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artiq_experiments
Commit cd346c12 authored by Nicolas Nunez Barreto's avatar Nicolas Nunez Barreto
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Merge branch 'master' of https://code.df.uba.ar/nnunez/artiq_experiments

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analisis/plots/20221006_transitoriosv2/Simulaciones/plot_decaytimes_vs_intensity_detunings.py
View file @ cd346c12
...@@ -3,7 +3,7 @@ import numpy as np ...@@ -3,7 +3,7 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
#plt.ion() #plt.ion()
dataREAL = pd.read_csv("medidos_powers_taus.csv") #dataREAL = pd.read_csv("medidos_powers_taus.csv")
dataSIM = pd.read_csv("sim_intensidad_taus_full.csv") dataSIM = pd.read_csv("sim_intensidad_taus_full.csv")
errsSIM = pd.read_csv("error_teorico_tau_det20.csv") errsSIM = pd.read_csv("error_teorico_tau_det20.csv")
I_sat = 43.3*1e-5 #uW/um2 I_sat = 43.3*1e-5 #uW/um2
...@@ -11,50 +11,51 @@ I_sat = 43.3*1e-5 #uW/um2 ...@@ -11,50 +11,51 @@ I_sat = 43.3*1e-5 #uW/um2
#%% ##################################################################################### #%% #####################################################################################
### Plot de lineas teoricas tau-intensidad variando detuning + datos medidos ### Plot de lineas teoricas tau-intensidad variando detuning + datos medidos
UVpotVec=[4,6,12,17,26,36, 48, 77, 112, 151, 190, 226, 255, 279]
Taus = [2.6474524026975392, 1.651982852985559, 1.0793431020294597, 0.8171909874107741, 0.6329192159654833, 0.5246427838474966, 0.468752585310023, 0.3943320198588099, 0.34233658233849174, 0.314035400291993, 0.30338246040910105,0.2891087574893047, 0.2861944000648665,0.2804274973493638]
Taus_v2 = [1.6655917442406167, 1.1754160164890701, 1.0129299348502132, 0.6747311668331314, 0.5395244011650271, 0.4699144306437099, 0.41805942894076137, 0.3948601586682529, 0.3849453824721735, 0.3752507710597908]
fig2, ax2 = plt.subplots(figsize=(4,3)) fig2, ax2 = plt.subplots(figsize=(4,3))
detuning_lines = ['Det10', 'Det20', 'Det30', 'Det40', 'Det50', 'Det60', 'Det70', 'Det80', 'Det90'] detuning_lines = ['Det10', 'Det20', 'Det30', 'Det40', 'Det50', 'Det60', 'Det70', 'Det80', 'Det90']
# Curvas de las simulaciones con tau en unidades de microseg # Curvas de las simulaciones con tau en unidades de microseg
for det in detuning_lines: for det in detuning_lines:
ax2.plot(dataSIM.I, dataSIM[det]*1e6, 'k-', ms=1, lw=0.5) ax2.plot(dataSIM.I**1, [d for d in dataSIM[det]*1e6], 'k-', ms=1, lw=0.5)
# Curvas de las mediciones cambiando el radio posible con tau en microseg # Curvas de las mediciones cambiando el radio posible con tau en microseg
for cambio in [0]: # esto es para shiftear la potencia medida for cambio in [0]: # esto es para shiftear la potencia medida
potencias = dataREAL.Pow - cambio potencias = dataREAL.Pow - cambio
for rad in [85.7*1.5]: # esto evalua con distintos radios for rad in [128]: # esto evalua con distintos radios
ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus, yerr=np.mean(1e6*errsSIM.stdval.values), ax2.errorbar([(p/(np.pi*(rad**2)))**1 for p in UVpotVec], [t for t in Taus], yerr=np.mean(1e6*errsSIM.stdval.values),
fmt='.--', ms=6, lw=.5, \ fmt='.--', ms=6, lw=.5, \
color="#3949ab", color="#3949ab",
capsize=2) capsize=2)
ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec][4:], Taus_v2[4:], yerr=np.mean(1e6*errsSIM.stdval.values), ax2.errorbar([(p/(np.pi*(rad**2)))**1 for p in UVpotVec][4:], [t for t in Taus_v2], yerr=np.mean(1e6*errsSIM.stdval.values),
fmt='.--', ms=6, lw=.5, \ fmt='.--', ms=6, lw=.5, \
color="#3949ab", color="#3949ab",
capsize=2) 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 fs = 9
ax2.set_xlim([0.47e-4, 0.8e-2]) ax2.set_xlim([0.47e-4, 0.8e-2])
ax2.set_ylim([2e-1, 4e1]) #ax2.set_xlim([0., 0.000005])
#ax2.set_ylim([2e-1, 4e1])
ax2.set_ylabel(r"Characteristic time ($\mu$s)", fontname='STIXGeneral') 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"$I = P / (\pi\,r^2)$ [$\mu$W/$\mu$m$^2$]")
ax2.set_xlabel(r'UV intensity ($\mu$W/$\mu$m$^2$)', fontname='STIXGeneral') ax2.set_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.set_title(r"datos(punteadas) $r \in [30; 190]\ \mu m$ | simulacion(llenas) $\Delta \in -[10; 90]$ MHz")
#ax2.legend(markerscale=2) #ax2.legend(markerscale=2)
ax2.set_xticks([1e-4, 1e-3]) #ax2.set_xticks([1e-4, 1e-3])
ax2.set_xticklabels([1e-4, 1e-3], fontsize=fs, fontname='STIXGeneral') ax2.set_xticklabels([1e-4, 1e-3], fontsize=fs, fontname='STIXGeneral')
ax2.set_yticks([1e0, 1e1]) #ax2.set_yticks([1e0, 1e1])
ax2.set_yticklabels([1e0, 1e1], fontsize=fs, fontname='STIXGeneral') ax2.set_yticklabels([1e0, 1e1], fontsize=fs, fontname='STIXGeneral')
ax2.grid(which='minor', alpha=0.2) ax2.grid(which='minor', alpha=0.2)
ax2.set_xscale("log") ax2.set_xscale("log")
ax2.set_yscale("log") ax2.set_yscale("log")
plt.savefig('Fig03_varyingUVpower.pdf')
plt.savefig('Fig03_varyingUVpower.svg')
analisis/plots/20221006_transitoriosv2/Transitorios_02.py
View file @ cd346c12
...@@ -9,9 +9,9 @@ import os ...@@ -9,9 +9,9 @@ import os
os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221006_transitoriosv2') os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20221006_transitoriosv2')
# Solo levanto algunos experimentos # Solo levanto algunos experimentos
SP_files = [8445, 8457, 8458, 8459, 8460, 8461, 8462, 8463, 8464, 8465, 8466, 8467, 8468, 8469] 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] SP_files_v2 = [8763, 8764, 8765, 8766, 8767, 8768, 8769, 8770, 8771, 8772, 8773, 8774, 8775, 8776]
DP_files = [8472, 8473, 8474, 8475, 8476, 8477, 8478, 8479, 8480, 8481, 8482] DP_files = [8472, 8473, 8474, 8475, 8476, 8477, 8478, 8479, 8480, 8481, 8482, 8749]
Long_files = ['DP_long', 'SP_long', 'DP_long_v2', 'DP_long_v3', 'DP_long_new', 'SP_long_new'] Long_files = ['DP_long', 'SP_long', 'DP_long_v2', 'DP_long_v3', 'DP_long_new', 'SP_long_new']
Calib_files = ['Cal_DP_5M', 'Cal_SP_5M', 'Cal_DP_25M', 'Cal_SP_25M'] Calib_files = ['Cal_DP_5M', 'Cal_SP_5M', 'Cal_DP_25M', 'Cal_SP_25M']
...@@ -307,7 +307,7 @@ popt_vec_v2 = [] ...@@ -307,7 +307,7 @@ popt_vec_v2 = []
pcov_vec_v2 = [] pcov_vec_v2 = []
ki = 80 ki = 80
plt.figure() plt.figure()
for j in range(len(SP_Heigths_v2)): for j in range(len(SP_Heigths_v2)):
...@@ -352,116 +352,109 @@ plt.plot(UVpotVec, Offsets_v2,'o') ...@@ -352,116 +352,109 @@ plt.plot(UVpotVec, Offsets_v2,'o')
""" """
FIGURA PAPER 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] jselected = [1, 5, 12]
t0=0.18 t0=0.18
plt.figure()
plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[0]])
plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[0]]) for r in RefBins[90:]])
plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[1]])
plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[1]]) for r in RefBins[90:]])
plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], SP_Heigths[jselected[2]])
plt.plot([r-t0 for r in RefBins[90:]], [expo(r, *popt_vec[jselected[2]]) for r in RefBins[90:]])
plt.xlim(-0.5,3)
plt.xlabel('Time (us)')
plt.ylabel('Counts')
s1 = 0
s2 = 700
s3 = 2*s2
kini = 75
colors=sns.color_palette("rocket", 10)
color3 = colors[0]
color2 = colors[3]
color1 = colors[8]
plt.figure(figsize=(3.5,3))
plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], [s+s1 for s in SP_Heigths[jselected[0]]],color=color1,alpha=0.7)
plt.plot([r-t0 for r in RefBins[kini:]], [expo(r, *popt_vec[jselected[0]])+s1 for r in RefBins[kini:]],color=color1)
plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], [s+s2 for s in SP_Heigths[jselected[1]]],color=color2,alpha=0.7)
plt.plot([r-t0 for r in RefBins[kini:]], [expo(r, *popt_vec[jselected[1]])+s2 for r in RefBins[kini:]],color=color2)
plt.plot([f*1e6-t0 for f in SP_Bins[jselected[0]][:-1]], [s+s3 for s in SP_Heigths[jselected[2]]],color=color3,alpha=0.7)
plt.plot([r-t0 for r in RefBins[kini:]], [expo(r, *popt_vec[jselected[2]])+s3 for r in RefBins[kini:]],color=color3)
plt.xlim(-0.3,2)
plt.xlabel('Time (us)', fontname='STIXGeneral', fontsize=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.pdf')
plt.savefig('fig3_01.svg') plt.savefig('fig3_01.svg')
#%% #%%
""" """
Defino los vectores de las mediciones largas para obtener branching fractions, 25 M de mediciones VEMOS UNA DP CON POTENCIA ALTA A VER SI DA LO QUE TIENE QUE DAR EL ANCHO DE LINEA
""" """
#Bins_long = [t*1e6 for t in Long_Bins[0][:-1]] BinsIRhigh = [t*1e6 for t in DP_Bins[-1][:-1]]
Bins_long = [t*1e6 for t in Long_Bins[-1][:-1]] HeightsIRhigh = DP_Heigths[-1]
#DP_long_raw = Long_Heigths[3] #la 0 y la 2 salieron mal pero las dejo por las dudas. uso la 3
#SP_long_raw = Long_Heigths[1]
DP_long_raw = Long_Heigths[-2] #la 0 y la 2 salieron mal pero las dejo por las dudas. uso la 3
SP_long_raw = Long_Heigths[-1]
DP_bkg_long_raw = Calib_Heigths[2] #calibracion del DP, esta al final ni la uso
SP_bkg_long_raw = Calib_Heigths[3] #calibracion del SP
from scipy.optimize import curve_fit
#Genero el vector de background para el SP def expo(T, tau, N0, C, T0):
BackgroundVectorLong = SP_Bkgr_builder(np.mean(SP_long_raw[0:50]),np.mean(SP_long_raw[-50:]),59,67,965) return N0*np.exp(-(T-T0)/tau) + C
SP_long_corrected = [SP_long_raw[k]-BackgroundVectorLong[k] for k in range(len(SP_long_raw))] popt_IR, pcov_IR = curve_fit(expo, BinsIRhigh[int(0.1*len(BinsIRhigh)):], HeightsIRhigh[int(0.1*len(BinsIRhigh)):], p0=(5, 100, 200, 1))
DP_long_corrected = [DP_long_raw[k]-np.mean(DP_long_raw[-100:]) for k in range(len(DP_long_raw))]
plt.figure() plt.figure()
plt.plot(SP_long_raw) plt.plot(BinsIRhigh, HeightsIRhigh)
plt.plot(BackgroundVectorLong,'o') plt.plot(BinsIRhigh, [expo(r, *popt_IR) for r in BinsIRhigh])
plt.xlim(0,100)
#%%
""" tauIR = popt_IR[0]
Ploteo las mediciones largas print(tauIR)
"""
plt.figure()
plt.plot(Bins_long, SP_long_corrected)
plt.plot([b+0.25 for b in Bins_long], DP_long_corrected)
plt.grid()
#plt.xlim(0, 1.5)
#plt.ylim(-100,100)
stotal = np.sum(SP_long_corrected)
dtotal = np.sum(DP_long_corrected)
print(stotal/(dtotal+stotal))
#%% #%%
""" """
aca se plotea la SP con su curva de calibracion sin ion para restar VEMOS UNA SP CON POTENCIA ALTA A VER SI DA LO QUE TIENE QUE DAR EL ANCHO DE LINEA
"""
plt.figure()
#plt.plot(Bins_long, SP_long_raw)
plt.plot(Bins_long, SP_bkg_long_raw)
plt.xlim(-1, 2)
#%%
"""
Restamos el fondo a ambas curvas. A la DP le restamos un promedio de la curva con el ion
apagado porque en ningun momento esta prendido el UV
""" """
#Pruebo restar background directamente
#DP_long_subs = [DP_long_raw[j]-DP_bkg_long_raw[j] for j in range(len(DP_long_raw))]
DP_long_subs = [d-int(np.mean(DP_long_raw[-100:])) for d in DP_long_raw]
SP_long_subs = [SP_long_raw[j]-SP_bkg_long_raw[j] for j in range(len(SP_long_raw))]
stot = np.sum(SP_long_subs) # k=5
dtot = np.sum(DP_long_subs) # BinsUVhigh = [t*1e6 for t in Long_Bins[k][:-1]]
# HeightsUVhigh = Long_Heigths[k]
prob = stot/(stot+dtot)
eff_deteccion = dtot/25e6 k=-3
print(f'p={prob}')
print(f'Eff de deteccion: {eff_deteccion*100}')
#%%
plt.figure()
plt.plot(Bins_long, SP_long_subs)
plt.plot([b+0.25 for b in Bins_long[0:475]], DP_long_subs)
plt.xlim(-0.2, 2)
plt.grid()
#%%
plt.figure()
plt.plot(Random_Bins[0][:-1], Random_Heigths[0],'o')
BinsUVhigh = [t*1e6 for t in SP_Bins[k][:-1]]
HeightsUVhigh = SP_Heigths[k]
from scipy.optimize import curve_fit
def expo(T, tau, N0, C, T0):
return N0*np.exp(-(T-T0)/tau) + C
initi=0.10
popt_UV, pcov_UV = curve_fit(expo, BinsUVhigh[int(initi*len(BinsUVhigh)):], HeightsUVhigh[int(initi*len(BinsUVhigh)):], p0=(1, 1000, 1800, 1), bounds=((0,0,0,0),(20, 1e7, 1e5, 1e3)))
plt.figure()
plt.plot(BinsUVhigh, HeightsUVhigh)
plt.plot(BinsUVhigh, [expo(r, *popt_UV) for r in BinsUVhigh])
plt.plot(BinsUVhigh[int(initi*len(BinsUVhigh))], HeightsUVhigh[int(initi*len(BinsUVhigh))],'o',markersize=5)
#plt.ylim(-1000,20000)
tauUV = popt_UV[0]
print(tauUV)
......
