Merge branch 'master' of https://code.df.uba.ar/nnunez/artiq_experiments

analisis/plots/20221006_transitoriosv2/Simulaciones/plot_decaytimes_vs_intensity_detunings.py

@@ -11,7 +11,7 @@ I_sat = 43.3*1e-5 #uW/um2
 #%% #####################################################################################
 ### Plot de lineas teoricas tau-intensidad variando detuning + datos medidos
 
-fig2, ax2 = plt.subplots()
+fig2, ax2 = plt.subplots(figsize=(4,3))
 detuning_lines = ['Det10', 'Det20', 'Det30', 'Det40', 'Det50', 'Det60', 'Det70', 'Det80', 'Det90']
 
 # Curvas de las simulaciones con tau en unidades de microseg
@@ -22,11 +22,15 @@ for det in detuning_lines:
 
 for cambio in [0]: # esto es para shiftear la potencia medida
     potencias = dataREAL.Pow - cambio
-    for rad in [85.7]: # esto evalua con distintos radios
+    for rad in [85.7*1.5]: # esto evalua con distintos radios
         ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus, yerr=np.mean(1e6*errsSIM.stdval.values),
                     fmt='.--', ms=6, lw=.5, \
                     color="#3949ab",
-                    capsize=3)
+                    capsize=2)
+        ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec][4:], Taus_v2[4:], yerr=np.mean(1e6*errsSIM.stdval.values),
+                    fmt='.--', ms=6, lw=.5, \
+                    color="#3949ab",
+                    capsize=2)
     
     
         #ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \
@@ -36,13 +40,19 @@ for cambio in [0]: # esto es para shiftear la potencia medida
         #             capsize=3,
         #             label=f'r={rad}; cambio={cambio}')
 
-ax2.set_xlim([1.2e-4, 0.2e-1])
-ax2.set_ylim([2e-1, 0.8e1])
-ax2.set_ylabel(r"$\tau$ [$\mu$s]")
+fs = 9
+
+ax2.set_xlim([0.47e-4, 0.8e-2])
+ax2.set_ylim([2e-1, 4e1])
+ax2.set_ylabel(r"Characteristic time ($\mu$s)", fontname='STIXGeneral')
 #ax2.set_xlabel(r"$I = P / (\pi\,r^2)$ [$\mu$W/$\mu$m$^2$]")
-ax2.set_xlabel(r'UV intensity ($\mu$W/$\mu$m$^2$)')
+ax2.set_xlabel(r'UV intensity ($\mu$W/$\mu$m$^2$)', fontname='STIXGeneral')
 # ax2.set_title(r"datos(punteadas) $r \in [30; 190]\ \mu m$   |   simulacion(llenas) $\Delta \in -[10; 90]$ MHz")
 #ax2.legend(markerscale=2)
+ax2.set_xticks([1e-4, 1e-3])
+ax2.set_xticklabels([1e-4, 1e-3], fontsize=fs, fontname='STIXGeneral')
+ax2.set_yticks([1e0, 1e1])
+ax2.set_yticklabels([1e0, 1e1], fontsize=fs, fontname='STIXGeneral')
 ax2.grid(which='minor', alpha=0.2)
 ax2.set_xscale("log")
 ax2.set_yscale("log")

analisis/plots/20221017_IonStatistics/IonStatistics.py

@@ -9,11 +9,8 @@ import os
 import scipy.stats as sts
 import seaborn as sns
 
-colors1=sns.color_palette("rocket", 10)
-colors2=sns.color_palette("mako", 10)
-color1 = colors1[5]
-color2 = colors1[3]
-color3 = colors1[1]
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221017_IonStatistics')
 
 plt.rcParams.update({
     "font.family": "STIXGeneral"
@@ -22,7 +19,7 @@ plt.rcParams.update({
     "font.size": 14
 })
 # Solo levanto algunos experimentos
-Stat_files = [8731, 8738, 8745]
+Stat_files = [8731, 8738, 8745, 8819, 8820, 8822]
 
 def expo(T, tau, N0, C):
     global T0
@@ -50,7 +47,10 @@ T0 = 0.0e-6
 Stat_Heigths = []
 Stat_Bins = []
 
