lo qeu pediste nico

analisis/plots/20250722_beam_profile/plot_profiles.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 20 17:30:37 2025
+
+@author: muri
+"""
+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import os 
+from scipy.optimize import curve_fit
+from scipy.stats import norm
+
+def fit_gauss(x,A,B,mu,sigma):
+    return A*norm.pdf(x,mu,scale = sigma) + B
+
+direc = './data/'
+files  = os.listdir(direc) 
+
+name = [f[4:9] for f in files ]
+
+
+# f = h5py.File(direc + files[0], 'r')
+index = [name.index('23002')]
+
+nmin = 15
+nmax  = 110
+volts = np.linspace(59,65,nmax-nmin)
+x = (volts-62)*6.9
+for i in index:
+        
+    f = h5py.File(direc + files[i], 'r')
+    count = np.array(f['datasets']['counts_spectrum'])[nmin:nmax]
+# count_base = f['datasets']['count_base']
+    # plt.plot(count,'.')
+    plt.plot(x,count,'.')
+    # plt.title(f'$4\sigma = $ {4*popt[-1]:.1f} $\mu$m')
+    
+plt.xlabel('x (aprox) (um)')
+plt.ylabel('cuentas')
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analisis/plots/20250724_espectros_CPT/CPT_bladetrap.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
+
+"""
+Primero tengo mediciones de espectros cpt de un ion variando la tension dc_A
+"""
+
+
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20231123_CPTconmicromocion3/Data/')
+#os.chdir('/home/nico/Documents/artiq_experiments/analisis/plots/20250516_CPT_bladetrap/Data')
+#os.chdir(r'C:\Users\Carolina\Documents\artiq_experiments\analisis\plots\20250516_CPT_bladetrap\Data')
+direc  = "./Data/"
+files  = os.listdir(direc) 
+
+name = [f[4:9] for f in files ]
+
+files = [x for _,x in sorted(zip(name,files))]
+name = sorted(name)
+
+
+CPT_FILES = files
+
+
+def SeeKeys(files):
+    for i, fname in zip(range(len(files)),CPT_FILES):
+        data = h5py.File(direc + fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+        print(fname)
+        print(list(data['datasets'].keys()))
+
+print(SeeKeys(CPT_FILES))
+#%%
+#carpeta pc nico labo escritorio:
+#C:\Users\Usuario\Documents\artiq\artiq_experiments\analisis\plots\20211101_CPT_DosLaseres_v03\Data
+
+Counts = []
+Freqs = []
+
+AmpTisa = []
+UVCPTAmp = []
+No_measures = []
+Voltages = []
+
+for i, fname in zip(range(len(files)),CPT_FILES):
+    print(str(i) + ' - ' + fname)
+    #print(fname)
+    data = h5py.File(direc + fname, 'r') # Leo el h5: Recordar que nuestros datos estan en 'datasets'
+
+    # Aca hago algo repugnante para poder levantar los strings que dejamos
+    # que además tenian un error de tipeo al final. Esto no deberá ser necesario
+    # cuando se solucione el error este del guardado.
+    Freqs.append(np.array(data['datasets']['IR1_Frequencies']))
+    Counts.append(np.array(data['datasets']['counts_spectrum']))
+    #AmpTisa.append(np.array(data['datasets']['TISA_CPT_amp']))
+    print(np.array(data['datasets']['no_measures']))
+
+
+#%%
+
+"""
+Ploteo curvas para ver que tal son
+"""
+
+jvec = [11,12,13] # de la 1 a la 9 vale la pena, despues no
+
+t_read = [100,400,800]
+plt.figure()
+i = 0
+for j in jvec:
+    Frequencies = Freqs[j]
+    plt.errorbar([2*f*1e-6 for f in Frequencies], Counts[j], yerr=np.sqrt(Counts[j]), fmt='o', capsize=2, markersize=2,label = f't_readout = {t_read[i]} $\mu$ s')
+    i = i + 1
+plt.xlabel('Frecuencia (MHz)')
+plt.ylabel('Counts')
+plt.grid()
+plt.legend()
+
+
+
+
+
+#%%
+"""
+HAGO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
+
+ajusto una medicion con muy poca micromocion
+
+"""
+
+
+from EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
+from scipy.optimize import curve_fit
+import time
+
+"""
+SUPER AJUSTE (SA)
+"""
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+Temp = 0.5e-3
