files_to_use = [0,1,2,3,5,6,7,8,9,10,11,12] fn = hf.get_files_to_use(os.getcwd(), files_to_use)[0] amps = [0.2, 0.1, 0.025, 0.05, 0.159, 0.08,10,5,2.5,0.25,0.5,1.0] base_path = os.getcwd() fns = [os.listdir(base_path)[i] for i in files_to_use] fns = [i for i in fns if not i.startswith('.DS_Store')] num_fns = len(fns) for i,fn in enumerate(fns): folder = os.path.join(base_path,fn) os.chdir(folder) files = os.listdir(os.getcwd()) print( "______________Data set: {}__________________".format(fn[:-4])) bm = hf.get_ionProp_value('detection%det_brightMean') dm = hf.get_ionProp_value('detection%det_darkMean') G_tot = 0.5*( hf.get_ionProp_value('raman%align%spontem%G_du') + hf.get_ionProp_value('raman%align%spontem%G_ud') + hf.get_ionProp_value('raman%align%spontem%G_el') ) k = bm-dm det_t = hf.get_ionProp_value('detection%det_t') N = k/(hf.get_ionProp_value('detection%det_phtperionperms')*det_t*1e-3) J1kHz = hf.get_ionProp_value('raman%raman_J1kHz') t = hf.get_ionProp_value('sf%fitParams%sf_fitParam_tpi')*1e-6 l_pwr = hf.get_ionProp_value('raman%align%lower_odf_voltage')/hf.get_ionProp_value('raman%align%lower_odf_voltage_max') u_pwr = hf.get_ionProp_value('raman%align%upper_odf_voltage')/hf.get_ionProp_value('raman%align%upper_odf_voltage_max') u_ang = 180-2*(hf.get_ionProp_value('raman%wp%raman_wp_upper_acssn_a0')-hf.get_ionProp_value('raman%wp%raman_wp_lower_home_offset')) l_ang = 2*(hf.get_ionProp_value('raman%wp%raman_wp_lower_acssn_a0')-hf.get_ionProp_value('raman%wp%raman_wp_upper_home_offset')) mVpp = hf.get_ionProp_value('raman%force_sensing%drive_amp_mVpp') z = (mVpp*1e-9)/2
files = os.listdir(os.getcwd()) #Load properties data prop_name = [x for x in files if "_props.csv" in x][0] file_name, data_p = hf.get_gen_csv(prop_name, skip_header=False) bm = data_p['det_brightMean'] dm = data_p["det_darkMean"] det_t = data_p["det_t"] #int_t[i] = 2e-6*data_p["squeeze_arm_t"] #total interaction time in secs J1ks[i] = data_p['raman_J1kHz'] Ncal = data_p['det_phtperionperms'] k[i] = bm - dm # phtns per N atoms N[i] = k[i] / (det_t * 1e-3) / Ncal t = data_p['sf_fitParam_tpi'] * 1e-6 u_ang = 180 - 2 * ( hf.get_ionProp_value('raman%wp%raman_wp_upper_acssn_a0') - hf.get_ionProp_value('raman%wp%raman_wp_lower_home_offset')) l_ang = 2 * (hf.get_ionProp_value('raman%wp%raman_wp_lower_acssn_a0') - hf.get_ionProp_value('raman%wp%raman_wp_upper_home_offset')) k_ion = k[i] / N[i] G_tot = 0.5 * (hf.get_ionProp_value('raman%align%spontem%G_du') + hf.get_ionProp_value('raman%align%spontem%G_ud') + hf.get_ionProp_value('raman%align%spontem%G_el')) mVpp = hf.get_ionProp_value('raman%force_sensing%drive_amp_mVpp') z = (mVpp * 1e-9) / 2 # load experiment data, but ignore as I want to do cacls from the # raw data here for file in os.listdir(os.getcwd()): if file.startswith("histData.") and file.endswith(".csv"): file_name = file
bp_bck = [] err = [] mVpps = [] names = [] As = [] sig_max_all = [] stds = [] # loop through different data sets here for i, fn in enumerate(fns): folder = os.path.join(base_path, fn) os.chdir(folder) files = os.listdir(os.getcwd()) print("______________Data set: {}__________________".format(fn[:-4])) bm = hf.get_ionProp_value('detection%det_brightMean') dm = hf.get_ionProp_value('detection%det_darkMean') G_tot = 0.5 * (hf.get_ionProp_value('raman%align%spontem%G_du') + hf.get_ionProp_value('raman%align%spontem%G_ud') + hf.get_ionProp_value('raman%align%spontem%G_el')) k = bm - dm det_t = hf.get_ionProp_value('detection%det_t') N = k / (hf.get_ionProp_value('detection%det_phtperionperms') * det_t * 1e-3) J1kHz = hf.get_ionProp_value('raman%raman_J1kHz') t = hf.get_ionProp_value('sf%fitParams%sf_fitParam_tpi') * 1e-6 name, scandata, counts_data, data = hf.get_raw_counts_hist( combine_scans=avg_scans) os.chdir(base_path)