METHOD_OPTIONS¶

Just specifies the script type:-

METHOD_OPTIONS: {py_module: p2ndat}

See METHOD_OPTIONS for more info.

FILE_PATHS¶

Just specifies the input PoPy data file and output Nonmem data file:-

FILE_PATHS:
    input_popy_file: fit_example1_data.csv
    output_nonmem_file: fit_example1_nm_data.csv

INPUT_POPY_FIELDS¶

Describes the columns of the input PoPy file:-

INPUT_POPY_FIELDS:
    time_field: TIME
    id_field: ID
    type_field: TYPE
    dv_fields: ['DV_CENTRAL']
    amt_fields: ['AMT']
    rate_fields: []
    dur_fields: []
    dose_labels: ['']

Here ‘time_field’, ‘id_field’ and ‘type_field’ are the PoPy data file Required Fields. They default to the above values.

The ‘dv_fields’ is a list of PoPy Observation Fields that will be moved into the Nonmem DV field. 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 ‘amt_fields’ is a list of PoPy Dosing Fields, i.e. columns that contain dose amounts. Similar to the ‘dv_fields’, if you specify multiple dosing amount columns, then the Nonmem data output will contain extra rows, as Nonmem only has one AMT field.

The ‘rate_fields’ and ‘dur_fields’ are blank because we only have bolus dosing here. If you have infusion dosing then add the @inf_rate and @inf_dur rate and duration parameters here.

The ‘dose_labels’ field contains the dosing names used in the PoPy data file. In this case dose_labels= [‘’] means PoPy dose names are not used. i.e. the TYPE column just uses ‘dose’ values. If you use ‘dose:my_dose_name’, ‘dose:my_other_dose_name’ in your PoPy data file, to describe Multiple Dose Types, then you need to list the names here, e.g. [‘my_dose_name’, ‘my_other_dose_name’].

OUTPUT_NONMEM_FIELDS¶

Describes the columns of the output Nonmem file:-

OUTPUT_NONMEM_FIELDS:
    comment_prefix: '#'
    column_names: auto
    time_field: TIME
    id_field: ID
    evid_field: EVID
    dv_field: DV
    mdv_field: MDV
    amt_field: AMT
    rate_field: none
    dur_field: none
    cmt_field: CMT
    obs_cmt_numbers: [1]
    dose_cmt_numbers: [1]

Here ‘comment_prefix’ allows loading of Nonmem data files with comment lines. Lines starting with the ‘comment_prefix’ symbol are ignored.

‘column_names: auto’, uses the columns names in the ‘.csv’ data file. You could rename them using a list here, a bit like the Nonmem $INPUT section.

The ‘time_field’, ‘id_field’, ‘evid_field’, ‘dv_field’, ‘mdv_field’, ‘rate_field’, ‘dur_field’ and ‘cmt_field’ allows you to specify the Nonmem key fields ‘ID’, ‘EVID’, ‘DV’, ‘MDV’, ‘AMT’, ‘RATE’, ‘DUR’ and ‘CMT’. These fields default to the Nonmem key names.

Note that if you do not require some of the Nonmem fields, e.g. in this case ‘rate_field’ and ‘dur_field’, because these only relate to infusion dosing and there is only bolus dosing in this example. Then you can assign null values using ‘none’.

The ‘obs_cmt_numbers’ is a list of compartment indices to appear in the CMT column to be created by the P2NDat Script. The ‘OUTPUT_NONMEM_FIELDS->obs_cmt_numbers’ list must be the same length as the ‘INPUT_POPY_FIELDS->dv_fields’ list. The elements of both lists must correspond to the same type of observation. e.g. in this case all PoPy observations ‘DV_CENTRAL’ occur in Nonmem compartment one. The P2NDat Script will copy the PoPy ‘DV_CENTRAL’ value into the Nonmem DV column for all rows with TYPE =’obs’ and set MDV =0 for these rows.

The ‘dose_cmt_numbers’ is a list of compartment indices to appear in the CMT column to be created by the P2NDat Script. The ‘OUTPUT_POPY_FIELDS->dose_cmt_numbers’ list must be the same length as the ‘INPUT_POPY_FIELDS->amt_fields’ list. The elements of both lists must correspond to the same type of dose. e.g. in this case all PoPy dose amounts ‘AMT’ occur in Nonmem compartment one. The P2NDat Script will copy the PoPy ‘AMT’ value into the Nonmem AMT column for all rows with TYPE =’dose’ and set AMT =0.0 for other rows.

