Welcome to coala API Documentation

_images/coala-header.png

Hey there! You’re in the right place if you:

  • want to develop coala itself!
  • want to develop a bear for coala.

If you’re trying to use coala, you should have a look at our user documentation instead.

coalib package

Subpackages

coalib.bearlib package

Subpackages
coalib.bearlib.abstractions package
Submodules
coalib.bearlib.abstractions.ExternalBearWrap module
coalib.bearlib.abstractions.ExternalBearWrap.external_bear_wrap(executable: str, **options)[source]
coalib.bearlib.abstractions.Linter module
coalib.bearlib.abstractions.Linter.linter(executable: str, global_bear: bool = False, use_stdin: bool = False, use_stdout: bool = True, use_stderr: bool = False, normalize_line_numbers: bool = False, normalize_column_numbers: bool = False, config_suffix: str = '', executable_check_fail_info: str = '', prerequisite_check_command: tuple = (), output_format: (<class 'str'>, None) = None, strip_ansi: bool = False, **options)[source]

Decorator that creates a Bear that is able to process results from an external linter tool. Depending on the value of global_bear this can either be a LocalBear or a GlobalBear.

The main functionality is achieved through the create_arguments() function that constructs the command-line-arguments that get passed to your executable.

>>> @linter('xlint', output_format='regex', output_regex='...')
... class XLintBear:
...     @staticmethod
...     def create_arguments(filename, file, config_file):
...         return '--lint', filename

Or for a GlobalBear without the filename and file:

>>> @linter('ylint',
...         global_bear=True,
...         output_format='regex',
...         output_regex='...')
... class YLintBear:
...     def create_arguments(self, config_file):
...         return '--lint', self.file_dict.keys()

Requiring settings is possible like in Bear.run() with supplying additional keyword arguments (and if needed with defaults).

>>> @linter('xlint', output_format='regex', output_regex='...')
... class XLintBear:
...     @staticmethod
...     def create_arguments(filename,
...                          file,
...                          config_file,
...                          lintmode: str,
...                          enable_aggressive_lints: bool=False):
...         arguments = ('--lint', filename, '--mode=' + lintmode)
...         if enable_aggressive_lints:
...             arguments += ('--aggressive',)
...         return arguments

Sometimes your tool requires an actual file that contains configuration. linter allows you to just define the contents the configuration shall contain via generate_config() and handles everything else for you.

>>> @linter('xlint', output_format='regex', output_regex='...')
... class XLintBear:
...     @staticmethod
...     def generate_config(filename,
...                         file,
...                         lintmode,
...                         enable_aggressive_lints):
...         modestring = ('aggressive'
...                       if enable_aggressive_lints else
...                       'non-aggressive')
...         contents = ('<xlint>',
...                     '    <mode>' + lintmode + '</mode>',
...                     '    <aggressive>' + modestring + '</aggressive>',
...                     '</xlint>')
...         return '\n'.join(contents)
...
...     @staticmethod
...     def create_arguments(filename,
...                          file,
...                          config_file):
...         return '--lint', filename, '--config', config_file

As you can see you don’t need to copy additional keyword-arguments you introduced from create_arguments() to generate_config() and vice-versa. linter takes care of forwarding the right arguments to the right place, so you are able to avoid signature duplication.

If you override process_output, you have the same feature like above (auto-forwarding of the right arguments defined in your function signature).

Note when overriding process_output: Providing a single output stream (via use_stdout or use_stderr) puts the according string attained from the stream into parameter output, providing both output streams inputs a tuple with (stdout, stderr). Providing use_stdout=False and use_stderr=False raises a ValueError. By default use_stdout is True and use_stderr is False.

Every linter is also a subclass of the LinterClass class.

>>> issubclass(XLintBear, LinterClass)
True

Documentation: Bear description shall be provided at class level. If you document your additional parameters inside create_arguments, generate_config and process_output, beware that conflicting documentation between them may be overridden. Document duplicated parameters inside create_arguments first, then in generate_config and after that inside process_output.

For the tutorial see: http://api.coala.io/en/latest/Developers/Writing_Linter_Bears.html

Parameters:
  • executable – The linter tool.
  • use_stdin – Whether the input file is sent via stdin instead of passing it over the command-line-interface.
  • use_stdout – Whether to use the stdout output stream. Incompatible with global_bear=True.
  • use_stderr – Whether to use the stderr output stream.
  • normalize_line_numbers – Whether to normalize line numbers (increase by one) to fit coala’s one-based convention.
  • normalize_column_numbers – Whether to normalize column numbers (increase by one) to fit coala’s one-based convention.
  • config_suffix – The suffix-string to append to the filename of the configuration file created when generate_config is supplied. Useful if your executable expects getting a specific file-type with specific file-ending for the configuration file.
  • executable_check_fail_info – Information that is provided together with the fail message from the normal executable check. By default no additional info is printed.
  • prerequisite_check_command – A custom command to check for when check_prerequisites gets invoked (via subprocess.check_call()). Must be an Iterable.
  • prerequisite_check_fail_message – A custom message that gets displayed when check_prerequisites fails while invoking prerequisite_check_command. Can only be provided together with prerequisite_check_command.
  • global_bear – Whether the created bear should be a GlobalBear or not. Global bears will be run once on the whole project, instead of once per file. Incompatible with use_stdin=True.
  • output_format –

    The output format of the underlying executable. Valid values are

    • None: Define your own format by overriding process_output. Overriding process_output is then mandatory, not specifying it raises a ValueError.
    • 'regex': Parse output using a regex. See parameter output_regex.
    • 'corrected': The output is the corrected of the given file. Diffs are then generated to supply patches for results.
    • 'unified-diff': The output is the unified diff of the corrections. Patches are then supplied for results using this output.

    Passing something else raises a ValueError.

  • output_regex –

    The regex expression as a string that is used to parse the output generated by the underlying executable. It should use as many of the following named groups (via (?P<name>...)) to provide a good result:

    • filename - The name of the linted file. This is relevant for
      global bears only.
    • line - The line where the issue starts.
    • column - The column where the issue starts.
    • end_line - The line where the issue ends.
    • end_column - The column where the issue ends.
    • severity - The severity of the issue.
    • message - The message of the result.
    • origin - The origin of the issue.
    • additional_info - Additional info provided by the issue.

    The groups line, column, end_line and end_column don’t have to match numbers only, they can also match nothing, the generated Result is filled automatically with None then for the appropriate properties.

    Needs to be provided if output_format is 'regex'.

  • severity_map –

    A dict used to map a severity string (captured from the output_regex with the named group severity) to an actual coalib.results.RESULT_SEVERITY for a result. Severity strings are mapped case-insensitive!

    • RESULT_SEVERITY.MAJOR: Mapped by critical, c, fatal, fail, f, error, err or e.
    • RESULT_SEVERITY.NORMAL: Mapped by warning, warn or w.
    • RESULT_SEVERITY.INFO: Mapped by information, info, i, note or suggestion.

    A ValueError is raised when the named group severity is not used inside output_regex and this parameter is given.

  • diff_severity – The severity to use for all results if output_format is 'corrected' or 'unified-diff'. By default this value is coalib.results.RESULT_SEVERITY.NORMAL. The given value needs to be defined inside coalib.results.RESULT_SEVERITY.
  • result_message – The message-string to use for all results. Can be used only together with corrected or unified-diff or regex output format. When using corrected or unified-diff, the default value is 'Inconsistency found.', while for regex this static message is disabled and the message matched by output_regex is used instead.
  • diff_distance – Number of unchanged lines that are allowed in between two changed lines so they get yielded as one diff if corrected or unified-diff output-format is given. If a negative distance is given, every change will be yielded as an own diff, even if they are right beneath each other. By default this value is 1.
  • strip_ansi – Supresses colored output from linters when enabled by stripping the ascii characters around the text.
Raises:
  • ValueError – Raised when invalid options are supplied.
  • TypeError – Raised when incompatible types are supplied. See parameter documentations for allowed types.
Returns:

A LocalBear derivation that lints code using an external tool.
coalib.bearlib.abstractions.LinterClass module
class coalib.bearlib.abstractions.LinterClass.LinterClass[source]

Bases: object

Every linter is also a subclass of the LinterClass class.

coalib.bearlib.abstractions.SectionCreatable module
class coalib.bearlib.abstractions.SectionCreatable.SectionCreatable[source]

Bases: object

A SectionCreatable is an object that is creatable out of a section object. Thus this is the class for many helper objects provided by the bearlib.

If you want to use an object that inherits from this class the following approach is recommended: Instantiate it via the from_section method. You can provide default arguments via the lower case keyword arguments.

Example:

SpacingHelper.from_section(section, tabwidth=8)

creates a SpacingHelper and if the “tabwidth” setting is needed and not contained in section, 8 will be taken.

It is recommended to write the prototype of the __init__ method according to this example:

def __init__(self, setting_one: int, setting_two: bool=False):
    pass  # Implementation

This way the get_optional_settings and the get_non_optional_settings method will extract automatically that:

  • setting_one should be an integer
  • setting_two should be a bool and defaults to False

If you write a documentation comment, you can use :param to add descriptions to your parameters. These will be available too automatically.

classmethod from_section(section, **kwargs)[source]

Creates the object from a section object.

Parameters:
  • section – A section object containing at least the settings specified by get_non_optional_settings()
  • kwargs – Additional keyword arguments
classmethod get_metadata()[source]
classmethod get_non_optional_settings()[source]

Retrieves the minimal set of settings that need to be defined in order to use this object.

Returns:a dictionary of needed settings as keys and help texts as values
classmethod get_optional_settings()[source]

Retrieves the settings needed IN ADDITION to the ones of get_non_optional_settings to use this object without internal defaults.

Returns:a dictionary of needed settings as keys and help texts as values
Module contents

The abstractions package contains classes that serve as interfaces for helper classes in the bearlib.

coalib.bearlib.aspects package
Submodules
coalib.bearlib.aspects.Formatting module
class coalib.bearlib.aspects.Formatting.BlankLine(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.BlankLine, coalib.bearlib.aspects.base.aspectbase

class BlankLineAfterClass(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterClass, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterDeclaration(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterProcedure(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class NewlineAtEOF(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.NewlineAtEOF, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {'BlankLineAfterProcedure': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterProcedure'>, 'BlankLineAfterDeclaration': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterDeclaration'>, 'BlankLineAfterClass': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterClass'>, 'NewlineAtEOF': <aspectclass 'Root.Formatting.Spacing.BlankLine.NewlineAtEOF'>}
class coalib.bearlib.aspects.Formatting.BlankLineAfterClass(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterClass, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class coalib.bearlib.aspects.Formatting.FileLength(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.FileLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
class coalib.bearlib.aspects.Formatting.Formatting(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.Formatting, coalib.bearlib.aspects.base.aspectbase

class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Length, coalib.bearlib.aspects.base.aspectbase

class FileLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.FileLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
class LineLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.LineLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'FileLength': <aspectclass 'Root.Formatting.Length.FileLength'>, 'LineLength': <aspectclass 'Root.Formatting.Length.LineLength'>}
class Quotation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Quotation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {}
class Spacing(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Spacing, coalib.bearlib.aspects.base.aspectbase

class BlankLine(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLine, coalib.bearlib.aspects.base.aspectbase

class BlankLineAfterClass(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterClass, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterDeclaration(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterProcedure(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class NewlineAtEOF(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.NewlineAtEOF, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {'BlankLineAfterProcedure': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterProcedure'>, 'BlankLineAfterDeclaration': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterDeclaration'>, 'BlankLineAfterClass': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterClass'>, 'NewlineAtEOF': <aspectclass 'Root.Formatting.Spacing.BlankLine.NewlineAtEOF'>}
class Indentation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Indentation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class SpacesAroundOperator(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.SpacesAroundOperator, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class TrailingSpace(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.TrailingSpace, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'TrailingSpace': <aspectclass 'Root.Formatting.Spacing.TrailingSpace'>, 'SpacesAroundOperator': <aspectclass 'Root.Formatting.Spacing.SpacesAroundOperator'>, 'BlankLine': <aspectclass 'Root.Formatting.Spacing.BlankLine'>, 'Indentation': <aspectclass 'Root.Formatting.Spacing.Indentation'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'Quotation': <aspectclass 'Root.Formatting.Quotation'>, 'Spacing': <aspectclass 'Root.Formatting.Spacing'>, 'Length': <aspectclass 'Root.Formatting.Length'>}
class coalib.bearlib.aspects.Formatting.Indentation(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.Indentation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class coalib.bearlib.aspects.Formatting.Length(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.Length, coalib.bearlib.aspects.base.aspectbase

class FileLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.FileLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
class LineLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.LineLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'FileLength': <aspectclass 'Root.Formatting.Length.FileLength'>, 'LineLength': <aspectclass 'Root.Formatting.Length.LineLength'>}
class coalib.bearlib.aspects.Formatting.LineLength(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.LineLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
class coalib.bearlib.aspects.Formatting.NewlineAtEOF(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.NewlineAtEOF, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class coalib.bearlib.aspects.Formatting.Quotation(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.Quotation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {}
class coalib.bearlib.aspects.Formatting.SpacesAroundOperator(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.SpacesAroundOperator, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class coalib.bearlib.aspects.Formatting.Spacing(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.Spacing, coalib.bearlib.aspects.base.aspectbase

class BlankLine(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLine, coalib.bearlib.aspects.base.aspectbase

class BlankLineAfterClass(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterClass, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterDeclaration(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterProcedure(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class NewlineAtEOF(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.NewlineAtEOF, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {'BlankLineAfterProcedure': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterProcedure'>, 'BlankLineAfterDeclaration': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterDeclaration'>, 'BlankLineAfterClass': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterClass'>, 'NewlineAtEOF': <aspectclass 'Root.Formatting.Spacing.BlankLine.NewlineAtEOF'>}
class Indentation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Indentation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class SpacesAroundOperator(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.SpacesAroundOperator, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class TrailingSpace(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.TrailingSpace, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'TrailingSpace': <aspectclass 'Root.Formatting.Spacing.TrailingSpace'>, 'SpacesAroundOperator': <aspectclass 'Root.Formatting.Spacing.SpacesAroundOperator'>, 'BlankLine': <aspectclass 'Root.Formatting.Spacing.BlankLine'>, 'Indentation': <aspectclass 'Root.Formatting.Spacing.Indentation'>}
class coalib.bearlib.aspects.Formatting.TrailingSpace(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Formatting.TrailingSpace, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
coalib.bearlib.aspects.Metadata module
class coalib.bearlib.aspects.Metadata.Body(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Body, coalib.bearlib.aspects.base.aspectbase

class Existence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Existence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'Length': <aspectclass 'Root.Metadata.CommitMessage.Body.Length'>, 'Existence': <aspectclass 'Root.Metadata.CommitMessage.Body.Existence'>}
class coalib.bearlib.aspects.Metadata.ColonExistence(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.ColonExistence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class coalib.bearlib.aspects.Metadata.CommitMessage(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.CommitMessage, coalib.bearlib.aspects.base.aspectbase

class Body(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Body, coalib.bearlib.aspects.base.aspectbase

class Existence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Existence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'Length': <aspectclass 'Root.Metadata.CommitMessage.Body.Length'>, 'Existence': <aspectclass 'Root.Metadata.CommitMessage.Body.Existence'>}
class Emptiness(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Emptiness, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {}
class Shortlog(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Shortlog, coalib.bearlib.aspects.base.aspectbase

class ColonExistence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.ColonExistence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class FirstCharacter(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.FirstCharacter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Tense(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Tense, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class TrailingPeriod(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.TrailingPeriod, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'TrailingPeriod': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.TrailingPeriod'>, 'ColonExistence': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.ColonExistence'>, 'FirstCharacter': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.FirstCharacter'>, 'Length': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Length'>, 'Tense': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Tense'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Metadata

subaspects = {'Shortlog': <aspectclass 'Root.Metadata.CommitMessage.Shortlog'>, 'Body': <aspectclass 'Root.Metadata.CommitMessage.Body'>, 'Emptiness': <aspectclass 'Root.Metadata.CommitMessage.Emptiness'>}
class coalib.bearlib.aspects.Metadata.Emptiness(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Emptiness, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {}
class coalib.bearlib.aspects.Metadata.Existence(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Existence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class coalib.bearlib.aspects.Metadata.FirstCharacter(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.FirstCharacter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class coalib.bearlib.aspects.Metadata.Length(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class coalib.bearlib.aspects.Metadata.Metadata(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Metadata, coalib.bearlib.aspects.base.aspectbase

class CommitMessage(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.CommitMessage, coalib.bearlib.aspects.base.aspectbase

class Body(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Body, coalib.bearlib.aspects.base.aspectbase

class Existence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Existence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'Length': <aspectclass 'Root.Metadata.CommitMessage.Body.Length'>, 'Existence': <aspectclass 'Root.Metadata.CommitMessage.Body.Existence'>}
class Emptiness(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Emptiness, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {}
class Shortlog(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Shortlog, coalib.bearlib.aspects.base.aspectbase

class ColonExistence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.ColonExistence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class FirstCharacter(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.FirstCharacter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Tense(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Tense, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class TrailingPeriod(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.TrailingPeriod, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'TrailingPeriod': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.TrailingPeriod'>, 'ColonExistence': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.ColonExistence'>, 'FirstCharacter': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.FirstCharacter'>, 'Length': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Length'>, 'Tense': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Tense'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Metadata

subaspects = {'Shortlog': <aspectclass 'Root.Metadata.CommitMessage.Shortlog'>, 'Body': <aspectclass 'Root.Metadata.CommitMessage.Body'>, 'Emptiness': <aspectclass 'Root.Metadata.CommitMessage.Emptiness'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'CommitMessage': <aspectclass 'Root.Metadata.CommitMessage'>}
class coalib.bearlib.aspects.Metadata.Shortlog(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Shortlog, coalib.bearlib.aspects.base.aspectbase

class ColonExistence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.ColonExistence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class FirstCharacter(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.FirstCharacter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Tense(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Tense, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class TrailingPeriod(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.TrailingPeriod, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'TrailingPeriod': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.TrailingPeriod'>, 'ColonExistence': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.ColonExistence'>, 'FirstCharacter': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.FirstCharacter'>, 'Length': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Length'>, 'Tense': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Tense'>}
class coalib.bearlib.aspects.Metadata.Tense(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.Tense, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class coalib.bearlib.aspects.Metadata.TrailingPeriod(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Metadata.TrailingPeriod, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
coalib.bearlib.aspects.Redundancy module
class coalib.bearlib.aspects.Redundancy.Clone(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.Clone, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class coalib.bearlib.aspects.Redundancy.Redundancy(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.Redundancy, coalib.bearlib.aspects.base.aspectbase

class Clone(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.Clone, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class UnreachableCode(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableCode, coalib.bearlib.aspects.base.aspectbase

class UnreachableStatement(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableStatement, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
class UnusedFunction(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedFunction, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedFunction': <aspectclass 'Root.Redundancy.UnreachableCode.UnusedFunction'>, 'UnreachableStatement': <aspectclass 'Root.Redundancy.UnreachableCode.UnreachableStatement'>}
class UnusedImport(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedImport, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class UnusedVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedVariable, coalib.bearlib.aspects.base.aspectbase

class UnusedGlobalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedGlobalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedLocalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedLocalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedParameter(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedParameter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedParameter': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedParameter'>, 'UnusedLocalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedLocalVariable'>, 'UnusedGlobalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedGlobalVariable'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'UnreachableCode': <aspectclass 'Root.Redundancy.UnreachableCode'>, 'UnusedImport': <aspectclass 'Root.Redundancy.UnusedImport'>, 'UnusedVariable': <aspectclass 'Root.Redundancy.UnusedVariable'>, 'Clone': <aspectclass 'Root.Redundancy.Clone'>}
class coalib.bearlib.aspects.Redundancy.UnreachableCode(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnreachableCode, coalib.bearlib.aspects.base.aspectbase

class UnreachableStatement(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableStatement, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
class UnusedFunction(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedFunction, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedFunction': <aspectclass 'Root.Redundancy.UnreachableCode.UnusedFunction'>, 'UnreachableStatement': <aspectclass 'Root.Redundancy.UnreachableCode.UnreachableStatement'>}
class coalib.bearlib.aspects.Redundancy.UnreachableStatement(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnreachableStatement, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
class coalib.bearlib.aspects.Redundancy.UnusedFunction(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnusedFunction, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
class coalib.bearlib.aspects.Redundancy.UnusedGlobalVariable(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnusedGlobalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class coalib.bearlib.aspects.Redundancy.UnusedImport(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnusedImport, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class coalib.bearlib.aspects.Redundancy.UnusedLocalVariable(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnusedLocalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class coalib.bearlib.aspects.Redundancy.UnusedParameter(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnusedParameter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class coalib.bearlib.aspects.Redundancy.UnusedVariable(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Redundancy.UnusedVariable, coalib.bearlib.aspects.base.aspectbase

class UnusedGlobalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedGlobalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedLocalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedLocalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedParameter(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedParameter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedParameter': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedParameter'>, 'UnusedLocalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedLocalVariable'>, 'UnusedGlobalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedGlobalVariable'>}
coalib.bearlib.aspects.Security module
class coalib.bearlib.aspects.Security.Security(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Security.Security, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {}
coalib.bearlib.aspects.Smell module
class coalib.bearlib.aspects.Smell.ClassConstants(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ClassConstants, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class coalib.bearlib.aspects.Smell.ClassInstanceVariables(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ClassInstanceVariables, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class coalib.bearlib.aspects.Smell.ClassLength(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ClassLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class coalib.bearlib.aspects.Smell.ClassMethods(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ClassMethods, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class coalib.bearlib.aspects.Smell.ClassSize(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ClassSize, coalib.bearlib.aspects.base.aspectbase

class ClassConstants(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassConstants, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassInstanceVariables(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassInstanceVariables, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassMethods(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassMethods, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {'ClassConstants': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassConstants'>, 'ClassMethods': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassMethods'>, 'ClassInstanceVariables': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassInstanceVariables'>, 'ClassLength': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassLength'>}
class coalib.bearlib.aspects.Smell.ClassSmell(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ClassSmell, coalib.bearlib.aspects.base.aspectbase

class ClassSize(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSize, coalib.bearlib.aspects.base.aspectbase

class ClassConstants(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassConstants, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassInstanceVariables(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassInstanceVariables, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassMethods(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassMethods, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {'ClassConstants': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassConstants'>, 'ClassMethods': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassMethods'>, 'ClassInstanceVariables': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassInstanceVariables'>, 'ClassLength': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassLength'>}
class DataClump(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.DataClump, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
class FeatureEnvy(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.FeatureEnvy, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ClassSize': <aspectclass 'Root.Smell.ClassSmell.ClassSize'>, 'FeatureEnvy': <aspectclass 'Root.Smell.ClassSmell.FeatureEnvy'>, 'DataClump': <aspectclass 'Root.Smell.ClassSmell.DataClump'>}
class coalib.bearlib.aspects.Smell.Complexity(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.Complexity, coalib.bearlib.aspects.base.aspectbase

class CylomaticComplexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.CylomaticComplexity, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
class MaintainabilityIndex(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MaintainabilityIndex, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'MaintainabilityIndex': <aspectclass 'Root.Smell.Complexity.MaintainabilityIndex'>, 'CylomaticComplexity': <aspectclass 'Root.Smell.Complexity.CylomaticComplexity'>}
class coalib.bearlib.aspects.Smell.CylomaticComplexity(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.CylomaticComplexity, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
class coalib.bearlib.aspects.Smell.DataClump(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.DataClump, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
class coalib.bearlib.aspects.Smell.FeatureEnvy(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.FeatureEnvy, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
class coalib.bearlib.aspects.Smell.MaintainabilityIndex(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.MaintainabilityIndex, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
class coalib.bearlib.aspects.Smell.MethodLength(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.MethodLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
class coalib.bearlib.aspects.Smell.MethodSmell(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.MethodSmell, coalib.bearlib.aspects.base.aspectbase

class MethodLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
class ParameterListLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ParameterListLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ParameterListLength': <aspectclass 'Root.Smell.MethodSmell.ParameterListLength'>, 'MethodLength': <aspectclass 'Root.Smell.MethodSmell.MethodLength'>}
class coalib.bearlib.aspects.Smell.Naming(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.Naming, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {}
class coalib.bearlib.aspects.Smell.ParameterListLength(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.ParameterListLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
class coalib.bearlib.aspects.Smell.Smell(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Smell.Smell, coalib.bearlib.aspects.base.aspectbase

class ClassSmell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSmell, coalib.bearlib.aspects.base.aspectbase

class ClassSize(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSize, coalib.bearlib.aspects.base.aspectbase

class ClassConstants(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassConstants, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassInstanceVariables(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassInstanceVariables, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassMethods(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassMethods, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {'ClassConstants': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassConstants'>, 'ClassMethods': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassMethods'>, 'ClassInstanceVariables': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassInstanceVariables'>, 'ClassLength': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassLength'>}
class DataClump(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.DataClump, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
class FeatureEnvy(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.FeatureEnvy, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ClassSize': <aspectclass 'Root.Smell.ClassSmell.ClassSize'>, 'FeatureEnvy': <aspectclass 'Root.Smell.ClassSmell.FeatureEnvy'>, 'DataClump': <aspectclass 'Root.Smell.ClassSmell.DataClump'>}
class Complexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Complexity, coalib.bearlib.aspects.base.aspectbase

class CylomaticComplexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.CylomaticComplexity, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
class MaintainabilityIndex(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MaintainabilityIndex, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'MaintainabilityIndex': <aspectclass 'Root.Smell.Complexity.MaintainabilityIndex'>, 'CylomaticComplexity': <aspectclass 'Root.Smell.Complexity.CylomaticComplexity'>}
class MethodSmell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodSmell, coalib.bearlib.aspects.base.aspectbase

class MethodLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
class ParameterListLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ParameterListLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ParameterListLength': <aspectclass 'Root.Smell.MethodSmell.ParameterListLength'>, 'MethodLength': <aspectclass 'Root.Smell.MethodSmell.MethodLength'>}
class Naming(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Naming, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'ClassSmell': <aspectclass 'Root.Smell.ClassSmell'>, 'Complexity': <aspectclass 'Root.Smell.Complexity'>, 'Naming': <aspectclass 'Root.Smell.Naming'>, 'MethodSmell': <aspectclass 'Root.Smell.MethodSmell'>}
coalib.bearlib.aspects.Spelling module
class coalib.bearlib.aspects.Spelling.DictionarySpelling(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Spelling.DictionarySpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
class coalib.bearlib.aspects.Spelling.OrgSpecificWordSpelling(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Spelling.OrgSpecificWordSpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
class coalib.bearlib.aspects.Spelling.Spelling(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.Spelling.Spelling, coalib.bearlib.aspects.base.aspectbase

class DictionarySpelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.DictionarySpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
class OrgSpecificWordSpelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.OrgSpecificWordSpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'OrgSpecificWordSpelling': <aspectclass 'Root.Spelling.OrgSpecificWordSpelling'>, 'DictionarySpelling': <aspectclass 'Root.Spelling.DictionarySpelling'>}
coalib.bearlib.aspects.base module
class coalib.bearlib.aspects.base.LeafAspectGetter[source]

Bases: object

Descriptor class for get_leaf_aspects() method in aspectbase.

This class is required to make the get_leaf_aspects() accessible from both aspectclass and aspectclass instance.

class coalib.bearlib.aspects.base.SubaspectGetter[source]

Bases: object

Special “getter” class to implement get() method in aspectbase that could be accessed from the aspectclass or aspectclass instance.

class coalib.bearlib.aspects.base.aspectbase(language, **taste_values)[source]

Bases: object

Base class for aspectclasses with common features for their instances.

Derived classes must use coalib.bearlib.aspects.meta.aspectclass as metaclass. This is automatically handled by coalib.bearlib.aspects.meta.aspectclass.subaspect() decorator.

get = functools.partial(<function get_subaspect>, <class 'coalib.bearlib.aspects.base.aspectbase'>)
get_leaf_aspects = functools.partial(<function _get_leaf_aspects>, <class 'coalib.bearlib.aspects.base.aspectbase'>)
tastes

Get a dictionary of all taste names mapped to their specific values, including parent tastes.

coalib.bearlib.aspects.base.get_subaspect(parent, subaspect)[source]

Get a subaspect from an aspectclass or aspectclass instance.

>>> import coalib.bearlib.aspects as coala_aspects
>>> metadata = coala_aspects['Metadata']
>>> commit_msg = coala_aspects['CommitMessage']
>>> shortlog = coala_aspects['Shortlog']

We can get direct children.

>>> get_subaspect(metadata, commit_msg)
<aspectclass 'Root.Metadata.CommitMessage'>

Or even a grandchildren.

>>> get_subaspect(metadata, shortlog)
<aspectclass 'Root.Metadata.CommitMessage.Shortlog'>

Or with string of aspect name

>>> get_subaspect(metadata, 'shortlog')
<aspectclass 'Root.Metadata.CommitMessage.Shortlog'>

We can also get child instance of an aspect instance.

>>> get_subaspect(metadata('Python'), commit_msg)
<...CommitMessage object at 0x...>

But, passing subaspect instance as argument is prohibited, because it doesn’t really make sense.

>>> get_subaspect(metadata('Python'), commit_msg('Java'))
Traceback (most recent call last):
...
AttributeError: Cannot search an aspect instance using another ...
Parameters:
  • parent – The parent aspect that should be searched.
  • subaspect – An subaspect that we want to find in an aspectclass.
Returns:

An aspectclass. Return None if not found.
coalib.bearlib.aspects.collections module
class coalib.bearlib.aspects.collections.AspectList(seq=(), exclude=None, languages=None)[source]

Bases: list

List-derived container to hold aspects.

get(aspect)[source]

Return first item that match or contain an aspect. See coalib.bearlib.aspects.aspectbase.get() for further example.

Parameters:aspect – An aspectclass OR name of an aspect.
Returns:An aspectclass OR aspectclass instance, depend on AspectList content. Return None if no match found.
get_leaf_aspects()[source]

Breakdown all of item in self into their leaf subaspects.

Returns:An AspectList contain ONLY leaf aspects.
coalib.bearlib.aspects.decorators module
coalib.bearlib.aspects.decorators.map_setting_to_aspect(**aspectable_setting)[source]

Map function arguments with aspect and override it if appropriate.

This decorator can be used by Bear.run() to automatically map and override bear’s setting value with their equivalent aspect or taste.