analisis/plots/20221006_transitoriosv2/fig3_01.svg
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analisis/plots/20221017_IonStatistics/IonStatistics.py
View file @ cd346c12
...@@ -140,8 +140,8 @@ plt.ylabel('Event frequency', fontname='STIXGeneral') ...@@ -140,8 +140,8 @@ plt.ylabel('Event frequency', fontname='STIXGeneral')
name='fig01a' 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+'.pdf')
plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg') #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg')
...@@ -173,8 +173,8 @@ plt.tight_layout() ...@@ -173,8 +173,8 @@ plt.tight_layout()
name='fig01b' 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+'.pdf')
plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'.svg') #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2022 Transient Phenomena JOSA B/Figures/'+name+'_dump.svg')
......
analisis/plots/20221017_IonStatistics/Simulaciones/plot_decaytimes_vs_intensity_detunings.py
View file @ cd346c12
...@@ -27,14 +27,19 @@ for cambio in [0]: # esto es para shiftear la potencia medida ...@@ -27,14 +27,19 @@ for cambio in [0]: # esto es para shiftear la potencia medida
fmt='.--', ms=6, lw=.5, \ fmt='.--', ms=6, lw=.5, \
color="#3949ab", color="#3949ab",
capsize=3) capsize=3)
ax2.errorbar([p/(np.pi*(rad**2)) for p in UVpotVec], Taus_v2, yerr=np.mean(1e6*errsSIM.stdval.values),
fmt='.--', ms=6, lw=.5, \
color="#3949ab",
capsize=3)
ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \ # ax2.errorbar(potencias/(np.pi*(rad**2)), dataREAL.Tau*1e6, \
yerr = 1e6*errsSIM.stdval.values, \ # yerr = 1e6*errsSIM.stdval.values, \
fmt='.--', ms=6, lw=.5, \ # fmt='.--', ms=6, lw=.5, \
color="#3949ab", # color="#3949ab",
capsize=3, # capsize=3,
label=f'r={rad}; cambio={cambio}') # label=f'r={rad}; cambio={cambio}')
ax2.set_xlim([1.2e-4, 0.2e-1]) ax2.set_xlim([1.2e-4, 0.2e-1])
ax2.set_ylim([2e-1, 0.8e1]) ax2.set_ylim([2e-1, 0.8e1])
......
analisis/plots/20230306_DD_DarkResonancesAnalysis/CPT_plotter_20230306.py 0 → 100644
View file @ cd346c12
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
ALL_FILES = """000010146-IR_Scan_withcal_optimized
000010147-IR_Scan_withcal_optimized
"""
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(ALL_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
for i, fname in enumerate(ALL_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures.append(np.array(data['datasets']['no_measures']))
#%%
#Barriendo angulo del IR con tisa apagado y prendido
jvec = [0, 1]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
#%%
#jselected = [2, 6, 5, 3, 4]
#Angsselected = ['Off', 0, 30, 60, 90]
jselected = [2, 6, 7, 3, 4]
Angsselected = ['Off', 0, 15, 60, 90]
fig, ax = plt.subplots(2, 2)
cs = 1
ms = 1
ax[0,0].errorbar([2*f*1e-6 for f in Freqs[jselected[1]]], Counts[jselected[1]], yerr=np.sqrt(Counts[jselected[1]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[0,0].errorbar([2*f*1e-6 for f in Freqs[jselected[0]]], Counts[jselected[0]], yerr=np.sqrt(Counts[jselected[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[0,0].set_ylim(0, 2100)
ax[0,0].set_title(f'TISA ang: {Angsselected[1]}º')
ax[0,0].set_xlabel('Detuning (MHz)')
ax[0,0].set_ylabel('Counts')
ax[0,0].grid()
ax[0,1].errorbar([2*f*1e-6 for f in Freqs[jselected[2]]], Counts[jselected[2]], yerr=np.sqrt(Counts[jselected[2]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[0,1].errorbar([2*f*1e-6 for f in Freqs[jselected[0]]], Counts[jselected[0]], yerr=np.sqrt(Counts[jselected[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[0,1].set_ylim(0, 2100)
ax[0,1].set_title(f'TISA ang: {Angsselected[2]}º')
ax[0,1].set_xlabel('Detuning (MHz)')
ax[0,1].set_ylabel('Counts')
ax[0,1].grid()
ax[1,0].errorbar([2*f*1e-6 for f in Freqs[jselected[3]]], Counts[jselected[3]], yerr=np.sqrt(Counts[jselected[3]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[1,0].errorbar([2*f*1e-6 for f in Freqs[jselected[0]]], Counts[jselected[0]], yerr=np.sqrt(Counts[jselected[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[1,0].set_ylim(0, 2100)
ax[1,0].set_title(f'TISA ang: {Angsselected[3]}º')
ax[1,0].set_xlabel('Detuning (MHz)')
ax[1,0].set_ylabel('Counts')
ax[1,0].grid()
ax[1,1].errorbar([2*f*1e-6 for f in Freqs[jselected[4]]], Counts[jselected[4]], yerr=np.sqrt(Counts[jselected[4]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[1,1].errorbar([2*f*1e-6 for f in Freqs[jselected[0]]], Counts[jselected[0]], yerr=np.sqrt(Counts[jselected[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[1,1].set_ylim(0, 2100)
ax[1,1].set_title(f'TISA ang: {Angsselected[4]}º')
ax[1,1].set_xlabel('Detuning (MHz)')
ax[1,1].set_ylabel('Counts')
ax[1,1].grid()
fig.tight_layout()
fig.subplots_adjust(top=0.9, left=0.1, right=0.9, bottom=0.01) # create some space below the plots by increasing the bottom-value
ax.flatten()[-2].legend(loc='upper center', bbox_to_anchor=(1.1, -0.4), ncol=3)
#%%
#from EITfit.threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
#from EITfit.threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
#from scipy.optimize import curve_fit
#Ajuste con el Tisa apagado primero para calibrar el eje X en el plot que voy a mostrar
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 33.5e6
c=3e8
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -5.5-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs_A[2]]
CountsDRpi_3 = Counts_A[2]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SG, SP, scale, offset):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
popt_tisaoff, pcov_tisaoff = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5, 1e4, 1e5], bounds=((0, 0, 0, 0), (2, 10, 1e6, 1e6)))
print(popt_tisaoff)
Sat_3 = popt_tisaoff[0]
Det_3 = popt_tisaoff[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt_tisaoff)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
FreqsCalibradas_A = FreqsDRpi_3
Scale_A = popt_tisaoff[2]
Offset_A = popt_tisaoff[3]
#%%
"""
FIGURA PARA EL PAPER
Esto plotea, arriba, 3 plots para 3 angulos distintos de la polarizacion del TISA (el que no scaneo), al prenderlo y al apagarlo.
Esto muestra esquematicamente la DR kicking segun la polarización del láser.
Abajo, 3 plots para 3 angulos distintos de la polarizacion del laser IR (el que scaneo), al prender y apagar
el TISA (el que no scaneo). Esto muestra como aparecen las DR pi, que no se ven bien en caso contrario.
Para correr esto, hay que correr:
El bloque anterior para calibrar el eje X de frecuencia de los plots superiores
El bloque para calibrar figura del 20211223
"""
#Aca pongo solo 3 que son los mismos angulos de las curvas barriendo angulo IR
jselected_A = [2, 6, 7, 3, 4]
Angsselected_A = ['Off', 0, 15, 60, 90]
#jselected = [2, 6, 7, 4]
#Angsselected = ['Off', 0, 15, 90]
fig, ax = plt.subplots(2, 3, figsize=(20, 12))
cs = 1
ms = 4
fs = 25
xscale = np.arange(-40, 10, 20)
ax[1,0].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[1,0].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[1]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[1]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[1,0].set_xlim(-51, 15)
ax[1,0].set_ylim(0, 14)
ax[1,0].set_title(f'TISA angle: {Angsselected_A[1]}º', fontsize=fs)
ax[1,0].set_xlabel('Detuning (MHz)', fontsize=fs)
ax[1,0].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[1,0].set_yticks(np.arange(0, 15, 5))
ax[1,0].set_yticklabels(np.arange(0, 15, 5), fontsize=fs)
ax[1,0].set_xticks(xscale)
ax[1,0].set_xticklabels(xscale, fontsize=fs)
ax[1,0].grid()
ax[1,1].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[1,1].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[2]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[2]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[1,1].set_xlim(-51, 15)
ax[1,1].set_ylim(0, 14)
ax[1,1].set_title(f'TISA angle: {Angsselected_A[2]}º', fontsize=fs)
ax[1,1].set_xlabel('Detuning (MHz)', fontsize=fs)
#ax[1,1].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[1,1].set_yticks(np.arange(0, 15, 5))
ax[1,1].set_yticklabels(np.arange(0, 15, 5), fontsize=fs)
ax[1,1].set_xticks(xscale)
ax[1,1].set_xticklabels(xscale, fontsize=fs)
ax[1,1].grid()
ax[1,2].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[1,2].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[4]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[4]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[1,2].set_xlim(-51, 15)
ax[1,2].set_ylim(0, 14)
ax[1,2].set_title(f'TISA angle: {Angsselected_A[4]}º', fontsize=fs)
ax[1,2].set_xlabel('Detuning (MHz)', fontsize=fs)
#ax[1,2].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[1,2].set_yticks(np.arange(0, 15, 5))
ax[1,2].set_yticklabels(np.arange(0, 15, 5), fontsize=fs)
ax[1,2].set_xticks(xscale)
ax[1,2].set_xticklabels(xscale, fontsize=fs)
ax[1,2].grid()
jselected_B = [3, 2, 4, 1, 5, 0]
Angsselected_B = ['Off', 0, 'Off', 15, 'Off', 90]
#jselected = [2, 6, 7, 4]
#Angsselected = ['Off', 0, 15, 90]
cs = 1
ms = 4
fs = 25
Offset_B=0 #agrego ad hoc porque el offset del ajuste no da bien
ax[0,0].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[1]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[1]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[0,0].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[0]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[0,0].set_xlim(-51, 15)
ax[0,0].set_ylim(0, 16.5)
ax[0,0].set_title(f'IR angle: {Angsselected_B[1]}º', fontsize=fs)
ax[0,0].set_xlabel('Detuning (MHz)', fontsize=fs)
ax[0,0].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[0,0].set_yticks(np.arange(0, 20, 5))
ax[0,0].set_yticklabels(np.arange(0, 20, 5), fontsize=fs)
ax[0,0].set_xticks(xscale)
ax[0,0].set_xticklabels(xscale, fontsize=fs)
ax[0,0].grid()
ax[0,1].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[3]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[3]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[0,1].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[2]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[2]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[0,1].set_xlim(-51, 15)
ax[0,1].set_ylim(0, 16.5)
ax[0,1].set_title(f'IR angle: {Angsselected_B[3]}º', fontsize=fs)
ax[0,1].set_xlabel('Detuning (MHz)', fontsize=fs)
#ax[0,1].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[0,1].set_yticks(np.arange(0, 20, 5))
ax[0,1].set_yticklabels(np.arange(0, 20, 5), fontsize=fs)
ax[0,1].set_xticks(xscale)
ax[0,1].set_xticklabels(xscale, fontsize=fs)
ax[0,1].grid()
ax[0,2].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[5]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[5]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[0,2].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[4]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[4]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[0,2].set_xlim(-51, 15)
ax[0,2].set_ylim(0, 16.5)
ax[0,2].set_title(f'IR angle: {Angsselected_B[5]}º', fontsize=fs)
ax[0,2].set_xlabel('Detuning (MHz)', fontsize=fs)
#ax[0,2].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[0,2].set_yticks(np.arange(0, 20, 5))
ax[0,2].set_yticklabels(np.arange(0, 20, 5), fontsize=fs)
ax[0,2].set_xticks(xscale)
ax[0,2].set_xticklabels(xscale, fontsize=fs)
ax[0,2].grid()
fig.tight_layout()
#fig.subplots_adjust(top=0.9, left=0.1, right=0.9, bottom=0.01) # create some space below the plots by increasing the bottom-value
#ax.flatten()[-2].legend(loc='upper center', bbox_to_anchor=(1.1, -0.4), ncol=3)
#%%
"""
FIGURA PARA EL PAPER VERSION 2
Esto plotea, arriba, 3 plots para 3 angulos distintos de la polarizacion del TISA (el que no scaneo), al prenderlo y al apagarlo.
