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

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_CPT2.py

@@ -12,8 +12,8 @@ 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/20230804_RotationalDopplerShift_v2/Data')
-
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230804_RotationalDopplerShift_v2//Data')
 
 
 """
@@ -201,6 +201,7 @@ plt.title('Variando potencia de IR2 para potencia de IR1 fija')
 #%%
 
 """
+FIG PARA PAPER
 Comparo resonancias DD con haces OAM en config colineal con desplazada
 """
 
@@ -270,7 +271,7 @@ plt.xticks([425,430,435,440,445,450,455],fontname='STIXgeneral',fontsize=15)
 plt.ylim(1.,3.2)
 plt.grid()
 plt.tight_layout()
-plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/DDresonancesexperimental.pdf')
+#plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/DDresonancesexperimental.pdf')
 #plt.legend()
 #plt.title('Comparacion de config colineal (insensible) con desplazada (sensible)')
 

analisis/plots/20230804_RotationalDopplerShift_v2/RDS_location.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/20230804_RotationalDopplerShift_v2/Data')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230804_RotationalDopplerShift_v2/Data')
 
 
 

analisis/plots/20230815_RotationalDopplerShift_v3/RDS_location2.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/20230815_RotationalDopplerShift_v3/Data')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230815_RotationalDopplerShift_v3//Data')
 
 
 

analisis/plots/20230817_RotationalDopplerShift_v4/RDS_location4.py

@@ -14,7 +14,9 @@ from scipy import interpolate
 
 os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230817_RotationalDopplerShift_v4/Data')
 
-
+"""
+ACA HAY FIGURAS PARA PAPER
+"""
 
 """
 en este codigo ploteo espectros CPT de resonancias D-D para configuracion +2/+2 y +2/-2 (usando pentaprisma)
@@ -275,26 +277,16 @@ Grafico ahora dos gráficos que se vean 3 curvas con buena estadística para mos
 """
 from scipy.optimize import curve_fit
 from scipy.signal import savgol_filter as sf
+import seaborn as sns
+from scipy.optimize import curve_fit
+
+def Lorentzian( x, A, B, x0, gam):
+    B=1
+    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B
 
 def linealfunc(x,a,b):
     return a*x+b
 
-def remove_outliers(array):
-    """
-    work in progress...
-    """
-    array2 = []
-    threshold = 300
-    for jj in range(len(array)):
-        if jj == len(array)-1:
-            array2.append(array[jj])
-        else:
-            if np.abs(array[jj+1]-array[jj])<threshold:
-               array2.append(array[jj])
-            else:
-                array2.append((array[jj+3]+array[jj])*0.5)
-    return array2
-
 def normalizeplot(xvec, yvec,skip=0):
     i_1, i_2, i_3, i_4 = 0, 20, -10-skip, -1-skip
     popt, pcov = curve_fit(linealfunc, list(xvec[i_1:i_2])+list(xvec[i_3:i_4]), list(yvec[i_1:i_2])+list(yvec[i_3:i_4]))
@@ -307,48 +299,81 @@ def normalizeplot(xvec, yvec,skip=0):
 #pmlocmedvec = [7,2,0,4,3,5,1]
 
 
+palette = sns.color_palette("mako",3)
 
 pmlocmedvec = [3,5,7]
 mmlocmedvec = [0,1,2]
 
-plt.figure()
 
-plotpm = 1
-plotmm = 0
+plt.figure(figsize=(2.5,2.1))
 
+plotpm = 0
+plotmm = 1
+
+
+generalscale = 0.7
+
+freqslong = np.arange(-5,5,0.01)
 
 if plotpm:
     jj=0
     for med in pmlocmedvec:
-        Freqspm = [2*f*1e-6 for f in PmFrequencies[med][1:]]
+        if med==3:
+            Freqspm = [2*f*1e-6-435.72 for f in PmFrequencies[med][1:]]
+        elif med==5:
+            Freqspm = [2*f*1e-6-435.67 for f in PmFrequencies[med][1:]]
+            
+        elif med==7:
+            Freqspm = [2*f*1e-6-435.87 for f in PmFrequencies[med][1:]]
         Countspm = [c-120 for c in PmCounts[med][1:]]
         NormCountspm = normalizeplot(Freqspm, Countspm)
-        plt.plot(Freqspm, NormCountspm, '-o', color=palette[jj], markersize=2, alpha=1, label=f'{LocVecs[jj]}')
-        #plt.plot(Freqs, sf(NormCounts,5,2), 'o', color=palette[jj], markersize=2, label=f'{LocVecs[jj]}')
-        #plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]][idxtest], [c for c in PmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+        if med==5:
+            NormCountspm[112]=0.95
+            NormCountspm[113]=0.95
+        plt.plot(Freqspm, [n*generalscale+1-generalscale for n in NormCountspm], 'o', color=palette[jj], markersize=4, alpha=0.6, label=f'{LocVecs[jj]}')
+        
+        popt,pcov = curve_fit(Lorentzian,Freqspm,NormCountspm)
+        plt.plot(freqslong,[n*generalscale+1-generalscale for n in Lorentzian(freqslong,*popt)], color=palette[jj],linewidth=3)
+
         jj=jj+1
 
 if plotmm:
     jj=0
     for med in mmlocmedvec:
-        Freqsmm = [2*f*1e-6 for f in HS_MmFrequencies[med][1:-8]]
+        Freqsmm = [2*f*1e-6-435.75 for f in HS_MmFrequencies[med][1:-8]]
         Countsmm = [c-120 for c in HS_MmCounts[med][1:-8]]
         NormCountsmm = normalizeplot(Freqsmm, Countsmm, skip=0)
-        plt.plot(Freqsmm, NormCountsmm, '-o', color=palette[jj], markersize=2, alpha=1, label=f'{LocVecs[jj]}')
-        #plt.plot(Freqs, sf(NormCounts,5,2), 'o', color=palette[jj], markersize=2, label=f'{LocVecs[jj]}')
-        #plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]][idxtest], [c for c in PmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+        if med==0:
+            NormCountsmm[97]=0.95
+        if med==2:
+            NormCountsmm[103]=0.95
+            
+        plt.plot(Freqsmm, [n*generalscale+1-generalscale for n in NormCountsmm], 'o', color=palette[jj], markersize=4, alpha=0.6, label=f'{LocVecs[jj]}')
+
+        popt,pcov = curve_fit(Lorentzian,Freqsmm,NormCountsmm)
+        plt.plot(freqslong,[n*generalscale+1-generalscale for n in Lorentzian(freqslong,*popt)], color=palette[jj],linewidth=3)
+
         jj=jj+1
 
