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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/20230804_RotationalDopplerShift_v2/Data')
"""
en este codigo ploteo espectros CPT de resonancias D-D para configuracion colineal (insensible a velocidad perpendicular)
y configuracion desplazada (sensible).
Primero una gaussiana variando la potencia (power_files).
Luego, variando compensacion con electrodo DCA y con electrodo OVEN.
"""
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
POWER_FILES = """VaryingPower/000014020-IR_Scan_withcal_optimized
VaryingPower/000014021-IR_Scan_withcal_optimized
VaryingPower/000014024-IR_Scan_withcal_optimized
VaryingPower/000014025-IR_Scan_withcal_optimized
VaryingPower/000014026-IR_Scan_withcal_optimized
"""
EXTRA_FILES = """Extra/000014032-IR_Scan_withcal_optimized
Extra/000014060-IR_Scan_withcal_optimized
Extra/000014061-IR_Scan_withcal_optimized"""
COMP_COL_FILES = """VaryingComp/Col/000014071-IR_Scan_withcal_optimized
VaryingComp/Col/000014072-IR_Scan_withcal_optimized
VaryingComp/Col/000014073-IR_Scan_withcal_optimized
VaryingComp/Col/000014074-IR_Scan_withcal_optimized
VaryingComp/Col/000014075-IR_Scan_withcal_optimized
VaryingComp/Col/000014076-IR_Scan_withcal_optimized
VaryingComp/Col/000014107-IR_Scan_withcal_optimized
VaryingComp/Col/000014108-IR_Scan_withcal_optimized
VaryingComp/Col/000014109-IR_Scan_withcal_optimized
VaryingComp/Col/000014110-IR_Scan_withcal_optimized
VaryingComp/Col/000014111-IR_Scan_withcal_optimized
VaryingComp/Col/000014112-IR_Scan_withcal_optimized
VaryingComp/Col/000014113-IR_Scan_withcal_optimized
VaryingComp/Col/000014114-IR_Scan_withcal_optimized
VaryingComp/Col/000014115-IR_Scan_withcal_optimized
VaryingComp/Col/000014118-IR_Scan_withcal_optimized
VaryingComp/Col/000014119-IR_Scan_withcal_optimized"""
COMP_DESP_FILES = """VaryingComp/Desp/000014077-IR_Scan_withcal_optimized
VaryingComp/Desp/000014083-IR_Scan_withcal_optimized
VaryingComp/Desp/000014078-IR_Scan_withcal_optimized
VaryingComp/Desp/000014079-IR_Scan_withcal_optimized
VaryingComp/Desp/000014080-IR_Scan_withcal_optimized
VaryingComp/Desp/000014081-IR_Scan_withcal_optimized
VaryingComp/Desp/000014082-IR_Scan_withcal_optimized
VaryingComp/Desp/000014083-IR_Scan_withcal_optimized
VaryingComp/Desp/000014084-IR_Scan_withcal_optimized
VaryingComp/Desp/000014085-IR_Scan_withcal_optimized
VaryingComp/Desp/000014086-IR_Scan_withcal_optimized
VaryingComp/Desp/000014087-IR_Scan_withcal_optimized
VaryingComp/Desp/000014088-IR_Scan_withcal_optimized
VaryingComp/Desp/000014089-IR_Scan_withcal_optimized
VaryingComp/Desp/000014090-IR_Scan_withcal_optimized
VaryingComp/Desp/000014093-IR_Scan_withcal_optimized
VaryingComp/Desp/000014094-IR_Scan_withcal_optimized
VaryingComp/Desp/000014095-IR_Scan_withcal_optimized
VaryingComp/Desp/000014096-IR_Scan_withcal_optimized
VaryingComp/Desp/000014097-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(POWER_FILES))
#carpeta pc nico labo escritorio:
#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
PowerCounts = []
PowerIR1_Freqs = []
for i, fname in enumerate(POWER_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
#Amplitudes.append(np.array(data['datasets']['amplitudes']))
PowerCounts.append(np.array(data['datasets']['counts_spectrum']))
PowerIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
ExtraCounts = []
ExtraIR1_Freqs = []
for i, fname in enumerate(EXTRA_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
#Amplitudes.append(np.array(data['datasets']['amplitudes']))
ExtraCounts.append(np.array(data['datasets']['counts_spectrum']))
ExtraIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
CompColCounts = []
CompColIR1_Freqs = []
for i, fname in enumerate(COMP_COL_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
#Amplitudes.append(np.array(data['datasets']['amplitudes']))
CompColCounts.append(np.array(data['datasets']['counts_spectrum']))
CompColIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
CompDespCounts = []
CompDespIR1_Freqs = []
for i, fname in enumerate(COMP_DESP_FILES.split()):
print(str(i) + ' - ' + fname)
data = h5py.File(fname+'.h5', 'r')
