varsion luda

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_CPT2_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
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Carga de datos experimentales 
+
+
+#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+# os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
+
+
+os.chdir('Data')
+
+
+
+"""
+en este codigo ploteo espectros CPT de resonancias D-D para configuracion colineal (insensible a velocidad perpendicular)
+y configuracion desplazada (sensible).
+Primero una gaussiana variando la potencia (power_files).
+Luego, variando compensacion con electrodo DCA y con electrodo OVEN.
+"""
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+# Levanto carpetas / archivos de datos por experimento
+POWER_FILES     = sorted(glob('VaryingPower/*'))
+EXTRA_FILES     = sorted(glob('Extra/*')) 
+COMP_COL_FILES  = sorted(glob('VaryingComp/Col/*')) 
+COMP_DESP_FILES = sorted(glob('VaryingComp/Desp/*')) 
+
+
+# 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()))
+
+# Veamos si está bien...
+print(SeeKeys(POWER_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+PowerCounts = []
+PowerIR1_Freqs = []
+
+for i, fname in enumerate(POWER_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    PowerCounts.append(np.array(data['datasets']['counts_spectrum']))
+    PowerIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+ExtraCounts = []
+ExtraIR1_Freqs = []
+
+for i, fname in enumerate(EXTRA_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    ExtraCounts.append(np.array(data['datasets']['counts_spectrum']))
+    ExtraIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    
+CompColCounts = []
+CompColIR1_Freqs = []
+
+for i, fname in enumerate(COMP_COL_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CompColCounts.append(np.array(data['datasets']['counts_spectrum']))
+    CompColIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    
+CompDespCounts = []
+CompDespIR1_Freqs = []
+
+for i, fname in enumerate(COMP_DESP_FILES):
+    print(f"{i:02d} - {fname}")
+    data = h5py.File(fname, 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    CompDespCounts.append(np.array(data['datasets']['counts_spectrum']))
+    CompDespIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+
+#%%
+
+"""
+Resonancias DD variando la potencia del IR2
+"""
+
+powermedvec = [3,4,0,1,2]
+
+AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+jj=0
+for med in powermedvec:
+    plt.plot([2*f*1e-6 for f in PowerIR1_Freqs[med][1:]], [c for c in PowerCounts[med][1:]], '-o', markersize=2, label=f'amp:{AmpsVecs[jj]}')
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.grid()
+plt.legend()
+plt.title('Variando potencia de IR2 para potencia de IR1 fija')
+
+
+#%%
+
+"""
+Resonancias DD en config colineal, haces OAM, lindas con algunos parametros (ver cuaderno)
+"""
+
+extramedvec = [1]
+
+#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in extramedvec:
+    plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[med][1:]], [c for c in ExtraCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(433.5, 438)
+plt.ylim(1100,3200)
+plt.axvline(freqDR)
+plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Variando potencia de IR2 para potencia de IR1 fija')
+
+
+#%%
+
+"""
+Comparo resonancias DD con haces OAM en config colineal con desplazada
+"""
+
+extramedvec = [1,2]
+
+#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+secular=False    
+
+
+plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[1][1:]], [c for c in ExtraCounts[1][1:]], '-o', markersize=2, label='Colineales')
+plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[2][1:]], [c for c in ExtraCounts[2][1:]], '-o', markersize=2, label='Desplazadas')
+
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(425, 455)
+plt.ylim(1100,3200)
+
+if secular:
+    plt.axvline(freqDR)
+    plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+    plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Comparacion de config colineal (insensible) con desplazada (sensible)')
+
+
+#%%
+
+"""
+Resonancias DD config colineal OAM variando compensacion dcA
+"""
+
+import seaborn as sns
+paleta = sns.color_palette("tab10")
+
+compcolmedvec = [5,0,1,2,3,4]
+
+CompsDCA = [-100, -150, -200, -250, -300, -350]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+MinimosFluos_Col = []
+ErrorMinimosFluos_Col = []
+
+MinimosFluos_Col_Relativos = []
+
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
