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

.gitignore

 **/__pycache__/
 *.pyc
 *.pyon
+**/__*.py
+**/__*.md
+analisis/plots/20231123_CPTconmicromocion3/grafico_*.png
+analisis/plots/20231123_CPTconmicromocion3/grafico_*.pdf

analisis/plots/20230912_RotationalDopplerShift_v6/RDS_piezobeamsizes.py

@@ -551,16 +551,16 @@ if depthscurve:
     plt.grid()
     #plt.axvline(3, color='salmon')
     plt.legend()
-
+#%%
     plt.figure()
-    #plt.plot(np.arange(0,len(Intensityver1),1), [i/np.max(Intensityver1) for i in Intensityver1], '-o',markersize=8)
-    #plt.errorbar(np.arange(0,len(Intensityver1),1), [p for p in pmdepthsdrver1], yerr= errorpmdepthsdrver1, fmt='o', capsize=3, markersize=8)
-    plt.plot(np.arange(0,len(Intensityver2),1), [i/np.max(Intensityver2) for i in Intensityver2], '-o',markersize=8)
-    plt.errorbar(np.arange(0,len(Intensityver2),1), [p for p in pmdepthsdrver2], yerr= errorpmdepthsdrver2, fmt='o', capsize=3, markersize=8)
+    # plt.plot(np.arange(0,len(Intensityver1),1), [i/np.max(Intensityver1) for i in Intensityver1], '-o',markersize=8)
+    # plt.errorbar(np.arange(0,len(Intensityver1),1), [p for p in pmdepthsdrver1], yerr= errorpmdepthsdrver1, fmt='o', capsize=3, markersize=8)
+    # plt.plot(np.arange(0,len(Intensityver2),1), [i/np.max(Intensityver2) for i in Intensityver2], '-o',markersize=8)
+    # plt.errorbar(np.arange(0,len(Intensityver2),1), [p for p in pmdepthsdrver2], yerr= errorpmdepthsdrver2, fmt='o', capsize=3, markersize=8)
     plt.plot(np.arange(0,len(Intensityver3),1), [i/np.max(Intensityver3) for i in Intensityver3], '-o',color='darkgreen',markersize=8)
     plt.errorbar(np.arange(0,len(Intensityver3),1), [p for p in pmdepthsdrver3], yerr= errorpmdepthsdrver3, color='darkgreen',fmt='o', alpha=0.7, capsize=3, markersize=8)
-    #plt.plot([m for m in np.arange(0,len(Intensityver4),1)], [i/np.max(Intensityver4) for i in Intensityver4], '-o',color='navy',markersize=8)
-    #plt.errorbar([m for m in np.arange(0,len(Intensityver4),1)], [p for p in pmdepthsdrver4], yerr=list(errorpmdepthsdrver4), fmt='o', color='navy', alpha=0.7,capsize=3, markersize=8)
+    plt.plot([m for m in np.arange(0,len(Intensityver4),1)], [i/np.max(Intensityver3) for i in Intensityver4], '-o',color='navy',markersize=8)
+    plt.errorbar([m for m in np.arange(0,len(Intensityver4),1)], [p for p in pmdepthsdrver4], yerr=list(errorpmdepthsdrver4), fmt='o', color='navy', alpha=0.7,capsize=3, markersize=8)
     #plt.xlim(-0.5, 12.7)
     plt.ylim(-0.1,1.1)
     plt.grid()
@@ -657,7 +657,7 @@ plt.xticks([-50,-25,0,25,50],fontname='STIXgeneral',fontsize=35)
 plt.yticks([3.5,4,4.5],fontname='STIXgeneral',fontsize=35)
 plt.tight_layout()
 plt.grid()
-plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/DDresonancesexperimental_fine.pdf')
+#plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/DDresonancesexperimental_fine.pdf')
 # plt.legend()
 print(f'Ancho: {round(1e3*popt[3],2)} kHz')
 

analisis/plots/20230912_RotationalDopplerShift_v6/RDS_piezobeamsizes_vert.py

@@ -447,13 +447,18 @@ if plotcurvita:
 