The order of setting override from the lowest to highest is: - Setting default (in bear’s run argument) - Aspect/taste default (if aspect is activated in Section) - Explicit aspect/taste default (if aspect is activated in Section) - Explicit setting

Parameters:aspectable_setting – A dictionary of settings as keys and their equivalent aspect or taste as value.
coalib.bearlib.aspects.docs module
class coalib.bearlib.aspects.docs.Documentation(definition: str = '', example: str = '', example_language: str = '', importance_reason: str = '', fix_suggestions: str = '')[source]

Bases: object

This class contains documentation about an aspectclass. The documentation is consistent if all members are given:

>>> Documentation('defined').check_consistency()
False
>>> Documentation('definition', 'example',
...               'example_language', 'importance',
...               'fix').check_consistency()
True
check_consistency()
coalib.bearlib.aspects.exceptions module
exception coalib.bearlib.aspects.exceptions.AspectLookupError(aspectname, message=None)[source]

Bases: LookupError

Error raised when trying to search aspect.

exception coalib.bearlib.aspects.exceptions.AspectNotFoundError(aspectname)[source]

Bases: coalib.bearlib.aspects.exceptions.AspectLookupError

No aspect found.

exception coalib.bearlib.aspects.exceptions.AspectTypeError(item)[source]

Bases: TypeError

This error is raised when an object is not an aspectclass or an instance of aspectclass

exception coalib.bearlib.aspects.exceptions.MultipleAspectFoundError(aspectname, other_aspects)[source]

Bases: coalib.bearlib.aspects.exceptions.AspectLookupError

Multiple aspect are found.

coalib.bearlib.aspects.meta module
class coalib.bearlib.aspects.meta.aspectclass(clsname, bases, clsattrs)[source]

Bases: type

Metaclass for aspectclasses.

Root aspectclass is coalib.bearlib.aspects.Root.

subaspect(subcls)[source]

The sub-aspectclass decorator.

See coalib.bearlib.aspects.Root for description and usage.

tastes

Get a dictionary of all taste names mapped to their coalib.bearlib.aspects.Taste instances.

coalib.bearlib.aspects.meta.assert_aspect(item)[source]

This function raises AspectTypeError when an object is not an aspectclass or an instance of aspectclass

coalib.bearlib.aspects.meta.isaspect(item)[source]

This function checks whether or not an object is an aspectclass or an instance of aspectclass

coalib.bearlib.aspects.meta.issubaspect(subaspect, aspect)[source]

This function checks whether or not subaspect is a subaspect of aspect.

coalib.bearlib.aspects.root module
class coalib.bearlib.aspects.root.Root(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.base.aspectbase

The root aspectclass.

Define sub-aspectclasses with class-bound .subaspect decorator. Definition string is taken from doc-string of decorated class. Remaining docs are taken from a nested docs class. Tastes are defined as class attributes that are instances of coalib.bearlib.aspects.Taste.

>>> from coalib.bearlib.aspects import Taste

>>> @Root.subaspect
... class Formatting:
...     """
...     A parent aspect for code formatting aspects...
...     """
...     class docs:
...        example = "..."
...        example_language = "..."
...        importance_reason = "..."
...        fix_suggestions = "..."

We can now create subaspects like this:

>>> @Formatting.subaspect
... class LineLength:
...     """
...     This aspect controls the length of a line...
...     """
...     class docs:
...        example = "..."
...        example_language = "..."
...        importance_reason = "..."
...        fix_suggestions = "..."
...
...     max_line_length = Taste[int](
...         "Maximum length allowed for a line.",
...         (80, 90, 120), default=80)

The representation will show the full “path” to the leaf of the tree:

>>> Root.Formatting.LineLength
<aspectclass 'Root.Formatting.LineLength'>

We can see, which settings are availables:

>>> Formatting.tastes
{}
>>> LineLength.tastes
{'max_line_length': <....Taste[int] object at ...>}

And instantiate the aspect with the values, they will be automatically converted:

>>> Formatting('Python')
<....Root.Formatting object at 0x...>
>>> LineLength('Python', max_line_length="100").tastes
{'max_line_length': 100}

If no settings are given, the defaults will be taken:

>>> LineLength('Python').tastes
{'max_line_length': 80}

Tastes can also be made available for only specific languages:

>>> from coalib.bearlib.languages import Language
>>> @Language
... class GreaterTrumpScript:
...     pass

>>> @Formatting.subaspect
... class Greatness:
...     """
...     This aspect controls the greatness of a file...
...     """
...     class docs:
...        example = "..."
...        example_language = "..."
...        importance_reason = "..."
...        fix_suggestions = "..."
...     min_greatness = Taste[int](
...         "Minimum greatness factor needed for a TrumpScript file. "
...         "This is fact.",
...         (1000000, 1000000000, 1000000000000), default=1000000,
...         languages=('GreaterTrumpScript' ,))

>>> Greatness.tastes
{'min_greatness': <....Taste[int] object at ...>}
>>> Greatness('GreaterTrumpScript').tastes
{'min_greatness': 1000000}
>>> Greatness('GreaterTrumpScript', min_greatness=1000000000000).tastes
{'min_greatness': 1000000000000}

>>> Greatness('Python').tastes
{}

>>> Greatness('Python', min_greatness=1000000000)
... 
Traceback (most recent call last):
  ...
coalib.bearlib.aspects.taste.TasteError:
Root.Formatting.Greatness.min_greatness is not available ...

>>> Greatness('Python').min_greatness
... 
Traceback (most recent call last):
  ...
coalib.bearlib.aspects.taste.TasteError:
Root.Formatting.Greatness.min_greatness is not available ...
class Formatting(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Formatting, coalib.bearlib.aspects.base.aspectbase

class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Length, coalib.bearlib.aspects.base.aspectbase

class FileLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.FileLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
class LineLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.LineLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'FileLength': <aspectclass 'Root.Formatting.Length.FileLength'>, 'LineLength': <aspectclass 'Root.Formatting.Length.LineLength'>}
class Quotation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Quotation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {}
class Spacing(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Spacing, coalib.bearlib.aspects.base.aspectbase

class BlankLine(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLine, coalib.bearlib.aspects.base.aspectbase

class BlankLineAfterClass(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterClass, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterDeclaration(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterProcedure(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class NewlineAtEOF(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.NewlineAtEOF, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {'BlankLineAfterProcedure': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterProcedure'>, 'BlankLineAfterDeclaration': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterDeclaration'>, 'BlankLineAfterClass': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterClass'>, 'NewlineAtEOF': <aspectclass 'Root.Formatting.Spacing.BlankLine.NewlineAtEOF'>}
class Indentation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Indentation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class SpacesAroundOperator(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.SpacesAroundOperator, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class TrailingSpace(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.TrailingSpace, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'TrailingSpace': <aspectclass 'Root.Formatting.Spacing.TrailingSpace'>, 'SpacesAroundOperator': <aspectclass 'Root.Formatting.Spacing.SpacesAroundOperator'>, 'BlankLine': <aspectclass 'Root.Formatting.Spacing.BlankLine'>, 'Indentation': <aspectclass 'Root.Formatting.Spacing.Indentation'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'Quotation': <aspectclass 'Root.Formatting.Quotation'>, 'Spacing': <aspectclass 'Root.Formatting.Spacing'>, 'Length': <aspectclass 'Root.Formatting.Length'>}
class Metadata(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Metadata, coalib.bearlib.aspects.base.aspectbase

class CommitMessage(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.CommitMessage, coalib.bearlib.aspects.base.aspectbase

class Body(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Body, coalib.bearlib.aspects.base.aspectbase

class Existence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Existence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'Length': <aspectclass 'Root.Metadata.CommitMessage.Body.Length'>, 'Existence': <aspectclass 'Root.Metadata.CommitMessage.Body.Existence'>}
class Emptiness(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Emptiness, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {}
class Shortlog(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Shortlog, coalib.bearlib.aspects.base.aspectbase

class ColonExistence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.ColonExistence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class FirstCharacter(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.FirstCharacter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Tense(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Tense, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class TrailingPeriod(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.TrailingPeriod, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'TrailingPeriod': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.TrailingPeriod'>, 'ColonExistence': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.ColonExistence'>, 'FirstCharacter': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.FirstCharacter'>, 'Length': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Length'>, 'Tense': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Tense'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Metadata

subaspects = {'Shortlog': <aspectclass 'Root.Metadata.CommitMessage.Shortlog'>, 'Body': <aspectclass 'Root.Metadata.CommitMessage.Body'>, 'Emptiness': <aspectclass 'Root.Metadata.CommitMessage.Emptiness'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'CommitMessage': <aspectclass 'Root.Metadata.CommitMessage'>}
class Redundancy(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.Redundancy, coalib.bearlib.aspects.base.aspectbase

class Clone(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.Clone, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class UnreachableCode(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableCode, coalib.bearlib.aspects.base.aspectbase

class UnreachableStatement(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableStatement, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
class UnusedFunction(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedFunction, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedFunction': <aspectclass 'Root.Redundancy.UnreachableCode.UnusedFunction'>, 'UnreachableStatement': <aspectclass 'Root.Redundancy.UnreachableCode.UnreachableStatement'>}
class UnusedImport(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedImport, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class UnusedVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedVariable, coalib.bearlib.aspects.base.aspectbase

class UnusedGlobalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedGlobalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedLocalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedLocalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedParameter(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedParameter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedParameter': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedParameter'>, 'UnusedLocalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedLocalVariable'>, 'UnusedGlobalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedGlobalVariable'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'UnreachableCode': <aspectclass 'Root.Redundancy.UnreachableCode'>, 'UnusedImport': <aspectclass 'Root.Redundancy.UnusedImport'>, 'UnusedVariable': <aspectclass 'Root.Redundancy.UnusedVariable'>, 'Clone': <aspectclass 'Root.Redundancy.Clone'>}
class Security(language, **taste_values)

Bases: coalib.bearlib.aspects.Security.Security, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {}
class Smell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Smell, coalib.bearlib.aspects.base.aspectbase

class ClassSmell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSmell, coalib.bearlib.aspects.base.aspectbase

class ClassSize(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSize, coalib.bearlib.aspects.base.aspectbase

class ClassConstants(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassConstants, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassInstanceVariables(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassInstanceVariables, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassMethods(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassMethods, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {'ClassConstants': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassConstants'>, 'ClassMethods': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassMethods'>, 'ClassInstanceVariables': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassInstanceVariables'>, 'ClassLength': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassLength'>}
class DataClump(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.DataClump, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
class FeatureEnvy(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.FeatureEnvy, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ClassSize': <aspectclass 'Root.Smell.ClassSmell.ClassSize'>, 'FeatureEnvy': <aspectclass 'Root.Smell.ClassSmell.FeatureEnvy'>, 'DataClump': <aspectclass 'Root.Smell.ClassSmell.DataClump'>}
class Complexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Complexity, coalib.bearlib.aspects.base.aspectbase

class CylomaticComplexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.CylomaticComplexity, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
class MaintainabilityIndex(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MaintainabilityIndex, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'MaintainabilityIndex': <aspectclass 'Root.Smell.Complexity.MaintainabilityIndex'>, 'CylomaticComplexity': <aspectclass 'Root.Smell.Complexity.CylomaticComplexity'>}
class MethodSmell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodSmell, coalib.bearlib.aspects.base.aspectbase

class MethodLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
class ParameterListLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ParameterListLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ParameterListLength': <aspectclass 'Root.Smell.MethodSmell.ParameterListLength'>, 'MethodLength': <aspectclass 'Root.Smell.MethodSmell.MethodLength'>}
class Naming(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Naming, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'ClassSmell': <aspectclass 'Root.Smell.ClassSmell'>, 'Complexity': <aspectclass 'Root.Smell.Complexity'>, 'Naming': <aspectclass 'Root.Smell.Naming'>, 'MethodSmell': <aspectclass 'Root.Smell.MethodSmell'>}
class Spelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.Spelling, coalib.bearlib.aspects.base.aspectbase

class DictionarySpelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.DictionarySpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
class OrgSpecificWordSpelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.OrgSpecificWordSpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'OrgSpecificWordSpelling': <aspectclass 'Root.Spelling.OrgSpecificWordSpelling'>, 'DictionarySpelling': <aspectclass 'Root.Spelling.DictionarySpelling'>}
parent = None
subaspects = {'Metadata': <aspectclass 'Root.Metadata'>, 'Spelling': <aspectclass 'Root.Spelling'>, 'Redundancy': <aspectclass 'Root.Redundancy'>, 'Smell': <aspectclass 'Root.Smell'>, 'Security': <aspectclass 'Root.Security'>, 'Formatting': <aspectclass 'Root.Formatting'>}
coalib.bearlib.aspects.taste module
class coalib.bearlib.aspects.taste.Taste(description: str = '', suggested_values: tuple = (), default=None, languages: tuple = ())[source]

Bases: object

Defines tastes in aspectclass definitions.

Tastes can be made only available for certain languages by providing a tuple of language identifiers on instantiation:

>>> Taste[bool](
...     'Ignore ``using`` directives in C#.',
...     (True, False), default=False,
...     languages=('CSharp', )
... ).languages
(C#,)

If no languages are given, they will be available for any language. See coalib.bearlib.aspects.Root for further usage.

cast_type

alias of str

exception coalib.bearlib.aspects.taste.TasteError[source]

Bases: AttributeError

A taste is not allowed to be accessed.

class coalib.bearlib.aspects.taste.TasteMeta[source]

Bases: type

Metaclass for coalib.bearlib.aspects.Taste

Allows defining taste cast type via __getitem__(), like:

>>> Taste[int]().cast_type
<class 'int'>
Module contents
class coalib.bearlib.aspects.Root(language, **taste_values)[source]

Bases: coalib.bearlib.aspects.base.aspectbase

The root aspectclass.

Define sub-aspectclasses with class-bound .subaspect decorator. Definition string is taken from doc-string of decorated class. Remaining docs are taken from a nested docs class. Tastes are defined as class attributes that are instances of coalib.bearlib.aspects.Taste.

>>> from coalib.bearlib.aspects import Taste

>>> @Root.subaspect
... class Formatting:
...     """
...     A parent aspect for code formatting aspects...
...     """
...     class docs:
...        example = "..."
...        example_language = "..."
...        importance_reason = "..."
...        fix_suggestions = "..."

We can now create subaspects like this:

>>> @Formatting.subaspect
... class LineLength:
...     """
...     This aspect controls the length of a line...
...     """
...     class docs:
...        example = "..."
...        example_language = "..."
...        importance_reason = "..."
...        fix_suggestions = "..."
...
...     max_line_length = Taste[int](
...         "Maximum length allowed for a line.",
...         (80, 90, 120), default=80)

The representation will show the full “path” to the leaf of the tree:

>>> Root.Formatting.LineLength
<aspectclass 'Root.Formatting.LineLength'>

We can see, which settings are availables:

>>> Formatting.tastes
{}
>>> LineLength.tastes
{'max_line_length': <....Taste[int] object at ...>}

And instantiate the aspect with the values, they will be automatically converted:

>>> Formatting('Python')
<....Root.Formatting object at 0x...>
>>> LineLength('Python', max_line_length="100").tastes
{'max_line_length': 100}

If no settings are given, the defaults will be taken:

>>> LineLength('Python').tastes
{'max_line_length': 80}

Tastes can also be made available for only specific languages:

>>> from coalib.bearlib.languages import Language
>>> @Language
... class GreaterTrumpScript:
...     pass

>>> @Formatting.subaspect
... class Greatness:
...     """
...     This aspect controls the greatness of a file...
...     """
...     class docs:
...        example = "..."
...        example_language = "..."
...        importance_reason = "..."
...        fix_suggestions = "..."
...     min_greatness = Taste[int](
...         "Minimum greatness factor needed for a TrumpScript file. "
...         "This is fact.",
...         (1000000, 1000000000, 1000000000000), default=1000000,
...         languages=('GreaterTrumpScript' ,))

>>> Greatness.tastes
{'min_greatness': <....Taste[int] object at ...>}
>>> Greatness('GreaterTrumpScript').tastes
{'min_greatness': 1000000}
>>> Greatness('GreaterTrumpScript', min_greatness=1000000000000).tastes
{'min_greatness': 1000000000000}

>>> Greatness('Python').tastes
{}

>>> Greatness('Python', min_greatness=1000000000)
... 
Traceback (most recent call last):
  ...
coalib.bearlib.aspects.taste.TasteError:
Root.Formatting.Greatness.min_greatness is not available ...

>>> Greatness('Python').min_greatness
... 
Traceback (most recent call last):
  ...
coalib.bearlib.aspects.taste.TasteError:
Root.Formatting.Greatness.min_greatness is not available ...
class Formatting(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Formatting, coalib.bearlib.aspects.base.aspectbase

class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Length, coalib.bearlib.aspects.base.aspectbase

class FileLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.FileLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
class LineLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.LineLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Length

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'FileLength': <aspectclass 'Root.Formatting.Length.FileLength'>, 'LineLength': <aspectclass 'Root.Formatting.Length.LineLength'>}
class Quotation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Quotation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {}
class Spacing(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Spacing, coalib.bearlib.aspects.base.aspectbase

class BlankLine(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLine, coalib.bearlib.aspects.base.aspectbase

class BlankLineAfterClass(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterClass, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterDeclaration(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterDeclaration, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class BlankLineAfterProcedure(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.BlankLineAfterProcedure, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
class NewlineAtEOF(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.NewlineAtEOF, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of BlankLine

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {'BlankLineAfterProcedure': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterProcedure'>, 'BlankLineAfterDeclaration': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterDeclaration'>, 'BlankLineAfterClass': <aspectclass 'Root.Formatting.Spacing.BlankLine.BlankLineAfterClass'>, 'NewlineAtEOF': <aspectclass 'Root.Formatting.Spacing.BlankLine.NewlineAtEOF'>}
class Indentation(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.Indentation, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class SpacesAroundOperator(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.SpacesAroundOperator, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
class TrailingSpace(language, **taste_values)

Bases: coalib.bearlib.aspects.Formatting.TrailingSpace, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spacing

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Formatting

subaspects = {'TrailingSpace': <aspectclass 'Root.Formatting.Spacing.TrailingSpace'>, 'SpacesAroundOperator': <aspectclass 'Root.Formatting.Spacing.SpacesAroundOperator'>, 'BlankLine': <aspectclass 'Root.Formatting.Spacing.BlankLine'>, 'Indentation': <aspectclass 'Root.Formatting.Spacing.Indentation'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'Quotation': <aspectclass 'Root.Formatting.Quotation'>, 'Spacing': <aspectclass 'Root.Formatting.Spacing'>, 'Length': <aspectclass 'Root.Formatting.Length'>}
class Metadata(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Metadata, coalib.bearlib.aspects.base.aspectbase

class CommitMessage(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.CommitMessage, coalib.bearlib.aspects.base.aspectbase

class Body(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Body, coalib.bearlib.aspects.base.aspectbase

class Existence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Existence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Body

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'Length': <aspectclass 'Root.Metadata.CommitMessage.Body.Length'>, 'Existence': <aspectclass 'Root.Metadata.CommitMessage.Body.Existence'>}
class Emptiness(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Emptiness, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {}
class Shortlog(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Shortlog, coalib.bearlib.aspects.base.aspectbase

class ColonExistence(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.ColonExistence, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class FirstCharacter(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.FirstCharacter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Length(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Length, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class Tense(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.Tense, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
class TrailingPeriod(language, **taste_values)

Bases: coalib.bearlib.aspects.Metadata.TrailingPeriod, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Shortlog

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of CommitMessage

subaspects = {'TrailingPeriod': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.TrailingPeriod'>, 'ColonExistence': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.ColonExistence'>, 'FirstCharacter': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.FirstCharacter'>, 'Length': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Length'>, 'Tense': <aspectclass 'Root.Metadata.CommitMessage.Shortlog.Tense'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Metadata

subaspects = {'Shortlog': <aspectclass 'Root.Metadata.CommitMessage.Shortlog'>, 'Body': <aspectclass 'Root.Metadata.CommitMessage.Body'>, 'Emptiness': <aspectclass 'Root.Metadata.CommitMessage.Emptiness'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'CommitMessage': <aspectclass 'Root.Metadata.CommitMessage'>}
class Redundancy(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.Redundancy, coalib.bearlib.aspects.base.aspectbase

class Clone(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.Clone, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class UnreachableCode(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableCode, coalib.bearlib.aspects.base.aspectbase

class UnreachableStatement(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnreachableStatement, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
class UnusedFunction(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedFunction, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnreachableCode

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedFunction': <aspectclass 'Root.Redundancy.UnreachableCode.UnusedFunction'>, 'UnreachableStatement': <aspectclass 'Root.Redundancy.UnreachableCode.UnreachableStatement'>}
class UnusedImport(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedImport, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {}
class UnusedVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedVariable, coalib.bearlib.aspects.base.aspectbase

class UnusedGlobalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedGlobalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedLocalVariable(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedLocalVariable, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
class UnusedParameter(language, **taste_values)

Bases: coalib.bearlib.aspects.Redundancy.UnusedParameter, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of UnusedVariable

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Redundancy

subaspects = {'UnusedParameter': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedParameter'>, 'UnusedLocalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedLocalVariable'>, 'UnusedGlobalVariable': <aspectclass 'Root.Redundancy.UnusedVariable.UnusedGlobalVariable'>}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'UnreachableCode': <aspectclass 'Root.Redundancy.UnreachableCode'>, 'UnusedImport': <aspectclass 'Root.Redundancy.UnusedImport'>, 'UnusedVariable': <aspectclass 'Root.Redundancy.UnusedVariable'>, 'Clone': <aspectclass 'Root.Redundancy.Clone'>}
class Security(language, **taste_values)

Bases: coalib.bearlib.aspects.Security.Security, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {}
class Smell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Smell, coalib.bearlib.aspects.base.aspectbase

class ClassSmell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSmell, coalib.bearlib.aspects.base.aspectbase

class ClassSize(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassSize, coalib.bearlib.aspects.base.aspectbase

class ClassConstants(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassConstants, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassInstanceVariables(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassInstanceVariables, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
class ClassMethods(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ClassMethods, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSize

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {'ClassConstants': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassConstants'>, 'ClassMethods': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassMethods'>, 'ClassInstanceVariables': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassInstanceVariables'>, 'ClassLength': <aspectclass 'Root.Smell.ClassSmell.ClassSize.ClassLength'>}
class DataClump(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.DataClump, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
class FeatureEnvy(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.FeatureEnvy, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of ClassSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ClassSize': <aspectclass 'Root.Smell.ClassSmell.ClassSize'>, 'FeatureEnvy': <aspectclass 'Root.Smell.ClassSmell.FeatureEnvy'>, 'DataClump': <aspectclass 'Root.Smell.ClassSmell.DataClump'>}
class Complexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Complexity, coalib.bearlib.aspects.base.aspectbase

class CylomaticComplexity(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.CylomaticComplexity, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
class MaintainabilityIndex(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MaintainabilityIndex, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Complexity

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'MaintainabilityIndex': <aspectclass 'Root.Smell.Complexity.MaintainabilityIndex'>, 'CylomaticComplexity': <aspectclass 'Root.Smell.Complexity.CylomaticComplexity'>}
class MethodSmell(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodSmell, coalib.bearlib.aspects.base.aspectbase

class MethodLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.MethodLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
class ParameterListLength(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.ParameterListLength, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of MethodSmell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {'ParameterListLength': <aspectclass 'Root.Smell.MethodSmell.ParameterListLength'>, 'MethodLength': <aspectclass 'Root.Smell.MethodSmell.MethodLength'>}
class Naming(language, **taste_values)

Bases: coalib.bearlib.aspects.Smell.Naming, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Smell

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'ClassSmell': <aspectclass 'Root.Smell.ClassSmell'>, 'Complexity': <aspectclass 'Root.Smell.Complexity'>, 'Naming': <aspectclass 'Root.Smell.Naming'>, 'MethodSmell': <aspectclass 'Root.Smell.MethodSmell'>}
class Spelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.Spelling, coalib.bearlib.aspects.base.aspectbase

class DictionarySpelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.DictionarySpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
class OrgSpecificWordSpelling(language, **taste_values)

Bases: coalib.bearlib.aspects.Spelling.OrgSpecificWordSpelling, coalib.bearlib.aspects.base.aspectbase

docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Spelling

subaspects = {}
docs = <coalib.bearlib.aspects.docs.Documentation object>
parent

alias of Root

subaspects = {'OrgSpecificWordSpelling': <aspectclass 'Root.Spelling.OrgSpecificWordSpelling'>, 'DictionarySpelling': <aspectclass 'Root.Spelling.DictionarySpelling'>}
parent = None
subaspects = {'Metadata': <aspectclass 'Root.Metadata'>, 'Spelling': <aspectclass 'Root.Spelling'>, 'Redundancy': <aspectclass 'Root.Redundancy'>, 'Smell': <aspectclass 'Root.Smell'>, 'Security': <aspectclass 'Root.Security'>, 'Formatting': <aspectclass 'Root.Formatting'>}
class coalib.bearlib.aspects.Taste(description: str = '', suggested_values: tuple = (), default=None, languages: tuple = ())[source]

Bases: object

Defines tastes in aspectclass definitions.

Tastes can be made only available for certain languages by providing a tuple of language identifiers on instantiation:

>>> Taste[bool](
...     'Ignore ``using`` directives in C#.',
...     (True, False), default=False,
...     languages=('CSharp', )
... ).languages
(C#,)

If no languages are given, they will be available for any language. See coalib.bearlib.aspects.Root for further usage.

cast_type

alias of str

exception coalib.bearlib.aspects.TasteError[source]

Bases: AttributeError

A taste is not allowed to be accessed.

class coalib.bearlib.aspects.aspectclass(clsname, bases, clsattrs)[source]

Bases: type

Metaclass for aspectclasses.

Root aspectclass is coalib.bearlib.aspects.Root.

subaspect(subcls)[source]

The sub-aspectclass decorator.

See coalib.bearlib.aspects.Root for description and usage.

tastes

Get a dictionary of all taste names mapped to their coalib.bearlib.aspects.Taste instances.

class coalib.bearlib.aspects.aspectbase(language, **taste_values)[source]

Bases: object

Base class for aspectclasses with common features for their instances.

Derived classes must use coalib.bearlib.aspects.meta.aspectclass as metaclass. This is automatically handled by coalib.bearlib.aspects.meta.aspectclass.subaspect() decorator.

get = functools.partial(<function get_subaspect>, <class 'coalib.bearlib.aspects.base.aspectbase'>)
get_leaf_aspects = functools.partial(<function _get_leaf_aspects>, <class 'coalib.bearlib.aspects.base.aspectbase'>)
tastes

Get a dictionary of all taste names mapped to their specific values, including parent tastes.

exception coalib.bearlib.aspects.AspectTypeError(item)[source]

Bases: TypeError

This error is raised when an object is not an aspectclass or an instance of aspectclass

exception coalib.bearlib.aspects.AspectNotFoundError(aspectname)[source]

Bases: coalib.bearlib.aspects.exceptions.AspectLookupError

No aspect found.

exception coalib.bearlib.aspects.MultipleAspectFoundError(aspectname, other_aspects)[source]

Bases: coalib.bearlib.aspects.exceptions.AspectLookupError

Multiple aspect are found.

class coalib.bearlib.aspects.AspectList(seq=(), exclude=None, languages=None)[source]

Bases: list

List-derived container to hold aspects.

get(aspect)[source]

Return first item that match or contain an aspect. See coalib.bearlib.aspects.aspectbase.get() for further example.

Parameters:aspect – An aspectclass OR name of an aspect.
Returns:An aspectclass OR aspectclass instance, depend on AspectList content. Return None if no match found.
get_leaf_aspects()[source]

Breakdown all of item in self into their leaf subaspects.

Returns:An AspectList contain ONLY leaf aspects.
coalib.bearlib.aspects.map_setting_to_aspect(**aspectable_setting)[source]

Map function arguments with aspect and override it if appropriate.

This decorator can be used by Bear.run() to automatically map and override bear’s setting value with their equivalent aspect or taste.

The order of setting override from the lowest to highest is: - Setting default (in bear’s run argument) - Aspect/taste default (if aspect is activated in Section) - Explicit aspect/taste default (if aspect is activated in Section) - Explicit setting

Parameters:aspectable_setting – A dictionary of settings as keys and their equivalent aspect or taste as value.
coalib.bearlib.languages package
Subpackages
coalib.bearlib.languages.definitions package
Submodules
coalib.bearlib.languages.definitions.C module
coalib.bearlib.languages.definitions.CPP module
coalib.bearlib.languages.definitions.CSS module
coalib.bearlib.languages.definitions.CSharp module
coalib.bearlib.languages.definitions.Fortran module
coalib.bearlib.languages.definitions.Golang module
coalib.bearlib.languages.definitions.JSP module
coalib.bearlib.languages.definitions.Java module
coalib.bearlib.languages.definitions.JavaScript module
coalib.bearlib.languages.definitions.Jinja2 module
coalib.bearlib.languages.definitions.Markdown module
coalib.bearlib.languages.definitions.Matlab module
coalib.bearlib.languages.definitions.ObjectiveC module
coalib.bearlib.languages.definitions.PHP module
coalib.bearlib.languages.definitions.PLSQL module
coalib.bearlib.languages.definitions.Python module
coalib.bearlib.languages.definitions.Ruby module
coalib.bearlib.languages.definitions.Scala module
coalib.bearlib.languages.definitions.Shell module
coalib.bearlib.languages.definitions.Swift module
coalib.bearlib.languages.definitions.TypeScript module
coalib.bearlib.languages.definitions.Unknown module
coalib.bearlib.languages.definitions.Vala module
coalib.bearlib.languages.definitions.html module
Module contents

This directory holds language definitions.

Language definitions hold expressions that help defining specific syntax elements for a programming language.

Currently defined keys are:

names extensions comment_delimiter multiline_comment_delimiters string_delimiters multiline_string_delimiters keywords special_chars
coalib.bearlib.languages.documentation package
Submodules
coalib.bearlib.languages.documentation.DocBaseClass module
class coalib.bearlib.languages.documentation.DocBaseClass.DocBaseClass[source]

Bases: object

DocBaseClass holds important functions which will extract, parse and generates diffs for documentation. All bears that processes documentation should inherit from this.

static extract(content, language, docstyle)[source]

Extracts all documentation texts inside the given source-code-string using the coala docstyle definition files.

The documentation texts are sorted by their order appearing in content.

For more information about how documentation comments are identified and extracted, see DocstyleDefinition.doctypes enumeration.

Parameters:
  • content – The source-code-string where to extract documentation from. Needs to be a list or tuple where each string item is a single line(including ending whitespaces like \n).
  • language – The programming language used.
  • docstyle – The documentation style/tool used (e.g. doxygen).
Raises:
  • FileNotFoundError – Raised when the docstyle definition file was not found.
  • KeyError – Raised when the given language is not defined in given docstyle.
  • ValueError – Raised when a docstyle definition setting has an invalid format.
Returns:

An iterator returning instances of DocumentationComment or MalformedComment found in the content.
static generate_diff(file, doc_comment, new_comment)[source]

Generates diff between the original doc_comment and its fix new_comment which are instances of DocumentationComment.