+    MinimosFluos_Col_Relativos.append([1-(c-30)/(np.mean(CompColCounts[med][0:20])-30) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(433.5, 438)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+#%%
+
+"""
+Resonancias DD config desplazada OAM variando compensacion dcA
+"""
+import seaborn as sns
+paleta = sns.color_palette("tab10")
+
+compdespmedvec = [0,2,3,4,5,6]
+
+CompsDCA = [-100, -100, -150, -200, -250, -300, -350]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR2)
+
+
+freqDR=435.8
+
+MinimosFluos_Desp = []
+ErrorMinimosFluos_Desp = []
+
+MinimosFluos_Desp_Relativos = []
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Desp.append([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])])
+    MinimosFluos_Desp_Relativos.append([1-(c-30)/(np.mean(CompDespCounts[med][0:20])-30) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+
+
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.ylim(400, 1280)
+#plt.ylim(0,100)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config desplazada OAM')
+
+
+
+
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Version Luda de los expectros al desplazar  ###############################
+from scipy.signal import savgol_filter as savgol
+
+# Grafico con subplots
+fig,axx = plt.subplots(2,2,figsize=(12,8), constrained_layout=True , sharey='row', sharex='row' )
+fig.set_constrained_layout_pads(w_pad=1/72, h_pad=0, hspace=0, wspace=0)
+
+
+# En la fila de arriba los espectros ..........................................
+
+####################################
+ax = axx[0][0]
+
+compcolmedvec = [5,0,1,2,3,4]
+CompsDCA_col = [-100, -150, -200, -250, -300, -350]
+
+ftrap = 22.1
+DR1 = 435.8
+DR2 = 444.2
+freqDR=435.8
+
+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(450,490).tolist()
+I_base_fit = np.array(I_base_fit)
+
+picos = {0:[], 1:[]}
+
+for med in compcolmedvec:
+
+    frecuencia = CompColIR1_Freqs[med][1:]*2e-6
+    cuentas    = CompColCounts[med][1:]
+    
+    if med==4:
+        # FIX OUTLIER
+        cuentas = cuentas.tolist()
+        cuentas.pop(291); cuentas.pop(291); cuentas.pop(291); 
+        cuentas = np.array(cuentas)
+        
+        frecuencia = frecuencia.tolist()
+        frecuencia.pop(291); frecuencia.pop(291); frecuencia.pop(291); 
+        frecuencia = np.array(frecuencia)
+    
+    
+    cuentas_suave    = savgol(cuentas,13,2)
+    
+    ax.plot(frecuencia,cuentas, '.', alpha=0.5)
+    ax.plot(frecuencia,cuentas_suave, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+    
+    mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+    ax.plot( [420,460], np.polyval(mm,[420,460]) , '-',  color=ax.get_lines()[-1].get_color(), alpha=0.5)
+    
+    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], 'o', ms=12, 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 )
+        
+    
+    # ax.plot(cuentas)
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel(r'frecuencia [kHz]')
+ax.set_ylabel(r'Cuentas')
+ax.set_title("Colineal")
+
+picos_col_dr1 = np.array(picos[0])
+picos_col_dr2 = np.array(picos[1])
+
+
+
+
+
+
+
+
+
+
+####################################
+ax = axx[0][1]
+
+compdespmedvec = [0,2,3,4,5,6]
+CompsDCA_desp = [-100, -150, -200, -250, -300, -350]
+ftrap = 22.1
+# DR1 = 435.8
+# DR2 = 444.2
+
+rango_dr1 = np.array([435,437])
+rango_dr2 = np.array([443,445])
+
+picos = {0:[], 1:[]}
+
+
+for med in compdespmedvec:
+
+    frecuencia = CompDespIR1_Freqs[med][1:]*2e-6
+    cuentas    = CompDespCounts[med][1:]
+    
+    if med==0:
+        # FIX OUTLIER
+        cuentas[207] = cuentas[206:208:2].mean()
+    
+    cuentas_suave    = savgol(cuentas,13,2)
+    
+    
+    ax.plot(frecuencia,cuentas, '.', alpha=0.5)
+    ax.plot(frecuencia,cuentas_suave, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+    
+    mm = np.polyfit(frecuencia[I_base_fit], cuentas[I_base_fit],1)
+    ax.plot( [420,460], np.polyval(mm,[420,460]) , '-',  color=ax.get_lines()[-1].get_color(), alpha=0.5)
+
+    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], 'o', ms=12, 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 )    
+    
+    # ax.plot(cuentas)
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel(r'frecuencia [kHz]')
+# ax.set_ylabel(r'Cuentas')
+ax.set_title("Desplazado")
+
+picos_desp_dr1 = np.array(picos[0])
+picos_desp_dr2 = np.array(picos[1])
+
+ax.set_xlim(427,453)
+
+
+
+
+# En la fila de abajo las profundidades .......................................
