Output

The cash flow model generates three types of output files: output, diagnostic, and log. These files are organized within the output folder, as shown below:

.
└── output/
    └── <timestamp>_diagnostic.csv
    └── <timestamp>_log.txt
    └── <timestamp>_output.csv

The numerical results of the model variables are stored in the <timestamp>_output.csv file. Users can review and manipulate the results from this file or utilize the output dataframe returned by the run.py script.

Structure

The output of a cash flow model is presented as a table, where rows represent time periods, and columns represent model variables. The structure of the output depends on the following settings:

• AGGREGATE - this flag determines whether the results should be aggregated or not.

• T_MAX_OUTPUT - specifies the number of projection periods included in the output.

• OUTPUT_COLUMNS - a list of output columns to be included (by default, all columns are included).

Let’s explore these settings with examples.

Aggregated output

You can create aggregated output by setting the AGGREGATE option to True, which is the default setting.

settings.py

settings = {
    ...
    "AGGREGATE": True,
    ...
}

In the aggregated output, results are summed across all model points, resulting in a single set of projections. The number of rows in this output is determined by T_MAX_OUTPUT + 1, where the +1 accounts for the projection starting at time period 0.

You can also aggregate data by groups. To do this, set the AGGREGATE option to True and specify the GROUP_BY_COLUMN as the column name containing the grouping variable from your ‘main’ model point set.

For example, to group results by product_code, configure your settings as follows:

settings.py

settings = {
    ...
    "AGGREGATE": True,
    "GROUP_BY_COLUMN": "product_code",
    ...
}

Individual output

To generate individual output, set the AGGREGATE option to False.

settings.py

settings = {
    ...
    "AGGREGATE": False,
    ...
}

In this case, each model point has its own set of results.

Subset of columns

If you only require specific variables in the output, you can select them using the OUTPUT_COLUMNS setting:

settings.py

settings = {
    ...
    "OUTPUT_COLUMNS": ["bel"],
    ...
}

The OUTPUT_COLUMNS setting takes a list of variables names to include in the output. By default, when this setting is an empty list, results for all model variables are generated.

Default vs. custom output

Default output

By default, the model’s results are saved to a CSV file. This file is saved in the output folder within the model’s directory. The filename follows the format <timestamp>_output.csv, where <timestamp> represents the date and time when the model was executed (e.g., 20231125_173512_output.csv).

Custom output

The default output behavior can be customized to suit specific requirements, such as saving results to different file formats or uploading them to a database. To use custom output, follow these steps:

1. Set the SAVE_OUTPUT setting to False in your settings.py file. This prevents the model from saving the output in the default manner:

settings.py

settings = {
    ...
    "SAVE_OUTPUT": False,
    ...
}

2. Modify the run.py script to handle custom output. For instance, you can save results as a text file without timestamps:

run.py

if __name__ == "__main__":
    output = run(settings)
    output.to_string("output.txt")

Now, instead of creating an <timestamp>_output.csv file, the script will generate an output.txt file with the results.