Parameters:
  • doc_comment – Original instance of DocumentationComment.
  • new_comment – Fixed instance of DocumentationComment.
Returns:

Diff instance.
process_documentation(*args, **kwargs)[source]

Checks and handles the fixing part of documentation.

Returns:A tuple of processed documentation and warning_desc.
coalib.bearlib.languages.documentation.DocstyleDefinition module
class coalib.bearlib.languages.documentation.DocstyleDefinition.DocstyleDefinition(language: str, docstyle: str, markers: (<class 'collections.abc.Iterable'>, <class 'str'>), metadata: coalib.bearlib.languages.documentation.DocstyleDefinition.Metadata, class_padding: coalib.bearlib.languages.documentation.DocstyleDefinition.ClassPadding, function_padding: coalib.bearlib.languages.documentation.DocstyleDefinition.FunctionPadding, docstring_type_regex: coalib.bearlib.languages.documentation.DocstyleDefinition.DocstringTypeRegex, docstring_position: str)[source]

Bases: object

The DocstyleDefinition class holds values that identify a certain type of documentation comment (for which language, documentation style/tool used etc.).

class ClassPadding(top_padding, bottom_padding)

Bases: tuple

bottom_padding

Alias for field number 1

top_padding

Alias for field number 0

class DocstringTypeRegex(class_sign, func_sign)

Bases: tuple

class_sign

Alias for field number 0

func_sign

Alias for field number 1

class FunctionPadding(top_padding, bottom_padding)

Bases: tuple

bottom_padding

Alias for field number 1

top_padding

Alias for field number 0

class Metadata(param_start, param_end, exception_start, exception_end, return_sep)

Bases: tuple

exception_end

Alias for field number 3

exception_start

Alias for field number 2

param_end

Alias for field number 1

param_start

Alias for field number 0

return_sep

Alias for field number 4

class_padding

A namedtuple ClassPadding consisting of values about blank lines before and after the documentation of docstring_type class.

These values are official standard of following blank lines before and after the documentation of docstring_type class.

docstring_position

Defines the position, where the regex of docstring type is present. Depending on different languages the docstrings are present below or above the defined class or function. This expicitly defines where the class regex or function regex is present(i.e. top or bottom).

docstring_type_regex

A namedtuple DocstringTypeRegex consisting of regex about class and function of a language, which is used to determine docstring_type of DocumentationComment.

docstyle

The documentation style/tool used to document code.

Returns:A lower-case string defining the docstyle (i.e. “default” or “doxygen”).
function_padding

A namedtuple FunctionPadding consisting of values about blank lines before and after the documentation of docstring_type function.

These values are official standard of following blank lines before and after the documentation of docstring_type function.

static get_available_definitions()[source]

Returns a sequence of pairs with (docstyle, language) which are available when using load().

Returns:A sequence of pairs with (docstyle, language).
language

The programming language.

Returns:A lower-case string defining the programming language (i.e. “cpp” or “python”).
classmethod load(language: str, docstyle: str, coalang_dir=None)[source]

Loads a DocstyleDefinition from the coala docstyle definition files.

This function considers all settings inside the according coalang-files as markers, except param_start, param_end and return_sep which are considered as special metadata markers.

Note

When placing new coala docstyle definition files, these must consist of only lowercase letters and end with .coalang!

Parameters:
  • language – The case insensitive programming language of the documentation comment as a string.
  • docstyle – The case insensitive documentation style/tool used to document code, e.g. "default" or "doxygen".
  • coalang_dir – Path to directory with coalang docstyle definition files. This replaces the default path if given.
Raises:
  • FileNotFoundError – Raised when the given docstyle was not found.
  • KeyError – Raised when the given language is not defined for given docstyle.
Returns:

The DocstyleDefinition for given language and docstyle.
markers

A tuple of marker sets that identify a documentation comment.

Marker sets consist of 3 entries where the first is the start-marker, the second one the each-line marker and the last one the end-marker. For example a marker tuple with a single marker set (("/**", "*", "*/"),) would match following documentation comment:

/**
 * This is documentation.
 */

It’s also possible to supply an empty each-line marker (("/**", "", "*/")):

/**
 This is more documentation.
 */

Markers are matched “greedy”, that means it will match as many each-line markers as possible. I.e. for ("///", "///", "///")):

/// Brief documentation.
///
/// Detailed documentation.
Returns:A tuple of marker/delimiter string tuples that identify a documentation comment.
metadata

A namedtuple of certain attributes present in the documentation.

These attributes are used to define parts of the documentation.

coalib.bearlib.languages.documentation.DocumentationComment module
class coalib.bearlib.languages.documentation.DocumentationComment.DocumentationComment(documentation, docstyle_definition, indent, marker, position)[source]

Bases: object

The DocumentationComment holds information about a documentation comment inside source-code, like position etc.

class Description(desc)

Bases: tuple

desc

Alias for field number 0

class ExceptionValue(name, desc)

Bases: tuple

desc

Alias for field number 1

name

Alias for field number 0

class Parameter(name, desc)

Bases: tuple

desc

Alias for field number 1

name

Alias for field number 0

class Reference(type_ref, ref_addr)

Bases: tuple

ref_addr

Alias for field number 1

type_ref

Alias for field number 0

class ReturnValue(desc)

Bases: tuple

desc

Alias for field number 0

assemble[source]

Assembles parsed documentation to the original documentation.

This function assembles the whole documentation comment, with the given markers and indentation.

bottom_padding = 0
docstring_type = 'others'
docstyle
classmethod from_metadata(doccomment, docstyle_definition, marker, indent, position)[source]

Assembles a list of parsed documentation comment metadata.

This function just assembles the documentation comment itself, without the markers and indentation.

>>> from coalib.bearlib.languages.documentation.DocumentationComment \
...     import DocumentationComment
>>> from coalib.bearlib.languages.documentation.DocstyleDefinition \
...     import DocstyleDefinition
>>> from coalib.results.TextPosition import TextPosition
>>> Description = DocumentationComment.Description
>>> Parameter = DocumentationComment.Parameter
>>> python_default = DocstyleDefinition.load("python3", "default")
>>> parsed_doc = [Description(desc='\nDescription\n'),
...               Parameter(name='age', desc=' Age\n')]
>>> str(DocumentationComment.from_metadata(
...         parsed_doc, python_default,
...         python_default.markers[0], '    ',
...         TextPosition(1, 1)))
'\nDescription\n:param age: Age\n'
Parameters:
  • doccomment – The list of parsed documentation comment metadata.
  • docstyle_definition – The DocstyleDefinition instance that defines what docstyle is being used in a documentation comment.
  • marker – The markers to be used in the documentation comment.
  • indent – The indentation to be used in the documentation comment.
  • position – The starting position of the documentation comment.
Returns:

A DocumentationComment instance of the assembled documentation.
language
metadata
parse()[source]

Parses documentation independent of language and docstyle.

Returns:The list of all the parsed sections of the documentation. Every section is a namedtuple of either Description or Parameter or ReturnValue.
Raises:NotImplementedError – When no parsing method is present for the given language and docstyle.
top_padding = 0
class coalib.bearlib.languages.documentation.DocumentationComment.MalformedComment(message, line)[source]

Bases: object

The MalformedComment holds information about the errors generated by the DocumentationExtraction, DocumentationComment, DocstyleDefinition and DocBaseClass.

When these classes are unable to parse certain docstrings, an instance of MalformedComment will be returned instead of DocumentationComment.

coalib.bearlib.languages.documentation.DocumentationExtraction module

Language and docstyle independent extraction of documenation comments.

Each of the functions is built upon one another, and at the last, exposes a single function extract_documentation_with_markers() which is used by DocBaseClass, to extract documentation.

coalib.bearlib.languages.documentation.DocumentationExtraction.extract_documentation_with_markers(content, docstyle_definition)[source]

Extracts all documentation texts inside the given source-code-string.

Parameters:
  • content – The source-code-string where to extract documentation from. Needs to be a list or tuple where each string item is a single line (including ending whitespaces like \n).
  • docstyle_definition – The DocstyleDefinition instance that defines what docstyle is being used in the documentation.
Returns:

An iterator returning each DocumentationComment found in the content.
Module contents

Provides facilities to extract, parse and assemble documentation comments for different languages and documentation tools.

Submodules
coalib.bearlib.languages.Language module
class coalib.bearlib.languages.Language.Language(*versions)[source]

Bases: object

This class defines programming languages and their versions.

You can define a new programming language as follows:

>>> @Language
... class TrumpScript:
...     __qualname__ = "America is great."
...     aliases = 'ts',
...     versions = 2.7, 3.3, 3.4, 3.5, 3.6
...     comment_delimiter = '#'
...     string_delimiter = {"'": "'"}

From a bear, you can simply parse the user given language string to get the instance of the Language you desire:

>>> Language['trumpscript']
America is great. 2.7, 3.3, 3.4, 3.5, 3.6
>>> Language['ts 3.4, 3.6']
America is great. 3.4, 3.6
>>> Language['TS 3']
America is great. 3.3, 3.4, 3.5, 3.6
>>> Language['tS 1']
Traceback (most recent call last):
 ...
ValueError: No versions left

All given versions will be stored as a sorted tuple of packaging.version.Version instances:

>>> Language.TrumpScript(3.4, 3.3).versions
(<Version('3.3')>, <Version('3.4')>)

The attributes are not accessible unless you have selected one - and only one - version of your language:

>>> Language.TrumpScript(3.3, 3.4).comment_delimiter
Traceback (most recent call last):
 ...
AttributeError: You have to specify ONE version ...
>>> Language.TrumpScript(3.3).comment_delimiter
'#'

If you don’t know which version is the right one, just use this:

>>> Language.TrumpScript().get_default_version()
America is great. 3.6

To see which attributes are available, use the attributes property:

>>> Language.TrumpScript(3.3).attributes
['comment_delimiter', 'string_delimiter']

You can access a dictionary of the attribute values for every version from the class:

>>> Language.TrumpScript.comment_delimiter
OrderedDict([(<Version('2.7')>, '#'), (<Version('3.3')>, '#'), (<Version('3.4')>, '#'), (<Version('3.5')>, '#'), (<Version('3.6')>, '#')])

Any nonexistent item will of course not be served:

>>> Language.TrumpScript.unknown_delimiter
Traceback (most recent call last):
 ...
AttributeError

You now know the most important parts for writing a bear using languages. Read ahead if you want to know more about working with multiple versions of programming languages as well as derivative languages!

We can define derivative languages as follows:

>>> @Language
... class TrumpScriptDerivative(Language.TrumpScript):
...     __qualname__ = 'Shorter'
...     comment_delimiter = '//'
...     keywords = None

>>> Language.TrumpScriptDerivative()
Shorter 2.7, 3.3, 3.4, 3.5, 3.6

>>> Language.TrumpScriptDerivative().get_default_version().attributes
['comment_delimiter', 'keywords', 'string_delimiter']
>>> Language.TrumpScriptDerivative().get_default_version().keywords
>>> Language.TrumpScriptDerivative().get_default_version().comment_delimiter
'//'
>>> Language.TrumpScriptDerivative().get_default_version().string_delimiter
{"'": "'"}

We can get an instance via this syntax as well:

>>> Language[Language.TrumpScript]
America is great. 2.7, 3.3, 3.4, 3.5, 3.6
>>> Language[Language.TrumpScript(3.6)]
America is great. 3.6

As you see, you can use the __qualname__ property. This will also affect the string representation and work as an implicit alias:

>>> str(Language.TrumpScript(3.4))
'America is great. 3.4'

We can specify the version by instantiating the TrumpScript class now:

>>> str(Language.TrumpScript(3.6))
'America is great. 3.6'

You can also define ranges of versions of languages:

>>> (Language.TrumpScript > 3.3) <= 3.5
America is great. 3.4, 3.5

>>> Language.TrumpScript == 3
America is great. 3.3, 3.4, 3.5, 3.6

Those can be combined by the or operator:

>>> (Language.TrumpScript == 3.6) | (Language.TrumpScript == 2)
America is great. 2.7, 3.6

The __contains__ operator of the class is defined as well for strings and instances. This is case insensitive and aliases are allowed:

>>> Language.TrumpScript(3.6) in Language.TrumpScript
True
>>> 'ts 3.6, 3.5' in Language.TrumpScript
True
>>> 'TrumpScript 2.6' in Language.TrumpScript
False
>>> 'TrumpScript' in Language.TrumpScript
True

This also works on instances:

>>> 'ts 3.6, 3.5' in (Language.TrumpScript == 3)
True
>>> 'ts 3.6,3.5' in ((Language.TrumpScript == 2)
...                  | Language.TrumpScript(3.5))
False
>>> Language.TrumpScript(2.7, 3.5) in (Language.TrumpScript == 3)
False
>>> Language.TrumpScript(3.5) in (Language.TrumpScript == 3)
True

Any undefined language will obviously not be available:

>>> Language.Cobol
Traceback (most recent call last):
 ...
UnknownLanguageError: No language found for `Cobol`
attributes

Retrieves the names of all attributes that are available for this language.

get_default_version()[source]

Retrieves the latest version the user would want to choose from the given versions in self.

(At a later point this might also retrieve a default version specifiable by the language definition, so keep using this!)

versions = ()
class coalib.bearlib.languages.Language.LanguageMeta(clsname, bases, clsattrs)[source]

Bases: type

Metaclass for coalib.bearlib.languages.Language.Language.

Allows it being used as a decorator as well as implements the __contains__() operation and stores all languages created with the decorator.

Ensures that .versions defined in language classes will be turned into sorted tuples of packaging.version.Version instances.

The operators are defined on the class as well, so you can do the following:

>>> @Language
... class SomeLang:
...     versions = 2.7, 3.3, 3.4, 3.5, 3.6
>>> Language.SomeLang > 3.4
SomeLang 3.5, 3.6
>>> Language.SomeLang < 3.4
SomeLang 2.7, 3.3
>>> Language.SomeLang >= 3.4
SomeLang 3.4, 3.5, 3.6
>>> Language.SomeLang <= 3.4
SomeLang 2.7, 3.3, 3.4
>>> Language.SomeLang == 3.4
SomeLang 3.4
>>> Language.SomeLang != 3.4
SomeLang 2.7, 3.3, 3.5, 3.6
>>> Language.SomeLang == 1.0
Traceback (most recent call last):
 ...
ValueError: No versions left
class coalib.bearlib.languages.Language.LanguageUberMeta[source]

Bases: type

This class is used to hide the all attribute from the Language class.

all = [<class 'coalib.bearlib.languages.Language.Unknown'>, <class 'coalib.bearlib.languages.Language.C'>, <class 'coalib.bearlib.languages.Language.CPP'>, <class 'coalib.bearlib.languages.Language.C#'>, <class 'coalib.bearlib.languages.Language.CSS'>, <class 'coalib.bearlib.languages.Language.Fortran'>, <class 'coalib.bearlib.languages.Language.Golang'>, <class 'coalib.bearlib.languages.Language.Hypertext Markup Language'>, <class 'coalib.bearlib.languages.Language.Java'>, <class 'coalib.bearlib.languages.Language.JavaScript'>, <class 'coalib.bearlib.languages.Language.JavaServer Pages'>, <class 'coalib.bearlib.languages.Language.Matlab'>, <class 'coalib.bearlib.languages.Language.Markdown'>, <class 'coalib.bearlib.languages.Language.ObjectiveC'>, <class 'coalib.bearlib.languages.Language.PHP'>, <class 'coalib.bearlib.languages.Language.PLSQL'>, <class 'coalib.bearlib.languages.Language.Python'>, <class 'coalib.bearlib.languages.Language.Ruby'>, <class 'coalib.bearlib.languages.Language.Scala'>, <class 'coalib.bearlib.languages.Language.Swift'>, <class 'coalib.bearlib.languages.Language.Vala'>, <class 'coalib.bearlib.languages.Language.TypeScript'>, <class 'coalib.bearlib.languages.Language.Shell'>, <class 'coalib.bearlib.languages.Language.Jinja2'>]
class coalib.bearlib.languages.Language.Languages[source]

Bases: tuple

A tuple-based container for coalib.bearlib.languages.Language instances. It supports language identifiers in any format accepted by Language[...]:

>>> Languages(['C#', Language.Python == 3])
(C#, Python 3.3, 3.4, 3.5, 3.6)
>>> Languages(['C#', Language.Python == '3.6'])
(C#, Python 3.6)
>>> Languages(['C#', 'Python 2.7'])
(C#, Python 2.7)

It provides __contains__() for checking if a given language identifier is included:

>>> 'Python 2.7, 3.5' in Languages([Language.Python()])
True
>>> 'Py 3.3' in Languages(['Python 2'])
False
>>> 'csharp' in Languages(['C#', Language.Python == 3.6])
True
exception coalib.bearlib.languages.Language.UnknownLanguageError[source]

Bases: AttributeError, KeyError

This exception occurs when an unknown language is requested.

coalib.bearlib.languages.Language.limit_versions(language, limit, operator)[source]

Limits given languages with the given operator:

Parameters:
  • language – A Language instance.
  • limit – A number to limit the versions.
  • operator – The operator to use for the limiting.
Returns:

A new Language instance with limited versions.
Raises:

ValueError – If no version is left anymore.
coalib.bearlib.languages.Language.parse_lang_str(string)[source]

Parses any given language string into name and a list of either int, float, or str versions (ignores leading whitespace):

>>> parse_lang_str("Python")
('Python', [])
>>> parse_lang_str("Python 3.3")
('Python', [3.3])
>>> parse_lang_str("Python 3.6, 3.3.1")
('Python', [3.6, '3.3.1'])
>>> parse_lang_str("Objective C 3.6, 3")
('Objective C', [3.6, 3])
>>> parse_lang_str("Cobol, stupid!")
Traceback (most recent call last):
 ...
packaging.version.InvalidVersion: Invalid version: 'stupid!'
>>> parse_lang_str("Cobol seems at least stupid ;)")
('Cobol seems at least stupid ;)', [])
coalib.bearlib.languages.LanguageDefinition module
class coalib.bearlib.languages.LanguageDefinition.LanguageDefinition(language: str, coalang_dir=None)[source]

Bases: coalib.bearlib.abstractions.SectionCreatable.SectionCreatable

This class is deprecated! Use the Language class instead.

A Language Definition holds constants which may help parsing the language. If you want to write a bear you’ll probably want to use those definitions to keep your bear independent of the semantics of each language.

You can easily get your language definition by just creating it with the name of the language desired:

>>> list(LanguageDefinition("cpp")['extensions'])
['.c', '.cpp', '.h', '.hpp']

For some languages aliases exist, the name is case insensitive; they will behave just like before and return settings:

>>> dict(LanguageDefinition('C++')['comment_delimiter'])
{'//': ''}
>>> dict(LanguageDefinition('C++')['string_delimiters'])
{'"': '"'}

If no language exists, you will get a FileNotFoundError:

>>> LanguageDefinition("BULLSHIT!")
Traceback (most recent call last):
 ...
FileNotFoundError

Custom coalangs are no longer supported. You can simply register your languages to the Languages decorator. When giving a custom coalang directory a warning will be emitted and it will attempt to load the given Language anyway through conventional means:

>>> LanguageDefinition("custom", coalang_dir='somewhere')
Traceback (most recent call last):
 ...
FileNotFoundError

If you need a custom language, just go like this:

>>> @Language
... class MyLittlePony:
...     color = 'green'
...     legs = 5
>>> int(LanguageDefinition('mylittlepony')['legs'])
5

But seriously, just use Language - and mind that it’s already typed:

>>> Language['mylittlepony'].get_default_version().legs
5
Module contents

This directory holds means to get generic information for specific languages.

coalib.bearlib.naming_conventions package
Module contents
coalib.bearlib.naming_conventions.to_camelcase(string)[source]

Converts the given string to camel-case.

>>> to_camelcase('Hello_world')
'helloWorld'
>>> to_camelcase('__Init__file__')
'__initFile__'
>>> to_camelcase('')
''
>>> to_camelcase('alreadyCamelCase')
'alreadyCamelCase'
>>> to_camelcase('   string')
'___string'
Parameters:string – The string to convert.
Returns:The camel-cased string.
coalib.bearlib.naming_conventions.to_kebabcase(string)[source]

Converts the given string to kebab-case.

>>> to_kebabcase('HelloWorld')
'hello-world'
>>> to_kebabcase('__Init__File__')
'init-file'
>>> to_kebabcase('')
''
>>> to_kebabcase('already-kebab-case')
'already-kebab-case'
>>> to_kebabcase('   string  ')
'string'
>>> to_kebabcase('ABCde.F.G..H..IH')
'a-b-cde.f.g..h..i-h'
Parameters:string – The string to convert.
Returns:The kebab-cased string.
coalib.bearlib.naming_conventions.to_pascalcase(string)[source]

Converts the given to string pascal-case.

>>> to_pascalcase('hello_world')
'HelloWorld'
>>> to_pascalcase('__init__file__')
'__InitFile__'
>>> to_pascalcase('')
''
>>> to_pascalcase('AlreadyPascalCase')
'AlreadyPascalCase'
>>> to_pascalcase('   string')
'___String'
Parameters:string – The string to convert.
Returns:The pascal-cased string.
coalib.bearlib.naming_conventions.to_snakecase(string)[source]

Converts the given string to snake-case.

>>> to_snakecase('HelloWorld')
'hello_world'
>>> to_snakecase('__Init__File__')
'__init_file__'
>>> to_snakecase('')
''
>>> to_snakecase('already_snake_case')
'already_snake_case'
>>> to_snakecase('   string  ')
'___string__'
>>> to_snakecase('ABCde.F.G..H..IH')
'a_b_cde.f.g..h..i_h'
Parameters:string – The string to convert.
Returns:The snake-cased string.
coalib.bearlib.naming_conventions.to_spacecase(string)[source]

Converts the given string to space-case.

>>> to_spacecase('helloWorld')
'Hello World'
>>> to_spacecase('__Init__File__')
'Init File'
>>> to_spacecase('')
''
>>> to_spacecase('Already Space Case')
'Already Space Case'
>>> to_spacecase('  string  ')
'String'
Parameters:string – The string to convert.
Returns:The space-cased string.
coalib.bearlib.spacing package
Submodules
coalib.bearlib.spacing.SpacingHelper module
class coalib.bearlib.spacing.SpacingHelper.SpacingHelper(tab_width: int = 4)[source]

Bases: coalib.bearlib.abstractions.SectionCreatable.SectionCreatable

DEFAULT_TAB_WIDTH = 4
get_indentation(line: str)[source]

Checks the lines indentation.

Parameters:line – A string to check for indentation.
Returns:The indentation count in spaces.
replace_spaces_with_tabs(line: str)[source]

Replaces spaces with tabs where possible. However in no case only one space will be replaced by a tab.

Example: ” a_text another” will be converted to ” a_text another”, assuming the tab_width is set to 4.

Parameters:line – The string with spaces to replace.
Returns:The converted string.
replace_tabs_with_spaces(line: str)[source]

Replaces tabs in this line with the appropriate number of spaces.

Example: ” ” will be converted to ” “, assuming the tab_width is set to 4.

Parameters:line – The string with tabs to replace.
Returns:A string with no tabs.
yield_tab_lengths(input: str)[source]

Yields position and size of tabs in a input string.

Parameters:input – The string with tabs.
Module contents
Module contents

The bearlib is an optional library designed to ease the task of any Bear. Just as the rest of coala the bearlib is designed to be as easy to use as possible while offering the best possible flexibility.

coalib.bearlib.deprecate_bear(bear)[source]

Use this to deprecate a bear. Say we have a bear:

>>> class SomeBear:
...     def run(*args):
...         print("I'm running!")

To change the name from SomeOldBear to SomeBear you can keep the SomeOldBear.py around with those contents:

>>> @deprecate_bear
... class SomeOldBear(SomeBear): pass

Now let’s run the bear:

>>> import sys
>>> logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
>>> SomeOldBear().run()
WARNING:root:The bear SomeOldBear is deprecated. Use SomeBear instead!
I'm running!
Parameters:bear – An old bear class that inherits from the new one (so it gets its methods and can just contain a pass.)
Returns:A bear class that warns about deprecation on use.
coalib.bearlib.deprecate_settings(**depr_args)[source]

The purpose of this decorator is to allow passing old settings names to bears due to the heavy changes in their names.

>>> @deprecate_settings(new='old')
... def run(new):
...     print(new)

Now we can simply call the bear with the deprecated setting, we’ll get a warning - but it still works!

>>> import sys
>>> logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
>>> run(old="Hello world!")
WARNING:root:The setting `old` is deprecated. Please use `new` instead.
Hello world!
>>> run(new="Hello world!")
Hello world!

This example represents the case where the old setting name needs to be modified to match the new one.

>>> @deprecate_settings(new=('old', lambda a: a + 'coala!'))
... def func(new):
...     print(new)

>>> func(old="Welcome to ")
WARNING:root:The setting `old` is deprecated. Please use `new` instead.
Welcome to coala!
>>> func(new='coala!')
coala!

This example represents the case where the old and new settings are provided to the function.

>>> @deprecate_settings(new='old')
... def run(new):
...     print(new)
>>> # doctest:
... run(old="Hello!", new='coala is always written with lowercase `c`.')
WARNING:root:The setting `old` is deprecated. Please use `new` instead.
WARNING:root:The value of `old` and `new` are conflicting. `new` will...
coala is always written with lowercase `c`.
>>> @deprecate_settings(new='old')
... def run(new):
...     print(new)
>>> run(old='Hello!', new='Hello!')
WARNING:root:The setting `old` is deprecated. Please use `new` instead.
Hello!

Note that messages are cached. So the same message won’t be printed twice: >>> run(old=’Hello!’, new=’Hello!’) Hello!

Multiple deprecations can be provided for the same setting. The modifier function for each deprecated setting can be given as a value in a dict where the deprecated setting is the key. A default modifier may be specified at the end of the deprecated settings tuple.

>>> @deprecate_settings(new=({'old': lambda x: x + ' coala!'},
...                          'older',
...                           lambda x: x + '!' ))
... def run(new):
...     print(new)
>>> run(old='Hi')
WARNING:root:The setting `old` is deprecated. Please use `new` instead.
Hi coala!
>>> run(older='Hi')
WARNING:root:The setting `older` is deprecated. Please use `new` instead.
Hi!

The metadata for coala has been adjusted as well:

>>> list(run.__metadata__.non_optional_params.keys())
['new']
>>> list(run.__metadata__.optional_params.keys())
['old', 'older']
Parameters:depr_args – A dictionary of settings as keys and their deprecated names as values.

coalib.bears package

Submodules
coalib.bears.BEAR_KIND module
coalib.bears.Bear module
class coalib.bears.Bear.Bear(section: coalib.settings.Section.Section, message_queue, timeout=0)[source]

Bases: pyprint.Printer.Printer, coalib.output.printers.LogPrinter.LogPrinterMixin

A bear contains the actual subroutine that is responsible for checking source code for certain specifications. However it can actually do whatever it wants with the files it gets. If you are missing some Result type, feel free to contact us and/or help us extending the coalib.

This is the base class for every bear. If you want to write a bear, you will probably want to look at the GlobalBear and LocalBear classes that inherit from this class. In any case you’ll want to overwrite at least the run method. You can send debug/warning/error messages through the debug(), warn(), err() functions. These will send the appropriate messages so that they are outputted. Be aware that if you use err(), you are expected to also terminate the bear run-through immediately.

Settings are available at all times through self.section.

To indicate which languages your bear supports, just give it the LANGUAGES value which should be a set of string(s):

>>> from dependency_management.requirements.PackageRequirement import (
... PackageRequirement)
>>> from dependency_management.requirements.PipRequirement import (
... PipRequirement)
>>> class SomeBear(Bear):
...     LANGUAGES = {'C', 'CPP','C#', 'D'}

To indicate the requirements of the bear, assign REQUIREMENTS a set with instances of PackageRequirements.

>>> class SomeBear(Bear):
...     REQUIREMENTS = {
...         PackageRequirement('pip', 'coala_decorators', '0.2.1')}

If your bear uses requirements from a manager we have a subclass from, you can use the subclass, such as PipRequirement, without specifying manager:

>>> class SomeBear(Bear):
...     REQUIREMENTS = {PipRequirement('coala_decorators', '0.2.1')}

To specify additional attributes to your bear, use the following:

>>> class SomeBear(Bear):
...     AUTHORS = {'Jon Snow'}
...     AUTHORS_EMAILS = {'jon_snow@gmail.com'}
...     MAINTAINERS = {'Catelyn Stark'}
...     MAINTAINERS_EMAILS = {'catelyn_stark@gmail.com'}
...     LICENSE = 'AGPL-3.0'
...     ASCIINEMA_URL = 'https://asciinema.org/a/80761'

If the maintainers are the same as the authors, they can be omitted:

>>> class SomeBear(Bear):
...     AUTHORS = {'Jon Snow'}
...     AUTHORS_EMAILS = {'jon_snow@gmail.com'}
>>> SomeBear.maintainers
{'Jon Snow'}
>>> SomeBear.maintainers_emails
{'jon_snow@gmail.com'}

If your bear needs to include local files, then specify it giving strings containing relative file paths to the INCLUDE_LOCAL_FILES set:

>>> class SomeBear(Bear):
...     INCLUDE_LOCAL_FILES = {'checkstyle.jar', 'google_checks.xml'}

To keep track easier of what a bear can do, simply tell it to the CAN_FIX and the CAN_DETECT sets. Possible values:

>>> CAN_DETECT = {'Syntax', 'Formatting', 'Security', 'Complexity', 'Smell',
... 'Unused Code', 'Redundancy', 'Variable Misuse', 'Spelling',
... 'Memory Leak', 'Documentation', 'Duplication', 'Commented Code',
... 'Grammar', 'Missing Import', 'Unreachable Code', 'Undefined Element',
... 'Code Simplification', 'Statistics'}
>>> CAN_FIX = {'Syntax', ...}

Specifying something to CAN_FIX makes it obvious that it can be detected too, so it may be omitted:

>>> class SomeBear(Bear):
...     CAN_DETECT = {'Syntax', 'Security'}
...     CAN_FIX = {'Redundancy'}
>>> list(sorted(SomeBear.can_detect))
['Redundancy', 'Security', 'Syntax']

Every bear has a data directory which is unique to that particular bear:

>>> class SomeBear(Bear): pass
>>> class SomeOtherBear(Bear): pass
>>> SomeBear.data_dir == SomeOtherBear.data_dir
False

BEAR_DEPS contains bear classes that are to be executed before this bear gets executed. The results of these bears will then be passed to the run method as a dict via the dependency_results argument. The dict will have the name of the Bear as key and the list of its results as results:

>>> class SomeBear(Bear): pass
>>> class SomeOtherBear(Bear):
...     BEAR_DEPS = {SomeBear}
>>> SomeOtherBear.BEAR_DEPS
{<class 'coalib.bears.Bear.SomeBear'>}

Every bear resides in some directory which is specified by the source_location attribute:

>>> class SomeBear(Bear): pass
>>> SomeBear.source_location
'...Bear.py'

Every linter bear makes use of an executable tool for its operations. The SEE_MORE attribute provides a link to the main page of the linter tool:

>>> class PyLintBear(Bear):
...     SEE_MORE = 'https://www.pylint.org/'
>>> PyLintBear.SEE_MORE
'https://www.pylint.org/'

In the future, bears will not survive without aspects. aspects are defined as part of the class statement’s parameter list. According to the classic CAN_DETECT and CAN_FIX attributes, aspects can either be only 'detect'-able or also 'fix'-able:

>>> from coalib.bearlib.aspects.Metadata import CommitMessage

>>> class aspectsCommitBear(Bear, aspects={
...         'detect': [CommitMessage.Shortlog.ColonExistence],
...         'fix': [CommitMessage.Shortlog.TrailingPeriod],
... }, languages=['Python']):
...     pass

>>> aspectsCommitBear.aspects['detect']
[<aspectclass 'Root.Metadata.CommitMessage.Shortlog.ColonExistence'>]
>>> aspectsCommitBear.aspects['fix']
[<aspectclass 'Root.Metadata.CommitMessage.Shortlog.TrailingPeriod'>]

To indicate the bear uses raw files, set USE_RAW_FILES to True:

>>> class RawFileBear(Bear):
...     USE_RAW_FILES = True
>>> RawFileBear.USE_RAW_FILES
True

However if USE_RAW_FILES is enabled the Bear is in charge of managing the file (opening the file, closing the file, reading the file, etc).

