def time_correct(in_dir_name, dir_params, main_dir):
'''
Spectrums/Images time adjusted in TimeSort function above are loaded in
and the data is maintained. The file name is split and the first file
captured is set to 0, the following files within the directory are given
a time stamp respective to the zero file (a time step). This is useful
for later processing.
Args:
in_dir_name: <string> directory name containind time adjusted
spectrum files
dir_params: <array> directories are given a name equivalent to the
individual file names, the dir_params function splits
the directory name into an array that can be used to find
the correct spectrum files.
main_dir: <string> current working directory
'''
file_string = '_'.join(dir_params[0:2])
print(' ')
print(dir_params)
data_files = io.extract_files(dir_name=in_dir_name,
file_string=file_string)
zero_file_name = data_files[0]
zero_time_stamp = (zero_file_name.split('_')[::-1])[0]
for index, selected_file in enumerate(data_files):
file = os.path.join(in_dir_name, selected_file)
data = np.load(file)
file_name = io.get_filename(file)
split_file = file_name.split('_')[::-1]
time_correction = int(
float(split_file[0]) - float(zero_time_stamp[0:-4]))
out_dir_name = '_'.join(dir_params[0:-1]) + '_TimeCorrected'
out_dir = os.path.join(main_dir, out_dir_name)
io.check_dir_exists(out_dir)
joined = []
joined.append(file_string)
joined.append(str(time_correction))
new_file_name = '_'.join(joined)
io.array_save(array_name=data,
file_name=new_file_name,
dir_name=out_dir)
io.update_progress(index / len(data_files))
writer.writerow([time_stamp] + [peak] + [peak_shift])
io.update_progress(index / len(data_files))
shutil.copy(outfile_name, results_dir)
os.remove(outfile_name)
dir_params = dprep.solute_finder(solute_dir)
data_files = io.extract_files(dir_name=results_dir,
file_string='_'.join(dir_params) +
'_Peaks.csv')
print(f'\nFiles to be processed: {data_files}')
for index, selected_file in enumerate(data_files):
file = os.path.join(results_dir, selected_file)
file_name = io.get_filename(file)
bg_file_string = [
'1M_Salt_Background',
'_'.join(dir_params[0:2]) + '_Background',
'1M_Salt_Paper_Background',
'_'.join(dir_params[0:2]) + '_Background',
f'{sensor}_Background', 'DI_Background', 'IPA_Background'
]
bg_file = os.path.join(bg_dir, 'Background_Peaks.csv')
names, bg_peak, bg_peak_shift = np.genfromtxt(bg_file,
delimiter='\t',
dtype=(str),
unpack=True)