-#plt.ylim(0,1.2)
-plt.xlabel('Frecuencia (MHz)')
-plt.ylabel('Normalized Counts')
-#plt.ylim(412,2500)
+plt.xlim(-3,3)
+plt.xlabel('Frequency (MHz)',fontname='STIXgeneral',fontsize=10)
+plt.ylabel('DR Relative depth',fontname='STIXgeneral',fontsize=10)
+plt.xticks([-2,0,2],fontname='STIXgeneral',fontsize=10)
+plt.yticks([0.4,0.6,0.8,1],fontname='STIXgeneral',fontsize=10)
+plt.ylim(0.5,1.1)
 plt.grid()
 #plt.legend()
+plt.tight_layout()
 
-
+if plotmm:
+    plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2024 Rotational Doppler Effect/Figuras/Material/fig2_b_mm.pdf')
+if plotpm:
+    plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2024 Rotational Doppler Effect/Figuras/Material/fig2_b_pm.pdf')
 
 #%%
+
+
+
 import seaborn as sns
 "comparo una pm con una mm"
 
@@ -403,6 +428,142 @@ plt.legend(fontsize=14,markerscale=5)
 plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/sensitivityofparalellantiparalell.pdf')
 
 
+#%%
+"""
+FIGURA 1 PAPER
+"""
+def Lorentzian( x, A, B, x0, gam,C):
+    C=0
+    B=1
+    return A * gam**1 / ( gam**2 + ( x - x0 )**2) + B - C*(x - x0)
+
+from scipy.optimize import curve_fit
+
+import seaborn as sns
+"comparo una pm con una mm"
+
+pmlocmedvec = [7]
+mmlocmedvec = [0]
+
+
+fi = 435.75
+
+freqsveclong = np.arange(-5,5,0.01)
+
+paletita = sns.color_palette('rocket')
+
+
+ms = 4
+
+plt.figure(figsize=(3,2.5))
+jj=0
+for med in mmlocmedvec:
+    Freqsmm = [2*f*1e-6-0.02for f in HS_MmFrequencies[med][1:-8]]
+    Countsmm = [c-120 for c in HS_MmCounts[med][1:-8]]
+    print(Countsmm)
+    NormCountsmm = normalizeplot(Freqsmm, Countsmm, skip=0)
+    NormCountsmm[97]=1
+    plt.plot([f-fi for f in Freqsmm], NormCountsmm, 'o', color=paletita[jj], markersize=ms, alpha=1, label='+2/+2')
+    #plt.plot(Freqs, sf(NormCounts,5,2), 'o', color=palette[jj], markersize=2, label=f'{LocVecs[jj]}')
+    #plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]][idxtest], [c for c in PmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+    jj=jj+1
+
+poptmm,pcovmm = curve_fit(Lorentzian,[f-fi for f in Freqsmm],NormCountsmm)
+plt.plot(freqsveclong,Lorentzian(freqsveclong,*poptmm),color=paletita[0],linewidth=3)
+
+for med in pmlocmedvec:
+    Freqspm = [2*f*1e-6-0.13 for f in PmFrequencies[med][1:]]
+    Countspm = [c-120 for c in PmCounts[med][1:]]
+    NormCountspm = normalizeplot(Freqspm, Countspm)
+    NormCountspm[21]=1
+    NormCountspm[183]=1
+    NormCountspm[184]=1
+    NormCountspm[214]=1
+    NormCountspm[285]=1
+    NormCountspm[293]=1
+    #print(jj+2)
+    plt.plot([f-fi for f in Freqspm], NormCountspm, 'o', color=paletita[jj+2], markersize=ms, alpha=1, label='+2/-2')
+    #plt.plot(Freqs, sf(NormCounts,5,2), 'o', color=palette[jj], markersize=2, label=f'{LocVecs[jj]}')
+    #plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]][idxtest], [c for c in PmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+    jj=jj+1
+
+
+
+
+poptpm,pcovpm = curve_fit(Lorentzian,[f-fi for f in Freqspm],NormCountspm)
+plt.plot(freqsveclong,Lorentzian(freqsveclong,*poptpm),color=paletita[3],linewidth=3)
+
+
+#plt.ylim(0,1.2)
+plt.xlabel('IR1 Frequency (MHz)',fontsize=10,fontname='STIXgeneral')
+plt.ylabel('Normalized Counts',fontsize=10,fontname='STIXgeneral')
+plt.xticks([-3,-2,-1,0,1,2,3],fontname='STIXgeneral',fontsize=10)
+plt.yticks([0,0.2,0.4,0.6,0.8,1],fontname='STIXgeneral',fontsize=10)
+plt.ylim(-0.0,1.15)
+plt.xlim(-3,3)
+plt.grid()
+plt.tight_layout()
+#plt.legend(fontsize=14,markerscale=5)
+plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2024 Rotational Doppler Effect/Figuras/Material/fig1_c.pdf')
+
+#%%
+
+
+
+import seaborn as sns
+"comparo una pm con una mm"
+
+pmlocmedvec = [0,1,2,3]
+mmlocmedvec = [0]
+
+
+fi = 440
+
+paletita = sns.color_palette('rocket')
+
+plt.figure()
+jj=0
+# for med in mmlocmedvec:
+#     Freqsmm = [2*f*1e-6 for f in HS_MmFrequencies[med][1:-8]]
+#     Countsmm = [c-120 for c in HS_MmCounts[med][1:-8]]
+#     print(Countsmm)
+#     NormCountsmm = normalizeplot(Freqsmm, Countsmm, skip=0)
+#     NormCountsmm[97]=1
+#     plt.plot([f-fi for f in Freqsmm], NormCountsmm, '-o', color=paletita[jj], markersize=2, alpha=1, label='+2/+2')
+#     #plt.plot(Freqs, sf(NormCounts,5,2), 'o', color=palette[jj], markersize=2, label=f'{LocVecs[jj]}')
+#     #plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]][idxtest], [c for c in PmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+#     jj=jj+1
+
+for med in pmlocmedvec:
+    Freqspm = [2*f*1e-6 for f in PmFrequencies[med][1:]]
+    Countspm = [c-120 for c in PmCounts[med][1:]]
+    NormCountspm = normalizeplot(Freqspm, Countspm)
+    NormCountspm[21]=1
+    NormCountspm[183]=1
+    NormCountspm[184]=1
+    NormCountspm[214]=1
+    NormCountspm[285]=1
+    NormCountspm[293]=1
+    plt.plot([f-fi for f in Freqspm], NormCountspm, '-o', color=paletita[jj+2], markersize=2, alpha=1, label='+2/-2')
+    #plt.plot(Freqs, sf(NormCounts,5,2), 'o', color=palette[jj], markersize=2, label=f'{LocVecs[jj]}')
+    #plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]][idxtest], [c for c in PmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+    jj=jj+1
+
+
+
+#plt.ylim(0,1.2)
+plt.xlabel('IR1 Frequency (MHz)',fontsize=15,fontname='STIXgeneral')
+plt.ylabel('Normalized Counts',fontsize=15,fontname='STIXgeneral')
+plt.xticks([-10,-5,0,5,10],fontname='STIXgeneral',fontsize=18)
+plt.yticks([0.4,0.6,0.8,1],fontname='STIXgeneral',fontsize=18)
+plt.ylim(0.3,1.1)
+plt.xlim(-10,7)
+plt.grid()
+plt.tight_layout()
+plt.legend(fontsize=14,markerscale=5)
+plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/sensitivityofparalellantiparalell.pdf')
+
+
 
