a ver

analisis/plots/20220526_CPTvariandoB_v2/CPT_plotter_20220520.py

@@ -12,7 +12,10 @@ from scipy import interpolate
 
 #/home/nico/Documents/artiq_experiments/analisis/plots/20220526_CPTvariandoB_v2/Data
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220526_CPTvariandoB_v2/Data')
+# os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20220526_CPTvariandoB_v2/Data')
+
+os.chdir("C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20220526_CPTvariandoB_v2//Data")
+
 
 ALL_FILES = """000007697-IR_Scan_withcal_optimized
 000007696-IR_Scan_withcal_optimized
@@ -273,7 +276,6 @@ plt.plot(freqslongpi_12, FittedEITpi_12)
 #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
 
 
-#%%
 
 #ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 13!!! pero ahora ajustando demas parametros
 
@@ -333,7 +335,8 @@ plt.figure()
 plt.errorbar(FreqsDRpi_13, CountsDRpi_13, yerr=2*np.sqrt(CountsDRpi_13), fmt='o', capsize=2, markersize=2)
 plt.plot(freqslongpi_13, FittedEITpi_13)
 
-#%%
+
+
 #ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 14!!!
 
 phidoppler, titadoppler = 0, 90
@@ -395,7 +398,7 @@ plt.errorbar(FreqsDRpi_14, CountsDRpi_14, yerr=2*np.sqrt(CountsDRpi_14), fmt='o'
 plt.plot(freqslongpi_14, FittedEITpi_14)
 #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
 
-#%%
+
 #ESTE CODIGO AJUSTA UNA DE LAS CURVAS, LA 15!!!
 
 phidoppler, titadoppler = 0, 90
@@ -605,7 +608,7 @@ plt.tick_params(axis="y", which="both", **visible_ticks)
 plt.grid()
 plt.tight_layout()
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/CPT_exp.png',dpi=500)
-plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig4_final.pdf')
+# plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig4_final.pdf')
 
 #%%
 from scipy.optimize import curve_fit
@@ -671,3 +674,166 @@ plt.ylim(0.1,0.43)
 plt.xlabel('Coil current (A)')
 plt.ylabel('Larmor frequency (MHz)')
 plt.grid()
+
+
+
+#%%
+#filtro y mejoro el plot
+"""
+PLOTS TESIS (LOS DEL PAPER SON LOS DE ANTES)
+"""
+
+Ufields = [popt_12[0], popt_13[0], popt_14[0], popt_15[0]]
+ErrorUfields = [np.sqrt(pcov_12)[0,0], np.sqrt(pcov_13)[0,0],np.sqrt(pcov_14)[0,0], np.sqrt(pcov_15)[0,0]]
+
+Bfields = [u/(2*np.pi)/1398190 for u in Ufields]
+ErrorBfields = [eu/(2*np.pi)/1398190 for eu in ErrorUfields]
+
+
+from scipy.signal import savgol_filter as sf
+import seaborn as sns
+
+offs = 7.53e2
+scal = 2.0917e5
+
+winl = 11
+po = 3
+
+FiltCounts12 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_12, winl, po)])
+#ErrorCounts12 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_12])
+ErrorCounts12 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_12, winl, po)])
+
+FiltCounts13 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_13, winl, po)])
+#ErrorCounts13 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_13])
+ErrorCounts13 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_13, winl, po)])
+
+FiltCounts14 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_14, winl, po)])
+#ErrorCounts14 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_14])
+ErrorCounts14 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_14, winl, po)])
+
+FiltCounts15 = np.array([(c-offs)*100/scal for c in sf(CountsDRpi_15, winl, po)])
+#ErrorCounts15 = np.array([0.5*np.sqrt(c/scal) for c in CountsDRpi_15])
+ErrorCounts15 = np.array([0.5*np.sqrt(c/scal) for c in sf(CountsDRpi_15, winl, po)])
+
+
+#plot con escalas atomicas y lindo
+
+colors=sns.color_palette("mako", 10)
+
+color1=colors[1]
+color2=colors[2]
+color3=colors[4]
+color4=colors[7]
+
+ms = 4
+
+plt.figure(figsize=(3.6, 3))
+
+# plt.plot(FreqsDRpi_12, FiltCounts12, 'o', markersize=ms, color=color1, label=fr'$B=${round(1e3*Bfields[0])}({round(1e3*ErrorBfields[0])}) mG', alpha=0.3)
+# plt.plot(FreqsDRpi_13, FiltCounts13, 'o', markersize=ms, color=color2, label=fr'$B=${round(1e3*Bfields[1])}({round(1e3*ErrorBfields[1])}) mG', alpha=0.3)
+# plt.plot(FreqsDRpi_14, FiltCounts14, 'o', markersize=ms, color=color3, label=fr'$B=${round(1e3*Bfields[2])}({round(1e3*ErrorBfields[2])}) mG', alpha=0.3)
+# plt.plot(FreqsDRpi_15, FiltCounts15, 'o', markersize=ms, color=color4, label=fr'$B=${round(1e3*Bfields[3])}({round(1e3*ErrorBfields[3])}) mG', alpha=0.3)
+
+# plt.plot(freqslongpi_12, [(c-offs)*100/scal for c in FittedEITpi_12], color=color1, linewidth=3)
+# plt.plot(freqslongpi_13, [(c-offs)*100/scal for c in FittedEITpi_13], color=color2, linewidth=3)
+# plt.plot(freqslongpi_14, [(c-offs)*100/scal for c in FittedEITpi_14], color=color3, linewidth=3)
+# plt.plot(freqslongpi_15, [(c-offs)*100/scal for c in FittedEITpi_15], color=color4, linewidth=3)
+
+plt.plot(freqslongpi_12, [c for c in FittedEITpi_12], color=color1, linewidth=3)
+plt.plot(freqslongpi_13, [c for c in FittedEITpi_13], color=color2, linewidth=3)
+plt.plot(freqslongpi_14, [c for c in FittedEITpi_14], color=color3, linewidth=3)
+plt.plot(freqslongpi_15, [c for c in FittedEITpi_15], color=color4, linewidth=3)
+
+plt.errorbar(FreqsDRpi_12, [c*scal/100+offs for c in FiltCounts12], yerr=np.sqrt(np.array([c*scal/100+offs for c in FiltCounts12])),  capsize=2,fmt='o', markersize=2, color=color1, label=fr'$B=${round(1e3*Bfields[0])}({round(1e3*ErrorBfields[0])}) mG', alpha=0.7)
+plt.errorbar(FreqsDRpi_13, [c*scal/100+offs for c in FiltCounts13], yerr=np.sqrt(np.array([c*scal/100+offs for c in FiltCounts13])),  capsize=2,fmt='o', markersize=2, color=color2, label=fr'$B=${round(1e3*Bfields[1])}({round(1e3*ErrorBfields[1])}) mG', alpha=0.7)
+plt.errorbar(FreqsDRpi_14, [c*scal/100+offs for c in FiltCounts14], yerr=np.sqrt(np.array([c*scal/100+offs for c in FiltCounts14])),  capsize=2,fmt='o', markersize=2, color=color3, label=fr'$B=${round(1e3*Bfields[2])}({round(1e3*ErrorBfields[2])}) mG', alpha=0.7)
+plt.errorbar(FreqsDRpi_15, [c*scal/100+offs for c in FiltCounts15], yerr=np.sqrt(np.array([c*scal/100+offs for c in FiltCounts15])),  capsize=2,fmt='o', markersize=2, color=color4, label=fr'$B=${round(1e3*Bfields[3])}({round(1e3*ErrorBfields[3])}) mG', alpha=0.7)
+
+
+# plt.plot(FreqsDRpi_13, [c*scal/100+offs for c in FiltCounts13], 'o', markersize=ms, color=color2, label=fr'$B=${round(1e3*Bfields[1])}({round(1e3*ErrorBfields[1])}) mG', alpha=0.3)
+# plt.plot(FreqsDRpi_14, [c*scal/100+offs for c in FiltCounts14], 'o', markersize=ms, color=color3, label=fr'$B=${round(1e3*Bfields[2])}({round(1e3*ErrorBfields[2])}) mG', alpha=0.3)
+# plt.plot(FreqsDRpi_15, [c*scal/100+offs for c in FiltCounts15], 'o', markersize=ms, color=color4, label=fr'$B=${round(1e3*Bfields[3])}({round(1e3*ErrorBfields[3])}) mG', alpha=0.3)
+
+
+
+# plt.fill_between(FreqsDRpi_12, FiltCounts12+ErrorCounts12, FiltCounts12-ErrorCounts12, color=color1, alpha=0.2)
+# plt.fill_between(FreqsDRpi_13, FiltCounts13+ErrorCounts13, FiltCounts13-ErrorCounts13, color=color2, alpha=0.2)
+# plt.fill_between(FreqsDRpi_14, FiltCounts14+ErrorCounts14, FiltCounts14-ErrorCounts14, color=color3, alpha=0.2)
+# plt.fill_between(FreqsDRpi_15, FiltCounts15+ErrorCounts15, FiltCounts15-ErrorCounts15, color=color4, alpha=0.2)
+
+plt.xlim(-40,30)
+# plt.ylim(-0.1,1.4)
+plt.xlabel('Desintonía IR (MHz)', fontsize=10)
+plt.ylabel('Cuentas', fontsize=10)
+plt.xticks([-40, -20, 0, 20], fontsize=10)
+# plt.yticks([0, 0.3, 0.6, 0.9, 1.2], fontsize=10)
+# visible_ticks = {
+#    "top": False,
+#    "right": False
+# }
+# plt.tick_params(axis="x", which="both", **visible_ticks)
+# plt.tick_params(axis="y", which="both", **visible_ticks)
+# plt.legend(loc='upper left', frameon=True, fontsize=7.6, handletextpad=0.1)
+plt.grid(alpha=0.5)
+plt.tight_layout()
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Work/2022 B vs k race/Figuras/Figuras jpg trabajadas/CPT_exp.png',dpi=500)
+# plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 B vs K eigenbasis/Figuras_finales/Finalesfinales/Fig4_final.pdf')
+
+plt.savefig('C://Users//nicon//Nextcloud//Nico//Doctorado//Tesis//Tesis_doctorado//Chapters//figures//Cap6//fig2test.pdf')
+
+
+#%%
+from scipy.optimize import curve_fit
+
+def LinearLarmortoCurrent(I, a, b):
+    Larmor = a*I+b
+    return Larmor
+
+def ConvertLarmortoBfield(u):
+    c = 1398190.0452488689
+    return u/(2*np.pi)/c
+
+"""
+mediciones = [10, 9, 11,  12,   13,    14, 15,   16]
+corrientes = [2, 0, -1, -1.2, -1.5, -1.75, -2, -2.2]
+"""
+
+"""
+#Esto anda:
+IFit = [-1, -1.5, -1.75]
+LarmorFit = [popt_13[0], popt_14[0], popt_15[0]]
+"""
+
+IFit = [-1.2, -1.5, -1.75, -2]
+LarmorFit = [popt_12[0], popt_13[0], popt_14[0], popt_15[0]]
+#LarmorFit = [287282.374931893, 203998.09641511342, 158507.0255951109] por si fallan los ajustes
+
+ErrorLarmorFit = [np.sqrt(pcov_12[0,0]),np.sqrt(pcov_13[0,0]),np.sqrt(pcov_14[0,0]),np.sqrt(pcov_15[0,0])]
+MeanError = 11139.353180216529 #por si fallan los ajustes
+
+Ilong = np.arange(2, -3, -0.01)
+popt_larmor, pcov_larmor = curve_fit(LinearLarmortoCurrent, IFit, LarmorFit)
+LarmorLong = LinearLarmortoCurrent(Ilong, *popt_larmor)
+
+print(popt_larmor)
+
+plt.figure()
+plt.plot(IFit, LarmorFit, 'o', markersize=5)
+plt.plot(Ilong, LarmorLong)
+
+Bfitted = [ConvertLarmortoBfield(u) for u in LarmorFit]
+ErrorBfitted = [ConvertLarmortoBfield(u) for u in ErrorLarmorFit]
+BLong = [ConvertLarmortoBfield(u) for u in LarmorLong]
+
+
+#ESTE GRAFICO TIENE QUE IR EN LA TESIS CUANDO PONGA
+#COMO CALIBRE EL CAMPO MAGNETICO CON LA CORRIENTE
+plt.figure(figsize=(3.6,3))
+plt.plot(Ilong, [b*1e3 for b in BLong])
+plt.plot(IFit, [b*1e3 for b in Bfitted], 'o', markersize=8)
+plt.xlim(-2.2,-1)
+plt.ylim(0.012*1e3,0.045*1e3)
+plt.xlabel('Corriente bobina superior (A)')
+plt.ylabel('Campo magnetico (mG)')
+plt.grid()
+