ASCIINEMA_URL = ''
AUTHORS = set()
AUTHORS_EMAILS = set()
BEAR_DEPS = set()
CAN_DETECT = set()
CAN_FIX = set()
INCLUDE_LOCAL_FILES = set()
LANGUAGES = set()
LICENSE = ''
MAINTAINERS = set()
MAINTAINERS_EMAILS = set()
PLATFORMS = {'any'}
REQUIREMENTS = set()
SEE_MORE = ''
USE_RAW_FILES = False
can_detect = set()
classmethod check_prerequisites()[source]

Checks whether needed runtime prerequisites of the bear are satisfied.

This function gets executed at construction.

Section value requirements shall be checked inside the run method. >>> from dependency_management.requirements.PipRequirement import ( … PipRequirement) >>> class SomeBear(Bear): … REQUIREMENTS = {PipRequirement(‘pip’)}

>>> SomeBear.check_prerequisites()
True

>>> class SomeOtherBear(Bear):
...     REQUIREMENTS = {PipRequirement('really_bad_package')}

>>> SomeOtherBear.check_prerequisites()
'really_bad_package is not installed. You can install it using ...'

>>> class anotherBear(Bear):
...     REQUIREMENTS = {PipRequirement('bad_package', '0.0.1')}

>>> anotherBear.check_prerequisites()
'bad_package 0.0.1 is not installed. You can install it using ...'
Returns:True if prerequisites are satisfied, else False or a string that serves a more detailed description of what’s missing.
data_dir = '/home/docs/.local/share/coala-bears/Bear'
download_cached_file(url, filename)[source]

Downloads the file if needed and caches it for the next time. If a download happens, the user will be informed.

Take a sane simple bear:

>>> from queue import Queue
>>> bear = Bear(Section("a section"), Queue())

We can now carelessly query for a neat file that doesn’t exist yet:

>>> from os import remove
>>> if exists(join(bear.data_dir, "a_file")):
...     remove(join(bear.data_dir, "a_file"))
>>> file = bear.download_cached_file("https://github.com/", "a_file")

If we download it again, it’ll be much faster as no download occurs:

>>> newfile = bear.download_cached_file("https://github.com/", "a_file")
>>> newfile == file
True
Parameters:
  • url – The URL to download the file from.
  • filename – The filename it should get, e.g. “test.txt”.
Returns:

A full path to the file ready for you to use!
execute(*args, debug=False, **kwargs)[source]
get_config_dir()[source]

Gives the directory where the configuration file is.

Returns:Directory of the config file.
classmethod get_metadata()[source]
Returns:Metadata for the run function. However parameters like self or parameters implicitly used by coala (e.g. filename for local bears) are already removed.
classmethod get_non_optional_settings(recurse=True)[source]

This method has to determine which settings are needed by this bear. The user will be prompted for needed settings that are not available in the settings file so don’t include settings where a default value would do.

Note: This function also queries settings from bear dependencies in recursive manner. Though circular dependency chains are a challenge to achieve, this function would never return on them!

Parameters:recurse – Get the settings recursively from its dependencies.
Returns:A dictionary of needed settings as keys and a tuple of help text and annotation as values.
static kind()[source]
Returns:The kind of the bear
log_message(log_message, timestamp=None, **kwargs)[source]
maintainers = set()
maintainers_emails = set()
classmethod missing_dependencies(lst)[source]

Checks if the given list contains all dependencies.

Parameters:lst – A list of all already resolved bear classes (not instances).
Returns:A set of missing dependencies.
name = 'Bear'
new_result

Returns a partial for creating a result with this bear already bound.

run(*args, dependency_results=None, **kwargs)[source]
run_bear_from_section(args, kwargs)[source]
static setup_dependencies()[source]

This is a user defined function that can download and set up dependencies (via download_cached_file or arbitrary other means) in an OS independent way.

source_location = '/home/docs/checkouts/readthedocs.org/user_builds/coala-api/envs/latest/lib/python3.5/site-packages/coala-0.12.0.dev20180614080551-py3.5.egg/coalib/bears/Bear.py'
coalib.bears.GlobalBear module
class coalib.bears.GlobalBear.GlobalBear(file_dict, section, message_queue, timeout=0)[source]

Bases: coalib.bears.Bear.Bear

A GlobalBear analyzes semantic facts across several files.

The results of a GlobalBear will be presented grouped by the origin Bear. Therefore Results spanning across multiple files are allowed and will be handled correctly.

If you are inspecting a single file at a time, you should consider using a LocalBear.

static kind()[source]
run(*args, dependency_results=None, **kwargs)[source]

Handles all files in file_dict.

Parameters:dependency_results – The dictionary of {bear name: result list}.
Returns:A list of Result type.

See coalib.bears.Bear for run method description.

coalib.bears.LocalBear module
class coalib.bears.LocalBear.LocalBear(section: coalib.settings.Section.Section, message_queue, timeout=0)[source]

Bases: coalib.bears.Bear.Bear

A LocalBear is a Bear that analyzes only one file at once. It therefore can not analyze semantical facts over multiple files.

This has the advantage that it can be highly parallelized. In addition, the results from multiple bears for one file can be shown together for that file, which is better to grasp for the user. coala takes care of all that.

Examples for LocalBear’s could be:

  • A SpaceConsistencyBear that checks every line for trailing whitespaces, tabs, etc.
  • A VariableNameBear that checks variable names and constant names for certain conditions
classmethod get_metadata()[source]
static kind()[source]
run(filename, file, *args, dependency_results=None, **kwargs)[source]

Handles the given file.

Parameters:
  • filename – The filename of the file
  • file – The file contents as string array
Returns:

A list of Result
coalib.bears.meta module
class coalib.bears.meta.bearclass(clsname, bases, clsattrs, *varargs, aspects=None, languages=None)[source]

Bases: type

Metaclass for coalib.bears.Bear.Bear and therefore all bear classes.

Pushing bears into the future… ;)

aspects = defaultdict(<function bearclass.<lambda>>, {})
Module contents

coalib.collecting package

Submodules
coalib.collecting.Collectors module
coalib.collecting.Collectors.collect_all_bears_from_sections(sections, log_printer=None, bear_globs=('**', ))[source]

Collect all kinds of bears from bear directories given in the sections.

Parameters:
  • sections – List of sections so bear_dirs are taken into account
  • log_printer – Log_printer to handle logging
  • bear_globs – List of glob patterns.
Returns:

Tuple of dictionaries of local and global bears. The dictionary key is section class and dictionary value is a list of Bear classes
coalib.collecting.Collectors.collect_bears(bear_dirs, bear_globs, kinds, log_printer=None, warn_if_unused_glob=True)[source]

Collect all bears from bear directories that have a matching kind matching the given globs.

Parameters:
  • bear_dirs – Directory name or list of such that can contain bears.
  • bear_globs – Globs of bears to collect.
  • kinds – List of bear kinds to be collected.
  • log_printer – log_printer to handle logging.
  • warn_if_unused_glob – True if warning message should be shown if a glob didn’t give any bears.
Returns:

Tuple of list of matching bear classes based on kind. The lists are in the same order as kinds.
coalib.collecting.Collectors.collect_bears_by_aspects(aspects, kinds)[source]

Collect bear based on aspects.

Return a list of bears that have capability to analyze all aspects from given AspectList requirement.

Parameters:
  • aspects – An AspectList that need to be covered.
  • kinds – List of bear kinds to be collected.
Returns:

Tuple of list of bear classes based on kind. The lists are in the same order as kinds.
coalib.collecting.Collectors.collect_dirs(dir_paths, ignored_dir_paths=None)[source]

Evaluate globs in directory paths and return all matching directories

Parameters:
  • dir_paths – File path or list of such that can include globs
  • ignored_dir_paths – List of globs that match to-be-ignored dirs
Returns:

List of paths of all matching directories
coalib.collecting.Collectors.collect_files(file_paths, log_printer=None, ignored_file_paths=None, limit_file_paths=None, section_name='')[source]

Evaluate globs in file paths and return all matching files

Parameters:
  • file_paths – File path or list of such that can include globs
  • ignored_file_paths – List of globs that match to-be-ignored files
  • limit_file_paths – List of globs that the files are limited to
  • section_name – Name of currently executing section
Returns:

List of paths of all matching files
coalib.collecting.Collectors.collect_registered_bears_dirs(entrypoint)[source]

Searches setuptools for the entrypoint and returns the bear directories given by the module.

Parameters:entrypoint – The entrypoint to find packages with.
Returns:List of bear directories.
coalib.collecting.Collectors.filter_capabilities_by_languages(bears, languages)[source]

Filters the bears capabilities by languages.

Parameters:
  • bears – Dictionary with sections as keys and list of bears as values.
  • languages – Languages that bears are being filtered on.
Returns:

New dictionary with languages as keys and their bears capabilities as values. The capabilities are stored in a tuple of two elements where the first one represents what the bears can detect, and the second one what they can fix.
coalib.collecting.Collectors.filter_section_bears_by_languages(bears, languages)[source]

Filters the bears by languages.

Parameters:
  • bears – The dictionary of the sections as keys and list of bears as values.
  • languages – Languages that bears are being filtered on.
Returns:

New dictionary with filtered out bears that don’t match any language from languages.
coalib.collecting.Collectors.get_all_bears()[source]

Get a list of all available bears.

coalib.collecting.Collectors.get_all_bears_names()[source]

Get a list of names of all available bears.

coalib.collecting.Collectors.icollect(file_paths, ignored_globs=None, match_cache={}, match_function=<function fnmatch>)[source]

Evaluate globs in file paths and return all matching files.

Parameters:
  • file_paths – File path or list of such that can include globs
  • ignored_globs – List of globs to ignore when matching files
  • match_cache – Dictionary to use for caching results
  • match_function – The function to use for glob matching
Returns:

Iterator that yields tuple of path of a matching file, the glob where it was found
coalib.collecting.Collectors.icollect_bears(bear_dir_glob, bear_globs, kinds, log_printer=None)[source]

Collect all bears from bear directories that have a matching kind.

Parameters:
  • bear_dir_glob – Directory globs or list of such that can contain bears
  • bear_globs – Globs of bears to collect
  • kinds – List of bear kinds to be collected
  • log_printer – Log_printer to handle logging
Returns:

Iterator that yields a tuple with bear class and which bear_glob was used to find that bear class.
coalib.collecting.Collectors.list_glob_results(values=None)[source]

Expands the globs of all given values and concatenates the results.

Parameters:values – List of file-globs or files.
Returns:List of matched files.
coalib.collecting.Collectors.match_dir_or_file_pattern(path, ignore_patterns=None)[source]

Tries to match the given path with the directory (substring match) or file (enforced full match) patterns.

Parameters:
  • path – Valid file path
  • ignore_patterns – List of regex patterns that match a file or a directory
Returns:

True if any of the given pattern match
coalib.collecting.Dependencies module
coalib.collecting.Dependencies.resolve(bears)[source]

Collects all dependencies of the given bears. This will also remove duplicates.

Parameters:bears – The given bears. Will not be modified.
Returns:The new list of bears, sorted so that it can be executed sequentially without dependency issues.
coalib.collecting.Importers module
coalib.collecting.Importers.iimport_objects(file_paths, names=None, types=None, supers=None, attributes=None, local=False, suppress_output=False)[source]

Import all objects from the given modules that fulfill the requirements

Parameters:
  • file_paths – File path(s) from which objects will be imported.
  • names – Name(s) an objects need to have one of.
  • types – Type(s) an objects need to be out of.
  • supers – Class(es) objects need to be a subclass of.
  • attributes – Attribute(s) an object needs to (all) have.
  • local – If True: Objects need to be defined in the file they appear in to be collected.
  • suppress_output – Whether console output from stdout shall be suppressed or not.
Returns:

An iterator that yields all matching python objects.
Raises:

Exception – Any exception that is thrown in module code or an ImportError if paths are erroneous.
coalib.collecting.Importers.import_objects(file_paths, names=None, types=None, supers=None, attributes=None, local=False, verbose=False)[source]

Import all objects from the given modules that fulfill the requirements

Parameters:
  • file_paths – File path(s) from which objects will be imported
  • names – Name(s) an objects need to have one of
  • types – Type(s) an objects need to be out of
  • supers – Class(es) objects need to be a subclass of
  • attributes – Attribute(s) an object needs to (all) have
  • local – if True: Objects need to be defined in the file they appear in to be collected
Returns:

list of all matching python objects
Raises:

Exception – Any exception that is thrown in module code or an ImportError if paths are erroneous.
coalib.collecting.Importers.object_defined_in(obj, file_path)[source]

Check if the object is defined in the given file.

>>> object_defined_in(object_defined_in, __file__)
True
>>> object_defined_in(object_defined_in, "somewhere else")
False

Builtins are always defined outside any given file:

>>> object_defined_in(False, __file__)
False
Parameters:
  • obj – The object to check.
  • file_path – The path it might be defined in.
Returns:

True if the object is defined in the file.
Module contents

coalib.core package

Submodules
coalib.core.Bear module
class coalib.core.Bear.Bear(section: coalib.settings.Section.Section, file_dict: dict)[source]

Bases: object

A bear contains the actual subroutine that is responsible for checking source code for certain specifications. However, it can actually do whatever it wants with the files it gets.

This is the base class for every bear. If you want to write a bear, you will probably want to look at the ProjectBear and FileBear classes that inherit from this class.

To indicate which languages your bear supports, just give it the LANGUAGES value which should be a set of string(s):

>>> class SomeBear(Bear):
...     LANGUAGES = {'C', 'CPP', 'C#', 'D'}

To indicate the requirements of the bear, assign REQUIREMENTS a set with instances of PackageRequirements.

>>> from dependency_management.requirements.PackageRequirement import (
...     PackageRequirement)
>>> class SomeBear(Bear):
...     REQUIREMENTS = {
...         PackageRequirement('pip', 'coala_decorators', '0.2.1')}

If your bear uses requirements from a manager we have a subclass from, you can use the subclass, such as PipRequirement, without specifying manager:

>>> from dependency_management.requirements.PipRequirement import (
...     PipRequirement)
>>> class SomeBear(Bear):
...     REQUIREMENTS = {PipRequirement('coala_decorators', '0.2.1')}

To specify additional attributes to your bear, use the following:

>>> class SomeBear(Bear):
...     AUTHORS = {'Jon Snow'}
...     AUTHORS_EMAILS = {'jon_snow@gmail.com'}
...     MAINTAINERS = {'Catelyn Stark'}
...     MAINTAINERS_EMAILS = {'catelyn_stark@gmail.com'}
...     LICENSE = 'AGPL-3.0'
...     ASCIINEMA_URL = 'https://asciinema.org/a/80761'

If the maintainers are the same as the authors, they can be omitted:

>>> class SomeBear(Bear):
...     AUTHORS = {'Jon Snow'}
...     AUTHORS_EMAILS = {'jon_snow@gmail.com'}
>>> SomeBear.maintainers
{'Jon Snow'}
>>> SomeBear.maintainers_emails
{'jon_snow@gmail.com'}

If your bear needs to include local files, then specify it giving strings containing relative file paths to the INCLUDE_LOCAL_FILES set:

>>> class SomeBear(Bear):
...     INCLUDE_LOCAL_FILES = {'checkstyle.jar', 'google_checks.xml'}

To keep track easier of what a bear can do, simply tell it to the CAN_FIX and the CAN_DETECT sets. Possible values are:

>>> CAN_DETECT = {'Syntax', 'Formatting', 'Security', 'Complexity',
... 'Smell', 'Unused Code', 'Redundancy', 'Variable Misuse', 'Spelling',
... 'Memory Leak', 'Documentation', 'Duplication', 'Commented Code',
... 'Grammar', 'Missing Import', 'Unreachable Code', 'Undefined Element',
... 'Code Simplification'}
>>> CAN_FIX = {'Syntax', ...}

Specifying something to CAN_FIX makes it obvious that it can be detected too, so it may be omitted:

>>> class SomeBear(Bear):
...     CAN_DETECT = {'Syntax', 'Security'}
...     CAN_FIX = {'Redundancy'}
>>> sorted(SomeBear.can_detect)
['Redundancy', 'Security', 'Syntax']

Every bear has a data directory which is unique to that particular bear:

>>> class SomeBear(Bear): pass
>>> class SomeOtherBear(Bear): pass
>>> SomeBear.data_dir == SomeOtherBear.data_dir
False

A bear can be dependent from other bears. BEAR_DEPS contains bear classes that are to be executed before this bear gets executed. The results of these bears will then be passed inside self.dependency_results as a dict. The dict will have the name of the bear as key and a list of its results as values:

>>> class SomeBear(Bear): pass
>>> class SomeOtherBear(Bear):
...     BEAR_DEPS = {SomeBear}
>>> SomeOtherBear.BEAR_DEPS
{<class 'coalib.core.Bear.SomeBear'>}
ASCIINEMA_URL = ''
AUTHORS = set()
AUTHORS_EMAILS = set()
BEAR_DEPS = set()
CAN_DETECT = set()
CAN_FIX = set()
INCLUDE_LOCAL_FILES = set()
LANGUAGES = set()
LICENSE = ''
MAINTAINERS = set()
MAINTAINERS_EMAILS = set()
PLATFORMS = {'any'}
REQUIREMENTS = set()
analyze(*args, **kwargs)[source]

Performs the code analysis.

Returns:An iterable of results.
can_detect = set()
classmethod check_prerequisites()[source]

Checks whether needed runtime prerequisites of the bear are satisfied.

This function gets executed at construction.

Section value requirements shall be checked inside the run method.

>>> from dependency_management.requirements.PipRequirement import (
...     PipRequirement)
>>> class SomeBear(Bear):
...     REQUIREMENTS = {PipRequirement('pip')}

>>> SomeBear.check_prerequisites()
True

>>> class SomeOtherBear(Bear):
...     REQUIREMENTS = {PipRequirement('really_bad_package')}

>>> SomeOtherBear.check_prerequisites()
'Following requirements are not installed: really_bad_package (...)'
Returns:True if prerequisites are satisfied, else False or a string that serves a more detailed description of what’s missing.
data_dir = '/home/docs/.local/share/coala-bears/Bear'
dependency_results

Contains all dependency results.

This variable gets set during bear execution from the core and can be used from analyze.

Modifications to the returned dictionary while the core is running leads to undefined behaviour.

>>> section = Section('my-section')
>>> file_dict = {'file1.txt': ['']}
>>> bear = Bear(section, file_dict)
>>> bear.dependency_results
defaultdict(<class 'list'>, {})
>>> dependency_bear = Bear(section, file_dict)
>>> bear.dependency_results[type(dependency_bear)] += [1, 2]
>>> bear.dependency_results
defaultdict(<class 'list'>, {<class 'coalib.core.Bear.Bear'>: [1, 2]})
Returns:A dictionary with bear-types as keys and their results received.
classmethod download_cached_file(url, filename)[source]

Downloads the file if needed and caches it for the next time. If a download happens, the user will be informed.

Take a sane simple bear:

>>> section = Section('my-section')
>>> file_dict = {'file1.txt': ['']}
>>> bear = Bear(section, file_dict)

We can now carelessly query for a neat file that doesn’t exist yet:

>>> from os import remove
>>> if exists(join(bear.data_dir, 'a_file')):
...     remove(join(bear.data_dir, 'a_file'))
>>> file = bear.download_cached_file('https://github.com/', 'a_file')

If we download it again, it’ll be much faster as no download occurs:

>>> newfile = bear.download_cached_file(
...     'https://github.com/', 'a_file')
>>> newfile == file
True
Parameters:
  • url – The URL to download the file from.
  • filename – The filename it should get, e.g. “test.txt”.
Returns:

A full path to the file ready for you to use!
execute_task(args, kwargs)[source]

Executes a task.

By default returns a list of results collected from this bear.

This function has to return something that is picklable to make bears work in multi-process environments.

Parameters:
  • args – The arguments of a task.
  • kwargs – The keyword-arguments of a task.
Returns:

A list of results from the bear.
generate_tasks()[source]

This method is responsible for providing the job arguments analyze gets called with.

Returns:An iterable containing the positional and keyword arguments organized in pairs: (args-tuple, kwargs-dict)
get_config_dir()[source]

Gives the directory where the configuration file resides.

Returns:Directory of the config file.
classmethod get_metadata()[source]
Returns:Metadata for the analyze function extracted from its signature. Excludes parameter self.
classmethod get_non_optional_settings()[source]

This method has to determine which settings are needed by this bear. The user will be prompted for needed settings that are not available in the settings file so don’t include settings where a default value would do.

Note: This function also queries settings from bear dependencies in recursive manner. Though circular dependency chains are a challenge to achieve, this function would never return on them!

Returns:A dictionary of needed settings as keys and a tuple of help text and annotation as values
maintainers = set()
maintainers_emails = set()
name = 'Bear'
new_result

Returns a partial for creating a result with this bear already bound.

static setup_dependencies()[source]

This is a user defined function that can download and set up dependencies (via download_cached_file or arbitrary other means) in an OS independent way.

source_location = '/home/docs/checkouts/readthedocs.org/user_builds/coala-api/envs/latest/lib/python3.5/site-packages/coala-0.12.0.dev20180614080551-py3.5.egg/coalib/core/Bear.py'
coalib.core.CircularDependencyError module
exception coalib.core.CircularDependencyError.CircularDependencyError(names=None)[source]

Bases: RuntimeError

An error identifying a circular dependency.

coalib.core.Core module
class coalib.core.Core.Session(bears, result_callback, cache=None, executor=None)[source]

Bases: object

Maintains a session for a coala execution. For each session, there are set of bears to run along with a callback function, which is called when results are available.

Dependencies of bears (provided via bear.BEAR_DEPS) are automatically handled. If BearA requires BearB as dependency, then on running BearA, first BearB will be executed, followed by BearA.

run()[source]

Runs the coala session.

coalib.core.Core.group(iterable, key=<function <lambda>>)[source]

Groups elements (out-of-order) together in the given iterable.

Supports non-hashable keys by comparing keys with ==.

Accessing the groups is supported using the iterator as follows:

>>> for key, elements in group([1, 3, 7, 1, 2, 1, 2]):
...     print(key, list(elements))
1 [1, 1, 1]
3 [3]
7 [7]
2 [2, 2]

You can control how elements are grouped by using the key parameter. It takes a function with a single parameter and maps to the group.

>>> data = [(1, 2), (3, 4), (1, 9), (2, 10), (1, 11), (7, 2), (10, 2),
...         (2, 1), (3, 7), (4, 5)]
>>> for key, elements in group(data, key=sum):
...     print(key, list(elements))
3 [(1, 2), (2, 1)]
7 [(3, 4)]
10 [(1, 9), (3, 7)]
12 [(2, 10), (1, 11), (10, 2)]
9 [(7, 2), (4, 5)]
Parameters:
  • iterable – The iterable to group elements in.
  • key – The key-function mapping an element to its group.
Returns:

An iterable yielding tuples with key, elements, where elements is also an iterable yielding the elements grouped under key.
coalib.core.Core.initialize_dependencies(bears)[source]

Initializes and returns a DependencyTracker instance together with a set of bears ready for scheduling.

This function acquires, processes and registers bear dependencies accordingly using a consumer-based system, where each dependency bear has only a single instance per section and file-dictionary.

The bears set returned accounts for bears that have dependencies and excludes them accordingly. Dependency bears that have themselves no further dependencies are included so the dependency chain can be processed correctly.

Parameters:bears – The set of instantiated bears to run that serve as an entry-point.
Returns:A tuple with (dependency_tracker, bears_to_schedule).
coalib.core.Core.run(bears, result_callback, cache=None, executor=None)[source]

Initiates a session with the given parameters and runs it.

Parameters:
  • bears – The bear instances to run.
  • result_callback –

    A callback function which is called when results are available. Must have following signature:

    def result_callback(result):
    pass

    Only those results are passed for bears that were explicitly requested via the bears parameter, implicit dependency results do not call the callback.

  • cache –

    A cache bears can use to speed up runs. If None, no cache will be used.

    The cache stores the results that were returned last time from the parameters passed to execute_task in bears. If the parameters to execute_task are the same from a previous run, the cache will be queried instead of executing execute_task.

    The cache has to be a dictionary-like object, that maps bear types to respective cache-tables. The cache-tables itself are dictionary-like objects that map hash-values (generated by PersistentHash.persistent_hash from the task objects) to actual bear results. When bears are about to be scheduled, the core performs a cache-lookup. If there’s a hit, the results stored in the cache are returned and the task won’t be scheduled. In case of a miss, execute_task is called normally in the executor.

  • executor – Custom executor used to run the bears. If None, a ProcessPoolExecutor is used using as many processes as cores available on the system.
coalib.core.DependencyBear module
class coalib.core.DependencyBear.DependencyBear(section, file_dict)[source]

Bases: coalib.core.Bear.Bear

This bear base class parallelizes tasks for each dependency result.

You can specify dependency bears with the BEAR_DEPS field.

generate_tasks()[source]
classmethod get_metadata()[source]
Returns:Metadata for the analyze function extracted from its signature. Excludes parameters self, filename and file.
coalib.core.DependencyTracker module
class coalib.core.DependencyTracker.DependencyTracker[source]

Bases: object

A DependencyTracker allows to register and manage dependencies between objects.

This class uses a directed graph to track relations.

Add a dependency relation between two objects:

>>> object1 = object()
>>> object2 = object()
>>> tracker = DependencyTracker()
>>> tracker.add(object2, object1)

This would define that object1 is dependent on object2.

If you define that object2 has its dependency duty fulfilled, you can resolve it:

>>> resolved = tracker.resolve(object2)
>>> resolved
{<object object at ...>}
>>> resolved_object = resolved.pop()
>>> resolved_object is object1
True

This returns all objects that are now freed, meaning they have no dependencies any more.

>>> object3 = object()
>>> tracker.add(object2, object1)
>>> tracker.add(object3, object1)
>>> tracker.resolve(object2)
set()
>>> tracker.resolve(object3)
{<object object at ...>}

The ones who instantiate a DependencyTracker are responsible for resolving dependencies in the right order. Dependencies which are itself dependent will be forcefully resolved and removed from their according dependencies too.

add(dependency, dependant)[source]

Add a dependency relation.

This function does not check for circular dependencies.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(0, 2)
>>> tracker.resolve(0)
{1, 2}
Parameters:
  • dependency – The object that is the dependency.
  • dependant – The object that is the dependant.
are_dependencies_resolved

Checks whether all dependencies in this DependencyTracker instance are resolved.

>>> tracker = DependencyTracker()
>>> tracker.are_dependencies_resolved
True
>>> tracker.add(0, 1)
>>> tracker.are_dependencies_resolved
False
>>> tracker.resolve(0)
{1}
>>> tracker.are_dependencies_resolved
True
Returns:True when all dependencies resolved, False if not.
check_circular_dependencies()[source]

Checks whether there are circular dependency conflicts.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(1, 0)
>>> tracker.check_circular_dependencies()
Traceback (most recent call last):
 ...
coalib.core.CircularDependencyError.CircularDependencyError: ...
Raises:CircularDependencyError – Raised on circular dependency conflicts.
dependants

Returns a set of all registered dependants.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(0, 2)
>>> tracker.add(1, 3)
>>> tracker.dependants
{1, 2, 3}
dependencies

Returns a set of all registered dependencies.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(0, 2)
>>> tracker.add(1, 3)
>>> tracker.dependencies
{0, 1}
get_all_dependants(dependency)[source]

Returns a set of all dependants of the given dependency, even indirectly related ones.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(1, 2)
>>> tracker.get_all_dependants(0)
{1, 2}
Parameters:dependency – The dependency to get all dependants for.
Returns:A set of dependants.
get_all_dependencies(dependant)[source]

Returns a set of all dependencies of the given dependants, even indirectly related ones.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(1, 2)
>>> tracker.get_all_dependencies(2)
{0, 1}
Parameters:dependant – The dependant to get all dependencies for.
Returns:A set of dependencies.
get_dependants(dependency)[source]

Returns all immediate dependants for the given dependency.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(0, 2)
>>> tracker.add(1, 3)
>>> tracker.get_dependants(0)
{1, 2}
>>> tracker.get_dependants(1)
{3}
>>> tracker.get_dependants(2)
set()
Parameters:dependency – The dependency to retrieve all dependants from.
Returns:A set of dependants.
get_dependencies(dependant)[source]

Returns all immediate dependencies of a given dependant.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(0, 2)
>>> tracker.add(1, 2)
>>> tracker.get_dependencies(0)
set()
>>> tracker.get_dependencies(1)
{0}
>>> tracker.get_dependencies(2)
{0, 1}
Parameters:dependant – The dependant to retrieve all dependencies from.
Returns:A set of dependencies.
resolve(dependency)[source]

Resolves all dependency-relations from the given dependency, and frees and returns dependants with no more dependencies. If the given dependency is itself a dependant, all those relations are also removed.