+
+sg = 0.544
+sp = 4.5
+sr = 0
+DetRepump = 0
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+
+u = 32.5e6
+
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
+alpha = 0
+
+
+drivefreq = 2*np.pi*7.262*1e6
+
+
+SelectedCurveVec = [1]
+
+popt_SA_vec = []
+pcov_SA_vec = []
+Detuningsshort_vec = []
+Counts_vec = []
+Detuningslong_vec = []
+FittedCounts_vec = []
+
+Betas_vec = []
+ErrorBetas_vec = []
+Temp_vec = []
+ErrorTemp_vec = []
+
+DetuningsUV_vec = []
+ErrorDetuningsUV_vec = []
+
+for selectedcurve in SelectedCurveVec:
+
+    FreqsDR = Freqs[selectedcurve]
+    CountsDR = Counts[selectedcurve]
+
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    CircPr = 1
+    alpha = 0
+
+
+    def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
+
+        freqs = [2*f*1e-6-offset for f in Freqs]
+        # BETA1=0
+        Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+
+        ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
+        if plot:
+            return ScaledFluo1, Detunings
+        else:
+            return ScaledFluo1
+        #return ScaledFluo1
+
+    t1 = time.time()
+
+    print(f'Beginning {selectedcurve}')
+    popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR[1:], CountsDR, p0=[440, -15, 0.3, 10, 1e4, 500, 2, (np.pi**2)*1e-3, 49.5e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 48e6), (1000, 0, 2, 20, 5e4, 2e3, 3, (np.pi**2)*10e-3, 50e6)))
+    print(f'Ended {selectedcurve}, time elapsed: {round(time.time()-t1)} s')
+
+    #popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
+
+    popt_SA_vec.append(popt_3_SA)
+    pcov_SA_vec.append(pcov_3_SA)
+
+
+#offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U
+
+    FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    
+    # popt_3_SA[4]=1
+    # popt_3_SA[5]=0
+    # popt_3_SA[6]=0
+    FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
+    corri = 0
+    DetuningsUV_vec.append(popt_3_SA[1])
+    ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
+
+    Betas_vec.append(popt_3_SA[6])
+    ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
+    Temp_vec.append(popt_3_SA[7])
+    ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
+
+    Detuningsshort_vec.append(Detunings_3_SA_short)
+    Counts_vec.append(CountsDR)
+    Detuningslong_vec.append(Detunings_3_SA_long)
+    FittedCounts_vec.append(FittedEITpi_3_SA_long)
+
+    plt.figure()
+    plt.errorbar(Detunings_3_SA_short[:], CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
+    plt.plot([c-corri for c in Detunings_3_SA_long], FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
+    #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+    plt.xlabel('Detuning (MHz)')
+    plt.ylabel('Counts')
+    # plt.legend(loc='upper left', fontsize=20)
+    plt.grid()
+
+    print(f'listo med {selectedcurve}')
+    print(popt_3_SA)
+    
+
+
+#%%
+"""
+HAGO SUPER AJUSTES O SEA CON OFFSETXPI Y DETDOPPLER INCLUIDOS
+
+Ajusto med 2, con un poco mas de micromocion supuestamente
+
+"""
+
+
+from EITfit.lolo_modelo_full_8niveles import PerformExperiment_8levels_MM
+from scipy.optimize import curve_fit
+import time
+
+"""
+SUPER AJUSTE (SA)
+"""
+
+phidoppler, titadoppler = 0, 90
+phirepump, titarepump = 0,  0
+phiprobe = 0
+titaprobe = 90
+
+Temp = 0.5e-3
+
+sg = 0.544
+sp = 4.5
+sr = 0
+DetRepump = 0
+
+
+lw = 0.1
+DopplerLaserLinewidth, RepumpLaserLinewidth, ProbeLaserLinewidth = lw, lw, lw #ancho de linea de los laseres
+
+
+u = 32.5e6
+
+
+gPS, gPD, = 2*np.pi*21.58e6, 2*np.pi*1.35e6
+alpha = 0
+
+
+drivefreq = 2*np.pi*7.262*1e6
+
+
+SelectedCurveVec = [2]
+
+popt_SA_vec = []