analisis/plots/20221004_transitorios/Transitorios_01.py

@@ -34,6 +34,8 @@ ALL_FILES_SP = """000008358-SingleLine
 000008382-SingleLine
 """
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20221004_transitorios//Data')
+
 #000001504-SingleLine.h5
 
 def expo(T, tau, N0, C):
@@ -160,5 +162,5 @@ plt.grid(alpha=0.3)
 plt.xlabel("Potencia [uW]")
 plt.ylabel("Alturas/Tau")
 
-plt.show()
+# plt.show()
 #input()

analisis/plots/20221006_transitoriosv2/Transitorios_02.py

@@ -8,6 +8,9 @@ from scipy.optimize import curve_fit
 import os
 
 #os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221006_transitoriosv2')
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20221006_transitoriosv2')
+
 # Solo levanto algunos experimentos
 SP_files = [8445, 8457, 8458, 8459, 8460, 8461, 8462, 8463, 8464, 8465, 8466, 8467, 8468, 8469, 84840, 88040]
 SP_files_v2 = [8763, 8764, 8765, 8766, 8767, 8768, 8769, 8770, 8771, 8772, 8773, 8774, 8775, 8776]
@@ -267,12 +270,6 @@ FIGURA PAPER SP CON AJUSTES
 import matplotlib
 import seaborn as sns
 
-matplotlib.rcParams['mathtext.fontset'] = 'stix'
-matplotlib.rcParams['font.family'] = 'STIXGeneral'
-plt.style.use('seaborn-bright')
-plt.rcParams.update({
-    "text.usetex": False,
-})
 
 plt.figure()
 plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
@@ -377,12 +374,6 @@ FIGURA PAPER
 import matplotlib
 import seaborn as sns
 
-matplotlib.rcParams['mathtext.fontset'] = 'stix'
-matplotlib.rcParams['font.family'] = 'STIXGeneral'
-plt.style.use('seaborn-bright')
-plt.rcParams.update({
-    "text.usetex": False,
-})
 
 jselected = [1, 5, 12]
 
@@ -415,9 +406,14 @@ plt.ylabel('Counts', fontname='STIXGeneral', fontsize=14)
 plt.xticks([0, 0.5, 1, 1.5, 2], fontsize=12)
 plt.yticks([0, 1000, 2000, 3000, 4000], fontsize=12)
 plt.grid()
-plt.savefig('fig3_01.pdf')
-plt.savefig('fig3_01.svg')
+# plt.savefig('fig3_01.pdf')
+# plt.savefig('fig3_01.svg')
+
 
+"""
+Hasta aca. lo de abajo fueron intentos fallidos....
+
+"""
 
 #%%
 

analisis/plots/20221017_IonStatistics/IonStatistics.py

@@ -11,13 +11,6 @@ import seaborn as sns
 
 
 #os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221017_IonStatistics')
-
-plt.rcParams.update({
-    "font.family": "STIXGeneral"
-})
-plt.rcParams.update({
-    "font.size": 14
-})
 # Solo levanto algunos experimentos
 Stat_files = [8731, 8738, 8745, 8819, 8820, 8822]
 
@@ -88,12 +81,6 @@ import seaborn as sns
 
 
 
-matplotlib.rcParams['mathtext.fontset'] = 'stix'
-matplotlib.rcParams['font.family'] = 'STIXGeneral'
-plt.style.use('seaborn-bright')
-plt.rcParams.update({
-    "text.usetex": False,
-})
 