>>> tracker = DependencyTracker()
>>> tracker.add(0, 1)
>>> tracker.add(0, 2)
>>> tracker.add(2, 3)
>>> tracker.resolve(0)
{1, 2}
>>> tracker.resolve(2)
{3}
>>> tracker.resolve(2)
set()
Parameters:dependency – The dependency.
Returns:Returns a set of dependants whose dependencies were all resolved.
coalib.core.FileBear module
class coalib.core.FileBear.FileBear(section, file_dict)[source]

Bases: coalib.core.Bear.Bear

This bear base class parallelizes tasks for each file given.

generate_tasks()[source]
classmethod get_metadata()[source]
Returns:Metadata for the analyze function extracted from its signature. Excludes parameters self, filename and file.
coalib.core.Graphs module
coalib.core.Graphs.traverse_graph(start_nodes, get_successive_nodes, run_on_edge=<function <lambda>>)[source]

Traverses all edges of a directed, possibly disconnected graph once. Detects cyclic graphs by raising a CircularDependencyError.

>>> graph = {1: [2], 2: [3, 4], 5: [3], 3: [6]}
>>> def get_successive_nodes(node):
...     return graph.get(node, [])
>>> edges = set()
>>> def append_to_edges(prev, nxt):
...     edges.add((prev, nxt))
>>> traverse_graph([1, 5], get_successive_nodes, append_to_edges)
>>> sorted(edges)
[(1, 2), (2, 3), (2, 4), (3, 6), (5, 3)]

You can also use this function to detect cyclic graphs:

>>> graph = {1: [2], 2: [3], 3: [1]}
>>> traverse_graph([1], get_successive_nodes)
Traceback (most recent call last):
 ...
coalib.core.CircularDependencyError.CircularDependencyError: ...
Parameters:
  • start_nodes – The nodes where to start traversing the graph.
  • get_successive_nodes – A callable that takes in a node and returns an iterable of nodes to traverse next.
  • run_on_edge – A callable that is run on each edge during traversing. Takes in two parameters, the previous- and next-node which form an edge. The default is an empty function.
Raises:

CircularDependencyError – Raised when the graph is cyclic.
coalib.core.PersistentHash module
coalib.core.PersistentHash.persistent_hash(obj)[source]

Calculates a persistent hash of the given object.

This hash method uses pickle (protocol 4) to serialize the given object and hash the byte-stream. The hashing algorithm used is SHA-1.

Parameters:obj – The object to calculate the persistent hash for.
Returns:The persistent hash.
coalib.core.ProjectBear module
class coalib.core.ProjectBear.ProjectBear(section, file_dict)[source]

Bases: coalib.core.Bear.Bear

This bear base class does not parallelize tasks at all, it runs on the whole file base provided.

generate_tasks()[source]
classmethod get_metadata()[source]
Returns:Metadata for the analyze function extracted from its signature. Excludes parameters self and files.
Module contents

coalib.misc package

Submodules
coalib.misc.BuildManPage module
class coalib.misc.BuildManPage.BuildManPage(dist)[source]

Bases: distutils.cmd.Command

Add a build_manpage command to your setup.py. To use this Command class add a command to call this class:

# For setuptools
setup(
      entry_points={
        "distutils.commands": [
            "build_manpage = coalib.misc.BuildManPage:BuildManPage"
        ]
      }
)

# For distutils
from coalib.misc.BuildManPage import BuildManPage
setup(
      cmdclass={'build_manpage': BuildManPage}
)

You can then use the following setup command to produce a man page:

$ python setup.py build_manpage --output=coala.1             --parser=coalib.parsing.DefaultArgParser:default_arg_parser

If automatically want to build the man page every time you invoke your build, add to your setup.cfg the following:

[build_manpage]
output = <appname>.1
parser = <path_to_your_parser>
finalize_options()[source]
initialize_options()[source]
run()[source]
user_options = [('output=', 'O', 'output file'), ('parser=', None, 'module path to an ArgumentParser instance(e.g. mymod:func, where func is a method or function which returnan arparse.ArgumentParser instance.')]
class coalib.misc.BuildManPage.ManPageFormatter(prog, indent_increment=2, max_help_position=24, width=None, desc=None, long_desc=None, ext_sections=None, parser=None)[source]

Bases: argparse.HelpFormatter

format_man_page()[source]
coalib.misc.Caching module
class coalib.misc.Caching.FileCache(log_printer, project_dir: str, flush_cache: bool = False)[source]

Bases: object

This object is a file cache that helps in collecting only the changed and new files since the last run. Example/Tutorial:

>>> import logging
>>> import copy, time
>>> logging.getLogger().setLevel(logging.CRITICAL)

To initialize the cache create an instance for the project:

>>> cache = FileCache(None, "test", flush_cache=True)

Now we can track new files by running:

>>> cache.track_files(["a.c", "b.c"])

Since all cache operations are lazy (for performance), we need to explicitly write the cache to disk for persistence in future uses: (Note: The cache will automatically figure out the write location)

>>> cache.write()

Let’s go into the future:

>>> time.sleep(1)

Let’s create a new instance to simulate a separate run:

>>> cache = FileCache(None, "test", flush_cache=False)

>>> old_data = copy.deepcopy(cache.data)

We can mark a file as changed by doing:

>>> cache.untrack_files({"a.c"})

Again write to disk after calculating the new cache times for each file:

>>> cache.write()
>>> new_data = cache.data

Since we marked ‘a.c’ as a changed file:

>>> "a.c" not in cache.data
True
>>> "a.c" in old_data
True

Since ‘b.c’ was untouched after the second run, its time was updated to the latest value:

>>> old_data["b.c"] < new_data["b.c"]
True
flush_cache()[source]

Flushes the cache and deletes the relevant file.

get_uncached_files(files)[source]

Returns the set of files that are not in the cache yet or have been untracked.

Parameters:files – The list of collected files.
Returns:A set of files that are uncached.
track_files(files)[source]

Start tracking files given in files by adding them to the database.

Parameters:files – A set of files that need to be tracked. These files are initialized with their last modified tag as -1.
untrack_files(files)[source]

Removes the given files from the cache so that they are no longer considered cached for this and the next run.

Parameters:files – A set of files to remove from cache.
write()[source]

Update the last run time on the project for each file to the current time. Using this object as a contextmanager is preferred (that will automatically call this method on exit).

coalib.misc.CachingUtilities module
coalib.misc.CachingUtilities.delete_files(log_printer, identifiers)[source]

Delete the given identifiers from the user’s coala data directory.

Parameters:
  • log_printer – A LogPrinter object to use for logging.
  • identifiers – The list of files to be deleted.
Returns:

True if all the given files were successfully deleted. False otherwise.
coalib.misc.CachingUtilities.get_data_path(log_printer, identifier)[source]

Get the full path of identifier present in the user’s data directory.

Parameters:
  • log_printer – A LogPrinter object to use for logging.
  • identifier – The file whose path needs to be expanded.
Returns:

Full path of the file, assuming it’s present in the user’s config directory. Returns None if there is a PermissionError in creating the directory.
coalib.misc.CachingUtilities.get_settings_hash(sections, targets=[], ignore_settings: list = ['disable_caching'])[source]

Compute and return a unique hash for the settings.

Parameters:
  • sections – A dict containing the settings for each section.
  • targets – The list of sections that are enabled.
  • ignore_settings – Setting keys to remove from sections before hashing.
Returns:

A MD5 hash that is unique to the settings used.
coalib.misc.CachingUtilities.hash_id(text)[source]

Hashes the given text.

Parameters:text – String to to be hashed
Returns:A MD5 hash of the given string
coalib.misc.CachingUtilities.pickle_dump(log_printer, identifier, data)[source]

Write data into the file filename present in the user config directory.

Parameters:
  • log_printer – A LogPrinter object to use for logging.
  • identifier – The name of the file present in the user config directory.
  • data – Data to be serialized and written to the file using pickle.
Returns:

True if the write was successful. False if there was a permission error in writing.
coalib.misc.CachingUtilities.pickle_load(log_printer, identifier, fallback=None)[source]

Get the data stored in filename present in the user config directory. Example usage:

>>> test_data = {'answer': 42}
>>> pickle_dump(None, 'test_project', test_data)
True
>>> pickle_load(None, 'test_project')
{'answer': 42}
>>> pickle_load(None, 'nonexistent_project')
>>> pickle_load(None, 'nonexistent_project', fallback=42)
42
Parameters:
  • log_printer – A LogPrinter object to use for logging.
  • identifier – The name of the file present in the user config directory.
  • fallback – Return value to fallback to in case the file doesn’t exist.
Returns:

Data that is present in the file, if the file exists. Otherwise the default value is returned.
coalib.misc.CachingUtilities.settings_changed(log_printer, settings_hash)[source]

Determine if the settings have changed since the last run with caching.

Parameters:
  • log_printer – A LogPrinter object to use for logging.
  • settings_hash – A MD5 hash that is unique to the settings used.
Returns:

Return True if the settings hash has changed Return False otherwise.
coalib.misc.CachingUtilities.update_settings_db(log_printer, settings_hash)[source]

Update the config file last modification date.

Parameters:
  • log_printer – A LogPrinter object to use for logging.
  • settings_hash – A MD5 hash that is unique to the settings used.
coalib.misc.Compatibility module
coalib.misc.Constants module
coalib.misc.Constants.get_system_coafile(coalib_root)[source]
coalib.misc.DeprecationUtilities module
coalib.misc.DeprecationUtilities.check_deprecation(param_list)[source]

Shows a deprecation warning message if the parameters passed are not None.

Parameters:param_list – A dictionary of parameters with their names mapped to their values being checked for deprecation. 
>>> from testfixtures import LogCapture
>>> from collections import OrderedDict
>>> param_list = OrderedDict([('foo', None),
...                           ('bar', 'Random'),
...                           ('baz', 1773)])
>>> with LogCapture() as capture:
...     check_deprecation(param_list)
...     print(capture)
root WARNING
  bar parameter is deprecated
root WARNING
  baz parameter is deprecated
coalib.misc.DictUtilities module
coalib.misc.DictUtilities.inverse_dicts(*dicts)[source]

Inverts the dicts, e.g. {1: 2, 3: 4} and {2: 3, 4: 4} will be inverted {2: [1], 3: [2], 4: [3, 4]}. This also handles dictionaries with Iterable items as values e.g. {1: [1, 2, 3], 2: [3, 4, 5]} and {2: [1], 3: [2], 4: [3, 4]} will be inverted to {1: [1, 2], 2: [1, 3], 3: [1, 2, 4], 4: [2, 4], 5: [2]}. No order is preserved.

Parameters:dicts (dict) – The dictionaries to invert.
Returns:The inversed dictionary which merges all dictionaries into one.
Return type:defaultdict
coalib.misc.DictUtilities.update_ordered_dict_key(dictionary, old_key, new_key)[source]
coalib.misc.Enum module
coalib.misc.Enum.enum(*sequential, **named)[source]
coalib.misc.Exceptions module
coalib.misc.Exceptions.get_exitcode(exception, log_printer=None)[source]
coalib.misc.Exceptions.log_exception(message, exception, log_level=40)[source]
coalib.misc.IterUtilities module
coalib.misc.IterUtilities.partition(iterable, predicate)[source]

Partitions the iterable into two iterables based on the given predicate.

Parameters:predicate – A function that takes an item of the iterable and returns a boolean
Returns:Two iterators pointing to the original iterable
coalib.misc.Shell module
class coalib.misc.Shell.ShellCommandResult(code, stdout, stderr)[source]

Bases: tuple

The result of a coalib.misc.run_shell_command() call.

It is based on a (stdout, stderr) string tuple like it is returned form subprocess.Popen.communicate and was originally returned from coalib.misc.run_shell_command(). So it is backwards-compatible.

It additionally stores the return .code:

>>> import sys
>>> process = Popen([sys.executable, '-c',
...                  'import sys; print(sys.stdin.readline().strip() +'
...                  '                  " processed")'],
...                 stdin=PIPE, stdout=PIPE, stderr=PIPE,
...                 universal_newlines=True)

>>> stdout, stderr = process.communicate(input='data')
>>> stderr
''
>>> result = ShellCommandResult(process.returncode, stdout, stderr)
>>> result[0]
'data processed\n'
>>> result[1]
''
>>> result.code
0
coalib.misc.Shell.run_interactive_shell_command(command, **kwargs)[source]

Runs a single command in shell and provides stdout, stderr and stdin streams.

This function creates a context manager that sets up the process (using subprocess.Popen()), returns to caller and waits for process to exit on leaving.

By default the process is opened in universal_newlines mode and creates pipes for all streams (stdout, stderr and stdin) using subprocess.PIPE special value. These pipes are closed automatically, so if you want to get the contents of the streams you should retrieve them before the context manager exits.

>>> with run_interactive_shell_command(["echo", "TEXT"]) as p:
...     stdout = p.stdout
...     stdout_text = stdout.read()
>>> stdout_text
'TEXT\n'
>>> stdout.closed
True

Custom streams provided are not closed except of subprocess.PIPE.

>>> from tempfile import TemporaryFile
>>> stream = TemporaryFile()
>>> with run_interactive_shell_command(["echo", "TEXT"],
...                                    stdout=stream) as p:
...     stderr = p.stderr
>>> stderr.closed
True
>>> stream.closed
False
Parameters:
  • command – The command to run on shell. This parameter can either be a sequence of arguments that are directly passed to the process or a string. A string gets splitted beforehand using shlex.split(). If providing shell=True as a keyword-argument, no shlex.split() is performed and the command string goes directly to subprocess.Popen().
  • kwargs – Additional keyword arguments to pass to subprocess.Popen that are used to spawn the process.
Returns:

A context manager yielding the process started from the command.
coalib.misc.Shell.run_shell_command(command, stdin=None, **kwargs)[source]

Runs a single command in shell and returns the read stdout and stderr data.

This function waits for the process (created using subprocess.Popen()) to exit. Effectively it wraps run_interactive_shell_command() and uses communicate() on the process.

See also run_interactive_shell_command().

Parameters:
  • command – The command to run on shell. This parameter can either be a sequence of arguments that are directly passed to the process or a string. A string gets splitted beforehand using shlex.split().
  • stdin – Initial input to send to the process.
  • kwargs – Additional keyword arguments to pass to subprocess.Popen that is used to spawn the process.
Returns:

A tuple with (stdoutstring, stderrstring).
Module contents

coalib.output package

Subpackages
coalib.output.printers package
Submodules
coalib.output.printers.LOG_LEVEL module
coalib.output.printers.ListLogPrinter module
class coalib.output.printers.ListLogPrinter.ListLogPrinter(log_level=30, timestamp_format='%X')[source]

Bases: pyprint.Printer.Printer, coalib.output.printers.LogPrinter.LogPrinterMixin

A ListLogPrinter is a log printer which collects all LogMessages to a list so that the logs can be used at a later time.

log_message(log_message, **kwargs)[source]
coalib.output.printers.LogPrinter module
class coalib.output.printers.LogPrinter.LogPrinter(printer=None, log_level=10, timestamp_format='%X')[source]

Bases: coalib.output.printers.LogPrinter.LogPrinterMixin

This class is deprecated and will be soon removed. To get logger use logging.getLogger(__name__). Make sure that you’re getting it when the logging configuration is loaded.

The LogPrinter class allows to print log messages to an underlying Printer.

This class is an adapter, means you can create a LogPrinter from every existing Printer instance.

log_level

Returns current log_level used in logger.

log_message(log_message, **kwargs)[source]
printer

Returns the underlying printer where logs are printed to.

class coalib.output.printers.LogPrinter.LogPrinterMixin[source]

Bases: object

Provides access to the logging interfaces (e.g. err, warn, info) by routing them to the log_message method, which should be implemented by descendants of this class.

debug(*messages, delimiter=' ', timestamp=None, **kwargs)[source]
err(*messages, delimiter=' ', timestamp=None, **kwargs)[source]
info(*messages, delimiter=' ', timestamp=None, **kwargs)[source]
log(log_level, message, timestamp=None, **kwargs)[source]
log_exception(message, exception, log_level=40, timestamp=None, **kwargs)[source]

If the log_level of the printer is greater than DEBUG, it prints only the message. If it is DEBUG or lower, it shows the message along with the traceback of the exception.

Parameters:
  • message – The message to print.
  • exception – The exception to print.
  • log_level – The log_level of this message (not used when logging the traceback. Tracebacks always have a level of DEBUG).
  • timestamp – The time at which this log occurred. Defaults to the current time.
  • kwargs – Keyword arguments to be passed when logging the message (not used when logging the traceback).
log_message(log_message, **kwargs)[source]

It is your reponsibility to implement this method, if you’re using this mixin.

warn(*messages, delimiter=' ', timestamp=None, **kwargs)[source]
Module contents

This package holds printer objects. Printer objects are general purpose and not tied to coala.

If you need logging capabilities please take a look at the LogPrinter object which adds logging capabilities “for free” if used as base class for any other printer.

Submodules
coalib.output.ConfWriter module
class coalib.output.ConfWriter.ConfWriter(file_name, key_value_delimiters=('=', ), comment_separators=('#', ), key_delimiters=(', ', ' '), section_name_surroundings=mappingproxy({'[': ']'}), section_override_delimiters=('.', ), unsavable_keys=('save', ), key_value_append_delimiters=('+=', ))[source]

Bases: pyprint.ClosableObject.ClosableObject

static is_comment(key)[source]
write_section(section)[source]
write_sections(sections)[source]
coalib.output.ConsoleInteraction module
class coalib.output.ConsoleInteraction.BackgroundMessageStyle[source]

Bases: pygments.style.Style

styles = {Token.Comment.PreprocFile: '', Token.Name.Builtin.Pseudo: '', Token.Keyword: '', Token.Generic.Heading: '', Token.Literal.String.Backtick: '', Token.Literal.Date: '', Token.Name.Variable.Magic: '', Token.Keyword.Namespace: '', Token.Generic.Output: '', Token.Other: '', Token.Name.Decorator: '', Token.Name.Builtin: '', Token.Keyword.Pseudo: '', Token.Generic.Error: '', Token.Escape: '', Token.Literal.Number.Oct: '', Token.Name: '', Token.Operator: '', Token.Literal.String.Doc: '', Token.Name.Property: '', Token.Comment.Preproc: '', Token.Text.Whitespace: '', Token.Literal.String.Other: '', Token.Literal.String.Heredoc: '', Token.Literal: '', Token.Literal.String.Escape: '', Token.Generic.Deleted: '', Token.Name.Exception: '', Token.Text: '', Token.Name.Variable.Global: '', Token.Literal.String: '', Token.Punctuation: '', Token.Generic.Subheading: '', Token.Name.Function: '', Token.Name.Variable.Class: '', Token.Name.Other: '', Token.Name.Function.Magic: '', Token.Keyword.Declaration: '', Token.Generic: '', Token.Comment.Hashbang: '', Token.Name.Variable: '', Token.Literal.String.Symbol: '', Token.Name.Tag: '', Token.Name.Entity: '', Token.Literal.String.Char: '', Token.Generic.Emph: '', Token.Literal.String.Delimiter: '', Token.Error: '', Token.Generic.Strong: '', Token.Name.Variable.Instance: '', Token.Comment.Single: '', Token.Literal.Number.Float: '', Token.Literal.Number.Integer.Long: '', Token.Literal.String.Single: '', Token.Name.Constant: '', Token.Keyword.Constant: '', Token.Generic.Prompt: '', Token.Comment.Special: '', Token.Name.Class: '', Token.Keyword.Type: '', Token.Keyword.Reserved: '', Token.Operator.Word: '', Token.Literal.Number.Integer: '', Token.Literal.Number.Hex: '', Token.Name.Namespace: '', Token.Generic.Traceback: '', Token.Name.Attribute: '', Token.Literal.String.Regex: '', Token.Comment.Multiline: '', Token.Literal.Number.Bin: '', Token.Literal.Number: '', Token.Comment: '', Token.Literal.String.Interpol: '', Token.Literal.String.Affix: '', Token.Literal.String.Double: '', Token: 'bold bg:#eee #111', Token.Name.Label: '', Token.Generic.Inserted: ''}
class coalib.output.ConsoleInteraction.BackgroundSourceRangeStyle[source]

Bases: pygments.style.Style

styles = {Token.Comment.PreprocFile: '', Token.Name.Builtin.Pseudo: '', Token.Keyword: '', Token.Generic.Heading: '', Token.Literal.String.Backtick: '', Token.Literal.Date: '', Token.Name.Variable.Magic: '', Token.Keyword.Namespace: '', Token.Generic.Output: '', Token.Other: '', Token.Name.Decorator: '', Token.Name.Builtin: '', Token.Keyword.Pseudo: '', Token.Generic.Error: '', Token.Escape: '', Token.Literal.Number.Oct: '', Token.Name: '', Token.Operator: '', Token.Literal.String.Doc: '', Token.Name.Property: '', Token.Comment.Preproc: '', Token.Text.Whitespace: '', Token.Literal.String.Other: '', Token.Literal.String.Heredoc: '', Token.Literal: '', Token.Literal.String.Escape: '', Token.Generic.Deleted: '', Token.Name.Exception: '', Token.Text: '', Token.Name.Variable.Global: '', Token.Literal.String: '', Token.Punctuation: '', Token.Generic.Subheading: '', Token.Name.Function: '', Token.Name.Variable.Class: '', Token.Name.Other: '', Token.Name.Function.Magic: '', Token.Keyword.Declaration: '', Token.Generic: '', Token.Comment.Hashbang: '', Token.Name.Variable: '', Token.Literal.String.Symbol: '', Token.Name.Tag: '', Token.Name.Entity: '', Token.Literal.String.Char: '', Token.Generic.Emph: '', Token.Literal.String.Delimiter: '', Token.Error: '', Token.Generic.Strong: '', Token.Name.Variable.Instance: '', Token.Comment.Single: '', Token.Literal.Number.Float: '', Token.Literal.Number.Integer.Long: '', Token.Literal.String.Single: '', Token.Name.Constant: '', Token.Keyword.Constant: '', Token.Generic.Prompt: '', Token.Comment.Special: '', Token.Name.Class: '', Token.Keyword.Type: '', Token.Keyword.Reserved: '', Token.Operator.Word: '', Token.Literal.Number.Integer: '', Token.Literal.Number.Hex: '', Token.Name.Namespace: '', Token.Generic.Traceback: '', Token.Name.Attribute: '', Token.Literal.String.Regex: '', Token.Comment.Multiline: '', Token.Literal.Number.Bin: '', Token.Literal.Number: '', Token.Comment: '', Token.Literal.String.Interpol: '', Token.Literal.String.Affix: '', Token.Literal.String.Double: '', Token: 'bold bg:#BB4D3E #111', Token.Name.Label: '', Token.Generic.Inserted: ''}
class coalib.output.ConsoleInteraction.NoColorStyle[source]

Bases: pygments.style.Style

styles = {Token.Comment.PreprocFile: '', Token.Name.Builtin.Pseudo: '', Token.Keyword: '', Token.Generic.Heading: '', Token.Literal.String.Backtick: '', Token.Literal.Date: '', Token.Name.Variable.Magic: '', Token.Keyword.Namespace: '', Token.Generic.Output: '', Token.Other: '', Token.Name.Decorator: '', Token.Name.Builtin: '', Token.Keyword.Pseudo: '', Token.Generic.Error: '', Token.Escape: '', Token.Literal.Number.Oct: '', Token.Name: '', Token.Operator: '', Token.Literal.String.Doc: '', Token.Name.Property: '', Token.Comment.Preproc: '', Token.Text.Whitespace: '', Token.Literal.String.Other: '', Token.Literal.String.Heredoc: '', Token.Literal: '', Token.Literal.String.Escape: '', Token.Generic.Deleted: '', Token.Name.Exception: '', Token.Text: '', Token.Name.Variable.Global: '', Token.Literal.String: '', Token.Punctuation: '', Token.Generic.Subheading: '', Token.Name.Function: '', Token.Name.Variable.Class: '', Token.Name.Other: '', Token.Name.Function.Magic: '', Token.Keyword.Declaration: '', Token.Generic: '', Token.Comment.Hashbang: '', Token.Name.Variable: '', Token.Literal.String.Symbol: '', Token.Name.Tag: '', Token.Name.Entity: '', Token.Literal.String.Char: '', Token.Generic.Emph: '', Token.Literal.String.Delimiter: '', Token.Error: '', Token.Generic.Strong: '', Token.Name.Variable.Instance: '', Token.Comment.Single: '', Token.Literal.Number.Float: '', Token.Literal.Number.Integer.Long: '', Token.Literal.String.Single: '', Token.Name.Constant: '', Token.Keyword.Constant: '', Token.Generic.Prompt: '', Token.Comment.Special: '', Token.Name.Class: '', Token.Keyword.Type: '', Token.Keyword.Reserved: '', Token.Operator.Word: '', Token.Literal.Number.Integer: '', Token.Literal.Number.Hex: '', Token.Name.Namespace: '', Token.Generic.Traceback: '', Token.Name.Attribute: '', Token.Literal.String.Regex: '', Token.Comment.Multiline: '', Token.Literal.Number.Bin: '', Token.Literal.Number: '', Token.Comment: '', Token.Literal.String.Interpol: '', Token.Literal.String.Affix: '', Token.Literal.String.Double: '', Token: 'noinherit', Token.Name.Label: '', Token.Generic.Inserted: ''}
coalib.output.ConsoleInteraction.acquire_actions_and_apply(console_printer, section, file_diff_dict, result, file_dict, cli_actions=None, apply_single=False)[source]

Acquires applicable actions and applies them.

Parameters:
  • console_printer – Object to print messages on the console.
  • section – Name of section to which the result belongs.
  • file_diff_dict – Dictionary containing filenames as keys and Diff objects as values.
  • result – A derivative of Result.
  • file_dict – A dictionary containing all files with filename as key.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
  • cli_actions – The list of cli actions available.
coalib.output.ConsoleInteraction.acquire_settings(log_printer, settings_names_dict, section)[source]

This method prompts the user for the given settings.

Parameters:
  • log_printer – Printer responsible for logging the messages. This is needed to comply with the interface.
  • settings_names_dict –

    A dictionary with the settings name as key and a list containing a description in [0] and the name of the bears who need this setting in [1] and following.

    Example:
 
{"UseTabs": ["describes whether tabs should be used instead of spaces",
             "SpaceConsistencyBear",
             "SomeOtherBear"]}
Parameters:section – The section the action corresponds to.
Returns:A dictionary with the settings name as key and the given value as value.
coalib.output.ConsoleInteraction.ask_for_action_and_apply(console_printer, section, metadata_list, action_dict, failed_actions, result, file_diff_dict, file_dict, applied_actions, apply_single=False)[source]

Asks the user for an action and applies it.

Parameters:
  • console_printer – Object to print messages on the console.
  • section – Currently active section.
  • metadata_list – Contains metadata for all the actions.
  • action_dict – Contains the action names as keys and their references as values.
  • failed_actions – A set of all actions that have failed. A failed action remains in the list until it is successfully executed.
  • result – Result corresponding to the actions.
  • file_diff_dict – If it is an action which applies a patch, this contains the diff of the patch to be applied to the file with filename as keys.
  • file_dict – Dictionary with filename as keys and its contents as values.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
  • applied_actions – A dictionary that contains the result, file_dict, file_diff_dict and the section for an action.
Returns:

Returns a boolean value. True will be returned, if it makes sense that the user may choose to execute another action, False otherwise. If apply_single isn’t set, always return False.
coalib.output.ConsoleInteraction.choose_action(console_printer, actions, apply_single=False)[source]

Presents the actions available to the user and takes as input the action the user wants to choose.

Parameters:
  • console_printer – Object to print messages on the console.
  • actions – Actions available to the user.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
Returns:

Return a tuple of lists, a list with the names of actions that needs to be applied and a list with with the description of the actions.
coalib.output.ConsoleInteraction.color_letter(console_printer, line)[source]
coalib.output.ConsoleInteraction.format_lines(lines, symbol='', line_nr='')[source]
coalib.output.ConsoleInteraction.get_action_info(section, action, failed_actions)[source]

Gets all the required Settings for an action. It updates the section with the Settings.

Parameters:
  • section – The section the action corresponds to.
  • action – The action to get the info for.
  • failed_actions – A set of all actions that have failed. A failed action remains in the list until it is successfully executed.
Returns:

Action name and the updated section.
coalib.output.ConsoleInteraction.highlight_text(no_color, text, style, lexer=<pygments.lexers.TextLexer>)[source]
coalib.output.ConsoleInteraction.nothing_done(log_printer=None)[source]

Will be called after processing a coafile when nothing had to be done, i.e. no section was enabled/targeted.

Parameters:log_printer – A LogPrinter object.
coalib.output.ConsoleInteraction.print_affected_files(console_printer, log_printer, result, file_dict)[source]

Prints all the affected files and affected lines within them.

Parameters:
  • console_printer – Object to print messages on the console.
  • log_printer – Printer responsible for logging the messages.
  • result – The result to print the context for.
  • file_dict – A dictionary containing all files with filename as key.
coalib.output.ConsoleInteraction.print_affected_lines(console_printer, file_dict, sourcerange)[source]

Prints the lines affected by the bears.

Parameters:
  • console_printer – Object to print messages on the console.
  • file_dict – A dictionary containing all files with filename as key.
  • sourcerange – The SourceRange object referring to the related lines to print.
coalib.output.ConsoleInteraction.print_bears(bears, show_description, show_params, console_printer, args=None)[source]

Presents all bears being used in a stylized manner.

Parameters:
  • bears – It’s a dictionary with bears as keys and list of sections containing those bears as values.
  • show_description – This parameter is deprecated.
  • show_params – This parameter is deprecated.
  • console_printer – Object to print messages on the console.
  • args – Args passed to coala command.
coalib.output.ConsoleInteraction.print_bears_formatted(bears, format=None)[source]
coalib.output.ConsoleInteraction.print_diffs_info(diffs, printer)[source]

Prints diffs information (number of additions and deletions) to the console.

Parameters:
  • diffs – List of Diff objects containing corresponding diff info.
  • printer – Object responsible for printing diffs on console.
coalib.output.ConsoleInteraction.print_lines(console_printer, file_dict, sourcerange)[source]

Prints the lines between the current and the result line. If needed they will be shortened.

Parameters:
  • console_printer – Object to print messages on the console.
  • file_dict – A dictionary containing all files as values with filenames as key.
  • sourcerange – The SourceRange object referring to the related lines to print.
coalib.output.ConsoleInteraction.print_result(console_printer, section, file_diff_dict, result, file_dict, interactive=True, apply_single=False)[source]

Prints the result to console.