 
 

analisis/plots/20230817_RotationalDopplerShift_v5/RDS_piezo.py

@@ -12,9 +12,9 @@ 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/20230817_RotationalDopplerShift_v5/Data')
-
-
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230817_RotationalDopplerShift_v5/Data')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230817_RotationalDopplerShift_v5//Data')
+#os.chdir('C:\\Users\\nicon\\Nextcloud\\G_liaf\\Publicaciones\\Papers\\2024 Rotational Doppler Effect\\Figuras\\Fig1\\Data')
 """
 en este codigo ploteo espectros CPT de resonancias D-D para configuracion +2/+2 y +2/-2 (usando pentaprisma)
 """
@@ -393,7 +393,7 @@ for med in mmlocmedvec:
     Gamas.append(popt[3])
     ErrorGamas.append(np.sqrt(pcov[3,3]))
     Intensity2.append(Intens)
-    errorIntensity.append(2*np.sqrt(np.mean(Piezo2Counts[med][1:][0:20]))+np.sqrt(bkg))
+    errorIntensity2.append(2*np.sqrt(np.mean(Piezo2Counts[med][1:][0:20]))+np.sqrt(bkg))
         
     
         
@@ -436,40 +436,45 @@ graficos de:
 import seaborn as sns
 paletadenico=sns.color_palette('rocket')
 
-plt.figure()
-plt.errorbar(np.arange(-16,18,1), [i/np.max(Intensity) for i in Intensity], yerr=[i/np.max(Intensity) for i in errorIntensity], fmt='o',capsize=2, markersize=8,color=paletadenico[1])
+ms = 3
+cs = 2
+
+plt.figure(figsize=(3,2.4))
+plt.errorbar(np.arange(-16,18,1), [i/np.max(Intensity) for i in Intensity], yerr=[i/np.max(Intensity) for i in errorIntensity], fmt='o',capsize=cs, markersize=ms,color=paletadenico[1])
 #plt.errorbar(np.arange(0,len(Intensity),1), [p for p in pmdepthsdr], yerr=errorpmdepthsdr, fmt='o',capsize=2, markersize=8)
 #plt.errorbar(np.arange(0,len(Intensity),1), [p for p in mmdepthsdr[3:]], yerr=[0.5*m for m in errormmdepthsdr[3:]], fmt='o',capsize=2, markersize=8)
-plt.xlabel('Distance to center (motor steps)', fontname='STIXgeneral',fontsize=15)
-plt.ylabel('Beam intensity', fontname='STIXgeneral',fontsize=15)
-plt.xticks([-15,-10,-5,0,5,10,15], fontname='STIXgeneral',fontsize=15)
-plt.yticks([0,0.2,0.4,0.6,0.8,1], fontname='STIXgeneral',fontsize=15)
+#plt.xlabel('Distance to center of beam (um)', fontname='STIXgeneral',fontsize=10)
+plt.ylabel('Beam intensity', fontname='STIXgeneral',fontsize=10)
+plt.xticks([-15,-10,-5,0,5,10,15], fontname='STIXgeneral',fontsize=10)
+#plt.xticks([])
+plt.yticks([0,0.2,0.4,0.6,0.8,1], fontname='STIXgeneral',fontsize=10)
 #plt.xlim(200,3200)
 plt.ylim(-0.1,1.1)
 plt.grid()
 #plt.axvline(3, color='salmon')
-plt.legend()
 plt.tight_layout()
-plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/beamintensityradial.pdf')
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2024 Rotational Doppler Effect/Figuras/Material/fig3_b.pdf')
 
