lolo graficos

analisis/plots/20230817_RotationalDopplerShift_v4/RDS_lolo.py

+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
+
+
+
+from glob import glob
+
+from scipy.signal import savgol_filter as savgol
+
+
+#%% Funciones auxiliares
+
+
+# Función para verificar lectura de archivos
+def SeeKeys(files):
+    for i, fname in enumerate(files):
+        data = h5py.File(fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+
+
+
+def clean_jumps_below(frec,amp,umbral=0):
+    """
+    función para filtrar los spickes menores a un humbral determinado
+    """
+    
+    frec = np.array(frec)
+    amp  = np.array(amp)
+
+    return frec[amp>umbral] , amp[amp>umbral]
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Carga de datos experimentales 
+
+
+# glob('/home/lolo/tmp/github/artiq_experiments/artiq_master/results/2023-08-16')
+
+DATAFILES = sorted(glob('../../../artiq_master/results/2023-08-*/*/*.h5'))
+
+
+""" Bitácora Nico:
+    
+Configuracion +2/+2 descompensando:
+
+Ubicación lateral
+DCA: 14506, 14507, 14508
+CompOVEN: 14509, 14510, 14511, 14512
+
+Ubicación superior:
+DCA: 14513, 14514, 14515
+CompOVEN: 14516, 14517, 14518, 14519
+
+Configuración Gaussianos:
+DCA: 14557, 14558. 14559
+CompOVEN: 14560, 14561, 14562, 14563, 14564
+
+
+"""
+
+DATAFILES_costado_dcA      = [  aa for aa in DATAFILES for jj in range(14506,14508+1) if f'{jj}' in aa ]
+
+DATAFILES_costado_compOVEN = [  aa for aa in DATAFILES for jj in range(14509,14512+1) if f'{jj}' in aa ]
+
+
+
+DATAFILES_arriba_dcA      = [  aa for aa in DATAFILES for jj in range(14513,14515+1) if f'{jj}' in aa ]
+
+DATAFILES_arriba_compOVEN = [  aa for aa in DATAFILES for jj in range(14516,14519+1) if f'{jj}' in aa ]
+
+
+DATAFILES_gauss_dcA      = [  aa for aa in DATAFILES for jj in range(14557,14559+1) if f'{jj}' in aa ]
+
+DATAFILES_gauss_compOVEN = [  aa for aa in DATAFILES for jj in range(14560,14564+1) if f'{jj}' in aa ]
+
+
+
+DATOS_COMPLETOS = []
+
+DATOS_COMPLETOS.append([DATAFILES_costado_dcA,'Configuracion +2/+2 colineales, ion al costado del haz barriendo dcA','conf_+2+2_lateral_dcA.png'])
+DATOS_COMPLETOS.append([DATAFILES_costado_compOVEN,'Configuracion +2/+2 colineales, ion al costado del haz barriendo compOven','conf_+2+2_lateral_compOven.png'])
+DATOS_COMPLETOS.append([DATAFILES_costado_dcA,'Configuracion +2/+2 colineales, ion arriba del haz barriendo dcA','conf_+2+2_arriba_dcA.png'])
+DATOS_COMPLETOS.append([DATAFILES_costado_compOVEN,'Configuracion +2/+2 colineales, ion arriba del haz barriendo compOven','conf_+2+2_arriba_compOven.png'])
+DATOS_COMPLETOS.append([DATAFILES_costado_dcA,'Configuracion gaussiana colineales barriendo dcA','conf_gauss_dcA.png'])
+DATOS_COMPLETOS.append([DATAFILES_costado_dcA,'Configuracion gaussiana colineales barriendo compOven','conf_gauss_compOven.png'])
+
+
+
+#%% inspect
+
+# SeeKeys(DATAFILES_costado_dcA[:1])
+# data = h5py.File(DATAFILES_costado_dcA[0], 'r')
+# for kk,vv in data['datasets'].items():
+#     print(f'{kk}:\t',vv)
+
+
+
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Configuracion +2/+2 colineales, ion al costado del haz barriendo dcA
+
+FACTOR_ESCALA = 10
+
+for ii,DATO in enumerate(DATOS_COMPLETOS):
+    
+
+    VECTOR_DE_DATOS, SUP_TITLE, FILENAME =  DATO
+    
+    
+    
+    # Grafico con subplots
+    fig,axx = plt.subplots(1,2,figsize=(12,8), constrained_layout=True , sharey=True )
+    fig.set_constrained_layout_pads(w_pad=1/72, h_pad=0, hspace=0, wspace=0)
+    
+    
+    
+    rango_dr1 = np.array([435,437])
+    rango_dr2 = np.array([443,445])
+    
+    I_base_fit = np.arange(0,50).tolist()+np.arange(230,280).tolist()+np.arange(450,459).tolist()
+    I_base_fit = np.array(I_base_fit)
+    
+    
+    
+    ax = axx[0]
+    
+    
+    # plt.figure()
+    
+    # ax = plt.gca()
+    
+    NORMALIZADO = True
+    
+    rango_dr1 = np.array([435,437])
+    rango_dr2 = np.array([443,446])
+    
+    picos = {0:[], 1:[]}
+    
+    dcA_vec = []
+    
+    
+    
+    
