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

analisis/plots/20210831_PowerStabilization/ScanPlotter.py

@@ -7,9 +7,23 @@ import ast
 from scipy.optimize import curve_fit
 import os
 from scipy import interpolate
+"""
+Programa que evidencia la calibración en frecuecnai de un AOM IR
+
+Hay que buscar en otros archivos si no hay una medicion asi pero sin variar la potencia del AOM, o sea, sin calibrar,
+para comparar y poiner la curva antes y despues de calibrar
+
+"""
+
 
 # Solo levanto algunos experimentos
-ALL_FILES = """000003316-LaserPowerCalibration.h5""" 
+ALL_FILES = """000003316-LaserPowerCalibration.h5
+""" 
+
+FILES_WITHOUTC = """000003283-LaserPowerCalibration.h5
+"""
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20210831_PowerStabilization/Data')
 
 def SeeKeys(files):
     for i, fname in enumerate(files.split()):
@@ -21,6 +35,10 @@ print(SeeKeys(ALL_FILES))
 #carpeta pc nico labo escritorio:
 #C:\Users\Usuario\Documents\artiq\artiq_experiments\artiq_master\results\2021-07-14\16
 
+Measurements_vec = []
+Calibration_amps_vec = []
+Calibration_freqs_vec = []
+
 for i, fname in enumerate(ALL_FILES.split()):
     print(i)
     print(fname)
@@ -29,20 +47,107 @@ for i, fname in enumerate(ALL_FILES.split()):
     # 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.
-    Calibration_amps = np.array(data['datasets']['Test_Experiment_amps'])
-    Calibration_freqs = np.array(data['datasets']['Test_Experiment_freqs'])
-    Measurements = np.array(data['datasets']['Test_Measured_PD_IR_counts'])
+    Calibration_amps_vec.append(np.array(data['datasets']['Test_Experiment_amps']))
+    Calibration_freqs_vec.append(np.array(data['datasets']['Test_Experiment_freqs']))
+    Measurements_vec.append(np.array(data['datasets']['Test_Measured_PD_IR_counts']))
+    
+NocMeasurements_vec = []
+NocCalibration_amps_vec = []
+NocCalibration_freqs_vec = []
+NocCalibration_freqsraw_vec = []
+
+NocCounts_vec = []
+
+for i, fname in enumerate(FILES_WITHOUTC.split()):
+    print(i)
+    print(fname)
+    data = h5py.File(fname, '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.
+    NocCalibration_amps_vec.append(np.array(data['datasets']['Calibration_amps_IR']))
+    NocCalibration_freqs_vec.append(np.array(data['datasets']['Calibration_freqs_IR']))
+    NocCalibration_freqsraw_vec.append(np.array(data['datasets']['Calibration_freqs_IR_Raw']))
+    NocCounts_vec.append(np.array(data['datasets']['PD_IR_counts']))
     
 
 #%%
 plt.figure()
-plt.hist(Measurements)
+plt.hist(Measurements_vec[0])
+#%%
+k=0
+plt.figure()
+plt.plot(Calibration_freqs_vec[k], Measurements_vec[k], 'o')
+plt.ylim(0,1)
+
+print(len(Measurements_vec[k]))
+
+#%%
+k=0
+plt.figure()
+plt.plot([f*1e-6 for f in Calibration_freqs_vec[k]], Calibration_amps_vec[k], 'o',markersize=5,color='purple')
+plt.xlabel('Frecuencia AOM (MHz)')
+plt.ylabel('Amplitud calibrada (a.u.)')
+
+#%%
+"""
+Sin calibrar. Estas son meds para distintas amps
+"""
 
 plt.figure()
-plt.plot(Calibration_freqs, Calibration_amps, 'o')
+plt.plot([f*1e-6 for f in NocCalibration_freqs_vec[k]], NocCounts_vec[k], 'o',markersize=5,color='purple')
+plt.xlabel('Frecuencia AOM (MHz)')
+plt.ylabel('Voltaje PD (V)')
+plt.ylim(0,1)
 
