When you install coverage.py, a command-line script simply called coverage is placed in your Python scripts directory. To help with multi-version installs, it will also create either a coverage2 or coverage3 alias, and a coverage-X.Y alias, depending on the version of Python you’re using. For example, when installing on Python 2.7, you will be able to use coverage, coverage2, or coverage-2.7 on the command line.

Coverage has a number of commands which determine the action performed:

  • run – Run a Python program and collect execution data.
  • report – Report coverage results.
  • html – Produce annotated HTML listings with coverage results.
  • xml – Produce an XML report with coverage results.
  • annotate – Annotate source files with coverage results.
  • erase – Erase previously collected coverage data.
  • combine – Combine together a number of data files.
  • debug – Get diagnostic information.

Help is available with the help command, or with the --help switch on any other command:

$ coverage help
$ coverage help run
$ coverage run --help

Version information for coverage.py can be displayed with coverage --version.

Any command can use a configuration file by specifying it with the --rcfile=FILE command-line switch. Any option you can set on the command line can also be set in the configuration file. This can be a better way to control coverage.py since the configuration file can be checked into source control, and can provide options that other invocation techniques (like test runner plugins) may not offer. See Configuration files for more details.

Execution

You collect execution data by running your Python program with the run command:

$ coverage run my_program.py arg1 arg2
blah blah ..your program's output.. blah blah

Your program runs just as if it had been invoked with the Python command line. Arguments after your file name are passed to your program as usual in sys.argv. Rather than providing a filename, you can use the -m switch and specify an importable module name instead, just as you can with the Python -m switch:

$ coverage run -m packagename.modulename arg1 arg2
blah blah ..your program's output.. blah blah

If you want branch coverage measurement, use the --branch flag. Otherwise only statement coverage is measured.

You can specify the code to measure with the --source, --include, and --omit switches. See Specifying source files for details of their interpretation. Remember to put options for run after “run”, but before the program invocation:

$ coverage run --source=dir1,dir2 my_program.py arg1 arg2
$ coverage run --source=dir1,dir2 -m packagename.modulename arg1 arg2

By default, coverage does not measure code installed with the Python interpreter, for example, the standard library. If you want to measure that code as well as your own, add the -L flag.

If your coverage results seem to be overlooking code that you know has been executed, try running coverage again with the --timid flag. This uses a simpler but slower trace method. Projects that use DecoratorTools, including TurboGears, will need to use --timid to get correct results. This option can also be enabled by setting the environment variable COVERAGE_OPTIONS to --timid.

If you are measuring coverage in a multi-process program, or across a number of machines, you’ll want the --parallel-mode switch to keep the data separate during measurement. See Combining data files below.

During execution, coverage.py may warn you about conditions it detects that could affect the measurement process. The possible warnings include:

  • “Trace function changed, measurement is likely wrong: XXX”

    Coverage measurement depends on a Python setting called the trace function. Other Python code in your product might change that function, which will disrupt coverage.py’s measurement. This warning indicate that has happened. The XXX in the message is the new trace function value, which might provide a clue to the cause.

  • “Module XXX has no Python source”

    You asked coverage.py to measure module XXX, but once it was imported, it turned out not to have a corresponding .py file. Without a .py file, coverage.py can’t report on missing lines.

  • “Module XXX was never imported”

    You asked coverage.py to measure module XXX, but it was never imported by your program.

  • “No data was collected”

    Coverage.py ran your program, but didn’t measure any lines as executed. This could be because you asked to measure only modules that never ran, or for other reasons.

The --debug option instructs coverage to log internal details of its operation, to help with diagnosing problems. It takes a comma-separated list of options, each indicating a facet of operation to log to stderr:

  • trace: print every decision about whether to trace a file or not. For files not being traced, the reason is also given.
  • config: before starting, dump all the configuration values.
  • sys: before starting, dump all the system and environment information, as with coverage debug sys.
  • dataio: log when reading or writing any data file.
  • pid: annotate all debug output with the process id.

Data file

Coverage collects execution data in a file called ”.coverage”. If need be, you can set a new file name with the COVERAGE_FILE environment variable.

By default,each run of your program starts with an empty data set. If you need to run your program multiple times to get complete data (for example, because you need to supply disjoint options), you can accumulate data across runs with the -a flag on the run command.

To erase the collected data, use the erase command:

$ coverage erase

Combining data files

If you need to collect coverage data from different machines or processes, coverage can combine multiple files into one for reporting. Use the -p flag during execution to append distinguishing information to the .coverage data file name.