Parameters:
  • console_printer – Object to print messages on the console.
  • section – Name of section to which the result belongs.
  • file_diff_dict – Dictionary containing filenames as keys and Diff objects as values.
  • result – A derivative of Result.
  • file_dict – A dictionary containing all files with filename as key.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
  • interactive – Variable to check whether or not to offer the user actions interactively.
coalib.output.ConsoleInteraction.print_results(log_printer, section, result_list, file_dict, file_diff_dict, console_printer, apply_single=False)[source]

Prints all the results in a section.

Parameters:
  • log_printer – Printer responsible for logging the messages.
  • section – The section to which the results belong to.
  • result_list – List containing the results
  • file_dict – A dictionary containing all files with filename as key.
  • file_diff_dict – A dictionary that contains filenames as keys and diff objects as values.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
  • console_printer – Object to print messages on the console.
coalib.output.ConsoleInteraction.print_results_formatted(log_printer, section, result_list, file_dict, *args)[source]

Prints results through the format string from the format setting done by user.

Parameters:
  • log_printer – Printer responsible for logging the messages.
  • section – The section to which the results belong.
  • result_list – List of Result objects containing the corresponding results.
coalib.output.ConsoleInteraction.print_results_no_input(log_printer, section, result_list, file_dict, file_diff_dict, console_printer, apply_single=False)[source]

Prints all non interactive results in a section

Parameters:
  • log_printer – Printer responsible for logging the messages.
  • section – The section to which the results belong to.
  • result_list – List containing the results
  • file_dict – A dictionary containing all files with filename as key.
  • file_diff_dict – A dictionary that contains filenames as keys and diff objects as values.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
  • console_printer – Object to print messages on the console.
coalib.output.ConsoleInteraction.print_section_beginning(console_printer, section)[source]

Will be called after initialization current_section in begin_section()

Parameters:
  • console_printer – Object to print messages on the console.
  • section – The section that will get executed now.
coalib.output.ConsoleInteraction.require_setting(setting_name, arr, section)[source]

This method is responsible for prompting a user about a missing setting and taking its value as input from the user.

Parameters:
  • setting_name – Name of the setting missing
  • arr – A list containing a description in [0] and the name of the bears who need this setting in [1] and following.
  • section – The section the action corresponds to.
  • return – Returns the setting value that was requested from the user.
coalib.output.ConsoleInteraction.show_bear(bear, show_description, show_params, console_printer, args=None)[source]

Displays all information about a bear.

Parameters:
  • bear – The bear to be displayed.
  • show_description – This parameter is deprecated.
  • show_params – This parameter is deprecated.
  • console_printer – Object to print messages on the console.
  • args – Args passed to coala command.
coalib.output.ConsoleInteraction.show_bears(local_bears, global_bears, show_description, show_params, console_printer, args=None)[source]

Extracts all the bears from each enabled section or the sections in the targets and passes a dictionary to the show_bears_callback method.

Parameters:
  • local_bears – Dictionary of local bears with section names as keys and bear list as values.
  • global_bears – Dictionary of global bears with section names as keys and bear list as values.
  • show_description – This parameter is deprecated.
  • show_params – This parameter is deprecated.
  • console_printer – Object to print messages on the console.
  • args – Args passed to coala command.
coalib.output.ConsoleInteraction.show_enumeration(console_printer, title, items, indentation, no_items_text)[source]

This function takes as input an iterable object (preferably a list or a dict) and prints it in a stylized format. If the iterable object is empty, it prints a specific statement given by the user. An e.g :

<indentation>Title: <indentation> * Item 1 <indentation> * Item 2

Parameters:
  • console_printer – Object to print messages on the console.
  • title – Title of the text to be printed
  • items – The iterable object.
  • indentation – Number of spaces to indent every line by.
  • no_items_text – Text printed when iterable object is empty.
coalib.output.ConsoleInteraction.show_language_bears_capabilities(language_bears_capabilities, console_printer)[source]

Displays what the bears can detect and fix.

Parameters:
  • language_bears_capabilities – Dictionary with languages as keys and their bears’ capabilities as values. The capabilities are stored in a tuple of two elements where the first one represents what the bears can detect, and the second one what they can fix.
  • console_printer – Object to print messages on the console.
coalib.output.ConsoleInteraction.try_to_apply_action(action_name, chosen_action, console_printer, section, metadata_list, action_dict, failed_actions, result, file_diff_dict, file_dict, applied_actions)[source]

Try to apply the given action.

Parameters:
  • action_name – The name of the action.
  • chosen_action – The action object that will be applied.
  • console_printer – Object to print messages on the console.
  • section – Currently active section.
  • metadata_list – Contains metadata for all the actions.
  • action_dict – Contains the action names as keys and their references as values.
  • failed_actions – A set of all actions that have failed. A failed action remains in the list until it is successfully executed.
  • result – Result corresponding to the actions.
  • file_diff_dict – If it is an action which applies a patch, this contains the diff of the patch to be applied to the file with filename as keys.
  • applied_actions – A dictionary that contains the result, file_dict, file_diff_dict and the section for an action.
  • file_dict – Dictionary with filename as keys and its contents as values.
coalib.output.Interactions module
coalib.output.Interactions.fail_acquire_settings(log_printer, settings_names_dict)[source]

This method throws an exception if any setting needs to be acquired.

Parameters:
  • log_printer – Printer responsible for logging the messages.
  • settings_names_dict – A dictionary with the settings name as key and a list containing a description in [0] and the name of the bears who need this setting in [1] and following.
Raises:
  • AssertionError – If any setting is required.
  • TypeError – If settings_names_dict is not a dictionary.
coalib.output.JSONEncoder module
coalib.output.JSONEncoder.create_json_encoder(**kwargs)[source]
coalib.output.Logging module
class coalib.output.Logging.CounterHandler(level=0)[source]

Bases: logging.Handler

A logging handler which counts the number of calls for each logging level.

classmethod emit(record)[source]
classmethod get_num_calls_for_level(level)[source]

Returns the number of calls registered for a given log level.

classmethod reset()[source]

Reset the counter to 0 for all levels

class coalib.output.Logging.JSONFormatter(fmt=None, datefmt=None, style='%')[source]

Bases: logging.Formatter

JSON formatter for python logging.

static format(record)[source]
coalib.output.Logging.configure_json_logging()[source]

Configures logging for JSON. :return: Returns a StringIO that captures the logs as JSON.

coalib.output.Logging.configure_logging(color=True)[source]

Configures the logging with hard coded dictionary.

Module contents

coalib.parsing package

Subpackages
coalib.parsing.filters package
Submodules
coalib.parsing.filters.CanDetectFilter module
coalib.parsing.filters.CanDetectFilter.can_detect_filter(bear, args)[source]

Filters the bears by CAN_DETECT.

Parameters:
  • bear – Bear object.
  • args – Set of detectable issue types on which bear is to be filtered.
Returns:

True if this bear matches the criteria inside args, False otherwise.
coalib.parsing.filters.CanFixFilter module
coalib.parsing.filters.CanFixFilter.can_fix_filter(bear, args)[source]

Filters the bears by CAN_FIX.

Parameters:
  • bear – Bear object.
  • args – Set of fixable issue types on which bear is to be filtered.
Returns:

True if this bear matches the criteria inside args, False otherwise.
coalib.parsing.filters.LanguageFilter module
coalib.parsing.filters.LanguageFilter.language_filter(bear, args)[source]

Filters the bears by LANGUAGES.

Parameters:
  • bear – Bear object.
  • args – Set of languages on which bear is to be filtered.
Returns:

True if this bear matches the criteria inside args, False otherwise.
Module contents

This package holds filter functions. Filter objects are used to get a list of bears with specific properties.

Submodules
coalib.parsing.CliParsing module
coalib.parsing.CliParsing.check_conflicts(sections)[source]

Checks if there are any conflicting arguments passed.

Parameters:sections – The {section_name: section_object} dictionary to check conflicts for.
Returns:True if no conflicts occur.
Raises:SystemExit – If there are conflicting arguments (exit code: 2)
coalib.parsing.CliParsing.parse_cli(arg_list=None, origin='/home/docs/checkouts/readthedocs.org/user_builds/coala-api/checkouts/latest/docs', arg_parser=None, args=None, key_value_delimiters=('=', ':'), comment_seperators=(), key_delimiters=(', ', ), section_override_delimiters=('.', ), key_value_append_delimiters=('+=', ))[source]

Parses the CLI arguments and creates sections out of it.

Parameters:
  • arg_list – The CLI argument list.
  • origin – Directory used to interpret relative paths given as argument.
  • arg_parser – Instance of ArgParser that is used to parse none-setting arguments.
  • args – Alternative pre-parsed CLI arguments.
  • key_value_delimiters – Delimiters to separate key and value in setting arguments where settings are being defined.
  • comment_seperators – Allowed prefixes for comments.
  • key_delimiters – Delimiter to separate multiple keys of a setting argument.
  • section_override_delimiters – The delimiter to delimit the section from the key name (e.g. the ‘.’ in sect.key = value).
  • key_value_append_delimiters – Delimiters to separate key and value in setting arguments where settings are being appended.
Returns:

A dictionary holding section names as keys and the sections themselves as value.
coalib.parsing.CliParsing.parse_custom_settings(sections, custom_settings_list, origin, line_parser)[source]

Parses the custom settings given to coala via -S something=value.

Parameters:
  • sections – The Section dictionary to add to (mutable).
  • custom_settings_list – The list of settings strings.
  • origin – The originating directory.
  • line_parser – The LineParser to use.
coalib.parsing.ConfParser module
class coalib.parsing.ConfParser.ConfParser(key_value_delimiters=('=', ), comment_seperators=('#', ), key_delimiters=(', ', ' '), section_name_surroundings=mappingproxy({'[': ']'}), remove_empty_iter_elements=True, key_value_append_delimiters=('+=', ))[source]

Bases: object

get_section(name, create_if_not_exists=False)[source]
parse(input_data, overwrite=False)[source]

Parses the input and adds the new data to the existing.

Parameters:
  • input_data – The filename to parse from.
  • overwrite – If True, wipes all existing Settings inside this instance and adds only the newly parsed ones. If False, adds the newly parsed data to the existing one (and overwrites already existing keys with the newly parsed values).
Returns:

A dictionary with (lowercase) section names as keys and their Setting objects as values.
coalib.parsing.DefaultArgParser module
class coalib.parsing.DefaultArgParser.CustomFormatter(prog, indent_increment=2, max_help_position=24, width=None)[source]

Bases: argparse.RawDescriptionHelpFormatter

A Custom Formatter that will keep the metavars in the usage but remove them in the more detailed arguments section.

class coalib.parsing.DefaultArgParser.PathArg[source]

Bases: str

Uni(xi)fying OS-native directory separators in path arguments.

Removing the pain from interactively using coala in a Windows cmdline, because backslashes are interpreted as escaping syntax and therefore removed when arguments are turned into coala settings

>>> import os
>>> PathArg(os.path.join('path', 'with', 'separators'))
'path/with/separators'
coalib.parsing.DefaultArgParser.default_arg_parser(formatter_class=None)[source]

This function creates an ArgParser to parse command line arguments.

Parameters:formatter_class – Formatting the arg_parser output into a specific form. For example: In the manpage format.
coalib.parsing.FilterHelper module
coalib.parsing.FilterHelper.apply_filter(filter_name, filter_args, all_bears=None)[source]

Returns bears after filtering based on filter_args. It returns all bears if nothing is present in filter_args.

Parameters:
  • filter_name – Name of the filter.
  • filter_args – Arguments of the filter to be passed in. For example: ['c', 'java']
  • all_bears – List of bears on which filter is to be applied. All the bears are loaded automatically by default.
Returns:

Filtered bears based on a single filter.
coalib.parsing.FilterHelper.apply_filters(filters, bears=None)[source]

Returns bears after filtering based on filters. It returns intersection of bears if more than one element is present in filters list.

Parameters:
  • filters – List of args based on bears has to be filtered. For example: [['language', 'c', 'java'], ['can_fix', 'syntax']]
  • bears – The bears to filter.
Returns:

Filtered bears.
coalib.parsing.FilterHelper.is_valid_filter(filter)[source]
coalib.parsing.Globbing module
coalib.parsing.Globbing.fnmatch(name, globs)[source]

Tests whether name matches one of the given globs.

An empty glob will match nothing and return false.

Parameters:
  • name – File or directory name
  • globs – Glob string with wildcards or list of globs
Returns:

Boolean: Whether or not name is matched by glob

Glob Syntax:

  • ‘[seq]’: Matches any character in seq. Cannot be empty. Any
    special character looses its special meaning in a set.
  • ‘[!seq]’: Matches any character not in seq. Cannot be empty. Any
    special character looses its special meaning in a set.
  • ‘(seq_a|seq_b)’: Matches either sequence_a or sequence_b as a whole.
    More than two or just one sequence can be given.
  • ‘?’: Matches any single character.
  • ‘*’: Matches everything but os.sep.
  • ‘**’: Matches everything.
coalib.parsing.Globbing.glob(pattern)[source]

Iterates all filesystem paths that get matched by the glob pattern. Syntax is equal to that of fnmatch.

Parameters:pattern – Glob pattern with wildcards
Returns:List of all file names that match pattern
coalib.parsing.Globbing.glob_escape(input_string)[source]

Escapes the given string with [c] pattern. Examples:

>>> from coalib.parsing.Globbing import glob_escape
>>> glob_escape('test (1)')
'test [(]1[)]'
>>> glob_escape('test folder?')
'test folder[?]'
>>> glob_escape('test*folder')
'test[*]folder'
Parameters:input_string – String that is to be escaped with [ ].
Returns:Escaped string in which all the special glob characters ()[]|?* are escaped.
coalib.parsing.Globbing.has_wildcard(pattern)[source]

Checks whether pattern has any wildcards.

Parameters:pattern – Glob pattern that may contain wildcards
Returns:Boolean: Whether or not there are wildcards in pattern
coalib.parsing.Globbing.iglob(pattern)[source]

Iterates all filesystem paths that get matched by the glob pattern. Syntax is equal to that of fnmatch.

Parameters:pattern – Glob pattern with wildcards
Returns:Iterator that yields all file names that match pattern
coalib.parsing.Globbing.relative_flat_glob(dirname, basename)[source]

Non-recursive glob for one directory. Does not accept wildcards.

Parameters:
  • dirname – Directory name
  • basename – Basename of a file in dir of dirname
Returns:

List containing Basename if the file exists
coalib.parsing.Globbing.relative_recursive_glob(dirname, pattern)[source]

Recursive Glob for one directory and all its (nested) subdirectories. Accepts only ‘**’ as pattern.

Parameters:
  • dirname – Directory name
  • pattern – The recursive wildcard ‘**’
Returns:

Iterator that yields all the (nested) subdirectories of the given dir
coalib.parsing.Globbing.relative_wildcard_glob(dirname, pattern)[source]

Non-recursive glob for one directory. Accepts wildcards.

Parameters:
  • dirname – Directory name
  • pattern – Glob pattern with wildcards
Returns:

List of files in the dir of dirname that match the pattern
coalib.parsing.Globbing.translate(pattern)[source]

Translates a pattern into a regular expression.

Parameters:pattern – Glob pattern with wildcards
Returns:Regular expression with the same meaning
coalib.parsing.InvalidFilterException module
exception coalib.parsing.InvalidFilterException.InvalidFilterException(filter_name)[source]

Bases: LookupError

coalib.parsing.LineParser module
class coalib.parsing.LineParser.LineParser(key_value_delimiters=('=', ), comment_separators=('#', ), key_delimiters=(', ', ' '), section_name_surroundings=None, section_override_delimiters=('.', ), key_value_append_delimiters=('+=', ))[source]

Bases: object

parse(line)[source]

Note that every value in the returned tuple besides the value is unescaped. This is so since the value is meant to be put into a Setting later thus the escapes may be needed there.

Parameters:line – The line to parse.
Returns:section_name (empty string if it’s no section name), [(section_override, key), …], value, comment
Module contents

The StringProcessing module contains various functions for extracting information out of strings.

Most of them support regexes for advanced pattern matching.

coalib.processes package

Subpackages
coalib.processes.communication package
Submodules
coalib.processes.communication.LogMessage module
class coalib.processes.communication.LogMessage.LogMessage(log_level, *messages, delimiter=' ', timestamp=None)[source]

Bases: object

to_string_dict()[source]

Makes a dictionary which has all keys and values as strings and contains all the data that the LogMessage has.

Returns:Dictionary with keys and values as string.
Module contents
Submodules
coalib.processes.BearRunning module
coalib.processes.BearRunning.get_global_dependency_results(global_result_dict, bear_instance)[source]

This method gets all the results originating from the dependencies of a bear_instance. Each bear_instance may or may not have dependencies.

Parameters:global_result_dict – The list of results out of which the dependency results are picked.
Returns:None if bear has no dependencies, False if dependencies are not met, the dependency dict otherwise.
coalib.processes.BearRunning.get_local_dependency_results(local_result_list, bear_instance)[source]

This method gets all the results originating from the dependencies of a bear_instance. Each bear_instance may or may not have dependencies.

Parameters:
  • local_result_list – The list of results out of which the dependency results are picked.
  • bear_instance – The instance of a local bear to get the dependencies from.
Returns:

Return none if there are no dependencies for the bear. Else return a dictionary containing dependency results.
coalib.processes.BearRunning.get_next_global_bear(timeout, global_bear_queue, global_bear_list, global_result_dict)[source]

Retrieves the next global bear.

Parameters:
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • global_bear_queue – queue (read, write) of indexes of global bear instances in the global_bear_list.
  • global_bear_list – A list containing all global bears to be executed.
  • global_result_dict – A Manager.dict that will be used to store global results. The list of results of one global bear will be stored with the bear name as key.
Returns:

(bear, bearname, dependency_results)
coalib.processes.BearRunning.run(file_name_queue, local_bear_list, global_bear_list, global_bear_queue, file_dict, local_result_dict, global_result_dict, message_queue, control_queue, timeout=0, debug=False)[source]

This is the method that is actually runs by processes.

If parameters type is ‘queue (read)’ this means it has to implement the get(timeout=TIMEOUT) method and it shall raise queue.Empty if the queue is empty up until the end of the timeout. If the queue has the (optional!) task_done() attribute, the run method will call it after processing each item.

If parameters type is ‘queue (write)’ it shall implement the put(object, timeout=TIMEOUT) method.

If the queues raise any exception not specified here the user will get an ‘unknown error’ message. So beware of that.

Parameters:
  • file_name_queue – queue (read) of file names to check with local bears. Each invocation of the run method needs one such queue which it checks with all the local bears. The queue could be empty. (Repeat until queue empty.)
  • local_bear_list – List of local bear instances.
  • global_bear_list – List of global bear instances.
  • global_bear_queue – queue (read, write) of indexes of global bear instances in the global_bear_list.
  • file_dict – dict of all files as {filename:file}, file as in file.readlines().
  • local_result_dict – A Manager.dict that will be used to store local results. A list of all local results. will be stored with the filename as key.
  • global_result_dict – A Manager.dict that will be used to store global results. The list of results of one global bear will be stored with the bear name as key.
  • message_queue – queue (write) for debug/warning/error messages (type LogMessage)
  • control_queue – queue (write). If any result gets written to the result_dict a tuple containing a CONTROL_ELEMENT (to indicate what kind of event happened) and either a bear name (for global results) or a file name to indicate the result will be put to the queue. If the run method finished all its local bears it will put (CONTROL_ELEMENT.LOCAL_FINISHED, None) to the queue, if it finished all global ones, (CONTROL_ELEMENT.GLOBAL_FINISHED, None) will be put there.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
coalib.processes.BearRunning.run_bear(message_queue, timeout, bear_instance, *args, debug=False, **kwargs)[source]

This method is responsible for executing the instance of a bear. It also reports or logs errors if any occur during the execution of that bear instance.

Parameters:
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • bear_instance – The instance of the bear to be executed.
  • args – The arguments that are to be passed to the bear.
  • kwargs – The keyword arguments that are to be passed to the bear.
Returns:

Returns a valid list of objects of the type Result if the bear executed successfully. None otherwise.
coalib.processes.BearRunning.run_global_bear(message_queue, timeout, global_bear_instance, dependency_results, debug=False)[source]

Runs an instance of a global bear. Checks if bear_instance is of type GlobalBear and then passes it to the run_bear to execute.

Parameters:
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • global_bear_instance – Instance of GlobalBear to run.
  • dependency_results – The results of all the bears on which the instance of the passed bear to be run depends on.
Returns:

Returns a list of results generated by the passed bear_instance.
coalib.processes.BearRunning.run_global_bears(message_queue, timeout, global_bear_queue, global_bear_list, global_result_dict, control_queue, debug=False)[source]

Run all global bears.

Parameters:
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • global_bear_queue – queue (read, write) of indexes of global bear instances in the global_bear_list.
  • global_bear_list – list of global bear instances
  • global_result_dict – A Manager.dict that will be used to store global results. The list of results of one global bear will be stored with the bear name as key.
  • control_queue – If any result gets written to the result_dict a tuple containing a CONTROL_ELEMENT (to indicate what kind of event happened) and either a bear name(for global results) or a file name to indicate the result will be put to the queue.
coalib.processes.BearRunning.run_local_bear(message_queue, timeout, local_result_list, file_dict, bear_instance, filename, debug=False)[source]

Runs an instance of a local bear. Checks if bear_instance is of type LocalBear and then passes it to the run_bear to execute.

Parameters:
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • local_result_list – Its a list that stores the results of all local bears.
  • file_dict – Dictionary containing contents of file.
  • bear_instance – Instance of LocalBear the run.
  • filename – Name of the file to run it on.
Returns:

Returns a list of results generated by the passed bear_instance.
coalib.processes.BearRunning.run_local_bears(filename_queue, message_queue, timeout, file_dict, local_bear_list, local_result_dict, control_queue, debug=False)[source]

Run local bears on all the files given.

Parameters:
  • filename_queue – queue (read) of file names to check with local bears.
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • file_dict – Dictionary that contains contents of files.
  • local_bear_list – List of local bears to run.
  • local_result_dict – A Manager.dict that will be used to store local bear results. A list of all local bear results will be stored with the filename as key.
  • control_queue – If any result gets written to the result_dict a tuple containing a CONTROL_ELEMENT (to indicate what kind of event happened) and either a bear name(for global results) or a file name to indicate the result will be put to the queue.
coalib.processes.BearRunning.run_local_bears_on_file(message_queue, timeout, file_dict, local_bear_list, local_result_dict, control_queue, filename, debug=False)[source]

This method runs a list of local bears on one file.

Parameters:
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • file_dict – Dictionary that contains contents of files.
  • local_bear_list – List of local bears to run on file.
  • local_result_dict – A Manager.dict that will be used to store local bear results. A list of all local bear results will be stored with the filename as key.
  • control_queue – If any result gets written to the result_dict a tuple containing a CONTROL_ELEMENT (to indicate what kind of event happened) and either a bear name(for global results) or a file name to indicate the result will be put to the queue.
  • filename – The name of file on which to run the bears.
coalib.processes.BearRunning.send_msg(message_queue, timeout, log_level, *args, delimiter=' ', end='')[source]

Puts message into message queue for a LogPrinter to present to the user.

Parameters:
  • message_queue – The queue to put the message into and which the LogPrinter reads.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • log_level – The log_level i.e Error,Debug or Warning.It is sent to the LogPrinter depending on the message.
  • args – This includes the elements of the message.
  • delimiter – It is the value placed between each arg. By default it is a ‘ ‘.
  • end – It is the value placed at the end of the message.
coalib.processes.BearRunning.task_done(obj)[source]

Invokes task_done if the given queue provides this operation. Otherwise passes silently.

Parameters:obj – Any object.
coalib.processes.BearRunning.validate_results(message_queue, timeout, result_list, name, args, kwargs)[source]

Validates if the result_list passed to it contains valid set of results. That is the result_list must itself be a list and contain objects of the instance of Result object. If any irregularity is found a message is put in the message_queue to present the irregularity to the user. Each result_list belongs to an execution of a bear.

Parameters:
  • message_queue – A queue that contains messages of type errors/warnings/debug statements to be printed in the Log.
  • timeout – The queue blocks at most timeout seconds for a free slot to execute the put operation on. After the timeout it returns queue Full exception.
  • result_list – The list of results to validate.
  • name – The name of the bear executed.
  • args – The args with which the bear was executed.
  • kwargs – The kwargs with which the bear was executed.
Returns:

Returns None if the result_list is invalid. Else it returns the result_list itself.
coalib.processes.CONTROL_ELEMENT module
coalib.processes.DebugProcessing module

Replacement for multiprocessing library in coala’s debug mode.

class coalib.processes.DebugProcessing.Manager[source]

Bases: object

A debug replacement for multiprocessing.Manager, just offering builtins.dict as .dict member.

class coalib.processes.DebugProcessing.Process[source]

Bases: functools.partial

A debug replacement for multiprocessing.Process, running the callable target without any process parallelization or threading.

start()[source]

Just call the underlying functools.partial instaed of any thread or parallel process creation.

class coalib.processes.DebugProcessing.Queue[source]

Bases: queue.Queue

A debug replacement for multiprocessing.Queue, directly processing any incoming coalib.processes.communication.LogMessage.LogMessage instances (if the queue was instantiated from a function with a local log_printer).

put(item)[source]

Add item to queue.

Except item is an instance of coalib.processes.communication.LogMessage.LogMessage and there is a self.log_printer. Then item is just sent to logger instead.

coalib.processes.LogPrinterThread module
class coalib.processes.LogPrinterThread.LogPrinterThread(message_queue, log_printer=None)[source]

Bases: threading.Thread

This is the Thread object that outputs all log messages it gets from its message_queue. Setting obj.running = False will stop within the next 0.1 seconds.

run()[source]
coalib.processes.Processing module
coalib.processes.Processing.autoapply_actions(results, file_dict, file_diff_dict, section, log_printer=None)[source]

Auto-applies actions like defined in the given section.

Parameters:
  • results – A list of results.
  • file_dict – A dictionary containing the name of files and its contents.
  • file_diff_dict – A dictionary that contains filenames as keys and diff objects as values.
  • section – The section.
  • log_printer – A log printer instance to log messages on.
Returns:

A list of unprocessed results.
coalib.processes.Processing.check_result_ignore(result, ignore_ranges)[source]

Determines if the result has to be ignored.

Any result will be ignored if its origin matches any bear names and its SourceRange overlaps with the ignore range.

Note that everything after a space in the origin will be cut away, so the user can ignore results with an origin like CSecurityBear (buffer) with just # Ignore CSecurityBear.

Parameters:
  • result – The result that needs to be checked.
  • ignore_ranges – A list of tuples, each containing a list of lower cased affected bearnames and a SourceRange to ignore. If any of the bearname lists is empty, it is considered an ignore range for all bears. This may be a list of globbed bear wildcards.
Returns:

True if the result has to be ignored.
coalib.processes.Processing.create_process_group(command_array, **kwargs)[source]
coalib.processes.Processing.execute_section(section, global_bear_list, local_bear_list, print_results, cache, log_printer, console_printer, debug=False, apply_single=False)[source]

Executes the section with the given bears.

The execute_section method does the following things:

  1. Prepare a Process - Load files - Create queues
  2. Spawn up one or more Processes
  3. Output results from the Processes
  4. Join all processes
Parameters:
  • section – The section to execute.
  • global_bear_list – List of global bears belonging to the section. Dependencies are already resolved.
  • local_bear_list – List of local bears belonging to the section. Dependencies are already resolved.
  • print_results – Prints all given results appropriate to the output medium.
  • cache – An instance of misc.Caching.FileCache to use as a file cache buffer.
  • log_printer – The log_printer to warn to.
  • console_printer – Object to print messages on the console.
  • debug – Bypass multiprocessing and run bears in debug mode, not catching any exceptions.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
Returns:

Tuple containing a bool (True if results were yielded, False otherwise), a Manager.dict containing all local results(filenames are key) and a Manager.dict containing all global bear results (bear names are key) as well as the file dictionary.
coalib.processes.Processing.fill_queue(queue_fill, any_list)[source]

Takes element from a list and populates a queue with those elements.

Parameters:
  • queue_fill – The queue to be filled.
  • any_list – List containing the elements.
coalib.processes.Processing.filter_raising_callables(it, exception, *args, debug=False, **kwargs)[source]

Filters all callable items inside the given iterator that raise the given exceptions.

Parameters:
  • it – The iterator to filter.
  • exception – The (tuple of) exception(s) to filter for.
  • args – Positional arguments to pass to the callable.
  • kwargs – Keyword arguments to pass to the callable.
coalib.processes.Processing.get_cpu_count()[source]
coalib.processes.Processing.get_default_actions(section)[source]

Parses the key default_actions in the given section.

Parameters:section – The section where to parse from.
Returns:A dict with the bearname as keys and their default actions as values and another dict that contains bears and invalid action names.
coalib.processes.Processing.get_file_dict(filename_list, log_printer=None, allow_raw_files=False)[source]

Reads all files into a dictionary.

Parameters:
  • filename_list – List of names of paths to files to get contents of.
  • log_printer – The logger which logs errors.
  • allow_raw_files – Allow the usage of raw files (non text files), disabled by default
Returns:

Reads the content of each file into a dictionary with filenames as keys.
coalib.processes.Processing.get_file_list(results)[source]

Get the set of files that are affected in the given results.

Parameters:results – A list of results from which the list of files is to be extracted.
Returns:A set of file paths containing the mentioned list of files.
coalib.processes.Processing.get_ignore_scope(line, keyword)[source]

Retrieves the bears that are to be ignored defined in the given line.

Parameters:
  • line – The line containing the ignore declaration.
  • keyword – The keyword that was found. Everything after the rightmost occurrence of it will be considered for the scope.
Returns:

A list of lower cased bearnames or an empty list (-> “all”)
coalib.processes.Processing.get_running_processes(processes)[source]
coalib.processes.Processing.instantiate_bears(section, local_bear_list, global_bear_list, file_dict, message_queue, console_printer, debug=False)[source]

Instantiates each bear with the arguments it needs.

Parameters:
  • section – The section the bears belong to.
  • local_bear_list – List of local bear classes to instantiate.
  • global_bear_list – List of global bear classes to instantiate.
  • file_dict – Dictionary containing filenames and their contents.
  • message_queue – Queue responsible to maintain the messages delivered by the bears.
  • console_printer – Object to print messages on the console.
Returns:

The local and global bear instance lists.
coalib.processes.Processing.instantiate_processes(section, local_bear_list, global_bear_list, job_count, cache, log_printer, console_printer, debug=False, use_raw_files=False)[source]

Instantiate the number of processes that will run bears which will be responsible for running bears in a multiprocessing environment.