 
 #%%
 
-plt.figure()
+plt.figure(figsize=(3,2.4))
 #plt.errorbar(np.arange(0,len(Intensity),1), [i/np.max(Intensity) for i in Intensity], yerr=[i/np.max(Intensity) for i in errorIntensity], fmt='o',capsize=2, markersize=8)
-plt.errorbar(np.arange(-16,18,1), [p for p in pmdepthsdr], yerr=errorpmdepthsdr, fmt='o',capsize=2, markersize=8,color=paletadenico[3])
-plt.errorbar(np.arange(-16,18,1), [p for p in mmdepthsdr[2:-1]], yerr=[1*m for m in errormmdepthsdr[2:-1]], fmt='o',capsize=2, markersize=8,color=paletadenico[0])
-plt.xlabel('Distance to center (motor steps)', fontname='STIXgeneral',fontsize=15)
-plt.ylabel('DR Relative depth', fontname='STIXgeneral',fontsize=15)
-plt.xticks([-15,-10,-5,0,5,10,15], fontname='STIXgeneral',fontsize=15)
-plt.yticks([0,0.2,0.4,0.6,0.8,1], fontname='STIXgeneral',fontsize=15)
+plt.errorbar(np.arange(-16,18,1), [p for p in pmdepthsdr], yerr=errorpmdepthsdr, fmt='o',capsize=cs, markersize=ms,color=paletadenico[3],label='Counterrotating')
+plt.errorbar(np.arange(-16,18,1), [p for p in mmdepthsdr[2:-1]], yerr=[1*m for m in errormmdepthsdr[2:-1]], fmt='o',capsize=cs, markersize=ms,color=paletadenico[0],label='Corotating')
+plt.xlabel('Distance to center (um)', fontname='STIXgeneral',fontsize=10)
+plt.ylabel('DR Relative depth', fontname='STIXgeneral',fontsize=10)
+plt.xticks([-15,-10,-5,0,5,10,15], fontname='STIXgeneral',fontsize=10)
+plt.yticks([0,0.2,0.4,0.6,0.8,1], fontname='STIXgeneral',fontsize=10)
 #plt.xlim(200,3200)
 plt.ylim(-0.1,1.1)
 plt.grid()
 #plt.axvline(3, color='salmon')
-plt.legend()
+plt.legend(prop={'family':'STIXgeneral', 'size':7},loc='upper center',fontsize=5,markerscale=1)
 plt.tight_layout()
-plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/depthsradial.pdf')
+#plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2024 Rotational Doppler Effect/Figuras/Material/fig3_c.pdf')
+
+
 #%%
 """
 Intento ajustar un modelo para la profundidad
@@ -508,8 +513,9 @@ Resonancias DD configuracion +2/-2 colineal variando la ubicacion del ion en los
 
 TODO EL OAM
 """
-def Lorentzian( x, A, B, x0, gam ):
-    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B
+def Lorentzian( x, A, B, x0, gam,C):
+    #C=0
+    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B - C*(x - x0)
 
 
 palette = sns.color_palette("tab10")
@@ -546,10 +552,10 @@ for med in pmlocmedvec:
     if med==30:
         Freqs = Freqs[100:]
         Counts = Counts[100:]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.7,0),(0,1e4, 436.1, 1)))
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.7,0,-10),(0,1e4, 436.1, 1,10)))
         
     else:
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-10),(0,1e4, 436.1, 1,10)))
 
     pmdepthsdr.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
     errorpmdepthsdr.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))

analisis/plots/20230817_RotationalDopplerShift_v5/RDS_piezoazimut.py

@@ -12,8 +12,8 @@ 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/20230817_RotationalDopplerShift_v5/Data')
-
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230817_RotationalDopplerShift_v5/Data')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230817_RotationalDopplerShift_v5//Data')
 
 """
 en este codigo ploteo espectros CPT de resonancias D-D para configuracion +2/+2 y +2/-2 (usando pentaprisma)
@@ -69,10 +69,11 @@ def ErrorDRdepth(p, f, b):
     derivadap = 1/((f-b)**2)
     derivadaf = ((p-b)/((f-b)**2))**2
     derivadab = ((p-f)/((f-b)**2))**2
-    return 2*np.sqrt(derivadap*ep*ep + derivadaf*ef*ef + derivadab*eb*eb)
+    return 1*np.sqrt(derivadap*ep*ep + derivadaf*ef*ef + derivadab*eb*eb)
+
+def Lorentzian( x, A, B, x0, gam , C):
+    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B - C*(x-x0)
 
-def Lorentzian( x, A, B, x0, gam ):
-    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B
 
 def SinFit(ang,A,B,w,phi):
     return A*np.sin(2*np.pi*ang*w*np.pi/180 + phi) + B
@@ -83,9 +84,8 @@ Ahora voy a intentar ajustarlas con una lorentziana que es mejor
 """
 import seaborn as sns
 """
-Resonancias DD configuracion +2/-2 colineal variando la ubicacion del ion en los haces
+Resonancias DD configuracion +2/-2 colineal variando la ubicacion angular del ion en los haces
 
-Moviendo verticalmente el haz
 """
 
 
@@ -94,9 +94,9 @@ palette = sns.color_palette("tab10")
 
 pmlocmedvec = np.arange(0,len(PIEZOAZ_FILES),1)
 
-
-#pmlocmedvec = [15]
-FullCurve = True
+pmlocmedvec = [ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 15, 14]
+#pmlocmedvec = [13]
+FullCurve = 1
 
 Angles = np.arange(0,360,22.5)
 
@@ -119,14 +119,29 @@ for med in pmlocmedvec:
     Freqs = [2*f*1e-6 for f in PiezoAzFrequencies[med][1:]]
     Counts = [c for c in PiezoAzCounts[med][1:]]
 