 #plt.figure()
 #plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
@@ -135,11 +122,11 @@ ax[1].plot(bins1+0.5,poisson1,color = color1,label = 'Ion',linewidth=1.5)
 ax[1].legend(loc='upper right', prop={'size': 10})
 ax[1].grid()
 ax[1].set_xticks([0, 100, 200, 300])
-ax[1].set_xticklabels([0, 100, 200, 300], fontsize=10,fontname='STIXGeneral')
+ax[1].set_xticklabels([0, 100, 200, 300], fontsize=10)
 ax[1].set_yticks([0,0.02, 0.04, 0.06, 0.08])
-ax[1].set_yticklabels([0,0.02, 0.04, 0.06, 0.08], fontsize=10,fontname='STIXGeneral')
-ax[1].set_xlabel('Cuentas', fontsize=10, fontname='STIXGeneral')
-ax[1].set_ylabel('Frecuencia de eventos', fontsize=10, fontname='STIXGeneral')
+ax[1].set_yticklabels([0,0.02, 0.04, 0.06, 0.08], fontsize=10)
+ax[1].set_xlabel('Cuentas', fontsize=10)
+ax[1].set_ylabel('Frecuencia de eventos', fontsize=10)
 
 #plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_poisson.pdf')
 
@@ -160,18 +147,22 @@ ax[0].plot([s*1e6 for s in OnOff_Bins[0][:-1]],[(OnOff_Heights[0][j]-OnOff_Heigh
 ax[0].plot([s*1e6 for s in OnOff_Bins[2][:-1]],OnOff_Heights[2], color=color1,linewidth=3)
 ax[0].set_xlim(0,1)
 ax[0].grid()
-ax[0].set_xlabel(r'Tiempo ($\mu$s)', fontname='STIXGeneral', fontsize=10)
-ax[0].set_ylabel(r'Cuentas /10$~\mu$s', fontname='STIXGeneral', fontsize=10)
+ax[0].set_xlabel(r'Tiempo ($\mu$s)', fontsize=10)
+ax[0].set_ylabel(r'Cuentas /10$~\mu$s',fontsize=10)
 ax[0].set_xticks([0,0.2, 0.4, 0.6, 0.8, 1])
 ax[0].set_yticks([0,5000, 10000])
-ax[0].set_xticklabels([0,0.2, 0.4, 0.6, 0.8, 1], fontname='STIXGeneral', fontsize=10)
-ax[0].set_yticklabels([0,5000, 10000], fontname='STIXGeneral', fontsize=10)
+ax[0].set_xticklabels([0,0.2, 0.4, 0.6, 0.8, 1], fontsize=10)
+ax[0].set_yticklabels([0,5000, 10000], fontsize=10)
 
 plt.tight_layout()
 
 name='fig01b'
 
-plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_statistics.pdf')
+# plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_statistics.pdf')
+
+# plt.savefig('C:/Users/nicon/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap5_statistics.pdf')
+
+plt.savefig('C://Users//nicon//Nextcloud//Nico//Doctorado//Tesis//Tesis_doctorado//Chapters//figures//Cap4//Cap4_statistics.pdf')
 
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.pdf')
 #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'_dump.svg')

analisis/plots/20230510_MotionalSpectrum/CPT_plotter_20230510.py

@@ -8,11 +8,11 @@ from scipy.optimize import curve_fit
 import os
 from scipy import interpolate
 
-#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/20230510_MotionalSpectrum/Data/')
+# os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/Data/')
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//Data')
 
 
 MOTIONAL_FILES = """000011490-AD9910RAM
@@ -83,6 +83,39 @@ for i, fname in enumerate(MOTIONAL_FILES.split()):
 
 
 
+#%%
+
+"""
+Ploteo las curvas de referencia 
+"""
+
+"""
+Valen la pena:
+    
+    16,19-20-21-22-23: supongo axial. se ven dos piquitos
+    25, 28: radial 1
+    26, 29: radial 2
+    31: se ven bien los dos picos enigmaticos
+    41: radial 1 fuerte
+    
+"""
+
+
+jvec = [41]
+
+plt.figure()
+i = 0
+for j in jvec:
+    plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='-o', capsize=2, markersize=2)
+    i = i + 1
+plt.xlabel('Frecuencia (kHz)')
+plt.ylabel('counts')
+# plt.xlim(851,856)
+# plt.ylim(600,1700)
+plt.grid()
+# plt.legend()
+
+
 #%%
 
 """
@@ -108,4 +141,3 @@ plt.grid()
 plt.legend()
 
 
-

analisis/plots/20230510_MotionalSpectrum/Electric/20220830/images/zzzlevantarimagenes.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr 23 09:38:53 2024
+
+@author: nicon
+"""
+
+import numpy as np 
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
+from matplotlib.colors import Normalize
+from scipy.ndimage import map_coordinates
+import os
+
+imagenes = []
+names = []
+
+for file in os.listdir()[:-1]:
+    names.append(file)
+    pic = np.load(str(file))
+    imagenes.append(pic)
+#%% 
+j=1
+print(names[j])
+plt.figure()
+plt.imshow(imagenes[j][580:630,940:990],vmin=105,vmax=130)
+
+
+#%% 
+j=0
+print(names[j])
+plt.figure()
+plt.imshow(imagenes[j][580:630,940:990],vmin=110,vmax=125)    
+
+#%% 
+j=2
+print(names[j])
+plt.figure()
+plt.imshow(imagenes[j][580:630,940:990],vmin=105,vmax=110)    
+
+
+#%% 
+j=3
+print(names[j])
+plt.figure()
+plt.imshow(imagenes[j][580:630,940:990],vmin=105,vmax=110)    
+
+
+#%% 
+j=2
+print(names[j])
+plt.figure()
+plt.imshow(imagenes[j][580:630,940:990],vmin=100,vmax=115)    
+
+
+

analisis/plots/20230510_MotionalSpectrum/MotioanlOptic2024.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
+
+#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/20230510_MotionalSpectrum/DataOpticTransitory/')
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//Data')
+
+MOTIONAL_FILES = """
+000011490-AD9910RAM
+000011491-AD9910RAM
+000011492-AD9910RAM
+000011493-AD9910RAM
+000011496-AD9910RAM
+000011499-AD9910RAM
+000011500-AD9910RAM
+000011501-AD9910RAM
+000011502-AD9910RAM
+000011503-AD9910RAM
+000011504-AD9910RAM
+000011505-AD9910RAM
+000011506-AD9910RAM
+000011507-AD9910RAM
+000011511-AD9910RAM
+000011512-AD9910RAM
+000011513-AD9910RAM
+000011514-AD9910RAM
+000011515-AD9910RAM
+000011516-AD9910RAM
+000011517-AD9910RAM
+000011518-AD9910RAM
+000011519-AD9910RAM
+000011521-AD9910RAM
+000011522-AD9910RAM
+000011560-AD9910RAM
+000011561-AD9910RAM
+000011562-AD9910RAM
+000011563-AD9910RAM
+000011564-AD9910RAM
+000011566-AD9910RAM
+000011567-AD9910RAM
+000011569-AD9910RAM
+000011571-AD9910RAM
+000011572-AD9910RAM
+000011574-AD9910RAM
+000011575-AD9910RAM
+000011576-AD9910RAM
+000011577-AD9910RAM
+000011580-AD9910RAM
+000011581-AD9910RAM
+000011582-AD9910RAM
+"""
+
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+print(SeeKeys(MOTIONAL_FILES))
+
+#%%
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+RealFreqs = []
+ErrorFreqs = []
+SetFreqs = []
+
+for i, fname in enumerate(MOTIONAL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    RealFreqs.append(np.array(data['datasets']['real_freq']))
+    Counts.append(np.array(data['datasets']['counts']))
+    ErrorFreqs.append(np.array(data['datasets']['error_freq']))
+    SetFreqs.append(np.array(data['datasets']['set_freq']))
+
+
+# Potencias = [7.4, 11.4, 16.8, 23.2, 31.1, 40.7, 51.5, 64, 78.2, 92.7, 108, 124, 134, 154, 167]
+
+# PotenciasUsadas = [7.4, 11.4, 16.8, 23.2, 31.1, 40.7, 51.5, 64, 78.2]
+
+
+#%%
+
+"""
+Ploteo una curva para buscar su minimo
+
+17,18,19,20,21,22,23: axial. doble pico. distintas pots uv.  11514, 11515, 11516, 11517, 11518, 11519, 11521
+
+25, 28: radial1 con mucha estadistica. 11560, 11563.
+
+29: radial2 con mucha estad. 11564
+30: radial2. clarisimo el doble pico. 11566
+31: radial2. mas claro todavia el doble pico. 11567
+
+37: me animaria a decir que lo anterior fue modulando el uv y este es modulando el IR. 11576
+40: tmb. pico chico. 11581
+
+41: radial1. no se modulando qué pero se ve bien. 11582
+
+"""
+
+
+
+
+jvec = [42]
+
+plt.figure()
+i = 0
+
+kmin = 106
+
+for j in jvec:
+    plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='-o', capsize=2, markersize=2)
+    i = i + 1
+plt.xlabel('Frecuencia (kHz)')
+plt.ylabel('counts')
+#plt.xlim(782,787)
+#plt.ylim(2000,5000)
+plt.grid()
+# plt.legend(loc='upper left')
+
+
+

analisis/plots/20230510_MotionalSpectrum/MotionalElectric_new.py

@@ -7,12 +7,14 @@ from scipy.optimize import curve_fit
 import os
 from scipy import interpolate
 
