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

analisis/plots/20211004_EspectrosUV/UV_spectrums.py

@@ -218,7 +218,7 @@ j = 26
 
 FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
 CountsChosen = Counts[j]
-
+ 
 popt, pcov = curve_fit(Lorentzian, FreqsChosen, CountsChosen)
 
 freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)

analisis/plots/20211006_EspectrosUV_FixedScript/UV_spectrums.py

@@ -259,7 +259,7 @@ freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-
 
 #plt.figure()
 plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.16 V')
-#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
 #plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
 #plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
 #plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
@@ -287,7 +287,7 @@ freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-
 
 #plt.figure()
 plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.20 V')
-#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
 #plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
 #plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
 #plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
@@ -347,7 +347,7 @@ freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-
 
 #plt.figure()
 plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.24 V')
-#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
 #plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
 #plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
 #plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
@@ -376,7 +376,7 @@ freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-
 
 #plt.figure()
 plt.plot(FreqsChosen, CountsChosen, 'o', label='dcB=-1.26 V')
-#plt.plot(freqslong, Lorentzian(freqslong, *popt))
+plt.plot(freqslong, Lorentzian(freqslong, *popt))
 #plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
 #plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
 #plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)

analisis/plots/20211013_EspectrosUV/UV_spectrums.py

+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+import re
+import ast
+from scipy.optimize import curve_fit
+import os
+from scipy import interpolate
+
+# Solo levanto algunos experimentos
+Calib_Files = """000004263-UV_Scan_withcalib_Haeffner
+000004265-UV_Scan_withcalib_Haeffner
+000004270-UV_Scan_withcalib_Haeffner
+000004273-UV_Scan_withcalib_Haeffner
+000004275-UV_Scan_withcalib_Haeffner
+000004282-UV_Scan_withcalib_Haeffner
+000004301-UV_Scan_withcalib_Haeffner
+000004303-UV_Scan_withcalib_Haeffner""" 
+
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211013_EspectrosUV\Data
+
+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()))
+
+#%%    
+
+Amps = []
+Freqs = []
+Counts = []
+
+for i, fname in enumerate(Calib_Files.split()):
+    print(SeeKeys(Calib_Files))
+    print(i)
+    print(fname)
+    data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+    print(list(data['datasets'].keys()))
+    Amps.append(np.array(data['datasets']['UV_Amplitudes']))
+    Freqs.append(np.array(data['datasets']['UV_Frequencies']))
+    Counts.append(np.array(data['datasets']['counts_spectrum']))
+
+
+#def GetBackground(countsper100ms, )
+
+
+
+#%%
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 10 #40 es el piso de ruido estimado
+
+j = 1 #UV_cooling en 90 MHz
+
+UV_cooling = 90
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+portion = 0.05
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='4 uW')
+plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz') 
+
+
+
+#%%
+
+def Lorentzian(f, A, gamma, x0, C):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + C #40 es el piso de ruido estimado
+
+j = 4 #UV_cooling en 90 MHz
+
+UV_cooling = 90
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+portion = 0.05