+    for data in sorted([ h5py.File(DataFile, 'r') for DataFile in VECTOR_DE_DATOS ],key=lambda x: np.array(x['datasets']['initialvoltage_dcA']) ):
+        frecuencia = np.array(data['datasets']['IR1_Frequencies'])*2e-6
+        voltages   = np.array(data['datasets']['scanning_voltages'])
+        for kk, dcA in enumerate( voltages ):
+        
+            # cuentas    = np.array(data['datasets']['data_array'])
+            cuentas   = np.array(data['datasets']['data_array']).reshape(len(voltages),-1)[kk][0:len(frecuencia)]
+            amplitudes = np.array(data['datasets']['IR1_Amplitudes'])
+            # dcA = np.array(data['datasets']['initialvoltage_dcA'])
+            
+            dcA_vec.append( dcA.tolist() )
+            
+            frecuencia,cuentas  = clean_jumps_below(frecuencia,cuentas, 200)
+            
+            cuentas_suave    = savgol(cuentas,13,2)
+            # cuentas_suave    = cuentas
+            
+            
+            I_base_fit  = np.arange(len(frecuencia))[:50].tolist()
+            I_base_fit += np.arange(len(frecuencia))[(frecuencia>=439)&(frecuencia<=442)].tolist()
+            # I_base_fit += np.arange(len(frecuencia))[(frecuencia>=500)&(frecuencia<=442)].tolist()
+            I_base_fit = np.array(I_base_fit)
+           
+            mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+            
+            
+            
+            base = np.polyval(mm,frecuencia)
+            
+            
+            CURVA       = (cuentas/base-1)/FACTOR_ESCALA + dcA
+            CURVA_SUAVE = (cuentas_suave/base-1)/FACTOR_ESCALA+ dcA
+            ax.plot(frecuencia,CURVA      , '.', alpha=0.1)
+            ax.plot(frecuencia,CURVA_SUAVE, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+            
+            
+            
+            
+            for jj,rango in enumerate([rango_dr1,rango_dr2]):
+                I = np.arange(len(frecuencia))[(frecuencia>rango.min())&(frecuencia<rango.max())]
+                idx = cuentas_suave[I].argmin() + I[0]
+                
+                ax.plot(frecuencia[idx],(cuentas_suave[idx]/base[idx]-1)/FACTOR_ESCALA+dcA, 'o', ms=6, color=ax.get_lines()[-1].get_color(),zorder=10)
+                
+                Base = np.polyval(mm,rango.mean())
+                val_pico = cuentas_suave[I].min()
+                picos[jj].append( (Base-val_pico)/Base )
+                print( (Base-val_pico)/Base , end="\t")
+            print('')
+        #     break
+        # break
+        # ax.plot(frecuencia,cuentas , label=dcA)
+        
+        # plt.pause(2)
+        # plt.cla()
+        
+        
+        # ax.plot(cuentas , label=dcA)
+        
+    # plt.plot(frecuencia,amplitudes )
+    # ax.legend()
+    
+    
+    
+    ax.axvline(rango_dr1.mean(), color='gray', zorder=-10, lw=3 , alpha=0.5)
+    ax.axvline(rango_dr2.mean(), color='gray', zorder=-10, lw=3, ls='--', alpha=0.5)
+    
+    ax = axx[1]
+    
+    ax.plot(  picos[0] , dcA_vec , '.-'  , color='gray')
+    for pico,dcA_val in zip(picos[0],dcA_vec):
+        ax.plot( pico, dcA_val, '.' ,ms=7)
+    ax.set_prop_cycle(None)
+
+    ax.plot(  picos[1] , dcA_vec , 'o--' , color='gray')
+    for pico,dcA_val in zip(picos[1],dcA_vec):
+        ax.plot( pico, dcA_val, 'o' ,ms=7)
+    
+    
+    profundidad_mean = [ np.array(np.array(picos[0])[dcA_vec==val].tolist()+np.array(picos[1])[dcA_vec==val].tolist()).mean() for val in np.unique(dcA_vec) ]
+    profundidad_dev  = [ np.array(np.array(picos[0])[dcA_vec==val].tolist()+np.array(picos[1])[dcA_vec==val].tolist()).std() for val in np.unique(dcA_vec) ]
+    dcA_uniq         = np.unique(dcA_vec)
+    
+    
+
+    
+    ax.set_xlabel('Profundidad')
+    
+    axx[0].set_ylabel(f'{SUP_TITLE.split()[-1]} [V]')
+    axx[0].set_xlabel('frecuencia [kHz]')
+    
+    
+    
+    for ax in axx:
+        ax.grid(b=True,linestyle=':',color='lightgray')
+    
+    
+    ax.set_xlim( 0, ax.get_xlim()[1] )
+    
+    fig.suptitle(SUP_TITLE)
+    
+    fig.savefig(f"{ii+1:02d}_{FILENAME}")
+    
+    
+    # break
+