-for i, fname in enumerate(Stat_files):
+OnOff_Heights = []
+OnOff_Bins = []
+
+for i, fname in enumerate(Stat_files[0:3]):
     #print(i)
     #print(fname)
     data = h5py.File('Data/00000'+str(fname)+'-IonStatistics.h5', 'r')
@@ -62,42 +62,125 @@ for i, fname in enumerate(Stat_files):
     Stat_Heigths.append(heigs)
     Stat_Bins.append(binsf)
     
+for i, fname in enumerate(Stat_files[3:6]):
+    #print(i)
+    #print(fname)
+    try:
+        data = h5py.File('Data/00000'+str(fname)+'-SingleLine.h5', 'r')
+    except:
+        data = h5py.File('Data/00000'+str(fname)+'-StationaryFluo.h5', 'r')
+    counts = np.array(data['datasets']['counts'])
+    bines = np.arange(counts.min(), counts.max()+BINW, BINW)
+
+    heigs, binsf  = np.histogram(counts, bines[bines>T0])
+    
+    OnOff_Heights.append(heigs)
+    OnOff_Bins.append(binsf)
+
+    
 #%%
+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])
 
 #plot figuras papers
 
-bins1 = np.arange(100,350, 1)
-bins2 = np.arange(10,50,1)
+colors1=sns.color_palette("rocket", 10)
+colors2=sns.color_palette("mako", 10)
+color2 = colors2[1]
+color3 = colors2[4]
+color1 = colors2[8]
+
+bins1 = np.arange(150,300, 1)
+bins2 = np.arange(0,50,1)
 bins3 = np.arange(30,100,1)
 
-plt.figure(figsize = (4.5,3))
-plt.hist(Stat_Heigths[0], bins=bins1, histtype='step',density = True,color = color1)#,label = 'BG')
-#plt.hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True)
-plt.hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True,color = color2)#,label = 'UV laser')
-#plt.hist(Stat_Heigths[3], bins=bins2, histtype='step',density = True)
-#plt.hist(Stat_Heigths[4], bins=bins2, histtype='step',density = True)
-plt.hist(Stat_Heigths[2], bins=bins3, histtype='step',density = True,color = color3)#,label = 'Ion')
+
+
+
+"""
+FIGURA ESTADISTICA POISSON
+"""
+
+plt.figure(figsize = (3.8,2.8))
+plt.hist(Stat_Heigths[0], bins=bins1, histtype='step',density = True,color = color1, alpha = 0.6)#,label = 'BG')
+plt.hist(Stat_Heigths[1], bins=bins2, histtype='step',density = True,color = color2, alpha = 0.6)#,label = 'UV laser')
+plt.hist(Stat_Heigths[2], bins=bins3, histtype='step',density = True,color = color3, alpha = 0.6)#,label = 'Ion')
 
 
 poisson1= sts.poisson.pmf(bins1,np.mean(Stat_Heigths[0]))
 poisson2= sts.poisson.pmf(bins2,np.mean(Stat_Heigths[1]))
 poisson3= sts.poisson.pmf(bins3,np.mean(Stat_Heigths[2]))
 
-
-plt.plot(bins2+0.5,poisson2,color = color2,alpha = 0.9,label = 'BG')
-plt.plot(bins3+0.5,poisson3,color = color3,alpha = 0.9,label = 'UV laser')
-plt.plot(bins1+0.5,poisson1,color = color1,alpha = 0.9,label = 'Ion')
-plt.legend()
+plt.plot(bins2+0.5,poisson2,color = color2,label = 'Background')
+plt.plot(bins3+0.5,poisson3,color = color3,label = 'UV laser')
+plt.plot(bins1+0.5,poisson1,color = color1,label = 'Ion')
+plt.legend(loc=(0.15,0.65), prop={'size': 11})
 plt.grid()
 plt.tight_layout()
-plt.xlabel('Counts')
-plt.ylabel('Event frequency')
+plt.xticks([0, 100, 200, 300], fontname='STIXGeneral')
+plt.yticks([0,0.02, 0.04, 0.06, 0.08], fontname='STIXGeneral')
+plt.xlabel('Counts', fontname='STIXGeneral')
+plt.ylabel('Event frequency', fontname='STIXGeneral')
 
