tuneando superajuste en un archivo aparte

analisis/plots/20231123_CPTconmicromocion3/lolo_CPT_plotter_20231123.py

@@ -1854,7 +1854,7 @@ if False:
     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)
@@ -1863,8 +1863,6 @@ if False:
     for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
         print(var)
         GUARDAR[var] = globals()[var]
-    
-    
-    np.savez('PARAMETROS.npz', **GUARDAR )
-    
 
+
+    np.savez('PARAMETROS.npz', **GUARDAR )

analisis/plots/20231123_CPTconmicromocion3/lolo_analisis_superajuste.py

@@ -112,31 +112,24 @@ CountsSplit_2ions = []
 CountsSplit_2ions.append(Split(Counts[4],len(Freqs[4])))
 
 
+#%% Cargo parámetros fiteados de antes
 
-#%% Definiciones de Numba
 
-@jit
-def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, plot=False):
-    #BETA = 1.8
-    # SG = 0.6
-    # SP = 8.1
-    # TEMP = 0.2e-3
+PARAMETROS = np.load('analisis_superajuste_PARAMETROS.npz',allow_pickle=True)
+for var_name in PARAMETROS.keys():
+    globals()[var_name] = PARAMETROS[var_name]
+    print(f'loaded: {var_name}')
 
-    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])
-
-    return ScaledFluo1
+if False:
+    # Esto es para correr en caso de necesidad de limpiar todos los vectores de parametros
+    print('Limpio los vectores de parámetros')
+    for var in [ kk for kk in globals().keys() if kk.endswith('_vec') ]:
+        print(f'del {var}')
+        del(globals()[var])
 
 
+#%% Definiciones de Numba
 
 @jit
 def FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
@@ -160,6 +153,14 @@ def FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BET
 
 
 
+@jit
+def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP):
+    "Esta verison de la función devuelve sólo el eje y, para usar de modelo en un ajuste"
+    return FitEIT_MM_single_plot(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP)[0]
+
+
+
+
 #%%
 """
 AHORA INTENTO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
@@ -280,7 +281,8 @@ if not 'popt_SA_vec' in globals().keys() or len(popt_SA_vec)==0:
         FittedCounts_vec.append(FittedEITpi_3_SA_long)
 
 
-#%%
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Graficamos todos los fiteos
 
 # tmp_datos=(Detuningsshort_vec,Counts_vec,Detuningslong_vec,FittedCounts_vec,SelectedCurveVec)
 
@@ -322,6 +324,32 @@ for ax in axx[:,0]:
 for ax in axx[-1,:]:
     ax.set_xlabel('Detuning (MHz)')
 
+
+
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Inspección de parámetros
+
+param_names = 'offset DetDoppler SG SP SCALE1 OFFSET BETA1 TEMP'.split()
+
+err_vecs = np.array([ np.sqrt(np.diag(el)) for el in pcov_SA_vec ])
+num_med  = np.arange(len(pcov_SA_vec)) +1
+
+fig, axx = plt.subplots( len(popt_SA_vec[0]),1, figsize=(13,8) ,  constrained_layout=True, sharex=True , sharey=False )
+fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
+
+for ax,param_vec,err_vec,par_name in zip(axx,popt_SA_vec.T,err_vecs.T,param_names) :
+    ax.plot(num_med, param_vec, '.-')
+    ax.errorbar( num_med, param_vec, yerr=err_vec, 
+                 fmt='s', mfc='none', elinewidth = 1, capsize=3, ms=1)
+
+    ax.grid(True, ls=":", color='lightgray')
+    ax.set_ylabel(par_name)
+
+ax.set_xticks(num_med)
+ax.set_xlabel('Num. de medición')
+
+
 #%%
 """
 Grafico distintas variables que salieron del SUper ajuste
@@ -556,22 +584,22 @@ plt.errorbar(np.arange(0,9,1),[popt_1[ki],popt_2[ki],popt_3[ki],popt_4[ki],popt_
 
 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.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('PARAMETROS.npz', **GUARDAR )
+    np.savez('analisis_superajuste_PARAMETROS.npz', **GUARDAR )