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
+
+freqslong = np.arange(min(FreqsChosen)-100, max(FreqsChosen)+100, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot(FreqsChosen, CountsChosen, 'o', label='1 uW')
+plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.axhline(popt[-1])
+plt.ylim(50, 1100)
+plt.legend()
+
+ 
+
+#%% COMPARATIVA DE AMBOS DONDE SE VE QUE UNO ES MAS FINO
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 10 #40 es el piso de ruido estimado
+
+j = 1 #UV_cooling en 90 MHz
+
+UV_cooling = 90
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+
+plt.figure()
+plt.plot(FreqsChosen, [c/max(CountsChosen) for c in CountsChosen], 'o', label='4 uW')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.legend()
+
+print(f'Ancho medido: {round(popt[1])} MHz') 
+
+
+j = 4 #UV_cooling en 90 MHz
+
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot(FreqsChosen, [c/max(CountsChosen) for c in CountsChosen], 'o', label='1 uW')
+#plt.plot(freqslong, Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.legend()
+
+
+#%%
+
+def Lorentzian(f, A, gamma, x0, C):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + C #40 es el piso de ruido estimado
+
+j = 5 #UV_cooling en 90 MHz
+
+UV_cooling = 90
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+portion = 0.6
+
+#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[:int(portion*len(FreqsChosen))], CountsChosen[:int(portion*len(FreqsChosen))], p0=[1e3, 40, 210, 100])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot([f-popt[2] for f in FreqsChosen], CountsChosen, 'o', label='1 uW')
+plt.plot([f-popt[2] for f in freqslong], Lorentzian(freqslong, *popt), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+plt.axhline(popt[-1])
+plt.legend()
+print(f'Ancho medido: {round(popt[1])} MHz') 
+
+
+#%%
+
+def Lorentzian(f, A, gamma, x0, C):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + C #40 es el piso de ruido estimado
+
+j = 6 #UV_cooling en 90 MHz
+
+UV_cooling = 90
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+portion = 1
+
+#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[:int(portion*len(FreqsChosen))], CountsChosen[:int(portion*len(FreqsChosen))], p0=[1e3, 40, 210, 100])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+plt.figure()
+plt.plot([f-popt[2] for f in FreqsChosen], CountsChosen/max(CountsChosen), 'o', label='2.5 uW')
+plt.plot([f-popt[2] for f in freqslong], Lorentzian(freqslong, *popt)/max(CountsChosen), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+#plt.axhline(popt[-1])
+plt.legend()
+print(f'Ancho medido: {round(popt[1])} MHz') 
+
+
+
+def Lorentzian(f, A, gamma, x0):
+    return (A/np.pi)*0.5*gamma/(((f-x0)**2)+((0.5*gamma)**2)) + 30 #40 es el piso de ruido estimado
+
+j = 7 #UV_cooling en 90 MHz
+
+UV_cooling = 90
+
+FreqsChosen = [2*f*1e-6 for f in Freqs[j]]
+CountsChosen = Counts[j]
+
+portion = 1
+
+#popt, pcov = curve_fit(Lorentzian, FreqsChosen[int(portion*len(FreqsChosen)):], CountsChosen[int(portion*len(FreqsChosen)):], p0=[1e3, 40, 210, 100])
+
+popt, pcov = curve_fit(Lorentzian, FreqsChosen[:int(portion*len(FreqsChosen))], CountsChosen[:int(portion*len(FreqsChosen))], p0=[1e3, 40, 210])
+
+freqslong = np.arange(min(FreqsChosen)-10, max(FreqsChosen)+10, (FreqsChosen[1]-FreqsChosen[0])*0.01)
+
+#plt.figure()
+plt.plot([f-popt[2] for f in FreqsChosen], CountsChosen/max(CountsChosen), 'o', label='2.5 uW')
+plt.plot([f-popt[2] for f in freqslong], Lorentzian(freqslong, *popt)/max(CountsChosen), label=f'FWHM {round(popt[1])} MHz')
+#plt.axvline(popt[2]-22.1, linestyle='--', linewidth=1)
+#plt.axvline(popt[2]+22.1, linestyle='--', linewidth=1)
+#plt.axvline(2*UV_cooling, linestyle='--', linewidth=1)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Cuentas')
+#plt.axhline(popt[-1])
+plt.legend()
+print(f'Ancho medido: {round(popt[1])} MHz') 
+