-#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/20230510_MotionalSpectrum/Data/')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataElectricNew')
+
+
 Data = []
 for ii in range(1,10):
     Data.append(f"ss0{ii}.dat")
@@ -33,6 +35,16 @@ for dd in Data:
 
     FrequencyVec.append([data[i][1] for i in range(len(data))])
     FluoVec.append([data[i][2] for i in range(len(data))])
+#%%
+#visualizo data
+ivec = [8]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
+# plt.xlim(0.78,0.8)    
+
+
 
 #%%
 #los voltajes son 2, 2.5, 3, 5, 7 y 9
@@ -98,7 +110,10 @@ Nuevas mediciones: dan mejor
 """
 
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataRawElectric/')
+# os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataRawElectric/')
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataRawElectric')
+
 
 Data = []
 for ii in range(1,10):
@@ -108,7 +123,11 @@ for ii in range(10,14):
 #for ii in range(40,49):
 #    Data.append(f"ss{ii}.dat")
 
-os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataRawElectric2/')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataRawElectric2/')
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataRawElectric2')
+
+
 
 for ii in range(1,10):
     Data.append(f"s0{ii}.dat")
@@ -117,8 +136,8 @@ for ii in range(10,24):
 #for ii in range(40,49):
 #    Data.append(f"ss{ii}.dat")
 
-FrequencyVec = []
-FluoVec = []
+FrequencyVec2 = []
+FluoVec2 = []
 
 for dd in Data:
     data = np.genfromtxt(dd,
@@ -127,8 +146,18 @@ for dd in Data:
                          dtype=None,
                          delimiter=' ')
 
-    FrequencyVec.append([data[i][1] for i in range(len(data))])
-    FluoVec.append([data[i][2] for i in range(len(data))])
+    FrequencyVec2.append([data[i][1] for i in range(len(data))])
+    FluoVec2.append([data[i][2] for i in range(len(data))])
+
+#%%
+#visualizo data
+ivec = [3]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec2[i]], FluoVec2[i],'o')
+# plt.xlim(0.78,0.8)    
+
 
 #%%
 """
@@ -138,7 +167,7 @@ ivec = [33]
 
 plt.figure()
 for i in ivec:
-    plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'-o')
+    plt.plot([f*1e-6 for f in FrequencyVec2[i]], FluoVec2[i],'-o')
 #plt.xlim(0.3,0.6)   
 
 
@@ -150,7 +179,7 @@ ivec = [23]
 
 plt.figure()
 for i in ivec:
-    plt.plot([f*1e-6 for f in FrequencyVec[i]], FluoVec[i],'o')
+    plt.plot([f*1e-6 for f in FrequencyVec2[i]], FluoVec2[i],'o')
 #plt.xlim(0.3,0.6)   
 
 #%%
@@ -163,15 +192,17 @@ ki=3500
 kf=-1500
 
 plt.figure()
-plt.plot([f*1e-6 for f in FrequencyVec[23]], FluoVec[23],'ro')
-plt.plot([f*1e-6 for f in FrequencyVec[33]][ki:kf], FluoVec[33][ki:kf],'ro')
+plt.plot([f*1e-6 for f in FrequencyVec2[27]], FluoVec2[27],'-o')
+#plt.plot([f*1e-6 for f in FrequencyVec2[33]][ki:kf], FluoVec2[33][ki:kf],'-o')
 plt.xlabel('Frecuencia (MHz)')
 plt.ylabel('Cuentas')
-plt.xlim(1.5,1.6)   
+#plt.xlim(1.5,1.6)   
 
 
 
 #%%
+
+
 """
 Miro modo radial con roi chiquita, una en el medio del ion y otra en donde el ion se estira en el costadito
 """

analisis/plots/20230510_MotionalSpectrum/MotionalElectric_newest_2024.py

+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
+
+
+#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/20230510_MotionalSpectrum/Data/')
+
+    
+
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataElectric')
+
+Data1 = []
+for ii in range(1,10):
+    Data1.append(f"ss0{ii}.dat")
+for ii in range(10,20):
+    Data1.append(f"ss{ii}.dat")
+for ii in range(20,40):
+    Data1.append(f"ss{ii}.dat")
+for ii in range(40,49):
+    Data1.append(f"ss{ii}.dat")
+
+FrequencyVec1 = []
+FluoVec1 = []
+
+for dd in Data1:
+    data = np.genfromtxt(dd,
+                         skip_header=1,
+                         names=True,
+                         dtype=None,
+                         delimiter=' ')
+
+    FrequencyVec1.append([data[i][1] for i in range(len(data))])
+    FluoVec1.append([data[i][2] for i in range(len(data))])
+    
+    
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataElectricNew')
+
+Data2 = []
+for ii in range(1,10):
+    Data2.append(f"ss0{ii}.dat")
+
+FrequencyVec2 = []
+FluoVec2 = []
+
+for dd in Data2:
+    data = np.genfromtxt(dd,
+                         skip_header=1,
+                         names=True,
+                         dtype=None,
+                         delimiter=' ')
+
+    FrequencyVec2.append([data[i][1] for i in range(len(data))])
+    FluoVec2.append([data[i][2] for i in range(len(data))])
+
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataElectricNew//Ion centro costado')
+
+Data2b = ['sscostado.dat']
+
+FrequencyVec2b = []
+FluoVec2b = []
+
+for dd in Data2b:
+    data = np.genfromtxt(dd,
+                         skip_header=1,
+                         names=True,
+                         dtype=None,
+                         delimiter=' ')
+
+    FrequencyVec2b.append([data[i][1] for i in range(len(data))])
+    FluoVec2b.append([data[i][2] for i in range(len(data))])
+
+    
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataRawElectric')
+
+Data3 = []
+for ii in range(1,10):
+    Data3.append(f"s0{ii}.dat")
+for ii in range(10,14):
+    Data3.append(f"s{ii}.dat")
+
+
+FrequencyVec3 = []
+FluoVec3 = []
+
+for dd in Data3:
+    data = np.genfromtxt(dd,
+                         skip_header=1,
+                         names=True,
+                         dtype=None,
+                         delimiter=' ')
+
+    FrequencyVec3.append([data[i][1] for i in range(len(data))])
+    FluoVec3.append([data[i][2] for i in range(len(data))])
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataRawElectric2')
+
+Data4 = []
+for ii in range(1,10):
+    Data4.append(f"s0{ii}.dat")
+for ii in range(10,24):
+    Data4.append(f"s{ii}.dat")
+
+
+FrequencyVec4 = []
+FluoVec4 = []
+
+for dd in Data4:
+    data = np.genfromtxt(dd,
+                         skip_header=1,
+                         names=True,
+                         dtype=None,
+                         delimiter=' ')
+
+    FrequencyVec4.append([data[i][1] for i in range(len(data))])
+    FluoVec4.append([data[i][2] for i in range(len(data))])
+
+
+#%%
+#del 1 al 49
+
+ivec = [47]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec1[i]], FluoVec1[i],'o')
+# plt.xlim(0.78,0.8)    
+
+
+#%%
+#del 0 al 8
+
+ivec = [8]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec2[i]], FluoVec2[i],'o')
+# plt.xlim(0.78,0.8)    
+
+#%%
+#solo el 0
+
+ivec = [0]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec2b[i]], FluoVec2b[i],'o')
+# plt.xlim(0.78,0.8)    
+
+#%%
+#desde el 0 hasta el 12
+
+"""
+0, 1: agarrarr el ion en el centro o el costado
+
+7: modo relativo!!!!! 10, 11 y 12 tmb pero no se ve tan bien anyways
+"""
+
+ivec = [9]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec3[i]], FluoVec3[i],'o')
+# plt.xlim(0.78,0.8)    
+
+#%%
+#desde 0 hasta 22
+
+ivec = [19]
+
+plt.figure()
+for i in ivec:
+    plt.plot([f*1e-6 for f in FrequencyVec4[i]], FluoVec4[i],'o')
+# plt.xlim(0.78,0.8)    
+
+
+
+#%%
+"""
+Primero un grafico de los tres modos
+"""
+import seaborn as sns
+
+i1 = 14
+i2 = 20
+
+Freqs1 = [f*1e-6 for f in FrequencyVec4[i1]]
+Counts1 = [c for c in FluoVec4[i1]]
+
+Freqs2 = [f*1e-6 for f in FrequencyVec4[i2]]
+Counts2 = [c for c in FluoVec4[i2]]
+
+for jj in range(1000,3500):
+    if Counts2[jj]<101.7:
+        Counts2[jj]=102.2
+
+for jj in range(4500,6000):
+    if Counts2[jj]<101.75:
+        Counts2[jj]=102.2
+
+k1 = 900
+k2 = 6000
+
+for kk in range(800,1500):
+    if Counts1[kk]<101.65:
+        Counts1[kk]=102.2
+
+Counts1[1722]=100.7
+Counts1[1723]=101
+
+lwidth = 3
+fs = 10
+
+bkg = 100
+
+pale = sns.color_palette("colorblind")
+
+col=3
+
+plt.figure(figsize=(5.5,4))
+plt.plot(Freqs1, [c-bkg for c in Counts1], linewidth=lwidth,color=pale[col])
+plt.plot(Freqs2[k1:k2], [c+0.06-bkg for c in Counts2[k1:k2]], linewidth=lwidth,color=pale[col])
+plt.ylim(-0.1,4)
+plt.xlim(0.7,1.75)
+plt.yticks([0,1,2,3])
+plt.ylabel('Cuentas por píxel')
+plt.xlabel('Frecuencia de excitación (MHz)')
+plt.grid(alpha=0.5)
+# plt.xlim(0.78,0.8)    
+
+
+
+#freq radial 1
+plt.figure(figsize=(3.5,3))
+plt.plot(Freqs1, [c-bkg for c in Counts1], linewidth=lwidth,color=pale[col])
+#plt.plot(Freqs2[k1:k2], [c+0.06-bkg for c in Counts2[k1:k2]], linewidth=lwidth,color=pale[col])
+plt.ylim(-0.1,3.2)
+plt.xlim(0.787,0.795)
+plt.yticks([0,1,2,3])
+plt.ylabel('Cuentas por píxel')
+plt.xlabel('Frecuencia de excitación (MHz)')
+plt.grid(alpha=0.5)
+plt.xticks([0.788, 0.791, 0.794])
+# plt.xlim(0.78,0.8)   
+
+
+#freq radial 1
+plt.figure(figsize=(3.5,3))
+plt.plot(Freqs1, [c-bkg for c in Counts1], linewidth=lwidth,color=pale[col])
+#plt.plot(Freqs2[k1:k2], [c+0.06-bkg for c in Counts2[k1:k2]], linewidth=lwidth,color=pale[col])
+plt.ylim(-0.1,3.2)
+plt.xlim(0.8745,0.8825)
+plt.yticks([0,1,2,3])
+plt.ylabel('Cuentas por píxel')
+plt.xlabel('Frecuencia de excitación (MHz)')
+plt.grid(alpha=0.5)
+plt.xticks([0.875, 0.878, 0.881])
+# plt.xlim(0.78,0.8)   
+
+
+

