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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
IRPOWER_FILES = """VaryingPower/000014529-IR_Scan_withcal_optimized
VaryingPower/000014530-IR_Scan_withcal_optimized
VaryingPower/000014523-IR_Scan_withcal_optimized
VaryingPower/000014528-IR_Scan_withcal_optimized
VaryingPower/000014522-IR_Scan_withcal_optimized
VaryingPower/000014526-IR_Scan_withcal_optimized
VaryingPower/000014524-IR_Scan_withcal_optimized
VaryingPower/000014527-IR_Scan_withcal_optimized
VaryingPower/000014525-IR_Scan_withcal_optimized
VaryingPower/000014531-IR_Scan_withcal_optimized
"""
UVPOWER_FILES = """VaryingUVPower/000014581-IR_Scan_withcal_optimized
VaryingUVPower/000014582-IR_Scan_withcal_optimized
VaryingUVPower/000014583-IR_Scan_withcal_optimized
VaryingUVPower/000014584-IR_Scan_withcal_optimized
VaryingUVPower/000014585-IR_Scan_withcal_optimized
VaryingUVPower/000014586-IR_Scan_withcal_optimized
VaryingUVPower/000014587-IR_Scan_withcal_optimized
VaryingUVPower/000014588-IR_Scan_withcal_optimized
VaryingUVPower/000014589-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(IRPOWER_FILES))
print(SeeKeys(UVPOWER_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
IrPowerCounts = []
IrPowerFrequencies = []
for i, fname in enumerate(IRPOWER_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
#Amplitudes.append(np.array(data['datasets']['amplitudes']))
IrPowerCounts.append(np.array(data['datasets']['counts_spectrum']))
IrPowerFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
UvPowerCounts = []
UvPowerFrequencies = []
UvPowerAmps = []
for i, fname in enumerate(UVPOWER_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
#Amplitudes.append(np.array(data['datasets']['amplitudes']))
UvPowerCounts.append(np.array(data['datasets']['counts_spectrum']))
UvPowerFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))
UvPowerAmps.append(np.array(data['datasets']['UV_CPT_amp']))
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 potencia del laser IR2
"""
palette = sns.color_palette("tab10")
# pmlocmedvec = [0, 4, 3, 5, 1, 7, 6, 2]
# idxvec = [95, 95, 95, 94, 93, 98, 97, 97]
irpowermedvec = [0,1,2,3,4,5,6,7,8,9]
idxvecdr1 = [159, 159, 159, 159, 159, 159, 159, 159, 160, 159]
idxvecdr2 = [394, 394, 394, 394, 394, 394, 394, 394, 394, 393]
#irpowermedvec = [0]
IrAmpVec = [0.03, 0.04, 0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.24, 0.33]
PotIr2Vec = [0.05, 0.08, 0.48, 2.5, 7.5, 16.8, 31.2, 50.8, 73.6, 143]
PotIr1 = 3.6
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
bkg = 130
powdepthsdr1=[]
errorpowdepthsdr1=[]
powdepthsdr2=[]
errorpowdepthsdr2=[]
idxtest = 393
jj=0
for med in irpowermedvec:
if med == 0:
powdepthsdr1.append(1-(IrPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][50:150])-bkg))
powdepthsdr2.append(1-(IrPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][-100:-1])-bkg))
errorpowdepthsdr1.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr1[jj]],np.mean(IrPowerCounts[med][1:][50:150]), bkg))
errorpowdepthsdr2.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr2[jj]],np.mean(IrPowerCounts[med][1:][-100:-1]), bkg))
else:
powdepthsdr1.append(1-(IrPowerCounts[med][1:][idxvecdr1[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][0:20])-bkg))
powdepthsdr2.append(1-(IrPowerCounts[med][1:][idxvecdr2[jj]]-bkg)/(np.mean(IrPowerCounts[med][1:][0:20])-bkg))