 #%%
 
+a=0.3
 
 plt.figure()
-plt.plot(np.arange(0,len(Intensityver1),1), [i/np.max(Intensityver1) for i in Intensityver1], '-o', color='navy',alpha=0.5,markersize=8)
-plt.errorbar(np.arange(0,len(Intensityver1),1), [p for p in pmdepthsdrver1], yerr= errorpmdepthsdrver1, fmt='o', color='navy',alpha=1, capsize=3, markersize=8)
+plt.plot([x*a for x in np.arange(0,len(Intensityver1),1)], [i/np.max(Intensityver2) for i in Intensityver1], '-o', color='navy',alpha=0.5,markersize=8)
+#plt.plot(np.arange(0,len(Intensityver1),1), [i/np.max(Intensityver1) for i in Intensityver1], '-o', color='navy',alpha=0.5,markersize=8)
+plt.errorbar([x*a for x in np.arange(0,len(Intensityver1),1)], [p for p in pmdepthsdrver1], yerr= errorpmdepthsdrver1, fmt='o', color='navy',alpha=1, capsize=3, markersize=8)
+
+
 plt.plot(np.arange(0,len(Intensityver2),1), [i/np.max(Intensityver2) for i in Intensityver2], '-o',color='firebrick',markersize=8,alpha=0.5)
+#plt.plot(np.arange(0,len(Intensityver2),1), [i/np.max(Intensityver2) for i in Intensityver2], '-o',color='firebrick',markersize=8,alpha=0.5)
 plt.errorbar(np.arange(0,len(Intensityver2),1), [p for p in pmdepthsdrver2], yerr= errorpmdepthsdrver2, fmt='o', color='firebrick',alpha=1,capsize=3, markersize=8)
-plt.xlim(-0.5, 36)
+plt.xlim(-0.5, 10)
 plt.ylim(-0.1,1.1)
 plt.grid()
 
@@ -960,3 +965,26 @@ plt.ylim(-0.1,1.1)
 plt.grid()
 
 
+
+#%%
+
+"""
+Analuisis posterior
+"""
+
+k = 0.4
+#ahora la anterior con haz grande y alguna de las 4 potencias con haz chico
+plt.figure()
+plt.plot([k*f for f in np.arange(0,len(Intensityver1),1)], [i/np.max(Intensityver31) for i in Intensityver1], '-o', color='navy',alpha=0.5,markersize=8)
+plt.errorbar([k*f for f in np.arange(0,len(Intensityver1),1)], [p for p in pmdepthsdrver1], yerr= errorpmdepthsdrver1, fmt='o', color='navy',alpha=1, capsize=3, markersize=8)
+# plt.plot(np.arange(0,len(Intensityver2),1), [i/np.max(Intensityver2) for i in Intensityver2], '-o', color='navy',alpha=0.5,markersize=8)
+# plt.errorbar(np.arange(0,len(Intensityver2),1), [p for p in pmdepthsdrver2], yerr= errorpmdepthsdrver2, fmt='o', color='navy',alpha=1, capsize=3, markersize=8)
+plt.plot(np.arange(0,len(Intensityver31),1), [i/np.max(Intensityver31) for i in Intensityver31], '-o',color='green',markersize=8,alpha=0.5)
+plt.errorbar(np.arange(0,len(Intensityver31),1), [0.8*p for p in pmdepthsdrver31], yerr= errorpmdepthsdrver31, fmt='o', color='green',alpha=1,capsize=3, markersize=8)
+plt.xlim(-0.5, 10)
+plt.ylim(-0.1,1.1)
+plt.grid()
+
+
+
+

analisis/plots/20230912_RotationalDopplerShift_v7/teoryanalysis.py

@@ -83,8 +83,8 @@ fact = (np.max(LG1)/np.max(LG2))
 
 
 plt.figure()
-plt.plot(r/w1,LG1)
-plt.plot(r/w2,LG2*fact)
+plt.plot(r,LG1)
+#plt.plot(r/w2,LG2*fact)
 plt.title('Scaled ratio between LG and RDE, x axis scaled with LG waist')
 
 

analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/lolo_modelo_full_8niveles.py

@@ -7,6 +7,11 @@
 
 reingenieria del código que anda
 
+Surge de fusionar
+Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
+Data/EITfit/MM_eightLevel_2repumps_python_scripts.py
+
+
 MAPA de FUNCIONES
 
 CPTspectrum8levels_MM

analisis/plots/20231123_CPTconmicromocion3/lolo_analisis_superajuste.py

 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Ploteo de datos y ajustes
+Ploteo de datos y ajustes del barrido en voltaje del Endcap
+equivalente a pasear el ion en algun entorno del punto de compensación ideal
+y ver los efectos de micromoción (y tal vez temperatura)
+
+
+
 @author: lolo
 """
 
@@ -24,6 +29,7 @@ from time import time
 
 # /home/lolo/Dropbox/marce/LIAF/Trampa_anular/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/EITfit/MM_eightLevel_2repumps_AnalysisFunctions.py
 