+#extraigo un chunk
+chu = 3
 plt.figure()
-plt.plot(Calibration_freqs, Measurements, 'o')
+plt.plot([f*1e-6 for f in NocCalibration_freqs_vec[k]][51*chu:51*(chu+1)], NocCounts_vec[k][51*chu:51*(chu+1)], 'o',markersize=5,color='purple')
+plt.xlabel('Frecuencia AOM (MHz)')
+plt.ylabel('Voltaje PD (V)')
+plt.ylim(0,1)
+
+Counts_noc = NocCounts_vec[k][51*chu:51*(chu+1)]
+
+#%%
+"""
+Figura tesis
+"""
+import seaborn as sns
+
+paleta=sns.color_palette("bright")
+
+fs = 10
+ms = 4
+
+fig,ax = plt.subplots(2, sharex=True,figsize=(6,6))
+fig.subplots_adjust(hspace=0.1)
+ax[0].plot([f*1e-6 for f in Calibration_freqs_vec[k]], Measurements_vec[k], 'o-', color=paleta[0], markersize=ms,label='Con calibración')
+ax[0].plot([f*1e-6 for f in Calibration_freqs_vec[k]],Counts_noc[:-1],'o-', color=paleta[1], markersize=ms,label='Sin calibración')
+ax[0].set_ylabel('Voltaje PD (V)',fontsize=fs,fontname='STIXgeneral')
+ax[0].set_yticks([0.3,0.35,0.4,0.45,0.5])
+ax[0].set_yticklabels([0.3,0.35,0.4,0.45,0.5], fontsize=fs, fontname='STIXGeneral')
+ax[0].set_ylim(0.26,0.54)
+ax[0].legend(prop={'family': 'STIXGeneral', 'size': fs})
+
+ax[1].plot([f*1e-6 for f in Calibration_freqs_vec[k]], Calibration_amps_vec[k], '^-',markersize=ms,color=paleta[0],alpha=0.6)
+#ax[1].axhline(0.23,linewidth=ms-1,color=paleta[1],alpha=0.5,linestyle='dashed')
+ax[1].set_ylim(0.19,0.274)
+ax[1].set_ylabel('Amplitud calibrada (a.u.)',fontsize=fs,fontname='STIXgeneral')
+ax[1].set_yticks([0.19,0.20,0.21,0.22,0.23,0.24,0.25,0.26,0.27])
+ax[1].set_yticklabels([0.19,0.20,0.21,0.22,0.23,0.24,0.25,0.26,0.27], fontsize=fs, fontname='STIXGeneral')
+ax[1].set_xticks([190,200,210,220,230])
+ax[1].set_xticklabels([190,200,210,220,230], fontsize=fs, fontname='STIXGeneral')
+ax[1].set_xlabel('Frecuencia AOM (MHz)',fontsize=fs,fontname='STIXgeneral')
+#ax[1].legend(prop={'family': 'STIXGeneral', 'size': fs})
+
+plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Tesis/Tesis_doctorado/Chapters/figures/Cap4/calibAOM.pdf')
+
+
+
+
 
 
 

analisis/plots/20231218_CPT_DosLaseres_Reflotoajustes/CPT_plotter_20231218.py

@@ -12,6 +12,9 @@ from scipy import interpolate
 CPT con tres laseres pero lso dos IR son el mismo entonces las DD son mas finas
 """
 
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20231218_CPT_DosLaseres_Reflotoajustes/Data/')
+
+
 #C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211223_CPT_DosLaseres_v07_ChristmasSpecial\Data
 
 ALL_FILES = """000016420-IR_Scan_withcal_optimized
@@ -140,6 +143,13 @@ if do_fit:
     freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
     FittedEITpi_1_long, Detunings_1_long = FitEIT_MM_1ion(freqslong, *popt_1, plot=True)
 
+
+DetDop = popt_1[1]
+DetRep = popt_1[2]
+Sdop = popt_1[3]
+Spr = popt_1[4]
+Srep = popt_1[5]
+
 #%%
 
 plt.figure()

analisis/plots/20231218_CPT_DosLaseres_Reflotoajustes/lolo_CPT_plotter_20231218.py

@@ -89,8 +89,8 @@ plt.legend()
 from scipy.optimize import curve_fit
 import time
 