Parameters:
  • section – The section the bears belong to.
  • local_bear_list – List of local bears belonging to the section.
  • global_bear_list – List of global bears belonging to the section.
  • job_count – Max number of processes to create.
  • cache – An instance of misc.Caching.FileCache to use as a file cache buffer.
  • log_printer – The log printer to warn to.
  • console_printer – Object to print messages on the console.
  • debug – Bypass multiprocessing and activate debug mode for bears, not catching any exceptions on running them.
  • use_raw_files – Allow the usage of raw files (non text files)
Returns:

A tuple containing a list of processes, and the arguments passed to each process which are the same for each object.
coalib.processes.Processing.print_result(results, file_dict, retval, print_results, section, log_printer, file_diff_dict, ignore_ranges, console_printer, apply_single=False)[source]

Takes the results produced by each bear and gives them to the print_results method to present to the user.

Parameters:
  • results – A list of results.
  • file_dict – A dictionary containing the name of files and its contents.
  • retval – It is True if no results were yielded ever before. If it is False this function will return False no matter what happens. Else it depends on if this invocation yields results.
  • print_results – A function that prints all given results appropriate to the output medium.
  • file_diff_dict – A dictionary that contains filenames as keys and diff objects as values.
  • ignore_ranges – A list of SourceRanges. Results that affect code in any of those ranges will be ignored.
  • apply_single – The action that should be applied for all results, If it’s not selected, has a value of False.
  • console_printer – Object to print messages on the console.
Returns:

Returns False if any results were yielded. Else True.
coalib.processes.Processing.process_queues(processes, control_queue, local_result_dict, global_result_dict, file_dict, print_results, section, cache, log_printer, console_printer, debug=False, apply_single=False)[source]

Iterate the control queue and send the results received to the print_result method so that they can be presented to the user.

Parameters:
  • processes – List of processes which can be used to run Bears.
  • control_queue – Containing control elements that indicate whether there is a result available and which bear it belongs to.
  • local_result_dict – Dictionary containing results respective to local bears. It is modified by the processes i.e. results are added to it by multiple processes.
  • global_result_dict – Dictionary containing results respective to global bears. It is modified by the processes i.e. results are added to it by multiple processes.
  • file_dict – Dictionary containing file contents with filename as keys.
  • print_results – Prints all given results appropriate to the output medium.
  • cache – An instance of misc.Caching.FileCache to use as a file cache buffer.
  • debug – Run in debug mode, expecting that no logger thread is running.
  • apply_single – The action that should be applied for all results. If it’s not selected, has a value of False.
Returns:

Return True if all bears execute successfully and Results were delivered to the user. Else False.
coalib.processes.Processing.simplify_section_result(section_result)[source]

Takes in a section’s result from execute_section and simplifies it for easy usage in other functions.

Parameters:section_result – The result of a section which was executed.
Returns:Tuple containing: - bool - True if results were yielded - bool - True if unfixed results were yielded - list - Results from all bears (local and global)
coalib.processes.Processing.yield_ignore_ranges(file_dict)[source]

Yields tuples of affected bears and a SourceRange that shall be ignored for those.

Parameters:file_dict – The file dictionary.
Module contents

coalib.results package

Subpackages
coalib.results.result_actions package
Submodules
coalib.results.result_actions.ApplyPatchAction module
class coalib.results.result_actions.ApplyPatchAction.ApplyPatchAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = 'Patch applied successfully.'
apply(result, original_file_dict, file_diff_dict, no_orig: bool = False)[source]

(A)pply patch

Parameters:no_orig – Whether or not to create .orig backup files
static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())
coalib.results.result_actions.DoNothingAction module
class coalib.results.result_actions.DoNothingAction.DoNothingAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = ''
apply(result, original_file_dict, file_diff_dict)[source]

Do (N)othing

static is_applicable(*args)
coalib.results.result_actions.GeneratePatchesAction module
class coalib.results.result_actions.GeneratePatchesAction.DefaultBear[source]

Bases: object

class coalib.results.result_actions.GeneratePatchesAction.GeneratePatchesAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = 'Patch generated successfully.'
apply(result, original_file_dict, file_diff_dict)[source]

(G)enerate patches

static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())
coalib.results.result_actions.GeneratePatchesAction.create_arg_parser(files, bears)[source]

A function that generates a default_arg_parser.

Parameters:
  • files – A list that contains filenames.
  • bears – A list that contains name of bears.
Returns:

An object of type default_arg_parser.
coalib.results.result_actions.GeneratePatchesAction.filter_bears(language)[source]

Filter bears by language.

Parameters:language – The language to filter with.
Returns:A list of bears.
coalib.results.result_actions.GeneratePatchesAction.find_language(filename)[source]

Find the language used in filename.

Parameters:filename – The name of the file.
Returns:The language used.
coalib.results.result_actions.GeneratePatchesAction.show_possibilities(console_printer, i, action)[source]
coalib.results.result_actions.IgnoreResultAction module
class coalib.results.result_actions.IgnoreResultAction.IgnoreResultAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = 'An ignore comment was added to your source code.'
apply(result, original_file_dict, file_diff_dict, language: str, no_orig: bool = False)[source]

Add (I)gnore comment

get_ignore_comment(origin, language)[source]

Returns a string of Ignore Comment, depending on the language Supports Single Line Comments

>>> IgnoreResultAction().get_ignore_comment("Bear", "css")
'/* Ignore Bear */\n'

And Multiline Comments

>>> IgnoreResultAction().get_ignore_comment("Bear", "c")
'// Ignore Bear\n'
static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())[source]

For being applicable, the result has to point to a number of files that have to exist i.e. have not been previously deleted. Additionally, the action should not have been applied to the current result before.

coalib.results.result_actions.OpenEditorAction module
class coalib.results.result_actions.OpenEditorAction.OpenEditorAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = 'Changes saved successfully.'
apply(result, original_file_dict, file_diff_dict, editor: str)[source]

(O)pen file

Parameters:editor – The editor to open the file with.
build_editor_call_args(editor, editor_info, filenames)[source]

Create argument list which will then be used to open an editor for the given files at the correct positions, if applicable.

Parameters:
  • editor – The editor to open the file with.
  • editor_info – A dict containing the keys args and file_arg_template, providing additional call arguments and a template to open files at a position for this editor.
  • filenames – A dict holding one entry for each file to be opened. Keys must be filename, line and column.
static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())[source]

For being applicable, the result has to point to a number of files that have to exist i.e. have not been previously deleted.

coalib.results.result_actions.PrintAspectAction module
class coalib.results.result_actions.PrintAspectAction.PrintAspectAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

apply(result, original_file_dict, file_diff_dict)[source]

Print Aspec(T) Information

static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())[source]
coalib.results.result_actions.PrintDebugMessageAction module
class coalib.results.result_actions.PrintDebugMessageAction.PrintDebugMessageAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

apply(result, original_file_dict, file_diff_dict)[source]

Print (D)ebug message

static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())[source]
coalib.results.result_actions.PrintMoreInfoAction module
class coalib.results.result_actions.PrintMoreInfoAction.PrintMoreInfoAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

apply(result, original_file_dict, file_diff_dict)[source]

Print (M)ore info

static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())[source]
coalib.results.result_actions.ResultAction module

A ResultAction is an action that is applicable to at least some results. This file serves the base class for all result actions, thus providing a unified interface for all actions.

class coalib.results.result_actions.ResultAction.ResultAction[source]

Bases: object

SUCCESS_MESSAGE = 'The action was executed successfully.'
apply(result, original_file_dict, file_diff_dict, **kwargs)[source]

No description. Something went wrong.

apply_from_section(result, original_file_dict: dict, file_diff_dict: dict, section: coalib.settings.Section.Section)[source]

Applies this action to the given results with all additional options given as a section. The file dictionaries are needed for differential results.

Parameters:
  • result – The result to apply.
  • original_file_dict – A dictionary containing the files in the state where the result was generated.
  • file_diff_dict – A dictionary containing a diff for every file from the state in the original_file_dict to the current state. This dict will be altered so you do not need to use the return value.
  • section – The section where to retrieve the additional information.
Returns:

The modified file_diff_dict.
classmethod get_metadata()[source]

Retrieves metadata for the apply function. The description may be used to advertise this action to the user. The parameters and their help texts are additional information that are needed from the user. You can create a section out of the inputs from the user and use apply_from_section to apply

:return A FunctionMetadata object.

static is_applicable(result, original_file_dict, file_diff_dict, applied_actions=())[source]

Checks whether the Action is valid for the result type.

Returns True or a string containing the not_applicable message.

Parameters:
  • result – The result from the coala run to check if an Action is applicable.
  • original_file_dict – A dictionary containing the files in the state where the result was generated.
  • file_diff_dict – A dictionary containing a diff for every file from the state in the original_file_dict to the current state. This dict will be altered so you do not need to use the return value.
Applied_actions:
 

List of actions names that have already been applied for the current result. Action names are stored in order of application.
coalib.results.result_actions.ShowAppliedPatchesAction module
class coalib.results.result_actions.ShowAppliedPatchesAction.ShowAppliedPatchesAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = 'Applied patches shown'
apply(result, original_file_dict, file_diff_dict)[source]

Show Applied (P)atches

static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())
coalib.results.result_actions.ShowAppliedPatchesAction.format_lines(lines, symbol='', line_nr='')[source]
coalib.results.result_actions.ShowPatchAction module
class coalib.results.result_actions.ShowPatchAction.ShowPatchAction[source]

Bases: coalib.results.result_actions.ResultAction.ResultAction

SUCCESS_MESSAGE = 'Displayed patch successfully.'
apply(result, original_file_dict, file_diff_dict, no_color: bool = False, show_result_on_top: bool = False)[source]

(S)how patch

Parameters:
  • no_color – Whether or not to use colored output.
  • show_result_on_top – Set this to True if you want to show the result info on top. (Useful for e.g. coala_ci.)
static is_applicable(result: coalib.results.Result.Result, original_file_dict, file_diff_dict, applied_actions=())[source]
coalib.results.result_actions.ShowPatchAction.format_line(line, real_nr='', sign=']', mod_nr='', symbol='')[source]
coalib.results.result_actions.ShowPatchAction.print_beautified_diff(difflines, printer)[source]
coalib.results.result_actions.ShowPatchAction.print_from_name(printer, line)[source]
coalib.results.result_actions.ShowPatchAction.print_to_name(printer, line)[source]
Module contents

The result_actions package holds objects deriving from ResultAction. A ResultAction represents an action that an be applied to a result.

Submodules
coalib.results.AbsolutePosition module
class coalib.results.AbsolutePosition.AbsolutePosition(text: (<class 'tuple'>, <class 'list'>, None) = None, position: (<class 'int'>, None) = None)[source]

Bases: coalib.results.TextPosition.TextPosition

position
coalib.results.AbsolutePosition.calc_line_col(text, position)[source]

Creates a tuple containing (line, column) by calculating line number and column in the text, from position.

The position represents the index of a character. In the following example ‘a’ is at position ‘0’ and it’s corresponding line and column are:

>>> calc_line_col(('a\n',), 0)
(1, 1)

All special characters(including the newline character) belong in the same line, and have their own position. A line is an item in the tuple:

>>> calc_line_col(('a\n', 'b\n'), 1)
(1, 2)
>>> calc_line_col(('a\n', 'b\n'), 2)
(2, 1)
Parameters:
  • text – A tuple/list of lines in which position is to be calculated.
  • position – Position (starting from 0) of character to be found in the (line, column) form.
Returns:

A tuple of the form (line, column), where both line and column start from 1.
coalib.results.Diff module
class coalib.results.Diff.Diff(file_list, rename=False, delete=False)[source]

Bases: object

A Diff result represents a difference for one file.

add_line(line_nr_before, line)[source]

Adds line after the given line number.

Parameters:
  • line_nr_before – Line number of the line before the addition. Use 0 to insert line before everything.
  • line – Line to add.
add_lines(line_nr_before, lines)[source]

Adds lines after the given line number.

Parameters:
  • line_nr_before – Line number of the line before the additions. Use 0 for insert lines before everything.
  • lines – A list of lines to add.
affected_code(filename)[source]

Creates a list of SourceRange objects which point to the related code. Changes on continuous lines will be put into one SourceRange.

Parameters:filename – The filename to associate the SourceRange’s to.
Returns:A list of all related SourceRange objects.
change_line(line_nr, original_line, replacement)[source]
delete
Returns:True if file is set to be deleted.
delete_line(line_nr)[source]

Mark the given line nr as deleted. The first line is line number 1.

Raises an exception if line number doesn’t exist in the diff.

delete_lines(line_nr_start, line_nr_end)[source]

Delete lines in a specified range, inclusively.

The range must be valid, i.e. lines must exist in diff, else an exception is raised.

classmethod from_string_arrays(file_array_1, file_array_2, rename=False)[source]

Creates a Diff object from two arrays containing strings.

If this Diff is applied to the original array, the second array will be created.

Parameters:
  • file_array_1 – Original array
  • file_array_2 – Array to compare
  • rename – False or str containing new name of file.
classmethod from_unified_diff(unified_diff, original_file)[source]

Creates a Diff object from given unified diff.

If the provided unified diff does not contain any patch, the Diff object initialized from the original file is returned.

Parameters:
  • unified_diff – Unified diff string.
  • original_file – The contents of the original file (line-splitted).
Raises:

RuntimeError – Raised when the context lines or the lines to be removed do not match in the original file and the unified diff.
insert(position, text)[source]

Inserts (multiline) text at arbitrary position.

>>> from coalib.results.TextPosition import TextPosition
>>> test_text = ['123\n', '456\n', '789\n']
>>> def insert(position, text):
...     diff = Diff(test_text)
...     diff.insert(position, text)
...     return diff.modified
>>> insert(TextPosition(2, 3), 'woopy doopy')
['123\n', '45woopy doopy6\n', '789\n']
>>> insert(TextPosition(1, 1), 'woopy\ndoopy')
['woopy\n', 'doopy123\n', '456\n', '789\n']
>>> insert(TextPosition(2, 4), '\nwoopy\ndoopy\n')
['123\n', '456\n', 'woopy\n', 'doopy\n', '\n', '789\n']
Parameters:
  • position – The TextPosition where to insert text.
  • text – The text to insert.
modified

Calculates the modified file, after applying the Diff to the original.

This property also adds linebreaks at the end of each line. If no newline was present at the end of file before, this state will be preserved, except if the last line is deleted.

modify_line(line_nr, replacement)[source]

Changes the given line with the given line number. The replacement will be there instead.

Given an empty diff object:

>>> diff = Diff(['Hey there! Gorgeous.\n',
...              "It's nice that we're here.\n"])

We can change a line easily:

>>> diff.modify_line(1,
...                  'Hey there! This is sad.\n')
>>> diff.modified
['Hey there! This is sad.\n', "It's nice that we're here.\n"]

We can even merge changes within one line:

>>> diff.modify_line(1,
...                  'Hello. :( Gorgeous.\n')
>>> diff.modified
['Hello. :( This is sad.\n', "It's nice that we're here.\n"]

However, if we change something that has been changed before, we’ll get a conflict:

>>> diff.modify_line(1, 'Hello. This is not ok. Gorgeous.\n')
Traceback (most recent call last):
 ...
coalib.results.LineDiff.ConflictError: ...
original

Retrieves the original file.

range(filename)[source]

Calculates a SourceRange spanning over the whole Diff. If something is added after the 0th line (i.e. before the first line) the first line will be included in the SourceRange.

The range of an empty diff will only affect the filename:

>>> range = Diff([]).range("file")
>>> range.file is None
False
>>> print(range.start.line)
None
Parameters:filename – The filename to associate the SourceRange with.
Returns:A SourceRange object.
remove(range)[source]

Removes a piece of text in a given range.

>>> from coalib.results.TextRange import TextRange
>>> test_text = ['nice\n', 'try\n', 'bro\n']
>>> def remove(range):
...     diff = Diff(test_text)
...     diff.remove(range)
...     return diff.modified
>>> remove(TextRange.from_values(1, 1, 1, 4))
['e\n', 'try\n', 'bro\n']
>>> remove(TextRange.from_values(1, 5, 2, 1))
['nicetry\n', 'bro\n']
>>> remove(TextRange.from_values(1, 3, 3, 2))
['niro\n']
>>> remove(TextRange.from_values(2, 1, 2, 1))
['nice\n', 'try\n', 'bro\n']
Parameters:range – The range to delete.
rename
Returns:string containing new name of the file.
replace(range, replacement)[source]

Replaces a part of text. Allows to span multiple lines.

This function uses add_lines and delete_lines accordingly, so calls of those functions on lines given range affects after usage or vice versa lead to ConflictError.

>>> from coalib.results.TextRange import TextRange
>>> test_text = ['hello\n', 'world\n', '4lines\n', 'done\n']
>>> def replace(range, text):
...     diff = Diff(test_text)
...     diff.replace(range, text)
...     return diff.modified
>>> replace(TextRange.from_values(1, 5, 4, 3), '\nyeah\ncool\nno')
['hell\n', 'yeah\n', 'cool\n', 'none\n']
>>> replace(TextRange.from_values(2, 1, 3, 5), 'b')
['hello\n', 'bes\n', 'done\n']
>>> replace(TextRange.from_values(1, 6, 4, 3), '')
['hellone\n']
Parameters:
  • range – The TextRange that gets replaced.
  • replacement – The replacement string. Can be multiline.
split_diff(distance=1)[source]

Splits this diff into small pieces, such that several continuously altered lines are still together in one diff. All subdiffs will be yielded.

A diff like this with changes being together closely won’t be splitted:

>>> diff = Diff.from_string_arrays([     'b', 'c', 'e'],
...                                ['a', 'b', 'd', 'f'])
>>> len(list(diff.split_diff()))
1

If we set the distance to 0, it will be splitted:

>>> len(list(diff.split_diff(distance=0)))
2

If a negative distance is given, every change will be yielded as an own diff, even if they are right beneath each other:

>>> len(list(diff.split_diff(distance=-1)))
3

If a file gets renamed or deleted only, it will be yielded as is:

>>> len(list(Diff([], rename='test').split_diff()))
1

An empty diff will not yield any diffs:

>>> len(list(Diff([]).split_diff()))
0
Parameters:distance – Number of unchanged lines that are allowed in between two changed lines so they get yielded as one diff.
stats()[source]

Returns tuple containing number of additions and deletions in the diff.

unified_diff

Generates a unified diff corresponding to this patch.

Each change will be displayed on its own line. Additionally, the unified diff preserves the EOF-state of the original file. This means that the Diff will only have a linebreak on the last line, if that was also present in the original file.

Note that the unified diff is not deterministic and thus not suitable for equality comparison.

coalib.results.HiddenResult module
class coalib.results.HiddenResult.HiddenResult(origin, contents, message: str = '', affected_code: (<class 'tuple'>, <class 'list'>) = (), severity: int = 1, additional_info: str = '', debug_msg='', diffs: (<class 'dict'>, None) = None, confidence: int = 100, aspect: (<class 'coalib.bearlib.aspects.base.aspectbase'>, None) = None, message_arguments: dict = {}, applied_actions: dict = {})[source]

Bases: coalib.results.Result.Result

This is a result that is not meant to be shown to the user. It can be used to transfer any data from a dependent bear to others.

coalib.results.LineDiff module
exception coalib.results.LineDiff.ConflictError[source]

Bases: Exception

class coalib.results.LineDiff.LineDiff(change=False, delete=False, add_after=False)[source]

Bases: object

A LineDiff holds the difference between two strings.

add_after
change
delete
coalib.results.RESULT_SEVERITY module
coalib.results.Result module
class coalib.results.Result.Result(origin, message: str, affected_code: (<class 'tuple'>, <class 'list'>) = (), severity: int = 1, additional_info: str = '', debug_msg='', diffs: (<class 'dict'>, None) = None, confidence: int = 100, aspect: (<class 'coalib.bearlib.aspects.base.aspectbase'>, None) = None, message_arguments: dict = {}, applied_actions: dict = {})[source]

Bases: object

A result is anything that has an origin and a message.

Optionally it might affect a file.

Result messages can also have arguments. The message is python style formatted with these arguments.

>>> r = Result('origin','{arg1} and {arg2}',            message_arguments={'arg1': 'foo', 'arg2': 'bar'})
>>> r.message
'foo and bar'

Message arguments may be changed later. The result message will also reflect these changes.

>>> r.message_arguments = {'arg1': 'spam', 'arg2': 'eggs'}
>>> r.message
'spam and eggs'
apply(file_dict: dict)[source]

Applies all contained diffs to the given file_dict. This operation will be done in-place.

Parameters:file_dict – A dictionary containing all files with filename as key and all lines a value. Will be modified.
classmethod from_values(origin, message: str, file: str, line: (<class 'int'>, None) = None, column: (<class 'int'>, None) = None, end_line: (<class 'int'>, None) = None, end_column: (<class 'int'>, None) = None, severity: int = 1, additional_info: str = '', debug_msg='', diffs: (<class 'dict'>, None) = None, confidence: int = 100, aspect: (<class 'coalib.bearlib.aspects.base.aspectbase'>, None) = None, message_arguments: dict = {})[source]

Creates a result with only one SourceRange with the given start and end locations.

Parameters:
  • origin – Class name or creator object of this object.
  • message – Base message to show with this result.
  • message_arguments – Arguments to be provided to the base message
  • file – The related file.
  • line – The first related line in the file. (First line is 1)
  • column – The column indicating the first character. (First character is 1)
  • end_line – The last related line in the file.
  • end_column – The column indicating the last character.
  • severity – Severity of this result.
  • additional_info – A long description holding additional information about the issue and/or how to fix it. You can use this like a manual entry for a category of issues.
  • debug_msg – A message which may help the user find out why this result was yielded.
  • diffs – A dictionary with filename as key and Diff object associated with it as value.
  • confidence – A number between 0 and 100 describing the likelihood of this result being a real issue.
  • aspect – An Aspect object which this result is associated to. Note that this should be a leaf of the aspect tree! (If you have a node, spend some time figuring out which of the leafs exactly your result belongs to.)
get_applied_actions()[source]
location_repr()[source]

Retrieves a string, that briefly represents the affected code of the result.

Returns:A string containing all of the affected files separated by a comma.
message
overlaps(ranges)[source]

Determines if the result overlaps with source ranges provided.

Parameters:ranges – A list SourceRange objects to check for overlap.
Returns:True if the ranges overlap with the result.
set_applied_actions(applied_actions)[source]
to_string_dict()[source]

Makes a dictionary which has all keys and values as strings and contains all the data that the base Result has.

FIXME: diffs are not serialized ATM. FIXME: Only the first SourceRange of affected_code is serialized. If there are more, this data is currently missing.

Returns:Dictionary with keys and values as string.
coalib.results.ResultFilter module
coalib.results.ResultFilter.basics_match(original_result, modified_result)[source]

Checks whether the following properties of two results match: * origin * message * severity * debug_msg

Parameters:
  • original_result – A result of the old files
  • modified_result – A result of the new files
Returns:

Boolean value whether or not the properties match
coalib.results.ResultFilter.ensure_files_present(original_file_dict, modified_file_dict)[source]

Ensures that all files are available as keys in both dicts.

Parameters:
  • original_file_dict – Dict of lists of file contents before changes
  • modified_file_dict – Dict of lists of file contents after changes
Returns:

Return a dictionary of renamed files.
coalib.results.ResultFilter.filter_results(original_file_dict, modified_file_dict, original_results, modified_results)[source]

Filters results for such ones that are unique across file changes

Parameters:
  • original_file_dict – Dict of lists of file contents before changes
  • modified_file_dict – Dict of lists of file contents after changes
  • original_results – List of results of the old files
  • modified_results – List of results of the new files
Returns:

List of results from new files that are unique from all those that existed in the old changes
coalib.results.ResultFilter.remove_range(file_contents, source_range)[source]

removes the chars covered by the sourceRange from the file

Parameters:
  • file_contents – list of lines in the file
  • source_range – Source Range
Returns:

list of file contents without specified chars removed
coalib.results.ResultFilter.remove_result_ranges_diffs(result_list, file_dict)[source]

Calculates the diffs to all files in file_dict that describe the removal of each respective result’s affected code.

Parameters:
  • result_list – list of results
  • file_dict – dict of file contents
Returns:

returnvalue[result][file] is a diff of the changes the removal of this result’s affected code would cause for the file.
coalib.results.ResultFilter.source_ranges_match(original_file_dict, diff_dict, original_result_diff_dict, modified_result_diff_dict, renamed_files)[source]

Checks whether the SourceRanges of two results match

Parameters:
  • original_file_dict – Dict of lists of file contents before changes
  • diff_dict – Dict of diffs describing the changes per file
  • original_result_diff_dict – diff for each file for this result
  • modified_result_diff_dict – guess
  • renamed_files – A dictionary containing file renamings across runs
Returns:

Boolean value whether the SourceRanges match
coalib.results.SourcePosition module
class coalib.results.SourcePosition.SourcePosition(file: str, line=None, column=None)[source]

Bases: coalib.results.TextPosition.TextPosition

file
coalib.results.SourceRange module
class coalib.results.SourceRange.SourceRange(start: coalib.results.SourcePosition.SourcePosition, end: (<class 'coalib.results.SourcePosition.SourcePosition'>, None) = None)[source]

Bases: coalib.results.TextRange.TextRange

affected_source(file_dict: dict)[source]

Tells which lines are affected in a specified file within a given range.

>>> from os.path import abspath
>>> sr = SourceRange.from_values('file_name', start_line=2, end_line=2)
>>> sr.affected_source({
...     abspath('file_name'): ('def fun():\n', '    x = 2  \n')
... })
('    x = 2  \n',)

If more than one line is affected.

>>> sr = SourceRange.from_values('file_name', start_line=2, end_line=3)
>>> sr.affected_source({
...     abspath('file_name'): ('def fun():\n',
...                            '    x = 2  \n', '    print(x)  \n')
... })
('    x = 2  \n', '    print(x)  \n')

If the file indicated at the source range is not in the file_dict or the lines are not given, this will return None:

>>> sr = SourceRange.from_values('file_name_not_present',
...     start_line=2, end_line=2)
>>> sr.affected_source({abspath('file_name'):
...     ('def fun():\n', '    x = 2  \n')})
Parameters:file_dict – It is a dictionary where the file names are the keys and the contents of the files are the values(which is of type tuple).
Returns:A tuple of affected lines in the specified file. If the file is not affected or the file is not present in file_dict return None.
expand(file_contents)[source]

Passes a new SourceRange that covers the same area of a file as this one would. All values of None get replaced with absolute values.

values of None will be interpreted as follows: self.start.line is None: -> 1 self.start.column is None: -> 1 self.end.line is None: -> last line of file self.end.column is None: -> last column of self.end.line

Parameters:file_contents – File contents of the applicable file
Returns:TextRange with absolute values
file
classmethod from_absolute_position(file: str, position_start: coalib.results.AbsolutePosition.AbsolutePosition, position_end: (<class 'coalib.results.AbsolutePosition.AbsolutePosition'>, None) = None)[source]

Creates a SourceRange from a start and end positions.

Parameters:
  • file – Name of the file.
  • position_start – Start of range given by AbsolutePosition.
  • position_end – End of range given by AbsolutePosition or None.
classmethod from_values(file, start_line=None, start_column=None, end_line=None, end_column=None)[source]
overlaps(other)[source]
renamed_file(file_diff_dict: dict)[source]

Retrieves the filename this source range refers to while taking the possible file renamings in the given file_diff_dict into account:

Parameters:file_diff_dict – A dictionary with filenames as key and their associated Diff objects as values.
coalib.results.TextPosition module
class coalib.results.TextPosition.TextPosition(line: (<class 'int'>, None) = None, column: (<class 'int'>, None) = None)[source]

Bases: object

column
line
exception coalib.results.TextPosition.ZeroOffsetError[source]

Bases: ValueError

coalib.results.TextRange module
class coalib.results.TextRange.TextRange(start: coalib.results.TextPosition.TextPosition, end: (<class 'coalib.results.TextPosition.TextPosition'>, None) = None)[source]

Bases: object

end
expand(text_lines)[source]

Passes a new TextRange that covers the same area of a file as this one would. All values of None get replaced with absolute values.

values of None will be interpreted as follows: self.start.line is None: -> 1 self.start.column is None: -> 1 self.end.line is None: -> last line of file self.end.column is None: -> last column of self.end.line

Parameters:text_lines – File contents of the applicable file
Returns:TextRange with absolute values
classmethod from_values(start_line=None, start_column=None, end_line=None, end_column=None)[source]

Creates a new TextRange.

Parameters:
  • start_line – The line number of the start position. The first line is 1.
  • start_column – The column number of the start position. The first column is 1.
  • end_line – The line number of the end position. If this parameter is None, then the end position is set the same like start position and end_column gets ignored.
  • end_column – The column number of the end position.
Returns:

A TextRange.
classmethod join(a, b)[source]

Creates a new TextRange that covers the area of two overlapping ones

Parameters:
  • a – TextRange (needs to overlap b)
  • b – TextRange (needs to overlap a)
Returns:

A new TextRange covering the union of the Area of a and b
overlaps(other)[source]
start
Module contents

coalib.settings package

Submodules
coalib.settings.Annotations module
coalib.settings.Annotations.typechain(*args)[source]

Returns function which applies the first transformation it can from args and returns transformed value, or the value itself if it is in args.

>>> function = typechain(int, 'a', ord, None)
>>> function("10")
10
>>> function("b")
98
>>> function("a")
'a'
>>> function(int)
<class 'int'>
>>> function(None) is None
True
>>> function("str")
Traceback (most recent call last):
    ...
ValueError: Couldn't convert value 'str' to any specified type or find it in specified values.
Raises:TypeError – Raises when either no functions are specified for checking.
coalib.settings.ConfigurationGathering module
coalib.settings.ConfigurationGathering.aspectize_sections(sections)[source]

Search for aspects related setting in a section, initialize it, and then embed the aspects information as AspectList object into the section itself.

Parameters:sections – List of section that potentially contain aspects setting.
Returns:The new sections.
coalib.settings.ConfigurationGathering.find_user_config(file_path, max_trials=10)[source]

Uses the filepath to find the most suitable user config file for the file by going down one directory at a time and finding config files there.

Parameters:
  • file_path – The path of the file whose user config needs to be found
  • max_trials – The maximum number of directories to go down to.
Returns:

The config file’s path, empty string if none was found
coalib.settings.ConfigurationGathering.gather_configuration(acquire_settings, log_printer=None, arg_list=None, arg_parser=None, args=None)[source]

Loads all configuration files, retrieves bears and all needed settings, saves back if needed and warns about non-existent targets.