+pcov_SA_vec = []
+Detuningsshort_vec = []
+Counts_vec = []
+Detuningslong_vec = []
+FittedCounts_vec = []
+
+Betas_vec = []
+ErrorBetas_vec = []
+Temp_vec = []
+ErrorTemp_vec = []
+
+DetuningsUV_vec = []
+ErrorDetuningsUV_vec = []
+
+for selectedcurve in SelectedCurveVec:
+
+    FreqsDR = Freqs[selectedcurve]
+    CountsDR = Counts[selectedcurve]
+
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    CircPr = 1
+    alpha = 0
+
+
+    def FitEIT_MM_single(Freqs, offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U, plot=False):
+
+        freqs = [2*f*1e-6-offset for f in Freqs]
+        # BETA1=0
+        Detunings, Fluorescence1 = PerformExperiment_8levels_MM(SG, SP, gPS, gPD, DetDoppler, U, DopplerLaserLinewidth, ProbeLaserLinewidth, TEMP, alpha, phidoppler, titadoppler, phiprobe, titaprobe,  BETA1, drivefreq, min(freqs), max(freqs)+(freqs[1]-freqs[0]), freqs[1]-freqs[0], circularityprobe=CircPr, plot=False, solvemode=1, detpvec=None)
+
+        ScaledFluo1 = np.array([f*SCALE1 + OFFSET for f in Fluorescence1])
+        if plot:
+            return ScaledFluo1, Detunings
+        else:
+            return ScaledFluo1
+        #return ScaledFluo1
+
+    t1 = time.time()
+
+    print(f'Beginning {selectedcurve}')
+    popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR[1:], CountsDR, p0=[440, -15, 0.3, 10, 1e4, 500, 2, (np.pi**2)*1e-3, 49.5e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 48e6), (1000, 0, 2, 20, 5e4, 2e3, 3, (np.pi**2)*10e-3, 50e6)))
+    print(f'Ended {selectedcurve}, time elapsed: {round(time.time()-t1)} s')
+
+    #popt_3_SA, pcov_3_SA = curve_fit(FitEIT_MM_single, FreqsDR, CountsDR, p0=[430, -25, 0.9, 6.2, 3e4, 1.34e3, 2, (np.pi**2)*1e-3, 32e6], bounds=((0, -50, 0, 0, 0, 0, 0, 0, 25e6), (1000, 0, 2, 20, 5e4, 5e4, 10, (np.pi**2)*10e-3, 40e6)))
+
+    popt_SA_vec.append(popt_3_SA)
+    pcov_SA_vec.append(pcov_3_SA)
+
+
+#offset, DetDoppler, SG, SP, SCALE1, OFFSET, BETA1, TEMP, U
+
+    FittedEITpi_3_SA_short, Detunings_3_SA_short = FitEIT_MM_single(FreqsDR, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
+    freqslong = np.arange(min(FreqsDR), max(FreqsDR)+FreqsDR[1]-FreqsDR[0], 0.1*(FreqsDR[1]-FreqsDR[0]))
+    
+    # popt_3_SA[4]=1
+    # popt_3_SA[5]=0
+    # popt_3_SA[6]=0
+    FittedEITpi_3_SA_long, Detunings_3_SA_long = FitEIT_MM_single(freqslong, *np.array([popt_3_SA[0],popt_3_SA[1],popt_3_SA[2],popt_3_SA[3],popt_3_SA[4],popt_3_SA[5],popt_3_SA[6],popt_3_SA[7],popt_3_SA[8]]), plot=True)
+    corri = 0
+    DetuningsUV_vec.append(popt_3_SA[1])
+    ErrorDetuningsUV_vec.append(np.sqrt(pcov_3_SA[1,1]))
+
+    Betas_vec.append(popt_3_SA[6])
+    ErrorBetas_vec.append(np.sqrt(pcov_3_SA[6,6]))
+    Temp_vec.append(popt_3_SA[7])
+    ErrorTemp_vec.append(np.sqrt(pcov_3_SA[7,7]))
+
+    Detuningsshort_vec.append(Detunings_3_SA_short)
+    Counts_vec.append(CountsDR)
+    Detuningslong_vec.append(Detunings_3_SA_long)
+    FittedCounts_vec.append(FittedEITpi_3_SA_long)
+#%%
+    plt.figure()
+    plt.errorbar(Detunings_3_SA_short[:], CountsDR, yerr=2*np.sqrt(CountsDR), fmt='o', color='darkgreen', alpha=0.5, capsize=2, markersize=2)
+    plt.plot([c-corri for c in Detunings_3_SA_long], FittedEITpi_3_SA_long, color='darkolivegreen', linewidth=3, label=f'med {selectedcurve}')
+    #plt.title(f'Sdop: {round(popt[0], 2)}, Spr: {round(popt[1], 2)}, T: {round(popt[2]*1e3, 2)} mK, detDop: {DetDoppler} MHz')
+    plt.xlabel('Detuning (MHz)')
+    plt.ylabel('Counts')
+    # plt.legend(loc='upper left', fontsize=20)
+    plt.grid()
+
+
+    print(f'listo med {selectedcurve}')
+    print(popt_3_SA)
+    
+    
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