analisis/plots/20211019_CPT_primeras4DR/CPT_plotter.py

+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+import re
+import ast
+from scipy.optimize import curve_fit
+import os
+from scipy import interpolate
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211019_CPT_primeras4DR\Data
+
+# Solo levanto algunos experimentos
+ALL_FILES = """000004475-IR_Scan_withcal_optimized
+000004476-IR_Scan_withcal_optimized
+000004477-IR_Scan_withcal_optimized
+000004478-IR_Scan_withcal_optimized
+000004479-IR_Scan_withcal_optimized
+000004480-IR_Scan_withcal_optimized
+000004481-IR_Scan_withcal_optimized
+000004482-IR_Scan_withcal_optimized
+000004483-IR_Scan_withcal_optimized
+000004484-IR_Scan_withcal_optimized
+000004485-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\artiq_master\results\2021-07-14\16
+
+Counts = []
+Freqs = []
+
+for i, fname in enumerate(ALL_FILES.split()):
+    print(i)
+    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']['IR_Frequencies']))
+    Counts.append(np.array(data['datasets']['counts_spectrum']))
+
+#%%
+
+j = 10
+
+plt.figure()
+plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-')
+plt.xlabel('Frecuencia MHz)')
+plt.ylabel('counts')
+plt.grid()
+
+
+
+
+
+
+
+
+
+
+
+
+
+

analisis/plots/20211101_CPT_DosLaseres_v03/CPT_plotter_20211102_variandoSR.py

@@ -395,11 +395,26 @@ popt, pcov = curve_fit(FitEITpi, FreqsDRpi, CountsDRpi, sigma=np.sqrt(CountsDRpi
 
 print(popt)
 
+DetRepumpFit = popt[-1]
+SatParRepumpFit = popt[2]
+SatParDopFit = popt[0]
+SatParDopFit = popt[1]
+
 FittedEITpi = FitEITpi(freqslongpi, *popt)
+#%%
 
-plt.figure()
-plt.errorbar(FreqsDRpi, CountsDRpi, yerr=1*np.sqrt(CountsDRpi), fmt='o', capsize=2, markersize=2)
-plt.plot(freqslongpi, FittedEITpi)
+ms = 20
+cs = 10
+lwidth = 10
+fs = 30
+
+plt.figure(figsize=(15,15))
+plt.errorbar(FreqsDRpi, [(c-9.05e2)*100/7e4 for c in CountsDRpi], yerr=[1*np.sqrt(c)*100/7e4 for c in CountsDRpi], fmt='o', capsize=cs, markersize=ms, color='rebeccapurple')
+plt.plot(freqslongpi, [(c-9.05e2)*100/7e4 for c in FittedEITpi], linewidth=lwidth, color='purple', alpha=0.7)
+plt.xlabel('Detuning (MHz)', fontsize=fs)
+plt.ylabel('Fluorescence (a.u.)', fontsize=fs)
+plt.yticks([7, 8, 9, 10], fontsize=fs)
+plt.xticks([-80, -60, -40, -20, 0], fontsize=fs)
 #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
 
 #%%
@@ -572,7 +587,7 @@ plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[
 #%%
 import matplotlib
 
-matplotlib.rcParams.update({'font.size': 18})
+matplotlib.rcParams.update({'font.size': 10})
 
 fig, ax = plt.subplots(1,2, figsize=(14,6))
 

analisis/plots/20211115_CPT_DosLaseres_v04/CPT_plotter_20211115.py

@@ -22,9 +22,6 @@ ALL_FILES = """000005717-IR_Scan_withcal_optimized
 000005724-IR_Scan_withcal_optimized
 000005725-IR_Scan_withcal_optimized
 000005726-IR_Scan_withcal_optimized
-000005728-IR_Scan_withcal_optimized
-000005729-IR_Scan_withcal_optimized
-000005730-IR_Scan_withcal_optimized
 000005910-IR_Scan_withcal_optimized
 000005911-IR_Scan_withcal_optimized
 000005912-IR_Scan_withcal_optimized
@@ -249,6 +246,10 @@ plt.grid()
 
