Testing GRASS GIS source code and modules

If you are already familiar with the basic concepts of GRASS testing framework, you might want to skip to one of:


For the testing in GRASS GIS, we are using a gunittest package and we usually refer to the system of writing and running tests as to a GRASS testing framework.

The framework is based on Python unittest package with a large number of GRASS-specific improvements, extensions and changes. These include things such as creation of GRASS-aware HTML test reports, or running of test in the way that process terminations potentially caused by C library functions does not influence the main testing process.

Some tests will run without any data but many tests require the small (basic) version of GRASS GIS sample Location for North Carolina (see GRASS GIS sample data).

Basic example

If you are writing a test of a GRASS module, create a Python script with the content derived from the example below. When using existing existing maps, suppose you are in North Carolina SPM GRASS sample location.

The file can contain one or more test case classes. Each class can contain one or more test methods (functions). Here we create one test case class with one test method. The other two methods are class methods ensuring the right environment for all test methods inside a test case class. When a test file becomes part of source code (which is the usual case) it must be placed into a directory named testsuite.

from grass.gunittest.case import TestCase
from grass.gunittest.main import test

# test case class must be derived from grass.gunittest.TestCase
class TestSlopeAspect(TestCase):

    def setUpClass(cls):
        """Ensures expected computational region"""
        # to not override mapset's region (which might be used by other tests)
        # cls.runModule or self.runModule is used for general module calls
        cls.runModule('g.region', raster='elevation')
        # note that the region set by default for NC location is the same as
        # the elevation raster map, this is an example shows what to do
        # in the general case

    def tearDownClass(cls):

    # test method must start with test_
    def test_limits(self):
        """Test that slope and aspect are in expected limits"""
        # we don't have to delete (g.remove) the maps
        # but we need to use unique names within one test file
        slope = 'limits_slope'
        aspect = 'limits_aspect'
        # self.assertModule is used to call module which we test
        # we expect module to finish successfully
        self.assertModule('r.slope.aspect', elevation='elevation',
                          slope=slope, aspect=aspect)
        # function tests if map's min and max are within expected interval
        self.assertRasterMinMax(map=slope, refmin=0, refmax=90,
                                msg="Slope in degrees must be between 0 and 90")
        self.assertRasterMinMax(map=aspect, refmin=0, refmax=360,
                                msg="Aspect in degrees must be between 0 and 360")

if __name__ == '__main__':

In the example we have used only two assert methods, one to check that module runs and end successfully and the other to test that map values are within an expect interval. There is a much larger selection of assert methods in TestCase class documentation and also in Python unittest package documentation.

To run the test, run GRASS GIS, use NC SPM sample location and create a separate mapset (name it test for example). Then go to the directory with the test file and run it:

python some_test_file.py

The output goes to the terminal in this case. Read further to see also more advanced ways of invoking the tests.

We have shown a test of a GRASS module using NC sample location. However, tests can be written also for C and Python library and also for internal functions in modules. See the rests of this document for a complete guide.

Building blocks and terminology


Some parts of the terminology should be revised to ensure understanding and acceptance.

test function and test method

A test function is a test of one particular feature or a test of one particular result. A test function is referred to as test method, individual test or just test.

assert function and assert method

An assert function (or assert method) refers to a function which checks that some predicate is fulfilled. For example, predicate can be that two raster maps does not differ from each other or that module run ends with successfully.

test case

The test methods testing one particular topic or feature are in one test case class.

From another point of view, one test case class contains all tests which require the same preparation and cleanup steps. In other words, a test case class contains all tests which are using the same test fixture.

There is also a general TestCase class which all concrete test case classes should inherit from to get all GRASS-specific testing functionality and also to be found by the testing framework.

test suite

A test suite, or also testsuite, is a set of tests focused on one topic, functionality or unit (similarly to test case). In GRASS GIS, it is a set of files in one testsuite directory. The test files in one testsuite directory are expected to test what is in the parent directory of a given testsuite directory. This is used to organize tests in the source code and also to generate test reports.