 #poissoneidad =  np.var(Stat_Heigths)/np.mean(Stat_Heigths)
 #plt.title('Varianza/media = {:.4f}'.format(poissoneidad))
-plt.savefig('bg_laser_ion_stats.pdf',dpi = 600 )
+#plt.savefig('bg_laser_ion_stats.pdf',dpi = 600 )
+
+name='fig01a'
+
+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+'.svg')
+
+
+
+#%%
+import matplotlib
+
+matplotlib.rcParams['mathtext.fontset'] = 'stix'
+matplotlib.rcParams['font.family'] = 'STIXGeneral'
+plt.style.use('seaborn-bright')
+plt.rcParams.update({
+    "text.usetex": False,
+})
+
+"""
+FIGURA BACKGROUND, ENCENDIDO AOM Y ENCENDIDO ION
+"""
+
+plt.figure(figsize=(4.5,3))
+plt.plot([s*1e6 for s in OnOff_Bins[1][:-1]],OnOff_Heights[1], color=color2)
+plt.plot([s*1e6 for s in OnOff_Bins[0][:-1]],[(OnOff_Heights[0][j]-OnOff_Heights[1][j])*3.13+OnOff_Heights[1][j] for j in range(len(OnOff_Heights[0]))], color=color3)
+plt.plot([s*1e6 for s in OnOff_Bins[2][:-1]],OnOff_Heights[2], color=color1)
+plt.xlim(0,1)
+plt.grid()
+plt.xlabel(r'Time ($\mu$s)', fontname='STIXGeneral')
+plt.ylabel(r'Counts /10$~\mu$s', fontname='STIXGeneral')
+plt.xticks([0,0.2, 0.4, 0.6, 0.8, 1], fontname='STIXGeneral')
+plt.yticks([0,5000, 10000], fontname='STIXGeneral')
+plt.tight_layout()
+
+name='fig01b'
+
+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+'.svg')
+
+
+
+
+
+
+
 
 