 
 #%%
+"""
+FIGURA PARA EL PAPER CPT 2 LASERES, 1 REPUMP
+"""
+
 
 #FITEOSSSSS
 
@@ -287,24 +288,56 @@ CountsDRpi_1 = Counts[12]
 freqslongpi_1 = np.arange(min(FreqsDRpi_1), max(FreqsDRpi_1)+FreqsDRpi_1[1]-FreqsDRpi_1[0], 0.1*(FreqsDRpi_1[1]-FreqsDRpi_1[0]))
 
 
-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]
+def FitEITpi(freqs, SG, SP, T):
+    #temp = 4.7e-3
+    MeasuredFreq, MeasuredFluo = GenerateNoisyCPT_fit(SG, 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*5.986e4 + 2.0905e3 for f in MeasuredFluo]
     return FinalFluo
 
-popt, pcov = curve_fit(FitEITpi, FreqsDRpi_1, CountsDRpi_1, p0=[1, 10, 1e4, 1e4], bounds=(0, [2, 30, 1e6, 1e6]))
+popt, pcov = curve_fit(FitEITpi, FreqsDRpi_1, CountsDRpi_1, p0=[1, 10, 4e-3], bounds=(0, [2, 30, 1e-2]))
 
 print(popt)
 
+#scale = popt[2]
+#offset = popt[3]
+
+scale = 5.986e4
+offset = 2.0905e3
 
 FittedEITpi_1 = FitEITpi(freqslongpi_1, *popt)
 
+
+
 plt.figure()
 plt.errorbar(FreqsDRpi_1, CountsDRpi_1, yerr=2*np.sqrt(CountsDRpi_1), fmt='o', capsize=2, markersize=2)
 plt.plot(freqslongpi_1, FittedEITpi_1)
 #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
 
+
+from scipy.signal import savgol_filter as sf
+import seaborn as sns
+
+colors=sns.color_palette("rocket", 3)
+colorscomp = sns.color_palette("viridis", 10)
+plt.style.use('seaborn-ticks')
+
+
+winl = 9
+po = 3
+
+FiltCounts = np.array([(c-offset)*100/scale for c in sf(CountsDRpi_1, winl, po)])
+ErrorCounts = np.array([1*np.sqrt(c/scale) for c in CountsDRpi_1])
+
+plt.figure()
+plt.plot(FreqsDRpi_1, FiltCounts, 'o', markersize=3, color='navy')
+plt.fill_between(FreqsDRpi_1, FiltCounts+ErrorCounts, FiltCounts-ErrorCounts, color='lightblue')
+plt.plot(freqslongpi_1, [(c-offset)*100/scale for c in FittedEITpi_1], label='Fitted spectrum', color='darkviolet')
+plt.legend()
+plt.xlim(-54, 28)
+plt.xlabel('Detuning probe (MHz)')
+plt.ylabel('Fluorescence (a.u.)')
+plt.grid()
+
 #%%
 #Esto es un ajuste de lorenziana por las afueras de la curva y la resta con el ajuste CPT para ver la profundidad de los picos
 
@@ -457,16 +490,28 @@ plt.plot(freqslongpi_3, FittedEITpi_3)
 #%%
 import seaborn as sns
 
-sns.set_theme(style="whitegrid")
+#sns.set_theme(style="whitegrid")
 sns.set_palette("rocket")
 