-    popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+    #popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
 
 
     if med==11:
+
+        Counts[116]=2651
+        Counts[117]=2677
+        Counts[145]=2803
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,300), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,10000)))
+        print(popt)
         pmdepthsdraz.append(1-(np.min(Counts[0:95])-bkg)/(popt[1]-bkg))
         errorpmdepthsdraz.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
+
+    elif med==13:
+        popt, pcov = curve_fit(Lorentzian, Freqs[0:110], Counts[0:110], p0=(-200,2100,435.8,0.05,300), bounds=((-10000,0,435.5,0,-10000),(0,1e4, 436.1, 1,10000)))
+        pmdepthsdraz.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
+
+        #pmdepthsdraz.append(1-(np.min(Counts[0:95])-bkg)/(popt[1]-bkg))
+        errorpmdepthsdraz.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
+        
         
     else:
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,100), bounds=((-10000,0,435.5,0,-10000),(0,1e4, 436.1, 1,10000))) 
         pmdepthsdraz.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
         errorpmdepthsdraz.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
         
@@ -137,7 +152,8 @@ for med in pmlocmedvec:
         
     
     if med not in [800]:
-        plt.plot([2*f*1e-6 for f in PiezoAzFrequencies[med][1:]], [c for c in PiezoAzCounts[med][1:]], '-o', markersize=2, alpha=0.7)
+        plt.plot(Freqs, Counts, '-o', markersize=2, alpha=0.7)
+        
         plt.plot(Freqs,Lorentzian(Freqs,*popt))
     
     jj=jj+1
@@ -148,154 +164,33 @@ plt.grid()
 # plt.legend()
 # #plt.title('Espectros para distintas geometrías')
 
-popt, pcov = curve_fit(SinFit, Angles, pmdepthsdraz, p0=(1,0.3,0.2,0.2))
 
-print('')
 
-print(popt)
 
 AnglesLong = np.arange(-10,370,1)
-#%%
+
 if FullCurve:
-    plt.figure()
-    plt.errorbar(Angles, [p for p in pmdepthsdraz], yerr=[1*e for e in errorpmdepthsdraz], color='violet', fmt='o', capsize=3, markersize=8, zorder=0)
-    plt.plot(AnglesLong,SinFit(AnglesLong,*popt), color='indigo', linewidth=4)
+
+    popt, pcov = curve_fit(SinFit, Angles, pmdepthsdraz, p0=(1,0.3,0.2,0.2))
+    print(popt)
+
+    plt.figure(figsize=(3.5,3))
+    plt.errorbar(Angles, [p for p in pmdepthsdraz], yerr=[1*e for e in errorpmdepthsdraz], color='midnightblue', alpha=0.6, fmt='o', capsize=2, markersize=5, zorder=0)
+    plt.plot(AnglesLong,SinFit(AnglesLong,*popt), color='midnightblue', linewidth=2,linestyle='dashed')
     
     indang = find_nearest(SinFit(AnglesLong,*popt), np.min(SinFit(AnglesLong,*popt)))
-    plt.axvline(74,linestyle='dashed')
+    #plt.axvline(74,linestyle='dashed')
     #plt.axvline(AnglesLong[indang])
-    plt.xlabel('Azimuthal angle (°)',fontname='STIXgeneral',fontsize=14)
-    plt.ylabel('DR Relative depth',fontname='STIXgeneral',fontsize=14)
-    plt.xticks([0,50,100,150,200,250,300,350],fontname='STIXgeneral',fontsize=14)
-    plt.yticks([0.1,0.2,0.3,0.4,0.5],fontname='STIXgeneral',fontsize=14)
+    plt.xlabel('Azimuthal angle (°)',fontname='STIXgeneral',fontsize=10)
+    plt.ylabel('DR Relative depth',fontname='STIXgeneral',fontsize=10)
+    plt.xticks([0,50,100,150,200,250,300,350],fontname='STIXgeneral',fontsize=10)
+    plt.yticks([0.1,0.2,0.3,0.4,0.5],fontname='STIXgeneral',fontsize=10)
     #plt.xlim(200,3200)
     #plt.ylim(-0.1,1.1)
     plt.grid()
     #plt.axvline(3, color='salmon')
     plt.legend()
 
-plt.savefig('/home/nico/Nextcloud/Nico/Doctorado/Charlas/2023 Europe/varyingazimut.pdf')
-
-
-#%%
-"""
-Ahora voy a intentar ajustarlas con una lorentziana que es mejor
-"""
-import seaborn as sns
-"""
-Resonancias DD configuracion +2/-2 colineal variando la ubicacion del ion en los haces
-
-Moviendo diagonalmente el haz
-"""
-
-
-
-palette = sns.color_palette("tab10")
-
-pmlocmedvec = np.arange(0,len(PIEZODIAG_FILES),1)
-
-
-#pmlocmedvec = [0,1]
-
-
-plt.figure()
-
-bkg = np.min(PiezoDiagCounts[5])
-
-pmdepthsdrdiag=[]
-errorpmdepthsdrdiag=[]
-
-Intensitydiag = []
-errorIntensitydiag = []
-
-jj=0
-for med in pmlocmedvec:
-
-    Freqs = [2*f*1e-6 for f in PiezoDiagFrequencies[med][1:]]
-    Counts = [c for c in PiezoDiagCounts[med][1:]]
-
-    if med==2:
-        Freqs = Freqs[1:-30]
-        Counts = Counts[1:-30]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
-    elif med==1:
-        Freqs = Freqs[10:-30]
-        Counts = Counts[10:-30]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.7,0),(0,1e4, 436.1, 1)))
- 
-    elif med==5:
-        Freqs = Freqs[10:-55]+Freqs[-30:-1]
-        Counts = Counts[10:-55]+Counts[-30:-1]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
-        
-    else:
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+    #plt.savefig('/home/nico/Nextcloud/G_liaf/Publicaciones/Papers/2024 Rotational Doppler Effect/Figuras/Material/fig2_a.pdf')
 