analisis/plots/20221024_BranchingFractionMeasurement/Simulaciones/OBE3levels_chtime_fitting.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import numpy as np
+import matplotlib.pyplot as plt
+from OBE3levels_functions import *
+from scipy.optimize import curve_fit
+from scipy.stats import poisson, norm
+import os
+import sys
+plt.ion()
+"""
+Este código simula la dinámica temporal de un ion de calcio sometido a un láser UV y dos láseres IR.
+
+"""
+def expo(T, tau, N0, C):
+    #global T0
+    return N0*np.exp(-(T-0e-6)/tau) + C
+
+ADD_NOISE = True
+NOISE_VAR = 30 # Varianza del ruido
+NOISE_ADJ = 78 # Escaleo del ruido versus el maximo
+NOISE_MEAN = 0 # Media del ruido
+NRO_REPS = 100 # Veces a promediar
+
+species = 'Calcium'
+#Parámetros de saturacion de los láseres rebombeo (IR), que son proporcionales a la intensidad de los mismos
+#Para reproducir SP, hay que poner 0 a los dos
+SatParRep1, SatParRep2 = 0, 0
+
+sat_intensity = 43.3e-5 # uW um-2
+g21 = 135591138.92893547
+
+#El número de los láseres IR aplicados en el experimento. Para el experimento de la transicion SP, es 0.
+Number_IR_lasers = 0
+
+#Detunings de los láseres en MHz. Si es 0, están en resonancia. En principio
+#podría ser un parámetro libre a ajustar porque influye mucho
+DetDoppler = -int(sys.argv[1])
+DetRep2 = 0
+DetRep1 = 0
+
+#Anchos de línea de los láseres, en MHz. No son tan cruciales, están entre 0.1 y 0.5 MHz
+DopplerLaserLinewidth, RepumpLaserLinewidth = 1e3, 1
+
+# Para trabajar con las curvas teóricas:
+# range_inicio = np.arange(sat_intensity/10, sat_intensity, sat_intensity/50)
+# range_fin = np.arange(sat_intensity, 30*sat_intensity, sat_intensity/5)
+# laser_intensities = np.append(range_inicio, range_fin)
+
+# Para trabajar con nuestros datos, saco las intensidades, a un radio de 100um:
+laser_intensities = np.array([0.00662085, 0.00582507, 0.0049338, 0.00397887, 0.00305577, 0.00226, 0.00155972, 0.00101859, 0.00060479, 0.00031831])
+
+
+OmegaRabi2 = (g21**2) * (laser_intensities / sat_intensity)
+SatParDopplerVec = OmegaRabi2 / ((DetDoppler*2*np.pi*1e6)**2 + g21**2)
+
+#Parámetros de la simulación, en us
+tfinOBE = 1
+pasoOBE = 1e-5
+
+#Estado electrónico inicial de la simulacion. Posibilidades: |S> = 1, |P> = 2, |D> = 3
+initial_state = 1
+
+#Corre y simula la dinámica. Devuelve elementos de matriz densidad
+# fig1, ax1 = plt.subplots()
+
+alltaus = list()
+allN0 = list()
+allmeans = list()
+allstds = list()
+
+temporal_taus = list()
+temporal_N0 = list()
+
+for i, SatParDoppler in enumerate(SatParDopplerVec):
+    t, RealRho11, RealRho22, RealRho33, AbsRho12, AbsRho13, AbsRho23 = Solve3LevelBloch(EquationSystemModel, species, SatParDoppler, SatParRep1, SatParRep2, DetDoppler, DetRep1, DetRep2, DopplerLaserLinewidth, RepumpLaserLinewidth, no_IR_lasers=Number_IR_lasers, initcond=initial_state, tfin=tfinOBE, paso=pasoOBE, printprogress=False)
+
+    k = int(0.1*len(t)) #parametro para que fitee solo la parte final
+    k = np.argmin(np.abs(t - 0.2e-6))
+
+    temporal_taus = list()
+    incert_estad = 0
+
+    for nro_rep in range(NRO_REPS):
+        poblacion_fit = RealRho22[k:].copy()
+        if ADD_NOISE:
+            # Genero ruido similar al medido
+            ruido_poisson = poisson.rvs(NOISE_VAR, size=len(poblacion_fit))
+            # Reescaleo el ruido para que sea compatible con las simulaciones
+            ruido_poisson *= np.max(poblacion_fit)/NOISE_ADJ
+
+            poblacion_fit += ruido_poisson
+            # poblacion_fit += norm.rvs(0, NOISE_VAR, size=len(poblacion_fit))*np.max(poblacion_fit)/NOISE_ADJ