#Amplitudes.append(np.array(data['datasets']['amplitudes']))
CompDespCounts.append(np.array(data['datasets']['counts_spectrum']))
CompDespIR1_Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
#%%
"""
Resonancias DD variando la potencia del IR2
"""
powermedvec = [3,4,0,1,2]
AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
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 PowerIR1_Freqs[med][1:]], [c for c in PowerCounts[med][1:]], '-o', markersize=2, label=f'amp:{AmpsVecs[jj]}')
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
plt.grid()
plt.legend()
plt.title('Variando potencia de IR2 para potencia de IR1 fija')
#%%
"""
Resonancias DD en config colineal, haces OAM, lindas con algunos parametros (ver cuaderno)
"""
extramedvec = [1]
#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
jj=0
for med in extramedvec:
plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[med][1:]], [c for c in ExtraCounts[med][1:]], '-o', markersize=2)
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
plt.xlim(433.5, 438)
plt.ylim(1100,3200)
plt.axvline(freqDR)
plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
plt.grid()
plt.legend()
plt.title('Variando potencia de IR2 para potencia de IR1 fija')
#%%
"""
Comparo resonancias DD con haces OAM en config colineal con desplazada
"""
extramedvec = [1,2]
#AmpsVecs = [0.05, 0.08, 0.12, 0.17, 0.22]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
secular=False
plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[1][1:]], [c for c in ExtraCounts[1][1:]], '-o', markersize=2, label='Colineales')
plt.plot([2*f*1e-6 for f in ExtraIR1_Freqs[2][1:]], [c for c in ExtraCounts[2][1:]], '-o', markersize=2, label='Desplazadas')
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
plt.xlim(425, 455)
plt.ylim(1100,3200)
if secular:
plt.axvline(freqDR)
plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
plt.grid()
plt.legend()
plt.title('Comparacion de config colineal (insensible) con desplazada (sensible)')
#%%
"""
Resonancias DD config colineal OAM variando compensacion dcA
"""
import seaborn as sns
paleta = sns.color_palette("tab10")
compcolmedvec = [5,0,1,2,3,4]
CompsDCA = [-100, -150, -200, -250, -300, -350]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
MinimosFluos_Col = []
ErrorMinimosFluos_Col = []
MinimosFluos_Col_Relativos = []
jj=0
for med in compcolmedvec:
plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
plt.plot([DR1, DR2], [c for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
MinimosFluos_Col_Relativos.append([1-(c-30)/(np.mean(CompColCounts[med][0:20])-30) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
#plt.xlim(433.5, 438)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compesnacion dcA config colineal OAM')
#%%
"""
Resonancias DD config desplazada OAM variando compensacion dcA
"""
import seaborn as sns
paleta = sns.color_palette("tab10")
compdespmedvec = [0,2,3,4,5,6]
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plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR1)
idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compdespmedvec[0]][1:]], DR2)
freqDR=435.8
MinimosFluos_Desp = []
ErrorMinimosFluos_Desp = []
MinimosFluos_Desp_Relativos = []
jj=0
for med in compdespmedvec:
plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
plt.plot([DR1, DR2], [c for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
ErrorMinimosFluos_Desp.append([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])])
MinimosFluos_Desp_Relativos.append([1-(c-30)/(np.mean(CompDespCounts[med][0:20])-30) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
plt.ylim(400, 1280)
#plt.ylim(0,100)
plt.grid()
plt.legend()
plt.title('Variando compesnacion dcA config desplazada OAM')
#%%
"""
Ahora grafico el valor de los minimos en funcion de la descompensacion dcA
"""
CompsDCAcol = [-100, -150, -200, -250, -300, -350]
CompsDCAdesp = [-100, -150, -200, -250, -300, -350]
MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
#DR1
plt.figure()
plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2, markersize=12, label='Colineal')
plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR1[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:]],fmt='o', capsize=2, markersize=12, label='Desplazada')
plt.legend()
plt.title('DR1')
plt.grid()
plt.xlabel('Endcap voltage (mV)')
plt.ylabel('DR1 counts - mean')
#DR2
plt.figure()
plt.errorbar(CompsDCAcol[:],MinimosFluos_Col_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[:]],fmt='o', capsize=2, markersize=12, label='Colineal')
plt.errorbar(CompsDCAdesp[:],MinimosFluos_Desp_DR2[:],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:]],fmt='o', capsize=2, markersize=12, label='Desplazada')
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plt.title('DR2')
plt.grid()
plt.xlabel('Endcap voltage (mV)')
plt.ylabel('DR2 counts - mean')
#%%
"""
Ahora veo los valores relativos a la fluorescencia al principio del espectro
"""
MinimosFluos_Col_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Col_Relativos))[0]
MinimosFluos_Col_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Col_Relativos))[1]
MinimosFluos_Desp_Relativos_DR1 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[0]
MinimosFluos_Desp_Relativos_DR2 = np.transpose(np.array(MinimosFluos_Desp_Relativos))[1]
plt.figure()
plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR1, 'o', markersize=12, label='Colineal')
plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR1, 'o', markersize=12, label='Desplazada')
plt.title('DR1')
plt.grid()
plt.xlabel('Endcap voltage (mV)')
plt.ylabel('DR1 counts relatives')
plt.legend()
plt.figure()
plt.semilogy(CompsDCAcol, MinimosFluos_Col_Relativos_DR2, 'o', markersize=12, label='Colineal')
plt.semilogy(CompsDCAdesp, MinimosFluos_Desp_Relativos_DR2, 'o', markersize=12, label='Desplazada')
plt.title('DR2')
plt.grid()
plt.xlabel('Endcap voltage (mV)')
plt.ylabel('DR2 counts relatives')
plt.legend()
#hasta aca
#%%
"""
Resonancias DD config colineal OAM variando compensacion OVEN
"""
import seaborn as sns
compcolmedvec = [6,7,8,9,10,11,12,13,14]
CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
jj=0
for med in compcolmedvec:
plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
#plt.xlim(433.5, 438)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compesnacion dcA config colineal OAM')
""" Le resto el fondo a ver como se ven"""
paleta = sns.color_palette("tab10")
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.25
idx1 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR1)
idx2 = find_nearest([2*f*1e-6 for f in CompColIR1_Freqs[compcolmedvec[0]][1:]], DR2)
MinimosFluos_Col = []
ErrorMinimosFluos_Col = []
freqDR=435.8
jj=0
for med in compcolmedvec:
plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c-np.mean(CompColCounts[med][1:20]) for c in CompColCounts[med][1:]], '-o', markersize=2)
plt.plot([DR1, DR2], [c-np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
MinimosFluos_Col.append([-c+np.mean(CompColCounts[med][1:20]) for c in [CompColCounts[med][idx1+1],CompColCounts[med][idx2+1]]])
ErrorMinimosFluos_Col.append([np.sqrt(CompColCounts[med][idx1+1]), np.sqrt(CompColCounts[med][idx2+1])])
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
#plt.xlim(443, 445)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compesnacion dcA config colineal OAM')
""" Estas son dos curvas con mas estadistica """
compcolmedvec = [15, 16]
CompsOVEN = [5.9,4.3]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
jj=0
for med in compcolmedvec:
plt.plot([2*f*1e-6 for f in CompColIR1_Freqs[med][1:]], [c for c in CompColCounts[med][1:]], '-o', markersize=2)
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
#plt.ylim(300, 1000)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compensacion OVEN config colineal OAM mas estadistica')
#%%
import seaborn as sns
"""
Resonancias DD config desplazada OAM variando compensacion OVEN
"""
compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
jj=0
for med in compdespmedvec:
plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
plt.ylim(300, 1000)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compensacion OVEN config desplazada OAM')
""" Le resto el fondo a ver como se ven y ploteo sus valores de DR"""
paleta = sns.color_palette("tab10")
