pulí un poco el lolo-superajuste

analisis/plots/20231123_CPTconmicromocion3/lolo_analisis_superajuste.py

@@ -405,7 +405,7 @@ for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,sele
     ax.grid(True, ls=":")
 
     print(f'listo med {selectedcurve}')
-    print(popt_3_SA)
+    # print(popt_3_SA)
 
 
 for ax in axx[:,0]:
@@ -452,58 +452,86 @@ ax.set_xticks(num_med)
 ax.set_xlabel('Num. de medición')
 
 
-#%%
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Endcap hiperbola (con residuos)
 """
-Grafico distintas variables que salieron del SUper ajuste
+Veamos cómo varía el Beta con el voltaje del endcap
 """
 
 import seaborn as sns
 paleta = sns.color_palette("rocket")
 
-voltages_dcA = Voltages[0][1:10]
+
+I = slice(None,9)
+
+voltages_dcA  = Voltages[0][SelectedCurveVec]
+
+
 
 def lineal(x,a,b):
     return a*x+b
 
-def hiperbola(x,a,b,c,x0):
-    return a*np.sqrt(((x-x0)**2+c**2))+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
+
+es_hiperbola = False
+
+    
+# par_inicial = (100,0.1,1,-0.15)
+#               a   y0   b  x0
+par_inicial = (12,0.1,1,-0.13)
 
-hiperbola_or_linear = True
 
-if hiperbola_or_linear:
-    popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
+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()
 
-    xhip = np.linspace(-0.23,0.005,200)
 
-    plt.figure()
-    plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-    plt.plot(xhip,hiperbola(xhip,*popthip))
-    plt.xlabel('Endcap voltage (V)')
-    plt.ylabel('Modulation factor')
-    plt.grid()
 
-else:
-    poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],Betas_vec[0:3])
-    poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],Betas_vec[4:])
 
-    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
+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 = axx[0]
+
+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')
 
-    xini = np.linspace(-0.23,-0.13,100)
-    xfin = np.linspace(-0.15,0.005,100)
+ax.set_ylabel('Modulation factor')
 
-    plt.figure()
-    plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,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()
+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])
+
+ax.set_ylabel('Res.')
+ax.set_xlabel('Endcap voltage (V)')
+
+for ax in axx:
+    ax.grid(True, ls=":", color='lightgray')
 
 
 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')
@@ -514,30 +542,11 @@ plt.ylabel('Temperature (mK)')
 plt.grid()
 #plt.ylim(0,2)
 
-#%%
-"""
-Ahora hago un ajuste con una hiperbola porque tiene mas sentido, por el hecho
-de que en el punto optimo el ion no esta en el centro de la trampa
-sino que esta a una distancia d
-"""
-def hiperbola(x,a,b,c,x0):
-    return a*np.sqrt(((x-x0)**2+c**2))+b
-
-popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
-
-xhip = np.linspace(-0.23,0.005,200)
-
-plt.figure()
-plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-plt.plot(xhip,hiperbola(xhip,*popthip))
-plt.xlabel('Endcap voltage (V)')
-plt.ylabel('Modulation factor')
-plt.grid()
-
 
 
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Ajuste de los betas y la temperatura
 
-#%%
 from scipy.special import jv