If you have multiple doses and multiple observation fields in your input PoPy data, then you have to specify the dv_fields/obs_cmt_numbers and amt_fields/dose_cmt_numbers list pairs carefully.

OUTPUT_OPTIONS¶

Describes the output options. Currently, the only option is to remove fields from the final data file:-

OUTPUT_OPTIONS:
    drop_fields: ['TYPE', 'DV_CENTRAL', 'DV_CENTRAL_FLAG']

Here we are removing the old PoPy fields from the Nonmem data output. This is useful in this case, as we wish to demonstrate regenerating the orig PoPy fields, when we use a N2PDat Script in the next section.

METHOD_OPTIONS¶

Specifies the script type:-

METHOD_OPTIONS: {py_module: n2pdat}

See METHOD_OPTIONS for more information.

FILE_PATHS¶

Specifies the input Nonmem data file and output PoPy data file:-

FILE_PATHS:
    input_nonmem_file: fit_example1_nm_data.csv
    output_popy_file: fit_example1_data_v2.csv

INPUT_NONMEM_FIELDS¶

Describes the columns of the input Nonmem file:-

INPUT_NONMEM_FIELDS:
    comment_prefix: '#'
    column_names: auto
    date_field: none
    date_format: none
    time_field: TIME
    id_field: ID
    evid_field: EVID
    dv_field: DV
    mdv_field: MDV
    amt_field: AMT
    rate_field: none
    dur_field: none
    cmt_field: CMT
    obs_cmt_numbers: [1]
    dose_cmt_numbers: [1]

This is the same as the OUTPUT_NONMEM_FIELDS section. The only difference is that this section is now describing an input Nonmem data file instead of an output Nonmem data file.

The ‘obs_cmt_numbers’ and ‘dose_cmt_numbers’ list have to correspond to the ‘dv_fields’ and ‘amt_fields’ in the OUTPUT_POPY_FIELDS section to get sensible PoPy data output. See below for more explanation.

OUTPUT_POPY_FIELDS¶

Describes the columns of the output PoPy file:-

OUTPUT_POPY_FIELDS:
    time_field: TIME
    id_field: ID
    type_field: TYPE
    dv_fields: ['DV_CENTRAL']
    amt_fields: ['AMT']
    rate_fields: []
    dur_fields: []
    dose_labels: ['']

This is the same as the INPUT_POPY_FIELDS section. The only difference is that this section is now describing an output PoPy data file instead of an input PoPy data file.

Here the ‘dv_fields’ is a list of PoPy observation columns to be created by the N2PDat Script, based on the input Nonmem DV field. The ‘OUTPUT_POPY_FIELDS->dv_fields’ list must be the same length as the ‘INPUT_NONMEM_FIELDS->obs_cmt_numbers’ list. The elements of both lists must correspond to the same type of observation. e.g. in this case all Nonmem observations occur in compartment one, so for Nonmem data rows with EVID =0 and CMT =1 the Nonmem DV value is copied into the PoPy DV_CENTRAL column with DV_CENTRAL_FLAG=1.

The ‘amt_fields’ is a list of PoPy dose amount columns to be created by the N2PDat Script, based on the input Nonmem AMT field. The ‘OUTPUT_POPY_FIELDS->amt_fields’ list must be the same length as the ‘INPUT_NONMEM_FIELDS->dose_cmt_numbers’ list. The elements of both list must correspond to the same type of dose. e.g. in this case all Nonmem doses occur in compartment one, so for Nonmem data rows with EVID =1 and CMT =1 the Nonmem AMT value is copied into the PoPy AMT column, with all other rows set to zero.

If you have multiple doses and multiple observation fields in your input Nonmem data, then you have to specify the obs_cmt_numbers/dv_fields and dose_cmt_numbers/amt_fields list pairs carefully.

OUTPUT_OPTIONS¶

Describes the output options, currently, just which fields to remove:-

OUTPUT_OPTIONS:
    drop_fields: ['DV', 'MDV', 'EVID', 'CMT']

Here we are removing the Nonmem specific fields. In a real life conversion it may be sensible to keep the Nonmem fields, so that you can perform a side by side sanity check from within the PoPy output file. Note the fields above will be of little use to a PoPy Fit Script, compared to the ‘DV_CENTRAL’, ‘DV_CENTRAL_FLAG’ and ‘TYPE’ fields, created by the N2PDat Script.