+
 from Data.EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
 
 
@@ -405,7 +411,7 @@ for Detunings_3_SA_short,CountsDR,Detunings_3_SA_long,FittedEITpi_3_SA_long,sele
     ax.grid(True, ls=":")
 
     print(f'listo med {selectedcurve}')
-    print(popt_3_SA)
+    # print(popt_3_SA)
 
 
 for ax in axx[:,0]:
@@ -452,58 +458,86 @@ ax.set_xticks(num_med)
 ax.set_xlabel('Num. de medición')
 
 
-#%%
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Endcap hiperbola (con residuos)
 """
-Grafico distintas variables que salieron del SUper ajuste
+Veamos cómo varía el Beta con el voltaje del endcap
 """
 
 import seaborn as sns
 paleta = sns.color_palette("rocket")
 
-voltages_dcA = Voltages[0][1:10]
+
+I = slice(None,9)
+
+voltages_dcA  = Voltages[0][SelectedCurveVec]
+
+
 
 def lineal(x,a,b):
     return a*x+b
 
-def hiperbola(x,a,b,c,x0):
-    return a*np.sqrt(((x-x0)**2+c**2))+b
+def hiperbola(x,a,y0,b,x0):
+    """
+    Hiperbola de ecuación:
+        1 =(y-y0)²/a² - (x-x0)²/b² 
+    """
+    return a*np.sqrt(((x-x0)**2+b**2))+y0
 
-hiperbola_or_linear = True
+es_hiperbola = False
 
-if hiperbola_or_linear:
-    popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
+    
+# par_inicial = (100,0.1,1,-0.15)
+#               a   y0   b  x0
+par_inicial = (12,0.1,1,-0.13)
+
+
+popthip,pcovhip = curve_fit(hiperbola,voltages_dcA[I],Betas_vec[I],p0=par_inicial)
+
+xhip = np.linspace(-0.23,0.005,200)
 
-    xhip = np.linspace(-0.23,0.005,200)
+# plt.figure()
+# plt.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+# plt.plot(xhip,hiperbola(xhip,*popthip))
+# # plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
+# plt.xlabel('Endcap voltage (V)')
+# plt.ylabel('Modulation factor')
+# plt.grid()
+
+
+
+
+fig, axx = plt.subplots( 2, figsize=(10,7) ,  
+                        constrained_layout=True, sharex=True,
+                        gridspec_kw=dict(height_ratios=[10,2]))
+fig.set_constrained_layout_pads(w_pad=2/72, h_pad=2/72, hspace=0, wspace=0)
 
-    plt.figure()
-    plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-    plt.plot(xhip,hiperbola(xhip,*popthip))
-    plt.xlabel('Endcap voltage (V)')
-    plt.ylabel('Modulation factor')
-    plt.grid()
+ax = axx[0]
 
-else:
-    poptini,pcovini = curve_fit(lineal,voltages_dcA[0:3],Betas_vec[0:3])
-    poptfin,pcovfin = curve_fit(lineal,voltages_dcA[4:],Betas_vec[4:])
+ax.errorbar(voltages_dcA[I],Betas_vec[I],yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+ax.plot(xhip,hiperbola(xhip,*popthip))
+# plt.plot(xhip,hiperbola(xhip,*par_inicial),'--',color='red')
 
-    minimum_voltage = -(poptini[1]-poptfin[1])/(poptini[0]-poptfin[0]) #voltaje donde se intersectan las rectas, es decir, donde deberia estar el minimo de micromocion
-    minimum_modulationfactor = lineal(minimum_voltage,*poptini) #es lo mismo si pongo *poptfin
+ax.set_ylabel('Modulation factor')
 
-    xini = np.linspace(-0.23,-0.13,100)
-    xfin = np.linspace(-0.15,0.005,100)
+ax = axx[1]
 
-    plt.figure()
-    plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-    plt.plot(xini,lineal(xini,*poptini))
-    plt.plot(xfin,lineal(xfin,*poptfin))
-    plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
-    plt.xlabel('Endcap voltage (V)')
-    plt.ylabel('Modulation factor')
-    plt.grid()
+ax.errorbar(voltages_dcA[I],Betas_vec[I]-hiperbola(voltages_dcA[I],*popthip),
+            yerr=ErrorBetas_vec[I],fmt='o',capsize=5,markersize=5,color=paleta[1])
+
+ax.set_ylabel('Res.')
+ax.set_xlabel('Endcap voltage (V)')
+
+for ax in axx:
+    ax.grid(True, ls=":", color='lightgray')
 
 
 print([t*1e3 for t in Temp_vec])
 
