Debugging

Specifying a Debugging Level

By default, GAMSPy will delete all temporary files generated by GAMS. In order to keep the temporary files, debugging_level parameter can be specified as keep or keep_on_error.

from gamspy import Container
m = Container(debugging_level="keep")
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specify your model here
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model.solve()
print(m.working_directory)

In this example, you keep your working directory in the temp directory in your operating system. The temp directories for Linux, Darwin, and Windows are usually /tmp, /var/tmp, and C:UsersusernameAppDataLocalTemp respectively. You can see the path for your model’s temporary files by printing container.working_directory.

Specifying the Working Directory

Alternatively, you can specify a working_directory to keep the temporary files generated by GAMS. If the working directory is specified, debugging_level will not have any effect.

from gamspy import Container
m = Container(working_directory=".")
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specify your model here
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model.solve()

In this example, specifying the working directory as the current directory causes temporary GAMS files (.gms, .lst, .g00. ,gdx files etc.) to be saved in the current directory.

Generating a Listing File

If one is only interested in the listing file that is generated after the solve statement, they can specify the path for the lst file through gamspy.Options().

from gamspy import Container, Options
m = Container()
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specify your model here
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model.solve(options=Options(listing_file="<path_to_the_listing_file>.lst"))

Generating a GDX File

Sometimes, it might be useful to generate a GDX file which contains the records of all symbols specified in the model. The path of the GDX file to be generated can be specified through gamspy.Options() as well.

from gamspy import Container, Options
m = Container()
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specify your model here
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model.solve(options=Options(gdx="<path_to_the_gdx_file>.gdx"))

Generating a Log File

The output of GAMS can be redirected to standard output or to a file by specifying the handle for the destination. For example:

import sys
from gamspy import Container
m = Container(working_directory=".")
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specify your model here
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model.solve(output=sys.stdout)

The code snippet above redirects the GAMS execution output to your console by specifying the output as standard output. You can also redirect the output to a file:

import sys
from gamspy import Container
m = Container(working_directory=".")
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specify your model here
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with open("mylog.txt", "w") as log:
    model.solve(output=log)

This code snippets redirects the output of the execution to a file named “mylog.txt”.

If you want to have your log file generated in the working directory, create_log_file argument can be provided.

import sys
from gamspy import Container
m = Container(working_directory=".")
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specify your model here
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model.solve(create_log_file=True)

This code snippet would generate a log file in the specified working directory. This argument is also useful for both redirecting the output to standard output and generating the log file at the same time.

import sys
from gamspy import Container
m = Container(working_directory=".")
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specify your model here
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model.solve(output=sys.stdout, create_log_file=True)

This code snippet would redirect the output to your console as well as saving the log file in your working directory.

Inspecting Generated GAMS String

Another alternative is to use the generateGamsString function. This function returns the GAMS code generated up to that point as a string.

from gamspy import Container
m = Container()
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print(m.generateGamsString())
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To see the generated GAMS statement for a certain symbol, getStatement function can be utilized.

from gamspy import Container, Set
m = Container()
i = Set(m, "i", records=['i1', 'i2'])
print(i.getStatement())

The code snippet above prints the GAMS statement for the symbol i:

'Set i(*);'

Inspecting Misbehaving (Infeasible) Models

Infeasibility is always a possible outcome when solving models. Infeasibilities in a model can be calculated by using gamspy.Model.compute_infeasibilities(). This would list the infeasibilities in all equations of the model. Infeasibilities in a single equation as well as infeasibilities in a single variable can be computed with gamspy.Equation.compute_infeasibilities(), gamspy.Variable.compute_infeasibilities() respectively.

from gamspy import Container
m = Container()
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specify your model here
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model.solve()
print(model.compute_infeasibilities())

Causes of infeasibility are not always easily identified. Solvers may report a particular equation as infeasible in cases where an entirely different equation is the cause. In these kind of complicated cases, one can dump all variables and equations in the listing file and inspect it.

from gamspy import Container, Options
m = Container()
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specify your model here
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model.solve(options=Options(equation_listing_limit=100, variable_listing_limit=100, listing_file="<path_to_the_listing_file>.lst"))