

PROSPECTOR


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About

Prospector is a tool to analyse Python code and output information about
errors, potential problems, convention violations and complexity.

It brings together the functionality of other Python analysis tools such
as Pylint, pep8, and McCabe complexity. See the
Supported Tools <http://prospector.landscape.io/en/master/supported_tools.html>
documentation section for a complete list.

The primary aim of Prospector is to be useful 'out of the box'. A common
complaint of other Python analysis tools is that it takes a long time to
filter through which errors are relevant or interesting to your own
coding style. Prospector provides some default profiles, which hopefully
will provide a good starting point and will be useful straight away, and
adapts the output depending on the libraries your project uses.


Installation

Prospector can be installed using pip by running the following command:

    pip install prospector

Optional dependencies for Prospector, such as pyroma can also be
installed by running:

    pip install prospector[with_pyroma]

For a list of all of the optional dependencies, see the optional extras
section on the ReadTheDocs page on supported tools.

For more detailed information on installing the tool, see the
installation section of the tool's main page on ReadTheDocs.


Documentation

Full documentation is available at ReadTheDocs.


Usage

Simply run prospector from the root of your project:

    prospector

This will output a list of messages pointing out potential problems or
errors, for example:

    prospector.tools.base (prospector/tools/base.py):
        L5:0 ToolBase: pylint - R0922
        Abstract class is only referenced 1 times

Options

Run prospector --help for a full list of options and their effects.

Output Format

The default output format of prospector is designed to be human
readable. For parsing (for example, for reporting), you can use the
--output-format json flag to get JSON-formatted output.

Profiles

Prospector is configurable using "profiles". These are composable YAML
files with directives to disable or enable tools or messages. For more
information, read the documentation about profiles.

If your code uses frameworks and libraries

Often tools such as pylint find errors in code which is not an error,
for example due to attributes of classes being created at run time by a
library or framework used by your project. For example, by default,
pylint will generate an error for Django models when accessing objects,
as the objects attribute is not part of the Model class definition.

Prospector mitigates this by providing an understanding of these
frameworks to the underlying tools.

Prospector will try to intuit which libraries your project uses by
detecting dependencies and automatically turning on support for the
requisite libraries. You can see which adaptors were run in the metadata
section of the report.

If Prospector does not correctly detect your project's dependencies, you
can specify them manually from the commandline:

    prospector --uses django celery

Additionally, if Prospector is automatically detecting a library that
you do not in fact use, you can turn off autodetection completely:

    prospector --no-autodetect

Note that as far as possible, these adaptors have been written as
plugins or augmentations for the underlying tools so that they can be
used without requiring Prospector. For example, the Django support is
available as a pylint plugin.

Strictness

Prospector has a configurable 'strictness' level which will determine
how harshly it searches for errors:

    prospector --strictness high

Possible values are verylow, low, medium, high, veryhigh.

Prospector does not include documentation warnings by default, but you
can turn this on using the --doc-warnings flag.


License

Prospector is available under the GPLv2 License.