+
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Este hay que armarlo aún
+
 plt.figure()
 plt.errorbar(voltages_dcA,[t*1e3 for t in Temp_vec],yerr=[t*1e3 for t in ErrorTemp_vec],fmt='o',capsize=5,markersize=5,color=paleta[3])
 # plt.axvline(minimum_voltage,linestyle='dashed',color='grey')
@@ -514,30 +548,11 @@ plt.ylabel('Temperature (mK)')
 plt.grid()
 #plt.ylim(0,2)
 
-#%%
-"""
-Ahora hago un ajuste con una hiperbola porque tiene mas sentido, por el hecho
-de que en el punto optimo el ion no esta en el centro de la trampa
-sino que esta a una distancia d
-"""
-def hiperbola(x,a,b,c,x0):
-    return a*np.sqrt(((x-x0)**2+c**2))+b
-
-popthip,pcovhip = curve_fit(hiperbola,voltages_dcA,Betas_vec,p0=(100,0.1,1,-0.15))
-
-xhip = np.linspace(-0.23,0.005,200)
-
-plt.figure()
-plt.errorbar(voltages_dcA,Betas_vec,yerr=ErrorBetas_vec,fmt='o',capsize=5,markersize=5,color=paleta[1])
-plt.plot(xhip,hiperbola(xhip,*popthip))
-plt.xlabel('Endcap voltage (V)')
-plt.ylabel('Modulation factor')
-plt.grid()
-
 
 
+#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+#%% Ajuste de los betas y la temperatura
 
-#%%
 from scipy.special import jv
 
 