-NormCountsDRpi_2 = [(c-popt[3])/popt[2] for c in CountsDRpi_2]
-NormCountsDRpi_3 = [(c-popt[3])/popt[2] for c in CountsDRpi_3]
-NormCountsDRpi_1 = [(c-popt[3])/popt[2] for c in CountsDRpi_1]
+#NormCountsDRpi_2 = [(c-popt[3])/popt[2] for c in CountsDRpi_2]
+#NormCountsDRpi_3 = [(c-popt[3])/popt[2] for c in CountsDRpi_3]
+#NormCountsDRpi_1 = [(c-popt[3])/popt[2] for c in CountsDRpi_1]
+
+#NormFittedEITpi_2 = [(c-popt[3])/popt[2] for c in FittedEITpi_2]
+#NormFittedEITpi_3 = [(c-popt[3])/popt[2] for c in FittedEITpi_3]
+#NormFittedEITpi_1 = [(c-popt[3])/popt[2] for c in FittedEITpi_1]
+
+
+
+
+NormCountsDRpi_2 = [(c-0)/popt[2] for c in CountsDRpi_2]
+NormCountsDRpi_3 = [(c-0)/popt[2] for c in CountsDRpi_3]
+NormCountsDRpi_1 = [(c-0)/popt[2] for c in CountsDRpi_1]
+
+NormFittedEITpi_2 = [(c-0)/popt[2] for c in FittedEITpi_2]
+NormFittedEITpi_3 = [(c-0)/popt[2] for c in FittedEITpi_3]
+NormFittedEITpi_1 = [(c-0)/popt[2] for c in FittedEITpi_1]
 
-NormFittedEITpi_2 = [(c-popt[3])/popt[2] for c in FittedEITpi_2]
-NormFittedEITpi_3 = [(c-popt[3])/popt[2] for c in FittedEITpi_3]
-NormFittedEITpi_1 = [(c-popt[3])/popt[2] for c in FittedEITpi_1]
 
 
 plt.figure()
@@ -477,7 +522,7 @@ plt.errorbar(FreqsDRpi_3, NormCountsDRpi_3, yerr=(np.sqrt(CountsDRpi_3))/popt[2]
 plt.plot(freqslongpi_2, NormFittedEITpi_1, zorder=2)
 plt.errorbar(FreqsDRpi_2, NormCountsDRpi_1, yerr=(np.sqrt(CountsDRpi_1))/popt[2], fmt='o', capsize=2, markersize=2, label=f'Sat Rep: 0', zorder=1)
 plt.legend(markerscale=3, loc='lower right')
-plt.ylim(0.018, 0.14)
+#plt.ylim(0.018, 0.14)
 plt.xlim(-55, 35)
 plt.xlabel('Repump Detuning [MHz]')
 plt.ylabel('Norm. Fluorescence [a.u.]')

analisis/plots/20211201_CPT_DosLaseres_v05/CPT_plotter_20211201.py

@@ -189,6 +189,85 @@ plt.ylabel('counts')
 plt.grid()
 #plt.legend()
 
+
+#%%
+
+#Variando potencia UV
+
+jvec = [16]
+
+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', label=f'Amp Tisa: {AmpTisa[i]}', capsize=2, markersize=2)
+    i = i + 1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+plt.grid()
+#plt.legend(
+
+
+
+#%%
+
+#FITEOSSSSS
+
+#Variando la polarizacion del UV
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+T = 0.6e-3
+
+sg = 0.49
+sp = 4
+sr = 0
+DetRepump = 0
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+
+u = 33.5e6
+
+B = (u/(2*np.pi))/c
+
+correccion = 8 #con 8 fitea bien
+
+offsetxpi = 440+1+correccion
+DetDoppler = -14-correccion
+
+FreqsDRpi_1 = [2*f*1e-6-offsetxpi+14 for f in Freqs[16]]
+CountsDRpi_1 = Counts[16]
+
+freqslongpi_1 = np.arange(min(FreqsDRpi_1), max(FreqsDRpi_1)+FreqsDRpi_1[1]-FreqsDRpi_1[0], 0.1*(FreqsDRpi_1[1]-FreqsDRpi_1[0]))
+
+
+def FitEITpi(freqs, SG, SP):
+    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*5.95591183e4 + 8.6e2 for f in MeasuredFluo]
+    return FinalFluo
+
+popt, pcov = curve_fit(FitEITpi, FreqsDRpi_1, CountsDRpi_1, p0=[0.7, 4], bounds=(0, [2, 30]))
+
+print(popt)
+
+
+FittedEITpi_1 = FitEITpi(freqslongpi_1, *popt)
+
+plt.figure()
+plt.errorbar(FreqsDRpi_1, CountsDRpi_1, yerr=2*np.sqrt(CountsDRpi_1), fmt='o', capsize=2, markersize=2)
+plt.plot(freqslongpi_1, FittedEITpi_1)
+#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+
+
+
+
 #%%
 
