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)