analisis/plots/20231212_Bstability/lolo_CPT_plotter_20231212.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Acá hay un análisis de la estabilidad del campo magnético (presuntamente).
+Se midieron N veces el mismo espectro y se analiza la variación del spliting
+de picos CPT que se debe al efecto Zeeman.
+
+El gráfico final muestra el corrimiento en procentual
+
+
+@author: lolo
+"""
+
+
+
+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 de una resonancia oscura DD multiples veces a lo largo de una noche para ver estabilidad de B
+"""
+
+#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/20231212_Bstability/Data/')
+
+# CPT_FILES = """000016432-IR_Scan_withcal_optimized
+# 000016433-IR_Scan_withcal_optimized
+# 000016434-IR_Scan_withcal_optimized
+# 000016435-IR_Scan_withcal_optimized
+# 000016436-IR_Scan_withcal_optimized
+# 000016437-IR_Scan_withcal_optimized
+# 000016438-IR_Scan_withcal_optimized
+# 000016439-IR_Scan_withcal_optimized
+# 000016440-IR_Scan_withcal_optimized
+# 000016441-IR_Scan_withcal_optimized
+# 000016442-IR_Scan_withcal_optimized
+# 000016443-IR_Scan_withcal_optimized
+# """ 
+
+folder = '../20231212_Bstability/Data/'
+CPT_FILES = f"""
+{folder}/000016434-IR_Scan_withcal_optimized
+{folder}/000016435-IR_Scan_withcal_optimized
+{folder}/000016436-IR_Scan_withcal_optimized
+{folder}/000016437-IR_Scan_withcal_optimized
+{folder}/000016438-IR_Scan_withcal_optimized
+{folder}/000016439-IR_Scan_withcal_optimized
+{folder}/000016440-IR_Scan_withcal_optimized
+{folder}/000016441-IR_Scan_withcal_optimized
+{folder}/000016442-IR_Scan_withcal_optimized
+{folder}/000016443-IR_Scan_withcal_optimized
+""".strip()
+
+
+CALIB_FILES = f"""{folder}/000016430-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(CPT_FILES))
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+Freqs = []
+
+CalibCounts = []
+CalibFreqs = []
+
+AmpTisa = []
+UVCPTAmp = []
+No_measures = []
+Voltages = []
+
+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']['data_array']))
+    #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']))
+    Voltages.append(np.array(data['datasets']['scanning_voltages']))
+
+
+for i, fname in enumerate(CALIB_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    CalibFreqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    CalibCounts.append(np.array(data['datasets']['counts_spectrum']))
+
+
+def Split(array,n):
+    length=len(array)/n
+    splitlist = []
+    jj = 0
+    while jj<length:
+        partial = []
+        ii = 0
+        while ii < n:
+            partial.append(array[jj*n+ii])
+            ii = ii + 1
+        splitlist.append(partial)
+        jj = jj + 1
+    return splitlist
+
+
+CountsSplit = []
+k=0
+for k in range(len(Counts)):
+    CountsSplit.append(Split(Counts[k],len(Freqs[k])))
+
+#%%
+from scipy.optimize import curve_fit
+
+def lorentzian(x,A,B,x0,g,C):
+    return 2*(A/np.pi)*g/(g**2 + 4*(x-x0)**2)+B+C*(x-x0)
+
+Freqscal = [2*f*1e-6 for f in CalibFreqs[0]]
+Countscal = CalibCounts[0]
+
+popt_dr1, pcov_dr1 = curve_fit(lorentzian,Freqscal[37:47],Countscal[37:47],p0=(-1000,1000,436,1,1))
+popt_dr2, pcov_dr2 = curve_fit(lorentzian,Freqscal[90:120],Countscal[90:120],p0=(-1000,1000,443,1,1))
+
+DeltaFreqs = popt_dr2[2]-popt_dr1[2]
+ZeroFrequency = 0.5*(popt_dr2[2]+popt_dr1[2])
+
+plt.figure()
+plt.plot(Freqscal,Countscal,'o')
+plt.plot(Freqscal,lorentzian(Freqscal,*popt_dr1))
+plt.plot(Freqscal,lorentzian(Freqscal,*popt_dr2))
+plt.axvline(ZeroFrequency)
+
+
+print(DeltaFreqs)
+
+"""
+Estas cuentas estan en el cuaderno SMILE MORE WORRY LESS pag 25.
+La resonancia de la izquierda esta a (-4/5)*u. La de la derecha esta a (4/5)*u. 
+Por ende la diferencia es (8/5)*u.
+Definimos u como 1.4 MHz/G * B. Entonces Despejamos B facilmente.
+"""
+
+ub = 9.27e-24
+h = 6.63e-34
+u = 1e-6*(ub/h)*1e-4 #en unidades de MHz/G
+
+MagneticField = DeltaFreqs/((8/5)*u)
+
+print(f'Magnetic field: {MagneticField}')
+
+
+
+#%%
+
+"""
+Ploteo la cpt de referencia / plotting the reference CPT
+"""
+
+freqs = [2*f*1e-6 for f in Freqs[0]]
+
+def lorentzian(x,A,B,x0,g,C):
+    return 2*(A/np.pi)*g/(g**2 + 4*(x-x0)**2)+B+C*(x-x0)
+
+# ii_plot = 11
+# jj_plot = 0
+
+ii_plot = 9
+jj_plot = 0
+
+ii_problematic = []
+jj_problematic = []
+
+Centers = []
+Widths = []
+test = []
+for ii in range(len(CountsSplit)):
+    for jj in range(len(CountsSplit[0])):    
+#        print(ii)
+#        print(jj)            
+        try:
+            if ii==2 and jj==11:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs[:-10], CountsSplit[ii][jj][:-10],p0=(-1000,1000,436,1,1))
+            
+            elif ii==2 and jj==12:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs[40:], CountsSplit[ii][jj][40:],p0=(-1000,1000,436,1,1))
+            
+            elif ii==4 and jj==1:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs[:-86], CountsSplit[ii][jj][:-86],p0=(-1000,1000,436,1,1))
+            
+            elif ii==4 and jj==2:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==4 and jj==7:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==4 and jj==12:
+                popt_lorentz = [0,0,0,0,0]
+    
+            elif ii==4 and jj==13:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==4 and jj==14:
+                popt_lorentz = [0,0,0,0,0]
+
+            elif ii==11 and jj==2:
+                popt_lorentz = [0,0,0,0,0]                
+
+            elif ii==11 and jj==3:
+                popt_lorentz = [0,0,0,0,0]                
+            
+            else:
+                popt_lorentz, pcov_lorentz = curve_fit(lorentzian, freqs, CountsSplit[ii][jj],p0=(-1000,1000,436,1,1))
+                
+            if popt_lorentz[2]>435.95 or popt_lorentz[2]<435.8:
+                if popt_lorentz[2]==0:
+                    pass
+                else:
+                    ii_problematic.append(ii)
+                    jj_problematic.append(jj)
+                    
+        except:
+            popt_lorentz=[0,0,0,0]
+            print("except")
+        print(ii,jj)
+        if ii == ii_plot and jj == jj_plot:
+            print('MATCH')
+            test.append(popt_lorentz)
+        Centers.append(popt_lorentz[2])
+        Widths.append(popt_lorentz[3])
+
+prob = 4
+
+print(ii_problematic[prob])
+print(jj_problematic[prob])
+
+kk=-83
+
+plt.figure()
+plt.plot(freqs, CountsSplit[ii_problematic[prob]][jj_problematic[prob]])
+plt.plot(freqs[kk], CountsSplit[ii_problematic[prob]][jj_problematic[prob]][kk],'o',markersize=10)
+plt.plot(freqs,lorentzian(freqs,*test[0]))
+#%%
+
+"""
+Usando que la DR de la izquierda esta a (-4/5)u, donde u = 1.4 MHz/G * B, 
+despejo y convierto la posicion de esa resonancia a campo magnetico
+"""
+
+def ConvertFreqsToMagneticField(f,zerofreq,u):
+    return np.abs(f-zerofreq)*(5/4)/(1.4)
+
+
+lentotal = len(CountsSplit)*len(CountsSplit[0])
+medtime=4/60
+timevec = np.linspace(0,medtime*lentotal, lentotal)
+
+plt.figure()
+plt.plot(timevec[4:],ConvertFreqsToMagneticField(Centers,ZeroFrequency,u)[4:],'o')
+plt.ylim(3.670,3.730)
+plt.xlabel('Time (h)')
+plt.ylabel('Magnetic field (G)')
+
+plt.figure()
+plt.plot(timevec[4:],[100*c/3.718 for c in ConvertFreqsToMagneticField(Centers,ZeroFrequency,u)][4:],'o')
+plt.ylim(98.5,100.1)
+plt.xlabel('Time (h)')
+plt.ylabel('Magnetic field variation (percent)')
+
+

