RDS_location2.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('C://Users//nicon//Doctorado//artiq_experiments//analisis//plots//20230815_RotationalDopplerShift_v3//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

LOC_FILES = """VaryingBeamlocation/000014398-IR_Scan_withcal_optimized
VaryingBeamlocation/000014399-IR_Scan_withcal_optimized
VaryingBeamlocation/000014400-IR_Scan_withcal_optimized
VaryingBeamlocation/000014401-IR_Scan_withcal_optimized
VaryingBeamlocation/000014402-IR_Scan_withcal_optimized
"""

COMPMERG_FILES = """VaryingCompMerged/000014441-IR_Scan_withcal_optimized
VaryingCompMerged/000014442-IR_Scan_withcal_optimized
"""

TEMP_FILES = """VaryingTemp/000015058-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(TEMP_FILES))


#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data

LocCounts = []
LocFrequencies = []

for i, fname in enumerate(LOC_FILES.split()):
    print(str(i) + ' - ' + fname)
    data = h5py.File(fname+'.h5', 'r')
    #Amplitudes.append(np.array(data['datasets']['amplitudes']))
    LocCounts.append(np.array(data['datasets']['counts_spectrum']))
    LocFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))

CompVoltages = []
CompCountsMerged = []
CompFrequencies = []

for i, fname in enumerate(COMPMERG_FILES.split()):
    print(str(i) + ' - ' + fname)
    data = h5py.File(fname+'.h5', 'r')
    CompVoltages.append(np.array(data['datasets']['scanning_voltages']))
    CompCountsMerged.append(np.array(data['datasets']['data_array']))
    CompFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))


CompCounts = []
for k in range(len(CompFrequencies)):
    CompCounts.append(Split(CompCountsMerged[k],len(CompFrequencies[k])))


TempTimes = []
TempCountsMerged = []
TempFrequencies = []

for i, fname in enumerate(TEMP_FILES.split()):
    print(str(i) + ' - ' + fname)
    data = h5py.File(fname+'.h5', 'r')
    TempTimes.append(np.array(data['datasets']['scanning_heattimes']))
    TempCountsMerged.append(np.array(data['datasets']['data_array']))
    TempFrequencies.append(np.array(data['datasets']['IR1_Frequencies']))


TempCounts = []
for k in range(len(TempFrequencies)):
    TempCounts.append(Split(TempCountsMerged[k],len(TempFrequencies[k])))


#%%
import seaborn as sns
"""
Resonancias DD configuracion +2/-2
"""

palette = sns.color_palette("tab10")

powermedvec = [0,1,2,3]

#powermedvec = [3]

AmpsVecs = ['Arriba', 'Abajo', 'Izquierda', 'Derecha']

plt.figure()

ftrap = 22.1

DR1 = 435.8
DR2 = 444.2

jj=0
for med in powermedvec:
    plt.plot([2*f*1e-6 for f in LocFrequencies[med][1:]], [c for c in LocCounts[med][1:]], '-o', color=palette[med],markersize=2, label=f'{AmpsVecs[jj]}')
    jj=jj+1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Counts')
plt.ylim(800,2200)
plt.grid()
#plt.legend()
plt.title('Espectros para distintas geometrías')

#%%
import seaborn as sns
"""
Resonancias DD configuracion +2/-2
"""

palette = sns.color_palette("tab10")

#powermedvec = [0,1,2,3]

powermedvec = [3,4]

plt.figure()

ftrap = 22.1

DR1 = 435.8
DR2 = 444.2

jj=0
for med in powermedvec:
    plt.plot([2*f*1e-6 for f in LocFrequencies[med][1:]], [c for c in LocCounts[med][1:]], '-o', color=palette[med],markersize=2, label=f'{AmpsVecs[jj]}')
    jj=jj+1
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Counts')
plt.ylim(800,2200)
plt.grid()
#plt.legend()
plt.title('Espectros para distintas geometrías')

#%%
"""
Pongo para barrer voltajes y que me lo guarde todo mergeado.
La med 0 es barriendo dcA. Hay 8 meds entre -0.33 y 0.02 V. Las dos ultimas no sirven porque se perdio el ion.
La med 1 es barriendo compOven. El ion se perdio asi que no sirven pero igual ya tenia esas meds
"""
med=0

Voltages = CompVoltages[med]

voltvec=[0,1,2,3,4,5]

plt.figure()
for volt in voltvec:
    plt.plot([2*f*1e-6 for f in CompFrequencies[med][1:]], CompCounts[med][volt][1:], label=Voltages[volt])
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Counts')
plt.grid()
plt.legend()



#%%
"""
Pongo para barrer temptimes y que me lo guarde todo mergeado. Salio mal igual...
"""
med=0

tempvec=[0,1,2,3,4]

plt.figure()
for med in tempvec:
    plt.plot([2*f*1e-6 for f in TempFrequencies[0][1:]], TempCounts[0][med][1:], label=TempTimes[0][med])
plt.xlabel('Frecuencia (MHz)')
plt.ylabel('Counts')
plt.grid()
plt.legend()