The ‘amt_fields’ is a list of PoPy Dosing Fields, i.e. Note you can specify multiple observed columns, each observed field will result in extra rows in the Nonmem data output, as Nonmem only ever has one DV observation column. The ‘dv_fields’ is a list of PoPy Observation Fields that will be moved into the Nonmem DV field. Here ‘time_field’, ‘id_field’ and ‘type_field’ are the PoPy data file Required Fields. INPUT_POPY_FIELDS : time_field : TIME id_field : ID type_field : TYPE dv_fields : amt_fields : rate_fields : dur_fields : dose_labels : You have to specify the ‘CMT’ value in your P2NDat Script manually, see OUTPUT_NONMEM_FIELDS. PoPy specifies the dosing compartment entirely in the script file, see Dosing Fields, so the output ‘CMT’ column has no corresponding column in the PoPy data file. The ‘fit_example1_nm_data.csv’ contains a ‘CMT’ field to specify that the Nonmem dosing occurs in compartment 1.

The P2NDat Script has removed the ‘TYPE’, ‘DV_CENTRAL’ and ‘DV_CENTRAL_FLAG’ PoPy fields, to leave ‘TIME’, ‘ID’ and ‘AMT’, then added the newly created Nonmem specific ‘DV’, ‘MDV’, ‘EVID’ and ‘CMT’ columns. Note the corresponding columns are not in the same order between ‘fit_example1_data.csv’ and ‘fit_example1_nm_data.csv’. Are summarised in the Table 50 Table 50 Comparing PoPy ‘fit_example1_data.csv’ and Nonmem ‘fit_example1_nm_data.csv’ ¶ Input PoPy column The differences between the input PoPy data Table 48 and the output Nonmem data Table 49. Table 49 Output data in Nonmem format (first ten rows) ¶ TIME The first ten rows are shown in Table 49. This will create a new Nonmem data file ‘fit_example1_nm_data.csv’.

Nonmem script example