Esto muestra esquematicamente la DR kicking segun la polarización del láser.
Abajo, 3 plots para 3 angulos distintos de la polarizacion del laser IR (el que scaneo), al prender y apagar
el TISA (el que no scaneo). Esto muestra como aparecen las DR pi, que no se ven bien en caso contrario.
Para correr esto, hay que correr:
El bloque anterior para calibrar el eje X de frecuencia de los plots superiores
El bloque para calibrar figura del 20211223
"""
from scipy.signal import savgol_filter as sf
import seaborn as sns
sns.set_theme(style='white')
#Aca pongo solo 3 que son los mismos angulos de las curvas barriendo angulo IR
jselected_A = [2, 6, 7, 3, 4]
Angsselected_A = ['Off', 0, 15, 60, 90]
#jselected = [2, 6, 7, 4]
#Angsselected = ['Off', 0, 15, 90]
fig, ax = plt.subplots(1, 2, figsize=(24, 12))
cs = 1
ms = 8
fs = 40
xscale = np.arange(-40, 10, 20)
winl = 13
po = 3
Counts_A_corr = Counts_A[jselected_A[1]]
Counts_A_corr[82] = 1620
Counts_A_corr[83] = 1625
Counts_A_corr[84] = 1631
#ax[1].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
#ax[1].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[1]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[1]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
#ax[1].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
#ax[1].errorbar(FreqsCalibradas_A, [(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[4]]], yerr=[np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[4]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
ax[1].plot(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A_corr], winl, po), '-o', label=r'$\alpha_{rep}=0$º', markersize=ms, color='darkgreen')
ax[1].plot(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[4]]], winl, po), '-o', label=r'$\alpha_{rep}=90º$', markersize=ms, color='maroon')
ax[1].plot(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], winl, po), '-o', label='Repumper off', markersize=ms, color='red')
#ax[1].plot(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], winl, po), '-o', label='TISA OFF', markersize=ms)
ax[1].fill_between(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], winl, po)+sf([np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], winl, po), sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[0]]], winl, po)-sf([np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[0]]], winl, po), color='lightsalmon')
ax[1].fill_between(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A_corr], winl, po)+sf([np.sqrt(c)*100/Scale_A for c in Counts_A_corr], winl, po), sf([(c-Offset_A)*100/Scale_A for c in Counts_A_corr], winl, po)-sf([np.sqrt(c)*100/Scale_A for c in Counts_A_corr], winl, po), color='paleturquoise')
ax[1].fill_between(FreqsCalibradas_A, sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[4]]], winl, po)+sf([np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[4]]], winl, po), sf([(c-Offset_A)*100/Scale_A for c in Counts_A[jselected_A[4]]], winl, po)-sf([np.sqrt(c)*100/Scale_A for c in Counts_A[jselected_A[4]]], winl, po), color='thistle')
ax[1].set_xlim(-51, 15)
ax[1].set_ylim(0, 14)
ax[1].set_title(r'$\alpha_{pr}=90º$', fontsize=fs)
ax[1].set_xlabel('Probe detuning (MHz)', fontsize=fs)
ax[1].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[1].set_yticks(np.arange(0, 15, 5))
ax[1].set_yticklabels(np.arange(0, 15, 5), fontsize=fs)
ax[1].set_xticks(xscale)
ax[1].set_xticklabels(xscale, fontsize=fs)
ax[1].grid()
lgnd = ax[1].legend(loc='upper left', prop={'size': 26}, markerscale=2)
jselected_B = [3, 2, 4, 1, 5, 0]
Angsselected_B = ['Off', 0, 'Off', 15, 'Off', 90]
#jselected = [2, 6, 7, 4]
#Angsselected = ['Off', 0, 15, 90]
cs = 1
ms = 8
fs = 40
Offset_B=0 #agrego ad hoc porque el offset del ajuste no da bien
Counts_B_corr1 = Counts_B[jselected_B[0]]
Counts_B_corr1[44] = 5536
Counts_B_corr2 = Counts_B[jselected_B[4]]
Counts_B_corr2[47] = 6115
#ax[0].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[1]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[1]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
#ax[0].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[0]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[0]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
#ax[0].errorbar(FreqsCalibradas_B, [(c-Offset_B-1000)*100/Scale_B for c in Counts_B[jselected_B[5]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[5]]], fmt='o', label='TISA ON', capsize=cs, markersize=ms)
#ax[0].errorbar(FreqsCalibradas_B, [(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[4]]], yerr=[2*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[4]]], fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
ax[0].plot(FreqsCalibradas_B, sf([(c-Offset_B)*100/Scale_B for c in Counts_B_corr2], winl, po), '-o', label=r'$\alpha_{pr}=90º$, repumper ON', markersize=ms, color='maroon')
ax[0].plot(FreqsCalibradas_B, sf([(c-Offset_B)*100/Scale_B for c in Counts_B_corr1], winl, po), '-o', label=r'$\alpha_{pr}=5º$, repumper ON', markersize=ms, color='midnightblue')
ax[0].plot(FreqsCalibradas_B, sf([(c-Offset_B-1000)*100/Scale_B for c in Counts_B[jselected_B[5]]], winl, po), '-o', label=r'$\alpha_{pr}=90º$, repumper OFF', markersize=ms, color='red')
ax[0].plot(FreqsCalibradas_B, sf([(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[1]]], winl, po), '-o', label=r'$\alpha_{pr}=5º$, rep OFF', markersize=ms, color='royalblue')
sqrtfactor = 2
ax[0].fill_between(FreqsCalibradas_B, sf([(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[1]]], winl, po)+sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[1]]], winl, po), sf([(c-Offset_B)*100/Scale_B for c in Counts_B[jselected_B[1]]], winl, po)-sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[1]]], winl, po), color='lightsalmon')
ax[0].fill_between(FreqsCalibradas_B, sf([(c-Offset_B)*100/Scale_B for c in Counts_B_corr1], winl, po)+sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B_corr1], winl, po), sf([(c-Offset_B)*100/Scale_B for c in Counts_B_corr1], winl, po)-sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B_corr1], winl, po), color='plum')
ax[0].fill_between(FreqsCalibradas_B, sf([(c-Offset_B-1000)*100/Scale_B for c in Counts_B[jselected_B[5]]], winl, po)+sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[5]]], winl, po), sf([(c-Offset_B-1000)*100/Scale_B for c in Counts_B[jselected_B[5]]], winl, po)-sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B[jselected_B[5]]], winl, po), color='lightsalmon')
ax[0].fill_between(FreqsCalibradas_B, sf([(c-Offset_B)*100/Scale_B for c in Counts_B_corr2], winl, po)+sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B_corr2], winl, po), sf([(c-Offset_B)*100/Scale_B for c in Counts_B_corr2], winl, po)-sf([sqrtfactor*np.sqrt(c)*100/Scale_B for c in Counts_B_corr2], winl, po), color='thistle')
ax[0].set_xlim(-51, 15)
ax[0].set_ylim(0, 16.5)
ax[0].set_title(r'$\alpha_{rep}=90º$', fontsize=fs)
ax[0].set_xlabel('Probe detuning (MHz)', fontsize=fs)
ax[0].set_ylabel('Fluorescence (a.u.)', fontsize=fs)
ax[0].set_yticks(np.arange(0, 20, 5))
ax[0].set_yticklabels(np.arange(0, 20, 5), fontsize=fs)
ax[0].set_xticks(xscale)
ax[0].set_xticklabels(xscale, fontsize=fs)
ax[0].grid()
lgnd = ax[0].legend(loc='upper left', prop={'size': 26}, markerscale=2)
#fig.tight_layout()
#fig.subplots_adjust(top=0.9, left=0.1, right=0.9, bottom=0.01) # create some space below the plots by increasing the bottom-value
#ax.flatten()[-2].legend(loc='upper center', bbox_to_anchor=(1.1, -0.4), ncol=3)
#%%
plt.figure()
plt.errorbar([2*f*1e-6 for f in Freqs[jselected[0]]], Counts[jselected[0]], yerr=np.sqrt(Counts[jselected[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
#%%
"""
Este bloque agarra las 7 mediciones con el tisa prendido barriendo angulo, entre 0º y 90º,
y les hace un fiteo para buscar parametro de saturacion y detuning. a ese fiteo le busca los minimos locales
que son las 4 resonancias oscuras (DR), y en las ubicaciones de esos minimos calcula la fluorescencia medida.
ese valor de fluorescencia es el que, en otro codigo, se usa para comparar con el valor teorico con su error.
si encuentra solo 3, calcula el 4to a ojo poniendo un indice de donde deberia estar ese minimo, porque
puede haber solo 3 porque al prender el tisa hay un kickeo de resonancias oscuras.
"""
from scipy.signal import argrelextrema
SatsTisa = []
DetsTisa = []
AngsTisa = []
FluosMinis = []
FluosMinisError = []
#Ajuste con el Tisa prendido en 0º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -5.1-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[6]]
CountsDRpi_3 = Counts[6]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[69]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[69]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 0º')
#SIGUIENTE ANGULO
#Ajuste con el Tisa en 30º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 30
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -5.1-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[5]]
CountsDRpi_3 = Counts[5]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 1e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[73]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[73]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 30º')
#SIGUIENTE ANGULO
#Ajuste con el Tisa en 60º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 60
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4
offsetxpi = 440+1+correccion
DetDoppler = -5.1-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[3]]
CountsDRpi_3 = Counts[3]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
plt.title('60º')
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
print(Fluosminis)
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[73]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[73]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 60º')
#SIGUIENTE ANGULO
#Ajuste con el Tisa prendido a 90º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -5.1-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[4]]
CountsDRpi_3 = Counts[4]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
def FitEITpi_withoutscale(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*100 + 0 for f in MeasuredFluo]
return FinalFluo
FittedEITpi_3 = FitEITpi_withoutscale(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, [100*(c- popt_tisaoff[3])/ popt_tisaoff[2] for c in CountsDRpi_3], yerr=(100/popt_tisaoff[2])*2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
#plt.plot(FreqsDRpi_3, [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3])
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[73]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[73]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 90º')
#SIGUIENTE ANGULO
#Ajuste con el Tisa prendido a 15º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 15
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -5.1-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[7]]
CountsDRpi_3 = Counts[7]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
def FitEITpi_withoutscale(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*100 + 0 for f in MeasuredFluo]
return FinalFluo
FittedEITpi_3 = FitEITpi_withoutscale(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, [100*(c- popt_tisaoff[3])/ popt_tisaoff[2] for c in CountsDRpi_3], yerr=(100/popt_tisaoff[2])*2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
#plt.plot(FreqsDRpi_3, [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3])
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[69]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[69]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 90º')
#SIGUIENTE ANGULO
#Ajuste con el Tisa prendido a 45º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 45
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -3.5-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[8]]
CountsDRpi_3 = Counts[8]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
def FitEITpi_withoutscale(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*100 + 0 for f in MeasuredFluo]
return FinalFluo
FittedEITpi_3 = FitEITpi_withoutscale(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, [100*(c- popt_tisaoff[3])/ popt_tisaoff[2] for c in CountsDRpi_3], yerr=(100/popt_tisaoff[2])*2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
#plt.plot(FreqsDRpi_3, [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3])
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
print(minsss)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[73]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[73]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 45º')
#SIGUIENTE ANGULO
#Ajuste con el Tisa prendido a 75º
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 75
phiprobe = 0
titaprobe = 90
T = 0.6e-3
sg = popt_tisaoff[0]
sp = popt_tisaoff[1]
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 31.9e6
B = (u/(2*np.pi))/c
correccion = 4 #con 8 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -3-correccion
FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs[9]]
CountsDRpi_3 = Counts[9]
freqslongpi_3 = np.arange(min(FreqsDRpi_3), max(FreqsDRpi_3)+FreqsDRpi_3[1]-FreqsDRpi_3[0], 0.1*(FreqsDRpi_3[1]-FreqsDRpi_3[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*popt_tisaoff[2] + popt_tisaoff[3] for f in MeasuredFluo]
return FinalFluo
popt, pcov = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[10, 100], bounds=((0, 0), (50, 300)))
print(popt)
Sat_3 = popt[0]
Det_3 = popt[1]
FittedEITpi_3 = FitEITpi(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, CountsDRpi_3, yerr=2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
SatsTisa.append(popt[0])
DetsTisa.append(popt[1])
AngsTisa.append(titarepump)
def FitEITpi_withoutscale(freqs, SR, DETREPUMP):
temp = 2e-3
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(sg, SR, sp, gPS, gPD, DetDoppler, DETREPUMP, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
FinalFluo = [f*100 + 0 for f in MeasuredFluo]
return FinalFluo
FittedEITpi_3 = FitEITpi_withoutscale(freqslongpi_3, *popt)
plt.figure()
plt.errorbar(FreqsDRpi_3, [100*(c- popt_tisaoff[3])/ popt_tisaoff[2] for c in CountsDRpi_3], yerr=(100/popt_tisaoff[2])*2*np.sqrt(CountsDRpi_3), fmt='o', capsize=2, markersize=2)
#plt.plot(FreqsDRpi_3, [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3])
plt.plot(freqslongpi_3, FittedEITpi_3)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
vector = [((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3]
minsss = argrelextrema(np.array(FittedEITpi_3), np.less)
minsssmeas = [int(m*len(vector)/len(FittedEITpi_3)) for m in minsss[0]]
Fluosminis = [[((c- popt_tisaoff[3])/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
FluosminisError = [[((np.sqrt(c))/ popt_tisaoff[2])*100 for c in CountsDRpi_3][m] for m in minsssmeas]
if len(Fluosminis) == 4:
print('hay 4')
FluosMinis.append(Fluosminis)
FluosMinisError.append(FluosminisError)
elif len(Fluosminis) == 3:
print('hay 3')
FluosMinis.append([Fluosminis[0], (CountsDRpi_3[73]-popt_tisaoff[3])*100/popt_tisaoff[2], Fluosminis[1], Fluosminis[2]])
FluosMinisError.append([FluosminisError[0], (np.sqrt(CountsDRpi_3[73]))*100/popt_tisaoff[2], FluosminisError[1], FluosminisError[2]])
else:
print('paso algo che')
print('fin 75º')
analisis/plots/20230306_DD_DarkResonancesAnalysis/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ cd346c12
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
else:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
def GetClosestIndex(Vector, value, tolerance=1e-3):
i = 0
while i<len(Vector):
if abs(Vector[i] - value) < tolerance:
return i
else:
i = i + 1
return GetClosestIndex(Vector, value, tolerance=2*tolerance)
def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
"""
Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
Si no encuentra, devuelve el valor teórico.