errorpowdepthsdr1.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr1[jj]],np.mean(IrPowerCounts[med][1:][0:20]), bkg))
errorpowdepthsdr2.append(ErrorDRdepth(IrPowerCounts[med][1:][idxvecdr2[jj]],np.mean(IrPowerCounts[med][1:][0:20]), bkg))
plt.plot([2*f*1e-6 for f in IrPowerFrequencies[med][1:]], [c for c in IrPowerCounts[med][1:]], '-o', color=palette[med],markersize=2, alpha=0.7, label=f'{IrAmpVec[jj]}')
plt.plot([2*f*1e-6 for f in IrPowerFrequencies[med][1:]][idxtest], [c for c in IrPowerCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14)
jj=jj+1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Counts')
#plt.xlim(442,445)
plt.grid()
#plt.legend()
#plt.title('Espectros para distintas geometrías')
fig, ax = plt.subplots()
ax.errorbar(PotIr2Vec, powdepthsdr1, yerr=errorpowdepthsdr1, fmt='o',capsize=4, markersize=10, label='DR left')
ax.errorbar(PotIr2Vec, powdepthsdr2, yerr=errorpowdepthsdr2, fmt='o',capsize=4, markersize=10, label='DR right')
ax.set_xlabel('IR1 amp')
ax.set_ylabel('DR Relative depth')
ax.set_xlim(-5,80)
ax.set_xscale('linear')
ax.set_ylim(0,1)
ax.axvline(PotIr1, label='IR1 power', linestyle='--', color='firebrick', zorder=0, alpha=0.7)
plt.legend()
plt.grid()
#%%
import seaborn as sns
"""
Resonancias DD configuracion +2/-2 colineal variando la potencia del laser UV
"""
palette = sns.color_palette("tab10")
# pmlocmedvec = [0, 4, 3, 5, 1, 7, 6, 2]
# idxvec = [95, 95, 95, 94, 93, 98, 97, 97]
uvpowermedvec = [0,1,2,3,4,5,6,7,8]
idxvecdr1 = [95, 95, 95, 95, 95, 95,95,95,95]
idxvecdr2 = [236, 236, 236, 236, 236, 236, 236, 236, 236, 236]
#uvpowermedvec = [2]
PotUvVec = [4, 0.76, 11, 22.4, 36, 50, 61, 65, 1.5]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
bkg = 120
powdepthsdr1=[]
errorpowdepthsdr1=[]
powdepthsdr2=[]
errorpowdepthsdr2=[]
idxtest = 236
jj=0
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))
errorpowdepthsdr2.append(ErrorDRdepth(UvPowerCounts[med][1:][idxvecdr2[jj]],np.mean(UvPowerCounts[med][1:][0:20]), bkg))
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plt.plot([2*f*1e-6 for f in UvPowerFrequencies[med][1:]], [c for c in UvPowerCounts[med][1:]], '-o', color=palette[med],markersize=2, alpha=0.7, label=f'{UvPowerAmps[jj]}')
#plt.plot([2*f*1e-6 for f in UvPowerFrequencies[med][1:]][idxtest], [c for c in UvPowerCounts[med][1:]][idxtest], 'o', color=palette[med],markersize=14)
jj=jj+1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Counts')
#plt.xlim(442,445)
plt.grid()
#plt.legend()
#plt.title('Espectros para distintas geometrías')
fig, ax = plt.subplots()
ax.errorbar(PotUvVec, powdepthsdr1, yerr=errorpowdepthsdr1, fmt='o',capsize=4, markersize=10, label='DR left')
ax.errorbar(PotUvVec, powdepthsdr2, yerr=errorpowdepthsdr2, fmt='o',capsize=4, markersize=10, label='DR right')
ax.set_xlabel('Uv power (uW)')
ax.set_ylabel('DR Relative depth')
#ax.set_xlim(-5,80)
ax.set_xscale('linear')
ax.set_ylim(0,1)
plt.legend()
plt.grid()
#%%
"""
EXTRA: sensibilidad de CPT D-D con OAM +2/-2 a movimiento térmico del ion
comparado con CPT tradicional S-D
"""
import numpy as np
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)
plt.figure()
plt.semilogy([r*1e6 for r in radius],sens(radius), linewidth=3, color='coral', label='D-D CPT +2/-2 OAM ')
plt.xlabel('OAM radius (um)')
plt.ylabel('Thermometry sensitivity')
plt.axhline(8.6e6, color='indigo', linestyle='--', label='S-P CPT gaussian sensitivity')
plt.axhline(8.6e6, color='indigo', linestyle='--', label='S-P CPT gaussian sensitivity')