compdespmedvec = [7,8,9,10,11,12,13,14,15,16]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
idx1 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR1)
idx2 = find_nearest([2*f*1e-6 for f in CompDespIR1_Freqs[compcolmedvec[0]][1:]], DR2)
freqDR=435.8
MinimosFluos_Desp = []
ErrorMinimosFluos_Desp = []
jj=0
for med in compdespmedvec:
plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c-np.mean(CompDespCounts[med][1:20]) for c in CompDespCounts[med][1:]], '-o', color=paleta[jj], markersize=2, zorder=0)
plt.plot([DR1, DR2], [c-np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]] , 'o', color=paleta[jj], markersize=15)
MinimosFluos_Desp.append([-c+np.mean(CompDespCounts[med][1:20]) for c in [CompDespCounts[med][idx1+1],CompDespCounts[med][idx2+1]]])
ErrorMinimosFluos_Desp.append(([np.sqrt(CompDespCounts[med][idx1+1]), np.sqrt(CompDespCounts[med][idx2+1])]))
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
#plt.ylim(300, 1000)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compensacion OVEN config desplazada OAM')
""" Estas son dos curvas con mas estadistica """
compdespmedvec = [17, 18]
CompsOVEN = [5.9,4.3]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
freqDR=435.8
jj=0
for med in compdespmedvec:
plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[med][1:]], [c for c in CompDespCounts[med][1:]], '-o', markersize=2)
jj=jj+1
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
#plt.ylim(300, 1000)
#plt.ylim(1100,3200)
plt.grid()
plt.legend()
plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
#%%
""" Agarro la desplazada compensada para ver si se ven bandas laterales de movimiento (no)"""
CompsOVEN = [5.9,4.3]
plt.figure()
ftrap = 22.1
DR1 = 435.8
DR2 = 444.2
#freqDR=435.8
freqDR=DR2
jj=0
plt.plot([2*f*1e-6 for f in CompDespIR1_Freqs[18][1:]], [c for c in CompDespCounts[18][1:]], '-o', markersize=2)
plt.xlabel('Frecuencia')
plt.ylabel('Counts')
plt.xlim(442, 446)
plt.axvline(freqDR)
plt.axvline(freqDR+0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-0.8,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR+0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-0.9,color='red',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR+1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
plt.axvline(freqDR-1.65,color='green',linestyle='dashed',zorder=0,alpha=0.5)
plt.grid()
plt.legend()
plt.title('Variando compensacion OVEN config desplazada OAM mas estadistica')
#%%
"""
Ahora grafico el valor de los minimos en funcion de la descompensacion OVEN
"""
CompsOVENcol = [6.1, 5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5]
CompsOVENdesp = [5.9, 5.7, 5.5, 5.3, 5.1, 4.9, 4.7, 4.5, 4.3, 4.1]
MinimosFluos_Col_DR1 = np.transpose(np.array(MinimosFluos_Col))[0]
MinimosFluos_Col_DR2 = np.transpose(np.array(MinimosFluos_Col))[1]
MinimosFluos_Desp_DR1 = np.transpose(np.array(MinimosFluos_Desp))[0]
MinimosFluos_Desp_DR2 = np.transpose(np.array(MinimosFluos_Desp))[1]
ErrorMinimosFluos_Col_DR1 = np.transpose(np.array(ErrorMinimosFluos_Col))[0]
ErrorMinimosFluos_Col_DR2 = np.transpose(np.array(ErrorMinimosFluos_Col))[1]
ErrorMinimosFluos_Desp_DR1 = np.transpose(np.array(ErrorMinimosFluos_Desp))[0]
ErrorMinimosFluos_Desp_DR2 = np.transpose(np.array(ErrorMinimosFluos_Desp))[1]
#DR1
plt.figure()
plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR1[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2, markersize=12, label='Colineal')
plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR1[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR2)[:-2]],fmt='o', capsize=2, markersize=12, label='Desplazada')
plt.legend()
plt.title('DR1')
plt.grid()
plt.xlabel('CompOVEN voltage')
plt.ylabel('DR1 counts - mean')
#DR2
plt.figure()
plt.errorbar(CompsOVENcol[1:],MinimosFluos_Col_DR2[1:],yerr=[2*i for i in list(ErrorMinimosFluos_Col_DR1)[1:]],fmt='o', capsize=2, markersize=12, label='Colineal')
plt.errorbar(CompsOVENdesp[:-2],MinimosFluos_Desp_DR2[:-2],yerr=[2*i for i in list(ErrorMinimosFluos_Desp_DR1)[:-2]],fmt='o', capsize=2, markersize=12, label='Desplazada')
plt.legend()
plt.title('DR2')
plt.grid()
plt.xlabel('CompOVEN voltage')
plt.ylabel('DR2 counts - mean')