+            poblacion_fit[np.where(poblacion_fit < 0 )] = 0 # Limpio posibles valores negativos
+
+        t_fit = t[k+1:]
+
+        popt, pcov = curve_fit(expo, t_fit, poblacion_fit, p0=(1e-6, 1e-2, 1e-2))
+
+        temporal_taus.append(popt[0])
+        incert_estad += np.sqrt(pcov[0,0])
+        # temporal_N0.append(popt[1])
+        if not ADD_NOISE: # si no quiere que agregue ruido, entonces solo hace el for una vez
+            break
+
+        print(f" {nro_rep}  ", end='\r')
+
+    if ADD_NOISE:
+        allmeans.append(np.mean(temporal_taus))
+        allstds.append(np.std(temporal_taus) + incert_estad/NRO_REPS)
+    else:
+        allmeans.append(np.mean(temporal_taus))
+
+
+    #ploteo en funcion de 100*Rho22 para que me devuelva en porcentaje la población del excitado que es proporcional a la fluorescencia detectada
+    # lbl='Detuning Rep1:' + str(DetRep1) + ' MHz'
+    # ax1.plot(t[:-1]*1e6, RealRho22, '.', label=lbl, markersize=1)
+    # ax1.plot(t[:-1]*1e6, 100*RealRho22, '.', label=lbl, markersize=1)
+    # ax1.plot(t_fit*1e6, 100*expo(t_fit, *popt))
+    print(f'({i:02d}/{len(SatParDopplerVec)}) Done {SatParDoppler}'.ljust(50, ' '))
+
+# ax1.legend(SatParDopplerVec)
+# ax1.set_xlabel('Time (us)')
+# ax1.set_ylabel('100*Rho22')
+
+#%% #####################################################################################
+### Guardo valores al CSV para analizar luego
+# nDet=f"Det{np.abs(DetDoppler)}"
+# CSV_FNAME = f"error_teorico_tau_{nDet}.csv"
+# try:
+#     import pandas as pd
+#     data = pd.read_csv(CSV_FNAME)
+#     data["meanval"] = allmeans
+#     data["stdval"] = allstds
+#     data.to_csv(CSV_FNAME, index=False)
+#     print(f"SAVED {nDet}".ljust(50, ' '))
+# except FileNotFoundError:
+#     data = pd.DataFrame({'I': laser_intensities, "meanval": allmeans, "stdval": allstds})
+#     data.to_csv(CSV_FNAME, index=False)
+#     print(f"CREATED CSV FILE".ljust(50, ' '))
+#     print(f"SAVED DETUNING {-DetDoppler}".ljust(50, ' '))
+
+#%% ####################################################################################
+### Plots de los valores de intensidades medidas para distintos parametros
+### Cada axes arma un eje horizontal distinto, es para comparar las posibilidades
+
+# fig4, ax4 = plt.subplots()
+# allmeans = [t*1e6 for t in allmeans]
+
+# ax4b = ax4.twinx()
+# ax4.plot(SatParDopplerVec, allmeans, '-o', lw=0.4)
+# ax4b.plot(SatParDopplerVec, allN0, 'k-^', lw=0.4)
+# ax4.set_xlabel("Parametro de saturación")
+# ax4.set_ylabel("Tau (circulo)")
+# ax4b.set_ylabel("Alturas (triang)")
+# ax4.set_title(f"SP; IR_Lasers=0; DetuningDoppler={DetDoppler}; LineWidth={DopplerLaserLinewidth}")
+
+# ax4by = ax4.twiny()
+# ax4by.plot(laser_intensities, allmeans, '-', lw=0)
+# ax4by.xaxis.set_label_position('bottom')
+# ax4by.xaxis.tick_bottom()
+# ax4by.spines['bottom'].set_position(("axes", -0.27))
+# ax4by.set_xlabel(r"Intensidad [ $\mu$W/$\mu$m$^2$ ]")
+
+# ax4cy = ax4.twiny()
+# ax4cy.plot(laser_intensities*np.pi*(35**2), allmeans, '-', lw=0)
+# ax4cy.xaxis.set_label_position('bottom')
+# ax4cy.xaxis.tick_bottom()
+# ax4cy.spines['bottom'].set_position(("axes", -0.58))
+# ax4cy.set_xlabel(r"Potencia (r=35$\mu$m) [ $\mu$W ]")
+
+# ax4dy = ax4.twiny()
+# ax4dy.plot(laser_intensities*np.pi*(50**2), allmeans, '-', lw=0)
+# ax4dy.xaxis.set_label_position('bottom')
+# ax4dy.xaxis.tick_bottom()
+# ax4dy.spines['bottom'].set_position(("axes", -0.9))
+# ax4dy.set_xlabel(r"Potencia (r=50$\mu$m) [ $\mu$W ]")

