todo

analisis/plots/20230815_RotationalDopplerShift_v3/Geometry.svg

@@ -28,9 +28,9 @@
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analisis/plots/20230817_RotationalDopplerShift_v4/RDS_IR2power.py

@@ -234,8 +234,10 @@ for med in uvpowermedvec:
     if med == 1:
         powdepthsdr1.append(1-(UvPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][6:26])-bkg))
         powdepthsdr2.append(1-(UvPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][6:26])-bkg))
+        errorpowdepthsdr1.append(ErrorDRdepth(UvPowerCounts[med][1:][idxvecdr1[jj]],np.mean(UvPowerCounts[med][1:][6:26]), bkg))
+        errorpowdepthsdr2.append(ErrorDRdepth(UvPowerCounts[med][1:][idxvecdr2[jj]],np.mean(UvPowerCounts[med][1:][6:26]), bkg))
 
-
+    else:
         powdepthsdr1.append(1-(UvPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][2:20])-bkg))
         powdepthsdr2.append(1-(UvPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(UvPowerCounts[med][1:][2:20])-bkg))
         errorpowdepthsdr1.append(ErrorDRdepth(UvPowerCounts[med][1:][idxvecdr1[jj]],np.mean(UvPowerCounts[med][1:][0:20]), bkg))
@@ -275,6 +277,9 @@ import matplotlib.pyplot as plt
 def sens(r,l=2):
     return 2*l/r
 
+def sensgauss(angle):
+    k = 2*np.pi/866e-9
+    return np.sqrt(k**2 + k**2 - 2*k*k*np.cos(angle*np.pi/180))
 
 radius = np.arange(0.1e-6,10e-6,0.01e-6)
 