 #AHORA SI: VARIANDO POTENCIA TISA, CON DOS IR EN SIGMAM M SIGMAM

analisis/plots/20211223_CPT_DosLaseres_v07_ChristmasSpecial/CPT_plotter_20211223.py

@@ -55,6 +55,21 @@ for i, fname in enumerate(ALL_FILES.split()):
     UVCPTAmp.append(np.array(data['datasets']['UV_CPT_amp']))
     No_measures.append(np.array(data['datasets']['no_measures']))
 
+Counts_B = []
+Freqs_B = []
+
+
+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_B.append(np.array(data['datasets']['IR_Frequencies']))
+    Counts_B.append(np.array(data['datasets']['counts_spectrum']))
+
 
 
 #%%
@@ -127,6 +142,126 @@ plt.legend()
 
 
 
+#%%
+"""
+Bloque para calibrar eje x en figura
+"""
+#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 = 32.5e6
+
+B = (u/(2*np.pi))/c
+
+correccion = 8 #con 8 fitea bien
+
+offsetxpi = 440+1+correccion
+DetDoppler = -5.0-correccion
+
+FreqsDRpi_3 = [2*f*1e-6-offsetxpi+14 for f in Freqs_B[5]]
+CountsDRpi_3 = Counts_B[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, SG, SP):
+    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*6.554e4 + 1.863e3 for f in MeasuredFluo]
+    return FinalFluo
+
+popt_tisaoff, pcov_tisaoff = curve_fit(FitEITpi, FreqsDRpi_3, CountsDRpi_3, p0=[0.5, 4.5], bounds=((0, 0), (2, 10)))
+
+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_B = FreqsDRpi_3
+
+Scale_B = 6.554e4
+Offset_B = 1.863e3
+
+#%%
+#Aca pongo solo 3 que son los mismos angulos de las curvas barriendo angulo TISA (para mostrar)
+
+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]
+
+fig, ax = plt.subplots(1, 3, figsize=(20, 6))
+
+cs = 1
+ms = 4
+fs = 25
+
+ax[0].errorbar(FreqsCalibradas_B, Counts_B[jselected_B[1]], yerr=np.sqrt(Counts_B[jselected_B[1]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
+ax[0].errorbar(FreqsCalibradas_B, Counts_B[jselected_B[0]], yerr=np.sqrt(Counts_B[jselected_B[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
+#ax[0].set_ylim(0, 2100)
+ax[0].set_title(f'TISA ang: {Angsselected_B[1]}º', fontsize=fs)
+ax[0].set_xlabel('Detuning (MHz)', fontsize=fs)
+ax[0].set_ylabel('Counts', fontsize=fs)
+#ax[0].set_yticks(np.arange(0, 2500, 500))
+#ax[0].set_yticklabels(np.arange(0, 2500, 500), fontsize=fs)
+#ax[0].set_xticks(np.arange(-50, 50, 25))
+#ax[0].set_xticklabels(np.arange(-50, 50, 25), fontsize=fs)
+ax[0].grid()
+
+ax[1].errorbar(FreqsCalibradas_B, Counts_B[jselected_B[3]], yerr=np.sqrt(Counts_B[jselected_B[2]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
+ax[1].errorbar(FreqsCalibradas_B, Counts_B[jselected_B[2]], yerr=np.sqrt(Counts_B[jselected_B[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
+#ax[1].set_ylim(0, 2100)
+ax[1].set_title(f'TISA ang: {Angsselected_B[3]}º', fontsize=fs)
+ax[1].set_xlabel('Detuning (MHz)', fontsize=fs)
+ax[1].set_ylabel('Counts', fontsize=fs)
+#ax[1].set_yticks(np.arange(0, 2500, 500))
+#ax[1].set_yticklabels(np.arange(0, 2500, 500), fontsize=fs)
+#ax[1].set_xticks(np.arange(-50, 50, 25))
+#ax[1].set_xticklabels(np.arange(-50, 50, 25), fontsize=fs)
+ax[1].grid()
+
+ax[2].errorbar(FreqsCalibradas_B, Counts_B[jselected_B[5]], yerr=np.sqrt(Counts_B[jselected_B[4]]), fmt='o', label='TISA ON', capsize=cs, markersize=ms)
+ax[2].errorbar(FreqsCalibradas_B, Counts_B[jselected_B[4]], yerr=np.sqrt(Counts_B[jselected_B[0]]), fmt='o', label='TISA OFF', capsize=cs, markersize=ms)
+#ax[2].set_ylim(0, 2100)
+ax[2].set_title(f'TISA ang: {Angsselected_B[5]}º', fontsize=fs)
+ax[2].set_xlabel('Detuning (MHz)', fontsize=fs)
+ax[2].set_ylabel('Counts', fontsize=fs)
+#ax[2].set_yticks(np.arange(0, 2500, 500))
+#ax[2].set_yticklabels(np.arange(0, 2500, 500), fontsize=fs)
+#ax[2].set_xticks(np.arange(-50, 50, 25))
+#ax[2].set_xticklabels(np.arange(-50, 50, 25), fontsize=fs)
+ax[2].grid()
+
+fig.tight_layout()
+
+
+
+
 #%%
 