The term test suite may also refer to TestSuite class which is part of Python unittest test invocation mechanism used by gunittest internally.

More generally, a test suite is a group of test cases or any tests (test methods, test cases and other test suites) in one or more files.

test file

A test file is a Python script executable as a standalone process. It does not set up any special environment and runs where it was invoked. The testing framework does not rely on the file to end in a standard way which means that if one file ends with segmentation fault the testing framework can continue in testing of other test files. Test files are central part gunittest system and are also the biggest difference from Python unittest. Test file name should be unique but does not have to contain all parent directory names, for example it can consist from a simplified name of a module plus a word or two describing which functionality is tested. The name should not contain dots (except for the .py suffix).

Alternatively, a test file could be called test script or test module (both in Python and GRASS sense) but note that none of these is used.

test runner and test invoker

Both test runner and test invoker refer to classes, functions or scripts used to run (invoke) tests or test files. One of the terms may fade of in the future (probably invoke because it is not used by Python unittest).

test fixture (test set up and tear down)

The preparation of the test is called setup or set up and the cleaning after the test is called teardown or tear down. A test fixture refers to these two steps and also to the environment where the test or tests are executed.

Each test case class can define setUp, setUpClass, tearDown and tearDownClass methods to implement preparation and cleanup steps for tests it contains. The methods ending with Class are class methods (in Python terminology) and should be defined using @classmethod decorator and with cls as first argument. These methods are executed once for the whole class while the methods without Class are executed for each test method.

In GRASS GIS, the preparation may, but does not have to, contain imports of maps, using temporary region, setting computational region, or generating random maps. The cleanup step should remove temporary region as well as remove all created maps and files.

test report

A test report is a document or set of documents with results of all executed tests together with additional information such as output of test.

Note that also test result is used also used in similar context because the class responsible for representing or creating the report in Python unittest package is called TestResult.

test failure and test error

A test failure occurs when a assert fails, e.g. value of a parameter given to assertTrue() function is False. A test error occurs when something what is not tested fails, i.e. when exception is risen for example preparation code or a test method itself.

Testing with gunittest package in general

The tests should be in files in a testsuite directory which is a subdirectory of the directory with tested files (module, package, library). Each test file (testing file) can have can have several test cases (testing classes). All test file names should have pattern test*.py or *.py if another naming convention seems more appropriate.

GRASS GIS gunittest package and testing framework is similar to the standard Python unittest package, so the ways to build tests are very similar. Test methods are in a test test case class and each test method tests one think using one or more assert methods.

from grass.gunittest.case import TestCase
from grass.gunittest.main import test

class TestPython(TestCase):

    def test_counting(self):
        """Test that Python can count to two"""
        self.assertEqual(1 + 1, 2)

if __name__ == '__main__':

Each test file should be able to run by itself accept certain set of command line parameters (currently none). This is done using if __name__ == '__main__' and gunittest.test() function.

To run a test file, start GRASS session in the location and mapset suitable for testing (typically, NC sample location) and go to the test file’s directory (it will be usually some testsuite directory in the source code) and run it as a Python script:

python test_something.py

When running individual test files, it is advisable to be in a separate mapset, so for example when using NC sample location, you should use a new mapset of arbitrary name but not one of the predefined mapsets).

To run all tests in the source tree, you have to be in the source code directory where you want to find tests, also you need to be inside a GRASS session and use command similar to this one:

python -m grass.gunittest.main --location nc_spm_grass7 --location-type nc

All test files in all testsuite directories will be executed and a report will be created in a newly created testreport directory. Open the file testreport/index.html to browse though the results. Note that again you need to be in GRASS session to run the tests in this way.

The --location-type parameter serves to filter tests according to data they can run successfully with. It is ignored for tests which does not have this specified.

In this case each running test file gets its own mapset and current working directory but all run are in one location.


The current location is ignored but you should not run tests in the location which is precious to you for the case that something fails and current location is used for tests.

When your are writing tests you can rely on having maps which are present in the NC sample location, or you can generate random maps. You can also import your data which you store inside data directory inside the given testsuite directory (for maps, ASCII formats are usually used). If you can create tests independent on location projection and location data it is much better then relying on given data but it is not at all required and all approaches are encouraged.