Once you have created a number of these files, you can copy them all to a single directory, and use the combine command to combine them into one .coverage data file:

$ coverage combine

If the different machines run your code from different places in their file systems, coverage won’t know how to combine the data. You can tell coverage how the different locations correlate with a [paths] section in your configuration file. See [paths] for details.

If you are collecting and renaming your own data files, you’ll need to name them properly for combine to find them. It looks for files named after the data file (defaulting to ”.coverage”, overridable with COVERAGE_FILE), with a dotted suffix. All such files in the current directory will be combined. Here are some examples of combinable data files:

.coverage.machine1
.coverage.20120807T212300
.coverage.last_good_run.ok

Reporting

Coverage provides a few styles of reporting, with the report, html, annotate, and xml commands. They share a number of common options.

The command-line arguments are module or file names to report on, if you’d like to report on a subset of the data collected.

The --include and --omit flags specify lists of filename patterns. They control which files to report on, and are described in more detail in Specifying source files.

The -i or --ignore-errors switch tells coverage.py to ignore problems encountered trying to find source files to report on. This can be useful if some files are missing, or if your Python execution is tricky enough that file names are synthesized without real source files.

If you provide a --fail-under value, the total percentage covered will be compared to that value. If it is less, the command will exit with a status code of 2, indicating that the total coverage was less than your target. This can be used as part of a pass/fail condition, for example in a continuous integration server. This option isn’t available for annotate.

Coverage summary

The simplest reporting is a textual summary produced with report:

$ coverage report
Name                      Stmts   Miss  Cover
---------------------------------------------
my_program                   20      4    80%
my_module                    15      2    86%
my_other_module              56      6    89%
---------------------------------------------
TOTAL                        91     12    87%

For each module executed, the report shows the count of executable statements, the number of those statements missed, and the resulting coverage, expressed as a percentage.

The -m flag also shows the line numbers of missing statements:

$ coverage report -m
Name                      Stmts   Miss  Cover   Missing
-------------------------------------------------------
my_program                   20      4    80%   33-35, 39
my_module                    15      2    86%   8, 12
my_other_module              56      6    89%   17-23
-------------------------------------------------------
TOTAL                        91     12    87%

You can restrict the report to only certain files by naming them on the command line:

$ coverage report -m my_program.py my_other_module.py
Name                      Stmts   Miss  Cover   Missing
-------------------------------------------------------
my_program                   20      4    80%   33-35, 39
my_other_module              56      6    89%   17-23
-------------------------------------------------------
TOTAL                        76     10    87%

Other common reporting options are described above in Reporting.

HTML annotation

Coverage can annotate your source code for which lines were executed and which were not. The html command creates an HTML report similar to the report summary, but as an HTML file. Each module name links to the source file decorated to show the status of each line.

Here’s a sample report.

Lines are highlighted green for executed, red for missing, and gray for excluded. The counts at the top of the file are buttons to turn on and off the highlighting.

A number of keyboard shortcuts are available for navigating the report. Click the keyboard icon in the upper right to see the complete list.

The title of the report can be set with the title setting in the [html] section of the configuration file, or the --title switch on the command line.

If you prefer a different style for your HTML report, you can provide your own CSS file to apply, by specifying a CSS file in the [html] section of the configuration file. See [html] for details.

The -d argument specifies an output directory, defaulting to “htmlcov”:

$ coverage html -d coverage_html

Other common reporting options are described above in Reporting.

Generating the HTML report can be time-consuming. Stored with the HTML report is a data file that is used to speed up reporting the next time. If you generate a new report into the same directory, coverage.py will skip generating unchanged pages, making the process faster.

Text annotation

The annotate command produces a text annotation of your source code. With a -d argument specifying an output directory, each Python file becomes a text file in that directory. Without -d, the files are written into the same directories as the original Python files.

Coverage status for each line of source is indicated with a character prefix:

> executed
! missing (not executed)
- excluded

For example:

# A simple function, never called with x==1

> def h(x):
      """Silly function."""
-     if 0:   #pragma: no cover
-         pass
>     if x == 1:
!         a = 1
>     else:
>         a = 2

Other common reporting options are described above in Reporting.

XML reporting

The xml command writes coverage data to a “coverage.xml” file in a format compatible with Cobertura.

You can specify the name of the output file with the -o switch.

Other common reporting options are described above in Reporting.

Diagnostics

The debug command shows internal information to help diagnose problems. If you are reporting a bug about coverage.py, including the output of this command can often help:

$ coverage debug sys > please_attach_to_bug_report.txt

Two types of information are available: sys to show system configuration, and data to show a summary of the collected coverage data.

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