analisis/plots/20221024_BranchingFractionMeasurement/Simulaciones/error_teorico_tau_det20.csv

+I,meanval,stdval
+0.00662085,2.63563456542463e-07,3.62795752e-08
+0.00582507,2.674966392578803e-07,3.99478821e-08
+0.0049338,2.7426723419290064e-07,3.66249293e-08
+0.00397887,2.8263714050526124e-07,3.83554920e-08
+0.00305577,2.9770905441813384e-07,3.90291265e-08
+0.00226,3.1786383950851434e-07,4.27893803e-08
+0.00155972,3.562791436115684e-07,4.67339215e-08
+0.00101859,4.198979464288562e-07,5.28900825e-08
+0.00060479,5.457916989936301e-07,6.95843326e-08
+0.00031831,8.275611779916097e-07,1.30369576e-07

analisis/plots/20221024_BranchingFractionMeasurement/Simulaciones/medidos_powers_taus.csv

+Pow,Tau,N0
+208.0,4.107136373675053e-07,242.3949275128121
+183.0,3.994370315889412e-07,255.700355179914
+155.0,4.4882562779198994e-07,226.84461233128172
+125.0,4.727871415748306e-07,217.84774878119222
+96.0,5.254285404397582e-07,222.57732196795308
+71.0,6.079817946589069e-07,189.5426628192835
+49.0,7.274218641328625e-07,168.69185328968453
+32.0,8.823248220314772e-07,140.40570782757854
+19.0,1.280491601444429e-06,90.5188484395718
+10.0,1.96934320559994e-06,60.092743064494314

analisis/plots/20221024_BranchingFractionMeasurement/Simulaciones/plot_decaytimes_vs_intensity_detunings.py

+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+#plt.ion()
+
+dataREAL = pd.read_csv("medidos_powers_taus.csv")
+dataSIM  = pd.read_csv("sim_intensidad_taus_full.csv")
+errsSIM  = pd.read_csv("error_teorico_tau_det20.csv")
+I_sat = 43.3*1e-5 #uW/um2
+
+#%% #####################################################################################
+### Plot de lineas teoricas tau-intensidad variando detuning + datos medidos
+
+fig2, ax2 = plt.subplots(figsize=(4,3))
+detuning_lines = ['Det10', 'Det20', 'Det30', 'Det40', 'Det50', 'Det60', 'Det70', 'Det80', 'Det90']
+
+# Curvas de las simulaciones con tau en unidades de microseg
+for det in detuning_lines:
+    ax2.plot(dataSIM.I, dataSIM[det]*1e6, 'k-', ms=1, lw=0.5)
+
+# Curvas de las mediciones cambiando el radio posible con tau en microseg
+
+for cambio in [0]: # esto es para shiftear la potencia medida
+    potencias = dataREAL.Pow - cambio
+    for rad in [85.7*1.5]: # esto evalua con distintos radios
+        ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus, yerr=np.mean(1e6*errsSIM.stdval.values),
+                    fmt='.--', ms=6, lw=.5, \
+                    color="#3949ab",
+                    capsize=2)
+        ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec][4:], Taus_v2[4:], yerr=np.mean(1e6*errsSIM.stdval.values),
+                    fmt='.--', ms=6, lw=.5, \
+                    color="#3949ab",
+                    capsize=2)
+    
+    
+        #ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \
+        #             yerr = 1e6*errsSIM.stdval.values, \
+        #             fmt='.--', ms=6, lw=.5, \
+        #             color="#3949ab",
+        #             capsize=3,
+        #             label=f'r={rad}; cambio={cambio}')
+
+fs = 9
+
+ax2.set_xlim([0.47e-4, 0.8e-2])
+ax2.set_ylim([2e-1, 4e1])
+ax2.set_ylabel(r"Characteristic time ($\mu$s)", fontname='STIXGeneral')
+#ax2.set_xlabel(r"$I = P / (\pi\,r^2)$ [$\mu$W/$\mu$m$^2$]")
+ax2.set_xlabel(r'UV intensity ($\mu$W/$\mu$m$^2$)', fontname='STIXGeneral')
+# ax2.set_title(r"datos(punteadas) $r \in [30; 190]\ \mu m$   |   simulacion(llenas) $\Delta \in -[10; 90]$ MHz")
+#ax2.legend(markerscale=2)
+ax2.set_xticks([1e-4, 1e-3])
+ax2.set_xticklabels([1e-4, 1e-3], fontsize=fs, fontname='STIXGeneral')
+ax2.set_yticks([1e0, 1e1])
+ax2.set_yticklabels([1e0, 1e1], fontsize=fs, fontname='STIXGeneral')
+ax2.grid(which='minor', alpha=0.2)
+ax2.set_xscale("log")
+ax2.set_yscale("log")
+
+