Whenever possible it is advantageous to use available assert methods. GRASS-specific assert methods are in gunittest.case.TestCase class. For general assert methods refer to Python unittest package documentation. Both are used in the same way; they are methods of a given test case class. In cases (which should be rare) when no assert methods fits the purpose, you can use your own checking finalized with a call of assertTrue() or assertFalse() method with the msg parameter parameter set to an informative message.

When you are using multiple assert methods in one test method, you must carefully consider what assert methods are testing and in which order you should put them. Consider the following example:

# incorrect order
def test_map_in_list_wrong(self):
    maps = get_list_of_maps()
    self.assertIn('elevation', maps)
    # there is no point in testing that
    # if list (or string) was empty or None execution of test ended
    # at the line with assertIn

# correct order
def test_map_in_list_correct(self):
    maps = get_list_of_maps()
    # see if list (or string) is not empty (or None)
    # and then see if the list fulfills more advanced conditions
    self.assertIn('elevation', maps)

If you are not sure when you would use multiple asserts consider the case when using only assertIn() function:

def test_map_in_list_short(self):
    maps = get_list_of_maps()
    self.assertIn('elevation', maps)

If the list (or string) is empty, the test fails and the message says something about elevation'' not being in the maps but it might be much more useful if it would tell us that the list maps does not contain any items. In case of maps being None, the situation is more complicated since we using assertIn with None will cause test error (not only failure). We must consider what is expected behavior of get_list_of_maps() function and what we are actually testing. For example, if we would be testing function interface, we probably should test separately different possibilities using assertIsNotNone() and then assertTrue() and then anything else.

Another reason for using multiple assert methods is that we may want to test different qualities of a result. Following the previous example, we can test that a list contains some map and does not contain some other. If you are testing a lot of things and they don’t have any clear order or dependencies, it might be more advantageous to split testing into several testing methods and do the preparation (creating a list in our example) in setUpClass() or setUp() method.

Tests of GRASS modules

This is applicable for both GRASS modules written in C or C++ and GRASS modules written in Python since we are testing the whole module (which is invoked as a subprocess).

def test_elevation(self):
    self.assertModule('r.info', map='elevation', flags='g')

Use method assertRasterMinMax() to test that a result is within expected range. This is a very general test which checks the basic correctness of the result and can be used with different maps in different locations.

def test_slope_limits(self):
    slope = 'limits_slope'
    self.assertModule('r.slope.aspect', elevation='elevation',
    self.assertRasterMinMax(map=slope, refmin=0, refmax=90,
                            msg="Slope in degrees must be between 0 and 90")

Especially if a module module has a lot of different parameters allowed in different combinations, you should test the if the wrong ones are really disallowed and proper error messages are provided (in addition, you can test things such as creation and removal of maps in error states).

from grass.gunittest.gmodules import SimpleModule

class TestRInfoParameterHandling(TestCase):
    """Test r.info handling of wrong input of parameters."""

    def test_rinfo_wrong_map(self):
        """Test input of map which does not exist."""
        map_name = 'does_not_exist'
        # create a module instance suitable for testing
        rinfo = SimpleModule('r.info', map=map_name, flags='g')
        # test that module fails (ends with non-zero return code)
        # test that error output is not empty
        # test that the right map is mentioned in the error message
        self.assertIn(map_name, stderr)

In some cases it might be advantageous to create a module instance in setUp() method and then modify it in test methods.

Tests of C and C++ code

There are basically two possibilities how to test C and C++ code. If you are testing GRASS library code the functions which are part of API these functions are exposed through Python ctypes and thus can be tested in Python. See section Tests of Python code for reference.

However, more advantageous and more preferable (although sometimes more complicated) solution is to write a special program, preferably GRASS module (i.e., using G_parser). The dedicated program can provide more direct interface to C and C++ functions used by a GRASS module then the module and can also serve for doing benchmarks which are not part of the testing. This can approach can be applied to both

See the example in lib/raster3d GRASS source code directory to create a proper Makefiles. A main() function should be written in the same way as for a standard module.