-cwd = os.getcwd()
-os.chdir('../20231123_CPTconmicromocion3')
+# cwd = os.getcwd()
+# os.chdir('../20231123_CPTconmicromocion3')
 from Data.EITfit.lolo_modelo_full_3niveles import GenerateNoisyCPT_fit
 os.chdir(cwd)
 

analisis/plots/20240312_RotationalDopplerShift_news/CPT_plotter_RDE.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
+
+"""
+Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
+"""
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20240312_RotationalDopplerShift_news/Data/CPT/')
+"""
+CPT con tres laseres pero lso dos IR son el mismo entonces las DD son mas finas
+"""
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211223_CPT_DosLaseres_v07_ChristmasSpecial\Data
+
+ALL_FILES = """000017121-IR_Scan_withcal_optimized
+000017123-IR_Scan_withcal_optimized
+000017170-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
+
+jvec = [1]
+
+jselected = jvec
+
+plt.figure()
+i = 0
+for j in jvec:
+    if j in jselected:
+        plt.errorbar([2*f*1e-6 for f in Freqs[j]], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2)
+    #plt.plot([2*f*1e-6 for f in Freqs[j]], Counts[j], 'o-', label=f'Amp Tisa: {AmpTisa[i]}', mb  arkersize=3)
+    i = i + 1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('counts')
+plt.grid()
+plt.legend()
+
+#%%
+from scipy.optimize import curve_fit
+import time
+
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  90
+phiprobe = 0
+titaprobe = 0.1
+
+Temp = 0.5e-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
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6 
+alpha = 0
+
+
+drivefreq = 2*np.pi*22.135*1e6
+noiseamplitude = 0
+
+selectedcurve=1
+
+FreqsDR = Freqs[selectedcurve]
+CountsDR = Counts[selectedcurve]
+   
+freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+CircPr = 1
+alpha = 0
+
+def FitEIT_MM_1ion(Freqs, offset, DetDoppler, DetRepump, SG, SP, SR, SCALE1, OFFSET, TEMP, U, plot=False):
+#def FitEIT_MM(freqs, SG, SP, SCALE1, OFFSET, BETA1):
+    # BETA1 = 0
+    # SG = 0.6
+    # SP = 8.1
+    # TEMP = 0.2e-3
+    # U = 32.5e6
+    freqs = [2*f*1e-6-offset for f in Freqs[:-1]]
+  
+    #Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+    Detunings, Fluorescence1 = 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)
+
+    ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
+    if plot:
+        return ScaledFluo1, Detunings
+    else:
+        return ScaledFluo1
+    #return ScaledFluo1
+
+do_fit = True
+if do_fit:
+    popt_2, pcov_2 = curve_fit(FitEIT_MM_1ion, FreqsDR, CountsDR, p0=[430, -25, 12, 0.5, 13, 11, 3e4, 2e3, 0.5e-3, 32e6], bounds=((0, -100, -20, 0, 0, 0, 0, 0, 0,20e6), (1000, 0, 50, 2, 20, 20, 5e6, 5e4, 15e-3,40e6)))
+
+    FittedEITpi_1_short, Detunings_1_short = FitEIT_MM_1ion(FreqsDR, *popt_2, plot=True)
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    FittedEITpi_1_long, Detunings_1_long = FitEIT_MM_1ion(freqslong, *popt_2, plot=True)
+
+
+DetDop = popt_2[1]
+DetRep = popt_2[2]
+Sdop = popt_2[3]
+Spr = popt_2[4]
+Srep = popt_2[5]
+#%%
+
+plt.figure()
+plt.errorbar(Detunings_1_short, CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='red', alpha=0.5, capsize=2, markersize=2)
+plt.plot(Detunings_1_long, FittedEITpi_1_long, color='darkolivegreen', linewidth=3, label='med 1')
+#plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+plt.xlabel('Detuning (MHz)')
+plt.ylabel('Counts')
+#plt.xlim(-20,0)
+plt.legend(loc='upper left', fontsize=20)
+plt.grid()
\ No newline at end of file

analisis/plots/20240312_RotationalDopplerShift_news/RDS_piezobeamsizes_2024_invariance.py

@@ -12,7 +12,7 @@ from scipy import interpolate
 
 #C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
 
-#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20240312_RotationalDopplerShift_news/Data')
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20240312_RotationalDopplerShift_news/Data')
 
 
 """
@@ -559,8 +559,9 @@ from scipy.optimize import curve_fit
 
 def modelo3(r,a,b,c):
     #c=1
-    s = (jv(0, b/r) )**2
-    return a*(s/(1+c*s))
+    s = a*(jv(0, b/(r)) )**2
+    return s
+    #return a*(s/(1+c*s))
 
 rfit = np.arange(x1,len(Intensityver2)*20+x2,20)
 yfit = [pmdepthsdrver1[0],pmdepthsdrver1[1],pmdepthsdrver1[2]] + [p for p in pmdepthsdrver2]