analisis/plots/20230817_RotationalDopplerShift_v4/RDS_location5.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
+
+#Mediciones barriendo angulo del TISA y viendo kicking de resonancias oscuras
+
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20220106_CPT_DosLaseres_v08_TISA_DR\Data
+
+os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20230817_RotationalDopplerShift_v4/Data')
+
+
+
+"""
+en este codigo ploteo espectros CPT de resonancias D-D para configuracion +2/+2 y +2/-2 (usando pentaprisma)
+"""
+
+def find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+
+PM_FILES = """VaryingBeamlocation2/000014686-IR_Scan_withcal_optimized
+VaryingBeamlocation2/000014687-IR_Scan_withcal_optimized
+VaryingBeamlocation2/000014688-IR_Scan_withcal_optimized
+VaryingBeamlocation2/000014689-IR_Scan_withcal_optimized
+VaryingBeamlocation2/000014691-IR_Scan_withcal_optimized
+"""
+
+
+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
+
+
+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(PM_FILES))
+#print(SeeKeys(MM_FILES))
+
+
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+PmCounts = []
+PmFrequencies = []
+
+for i, fname in enumerate(PM_FILES.split()):
+    print(str(i) + ' - ' + fname)
+    data = h5py.File(fname+'.h5', 'r')
+    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+    PmCounts.append(np.array(data['datasets']['counts_spectrum']))
+    PmFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+
+# MmCounts = []
+# MmFrequencies = []
+
+# for i, fname in enumerate(MM_FILES.split()):
+#     print(str(i) + ' - ' + fname)
+#     data = h5py.File(fname+'.h5', 'r')
+#     #Amplitudes.append(np.array(data['datasets']['amplitudes']))
+#     MmCounts.append(np.array(data['datasets']['counts_spectrum']))
+#     MmFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
+
+
+def ErrorDRdepth(p, f, b):
+    ep = np.sqrt(p)
+    ef = np.sqrt(f)
+    eb = np.sqrt(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)
+
+#%%
+import seaborn as sns
+"""
+Resonancias DD configuracion +2/-2 colineal variando la ubicacion del ion en los haces
+"""
+
+palette = sns.color_palette("tab10")
+
+pmlocmedvec = [0, 1, 2, 3, 4]
+
+#pmlocmedvec = []
+
+idxvecdr1 = [402,226, 226, 226, 226]
+idxvecdr2 = [1302,732, 732,732,732]
+
+#pmlocmedvec = [2]
+
+LocVecs = ['1', '2', '3', '4', '5']
+
+LocVecsint = np.arange(1,6,1)
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+bkg = 150
+
+pmdepthsdr1=[]
+errorpmdepthsdr1=[]
+
+pmdepthsdr2=[]
+errorpmdepthsdr2=[]
+
+
+idxtest = 732
+
+jj=0
+for med in pmlocmedvec:
+    pmdepthsdr1.append(1-(PmCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(PmCounts[med][1:][0:20])-bkg))
+    pmdepthsdr2.append(1-(PmCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(PmCounts[med][1:][0:20])-bkg))
+    errorpmdepthsdr1.append(ErrorDRdepth(PmCounts[med][1:][idxvecdr1[jj]],np.mean(PmCounts[med][1:][0:20]), bkg))
+    errorpmdepthsdr2.append(ErrorDRdepth(PmCounts[med][1:][idxvecdr2[jj]],np.mean(PmCounts[med][1:][0:20]), bkg))
+    plt.plot([2*f*1e-6 for f in PmFrequencies[med][1:]], [c for c in PmCounts[med][1:]], '-o', color=palette[med],markersize=2, alpha=0.7, 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.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+plt.xlim(432, 446.5)
+plt.grid()
+plt.legend()
+#plt.title('Espectros para distintas geometrías')
+
+
+plt.figure()
+plt.errorbar(LocVecsint, pmdepthsdr1, yerr=errorpmdepthsdr1, label='DR left', fmt='o',capsize=2, markersize=8)
+plt.errorbar(LocVecsint, pmdepthsdr2, yerr=errorpmdepthsdr2, label='DR right', fmt='o',capsize=2, markersize=8)
+plt.xlabel('Ion location')
+plt.ylabel('DR Relative depth')
+plt.xticks([1,2,3,4,5])
+plt.ylim(0,1)
+plt.grid()
+plt.axvline(3, color='salmon')
+plt.legend()
+
+#%%
+import seaborn as sns
+"""
+Resonancias DD configuracion -2/-2 colineal variando la ubicacion del ion en los 
+Mido la profundidad de las resonancias oscuras
+"""
+
+palette = sns.color_palette("tab10")
+
+# pmlocmedvec = [0, 4, 3, 5, 1, 7, 6, 2]
+# idxvec = [95, 95, 95, 94, 93, 98, 97, 97]
+
+mmlocmedvec = [0,1,2,3,4,5,6]
+
+idxvecdr1 = [160, 160, 160, 160, 159,159, 159]
+idxvecdr2 = [394, 394, 394, 394, 394, 394, 394]
+
+#mmlocmedvec = [7]
+
+LocVecs = ['1', '2', '3', '4', '5', '6', '7', '8']
+
+LocVecsint = np.arange(1,8,1)
+
+plt.figure()
+
+ftrap = 22.1
+
+DR1 = 435.8
+DR2 = 444.2
+
+bkg = 40
+
+mmdepthsdr1=[]
+errormmdepthsdr1=[]
+
+mmdepthsdr2=[]
+errormmdepthsdr2=[]
+
+
+idxtest = 394
+
+jj=0
+for med in mmlocmedvec:
+    mmdepthsdr1.append(1-(MmCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(MmCounts[med][1:][0:20])-bkg))
+    mmdepthsdr2.append(1-(MmCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(MmCounts[med][1:][0:20])-bkg))
+    errormmdepthsdr1.append(ErrorDRdepth(MmCounts[med][1:][idxvecdr1[jj]],np.mean(MmCounts[med][1:][0:20]), bkg))
+    errormmdepthsdr2.append(ErrorDRdepth(MmCounts[med][1:][idxvecdr2[jj]],np.mean(MmCounts[med][1:][0:20]), bkg))
+    plt.plot([2*f*1e-6 for f in MmFrequencies[med][1:]], [c for c in MmCounts[med][1:]], '-o', color=palette[med],markersize=2, alpha=0.7, label=f'{LocVecs[jj]}')
+    #plt.plot([2*f*1e-6 for f in MmFrequencies[med][1:]][idxtest], [c for c in MmCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14, label=f'{LocVecs[jj]}')
+    jj=jj+1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+#plt.xlim(442,445)
+plt.grid()
+plt.legend()
+plt.title('Espectros para distintas geometrías')
+
+
+plt.figure()
+plt.errorbar(LocVecsint, mmdepthsdr1, yerr=errormmdepthsdr1, fmt='o',capsize=2, markersize=5)
+plt.errorbar(LocVecsint, mmdepthsdr2, yerr=errormmdepthsdr2, fmt='o',capsize=2, markersize=5)
+plt.ylabel('DR1 Relative depth config -2/-2')
+plt.xticks([])
+plt.ylim(0,1)
+
+#%%
+"""
+grafico la profundidad de la resonancia oscura en funcion de la ubicacion del ion
+en el perfil del haz. la ubicacion 1 y 7 son horizontales, izquierda y derecha.
+la ubicacion 4 es el centro, arriba. el resto, intermedias
+"""
+
+plt.figure()
+plt.errorbar(LocVecsint, mmdepthsdr1, yerr=errormmdepthsdr1, fmt='o',capsize=2, markersize=6, color='darkred', label='Same OAM')
+plt.errorbar(LocVecsint, pmdepthsdr1, yerr=errorpmdepthsdr2, fmt='o',capsize=2, markersize=6, color='coral', label='Opposite OAM')
+plt.ylabel('DR1 Relative depth')
+plt.xlabel('Ion angular position')
+#plt.xticks([])
+plt.grid()
+plt.ylim(0,1)
+plt.legend()
+
+#%%
+"""
+Grafico ahora dos gráficos que se vean 3 curvas con buena estadística para mostrar
+"""
+from scipy.optimize import curve_fit
+from scipy.signal import savgol_filter as sf
+
+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]))
+    print(popt)
+    yvecnorm = []
+    for jj in range(len(yvec)):
+        print(linealfunc(xvec[jj],*popt))
+        yvecnorm.append(yvec[jj]/linealfunc(xvec[jj],*popt))
+    return yvecnorm
+#pmlocmedvec = [7,2,0,4,3,5,1]
+
+
+
+pmlocmedvec = [3,5,7]
+mmlocmedvec = [0,1,2]
+
+plt.figure()
+
+plotpm = 1
+plotmm = 0
+
+
+if plotpm:
+    jj=0
+    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)
+        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]}')
+        jj=jj+1
+
+if plotmm:
+    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]]
+        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]}')
+        jj=jj+1
+
+plt.ylim(0,1.2)
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Normalized Counts')
+#plt.ylim(412,2500)
+plt.grid()
+#plt.legend()
+
+
+
+
+
+
+