"""
IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
try:
FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
except:
FreqDR1 = TeoDR[0]
IndiceDR1 = IndiceDRteo1
try:
FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
except:
FreqDR2 = TeoDR[1]
IndiceDR2 = IndiceDRteo2
try:
FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
except:
FreqDR3 = TeoDR[2]
IndiceDR3 = IndiceDRteo3
try:
FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
except:
FreqDR4 = TeoDR[3]
IndiceDR4 = IndiceDRteo4
try:
FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
except:
FreqDR5 = TeoDR[4]
IndiceDR5 = IndiceDRteo5
try:
FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
except:
FreqDR6 = TeoDR[5]
IndiceDR6 = IndiceDRteo6
return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
"""
Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz
"""
IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
DistanciaFrecuenciaCociente = 25
if NormalizationCriterium==0:
print('che')
return FrequencyOfMinimums, FluorescenceOfMinimums
if NormalizationCriterium==1:
Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
indiceizquierda = k
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
indicederecha = l
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
#asintotico
FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
if NormalizationCriterium==4:
#este te tira la fluorescencia de detuning 0
FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
print('Esto: ', RelativeFluorescenceOfMinimums)
if NormalizationCriterium==2 and getindices==True:
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
"""
Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
La estructura es:
MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
"""
Mapa1 = MapasDR1[0]
Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
Fluo1 = RelMinMedido1[0]
Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
"""
Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
en blanco la zona donde el valor medido se puede hallar.
"""
CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
i = 0
while i<len(theoricalmap):
j = 0
while j<len(theoricalmap[0]):
if abs(theoricalmap[i][j]-target) < tolerance:
CoincidenceMatrix[i][j] = 1
j=j+1
i=i+1
return CoincidenceMatrix
def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
MatrixMapsVectors = []
for i in range(len(MapsVectors)):
MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
for i in range(len(MatrixMapsVectors)):
MatrixSum = MatrixSum + MatrixMapsVectors[i]
MaxNumberOfCoincidences = np.max(MatrixSum)
ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
for i in range(len(ListMatrixSum)):
for j in range(len(ListMatrixSum[0])):
if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
AbsoluteCoincidencesMatrix[i][j] = 1
return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
Xsum = 0
Xvec = []
Ysum = 0
Yvec = []
N = len(NonZeroIndices[0])
for i in range(N):
Xsum = Xsum + X[NonZeroIndices[1][i]]
Xvec.append(X[NonZeroIndices[1][i]])
Ysum = Ysum + Y[NonZeroIndices[0][i]]
Yvec.append(Y[NonZeroIndices[0][i]])
Xaverage = Xsum/N
Yaverage = Ysum/N
Xspread = np.std(Xvec)
Yspread = np.std(Yvec)
return Xaverage, Yaverage, N, Xspread, Yspread
def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
print('hola')
print(RelativeFluorescenceOfMinimums0)
return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def AddNoiseToCPT(Fluo, noisefactor):
return [f+noisefactor*(2*random.random()-1) for f in Fluo]
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
"""
FUNCION VIEJA
Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
Minimumcriterion:
1: Saca los minimos con funcion argelextrema
2: Directamente con las frecuencias teoricas busca las fluorescencias
Normalizationcriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
"""
FluorescenceOfMaximum = max(Fluo)
FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
#criterio para encontrar los minimos
#criterio usando minimos de la fluorescencia calculados con la curva
if MinimumCriterium == 1:
LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
#criterio con las DR teoricas
if MinimumCriterium == 2:
FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
FrequencyOfMinimums = []
FluorescenceOfMinimums =[]
print(FrecuenciasDRTeoricas)
k=0
ventanita = 0.001
while k < len(Freq):
if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
k = k + 1
print(FrequencyOfMinimums)
if len(FrequencyOfMinimums) != len(FrecuenciasDRTeoricas):
print('NO ANDA BIEN ESTO PAPI, revisalo')
#esto es para establecer un criterio para la fluorescencia relativa
DistanciaFrecuenciaCociente = 15
if NormalizationCriterium==1:
FluoNormDivisor = 1
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
FluoNormDivisor = Fluo[0]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
#primero buscamos las frecuencias referencia que se parezcan a las 6:
i = 0
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
while i < len(FrequencyOfMinimumsVector):
if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
print('Cool! Taking the DR identified with any curve')
break
else:
i = i + 1
if i==len(FrequencyOfMinimumsVector):
print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
if taketheoricalDR:
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
print('Ventana = ', Ventana)
DarkResonance1Frequency = []
DarkResonance1Fluorescence = []
DarkResonance2Frequency = []
DarkResonance2Fluorescence = []
DarkResonance3Frequency = []
DarkResonance3Fluorescence = []
DarkResonance4Frequency = []
DarkResonance4Fluorescence = []
DarkResonance5Frequency = []
DarkResonance5Fluorescence = []
DarkResonance6Frequency = []
DarkResonance6Fluorescence = []
i = 0
while i < len(FrequencyOfMinimumsVector):
j = 0
FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
while j < len(FrequencyOfMinimumsVector[i]):
if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[0] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[1] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[2] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[3] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[4] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[5] = 0
else:
#print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
pass
j = j + 1
if np.count_nonzero(FrecuenciasReferencia) > 0:
if FrecuenciasReferencia[0] != 0:
DarkResonance1Frequency.append(FrecuenciasReferencia[0])
DarkResonance1Fluorescence.append()
if FrecuenciasReferencia[1] != 0:
DarkResonance2Frequency.append(FrecuenciasReferencia[1])
DarkResonance2Fluorescence.append(0)
if FrecuenciasReferencia[2] != 0:
DarkResonance3Frequency.append(FrecuenciasReferencia[2])
DarkResonance3Fluorescence.append(0)
if FrecuenciasReferencia[3] != 0:
DarkResonance4Frequency.append(FrecuenciasReferencia[3])
DarkResonance4Fluorescence.append(0)
if FrecuenciasReferencia[4] != 0:
DarkResonance5Frequency.append(FrecuenciasReferencia[4])
DarkResonance5Fluorescence.append(0)
if FrecuenciasReferencia[5] != 0:
DarkResonance6Frequency.append(FrecuenciasReferencia[5])
DarkResonance6Fluorescence.append(0)
i = i + 1
return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
analisis/plots/20230306_DD_DarkResonancesAnalysis/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ cd346c12
#!/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/20230306_DD_DarkResonancesAnalysis/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ cd346c12
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
def H0matrix(Detg, Detp, u):
"""
Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning del repump y u es el campo magnético en Hz/Gauss.
Para esto se toma la energía del nivel P como 0
"""
eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5, Detp-2*u/5, Detp+2*u/5, Detp+6*u/5) #pagina 26 de Oberst. los lande del calcio son iguales a Bario.
H0 = np.diag(eigenEnergies)
return H0
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
Calcula la matriz de interacción Hsp + Hpd, en donde rabR es la frecuencia de rabi de la transición Doppler SP,
rabP es la frecuencia de rabi de la transición repump DP, y las componentes ei_r y ei_p son las componentes de la polarización
del campo eléctrico incidente de doppler y repump respectivamente. Deben estar normalizadas a 1
"""
HI = np.zeros((8, 8), dtype=np.complex_)
i, j = 1, 3
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 = 1, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 3
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(-1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
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.exp(-1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 6
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 7
HI[i-1, j-1] = rabP/np.sqrt(12) * np.sin(titaprobe)*np.exp(1j*phiprobe)
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.exp(-1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 7
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 8
HI[i-1, j-1] = (rabP/2) * np.sin(titaprobe)*np.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
Hintplus = np.zeros((8, 8), dtype=np.complex_)
Hintminus = np.zeros((8, 8), dtype=np.complex_)
Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
if forma==1:
Lplus = np.zeros((64, 64), dtype=np.complex_)
Lminus = np.zeros((64, 64), dtype=np.complex_)
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j==q:
if (k==2 or k==3) and r > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k])
if (r==2 or r==3) and k > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
elif r==k:
if (q==2 or q==3) and j > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q])
if (j==2 or j==3) and q > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
DeltaBar[i, i] = (1j)*(detr - detp)
return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
Sp = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwr, lwp):
"""
Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
"""
Leff = np.zeros((8, 8), dtype=np.complex_)
Leff[0, 0] = 2*lwg
Leff[1, 1] = 2*lwg
Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[4, 4] = 2*(lwr + lwp)
Leff[5, 5] = 2*(lwr + lwp)
Leff[6, 6] = 2*(lwr + lwp)
Leff[7, 7] = 2*(lwr + lwp)
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp):
"""
Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
ya que los componentes de Cm son reales.
Esta M es la suma de las 8 matrices M.
