Ajuste cuadratico')
-plt.plot(betaslong,InverseMicromotionSpectra_raw(betaslong,*popt_rho22_raw),label='Ajuste cuadratico')
+## Grafico central ###############################
+I = slice(None,9)
+par_inicial = (12,0.1,1,-0.13)
+param,pcov = curve_fit(hiperbola,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
+x_hip = np.linspace(-0.23,0.005,200)
 
-#plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
-#plt.axhline(0.538)
-plt.xlabel('Modulation factor')
-plt.ylabel('Temperature (mK)')
-plt.grid()
 
+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.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')
 
-#%%
-"""
-Esto no es del super ajuste sino de los ajustes anteriores en donde DetDoppler y offset son puestos a mano
-Aca grafico los betas con su error en funcion de la tension variada.
-Ademas, hago ajuste lineal para primeros y ultimos puntos, ya que espero que
-si la tension hace que la posicion del ion varie linealmente, el beta varia proporcional a dicha posicion.
-"""
+ax = ax_res
+ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
+            yerr=ErrorBetas_vec[I],fmt='o',capsize=4,markersize=3,color='C3')
+ax.axhline( 0 , color='C0')
+ax.set_ylabel('Res.', labelpad=-5)
+ax.set_xlabel('Endcap voltage [V]')
+ax.set_title(f'(h)', x=0.95, y=0.72, color='gray')
 
-import seaborn as sns
 
-def lineal(x,a,b):
-    return a*x+b
+ax_res.get_xticklabels()[-1].set_visible(False)
 
-paleta = sns.color_palette("rocket")
+for ax in [ax_central,ax_res]:
+    ax.grid(True, ls=":", color='lightgray')
 
-betavector = [beta1,beta2,beta3,beta4,beta5,beta6,beta7,beta8,beta9]
-errorbetavector = [errorbeta1,errorbeta2,errorbeta3,errorbeta4,errorbeta5,errorbeta6,errorbeta7,errorbeta8,errorbeta9]
-voltages_dcA = Voltages[0][1:10]
 
-poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],betavector[0:3])
-poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],betavector[4:])
+# print([t*1e3 for t in Temp_vec])
 
-minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
-minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
 
-xini = np.linspace(-0.23,-0.13,100)
-xfin = np.linspace(-0.15,0.005,100)
+# Anotaciones ##########################
+for jj,ax in zip(selection,axes_vec):
+    
+    Axes_x = 1 if axx.flatten().tolist().index(ax)%3==0 else 0
+    ax_central.annotate("",
+                         xy=(ax_central.get_lines()[0].get_xdata()[jj], ax_central.get_lines()[0].get_ydata()[jj]), 
+                         xycoords=ax_central.transData,
+                         xytext=(Axes_x, 0.5), textcoords=ax.transAxes,
+                         arrowprops=dict(arrowstyle="<-",connectionstyle="arc3,rad=-0.2", color='gray', alpha=0.5),
+                         zorder=-2)
+    
 
-plt.figure()
-plt.errorbar(voltages_dcA,betavector,yerr=errorbetavector,fmt='o',capsize=5,markersize=5,color=paleta[1])
-plt.plot(xini,lineal(xini,*poptini))
-plt.plot(xfin,lineal(xfin,*poptfin))
-plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
-plt.xlabel('Endcap voltage (V)')
-plt.ylabel('Modulation factor')
-plt.grid()
 
+# dibujos ##########################
 
-#%%
-"""
-Aca veo la temperatura del ion en funcion del voltaje del endcap, ya que
-al cambiar la cantidad de micromocion, cambia la calidad del enfriado
-"""
-tempvector = np.array([temp1,temp2,temp3,temp4,temp5,temp6,temp7,temp8,temp9])*1e3
-errortempvector = np.array([errortemp1,errortemp2,errortemp3,errortemp4,errortemp5,errortemp6,errortemp7,errortemp8,errortemp9])*1e3
+a, y0, b, x0 = param
 
-voltages_dcA = Voltages[0][1:10]
+ax_dib.plot( x_hip , x_hip*0+y0 , color='gray)
+ax_dib.set_ylim(0,y0*1.1')
 
-plt.figure()
-plt.errorbar(voltages_dcA,tempvector,yerr=errortempvector,fmt='o',capsize=5,markersize=5,color=paleta[3])
-plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
-plt.xlabel('Endcap voltage (V)')
-plt.ylabel('Temperature (mK)')
-plt.grid()
-plt.ylim(0,2)
 
+# 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')
 
-#%%
-"""
-Por las dudas, temperatura en funcion de beta
-"""
-plt.figure()
-plt.errorbar(betavector,tempvector,yerr=errortempvector,xerr=errorbetavector,fmt='o',capsize=5,markersize=5)
-plt.xlabel('Modulation factor')
-plt.ylabel('Temperature (mK)')
-plt.grid()
 
 
+fig.align_ylabels([ax_central,ax_res])
+fig.tight_layout()
 
-#%%
-"""
-Si quiero ver algun parametro del ajuste puntual. el orden es: 0:SG, 1:SP, 2:SCALE1, 3:OFFSET
-"""
-ki=2
-plt.errorbar(np.arange(0,9,1),[popt_1[ki],popt_2[ki],popt_3[ki],popt_4[ki],popt_5[ki],popt_6[ki],popt_7[ki],popt_8[ki],popt_9[ki]],yerr=[np.sqrt(pcov_1[ki,ki]),np.sqrt(pcov_2[ki,ki]),np.sqrt(pcov_3[ki,ki]),np.sqrt(pcov_4[ki,ki]),np.sqrt(pcov_5[ki,ki]),np.sqrt(pcov_6[ki,ki]),np.sqrt(pcov_7[ki,ki]),np.sqrt(pcov_8[ki,ki]),np.sqrt(pcov_9[ki,ki])], fmt='o',capsize=3,markersize=3)
 
+# fig.savefig('grafico_central_opcion_B.png', dpi=300)
+# fig.savefig('grafico_central_opcion_B.pdf')
 
-#%%
 
-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]
 
 
-    np.savez('analisis_superajuste_PARAMETROS.npz', **GUARDAR )