+
+
+
+####################################
+ax = axx[1][0]
+ax.plot( CompsDCA_col , picos_col_dr1 , 'o--' , color='gray')
+for comp,pic in zip(CompsDCA_col , picos_col_dr1):
+    ax.plot(comp,pic,'o')
+ax.set_prop_cycle(None)
+ax.plot( CompsDCA_col , picos_col_dr2 , 'o-' , color='gray')
+for comp,pic in zip(CompsDCA_col , picos_col_dr2):
+    ax.plot(comp,pic,'o')
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+
+ax.set_xlabel('Descompensación [mV]')
+ax.set_ylabel('Profundidad relativa')
+
+####################################
+ax = axx[1][1]
+ax.plot( CompsDCA_desp , picos_desp_dr1 , 'o--' , color='gray' )
+for comp,pic in zip(CompsDCA_desp , picos_desp_dr1):
+    ax.plot(comp,pic,'o')
+ax.set_prop_cycle(None)
+ax.plot( CompsDCA_desp , picos_desp_dr2 , 'o-'  , color='gray')
+for comp,pic in zip(CompsDCA_desp , picos_desp_dr2):
+    ax.plot(comp,pic,'o')
+
+ax.grid(b=True,linestyle=':',color='lightgray')
+ax.set_xlabel('Descompensación [mV]')
+
+ax.semilogy()
+ax.set_ylim(0.09,1.05)
+
+
+for ax in axx[1]:
+    ax.grid(b=True,linestyle=':',color='lightgray', which='minor', axis='y')
+
+from matplotlib.ticker import FormatStrFormatter
+ax.yaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
+
+for ax in axx[0]:
+    ax.axvline(DR1, ls='--', color='gray')
+    ax.axvline(DR2, ls='-', color='gray')
+
+
+
+
+
+#%%
+
+"""
+Ahora grafico el valor de los minimos en funcion de la descompensacion dcA
+"""
+
+CompsDCAcol = [-100, -150, -200, -250, -300, -350]
+CompsDCAdesp = [-100, -150, -200, -250, -300, -350]
+
+MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
+MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
+
+MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
+MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
+
+ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
+ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
+
+ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
+ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
+
+#DR1
+plt.figure()
+plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR1')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR1 counts - mean')
+#DR2
+plt.figure()
+plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR2')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR2 counts - mean')
+
+#%%
+
+"""
+Ahora veo los valores relativos a la fluorescencia al principio del espectro
+"""
+
+MinimosFluos_Col_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Col_Relativos))[0]
+MinimosFluos_Col_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Col_Relativos))[1]
+
+MinimosFluos_Desp_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[0]
+MinimosFluos_Desp_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[1]
+
+plt.figure()
+plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR1, 'o', markersize=12, label='Colineal')
+plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR1, 'o', markersize=12, label='Desplazada')
+plt.title('DR1')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR1 counts relatives')
+plt.legend()
+
+plt.figure()
+plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR2, 'o', markersize=12, label='Colineal')
+plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR2, 'o', markersize=12, label='Desplazada')
+plt.title('DR2')
+plt.grid()
+plt.xlabel('Endcap voltage (mV)')
+plt.ylabel('DR2 counts relatives')
+plt.legend()
+
+#hasta aca
+
+#%%
+"""
+Resonancias DD config colineal OAM variando compensacion OVEN
+"""
+
+import seaborn as sns
+
+compcolmedvec = [6,7,8,9,10,11,12,13,14]
+
+CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
+
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(433.5, 438)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+
+""" Le resto el fondo a ver como se ven"""
+
+paleta = sns.color_palette("tab10")
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.25
+
+idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+MinimosFluos_Col = []
+ErrorMinimosFluos_Col = []
+