"""
M = np.matrix(np.zeros((64, 64), dtype=np.complex_))
M[0,27] = (2/3)*gPS
M[9,18] = (2/3)*gPS
M[0,18] = (1/3)*gPS
M[1,19] = -(1/3)*gPS
M[8,26] = -(1/3)*gPS
M[9,27] = (1/3)*gPS
M[36,18] = (1/2)*gPD
M[37,19] = (1/np.sqrt(12))*gPD
M[44,26] = (1/np.sqrt(12))*gPD
M[45,27] = (1/6)*gPD
M[54,18] = (1/6)*gPD
M[55,19] = (1/np.sqrt(12))*gPD
M[62,26] = (1/np.sqrt(12))*gPD
M[63,27] = (1/2)*gPD
M[45,18] = (1/3)*gPD
M[46,19] = (1/3)*gPD
M[53,26] = (1/3)*gPD
M[54,27] = (1/3)*gPD
M[0,0] = 2*lwg
M[1,1] = 2*lwg
M[8,8] = 2*lwg
M[9,9] = 2*lwg
factor1 = 1
factor2 = 1
factor3 = 1
factor4 = 1
#M[36, 45] = lwp
M[36,36] = 2*(lwr + factor1*lwp)
M[37,37] = 2*(lwr + factor1*lwp)
M[38,38] = 2*(lwr + factor1*lwp)
M[39,39] = 2*(lwr + factor1*lwp)
M[44,44] = 2*(lwr + factor2*lwp)
M[45,45] = 2*(lwr + factor2*lwp)
M[46,46] = 2*(lwr + factor2*lwp)
M[47,47] = 2*(lwr + factor2*lwp)
M[52,52] = 2*(lwr + factor3*lwp)
M[53,53] = 2*(lwr + factor3*lwp)
M[54,54] = 2*(lwr + factor3*lwp)
M[55,55] = 2*(lwr + factor3*lwp)
M[60,60] = 2*(lwr + factor4*lwp)
M[61,61] = 2*(lwr + factor4*lwp)
M[62,62] = 2*(lwr + factor4*lwp)
M[63,63] = 2*(lwr + factor4*lwp)
return M
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
return gammaD
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
Heffdaga = np.matrix(Heff).getH()
Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j!=q and r!=k:
pass
elif j==q and r!=k:
if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2): #todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
elif j!=q and r==k:
if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
else:
if Heff[r,k] == Heffdaga[j,q]:
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
M = CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
nmax = 1
Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
factor1 = np.exp(1j*0.2*np.pi)
factor2 = np.exp(-1j*0.2*np.pi)
#print(factor)
L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
Lfull = L0 + L1
#NORMALIZACION DE RHO
i = 0
while i < 64:
if i%9 == 0:
Lfull[0, i] = 1
else:
Lfull[0, i] = 0
i = i + 1
return Lfull
"""
Scripts para correr un experimento y hacer el análisis de los datos
"""
def CalculoTeoricoDarkResonances(u, titadoppler):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return NegativeDR, PositiveDR
def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
rabG = sg*gPS
rabR = sr*gPD
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
#Fluo = np.abs(rhovectorized[coh])
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
if __name__ == "__main__":
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 25 #campo magnetico en gauss
u = c*B
sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
Detg = -25
Detr = 20 #detuning del doppler y repump
Temp = 0.0e-3 #temperatura en K
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe, titaprobe = 0, 90
plotCPT = False
freqMin = -50
freqMax = 50
freqStep = 5e-2
Frequencyvector, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
for PDR in PositiveDR:
plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
for NDR in NegativeDR:
plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
#parametros que andan piola:
"""
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 17 #campo magnetico en gauss
u = c*B
#u = 80e6
sr, sp = 0.53, 4.2
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
rabR, rabP = sr*gPS, sp*gPD
lw = 2*np.pi * 0.33e6
lwr, lwp = lw, lw #ancho de linea de los laseres
dr_spec = - 2*np.pi* 26e6
freqSteps = 500
freqMin = -100e6
freqMax = 100e6
dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
#dps = [-30e6]
alfar = 90*(np.pi/180)
ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
alfap = 90*(np.pi/180)
ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
"""
analisis/plots/20230321_heatingrate/CPT_plotter_20230321.py 0 → 100644
View file @ cd346c12
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
CPT_FILES = """000010420-IR_Scan_withcal_optimized
000010436-IR_Scan_withcal_optimized
000010437-IR_Scan_withcal_optimized
000010438-IR_Scan_withcal_optimized
000010439-IR_Scan_withcal_optimized
000010440-IR_Scan_withcal_optimized
000010441-IR_Scan_withcal_optimized
"""
HEATING_FILES = """000010422-HeatingRate"""
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(CPT_FILES))
print(SeeKeys(HEATING_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
Counts = []
Freqs = []
AmpTisa = []
UVCPTAmp = []
No_measures = []
for i, fname in enumerate(CPT_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
Counts.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures.append(np.array(data['datasets']['no_measures']))
Counts_heating = []
Times = []
AmpTisa_heating = []
UVCPTAmp_heating = []
No_measures_heating = []
for i, fname in enumerate(HEATING_FILES.split()):
print(str(i) + ' - ' + fname)
#print(fname)
data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
# Aca hago algo repugnante para poder levantar los strings que dejamos
# que además tenian un error de tipeo al final. Esto no deberá ser necesario
# cuando se solucione el error este del guardado.
Times.append(np.array(data['datasets']['heating_times']))
Counts_heating.append(np.array(data['datasets']['counts_spectrum']))
#AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
UVCPTAmp_heating.append(np.array(data['datasets']['UV_CPT_amp']))
No_measures_heating.append(np.array(data['datasets']['no_measures']))
#%%
"""
Ploteo la cpt de referencia
"""
jvec = [0]
plt.figure()
i = 0
for j in jvec:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.grid()
plt.legend()
#%%
from EITfit.threeLevel_2repumps_AnalysisFunctions import CalculoTeoricoDarkResonances_8levels, GetMinimaInfo, GetPlotsofFluovsAngle_8levels, PerformExperiment_8levels, FindDRFrequencies, FindRelativeFluorescencesOfDR, GenerateNoisyCPT, SmoothNoisyCPT, GetFinalMaps, GenerateNoisyCPT_fixedRabi, GenerateNoisyCPT_fit
from EITfit.threeLevel_2repumps_AnalysisFunctions import MeasureRelativeFluorescenceFromCPT, IdentifyPolarizationCoincidences, RetrieveAbsoluteCoincidencesBetweenMaps, GetClosestIndex
from scipy.optimize import curve_fit
"""
Ajusto un cpt para obtener todos los parámetros relevantes primero.
"""
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 0
phiprobe = 0
titaprobe = 90
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
alpha = 0
noiseamplitude = 0
T = 0.6e-3
sg = 0.544
sp = 4.5
sr = 0
DetRepump = 0
lw = 0.1
DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
u = 33.5e6
c=3e8
B = (u/(2*np.pi))/c
correccion = 6 #con 3 fitea bien
offsetxpi = 440+1+correccion
DetDoppler = -10.5-correccion
FreqsDRpi = [2*f*1e-6-offsetxpi+14 for f in Freqs[0]]
CountsDRpi = Counts[0]
freqslongpi = np.arange(min(FreqsDRpi), max(FreqsDRpi)+FreqsDRpi[1]-FreqsDRpi[0], 0.1*(FreqsDRpi[1]-FreqsDRpi[0]))
#[1.71811842e+04 3.34325038e-17]
def FitEITpi(freqs, SG, SP, scale, offset, temp):
#temp = 2.9e-4
MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, sr, SP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, temp, alpha, phidoppler, titadoppler, phiprobe, [titaprobe], phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=noiseamplitude)
#scale = 2.85164232e+04
#offset = 2.16392191e+03
FinalFluo = [f*scale + offset for f in MeasuredFluo]
return FinalFluo
#con doppler broadening solo en uv da: SG, SP, scale, offset, temp
#[4.57165783e-01 8.05907940e+00 6.80017204e+04 3.61150824e-17, 6.77295995e-03]
#con broadening en ambos laseres da
#[4.65604861e-01 8.06781613e+00 6.65914036e+04 4.12611199e-14, 1.80590400e-03]
#esos valores anteriores dan mal la ordenada al origen, cambie correccion y ahora da
#[4.71134671e-01, 7.63142299e+00, 7.30866544e+04, 1.80899906e+02, 1.20863371e-03]
print(f'Temperatura: ({round(1e3*popt_fullcpt[-1],2)} +- {round(1e3*np.sqrt(pcov_fullcpt[-1][-1]),2)}) mK')
popt_fullcpt, pcov_fullcpt = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, p0=[0.5, 4.5, 1e4, 1e3, 1e-3], bounds=((0, 0, 0, 0, 0), (2, 10, 1e5, 1e5, 10e-3)))
print(popt_fullcpt)
Sat = popt_fullcpt[0]
Det = popt_fullcpt[1]
FittedEITpi = FitEITpi(freqslongpi, *popt_fullcpt)
plt.figure()
plt.errorbar(FreqsDRpi, CountsDRpi, yerr=2*np.sqrt(CountsDRpi), fmt='o', capsize=2, markersize=2)
plt.plot(freqslongpi, FittedEITpi)
#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
#%%
"""
Simulo CPTs con todos esos parámetros para distintas temperaturas
"""
TempVecTeorico = list(np.arange(0, 31, 1))
CurvasTeoricas = []
for tempi in TempVecTeorico:
CurvasTeoricas.append(FitEITpi(freqslongpi, *popt_fullcpt[:-1], tempi*1e-3))
#%%
"""
Acá agarro la primera y busco el valor i_DR que corresponde a la resonancia oscura de interés
"""
curva_ref = CurvasTeoricas[0]
i_DR = 955
plt.figure()
plt.plot(freqslongpi, curva_ref)
plt.plot(freqslongpi[i_DR], curva_ref[i_DR],'o')
#%%
"""
Ahora interpolo los valores teóricos de las profundidades de esas resonancias
y aplico la interpolación a las mediciones para obtener temperaturas.
Luego, grafico las temperaturas en función de los tiempos de calentamiento.
"""
from scipy.interpolate import interp1d
FluosDRTeo = [CurvasTeoricas[k][i_DR] for k in range(len(CurvasTeoricas))]
interpolado = interp1d(FluosDRTeo, TempVecTeorico) #creo funcion que interpola
meas = 0
maxi = 9 #valor maximo, dsp el ion se calento
Heating_tim = Times[meas][:maxi]
Heating_tim_ms = [t*1e3 for t in Heating_tim]
Heating_med = [2*c for c in Counts_heating[meas][:maxi]]
ErrorHeating_med = [2*np.sqrt(c) for c in Counts_heating[meas][:maxi]]
Temperaturas_interpoladas = [float(interpolado(h)) for h in Heating_med]
Error_Temperaturas_interpoladas = [float(interpolado(Heating_med[k]+0.5*ErrorHeating_med[k]))-Temperaturas_interpoladas[k] for k in range(len(Heating_med))]
plt.figure()
plt.plot(FluosDRTeo, [1*t for t in TempVecTeorico], 'o', color='orange')
plt.plot(np.linspace(FluosDRTeo[0],FluosDRTeo[-1],1000), interpolado(np.linspace(FluosDRTeo[0],FluosDRTeo[-1],1000)))
plt.xlabel('Cuentas de DR teoricas')
plt.ylabel('Temperatura (mK)')
plt.figure()
#plt.plot(Heating_med, Heating_tim, 'o', color='blue')
plt.errorbar(Heating_tim, Heating_med, yerr=ErrorHeating_med, fmt='o', capsize=2, markersize=2)
plt.ylabel('Cuentas de DR medidas')
plt.xlabel('Heating time (s)')
def lineal(x,a,b):
return a*x+b
#p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas, sigma=Error_Temperaturas_interpoladas)
p1,p2 = curve_fit(lineal, Heating_tim_ms, Temperaturas_interpoladas)
#%%
"""
Grafico finalmente el plot del heating rate de la trampa
"""
plt.figure()
#plt.plot(Heating_tim_ms,Temperaturas_interpoladas,'o')
plt.errorbar(Heating_tim_ms,Temperaturas_interpoladas, yerr=np.array(Error_Temperaturas_interpoladas), fmt='o', capsize=2, markersize=7, color='black')
plt.plot(Heating_tim_ms, lineal(np.array(Heating_tim_ms), *p1), color='red')
plt.xlabel('Heating time (ms)', fontname='STIXGeneral', fontsize=15)
plt.ylabel('Temperature (mK)', fontname='STIXGeneral', fontsize=15)
plt.grid()
plt.xticks([0, 5, 10, 15, 20 ,25, 30, 35], fontname='STIXGeneral', fontsize=15)
plt.yticks([0, 5, 10, 15], fontname='STIXGeneral', fontsize=15)
plt.title(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms', fontname='STIXGeneral', fontsize=15)
plt.tight_layout()
plt.savefig('Fig_heatingrate.svg')
print(f'Heating rate: ({round(p1[0],2)} +- {round(np.sqrt(p2[0][0]),2)}) mK/ms')
#%%
"""
Ahora voy a ver CPT enteras con tiempos de calentamiento distintos.
"""
jvec = [3, 4]
plt.figure()
i = 0
for j in jvec:
if j==4:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==3:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='5 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,2900)
plt.grid()
plt.legend()
jvec = [1, 2]
plt.figure()
i = 0
for j in jvec:
if j==2:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==1:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='1 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,2900)
plt.grid()
plt.legend()
"""
La siguiente curva probablemente no este bien medida ya que inmediatamente
despues, los laseres se deslockearon. La dejo por las dudas.
"""
jvec = [5, 6]
plt.figure()
i = 0
for j in jvec:
if j==6:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='Without heating')
elif j==5:
plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2, label='10 ms heating')
i = i + 1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('counts')
plt.ylim(1000,3900)
plt.grid()
plt.legend()
plt.title('Ojo: medicion condicionada por derivas')
analisis/plots/20230321_heatingrate/Data/EITfit/threeLevel_2repumps_AnalysisFunctions.py 0 → 100644
View file @ cd346c12
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 16:30:09 2020
@author: oem
"""
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
from EITfit.threeLevel_2repumps_linealpol_python_scripts import CPTspectrum8levels, CPTspectrum8levels_fixedRabi
import random
from scipy.signal import savgol_filter as sf
def CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
else:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return [detuningdoppler + dr for dr in NegativeDR], [detuningrepump + dr for dr in PositiveDR]
def GetClosestIndex(Vector, value, tolerance=1e-3):
i = 0
while i<len(Vector):
if abs(Vector[i] - value) < tolerance:
return i
else:
i = i + 1
return GetClosestIndex(Vector, value, tolerance=2*tolerance)
def FindDRFrequencies(Freq, Fluo, TeoDR, entorno=3):
"""
Busca los indices y la frecuencia de los minimos en un entorno cercano al de la DR.
Si no encuentra, devuelve el valor teórico.