Having a GRASS module for the purpose of testing you can write test as if it would be standard GRASS module.

Tests of Python code

For testing of Python code contained in some package, use gunittest in the same way as unittest would be used. This basically means that if you will write tests of Python functions and C functions exposed to Python through ctypes API, you might want to focus more on unittest documentation since you will perhaps need the more standard assert functions rather then the GRASS-specific ones.

Testing Python code with doctest


The primary use of doctest is to ensure that the documentation for functions and classes is valid. Additionally, it can increase the number of tests when executed together with other tests.

In Python, the easiest thing to test are functions which performs some computations or string manipulations, i.e. they have some numbers or strings on the input and some other numbers or strings on the output.

At the beginning you can use doctest for this purpose. The syntax is as follows:

def sum_list(list_to_sum):
    """Here is some documentation in docstring.

    And here is the test::

    >>> sum_list([2, 5, 3])

In case of GRASS modules which are Python scripts, you can add something like this to your script:

if __name__ == "__main__":
    if len(sys.argv) == 2 and sys.argv[1] == '--doctest':
        import doctest

No output means that everything was successful. Note that you cannot use all the ways of running doctest since doctest will fail don the module file due to the dot or dots in the file name. Moreover, it is sometimes required that the file is accessible through sys.path which is not true for case of GRASS modules.

However, do not use use doctest for tests of edge cases, for tests which require generate complex data first, etc. In these cases use gunittest.

Tests as general scripts

GRASS testing framework supports also general Python or Shell scripts to be used as test files. This is strongly discouraged because it is not using standard gnunittest assert functions which only leads to reimplementing the functionality, relying on a person examining the data, or improper tests such as mere testing if the module executed without an error without looking at the actual results. Moreover, the testing framework does not have a control over what is executed and how which limits potential usage and features of testing framework. Doing this also prevents testing framework from creating a detailed report and thus better understanding of what is tested and what is failing. Shell scripts are also harder to execute on MS Windows where the interpreter might not be available or might not be on path.

The testing framework uses Shell interpreter with -e flag when executing the tests, so the tests does not have to use set -e and can rely on it being set from outside. The flag ensures that if some command fails, i.e. ends with non-zero return code (exit status), the execution of the script ends too. The testing framework also uses -x flag to print the executed commands which usually makes examining of the test output easier.

If multiple test files are executed using grass.gunittest.main module, the testing framework creates a temporary Mapset for the general Python and Shell scripts in the same way as it does for gunittest-based test files. When the tests are executed separately, the clean up in current Mapset and current working directory must be ensured by the user or the script itself (which is generally true for all test files).


This is a bad practice which prevents creation of detailed reports and usage of advanced gunittest features, so you should avoid it whenever possible.



Both the section and the practice itself are under development.

Most of the tests requires some input data. However, it is good to write a test in the way that it is independent on the available data. In case of GRASS, we have we can have tests of functions where some numbers or strings are input and some numbers or string are output. These tests does not require any data to be provided since the numbers can be part of the test. Then we have another category of tests, typically tests of GRASS modules, which require some maps to be on the input and thus the output (and test) depends on the specific data. Again, it it best to have tests which does not require any special data or generally environment settings (e.g. geographic projection) but it is much easier to write good tests with a given set of data. So, an compromises must be made and tests of different types should be written.

In the GRASS testing framework, each test file should be marked according to category it belongs to. Each category corresponds to GRASS location or locations where the test file can run successfully.

Universal tests

First category is universal. The tests in this category use some some hard coded constants, generated data, random data, or their own imported data as in input to function and GRASS modules. All the tests, input data and reference results should be projection independent. These tests will runs always regardless of available locations.

Standard names tests

Second category are tests using standard names. Tests rely on a certain set of maps with particular names to be present in the location. Moreover, the tests can rely also on the (semantic) meaning of the names, i.e. raster map named elevation will always contain some kind of digital elevation model of some area, so raster map elevation can be used to compute aspect. In other words, these tests should be able to (successfully) run in any location with a maps named in the same way as in the standard testing location(s).