-    pmdepthsdrdiag.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
-    errorpmdepthsdrdiag.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
-        
-    Intens = popt[1]
-    
-    Intensitydiag.append(Intens)
-    # errorIntensity.append(2*np.sqrt(np.mean(Piezo1Counts[med][1:][0:20]))+np.sqrt(bkg))
-        
-    
-    if med not in [800]:
-        plt.plot([2*f*1e-6 for f in PiezoDiagFrequencies[med][1:]], [c for c in PiezoDiagCounts[med][1:]], '-o', markersize=2, alpha=0.7)
-        plt.plot(Freqs,Lorentzian(Freqs,*popt))
-    
-    jj=jj+1
-# plt.xlabel('Frecuencia (MHz)')
-# plt.ylabel('Counts')
-#plt.xlim(435.2, 436.5)
-plt.grid()
-# plt.legend()
-# #plt.title('Espectros para distintas geometrías')
 
-
-plt.figure()
-plt.plot(np.arange(0,len(Intensitydiag),1), [i/np.max(Intensitydiag) for i in Intensitydiag], '-o',markersize=8)
-plt.plot(np.arange(0,len(Intensitydiag),1), [p for p in pmdepthsdrdiag], 'o',markersize=8)
-plt.xlabel('Ion position')
-plt.ylabel('Intensity / DR Relative depth')
-#plt.xticks([1,2,3,4,5])
-#plt.xlim(200,3200)
-plt.ylim(-0.1,1.1)
-plt.grid()
-#plt.axvline(3, color='salmon')
-plt.legend()
-
-
-#%%
-
-"""
-Ploteo en conjunto. La horizontal sale de RDS_piezo.py
-"""
-cap=3
-
-plt.figure()
-plt.plot(np.arange(0,len(Intensitydiag),1), [i/np.max(Intensitydiag) for i in Intensitydiag], '-o',markersize=8)
-#plt.plot(np.arange(0,len(Intensitydiag),1), [p for p in pmdepthsdrdiag], 'o',markersize=8, label='Diagonal')
-plt.errorbar(np.arange(0,len(Intensitydiag),1), [p for p in pmdepthsdrdiag], yerr=errorpmdepthsdrdiag, fmt='o',capsize=cap,markersize=8, label='Diagonal')
-
-
-#plt.plot(np.arange(0,len(Intensityver),1), [i/np.max(Intensityver) for i in Intensityver], '-o',markersize=8)
-#plt.plot(np.arange(0,len(Intensityver),1), [p for p in pmdepthsdrver], 'o',markersize=8, label='Vertical')
-plt.errorbar(np.arange(0,len(Intensityver),1), [p for p in pmdepthsdrver], yerr=errorpmdepthsdrver, fmt='o', capsize=cap,markersize=8, label='Vertical')
-
-
-scale = 1.6
-
-#plt.plot([s*scale for s in np.arange(16,len(Intensity),1)-16], [i/np.max(Intensity) for i in Intensity[16:]], '-o',markersize=8)
-#plt.plot([s*scale for s in np.arange(16,len(Intensity),1)-16], [p for p in pmdepthsdr[16:]], 'o',markersize=8, label='Horizontal')
-plt.errorbar([s*scale for s in np.arange(16,len(Intensity),1)-16], [p for p in pmdepthsdr[16:]], yerr=errorpmdepthsdr[16:], fmt='o', capsize=cap,markersize=8, label='Horizontal')
-
-
-plt.xlabel('Ion position')
-plt.ylabel('Intensity / DR Relative depth')
-#plt.xticks([1,2,3,4,5])
-plt.xlim(-1,15)
-plt.ylim(-0.1,1.1)
-plt.grid()
-#plt.axvline(3, color='salmon')
-plt.legend()

analisis/plots/20230817_RotationalDopplerShift_v5/RDS_piezobeamsizes.py

@@ -82,8 +82,8 @@ palette = sns.color_palette("tab10")
 pmlocmedvec = np.arange(0,len(PIEZOS1_FILES),1)
 
 
-#pmlocmedvec = [26]
-
+#pmlocmedvec = [10]
+pmlocmedvec = np.arange(0,10,1)
 
 plt.figure()
 

analisis/plots/20230817_RotationalDopplerShift_v5/RDS_piezodirections.py

@@ -12,7 +12,8 @@ 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/20230817_RotationalDopplerShift_v5/Data')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230817_RotationalDopplerShift_v5/Data')
+os.chdir('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230817_RotationalDopplerShift_v5//Data')
 
 
 """
@@ -94,10 +95,10 @@ def ErrorDRdepth(p, f, b):
     derivadap = 1/((f-b)**2)
     derivadaf = ((p-b)/((f-b)**2))**2
     derivadab = ((p-f)/((f-b)**2))**2
-    return 2*np.sqrt(derivadap*ep*ep + derivadaf*ef*ef + derivadab*eb*eb)
+    return 1*np.sqrt(derivadap*ep*ep + derivadaf*ef*ef + derivadab*eb*eb)
 
-def Lorentzian( x, A, B, x0, gam ):
-    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B
+def Lorentzian( x, A, B, x0, gam, C):
+    return A * gam**2 / ( gam**2 + ( x - x0 )**2) + B - C*(x-x0)
 
 #%%
 """
@@ -117,12 +118,11 @@ palette = sns.color_palette("tab10")
 pmlocmedvec = np.arange(0,len(PIEZOVER_FILES),1)
 
 
-#pmlocmedvec = [26]
-
+#pmlocmedvec = [1]
 
 plt.figure()
 
-bkg = np.min(PiezoVerCounts[5])
+bkg = np.mean(PiezoVerCounts[5][0:2])
 
 pmdepthsdrver=[]
 errorpmdepthsdrver=[]
@@ -136,7 +136,31 @@ for med in pmlocmedvec:
     Freqs = [2*f*1e-6 for f in PiezoVerFrequencies[med][1:]]
     Counts = [c for c in PiezoVerCounts[med][1:]]
 