analisis/plots/20230510_MotionalSpectrum/MotionalElectric_old.py

@@ -13,6 +13,9 @@ from scipy import interpolate
 
 #os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/Data/')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230707_MotionalSpectrum_v2//Data')
+
+
 Data = []
 for ii in range(1,10):
     Data.append(f"ss0{ii}.dat")

analisis/plots/20230510_MotionalSpectrum/MotionalOptic_TransitoryEffect.py

@@ -12,8 +12,9 @@ from scipy import interpolate
 
 #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/20230510_MotionalSpectrum/DataOpticTransitory/')
+# os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230510_MotionalSpectrum/DataOpticTransitory/')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230510_MotionalSpectrum//DataOpticTransitory')
 
 MOTIONAL_FILES = """
 000012033-AD9910RAM
@@ -56,13 +57,15 @@ for i, fname in enumerate(MOTIONAL_FILES.split()):
 Potencias = [7.4, 11.4, 16.8, 23.2, 31.1, 40.7, 51.5, 64, 78.2, 92.7, 108, 124, 134, 154, 167]
 
 PotenciasUsadas = [7.4, 11.4, 16.8, 23.2, 31.1, 40.7, 51.5, 64, 78.2]
+
+
 #%%
 
 """
 Ploteo una curva para buscar su minimo
 """
 
-jvec = [0,1,2]
+jvec = [9]
 
 plt.figure()
 i = 0
@@ -90,7 +93,7 @@ plt.legend(loc='upper left')
 Ploteo las curvas de referencia 
 """
 
-jvec = [0, 1, 2, 3, 8]
+jvec = [9]
 
 
 
@@ -136,9 +139,146 @@ plt.axhline(1)
 plt.xlabel('Potencia IR (uW)')
 
 
+#%%
+"""
+Ahora hago lo de antes pero bien, con ajustes, como debe ser
+"""
+
+def Lorentzian( x, A, B, x0, gam ):
+    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B
+
+
+def ErrorDRdepth(p, f, b):
+    ep = np.sqrt(p)
+    ef = np.sqrt(f)
+    eb = np.sqrt(b)
+    derivadap = 1/((f-b)**2)
+    derivadaf = ((p-b)/((f-b)**2))**2
+    derivadab = ((p-f)/((f-b)**2))**2
+    return 1*np.sqrt(derivadap*ep*ep + derivadaf*ef*ef + derivadab*eb*eb)
+
+def SaturationCurve_detzero(p, F0, a,b):
+    return b*(1-0.5*F0*a*p*(1/(1+a*p)))
+
+bkg = np.min(Counts[2])
+
+depths = []
+errordepths = []
+
+js = [0,1,2,3,4,5,6,7,8,9]
+#js=[9]
+
+
+poptsvec = []
+
+for k in js:
+    Freqs = [1*f*1e-3 for f in RealFreqs[k]]
+    Coun = [c for c in Counts[k]]
+    if k==4:
+        Coun[97]=3800
+    elif k==2:
+        Coun[129]=3400
+    
+    elif k==5:
+        Coun[115]=3750
+        Coun[116]=3850
+    elif k==8:
+        Coun[98]=4200
+        
+    elif k==9:
+        Coun[52]=4400
+        Coun[77]=4400
+        Coun[100]=4000
+        Coun[104]=3700
+        Coun[129]=4300
+        Coun[130]=4300
+        
+    #popt, pcov = curve_fit(Gaussian, Freqs, Counts, p0=(-200,2100,444.2,0.05))
+    popt, pcov = curve_fit(Lorentzian, Freqs, Coun, p0=(-2000,3000,784.2,0.05), bounds=((-10000,0,0,0),(0,1e4, 1e4, 1)))
+    poptsvec.append(popt)
+    if k==0:
+        depths.append(0)
+        errordepths.append(0.03)
+    else:
+        depths.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
+        errordepths.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
+
+    if len(js)==1:
+        plt.figure()
+        plt.plot(Freqs,Coun,'-o')
+        plt.plot(Freqs,Lorentzian(Freqs,*popt))
+        # plt.xlim(782.5,788)
+if not len(js)==1:
+    plt.figure()
+    #plt.errorbar(PotenciasUsadas+[92.7],depths,yerr=errordepths,fmt='o',capsize=3)
+    plt.errorbar(PotenciasUsadas,depths[:-1],yerr=errordepths[:-1],fmt='o',capsize=3)
+
+#%%
+
+# poptcurv, pcovcurv = curve_fit(SaturationCurve_detzero,PotenciasUsadas,depths[:-1],p0=(10,1e-3, -2))
+
+plong = np.arange(0,90,0.1)
+
+ms = 7
+cs = 3
+fs = 10
+
+plt.figure(figsize=(3.,3))
+plt.errorbar(PotenciasUsadas,depths[:-1],yerr=errordepths[:-1],fmt='o',color='black',capsize=cs,markersize=ms)
+plt.xlabel(r'Potencia UV ($\mu$W)',fontsize=fs)
+plt.ylabel('Profundidad',fontsize=fs)
+plt.grid(alpha=0.5)
+plt.ylim(0, 0.28)
+plt.xlim(0,82)
+plt.tight_layout()
+# plt.savefig('C://Users//nicon//Nextcloud//Nico//Doctorado//Tesis//Tesis_doctorado//Chapters//figures//Cap9//fig2b.pdf')
+
+#%%
+import seaborn as sns
+
+paleta = sns.color_palette("flare", 5)
+
+k0, k1, k2, k3, k4 = 0, 1, 2, 3, 8
+
+c0, c1, c2, c3, c4 = 0, 1, 2, 3, 4
+
+Coun0 = [c for c in Counts[k0]]
+Coun1 = [c for c in Counts[k1]]
+Coun2 = [c for c in Counts[k2]]
+Coun3 = [c for c in Counts[k3]]
+Coun4 = [c for c in Counts[k4]]
+
+Coun4[98]=4200
+
+
+lwidth = 4
+ms = 4
+fs = 10
+
+plt.figure(figsize=(3.,3))
+plt.plot(Freqs,Coun0,'o', color=paleta[c0], alpha=0.5, markersize = ms)
+plt.plot(Freqs,Lorentzian(Freqs,*poptsvec[k0]), color=paleta[c1], linewidth=lwidth)
+
+plt.plot([f+0.6 for f in Freqs],Coun1,'o', color=paleta[c1], alpha=0.5, markersize = ms)
+plt.plot([f+0.6 for f in Freqs],Lorentzian(Freqs,*poptsvec[k1]), color=paleta[c1], linewidth=lwidth)
+
+plt.plot([f-0.1 for f in Freqs],Coun2,'o', color=paleta[c2], alpha=0.5, markersize = ms)
+plt.plot([f-0.1 for f in Freqs],Lorentzian(Freqs,*poptsvec[k2]), color=paleta[c2], linewidth=lwidth)
+
+plt.plot(Freqs,Coun3,'o', color=paleta[c3], alpha=0.5, markersize = ms)
+plt.plot(Freqs,Lorentzian(Freqs,*poptsvec[k3]), color=paleta[c3], linewidth=lwidth)
 
