#obtain Infragold calibrated data
data[key+'_wavelength'], data[key+'_reflectance'] = IR_analysis.calibrate_data_csv(file, IG_filenames_csv[index])
print 'finished reading file: ', key
IR_analysis.write_file(key, data[key+'_wavelength'], data[key+'_reflectance'])
#PASA cashbox csv with bad scans (2013)
for index, file in enumerate(sample_filenames_csv_bad_scans):
#data stored in dictionary
#define key based on file name
key=file.split('samples/',1)[-1]
key=key.rstrip('.csv')
#store wavelength and reflectance data in key
data[key+'_wavelength']=[]
data[key+'_reflectance']=[]
#obtain Infragold calibrated data (integer = bad scans to remove)
data[key+'_wavelength'], data[key+'_reflectance'] = IR_analysis.calibrate_data_csv_bad_scans(file, IG_filenames_csv_bad_scans[index], 32)
print 'finished reading file: ', key
print '(removed bad scans)'
IR_analysis.write_file(key, data[key+'_wavelength'], data[key+'_reflectance'])
#PASA cashbox txt (2015, and later, current format)
#same analysis as PASA-Lite
for index, file in enumerate(sample_filenames_PASA_cashbox):
#data stored in dictionary
#define key based on file name
key=file.split('samples/',1)[-1]
key=key.rstrip('.txt')
#store wavelength and reflectance data in key
data[key+'_wavelength']=[]
data[key+'_reflectance']=[]
#obtain Infragold calibrated data