"""
IndiceDRteo1, IndiceEntornoinicialDRteo1, IndiceEntornofinalDRteo1 = GetClosestIndex(Freq, TeoDR[0]), GetClosestIndex(Freq, TeoDR[0]-entorno), GetClosestIndex(Freq, TeoDR[0]+entorno)
IndiceDRteo2, IndiceEntornoinicialDRteo2, IndiceEntornofinalDRteo2 = GetClosestIndex(Freq, TeoDR[1]), GetClosestIndex(Freq, TeoDR[1]-entorno), GetClosestIndex(Freq, TeoDR[1]+entorno)
IndiceDRteo3, IndiceEntornoinicialDRteo3, IndiceEntornofinalDRteo3 = GetClosestIndex(Freq, TeoDR[2]), GetClosestIndex(Freq, TeoDR[2]-entorno), GetClosestIndex(Freq, TeoDR[2]+entorno)
IndiceDRteo4, IndiceEntornoinicialDRteo4, IndiceEntornofinalDRteo4 = GetClosestIndex(Freq, TeoDR[3]), GetClosestIndex(Freq, TeoDR[3]-entorno), GetClosestIndex(Freq, TeoDR[3]+entorno)
IndiceDRteo5, IndiceEntornoinicialDRteo5, IndiceEntornofinalDRteo5 = GetClosestIndex(Freq, TeoDR[4]), GetClosestIndex(Freq, TeoDR[4]-entorno), GetClosestIndex(Freq, TeoDR[4]+entorno)
IndiceDRteo6, IndiceEntornoinicialDRteo6, IndiceEntornofinalDRteo6 = GetClosestIndex(Freq, TeoDR[5]), GetClosestIndex(Freq, TeoDR[5]-entorno), GetClosestIndex(Freq, TeoDR[5]+entorno)
EntornoFreqDR1, EntornoFreqDR2 = Freq[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Freq[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFreqDR3, EntornoFreqDR4 = Freq[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Freq[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFreqDR5, EntornoFreqDR6 = Freq[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Freq[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
EntornoFluoDR1, EntornoFluoDR2 = Fluo[IndiceEntornoinicialDRteo1:IndiceEntornofinalDRteo1], Fluo[IndiceEntornoinicialDRteo2:IndiceEntornofinalDRteo2]
EntornoFluoDR3, EntornoFluoDR4 = Fluo[IndiceEntornoinicialDRteo3:IndiceEntornofinalDRteo3], Fluo[IndiceEntornoinicialDRteo4:IndiceEntornofinalDRteo4]
EntornoFluoDR5, EntornoFluoDR6 = Fluo[IndiceEntornoinicialDRteo5:IndiceEntornofinalDRteo5], Fluo[IndiceEntornoinicialDRteo6:IndiceEntornofinalDRteo6]
IndiceFluoMinimaEntorno1, IndiceFluoMinimaEntorno2 = argrelextrema(np.array(EntornoFluoDR1), np.less)[0], argrelextrema(np.array(EntornoFluoDR2), np.less)[0]
IndiceFluoMinimaEntorno3, IndiceFluoMinimaEntorno4 = argrelextrema(np.array(EntornoFluoDR3), np.less)[0], argrelextrema(np.array(EntornoFluoDR4), np.less)[0]
IndiceFluoMinimaEntorno5, IndiceFluoMinimaEntorno6 = argrelextrema(np.array(EntornoFluoDR5), np.less)[0], argrelextrema(np.array(EntornoFluoDR6), np.less)[0]
try:
FreqDR1 = EntornoFreqDR1[int(IndiceFluoMinimaEntorno1)]
IndiceDR1 = GetClosestIndex(Freq, FreqDR1)
except:
FreqDR1 = TeoDR[0]
IndiceDR1 = IndiceDRteo1
try:
FreqDR2 = EntornoFreqDR2[int(IndiceFluoMinimaEntorno2)]
IndiceDR2 = GetClosestIndex(Freq, FreqDR2)
except:
FreqDR2 = TeoDR[1]
IndiceDR2 = IndiceDRteo2
try:
FreqDR3 = EntornoFreqDR3[int(IndiceFluoMinimaEntorno3)]
IndiceDR3 = GetClosestIndex(Freq, FreqDR3)
except:
FreqDR3 = TeoDR[2]
IndiceDR3 = IndiceDRteo3
try:
FreqDR4 = EntornoFreqDR4[int(IndiceFluoMinimaEntorno4)]
IndiceDR4 = GetClosestIndex(Freq, FreqDR4)
except:
FreqDR4 = TeoDR[3]
IndiceDR4 = IndiceDRteo4
try:
FreqDR5 = EntornoFreqDR5[int(IndiceFluoMinimaEntorno5)]
IndiceDR5 = GetClosestIndex(Freq, FreqDR5)
except:
FreqDR5 = TeoDR[4]
IndiceDR5 = IndiceDRteo5
try:
FreqDR6 = EntornoFreqDR6[int(IndiceFluoMinimaEntorno6)]
IndiceDR6 = GetClosestIndex(Freq, FreqDR6)
except:
FreqDR6 = TeoDR[5]
IndiceDR6 = IndiceDRteo6
return [IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6], [FreqDR1, FreqDR2, FreqDR3, FreqDR4, FreqDR5, FreqDR6]
def FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=-100, getindices=False):
"""
Toma los indices donde estan las DR y evalua su fluorescencia. Esos indices son minimos locales en un entorno
cercano a las DR teoricas y, si no hay ningun minimo, toma la teorica.
Luego, hace el cociente de esa fluorescencia y un factor de normalización segun NormalizationCriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
4: Deuelve el cociente entre la fluorescencia del minimo y el valor de fluorescencia a detuning 0 MHz
"""
IndiceDR1, IndiceDR2, IndiceDR3, IndiceDR4, IndiceDR5, IndiceDR6 = IndicesDR[0], IndicesDR[1], IndicesDR[2], IndicesDR[3], IndicesDR[4], IndicesDR[5]
FluorescenceOfMinimums = [Fluo[IndiceDR1], Fluo[IndiceDR2], Fluo[IndiceDR3], Fluo[IndiceDR4], Fluo[IndiceDR5], Fluo[IndiceDR6]]
FrequencyOfMinimums = [Freq[IndiceDR1], Freq[IndiceDR2], Freq[IndiceDR3], Freq[IndiceDR4], Freq[IndiceDR5], Freq[IndiceDR6]]
DistanciaFrecuenciaCociente = 25
if NormalizationCriterium==0:
print('che')
return FrequencyOfMinimums, FluorescenceOfMinimums
if NormalizationCriterium==1:
Fluorescenciacerodetuning = Fluo[GetClosestIndex(Freq, 0)]
Fluorescenciaasintotica = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
return FrequencyOfMinimums, np.array([Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica, Fluorescenciacerodetuning/Fluorescenciaasintotica])
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
indiceizquierda = k
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
indicederecha = l
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
#asintotico
FluoNormDivisor = Fluo[GetClosestIndex(Freq, frecuenciareferenciacriterioasintotico)]
if NormalizationCriterium==4:
#este te tira la fluorescencia de detuning 0
FluoNormDivisor = Fluo[GetClosestIndex(Freq, 0)]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
print('Esto: ', RelativeFluorescenceOfMinimums)
if NormalizationCriterium==2 and getindices==True:
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums, indiceizquierda, indicederecha
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetFinalMaps(MapasDR1, MapasDR2, MapasDR3, MapasDR4, MapasDR5, MapasDR6):
"""
Nota: esto vale para polarizacion del 397 sigma+ + sigma-. Sino hay que cambiar los coeficientes.
La estructura es:
MapasDRi = [MapaMedido_criterio1_DRi, MapaMedido_criterio2_DRi, MapaMedido_criterio3_DRi, MapaMedido_criterio4_DRi]
"""
Mapa1 = MapasDR1[0]
Mapa2pi = np.sqrt(3)*(MapasDR2[1] + MapasDR5[1])
Mapa2smas = np.sqrt(12/2)*MapasDR3[1] + (2/np.sqrt(2))*MapasDR6[1]
Mapa2smenos = (2/np.sqrt(2))*MapasDR1[1] + np.sqrt(12/2)*MapasDR4[1]
Mapa3pi = np.sqrt(3)*(MapasDR2[2] + MapasDR5[2])
Mapa3smas = np.sqrt(12/2)*MapasDR3[2] + (2/np.sqrt(2))*MapasDR6[2]
Mapa3smenos = (2/np.sqrt(2))*MapasDR1[2] + np.sqrt(12/2)*MapasDR4[2]
return Mapa1, [Mapa2pi, Mapa2smas, Mapa2smenos], [Mapa3pi, Mapa3smas, Mapa3smenos]
def CombinateDRwithCG(RelMinMedido1, RelMinMedido2, RelMinMedido3, RelMinMedido4):
Fluo1 = RelMinMedido1[0]
Fluo2pi = np.sqrt(3)*(RelMinMedido2[1] + RelMinMedido2[4])
Fluo2smas = np.sqrt(12/2)*RelMinMedido2[2] + (2/np.sqrt(2))*RelMinMedido2[5]
Fluo2smenos = (2/np.sqrt(2))*RelMinMedido2[0] + np.sqrt(12/2)*RelMinMedido2[3]
Fluo3pi = np.sqrt(3)*(RelMinMedido3[1] + RelMinMedido3[4])
Fluo3smas = np.sqrt(12/2)*RelMinMedido3[2] + (2/np.sqrt(2))*RelMinMedido3[5]
Fluo3smenos = (2/np.sqrt(2))*RelMinMedido3[0] + np.sqrt(12/2)*RelMinMedido3[3]
return Fluo1, [Fluo2pi, Fluo2smas, Fluo2smenos], [Fluo3pi, Fluo3smas, Fluo3smenos]
def IdentifyPolarizationCoincidences(theoricalmap, target, tolerance=1e-1):
"""
Busca en un mapa 2D la presencia de un valor target (medido) con tolerancia tolerance.
Si lo encuentra, pone un 1. Sino, un 0. Al plotear con pcolor se verá
en blanco la zona donde el valor medido se puede hallar.
"""
CoincidenceMatrix = np.zeros((len(theoricalmap), len(theoricalmap[0])))
i = 0
while i<len(theoricalmap):
j = 0
while j<len(theoricalmap[0]):
if abs(theoricalmap[i][j]-target) < tolerance:
CoincidenceMatrix[i][j] = 1
j=j+1
i=i+1
return CoincidenceMatrix
def RetrieveAbsoluteCoincidencesBetweenMaps(MapsVectors):
MatrixSum = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
AbsoluteCoincidencesMatrix = np.zeros((len(MapsVectors[0]), len(MapsVectors[0][0])))
MatrixMapsVectors = []
for i in range(len(MapsVectors)):
MatrixMapsVectors.append(np.matrix(MapsVectors[i]))
for i in range(len(MatrixMapsVectors)):
MatrixSum = MatrixSum + MatrixMapsVectors[i]
MaxNumberOfCoincidences = np.max(MatrixSum)
ListMatrixSum = [list(i) for i in list(np.array(MatrixSum))]
for i in range(len(ListMatrixSum)):
for j in range(len(ListMatrixSum[0])):
if ListMatrixSum[i][j] == MaxNumberOfCoincidences:
AbsoluteCoincidencesMatrix[i][j] = 1
return AbsoluteCoincidencesMatrix, MaxNumberOfCoincidences
def MeasureMeanValueOfEstimatedArea(AbsoluteCoincidencesMap, X, Y):
NonZeroIndices = np.nonzero(AbsoluteCoincidencesMap)
Xsum = 0
Xvec = []
Ysum = 0
Yvec = []
N = len(NonZeroIndices[0])
for i in range(N):
Xsum = Xsum + X[NonZeroIndices[1][i]]
Xvec.append(X[NonZeroIndices[1][i]])
Ysum = Ysum + Y[NonZeroIndices[0][i]]
Yvec.append(Y[NonZeroIndices[0][i]])
Xaverage = Xsum/N
Yaverage = Ysum/N
Xspread = np.std(Xvec)
Yspread = np.std(Yvec)
return Xaverage, Yaverage, N, Xspread, Yspread
def MeasureRelativeFluorescenceFromCPT(Freq, Fluo, u, titadoppler, detuningrepump, detuningdoppler, frefasint=-100, entorno=3):
ResonanciasTeoricas, ResonanciasPositivas = CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)
IndicesDR, FreqsDR = FindDRFrequencies(Freq, Fluo, ResonanciasTeoricas, entorno=entorno)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums0 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=0, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums1 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=1, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums2, indiceizquierda, indicederecha = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=2, frecuenciareferenciacriterioasintotico=frefasint, getindices=True)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums3 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=3, frecuenciareferenciacriterioasintotico=frefasint)
FrequencyOfMinimums, RelativeFluorescenceOfMinimums4 = FindRelativeFluorescencesOfDR(Freq, Fluo, IndicesDR, detuningdoppler, NormalizationCriterium=4, frecuenciareferenciacriterioasintotico=frefasint)
print('hola')
print(RelativeFluorescenceOfMinimums0)
return RelativeFluorescenceOfMinimums0, RelativeFluorescenceOfMinimums1, RelativeFluorescenceOfMinimums2, RelativeFluorescenceOfMinimums3, RelativeFluorescenceOfMinimums4, IndicesDR, [indiceizquierda, indicederecha]
def GenerateNoisyCPT(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def GenerateNoisyCPT_fit(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqs, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
Frequencyvector, Fluovector = PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, min(freqs), max(freqs) + freqs[1]-freqs[0], freqs[1]-freqs[0], plot=False, solvemode=1, detpvec=None)
NoisyFluovector = [fluo+noiseamplitude*(2*random.random()-1) for fluo in Fluovector]
return Frequencyvector, NoisyFluovector
def AddNoiseToCPT(Fluo, noisefactor):
return [f+noisefactor*(2*random.random()-1) for f in Fluo]
def SmoothNoisyCPT(Fluo, window=11, poly=3):
SmoothenFluo = sf(Fluo, window, poly)
return SmoothenFluo
def GetMinimaInfo(Freq, Fluo, u, titadoppler, detuningdoppler, detuningrepump, MinimumCriterium=2, NormalizationCriterium=1):
"""
FUNCION VIEJA
Esta funcion devuelve valores de frecuencias y fluorescencia relativa de los minimos.