+plt.plot([f-0.3 for f in Freqs],Coun4,'o', color=paleta[c4], alpha=0.5, markersize = ms)
+plt.plot([f-0.3 for f in Freqs],Lorentzian(Freqs,*poptsvec[k4]), color=paleta[c4], linewidth=lwidth)
 
+plt.ylim(1800,4700)
+plt.xlim(779,789)
 
+plt.xlabel('Frecuencia mod IR1 (kHz)', fontsize=fs)
+plt.ylabel('Cuentas', fontsize=fs)
 
+plt.tight_layout()
 
+plt.grid(alpha=0.5)
+# plt.savefig('C://Users//nicon//Nextcloud//Nico//Doctorado//Tesis//Tesis_doctorado//Chapters//figures//Cap9//fig2c.pdf')
 

analisis/plots/20230523_transitorioshighpower/Transitorios_03.py

@@ -9,12 +9,14 @@ import os
 
 #os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230523_transitorioshighpower/')
 # Solo levanto algunos experimentos
+
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230523_transitorioshighpower')
+
 SP_files = [12221, 12222, 12223, 12224]
 
 Random_files = [8749]
 
-def expo(T, tau, N0, C):
-    global T0
+def expo(T, tau, N0, C, T0):
     return N0*np.exp(-(T-T0)/tau) + C
 
 def pow_from_amp(amp):
@@ -91,7 +93,7 @@ for Height in [SP_Heigths[3]]:
     print(Height)
     plt.plot(RefBins, Height,'o')
     
-plt.xlim(0.15, 0.5)
+# plt.xlim(0.15, 0.5)
 
 #%%
 
@@ -116,7 +118,7 @@ popt_vec = []
 pcov_vec = []
 
 plt.figure()
-for j in range(len(SP_Heigths)):
+for j in [3]:
 #for j in [12]:
     print(j)
     #BackgroundVector = SP_Bkgr_builder(np.mean(SP_Heigths[j][0:50]),np.mean(SP_Heigths[j][-50:]),59,67,965)
@@ -125,17 +127,16 @@ for j in range(len(SP_Heigths)):
     
     CorrectedSP_Height = SP_Heigths[j]
     
-    if j==3:
-        popt, pcov = curve_fit(expo, RefBins[90:], CorrectedSP_Height[90:], p0=(5, 1000, 100))
-        popt_vec.append(popt)
-        pcov_vec.append(pcov)
-        plt.plot(RefBins, CorrectedSP_Height)
-        plt.plot(RefBins[90:], [expo(r, *popt) for r in RefBins][90:])
-        Taus.append(popt[0])
-        Amps.append(popt[1])
-        Offsets.append(popt[2])
-        ErrorTaus.append(np.sqrt(pcov)[0][0])
-        print(Taus)        
+    popt, pcov = curve_fit(expo, RefBins[700:], CorrectedSP_Height[700:], p0=(1e-2, 10000, 1, 1))
+    popt_vec.append(popt)
+    pcov_vec.append(pcov)
+    plt.plot(RefBins, CorrectedSP_Height)
+    plt.plot(RefBins[700:], [expo(r, *popt) for r in RefBins][700:])
+    Taus.append(popt[0])
+    Amps.append(popt[1])
+    Offsets.append(popt[2])
+    ErrorTaus.append(np.sqrt(pcov[0,0]))
+    print(Taus)        
 #%%
 plt.figure()
 plt.plot(UVpotVec[:-5], Taus[:-6],'o', markersize=10, color='purple')
@@ -164,13 +165,6 @@ FIGURA PAPER SP CON AJUSTES
 import matplotlib
 import seaborn as sns
 
-matplotlib.rcParams['mathtext.fontset'] = 'stix'
-matplotlib.rcParams['font.family'] = 'STIXGeneral'
-plt.style.use('seaborn-bright')
-plt.rcParams.update({
-    "text.usetex": False,
-})
-
 plt.figure()
 plt.plot(Stat_Bins[0][:-1], Stat_Heigths[0])
 
@@ -270,12 +264,7 @@ FIGURA PAPER
 import matplotlib
 import seaborn as sns
 
-matplotlib.rcParams['mathtext.fontset'] = 'stix'
-matplotlib.rcParams['font.family'] = 'STIXGeneral'
-plt.style.use('seaborn-bright')
-plt.rcParams.update({
-    "text.usetex": False,
-})
+
 
 jselected = [1, 5, 12]
 

analisis/plots/20230609_LocalizedSaturationInCrystal/CPT_plotter_20230510.py

@@ -12,8 +12,9 @@ from scipy import interpolate
 
 #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/20230609_LocalizedSaturationInCrystal/Data/')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230609_LocalizedSaturationInCrystal/Data/')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230609_LocalizedSaturationInCrystal//Data')
 
 MOTIONAL_FILES = """000012579-MeltingExperiment
 000012580-MeltingExperiment

analisis/plots/20230616_newUVspectra/UV_spectrums_new.py

@@ -19,7 +19,7 @@ Calib_Files = """000012744-UV_Scan_withcalib_Haeffner
 000012754-UV_Scan_withcalib_Haeffner
 000012755-UV_Scan_withcalib_Haeffner""" 
 
-
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230616_newUVspectra//Data')
 
 #carpeta pc nico labo escritorio:
 #C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220503_EspectrosUVnuevos\Data

analisis/plots/20230707_MotionalSpectrum_v2/MotionalOptic.py

@@ -12,8 +12,8 @@ from scipy import interpolate
 
 #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/20230707_MotionalSpectrum_v2/Data/')
-
+# os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230707_MotionalSpectrum_v2/Data/')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230707_MotionalSpectrum_v2//Data')
 
 MOTIONAL_FILES = """
 000013002-AD9910RAM_andor
@@ -49,7 +49,7 @@ for i, fname in enumerate(MOTIONAL_FILES.split()):
 
 Potencias_IR = [0,20,50]
 
-#%%
+    #%%
 
 """
 Ploteo una curva para buscar su minimo

analisis/plots/20230714_EspectrosCristal4iones/Espectros_cristal.py

@@ -12,8 +12,9 @@ from scipy import interpolate
 
 #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/20230714_EspectrosCristal4iones/Data/')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230714_EspectrosCristal4iones/Data/')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230714_EspectrosCristal4iones//Data')
 