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c-np.mean(CompColCounts[med][1:20]) for c in CompColCounts[med][1:]], '-o', markersize=2)
+    plt.plot([DR1, DR2], [c-np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.xlim(443, 445)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compesnacion dcA config colineal OAM')
+
+
+""" Estas son dos curvas con mas estadistica """
+compcolmedvec = [15, 16]
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compcolmedvec:
+    plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config colineal OAM mas estadistica')
+
+
+
+#%%
+import seaborn as sns
+
+"""
+Resonancias DD config desplazada OAM variando compensacion OVEN
+"""
+
+compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
+
+CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM')
+
+
+""" Le resto el fondo a ver como se ven y ploteo sus valores de DR"""
+
+paleta = sns.color_palette("tab10")
+
+compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR1)
+idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR2)
+
+freqDR=435.8
+
+MinimosFluos_Desp = []
+ErrorMinimosFluos_Desp = []
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c-np.mean(CompDespCounts[med][1:20]) for c in CompDespCounts[med][1:]], '-o', color=paleta[jj], markersize=2, zorder=0)
+    plt.plot([DR1, DR2], [c-np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
+    MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
+    ErrorMinimosFluos_Desp.append(([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])]))
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM')
+
+
+
+""" Estas son dos curvas con mas estadistica """
+compdespmedvec = [17, 18]
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+freqDR=435.8
+
+jj=0
+for med in compdespmedvec:
+    plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
+    jj=jj+1
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+#plt.ylim(300, 1000)
+#plt.ylim(1100,3200)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
+
+#%%
+""" Agarro la desplazada compensada para ver si se ven bandas laterales de movimiento (no)"""
+
+CompsOVEN = [5.9,4.3]
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+#freqDR=435.8
+freqDR=DR2
+jj=0
+
+plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[18][1:]], [c for c in CompDespCounts[18][1:]], '-o', markersize=2)
+
+plt.xlabel('Frecuencia')
+plt.ylabel('Counts')
+plt.xlim(442, 446)
+plt.axvline(freqDR)
+plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
+plt.grid()
+plt.legend()
+plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
+
+#%%
+
+"""
+Ahora grafico el valor de los minimos en funcion de la descompensacion OVEN
+"""
+
+CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
+CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
+
+MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
+MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
+
+MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
+MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
+
+ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
+ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
+
+ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
+ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
+
+#DR1
+plt.figure()
+plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR1[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR1[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:-2]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR1')
+plt.grid()
+plt.xlabel('CompOVEN voltage')
+plt.ylabel('DR1 counts - mean')
+#DR2
+plt.figure()
+plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR2[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2,  markersize=12, label='Colineal')
+plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR2[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:-2]],fmt='o', capsize=2,  markersize=12, label='Desplazada')
+plt.legend()
+plt.title('DR2')
+plt.grid()
+plt.xlabel('CompOVEN voltage')
+plt.ylabel('DR2 counts - mean')
\ No newline at end of file