Minimumcriterion:
1: Saca los minimos con funcion argelextrema
2: Directamente con las frecuencias teoricas busca las fluorescencias
Normalizationcriterium:
1: Devuelve la fluorescencia absoluta de los minimos
2: Devuelve el cociente entre la fluorescencia del minimo y un valor medio entre dos puntos lejanos, como si no
hubiera una resonancia oscura y hubiera una recta. Ese valor esta a DistanciaFrecuenciaCociente del detuning del azul (el punto medio entre las dos DR en este caso)
3: Devuelve el cociente entre la fluorescencia del minimo y el valor a -100 MHz (si se hizo de -100 a 100),
o el valor limite por izquierda de la curva
"""
FluorescenceOfMaximum = max(Fluo)
FrequencyOfMaximum = Freq[Fluo.index(FluorescenceOfMaximum)]
#criterio para encontrar los minimos
#criterio usando minimos de la fluorescencia calculados con la curva
if MinimumCriterium == 1:
LocationOfMinimums = argrelextrema(np.array(Fluo), np.less)[0]
FluorescenceOfMinimums = np.array([Fluo[i] for i in LocationOfMinimums])
FrequencyOfMinimums = np.array([Freq[j] for j in LocationOfMinimums])
#criterio con las DR teoricas
if MinimumCriterium == 2:
FrecuenciasDRTeoricas, FrecuenciasDRTeoricasPositivas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
FrequencyOfMinimums = []
FluorescenceOfMinimums =[]
print(FrecuenciasDRTeoricas)
k=0
ventanita = 0.001
while k < len(Freq):
if Freq[k] < FrecuenciasDRTeoricas[0] + ventanita and Freq[k] > FrecuenciasDRTeoricas[0] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[1] + ventanita and Freq[k] > FrecuenciasDRTeoricas[1] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[2] + ventanita and Freq[k] > FrecuenciasDRTeoricas[2] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[3] + ventanita and Freq[k] > FrecuenciasDRTeoricas[3] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[4] + ventanita and Freq[k] > FrecuenciasDRTeoricas[4] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
elif Freq[k] < FrecuenciasDRTeoricas[5] + ventanita and Freq[k] > FrecuenciasDRTeoricas[5] - ventanita:
FrequencyOfMinimums.append(Freq[k])
FluorescenceOfMinimums.append(Fluo[k])
k = k + 1
print(FrequencyOfMinimums)
if len(FrequencyOfMinimums) != len(FrecuenciasDRTeoricas):
print('NO ANDA BIEN ESTO PAPI, revisalo')
#esto es para establecer un criterio para la fluorescencia relativa
DistanciaFrecuenciaCociente = 15
if NormalizationCriterium==1:
FluoNormDivisor = 1
if NormalizationCriterium==2:
k = 0
while k < len(Freq):
if Freq[k] < detuningdoppler-DistanciaFrecuenciaCociente + 2 and Freq[k] > detuningdoppler-DistanciaFrecuenciaCociente - 2:
FluoIzquierda = Fluo[k]
print('Izq:', Freq[k])
break
else:
k = k + 1
l = 0
while l < len(Freq):
if Freq[l] < detuningdoppler+DistanciaFrecuenciaCociente + 2 and Freq[l] > detuningdoppler+DistanciaFrecuenciaCociente - 2:
FluoDerecha = Fluo[l]
print('Der: ', Freq[l])
break
else:
l = l + 1
FluoNormDivisor = 0.5*(FluoDerecha+FluoIzquierda)
print(FluoNormDivisor)
if NormalizationCriterium==3:
FluoNormDivisor = Fluo[0]
RelativeFluorescenceOfMinimums = np.array([Fluore/FluoNormDivisor for Fluore in FluorescenceOfMinimums])
return FrequencyOfMinimums, RelativeFluorescenceOfMinimums
def GetPlotsofFluovsAngle_8levels(FrequencyOfMinimumsVector, RelativeFluorescenceOfMinimumsVector, u, titadoppler, detuningdoppler, detuningrepump, ventana=0.25, taketheoricalDR=False):
#primero buscamos las frecuencias referencia que se parezcan a las 6:
i = 0
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[0]
FrecuenciasDRTeoricas = [darkresonance for darkresonance in CalculoTeoricoDarkResonances_8levels(u, titadoppler, detuningdoppler, detuningrepump)[0]]
while i < len(FrequencyOfMinimumsVector):
if len(FrequencyOfMinimumsVector[i])==len(FrecuenciasDRTeoricas):
FrecuenciasReferenciaBase = FrequencyOfMinimumsVector[i]
print('Cool! Taking the DR identified with any curve')
break
else:
i = i + 1
if i==len(FrequencyOfMinimumsVector):
print('No hay ningun plot con 5 resonancias oscuras. Tomo las teóricas')
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
if taketheoricalDR:
FrecuenciasReferenciaBase = FrecuenciasDRTeoricas
Ventana = abs(ventana*(FrecuenciasReferenciaBase[1] - FrecuenciasReferenciaBase[0])) #ventana separadora de resonancias
print('Ventana = ', Ventana)
DarkResonance1Frequency = []
DarkResonance1Fluorescence = []
DarkResonance2Frequency = []
DarkResonance2Fluorescence = []
DarkResonance3Frequency = []
DarkResonance3Fluorescence = []
DarkResonance4Frequency = []
DarkResonance4Fluorescence = []
DarkResonance5Frequency = []
DarkResonance5Fluorescence = []
DarkResonance6Frequency = []
DarkResonance6Fluorescence = []
i = 0
while i < len(FrequencyOfMinimumsVector):
j = 0
FrecuenciasReferencia = [i for i in FrecuenciasReferenciaBase]
while j < len(FrequencyOfMinimumsVector[i]):
if abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[0])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[0])-Ventana):
DarkResonance1Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance1Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[0] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[1])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[1])-Ventana):
DarkResonance2Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance2Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[1] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[2])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[2])-Ventana):
DarkResonance3Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance3Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[2] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[3])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[3])-Ventana):
DarkResonance4Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance4Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[3] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[4])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[4])-Ventana):
DarkResonance5Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance5Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[4] = 0
elif abs(FrequencyOfMinimumsVector[i][j]) < (abs(FrecuenciasReferencia[5])+Ventana) and abs(FrequencyOfMinimumsVector[i][j]) >= (abs(FrecuenciasReferencia[5])-Ventana):
DarkResonance6Frequency.append(FrequencyOfMinimumsVector[i][j])
DarkResonance6Fluorescence.append(RelativeFluorescenceOfMinimumsVector[i][j])
FrecuenciasReferencia[5] = 0
else:
#print('Algo anduvo mal, por ahi tenes que cambiar la ventana che')
pass
j = j + 1
if np.count_nonzero(FrecuenciasReferencia) > 0:
if FrecuenciasReferencia[0] != 0:
DarkResonance1Frequency.append(FrecuenciasReferencia[0])
DarkResonance1Fluorescence.append()
if FrecuenciasReferencia[1] != 0:
DarkResonance2Frequency.append(FrecuenciasReferencia[1])
DarkResonance2Fluorescence.append(0)
if FrecuenciasReferencia[2] != 0:
DarkResonance3Frequency.append(FrecuenciasReferencia[2])
DarkResonance3Fluorescence.append(0)
if FrecuenciasReferencia[3] != 0:
DarkResonance4Frequency.append(FrecuenciasReferencia[3])
DarkResonance4Fluorescence.append(0)
if FrecuenciasReferencia[4] != 0:
DarkResonance5Frequency.append(FrecuenciasReferencia[4])
DarkResonance5Fluorescence.append(0)
if FrecuenciasReferencia[5] != 0:
DarkResonance6Frequency.append(FrecuenciasReferencia[5])
DarkResonance6Fluorescence.append(0)
i = i + 1
return DarkResonance1Frequency, DarkResonance1Fluorescence, DarkResonance2Frequency, DarkResonance2Fluorescence, DarkResonance3Frequency, DarkResonance3Fluorescence, DarkResonance4Frequency, DarkResonance4Fluorescence, DarkResonance5Frequency, DarkResonance5Fluorescence, DarkResonance6Frequency, DarkResonance6Fluorescence, FrecuenciasReferenciaBase
def PerformExperiment_8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
def PerformExperiment_8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, phirepump, titarepump, freqMin, freqMax, freqStep, plot=False, solvemode=1, detpvec=None):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
Fluovectors = []
for titaprobe in titaprobeVec:
tinicial = time.time()
ProbeDetuningVectorL, Fluovector = CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, DetDoppler, DetRepump, u, DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=False, solvemode=1)
tfinal = time.time()
print('Done angle ', titarepump, ' Total time: ', round((tfinal-tinicial), 2), "s")
if plot:
plt.figure()
plt.xlabel('Repump detuning (MHz')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(ProbeDetuningVectorL, Fluovector, label=str(titarepump)+'º tita repump, T: ' + str(T*1e3) + ' mK')
plt.legend()
Fluovectors.append(Fluovector)
if len(titaprobeVec) == 1: #esto es para que no devuelva un vector de vectores si solo fijamos un angulo
Fluovectors = Fluovector
return ProbeDetuningVectorL, Fluovectors
analisis/plots/20230321_heatingrate/Data/EITfit/threeLevel_2repumps_CPTPlotter.py 0 → 100644
View file @ cd346c12
#!/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/20230321_heatingrate/Data/EITfit/threeLevel_2repumps_linealpol_python_scripts.py 0 → 100644
View file @ cd346c12
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 7 22:30:01 2020
@author: nico
"""
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.signal import argrelextrema
"""
Scripts para el calculo de la curva CPT
"""
def H0matrix(Detg, Detp, u):
"""
Calcula la matriz H0 en donde dr es el detuning del doppler, dp es el retuning del repump y u es el campo magnético en Hz/Gauss.
Para esto se toma la energía del nivel P como 0
"""
eigenEnergies = (Detg-u, Detg+u, -u/3, u/3, Detp-6*u/5, Detp-2*u/5, Detp+2*u/5, Detp+6*u/5) #pagina 26 de Oberst. los lande del calcio son iguales a Bario.
H0 = np.diag(eigenEnergies)
return H0
def HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe):
"""
Calcula la matriz de interacción Hsp + Hpd, en donde rabR es la frecuencia de rabi de la transición Doppler SP,
rabP es la frecuencia de rabi de la transición repump DP, y las componentes ei_r y ei_p son las componentes de la polarización
del campo eléctrico incidente de doppler y repump respectivamente. Deben estar normalizadas a 1
"""
HI = np.zeros((8, 8), dtype=np.complex_)
i, j = 1, 3
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 = 1, 4
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 2, 3
HI[i-1, j-1] = -(rabG/np.sqrt(3)) * np.sin(titadoppler)*np.exp(-1j*phidoppler)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
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.exp(-1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 6
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 3, 7
HI[i-1, j-1] = rabP/np.sqrt(12) * np.sin(titaprobe)*np.exp(1j*phiprobe)
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.exp(-1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 7
HI[i-1, j-1] = -(rabP/np.sqrt(3)) * np.cos(titaprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
i, j = 4, 8
HI[i-1, j-1] = (rabP/2) * np.sin(titaprobe)*np.exp(1j*phiprobe)
HI[j-1, i-1] = np.conjugate(HI[i-1, j-1])
return HI
def Lplusminus(detr, detp, phirepump, titarepump, forma=1):
Hintplus = np.zeros((8, 8), dtype=np.complex_)
Hintminus = np.zeros((8, 8), dtype=np.complex_)
Hintplus[4, 2] = (-1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[5, 2] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[6, 2] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintplus[5, 3] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintplus[6, 3] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintplus[7, 3] = (1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 4] = (-1/2)*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[2, 5] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[2, 6] = (1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(1j*phirepump)
Hintminus[3, 5] = (-1/(2*np.sqrt(3)))*np.sin(titarepump)*np.exp(-1j*phirepump)
Hintminus[3, 6] = (-1/np.sqrt(3))*np.cos(titarepump)
Hintminus[3, 7] = (1/2)*np.sin(titarepump)*np.exp(1j*phirepump)
if forma==1:
Lplus = np.zeros((64, 64), dtype=np.complex_)
Lminus = np.zeros((64, 64), dtype=np.complex_)
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j==q:
if (k==2 or k==3) and r > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(Hintplus[r,k])
if (r==2 or r==3) and k > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(Hintminus[r,k])
elif r==k:
if (q==2 or q==3) and j > 3:
Lplus[r*8+q][k*8+j] = (-1j)*(- Hintplus[j,q])
if (j==2 or j==3) and q > 3:
Lminus[r*8+q][k*8+j] = (-1j)*(- Hintminus[j,q])
if forma==2:
deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
Lplus = (-1j)*(np.kron(Hintplus, deltaKro) - np.kron(deltaKro, Hintplus))
Lminus = (-1j)*(np.kron(Hintminus, deltaKro) - np.kron(deltaKro, Hintminus))
DeltaBar = np.zeros((64, 64), dtype=np.complex_)
for i in range(64):
DeltaBar[i, i] = (1j)*(detr - detp)
return np.matrix(Lminus), np.matrix(Lplus), np.matrix(DeltaBar)
def GetL1(Lplus, Lminus, DeltaBar, L0, rabR, nmax):
"""
Devuelve Splus0 y Sminus0
"""
Sp = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 - (nmax+1)*DeltaBar))*np.matrix(Lplus))
Sm = (-1)*(0.5*rabR)*(np.matrix(np.linalg.inv(L0 + (nmax+1)*DeltaBar))*np.matrix(Lminus))
for n in list(range(nmax+1))[(nmax+1)::-1][0:len(list(range(nmax+1))[(nmax+1)::-1])-1]: #jaja esto solo es para que vaya de nmax a 1 bajando. debe haber algo mas facil pero kcio
Sp = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 - n*DeltaBar + rabR*(Lminus*np.matrix(Sp))))*np.matrix(Lplus))
Sm = (-1)*(rabR)*(np.matrix(np.linalg.inv(L0 + n*DeltaBar + rabR*(Lplus*np.matrix(Sm))))*np.matrix(Lminus))
L1 = 0.5*rabR*(np.matrix(Lminus)*np.matrix(Sp) + np.matrix(Lplus)*np.matrix(Sm))
return L1
def EffectiveL(gPS, gPD, lwg, lwr, lwp):
"""
Siendo Heff = H + EffectiveL, calcula dicho EffectiveL que es (-0.5j)*sumatoria(CmDaga*Cm) que luego sirve para calcular el Liouvilliano
"""
Leff = np.zeros((8, 8), dtype=np.complex_)
Leff[0, 0] = 2*lwg
Leff[1, 1] = 2*lwg
Leff[2, 2] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[3, 3] = ((2/3)+(1/3))*gPS + ((1/2) + (1/6) + (1/3))*gPD
Leff[4, 4] = 2*(lwr + lwp)
Leff[5, 5] = 2*(lwr + lwp)
Leff[6, 6] = 2*(lwr + lwp)
Leff[7, 7] = 2*(lwr + lwp)
return (-0.5j)*Leff
def CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp):
"""
Si tomamos el Liuvilliano como L = (-j)*(Heff*deltak - Heffdaga*deltak) + sum(Mm),
esta funcion calcula dichos Mm, que tienen dimensión 64x64 ya que esa es la dimensión del L. Estas componentes
salen de hacer la cuenta a mano conociendo los Cm y considerando que Mm[8*(r-1)+s, 8*(k-1)+j] = Cm[r,l] + Cmdaga[j,s] = Cm[r,l] + Cm[s,j]
ya que los componentes de Cm son reales.