Standard data tests

Third category of tests rely on standard data. These tests expect that the GRASS location they run in not only contains the maps with particular names as in the standard names but the tests rely also on the data being the same as in the standard testing location(s). However, the (geographic) projection or data storage can be different. This is expected to be the most common case but it is much better if the tests is one of the previous categories (universal or standard names). If it is possible the functions or modules with tests in this category should have also tests which will fit into one of the previous categories, even though these additional tests will not be as precise as the other tests.

Location specific tests

Finally, there are tests which requires certain concrete location. There is (or will be) a set of standard testing locations each will have the same data (maps) but the projections and data storage types will be different. The suggested locations are: NC sample location in SPM projection, NC in SPF, NC in LL, NC in XY, and perhaps NC in UTM, and NC in some custom projection (in case of strange not-fitting projection, there is a danger that the results of analyses can differer significantly). Moreover, the set can be extended by GRASS locations which are using different storage backends, e.g. PostGIS for vectors and PostgreSQL for temporal database. Tests can specify one or preferably more of these standard locations.

Specialized location tests

Additionally, an specialized location with a collection of strange, incorrect, or generally extreme data will be provided. In theory, more than one location like this can be created if the data cannot be together in one location or if the location itself is somehow special, e.g. because of projection.

Each category, or perhaps each location (will) have a set of external data available for import or other purposes. The standardization of this data is in question and thus this may be specific to each location or this can be a separate resource common to all tests using one of the standardized locations, or alternatively this data can be associated with the location with special data.


The more general category you choose for your tests the more testing data can applied to your tests and the more different circumstances can be tried with your tests.

Data specific to one test

If the data required by the test are not part of standard location and cannot be part of the test file itself, this data should be stored in files in data subdirectory of testsuite directory. The test should access the data using a relative path from its location, i.e. all data will be accessed using data/.... This data directory might be used directly when running test file directly in the directory in the source code or might be copied to the test current working directory when running tests by the main test invoking tool.

Tests creating separate Mapsets, Locations and GRASS Databases

If test is creating a custom Mapset or Mapsets, it can create them in the current Location or create a custom GRASS Database in the current directory. In any case, test has to take care of cleaning up (deleting) the created directories and it has to use names which will be unique enough (name of the test case class or the file is probably a good choice but completely unique identifier is probably much better).

If test needs custom Location or it tests something related to GRASS Database, it must always create a new GRASS Database in the current directory.

In any case, the author must try the tests cautiously and several times in the same Location to see if everything works as expected. Testing framework is using Mapsets to separate the tests and the functions does not explicitly check for the case where a test is using different Mapset then the one which has been given to it by the framework.

Analyzing quality of source code

Besides testing, you can also use some tools to check the quality of your code according to various standards and occurrence of certain code patterns.

For C/C++ code we additionally use the third party solution Coverity Scan where GRASS GIS is registered as project number 1038. Also you can use Cppcheck which will show a lot of errors which compilers do not check. In any case, set your compiler to high error and warning levels, check them and fix them in your code. Furthermore, Travis-CI is used to check if the source code can still be compiled after submitting changes to the repository.

For Python, we recommend pylint and then for style issues pep8 tool (and perhaps also pep257 tool). However, there is more tools available you can use them together with the recommend ones.

To provide a way to evaluate the Python source code in the whole GRASS source tree there is a Python script grass_py_static_check.py which uses pylint and pep8 with GRASS-specific settings. Run the tool in GRASS session in the source code root directory. A HTML report will be created in pylint_report directory.



grass_py_static_check.py is available in sandbox.

Additionally, if you are invoking your Python code manually using python command, e.g. when testing, use parameters:

python -Qwarn -tt -3 some_module.py

This will warn you about usage of old division semantics for integers and about incompatibilities with Python 3 (if you are using Python 2) which 2to3 tool cannot fix. Finally, it will issue errors if are using tabs for indentation inconsistently (note that you should not use tabs for indentation at all).