 #Aca veo como poniendo tisa en sigma se rompen las 4 resonancias oscuras

analisis/plots/20220106_CPT_DosLaseres_v08_TISA_DR/Data/dr1err.txt

+0.000000000000000000e+00 3.000000000000000000e+01 6.000000000000000000e+01 9.000000000000000000e+01 1.500000000000000000e+01 4.500000000000000000e+01 7.500000000000000000e+01
+1.784693448063240095e-01 2.108427153120442377e-01 2.091073548113952796e-01 2.187264577655991249e-01 1.895357908431558047e-01 2.191180900537179066e-01 2.304649447016792752e-01

analisis/plots/20220106_CPT_DosLaseres_v08_TISA_DR/Data/dr2err.txt

+0.000000000000000000e+00 3.000000000000000000e+01 6.000000000000000000e+01 9.000000000000000000e+01 1.500000000000000000e+01 4.500000000000000000e+01 7.500000000000000000e+01
+2.379691345730698959e-01 2.488013995521680122e-01 2.395849375251260516e-01 2.243382206115963617e-01 2.468988235645363516e-01 2.585188884870380233e-01 2.491457615156644767e-01

analisis/plots/20220106_CPT_DosLaseres_v08_TISA_DR/Data/dr3err.txt

+0.000000000000000000e+00 3.000000000000000000e+01 6.000000000000000000e+01 9.000000000000000000e+01 1.500000000000000000e+01 4.500000000000000000e+01 7.500000000000000000e+01
+2.634466295782048606e-01 2.601718393967488718e-01 2.325020388682937644e-01 2.179410819521661047e-01 2.628764862581587924e-01 2.650688532186117885e-01 2.313931198017891155e-01

analisis/plots/20220106_CPT_DosLaseres_v08_TISA_DR/Data/dr4err.txt

+0.000000000000000000e+00 3.000000000000000000e+01 6.000000000000000000e+01 9.000000000000000000e+01 1.500000000000000000e+01 4.500000000000000000e+01 7.500000000000000000e+01
+1.745838324094645955e-01 2.015920452755217973e-01 2.122611830210968942e-01 2.318558139647002025e-01 1.911124674842025473e-01 2.083885942837055416e-01 2.287847404371309845e-01