analisis/plots/20221024_BranchingFractionMeasurement/Transitorios_BF.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
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221024_BranchingFractionMeasurement')
+
+Long_files = ['DP_long', 'SP_long']
+Calib_files = ['Fondo_IR_50M', 'Fondo_UV_50M']
+
+def expo(T, tau, N0, C):
+    global T0
+    return N0*np.exp(-(T-T0)/tau) + C
+
+def pow_from_amp(amp):
+    """Paso de amplitud urukul a potencia medida por Nico"""
+    # Forma altamente ineficiente de hacer esto, pero me salio asi
+    amplitudes_UV = np.flip(np.array([0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30]))
+    assert amp in amplitudes_UV
+    potencias_UV = np.flip(np.array([4, 10, 19, 32, 49, 71, 96, 125, 155, 183, 208, 229]))
+    return potencias_UV[np.where(amplitudes_UV == amp)][0]
+
+
+def SP_Bkgr_builder(amp_in, amp_fin, derivadainicio, derivadafin, longbins):
+    CalibCurve = []
+    j=0
+    while j<longbins:
+        if j<=derivadainicio:
+            CalibCurve.append(amp_in)
+        elif j>=derivadainicio and j<=derivadafin:
+            pendiente=(amp_fin-amp_in)/(derivadafin-derivadainicio)
+            CalibCurve.append(amp_in+pendiente*(j-derivadainicio))
+        else:
+            CalibCurve.append(amp_fin)
+        j=j+1
+    return CalibCurve
+
+    
+
+"""
+plt.plot(amplitudes_UV, potencias_UV, 'ko-', lw=0.2)
+plt.xlabel("Amplitud Urukul")
+plt.ylabel("Potencia /uW")
+plt.grid()
+"""
+#%%
+
+BINW = 10e-9
+T0 = -0.4e-6
+
+
+BINW_long = 10e-9
+T0_long = -0.4e-6
+
+Long_Heigths = []
+Long_Bins = []
+
+for i, fname in enumerate(Long_files):
+    #print(i)
+    #print(fname)
+    data = h5py.File('Data/Largas/'+str(fname)+'.h5', 'r')
+    counts = np.array(data['datasets']['counts'])
+    bines = np.arange(counts.min(), counts.max()+BINW_long, BINW_long)
+
+    heigs, binsf  = np.histogram(counts, bines[bines>T0_long])
+    
+    Long_Heigths.append(heigs)
+    Long_Bins.append(binsf) 
+
+
+BINW_calib = 10e-9
+T0_calib = -0.4e-6
+
+Calib_Heigths = []
+Calib_Bins = []
+
+for i, fname in enumerate(Calib_files):
+    #print(i)
+    #print(fname)
+    data = h5py.File('Data/Calibrations/'+str(fname)+'.h5', 'r')
+    counts = np.array(data['datasets']['counts'])
+    bines = np.arange(counts.min(), counts.max()+BINW_calib, BINW_calib)
+
+    heigs, binsf  = np.histogram(counts, bines[bines>T0_calib])
+    
+    Calib_Heigths.append(heigs)
+    Calib_Bins.append(binsf) 
+
+
+
+#%%
+"""
+Vectores de amplitudes y potencias
+"""
+
+UVampVec = [0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26]
+UVpotVec = [4, 6, 12, 17, 26, 36, 48, 77, 112, 151, 190, 226, 255, 279]
+
+IRampVec = [0.10, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.2, 0.08]
+
+
+#%%
+
+RefBins = [t*1e6 for t in Long_Bins[0][:-1]]
+
+plt.figure()
+
+#for i in range(len(Long_Heigths)):
+#    plt.plot(Long_Bins[i][:-1], Long_Heigths[i])
+    
+for i in range(len(Calib_Heigths)):
+    plt.plot(Calib_Bins[i][:-1], Calib_Heigths[i])
+    
+#%%
+"""Calculo branching"""
+
+TotalDP = np.sum(Long_Heigths[0])
+TotalSP = np.sum(Long_Heigths[1])
+
+TotalDPbkg = np.sum(Calib_Heigths[0])
+TotalSPbkg = np.sum(Calib_Heigths[1])
+
+TotalDPfinal = TotalDP-TotalDPbkg
+TotalSPfinal = TotalSP-TotalSPbkg
+
+branch = TotalSPfinal/(TotalSPfinal+TotalDPfinal)
+
+print(branch)
+
+
+
+
+
+