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C coverage tool
This directory contains a tool to generate C code-coverage reports
using valgrind's callgrind tool.
* `Valgrind <>`_ (3.5.0 tested, earlier
versions may work)
* `Pygments <>`_ (0.11 or later required)
C code-coverage
Generating C code coverage reports requires two steps:
* Collecting coverage results (from valgrind)
* Generating a report from one or more sets of results
For most cases, it is good enough to do::
> python -c "import numpy; numpy.test()"
which will run all of the Numpy unit tests, create a directory called
`coverage` and place the coverage results there.
In a more advanced scenario, you may wish to run individual unit tests
(since running under valgrind slows things down) and combine multiple
results files together in a single report.
Collecting results
To collect coverage results, you merely run the python interpreter
under valgrind's callgrind tool. The `` helper
script will pass all of the required arguments to valgrind.
For example, in typical usage, you may want to run all of the Numpy
unit tests::
> python -c "import numpy; numpy.test()"
This will output a file ```` containing the results of
the run, where ``pid`` is the process id of the run.
Generating a report
To generate a report, you pass the ```` output file to
the `` script::
To combine multiple results files together, simply list them on the
commandline or use wildcards::
> callgrind.out.*
* ``--directory``: Specify a different output directory
* ``--pattern``: Specify a regex pattern to match for source files.
The default is `numpy`, so it will only include source files whose
path contains the string `numpy`. If, for instance, you wanted to
include all source files covered (that are available on your
system), pass ``--pattern=.``.
* ``--format``: Specify the output format(s) to generate. May be
either ``text`` or ``html``. If ``--format`` is not provided,
both formats will be output.
Reading a report
The C code coverage report is a flat directory of files, containing
text and/or html files. The files are named based on their path in
the original source tree with slashes converted to underscores.
Text reports
The text reports add a prefix to each line of source code:
- '>' indicates the line of code was run
- '!' indicates the line of code was not run
HTML reports
The HTML report highlights the code that was run in green.
The HTML report has special support for the "generated" functions in
Numpy. Each run line of code also contains a number in square
brackets indicating the number of different generated functions the
line was run in. Hovering the mouse over the line will display a list
of the versions of the function in which the line was run. These
numbers can be used to see if a particular line was run in all
versions of the function.
The coverage results occasionally misses lines that clearly must have
been run. This usually can be traced back to the compiler optimizer
removing lines because they are tautologically impossible or to
combine lines together. Compiling Numpy without optimizations helps,
but not completely. Even despite this flaw, this tool is still
helpful in identifying large missed blocks or functions.