Esta M es la suma de las 8 matrices M.
"""
M = np.matrix(np.zeros((64, 64), dtype=np.complex_))
M[0,27] = (2/3)*gPS
M[9,18] = (2/3)*gPS
M[0,18] = (1/3)*gPS
M[1,19] = -(1/3)*gPS
M[8,26] = -(1/3)*gPS
M[9,27] = (1/3)*gPS
M[36,18] = (1/2)*gPD
M[37,19] = (1/np.sqrt(12))*gPD
M[44,26] = (1/np.sqrt(12))*gPD
M[45,27] = (1/6)*gPD
M[54,18] = (1/6)*gPD
M[55,19] = (1/np.sqrt(12))*gPD
M[62,26] = (1/np.sqrt(12))*gPD
M[63,27] = (1/2)*gPD
M[45,18] = (1/3)*gPD
M[46,19] = (1/3)*gPD
M[53,26] = (1/3)*gPD
M[54,27] = (1/3)*gPD
M[0,0] = 2*lwg
M[1,1] = 2*lwg
M[8,8] = 2*lwg
M[9,9] = 2*lwg
factor1 = 1
factor2 = 1
factor3 = 1
factor4 = 1
#M[36, 45] = lwp
M[36,36] = 2*(lwr + factor1*lwp)
M[37,37] = 2*(lwr + factor1*lwp)
M[38,38] = 2*(lwr + factor1*lwp)
M[39,39] = 2*(lwr + factor1*lwp)
M[44,44] = 2*(lwr + factor2*lwp)
M[45,45] = 2*(lwr + factor2*lwp)
M[46,46] = 2*(lwr + factor2*lwp)
M[47,47] = 2*(lwr + factor2*lwp)
M[52,52] = 2*(lwr + factor3*lwp)
M[53,53] = 2*(lwr + factor3*lwp)
M[54,54] = 2*(lwr + factor3*lwp)
M[55,55] = 2*(lwr + factor3*lwp)
M[60,60] = 2*(lwr + factor4*lwp)
M[61,61] = 2*(lwr + factor4*lwp)
M[62,62] = 2*(lwr + factor4*lwp)
M[63,63] = 2*(lwr + factor4*lwp)
return M
def dopplerBroadening(wlg, wlp, alpha, T, mcalcio = 6.655e-23*1e-3):
"""
Calcula el broadening extra semiclásico por temperatura considerando que el ion atrapado se mueve.
wlg es la longitud de onda doppler, wlp la longitud de onda repump, T la temperatura del ion en kelvin, y alpha (en rads) el ángulo
que forman ambos láseres.
"""
kboltzmann = 1.38e-23 #J/K
gammaD = (2*np.pi)*np.sqrt((1/(wlg*wlg)) + (1/(wlp*wlp)) - 2*(1/(wlg*wlp))*np.cos(alpha))*np.sqrt(kboltzmann*T/(2*mcalcio))
return gammaD
def FullL_efficient(rabG, rabR, rabP, gPS = 0, gPD = 0, Detg = 0, Detr = 0, Detp = 0, u = 0, lwg = 0, lwr=0, lwp = 0,
phidoppler=0, titadoppler=0, phiprobe=0, titaprobe=0, phirepump=0, titarepump=0, T = 0, alpha = 0):
"""
Calcula el Liouvilliano total de manera explícita índice a índice. Suma aparte las componentes de las matrices M.
Es la más eficiente hasta ahora.
"""
db = dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)
#lwr = np.sqrt(lwr**2 + dopplerBroadening(0.397e-6, 0.866e-6, alpha, T)**2)
lwg = np.sqrt(lwg**2 + db**2)
lwr = np.sqrt(lwr**2 + db**2)
CC = EffectiveL(gPS, gPD, lwg, lwr, lwp)
Heff = H0matrix(Detg, Detp, u) + HImatrix(rabG, rabP, phidoppler, titadoppler, phiprobe, titaprobe) + CC
Heffdaga = np.matrix(Heff).getH()
Lfullpartial = np.zeros((64, 64), dtype=np.complex_)
for r in range(8):
for q in range(8):
for k in range(8):
for j in range(8):
if j!=q and r!=k:
pass
elif j==q and r!=k:
if (r < 2 and k > 3) or (k < 2 and r > 3) or (r > 3 and k > 3) or (r==0 and k==1) or (r==1 and k==0) or (r==2 and k==3) or (r==3 and k==2): #todo esto sale de analizar explicitamente la matriz y tratar de no calcular cosas de más que dan cero
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k])
elif j!=q and r==k:
if (j < 2 and q > 3) or (q < 2 and j > 3) or (j > 3 and q > 3) or (j==0 and q==1) or (j==1 and q==0) or (j==2 and q==3) or (j==3 and q==2):
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(-Heffdaga[j,q])
else:
if Heff[r,k] == Heffdaga[j,q]:
pass
else:
Lfullpartial[r*8+q][k*8+j] = (-1j)*(Heff[r,k]-Heffdaga[j,q])
M = CalculateSingleMmatrix(gPS, gPD, lwg, lwr, lwp)
L0 = np.array(np.matrix(Lfullpartial) + M)
nmax = 1
Lminus, Lplus, DeltaBar = Lplusminus(Detr, Detp, phirepump, titarepump)
factor1 = np.exp(1j*0.2*np.pi)
factor2 = np.exp(-1j*0.2*np.pi)
#print(factor)
L1 = GetL1(factor1*Lplus, factor2*Lminus, DeltaBar, L0, rabR, nmax)
Lfull = L0 + L1
#NORMALIZACION DE RHO
i = 0
while i < 64:
if i%9 == 0:
Lfull[0, i] = 1
else:
Lfull[0, i] = 0
i = i + 1
return Lfull
"""
Scripts para correr un experimento y hacer el análisis de los datos
"""
def CalculoTeoricoDarkResonances(u, titadoppler):
if titadoppler==0:
NegativeDR = [(-7/5)*u, (-3/5)*u, (-1/5)*u, (1/5)*u, (3/5)*u, (7/5)*u]
elif titadoppler==90:
NegativeDR = [(-11/5)*u, (-7/5)*u, (-3/5)*u, (3/5)*u, (7/5)*u, (11/5)*u]
PositiveDR = [(-8/5)*u, (-4/5)*u, 0, (4/5)*u, (8/5)*u]
return NegativeDR, PositiveDR
def CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
def CPTspectrum8levels_fixedRabi(sg, sr, sp, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump,
freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1):
"""
Hace un experimento barriendo ángulos de repump con el angulo de doppler fijo.
solvemode=1: resuelve con np.linalg.solve
solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
"""
phidoppler, titadoppler = phidoppler*(np.pi/180), titadoppler*(np.pi/180)
phiprobe, titaprobe = phiprobe*(np.pi/180), titaprobe*(np.pi/180)
phirepump, titarepump = phirepump*(np.pi/180), titarepump*(np.pi/180)
DetProbeVector = 2*np.pi*np.arange(freqMin*1e6, freqMax*1e6, freqStep*1e6)
Detg, Detr = 2*np.pi*Detg*1e6, 2*np.pi*Detr*1e6
#lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
lwg, lwr, lwp = lwg*1e6, lwr*1e6, lwp*1e6
rabG = sg*gPS
rabR = sr*gPD
rabP = sp*gPD
#u = 2*np.pi*u*1e6
Fluovector = []
tinicial = time.time()
for Detp in DetProbeVector:
L = FullL_efficient(rabG, rabR, rabP, gPS, gPD, Detg, Detr, Detp, u, lwg, lwr, lwp, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, Temp, alpha)
if solvemode == 1:
coh = 5
rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)]))
#Fluo = np.abs(rhovectorized[coh])
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
if solvemode == 2:
Linv = np.linalg.inv(L)
rhovectorized = [Linv[j][0] for j in range(len(Linv))]
Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27])) #estos son los rho33 + rho44
Fluovector.append(Fluo)
tfinal = time.time()
print('Done, Total time: ', round((tfinal-tinicial), 2), "s")
DetProbeVectorMHz = np.arange(freqMin, freqMax, freqStep)
if plot:
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
plt.plot(DetProbeVectorMHz, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.legend()
return DetProbeVectorMHz, Fluovector
#%%
if __name__ == "__main__":
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 25 #campo magnetico en gauss
u = c*B
sg, sr, sp = 0.5, 1.5, 4 #parámetros de saturación del doppler y repump
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 #anchos de linea de las transiciones
rabG, rabR, rabP = sg*gPS, sr*gPD, sp*gPD #frecuencias de rabi
lwg, lwr, lwp = 0.3, 0.3, 0.3 #ancho de linea de los laseres
Detg = -25
Detr = 20 #detuning del doppler y repump
Temp = 0.0e-3 #temperatura en K
alpha = 0*(np.pi/180) #angulo entre los láseres
phidoppler, titadoppler = 0, 90
phirepump, titarepump = 0, 90
phiprobe, titaprobe = 0, 90
plotCPT = False
freqMin = -50
freqMax = 50
freqStep = 5e-2
Frequencyvector, Fluovector = CPTspectrum8levels(rabG, rabR, rabP, gPS, gPD, Detg, Detr, u, lwg, lwr, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, phirepump, titarepump, freqMin=freqMin, freqMax=freqMax, freqStep=freqStep, plot=plotCPT, solvemode=1)
NegativeDR, PositiveDR = CalculoTeoricoDarkResonances(u/(2*np.pi*1e6), titadoppler)
plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
plt.xlabel('Probe detuning (MHz)')
plt.ylabel('Fluorescence (A.U.)')
for PDR in PositiveDR:
plt.axvline(Detr+PDR, linestyle='--', linewidth=0.5, color='red')
for NDR in NegativeDR:
plt.axvline(Detg+NDR, linestyle='--', linewidth=0.5, color='blue')
#parametros que andan piola:
"""
ub = 9.27e-24
h = 6.63e-34
c = (ub/h)*1e-4 #en unidades de MHz/G
B = 17 #campo magnetico en gauss
u = c*B
#u = 80e6
sr, sp = 0.53, 4.2
gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
rabR, rabP = sr*gPS, sp*gPD
lw = 2*np.pi * 0.33e6
lwr, lwp = lw, lw #ancho de linea de los laseres
dr_spec = - 2*np.pi* 26e6
freqSteps = 500
freqMin = -100e6
freqMax = 100e6
dps = 2*np.pi*np.linspace(freqMin, freqMax, freqSteps)
#dps = [-30e6]
alfar = 90*(np.pi/180)
ex_r, ey_r, ez_r = np.sin(alfar)*np.cos(0), np.sin(alfar)*np.sin(0), np.cos(alfar)
alfap = 90*(np.pi/180)
ex_p, ey_p, ez_p = np.sin(alfap)*np.cos(0), np.sin(alfap)*np.sin(0), np.cos(alfap)
"""
analisis/plots/20230321_heatingrate/Data/Fig_heatingrate.svg 0 → 100644
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