Question

介绍

Writing tests for the numpy.i SWIGopen in new window interface file is a combinatorial headache. At present, 12 different data types are supported, each with 74 different argument signatures, for a total of 888 typemaps supported “out of the box”. Each of these typemaps, in turn, might require several unit tests in order to verify expected behavior for both proper and improper inputs. Currently, this results in more than 1,000 individual unit tests executed when make test is run in the numpy/tools/swig subdirectory.

To facilitate this many similar unit tests, some high-level programming techniques are employed, including C and SWIGopen in new window macros, as well as Python inheritance. The purpose of this document is to describe the testing infrastructure employed to verify that the numpy.i typemaps are working as expected.

Testing Organization

There are three independent testing frameworks supported, for one-, two-, and three-dimensional arrays respectively. For one-dimensional arrays, there are two C++ files, a header and a source, named:

Vector.h
Vector.cxx

that contain prototypes and code for a variety of functions that have one-dimensional arrays as function arguments. The file:

Vector.i

is a SWIGopen in new window interface file that defines a python module Vector that wraps the functions in Vector.h while utilizing the typemaps in numpy.i to correctly handle the C arrays.

The Makefile calls swig to generate Vector.py and Vector_wrap.cxx, and also executes the setup.py script that compiles Vector_wrap.cxx and links together the extension module _Vector.so or _Vector.dylib, depending on the platform. This extension module and the proxy file Vector.py are both placed in a subdirectory under the build directory.

The actual testing takes place with a Python script named:

testVector.py

that uses the standard Python library module unittest, which performs several tests of each function defined in Vector.h for each data type supported.

Two-dimensional arrays are tested in exactly the same manner. The above description applies, but with Matrix substituted for Vector. For three-dimensional tests, substitute Tensor for Vector. For four-dimensional tests, substitute SuperTensor for Vector. For flat in-place array tests, substitute Flat for Vector. For the descriptions that follow, we will reference the Vector tests, but the same information applies to Matrix, Tensor and SuperTensor tests.

The command make test will ensure that all of the test software is built and then run all three test scripts.

Testing Header Files

Vector.h is a C++ header file that defines a C macro called TEST_FUNC_PROTOS that takes two arguments: TYPE, which is a data type name such as unsigned int; and SNAME, which is a short name for the same data type with no spaces, e.g. uint. This macro defines several function prototypes that have the prefix SNAME and have at least one argument that is an array of type TYPE. Those functions that have return arguments return a TYPE value.

TEST_FUNC_PROTOS is then implemented for all of the data types supported by numpy.i:

• signed char
• unsigned char
• short
• unsigned short
• int
• unsigned int
• long
• unsigned long
• long long
• unsigned long long
• float
• double

Testing Source Files

Vector.cxx is a C++ source file that implements compilable code for each of the function prototypes specified in Vector.h. It defines a C macro TEST_FUNCS that has the same arguments and works in the same way as TEST_FUNC_PROTOS does in Vector.h. TEST_FUNCS is implemented for each of the 12 data types as above.

Testing SWIG Interface Files

Vector.i is a SWIGopen in new window interface file that defines python module Vector. It follows the conventions for using numpy.i as described in this chapter. It defines a SWIGopen in new window macro %apply_numpy_typemaps that has a single argument TYPE. It uses the SWIGopen in new window directive %apply to apply the provided typemaps to the argument signatures found in Vector.h. This macro is then implemented for all of the data types supported by numpy.i. It then does a %include "Vector.h" to wrap all of the function prototypes in Vector.h using the typemaps in numpy.i.

Testing Python Scripts

After make is used to build the testing extension modules, testVector.py can be run to execute the tests. As with other scripts that use unittest to facilitate unit testing, testVector.py defines a class that inherits from unittest.TestCase:

class VectorTestCase(unittest.TestCase):

However, this class is not run directly. Rather, it serves as a base class to several other python classes, each one specific to a particular data type. The VectorTestCase class stores two strings for typing information:

• self.typeStr

  A string that matches one of the SNAME prefixes used in Vector.h and Vector.cxx. For example, "double".

• self.typeCode

  A short (typically single-character) string that represents a data type in numpy and corresponds to self.typeStr. For example, if self.typeStr is "double", then self.typeCode should be "d".

Each test defined by the VectorTestCase class extracts the python function it is trying to test by accessing the Vector module’s dictionary:

length = Vector.__dict__[self.typeStr + "Length"]

In the case of double precision tests, this will return the python function Vector.doubleLength.

We then define a new test case class for each supported data type with a short definition such as:

class doubleTestCase(VectorTestCase):
    def __init__(self, methodName="runTest"):
        VectorTestCase.__init__(self, methodName)
        self.typeStr  = "double"
        self.typeCode = "d"

Each of these 12 classes is collected into a unittest.TestSuite, which is then executed. Errors and failures are summed together and returned as the exit argument. Any non-zero result indicates that at least one test did not pass.