analisis/plots/20231214_CPTconmicromocioncristals/Data/EITfit/MM_eightLevel_2repumps_python_scripts.py

@@ -129,7 +129,7 @@ def LtempCalculus(beta, drivefreq, forma=1):
     if forma==2:
         deltaKro = np.diag([1, 1, 1, 1, 1, 1, 1, 1])
         Ltemp = (-1j)*(np.kron(Hint, deltaKro) - np.kron(deltaKro, Hint))
-
+    print(np.matrix(Ltemp))
     Omega = np.zeros((64, 64), dtype=np.complex_)
     
     for i in range(64):
@@ -399,7 +399,10 @@ if __name__ == "__main__":
     freqMax = 50
     freqStep = 5e-2
     
-    Frequencyvector, Fluovector = CPTspectrum8levels_MM(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)
+        
+    Freq, Fluo = CPTspectrum8levels_MM(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, Circularityprobe, beta, drivefreq, freqMin=-100, freqMax=100, freqStep=1e-1, plot=False, solvemode=1)
+    
+    #Frequencyvector, Fluovector = CPTspectrum8levels_MM(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)
     
     plt.plot(Frequencyvector, [100*f for f in Fluovector], label=str(titaprobe) + 'º, T: ' + str(Temp*1e3) + ' mK')
     plt.xlabel('Probe detuning (MHz)')

analisis/plots/20231226_CPTconmicromocion4/Data/EITfit/MM_eightLevel_2repumps_CPTPlotter.py

@@ -67,7 +67,7 @@ sp = 8 #nice range: 0.1 to 20 #p is for probe but is the repump
 
 drivefreq=2*np.pi*22.135*1e6 #ignore it
 #betavec = np.arange(0,1.1,0.1) #ignore it
-betavec=[0] #ignore it
+betavec=[10] #ignore it
 alphavec = [0] #ignore it
 
 
@@ -81,7 +81,7 @@ for sg in sgvec:
     for T in TempVec:
         for alpha in alphavec:
             for beta in betavec:
-                Frequencies, Fluorescence = PerformExperiment_8levels(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+                Frequencies, Fluorescence = PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  beta, drivefreq, freqMin, freqMax, freqStep, circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
                 
                 FrequenciesVec.append(Frequencies)
                 FluorescencesVec.append(Fluorescence)

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

@@ -135,7 +135,9 @@ def LtempCalculus(beta, drivefreq, forma=1):
     for i in range(64):
         Omega[i, i] = (1j)*drivefreq
     
-
+        
+    #print(np.allclose(np.matrix(Ltemp), np.matrix(Ltemp).T, rtol=1e-5, atol=1e-8))
+    print(np.max(np.matrix(Ltemp)))
     return np.matrix(Ltemp), np.matrix(Omega)