 MOTIONAL_FILES = """000013292-UV_Scan_withcal_optimized_andor
 000013293-UV_Scan_withcal_optimized_andor

analisis/plots/20230720_EspectrosCristal2iones/Espectroscpt_cristal.py

@@ -12,8 +12,9 @@ from scipy import interpolate
 
 #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/20230720_EspectrosCristal2iones/Data/')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230720_EspectrosCristal2iones/Data/')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230720_EspectrosCristal2iones//Data')
 
 MOTIONAL_FILES = """000013489-IR_Scan_withcal_optimized_andor
 000013490-IR_Scan_withcal_optimized_andor
@@ -132,7 +133,7 @@ plt.legend()
 
 
 #%%
-#from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels_MM, GenerateNoisyCPT_MM_fit
+# from EITfit.MM_eightLevel_2repumps_AnalysisFunctions import PerformExperiment_8levels_MM, GenerateNoisyCPT_MM_fit
 from scipy.optimize import curve_fit
 from time import time as titi
 

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_CPT2_lolo.py

@@ -16,7 +16,6 @@ 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
 
@@ -24,7 +23,7 @@ from glob import glob
 
 
 os.chdir('Data')
-
+#%%
 
 
 """

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_location.py

@@ -14,6 +14,7 @@ from scipy import interpolate
 
 #os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230804_RotationalDopplerShift_v2//Data')
 
 
 """

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_temperature.py

@@ -12,8 +12,9 @@ from scipy import interpolate
 
 #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('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
 
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230804_RotationalDopplerShift_v2//Data')
 
 
 """

analisis/plots/20230815_RotationalDopplerShift_v3/RDS_location2_lolo.py

@@ -325,12 +325,14 @@ fig.suptitle("Configuracion +2/-2 colineales, ion al costado del haz  dcA")
 # for kk,vv in data['datasets'].items():
 #     print(f'{kk}:\t',vv)
 
-
+"""
+FIGURA TESIS PARTE 1
+"""
 
 from scipy.signal import savgol_filter as savgol
 
 # Grafico con subplots
-fig,axx = plt.subplots(1,2,figsize=(12,8), constrained_layout=True , sharey=True )
+fig,axx = plt.subplots(1,2,figsize=(6.5,3.2), constrained_layout=True , sharey=True )
 fig.set_constrained_layout_pads(w_pad=1/72, h_pad=0, hspace=0, wspace=0)
 
 
@@ -379,8 +381,8 @@ for data in sorted([ h5py.File(DataFile, 'r') for DataFile in DATAFILES_costado_
     
     CURVA       = (cuentas/base-1)/5 + compOven
     CURVA_SUAVE = (cuentas_suave/base-1)/5+ compOven
-    ax.plot(frecuencia,CURVA      , '.', alpha=0.1)
-    ax.plot(frecuencia,CURVA_SUAVE, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
+    ax.plot([f-440 for f in frecuencia],CURVA      , '.', alpha=0.1)
+    ax.plot([f-440 for f in frecuencia],CURVA_SUAVE, '-', alpha=0.9, color=ax.get_lines()[-1].get_color())
     
     
     
@@ -389,7 +391,7 @@ for data in sorted([ h5py.File(DataFile, 'r') for DataFile in DATAFILES_costado_
         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)/5+compOven, 'o', ms=6, color=ax.get_lines()[-1].get_color(),zorder=10)
+        ax.plot([frecuencia[idx]-440],(cuentas_suave[idx]/base[idx]-1)/5+compOven, 'o', ms=6, color=ax.get_lines()[-1].get_color(),zorder=10)
         
         Base = np.polyval(mm,rango.mean())
         val_pico = cuentas_suave[I].min()
@@ -398,14 +400,14 @@ for data in sorted([ h5py.File(DataFile, 'r') for DataFile in DATAFILES_costado_
     print('')
     
 
-ax.legend()
-
+# ax.legend()
+ax.set_xlim(-9,9)
 
 
 ax = axx[1]
 
-ax.plot(  picos[0] , compOven_vec , '.')
-ax.plot(  picos[1] , compOven_vec , 'o' )
+# ax.plot(  picos[0] , compOven_vec , '.')
+# ax.plot(  picos[1] , compOven_vec , 'o' )
 
 
 
@@ -414,27 +416,29 @@ profundidad_dev  = [ np.array(np.array(picos[0])[compOven_vec==val].tolist()+np.
 compOven_uniq         = np.unique(compOven_vec)
 
 
-ax.plot(  profundidad_mean , compOven_uniq , 'o-', color='gray' , alpha=0.5 )
-ax.errorbar(  profundidad_mean , compOven_uniq , xerr=profundidad_dev , color='gray' ,capsize=5)
+ax.plot(  profundidad_mean , compOven_uniq , 'o-', color='black' , alpha=1 )
+ax.errorbar(  profundidad_mean , compOven_uniq , xerr=profundidad_dev , color='black' ,capsize=3)
 
 
-ax.set_xlabel('Profundidad')
+ax.set_xlabel('Profundidad media de resonancias')
 
-axx[0].set_ylabel('compOven [V]')
-axx[0].set_xlabel('frecuencia [kHz]')
+axx[0].set_ylabel('Voltaje electrodo (V)', fontsize=10)
+axx[0].set_xlabel('Desintonía IR1 (MHz)',fontsize=10)
 
+axx[0].grid(alpha=0.5)
+axx[1].grid(alpha=0.5)
 
-
-for ax in axx:
-    ax.grid(b=True,linestyle=':',color='lightgray')
+# for ax in axx:
+#     ax.grid(b=True,linestyle=':',color='lightgray')
 
 
 ax.set_xlim( 0, ax.get_xlim()[1] )
 
-fig.suptitle("Configuracion +2/-2 colineales, ion al costado del haz  compOven")
+# fig.suptitle("Configuración +2/-2 colineales")
 
 # fig.savefig("config+2-2_colineal_ion_al_costado_compOven.png")
 
+plt.savefig('C://Users//nicon//Nextcloud//Nico//Doctorado//Tesis//Tesis_doctorado//Chapters//figures//Cap8//fig3a.pdf')
 
 
 
@@ -572,7 +576,7 @@ ax.set_xlim( 0, ax.get_xlim()[1] )
 
 fig.suptitle("Configuracion +2/-2 colineales, ion arriba del haz  dcA")
 
-fig.savefig("config+2-2_colineal_ion_ariba_dcA.png")
+# fig.savefig("config+2-2_colineal_ion_ariba_dcA.png")
 
 
 

analisis/plots/20231226_CPTconmicromocion4/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py

@@ -58,7 +58,7 @@ def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circulari
     i, j = 2, 4
     HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.cos(titadoppler)
     HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])   
-    
+        
     i, j = 3, 5
     HI[i-1, j-1] = -(rabP/2) * np.sin(titaprobe)*(np.cos(phiprobe)-1j*np.sin(phiprobe)*circularityprobe)
     HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
@@ -72,6 +72,7 @@ def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe, circulari
     HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
     
     
+    
     i, j = 4, 6
     HI[i-1, j-1] = -(rabP/np.sqrt(12)) * np.sin(titaprobe)*(np.cos(phiprobe)-1j*np.sin(phiprobe)*circularityprobe)
     HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])