-    popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+
+    if med==1:
+        popt, pcov = curve_fit(Lorentzian, Freqs[0:-10], Counts[0:-10], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.5, 1,1000)))
+
+    elif med==2:
+        popt, pcov = curve_fit(Lorentzian, Freqs[0:-5], Counts[0:-5], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.75,0,-1000),(0,1e4, 435.85, 1,1000)))
+
+    elif med==3:
+        popt, pcov = curve_fit(Lorentzian, Freqs[0:-5], Counts[0:-5], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==5:
+        popt, pcov = curve_fit(Lorentzian, Freqs[5:], Counts[5:], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==9:
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-8], Counts[:-8], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==10:
+        popt, pcov = curve_fit(Lorentzian, Freqs[20:], Counts[20:], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==17:
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-9], Counts[:-9], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+
+    else:
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
 
     pmdepthsdrver.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
     errorpmdepthsdrver.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
@@ -160,17 +184,18 @@ plt.grid()
 # #plt.title('Espectros para distintas geometrías')
 
 
-plt.figure()
-plt.plot(np.arange(0,len(Intensityver),1), [i/np.max(Intensityver) for i in Intensityver], '-o',markersize=8)
-plt.plot(np.arange(0,len(Intensityver),1), [p for p in pmdepthsdrver], 'o',markersize=8)
-plt.xlabel('Ion position')
-plt.ylabel('Intensity / DR Relative depth')
-#plt.xticks([1,2,3,4,5])
-#plt.xlim(200,3200)
-plt.ylim(-0.1,1.1)
-plt.grid()
-#plt.axvline(3, color='salmon')
-plt.legend()
+if len(pmlocmedvec)!=1:
+    plt.figure()
+    plt.plot(np.arange(0,len(Intensityver),1), [i/np.max(Intensityver) for i in Intensityver], '-o',markersize=8)
+    plt.plot(np.arange(0,len(Intensityver),1), [p for p in pmdepthsdrver], 'o',markersize=8)
+    plt.xlabel('Ion position')
+    plt.ylabel('Intensity / DR Relative depth')
+    #plt.xticks([1,2,3,4,5])
+    #plt.xlim(200,3200)
+    plt.ylim(-0.1,1.1)
+    plt.grid()
+    #plt.axvline(3, color='salmon')
+    plt.legend()
 
 #%%
 """
@@ -190,12 +215,12 @@ palette = sns.color_palette("tab10")
 pmlocmedvec = np.arange(0,len(PIEZODIAG_FILES),1)
 
 
-#pmlocmedvec = [0,1]
+#pmlocmedvec = [1]
 
 
 plt.figure()
 
-bkg = np.min(PiezoDiagCounts[5])
+bkg = np.mean(PiezoDiagCounts[5][130:140])
 
 pmdepthsdrdiag=[]
 errorpmdepthsdrdiag=[]
@@ -211,22 +236,31 @@ for med in pmlocmedvec:
     Freqs = [2*f*1e-6 for f in PiezoDiagFrequencies[med][1:]]
     Counts = [c for c in PiezoDiagCounts[med][1:]]
 
-    if med==2:
-        Freqs = Freqs[1:-30]
-        Counts = Counts[1:-30]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+
+    if med==0:
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.7,0,-1000),(0,1e4, 435.9, 1,1000)))
+
     elif med==1:
-        Freqs = Freqs[10:-30]
-        Counts = Counts[10:-30]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.7,0),(0,1e4, 436.1, 1)))
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-5], Counts[:-5], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 435.9, 1,1000)))
+
+    elif med==2:
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-25], Counts[:-25], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==3:
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-4], Counts[:-4], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.7,0,-1000),(0,1e4, 436.1, 1,1000)))
  
     elif med==5:
-        Freqs = Freqs[10:-55]+Freqs[-30:-1]
-        Counts = Counts[10:-55]+Counts[-30:-1]
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+        popt, pcov = curve_fit(Lorentzian, Freqs[10:-53]+Freqs[-30:-1], Counts[10:-53]+Counts[-30:-1], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==20:
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-10], Counts[:-10], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.7,0,-1000),(0,1e4, 436.1, 1,1000)))
+
+    elif med==22:
+        popt, pcov = curve_fit(Lorentzian, Freqs[:-10], Counts[:-10], p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.7,0,-1000),(0,1e4, 436.1, 1,1000)))
+
         
     else:
-        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+        popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
 
     pmdepthsdrdiag.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
     errorpmdepthsdrdiag.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))
@@ -251,18 +285,19 @@ plt.grid()
 # #plt.title('Espectros para distintas geometrías')
 
 
-plt.figure()
-plt.plot(np.arange(0,len(Intensitydiag),1), [i/np.max(Intensitydiag) for i in Intensitydiag], '-o',markersize=8)
-plt.plot(np.arange(0,len(Intensitydiag),1), [p for p in pmdepthsdrdiag], 'o',markersize=8)
-plt.plot(np.arange(0,len(Intensitydiag),1), [p for p in Anchos], 'o',markersize=8)
-plt.xlabel('Ion position')
-plt.ylabel('Intensity / DR Relative depth')
-#plt.xticks([1,2,3,4,5])
-#plt.xlim(200,3200)
-plt.ylim(-0.1,1.1)
-plt.grid()
-#plt.axvline(3, color='salmon')
-plt.legend()
+if len(pmlocmedvec)!=1:
+    plt.figure()
+    plt.plot(np.arange(0,len(Intensitydiag),1), [i/np.max(Intensitydiag) for i in Intensitydiag], '-o',markersize=8)
+    plt.plot(np.arange(0,len(Intensitydiag),1), [p for p in pmdepthsdrdiag], 'o',markersize=8)
+    plt.plot(np.arange(0,len(Intensitydiag),1), [p for p in Anchos], 'o',markersize=8)
+    plt.xlabel('Ion position')
+    plt.ylabel('Intensity / DR Relative depth')
+    #plt.xticks([1,2,3,4,5])
+    #plt.xlim(200,3200)
+    plt.ylim(-0.1,1.1)
+    plt.grid()
+    #plt.axvline(3, color='salmon')
+    plt.legend()
 
 
 #%%
@@ -293,7 +328,7 @@ plt.errorbar([s*scale for s in np.arange(16,len(Intensity),1)-16], [p for p in p
 plt.xlabel('Ion position')
 plt.ylabel('Intensity / DR Relative depth')
 #plt.xticks([1,2,3,4,5])
-plt.xlim(-1,15)
+#plt.xlim(-1,15)
 plt.ylim(-0.1,1.1)
 plt.grid()
 #plt.axvline(3, color='salmon')
@@ -338,7 +373,7 @@ for med in pmlocmedvec:
     Freqs = [2*f*1e-6 for f in PiezoVerDFrequencies[med][1:]]
     Counts = [c for c in PiezoVerDCounts[med][1:]]
 
-    popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05), bounds=((-10000,0,435.5,0),(0,1e4, 436.1, 1)))
+    popt, pcov = curve_fit(Lorentzian, Freqs, Counts, p0=(-200,2100,435.8,0.05,0.1), bounds=((-10000,0,435.5,0,-1000),(0,1e4, 436.1, 1,1000)))
 
     pmdepthsdrverd.append(1-(np.min(Lorentzian(Freqs,*popt))-bkg)/(popt[1]-bkg))
     errorpmdepthsdrverd.append(ErrorDRdepth(np.min(Lorentzian(Freqs,*popt)),popt[1], bkg))

analisis/plots/20231214_CPTconmicromocioncristals/Data/EITfit/lolo_modelo_full_8niveles.py

@@ -69,6 +69,45 @@ def PerformExperiment_8levels_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLi
     return ProbeDetuningVectorL, Fluovector
 
 
+@njit
+def PerformExperiment_8levels_MM_lolo(  sg, sp, gPS, gPD, DetUV, u,
+                                        DopplerLaserLinewidth, ProbeLaserLinewidth,
+                                        T, alpha, phidoppler, titadoppler, phiprobe,
+                                        titaprobe, beta, drivefreq, DetIR,
+                                        circularityprobe=1):
+    """
+    solvemode=1: resuelve con np.linalg.solve
+    solvemode=2: resuelve invirtiendo L con la funcion np.linalg.inv
+
+    sg: saturacion doppler como cociente respecto al ancho de linea ( UV )
+    sp: "" para repump (IR)
+    gPS: ancho de linea de la Doppler UV
+    gPD: ancho de linea IR
+    DetUV: Detunning Doppler  (agregado como ancho efectivo del laser) en MHz (no angular)
+    u: Larmor frequency ( 4 G ~ 32 MHz)
+    DopplerLaserLinewidth: Ancho de line a UV ( < 100 kHz)
+    ProbeLaserLinewidth: ancho IR
+    T: Temperatura
+    alpha: angulo relativo entre los k de los laser
+    phidoppler:    |
+    titadoppler:   |--> Polarización de los lasers respecto al campo magnético (pol lineal)
+    phiprobe:      |
+    titaprobe:     |
+    beta: Modulation depth
+    drivefreq: de la trampa ~22 MHz *2pi
+    freqMin, freqMax, freqStep: Detuning del pump -- parametro del eje Z que es la frecuencia del repump
+    circularityprobe:  1 es lineal , 1j es circular
+    """
+    rta = CPTspectrum8levels_MM_lolo(sg, sp, gPS, gPD, DetUV, u,
+                                    DopplerLaserLinewidth, ProbeLaserLinewidth,
+                                    T, alpha, phidoppler, titadoppler, phiprobe,
+                                    titaprobe, circularityprobe, beta, drivefreq,
+                                    DetIR)
+    return rta
+
+
+
+
 def GenerateNoisyCPT_MM(sg, sp, gPS, gPD, DetDoppler, u, DopplerLaserLinewidth, ProbeLaserLinewidth, T, alpha, phidoppler, titadoppler, phiprobe, titaprobeVec, kg, kr, v0, drivefreq, freqMin, freqMax, freqStep, circularityprobe=1, plot=False, solvemode=1, detpvec=None, noiseamplitude=0.001):
     """
     Genera un resultado de PerformExperiment_8levels_MM con ruido normal agregado
@@ -514,6 +553,44 @@ def CPTspectrum8levels_MM(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phid
     return DetProbeVectorMHz, Fluovector
 
 
+
+
+
+@njit
+def CPTspectrum8levels_MM_lolo(sg, sp, gPS, gPD, Detg, u, lwg, lwp, Temp, alpha, phidoppler, titadoppler, phiprobe, titaprobe, Circularityprobe, beta, drivefreq, DetProbeVector):
+
+    """
+    ESTA ES LA FUNCION QUE ESTAMOS USANDO
+    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)
+
+    DetProbeVector = 2*np.pi*DetProbeVector*1e6
+    Detg = 2*np.pi*Detg*1e6
+    #lwg, lwr, lwp = 2*np.pi*lwg*1e6, 2*np.pi*lwr*1e6, 2*np.pi*lwp*1e6
+    lwg, lwp = lwg*1e6, lwp*1e6
+
+    rabG = sg*gPS
+    rabP = sp*gPD
+
+    Fluovector = []
+
+
+    for Detp in DetProbeVector:
+        L = FullL_MM(rabG, rabP, gPS, gPD, Detg, Detp, u, lwg, lwp, phidoppler, titadoppler, phiprobe, titaprobe, beta, drivefreq, Temp, alpha, Circularityprobe)
+        # if solvemode == 1:
+        rhovectorized = np.linalg.solve(L, np.array([int(i==0) for i in range(64)],dtype=np.complex_))
+        Fluo = np.real(rhovectorized[18] + np.real(rhovectorized[27]))
+        Fluovector.append(Fluo)
+
+    return Fluovector
+
+
+
 # @njit
 # def lolo():
 #     L = FullL_MM(100,200,12,123,14)