analisis/plots/20240515_MotionalSpectrum_v3_pendientes/Data/EITfit/threeLevel_2repumps_CPTPlotter.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep  1 17:58:39 2020
+
+@author: oem
+"""
+
+
+
+import os
+import numpy as np
+#os.chdir('/home/oem/Nextcloud/G_liaf/liaf-TrampaAnular/Código General/EIT-CPT/Buenos Aires/Experiment Simulations/CPT scripts/Eight Level 2 repumps')
+from threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
+import matplotlib.pyplot as plt
+import time
+from threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
+
+#C:\Users\Usuario\Nextcloud\G_liaf\liaf-TrampaAnular\Código General\EIT-CPT\Buenos Aires\Experiment Simulations\CPT scripts\Eight Level 2 repumps
+
+ub = 9.27e-24
+h = 6.63e-34
+c = (ub/h)*1e-4 #en unidades de MHz/G
+
+#u = 1e6
+u = 33.5e6
+
+B = (u/(2*np.pi))/c
+
+
+#sg, sp = 0.6, 5  #parámetros de control, saturación del doppler y repump
+#rabG, rabP = sg*gPS, sp*gPD #frecuencias de rabi
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
+
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+DetDoppler = -36 #42
+DetRepumpVec = [DetDoppler+29.6]
+
+Tvec = [0.7] #temperatura en mK
+alpha = 0*(np.pi/180) #angulo entre los láseres
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+#Calculo las resonancias oscuras teóricas
+#ResonanciasTeoricas, DRPositivas = CalculoTeoricoDarkResonances_8levels(u/(2*np.pi*1e6), titadoppler, DetDoppler, DetRepump)
+
+#Parametros de la simulacion cpt
+center = -45
+span = 80
+freqMin = center-span*0.5
+freqMax = center+span*0.5
+
+""" parametros para tener espectros coherentes
+freqMin = -56
+freqMax = 14
+"""
+freqStep = 1e-1
+
+noiseamplitude = 0
+
+RelMinMedido0Vector = []
+RelMinMedido1Vector = []
+RelMinMedido2Vector = []
+RelMinMedido3Vector = []
+RelMinMedido4Vector = []
+
+#Sr = np.arange(0, 10, 0.2)
+
+#Sg = np.arange(0.01, 1, 0.05)
+#Sp = np.arange(0.1, 6.1, 1)
+
+#Sg = [0.6**2]
+#Sp = [2.3**2]
+
+
+Sg = [1.4]
+Sp = [6]
+
+Sr = [11]
+
+
+
+
+i = 0
+
+save = False
+
+
+showFigures = True
+
+if not showFigures:
+    plt.ioff()
+else:
+    plt.ion()
+    
+fig1, ax1 = plt.subplots()
+
+offsetx = 464
+
+ax1.plot([f-offsetx for f in FreqsDR], CountsDR, 'o')
+
+run = True
+
+Scale = 730
+Offset = 600 #600 para 20k cuentas aprox
+
+MaxCoherenceValue = []
+
+for sg in Sg:
+    for sp in Sp:
+        rabG, rabP = sg*gPS, sp*gPD
+        for Ti in Tvec:
+            T = Ti*1e-3
+            for DetRepump in DetRepumpVec:
+                print(T)
+                for sr in Sr:
+                
+                    rabR = sr*gPD
+                    
+                    #MeasuredFreq, MeasuredFluo = GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+                    if run:
+                        MeasuredFreq4, MeasuredFluo4 = GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+                    
+                    #SmoothFluo = SmoothNoisyCPT(MeasuredFluo, window=9, poly=2)
+                    SmoothFluo4 = MeasuredFluo4
+                    
+                    #Scale = max(BestC)/max([100*s for s in SmoothFluo4])
+                    ax1.plot(MeasuredFreq4, [Scale*100*f + Offset for f in SmoothFluo4], label=f'Sr = {sr}')
+                    ax1.axvline(DetDoppler, linestyle='--', linewidth=1)
+                    #if sr != 0:
+                        #ax1.axvline(DetRepump, linestyle='--', linewidth=1)
+
+                    MaxCoherenceValue.append(np.max(SmoothFluo4))
+
+                    #print(titaprobe)
+                    ax1.set_xlabel('Detuning Rebombeo (MHz)')
+                    ax1.set_ylabel('Fluorescencia (AU)')
+                    ax1.set_title(f'B: {round(B, 2)} G, Sdop: {round(sg, 2)}, Sp: {round(sp, 2)}, Sr: {round(sr, 2)}, lw: {lw} MHz, T: {Ti} mK')
+                    #ax1.set_ylim(0, 8)
+                    #ax1.axvline(DetDoppler, linestyle='dashed', color='red', linewidth=1)
+                    #ax1.axvline(DetRepump, linestyle='dashed', color='black', linewidth=1)
+                    #ax1.set_title('Pol Doppler y Repump: Sigma+ Sigma-, Pol Probe: PI')
+                    #ax1.legend()
+                    ax1.grid()
+                    print (f'{i+1}/{len(Sg)*len(Sp)}')
+                    i = i + 1
+                    if save:
+                        plt.savefig(f'Mapa_plots_100k_1mk/CPT_SMSM_sdop{round(sg, 2)}_sp{round(sp, 2)}_sr{round(sr, 2)}.jpg')
+                    ax1.legend()
+                    
+"""
+plt.figure()
+plt.plot(Sr, MaxCoherenceValue, 'o')
+plt.xlabel('Sr')
+plt.ylabel('Coherence')
+"""
+
+"""
+                        
+plt.figure()
+plt.plot(MeasuredFreq, [100*f for f in SmoothFluo], color='darkred')
+plt.xlabel('Desintonía 866 (MHz)')
+plt.ylabel('Fluorescencia (A.U.)')
+plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
+plt.yticks(np.arange(0.4, 1.8, 0.2))
+plt.ylim(0.5, 1.6)
+plt.grid()
+              
+
+
+plt.figure()
+plt.plot(MeasuredFreq4, [100*f for f in SmoothFluo4], color='darkred')
+plt.xlabel('Desintonía 866 (MHz)')
+plt.ylabel('Fluorescencia (A.U.)')
+plt.axvline(-30, color='darkblue', linewidth=1.2, linestyle='--')
+plt.yticks(np.arange(0.8, 2.4, 0.4))
+plt.grid()
+              
+"""
+
+
+
+
+#%%
+from scipy.optimize import curve_fit
+
+T = 0.5e-3
+
+sg = 0.7
+sp = 6
+sr = 0
+DetDoppler = -14
+DetRepump = 0
+
+FitsSp = []
+FitsOffset = []
+
+Sg = [0.87]
+
+def FitEIT(freqs, SP, offset):
+    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(0.87, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
+    FinalFluo = [f*43000 + 2685 for f in MeasuredFluo]
+    return FinalFluo
+
+freqs = [f-offsetx+32 for f in FreqsDR]
+freqslong = np.arange(min(freqs), max(freqs)+freqs[1]-freqs[0], 0.1*(freqs[1]-freqs[0]))
+
+popt, pcov = curve_fit(FitEIT, freqs, CountsDR, p0=[5, 700], bounds=(0, [10, 1e6]))
+
+FitsSp.append(popt[0])
+FitsOffset.append(popt[1])
+
+print(popt)
+
+FittedEIT = FitEIT(freqslong, *popt)
+
+
+plt.figure()
+plt.errorbar(freqs, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', capsize=2, markersize=2)
+plt.plot(freqslong, FitEIT(freqslong, *popt))
+plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {T*1e3} mK, detDop: {DetDoppler} MHz')
+
+np.savetxt('CPT_measured.txt', np.transpose([freqs, CountsDR]))
+np.savetxt('CPT_fitted.txt', np.transpose([freqslong, FittedEIT]))

analisis/plots/20240515_MotionalSpectrum_v3_pendientes/MotionalOptic.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
+
+#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/20230707_MotionalSpectrum_v2/Data/')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20240515_MotionalSpectrum_v3_pendientes//Data')
+
+MOTIONAL_FILES = """
+000013002-AD9910RAM_andor
+000013003-AD9910RAM_andor
+000013004-AD9910RAM_andor
+"""
+
+
+def SeeKeys(files):
+    for i, fname in enumerate(files.split()):
+        data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+print(SeeKeys(MOTIONAL_FILES))
+
+#%%
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts_roi1 = []
+Counts_roi2 = []
+RealFreqs = []
+IR1_amp_vec = []
+
+for i, fname in enumerate(MOTIONAL_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    RealFreqs.append(np.array(data['datasets']['real_freq']))
+    Counts_roi1.append(np.array(data['datasets']['counts_roi1']))
+    Counts_roi2.append(np.array(data['datasets']['counts_roi2']))
+    IR1_amp_vec.append(np.array(data['datasets']['IR1_amp']))
+
+Potencias_IR = [0,20,50]
+
+    #%%
+
+"""
+Ploteo una curva para buscar su minimo
+"""
+
+jvec = [2,1,0]
+
+plt.figure()
+i = 0
+
+kmin = 106
+
+for j in jvec:
+    plt.errorbar([1*f*1e-3 for f in RealFreqs[j]], Counts_roi1[j], yerr=0.1*np.sqrt(Counts_roi1[j]), fmt='o', capsize=2, markersize=2, label=f'IR1 power: {Potencias_IR[j]} uW')
+    #plt.plot([1*f*1e-3 for f in RealFreqs[j]][kmin], Counts[j][kmin], 'o', markersize=15)
+    i = i + 1
+plt.xlabel('Frecuencia mod IR2 (kHz)')
+plt.ylabel('Cuentas/400 ms')
+plt.xlim(780,810)
+plt.ylim(18680,19650)
+plt.grid()
+plt.legend(loc='lower left')
+
+
+