This function:

  • Reads and merges all settings in sections from
    • Default config
    • User config
    • Configuration file
    • CLI
  • Collects all the bears
  • Fills up all needed settings
  • Writes back the new sections to the configuration file if needed
  • Gives all information back to caller
Parameters:
  • acquire_settings – The method to use for requesting settings. It will get a parameter which is a dictionary with the settings name as key and a list containing a description in [0] and the names of the bears who need this setting in all following indexes.
  • log_printer – The log printer to use for logging. The log level will be adjusted to the one given by the section.
  • arg_list – CLI args to use
  • arg_parser – Instance of ArgParser that is used to parse none-setting arguments.
  • args – Alternative pre-parsed CLI arguments.
Returns:

A tuple with the following contents:

  • A dictionary with the sections
  • Dictionary of list of local bears for each section
  • Dictionary of list of global bears for each section
  • The targets list
coalib.settings.ConfigurationGathering.get_all_bears(log_printer=None, arg_parser=None, silent=True, bear_globs=('**', ))[source]
Parameters:
  • log_printer – The log_printer to handle logging.
  • arg_parser – An ArgParser object.
  • silent – Whether or not to display warnings.
  • bear_globs – List of glob patterns.
Returns:

Tuple containing dictionaries of local bears and global bears.
coalib.settings.ConfigurationGathering.get_config_directory(section)[source]

Retrieves the configuration directory for the given section.

Given an empty section:

>>> section = Section("name")

The configuration directory is not defined and will therefore fallback to the current directory:

>>> get_config_directory(section) == os.path.abspath(".")
True

If the files setting is given with an originating coafile, the directory of the coafile will be assumed the configuration directory:

>>> section.append(Setting("files", "**", origin="/tmp/.coafile"))
>>> get_config_directory(section) == os.path.abspath('/tmp/')
True

However if its origin is already a directory this will be preserved:

>>> files = section['files']
>>> files.origin = os.path.abspath('/tmp/dir/')
>>> section.append(files)
>>> os.makedirs(section['files'].origin, exist_ok=True)
>>> get_config_directory(section) == section['files'].origin
True

The user can manually set a project directory with the project_dir setting:

>>> section.append(Setting('project_dir', os.path.abspath('/tmp'), '/'))
>>> get_config_directory(section) == os.path.abspath('/tmp')
True

If no section is given, the current directory is returned:

>>> get_config_directory(None) == os.path.abspath(".")
True

To summarize, the config directory will be chosen by the following priorities if possible in that order:

  • the project_dir setting
  • the origin of the files setting, if it’s a directory
  • the directory of the origin of the files setting
  • the current directory
Parameters:section – The section to inspect.
Returns:The directory where the project is lying.
coalib.settings.ConfigurationGathering.get_filtered_bears(languages, log_printer=None, arg_parser=None, silent=True)[source]
Parameters:
  • languages – List of languages.
  • log_printer – The log_printer to handle logging.
  • arg_parser – An ArgParser object.
  • silent – Whether or not to display warnings.
Returns:

Tuple containing dictionaries of local bears and global bears.
coalib.settings.ConfigurationGathering.load_config_file(filename, log_printer=None, silent=False)[source]

Loads sections from a config file. Prints an appropriate warning if it doesn’t exist and returns a section dict containing an empty default section in that case.

It assumes that the cli_sections are available.

Parameters:
  • filename – The file to load settings from.
  • log_printer – The log printer to log the warning/error to (in case).
  • silent – Whether or not to warn the user/exit if the file doesn’t exist.
Raises:

SystemExit – Exits when the given filename is invalid and is not the default coafile. Only raised when silent is False.
coalib.settings.ConfigurationGathering.load_configuration(arg_list, log_printer=None, arg_parser=None, args=None, silent=False)[source]

Parses the CLI args and loads the config file accordingly, taking default_coafile and the users .coarc into account.

Parameters:
  • arg_list – The list of CLI arguments.
  • log_printer – The LogPrinter object for logging.
  • arg_parser – An argparse.ArgumentParser instance used for parsing the CLI arguments.
  • args – Alternative pre-parsed CLI arguments.
  • silent – Whether or not to display warnings, ignored if save is enabled.
Returns:

A tuple holding (log_printer: LogPrinter, sections: dict(str, Section), targets: list(str)). (Types indicated after colon.)
coalib.settings.ConfigurationGathering.merge_section_dicts(lower, higher)[source]

Merges the section dictionaries. The values of higher will take precedence over the ones of lower. Lower will hold the modified dict in the end.

Parameters:
  • lower – A section.
  • higher – A section which values will take precedence over the ones from the other.
Returns:

The merged dict.
coalib.settings.ConfigurationGathering.save_sections(sections)[source]

Saves the given sections if they are to be saved.

Parameters:sections – A section dict.
coalib.settings.ConfigurationGathering.validate_aspect_config(section)[source]

Validate if a section contain required setting to run in aspects mode.

Parameters:section – The section that potentially contain aspect setting.
Returns:The validity of section.
coalib.settings.ConfigurationGathering.warn_config_absent(sections, argument, log_printer=None)[source]

Checks if at least 1 of the given arguments is present somewhere in the sections and emits a warning that code analysis can not be run without it.

Parameters:
  • sections – A dictionary of sections.
  • argument – An argument OR a list of arguments that at least 1 should present.
  • log_printer – A log printer to emit the warning to.
Returns:

Returns a boolean False if the given argument is present in the sections, else returns True.
coalib.settings.ConfigurationGathering.warn_nonexistent_targets(targets, sections, log_printer=None)[source]

Prints out a warning on the given log printer for all targets that are not existent within the given sections.

Parameters:
  • targets – The targets to check.
  • sections – The sections to search. (Dict.)
  • log_printer – The log printer to warn to.
coalib.settings.DocstringMetadata module
class coalib.settings.DocstringMetadata.DocstringMetadata(desc, param_dict, retval_desc)[source]

Bases: object

classmethod from_docstring(docstring)[source]

Parses a python docstring. Usable attributes are: :param @param :return @return

coalib.settings.FunctionMetadata module
class coalib.settings.FunctionMetadata.FunctionMetadata(name: str, desc: str = '', retval_desc: str = '', non_optional_params: (<class 'dict'>, None) = None, optional_params: (<class 'dict'>, None) = None, omit: (<class 'set'>, <class 'tuple'>, <class 'list'>, <class 'frozenset'>) = frozenset(), deprecated_params: (<class 'set'>, <class 'tuple'>, <class 'list'>, <class 'frozenset'>) = frozenset())[source]

Bases: object

add_deprecated_param(original, alias)[source]

Adds an alias for the original setting. The alias setting will have the same metadata as the original one. If the original setting is not optional, the alias will default to None.

Parameters:
  • original – The name of the original setting.
  • alias – The name of the alias for the original.
Raises:

KeyError – If the new setting doesn’t exist in the metadata.
create_params_from_section(section)[source]

Create a params dictionary for this function that holds all values the function needs plus optional ones that are available.

Parameters:section – The section to retrieve the values from.
Returns:The params dictionary.
desc

Returns description of the function.

filter_parameters(dct)[source]

Filters the given dict for keys that are declared as parameters inside this metadata (either optional or non-optional).

You can use this function to safely pass parameters from a given dictionary:

>>> def multiply(a, b=2, c=0):
...     return a * b + c
>>> metadata = FunctionMetadata.from_function(multiply)
>>> args = metadata.filter_parameters({'a': 10, 'b': 20, 'd': 30})

You can safely pass the arguments to the function now:

>>> multiply(**args)  # 10 * 20
200
Parameters:dct – The dict to filter.
Returns:A new dict containing the filtered items.
classmethod from_function(func, omit=frozenset())[source]

Creates a FunctionMetadata object from a function. Please note that any variable argument lists are not supported. If you do not want the first (usual named ‘self’) argument to appear please pass the method of an actual INSTANCE of a class; passing the method of the class isn’t enough. Alternatively you can add “self” to the omit set.

Parameters:
  • func – The function. If __metadata__ of the unbound function is present it will be copied and used, otherwise it will be generated.
  • omit – A set of parameter names that are to be ignored.
Returns:

The FunctionMetadata object corresponding to the given function.
classmethod merge(*metadatas)[source]

Merges signatures of FunctionMetadata objects.

Parameter (either optional or non-optional) and non-parameter descriptions are merged from left to right, meaning the right hand metadata overrides the left hand one.

>>> def a(x, y):
...     '''
...     desc of *a*
...     :param x: x of a
...     :param y: y of a
...     :return:  5*x*y
...     '''
...     return 5 * x * y
>>> def b(x):
...     '''
...     desc of *b*
...     :param x: x of b
...     :return:  100*x
...     '''
...     return 100 * x
>>> metadata1 = FunctionMetadata.from_function(a)
>>> metadata2 = FunctionMetadata.from_function(b)
>>> merged = FunctionMetadata.merge(metadata1, metadata2)
>>> merged.name
"<Merged signature of 'a', 'b'>"
>>> merged.desc
'desc of *b*'
>>> merged.retval_desc
'100*x'
>>> merged.non_optional_params['x'][0]
'x of b'
>>> merged.non_optional_params['y'][0]
'y of a'
Parameters:metadatas – The sequence of metadatas to merge.
Returns:A FunctionMetadata object containing the merged signature of all given metadatas.
non_optional_params

Retrieves a dict containing the name of non optional parameters as the key and a tuple of a description and the python annotation. Values that are present in self.omit will be omitted.

optional_params

Retrieves a dict containing the name of optional parameters as the key and a tuple of a description, the python annotation and the default value. Values that are present in self.omit will be omitted.

str_nodesc = 'No description given.'
str_optional = "Optional, defaults to '{}'."
coalib.settings.Section module
class coalib.settings.Section.Section(name, defaults=None)[source]

Bases: object

This class holds a set of settings.

To add settings and sections to a dictionary of sections we can use append_to_sections:

>>> sections = {}
>>> append_to_sections(sections,
...                    'test1',
...                    'val',
...                    'origin',
...                    section_name='all')
>>> 'all' in sections
True
>>> len(sections)
1
>>> str(sections)
"{'all': <Section object(... contents=OrderedDict([('test1', ..."

We can also add settings that can be appended to other settings. Basically it takes the default value of the setting which resides in the defaults of the section and appends the value of the setting in the second and returns the value of the setting:

>>> append_to_sections(sections,
...                    'test1',
...                    'val2',
...                    'origin',
...                    section_name='all.python',
...                    to_append=True)

When the section has no defaults:

>>> str(sections['all.python']['test1'])
'val2'

After assigning defaults:

>>> sections['all.python'].set_default_section(sections)
>>> str(sections['all.python']['test1'])
'val, val2'
add_or_create_setting(setting, custom_key=None, allow_appending=True)[source]

Adds the value of the setting to an existing setting if there is already a setting with the key. Otherwise creates a new setting.

append(setting, custom_key=None)[source]
bear_dirs()[source]
copy()[source]
Returns:a deep copy of this object
delete_setting(key)[source]

Delete a setting :param key: The key of the setting to be deleted

get(key, default='', ignore_defaults=False)[source]

Retrieves the item without raising an exception. If the item is not available an appropriate Setting will be generated from your provided default value.

Parameters:
  • key – The key of the setting to return.
  • default – The default value
  • ignore_defaults – Whether or not to ignore the default section.
Returns:

The setting.
is_enabled(targets)[source]

Checks if this section is enabled or, if targets is not empty, if it is included in the targets list.

Parameters:targets – List of target section names, all lower case.
Returns:True or False
set_default_section(sections, section_name=None)[source]

Find and set the defaults of a section from a dictionary of sections. The defaults are found on the basis of ‘.’ in section names:

>>> sections = {'all': Section('all')}
>>> section = Section('all.python')
>>> section.set_default_section(sections)
>>> section.defaults.name
'all'
>>> section = Section('all.python.syntax')
>>> section.set_default_section(sections)
>>> section.defaults.name
'all'

This works case insensitive. The key of the sections dict is expected to be lowered though!

>>> sections = {'c': Section('C'), 'cpp': Section('Cpp'),
...             'c.something': Section('C.something')}
>>> section = Section('C.something')
>>> section.set_default_section(sections)
>>> section.defaults.name
'C'
>>> section = Section('C.SOMETHING.else')
>>> section.set_default_section(sections)
>>> section.defaults.name
'C.something'
>>> section = Section('Cpp.SOMETHING.else')
>>> section.set_default_section(sections)
>>> section.defaults.name
'Cpp'
Parameters:
  • sections – A dictionary of sections.
  • section_name – Optional section name argument to find the default section for. If not given then use member section name.
update(other_section, ignore_defaults=False)[source]

Incorporates all keys and values from the other section into this one. Values from the other section override the ones from this one.

Default values from the other section override the default values from this only.

Parameters:
  • other_section – Another Section
  • ignore_defaults – If set to true, do not take default values from other
Returns:

self
update_setting(key, new_key=None, new_value=None)[source]

Updates a setting with new values. :param key: The old key string. :param new_key: The new key string. :param new_value: The new value for the setting

coalib.settings.Section.append_to_sections(sections, key, value, origin, section_name=None, from_cli=False, to_append=False)[source]

Appends the given data as a Setting to a Section with the given name. If the Section does not exist before it will be created empty.

Parameters:
  • sections – The sections dictionary to add to.
  • key – The key of the setting to add.
  • value – The value of the setting to add.
  • origin – The origin value of the setting to add.
  • section_name – The name of the section to add to.
  • from_cli – Whether or not this data comes from the CLI.
  • to_append – The boolean value if setting value needs to be appended to a setting in the defaults of a section.
coalib.settings.Section.extract_aspects_from_section(section)[source]

Extract aspects settings from a section into an AspectList.

Note that the section is assumed to already have valid and complete aspects related setting. This checking could be done by coalib.settings.ConfigurationGathering.validate_aspect_config().

Parameters:section – Section object.
Returns:AspectList containing aspectclass instance with user-defined tastes.
coalib.settings.SectionFilling module
coalib.settings.SectionFilling.fill_section(section, acquire_settings, log_printer, bears)[source]

Retrieves needed settings from given bears and asks the user for missing values.

If a setting is requested by several bears, the help text from the latest bear will be taken.

Parameters:
  • section – A section containing available settings. Settings will be added if some are missing.
  • acquire_settings – The method to use for requesting settings. It will get a parameter which is a dictionary with the settings name as key and a list containing a description in [0] and the names of the bears who need this setting in all following indexes.
  • log_printer – The log printer for logging.
  • bears – All bear classes or instances.
Returns:

The new section.
coalib.settings.SectionFilling.fill_settings(sections, targets, acquire_settings, log_printer=None, fill_section_method=<function fill_section>, **kwargs)[source]

Retrieves all bears and requests missing settings via the given acquire_settings method.

This will retrieve all bears and their dependencies.

Parameters:
  • sections – The sections to fill up, modified in place.
  • targets – List of section names to be executed which are passed from cli.
  • acquire_settings – The method to use for requesting settings. It will get a parameter which is a dictionary with the settings name as key and a list containing a description in [0] and the names of the bears who need this setting in all following indexes.
  • log_printer – The log printer to use for logging.
  • fill_section_method – Method to be used to fill the section settings.
  • kwargs – Any other arguments for the fill_section_method can be supplied via kwargs, which are passed directly to the fill_section_method.
Returns:

A tuple containing (local_bears, global_bears), each of them being a dictionary with the section name as key and as value the bears as a list.
coalib.settings.Setting module
class coalib.settings.Setting.Setting(key, value, origin: str = '', strip_whitespaces: bool = True, list_delimiters: collections.abc.Iterable = (', ', ';'), from_cli: bool = False, remove_empty_iter_elements: bool = True, to_append: bool = False)[source]

Bases: coala_utils.string_processing.StringConverter.StringConverter

A Setting consists mainly of a key and a value. It mainly offers many conversions into common data types.

key
value
coalib.settings.Setting.glob(obj, *args, **kwargs)[source]

Creates a path in which all special glob characters in all the parent directories in the given setting are properly escaped.

Parameters:obj – The Setting object from which the key is obtained.
Returns:Returns a path in which special glob characters are escaped.
coalib.settings.Setting.glob_list(obj, *args, **kwargs)[source]

Creates a list of paths in which all special glob characters in all the parent directories of all paths in the given setting are properly escaped.

Parameters:obj – The Setting object from which the key is obtained.
Returns:Returns a list of paths in which special glob characters are escaped.
coalib.settings.Setting.language(name)[source]

Convert a string into Language object.

Parameters:name – String containing language name.
Returns:Language object.
Raises:ValueError – If the name contain invalid language name.
coalib.settings.Setting.path(obj, *args, **kwargs)[source]
coalib.settings.Setting.path_list(obj, *args, **kwargs)[source]
coalib.settings.Setting.typed_dict(key_type, value_type, default)[source]

Creates a class that converts a setting into a dict with the given types.

Parameters:
  • key_type – The type conversion function for the keys.
  • value_type – The type conversion function for the values.
  • default – The default value to use if no one is given by the user.
Returns:

An instance of the created conversion class.
coalib.settings.Setting.typed_list(conversion_func)[source]

Creates a class that converts a setting into a list of elements each converted with the given conversion function.

Parameters:conversion_func – The conversion function that converts a string into your desired list item object.
Returns:An instance of the created conversion class.
coalib.settings.Setting.typed_ordered_dict(key_type, value_type, default)[source]

Creates a class that converts a setting into an ordered dict with the given types.

Parameters:
  • key_type – The type conversion function for the keys.
  • value_type – The type conversion function for the values.
  • default – The default value to use if no one is given by the user.
Returns:

An instance of the created conversion class.
coalib.settings.Setting.url(obj, *args, **kwargs)[source]
Module contents

coalib.testing package

Submodules
coalib.testing.BaseTestHelper module
class coalib.testing.BaseTestHelper.BaseTestHelper[source]

Bases: object

This is a base class for all Bears’ tests of coala’s testing API.

assert_result_equal(observed_result, expected_result)[source]

Asserts that an observed result from a bear is exactly same as the expected result from the bear.

Parameters:
  • observed_result – The observed result from a bear
  • expected_result – The expected result from a bear
coalib.testing.BearTestHelper module
coalib.testing.BearTestHelper.generate_skip_decorator(bear)[source]

Creates a skip decorator for a unittest module test from a bear.

check_prerequisites is used to determine a test skip.

Parameters:bear – The bear whose prerequisites determine the test skip.
Returns:A decorator that skips the test if appropriate.
coalib.testing.LocalBearTestHelper module
class coalib.testing.LocalBearTestHelper.LocalBearTestHelper(methodName='runTest')[source]

Bases: unittest.case.TestCase

This is a helper class for simplification of testing of local bears.

Please note that all abstraction will prepare the lines so you don’t need to do that if you use them.

If you miss some methods, get in contact with us, we’ll be happy to help!

assertComparableObjectsEqual(observed_result, expected_result)[source]
check_invalidity(local_bear, lines, filename=None, force_linebreaks=True, create_tempfile=True, tempfile_kwargs={}, settings={})[source]

Asserts that a check of the given lines with the given local bear yields results.

Parameters:
  • local_bear – The local bear to check with.
  • lines – The lines to check. (List of strings)
  • filename – The filename, if it matters.
  • force_linebreaks – Whether to append newlines at each line if needed. (Bears expect a n for every line)
  • create_tempfile – Whether to save lines in tempfile if needed.
  • tempfile_kwargs – Kwargs passed to tempfile.mkstemp().
check_line_result_count(local_bear, lines, results_num, filename=None, force_linebreaks=True, create_tempfile=True, tempfile_kwargs={}, settings={})[source]

Check many results for each line.

Parameters:
  • local_bear – The local bear to check with.
  • lines – The lines to check. (List of strings)
  • results_num – The expected list of many results each line.
  • filename – The filename, if it matters.
  • force_linebreaks – Whether to append newlines at each line if needed. (Bears expect a n for every line)
  • create_tempfile – Whether to save lines in tempfile if needed.
  • tempfile_kwargs – Kwargs passed to tempfile.mkstemp().
  • settings – A dictionary of keys and values (both strings) from which settings will be created that will be made available for the tested bear.
check_results(local_bear, lines, results, filename=None, check_order=False, force_linebreaks=True, create_tempfile=True, tempfile_kwargs={}, settings={})[source]

Asserts that a check of the given lines with the given local bear does yield exactly the given results.

Parameters:
  • local_bear – The local bear to check with.
  • lines – The lines to check. (List of strings)
  • results – The expected list of results.
  • filename – The filename, if it matters.
  • check_order – Whether to check that the elements of results and that of the actual list generated are in the same order or not.
  • force_linebreaks – Whether to append newlines at each line if needed. (Bears expect a n for every line)
  • create_tempfile – Whether to save lines in tempfile if needed.
  • tempfile_kwargs – Kwargs passed to tempfile.mkstemp().
  • settings – A dictionary of keys and values (both strings) from which settings will be created that will be made available for the tested bear.
check_validity(local_bear, lines, filename=None, valid=True, force_linebreaks=True, create_tempfile=True, tempfile_kwargs={}, settings={})[source]

Asserts that a check of the given lines with the given local bear either yields or does not yield any results.

Parameters:
  • local_bear – The local bear to check with.
  • lines – The lines to check. (List of strings)
  • filename – The filename, if it matters.
  • valid – Whether the lines are valid or not.
  • force_linebreaks – Whether to append newlines at each line if needed. (Bears expect a n for every line)
  • create_tempfile – Whether to save lines in tempfile if needed.
  • tempfile_kwargs – Kwargs passed to tempfile.mkstemp().
coalib.testing.LocalBearTestHelper.execute_bear(bear, *args, **kwargs)[source]
coalib.testing.LocalBearTestHelper.get_results(local_bear, lines, filename=None, force_linebreaks=True, create_tempfile=True, tempfile_kwargs={}, settings={})[source]
coalib.testing.LocalBearTestHelper.verify_local_bear(bear, valid_files, invalid_files, filename=None, settings={}, force_linebreaks=True, create_tempfile=True, timeout=None, tempfile_kwargs={})[source]

Generates a test for a local bear by checking the given valid and invalid file contents. Simply use it on your module level like:

YourTestName = verify_local_bear(YourBear, ([‘valid line’],),
([‘invalid line’],))
Parameters:
  • bear – The Bear class to test.
  • valid_files – An iterable of files as a string list that won’t yield results.
  • invalid_files – An iterable of files as a string list that must yield results.
  • filename – The filename to use for valid and invalid files.
  • settings – A dictionary of keys and values (both string) from which settings will be created that will be made available for the tested bear.
  • force_linebreaks – Whether to append newlines at each line if needed. (Bears expect a n for every line)
  • create_tempfile – Whether to save lines in tempfile if needed.
  • timeout – The total time to run the test for.
  • tempfile_kwargs – Kwargs passed to tempfile.mkstemp() if tempfile needs to be created.
Returns:

A unittest.TestCase object.
Module contents

Submodules

coalib.coala module

coalib.coala.main(debug=False)[source]

coalib.coala_ci module

coalib.coala_ci.main(debug=False)[source]

coalib.coala_delete_orig module

coalib.coala_delete_orig.main(log_printer=None, section: coalib.settings.Section.Section = None)[source]

coalib.coala_format module

coalib.coala_format.main(debug=False)[source]

coalib.coala_json module

coalib.coala_json.main(debug=False)[source]

coalib.coala_main module

coalib.coala_main.do_nothing(*args)[source]
coalib.coala_main.format_lines(lines, symbol='', line_nr='')[source]
coalib.coala_main.provide_all_actions()[source]
coalib.coala_main.run_coala(console_printer=None, log_printer=None, print_results=<function do_nothing>, acquire_settings=<function fail_acquire_settings>, print_section_beginning=<function do_nothing>, nothing_done=<function do_nothing>, autoapply=True, force_show_patch=False, arg_parser=None, arg_list=None, args=None, debug=False)[source]

This is a main method that should be usable for almost all purposes and reduces executing coala to one function call.

Parameters:
  • console_printer – Object to print messages on the console.
  • log_printer – A LogPrinter object to use for logging.
  • print_results – A callback that takes a LogPrinter, a section, a list of results to be printed, the file dict and the mutable file diff dict.
  • acquire_settings – The method to use for requesting settings. It will get a parameter which is a dictionary with the settings name as key and a list containing a description in [0] and the names of the bears who need this setting in all following indexes.
  • print_section_beginning – A callback that will be called with a section name string whenever analysis of a new section is started.
  • nothing_done – A callback that will be called with only a log printer that shall indicate that nothing was done.
  • autoapply – Set this to false to not autoapply any actions. If you set this to False, force_show_patch will be ignored.
  • force_show_patch – If set to True, a patch will be always shown. (Using ApplyPatchAction.)
  • arg_parser – Instance of ArgParser that is used to parse non-setting arguments.
  • arg_list – The CLI argument list.
  • args – Alternative pre-parsed CLI arguments.
  • debug – Run in debug mode, bypassing multiprocessing, and not catching any exceptions.
Returns:

A dictionary containing a list of results for all analyzed sections as key.

coalib.coala_modes module

coalib.coala_modes.mode_format(args, debug=False)[source]
coalib.coala_modes.mode_json(args, debug=False)[source]
coalib.coala_modes.mode_non_interactive(console_printer, args, debug=False)[source]
coalib.coala_modes.mode_normal(console_printer, log_printer, args, debug=False)[source]

This is the default coala mode. User interaction is allowed in this mode.

Parameters:
  • console_printer – Object to print messages on the console.
  • log_printer – Deprecated.
  • args – Alternative pre-parsed CLI arguments.
  • debug – Run in debug mode, bypassing multiprocessing, and not catching any exceptions.

Module contents

The coalib package is a collection of various subpackages regarding writing, executing and editing bears. Various other packages such as formatting and settings are also included in coalib.

coalib.assert_supported_version()[source]
coalib.get_version()[source]

Welcome to the Newcomers’ Guide!

DO NOT WORK ON ANY ISSUE WITHOUT ASSIGNMENT! If you do, someone else might work on it as well, and we might have no choice but to reject one of your Pull Requests. We hate it if someone’s time is wasted. For your own sake, please follow this guide. We put a lot of work into this for you!

Everyone in the coala community is expected to follow our Code of Conduct.

To become part of the coala developers team, there are a few steps you need to complete. The newcomer process is as follows:

You will start as a newcomer, which is kind of a trial. If you complete the following tasks, you will become a developer at coala:

  • run coala on a project of yours
  • merge a difficulty/newcomer Pull Request
  • review at least a difficulty/newcomer Pull Request
  • merge a difficulty/low Pull Request
  • review at least a difficulty/low or higher Pull Request

Note: After you have solved a difficulty/newcomer issue, please don’t take up any more. Instead, move on to more difficult issues.

Once you’ve run coala on a project, please fill out our usability survey. And once you’ve got your first Pull Request merged successfully, fill out our survey form. By doing so, you can help us make your experience better!

Once you’ve completed all five tasks, please fill our promotion request form to get promoted to the role of developer. GitLab contributions also count to the promotion of a newcomer to developer.

Note

Don’t just fix a newcomer issue! Supervising newcomers is really a lot of work. We’re all volunteers and we can’t keep this up if you don’t help us in other areas as well!

Of course, the order of the steps above is not important, although we recommend that you start with a newcomer issue, end with a low issue, and review other PRs in the meantime!

This is a step-based guide that will help you make your first contribution to coala, while getting you familiar with the workflow!

For more information about Pull Requests, keep reading!

Tip

You do not need to read the coala codebase to get started - this guide is intended to help you do that without reading tons of meaningless code. Nobody is good at that.

Most importantly, this guide is not intended to “check if you are fit” to contribute, but is rather a crash course to make you fit to contribute. We are a bit picky when it comes to code quality, but it’s actually not at all hard to get to this level if you bear with us through this guide.

Step 0. Run coala

As you prepare to join our community, you should find out what this project is about - if you didn’t do this already. We highly recommend you install coala and use it on at least one of your projects. Also, we recommend that you read our development setup notes to learn how to set up an environment to work on coala.

Most importantly, keep notes on what could be changed to make coala usage easier! What documentation was missing? What was hard to understand?

See also

Struggling with this? We have a very verbose guide on this topic in our Google Code In resources which can help you find a suitable repository and run coala on a bigger project.

Once you complete this, please take the time to fill out this form so we can get better!

Step 1. Meet the Community and Get an Invitation to the Organization

To get started, the first step is to meet the community. We use gitter to communicate, and there the helpful community will guide you. Gitter is an instant messaging service used by developers and users of GitHub. Gitter uses chatrooms, where developers can join in and can talk about a particular topic. coala has 2 types of chatrooms - repository chatrooms and discussion topics. Repository chatrooms are related to a specific repository and discussion chatrooms are related to general discussion topics like conferences, workshops, etc.

  • coala This is the main chatroom and repository chatroom of coala/coala.
  • gsoc This is where you discuss about Google Summer of Code.
  • coala-bears Repo chatroom for coala/coala-bears.
  • workshop Discussions related to workshops go here.
  • conferences Everything related to conferences.
  • offtopic Anything fun! Our gaming sessions start here.

The list of all available chatrooms are available here - channel list

But before joining the community, here are few things that you should keep in mind.

  • Only log into Gitter using your GitHub account and not your Twitter account since the Gitter bot corobo identifies each user from their GitHub username which makes it possible to automate certain tasks such as asking the bot to assign an issue to your profile.
  • Don’t @-mention or private message people, unless its utterly important. @ mentions generate notifications on the various gitter clients the user may be signed into, you might even wake someone on the other side of the world up. Also it discourages other people to answer the question, so you might wait longer for an answer.
  • Do not mention users unrelated to a particular issue/question. This also means you should not mention the person that was last online. For example, mentioning someone on a Github issue will subscribe them to that issue, even if they don’t want to be a part of the discussion. However, mentioning someone is okay if they really need to see your message.
  • Don’t use /all if you are a newcomer or do not have a critical reason.
  • Don’t repeatedly @-mention people in an ongoing conversation.
  • You should ask someone before mentioning them.

Now you are ready to join coala community at coala gitter. The newcomers should ping us “Hello World” to let us know they are here because we care!

When you say “Hello World” in chat corobo (our gitter bot) will invite you to be part of the Newcomer team. The invitation will be sent by mail and you will have to accept it to join. If you don’t find the invitation, accept it here.

Congratulations! Now that you are part of our organization, you can start working on issues. If you are familiar with git, you can skip the next section and pick an issue.

Note

If you have any questions, ask them in a precise and respectful way that maximizes your chances of getting the answer you’re looking for. If you’re unsure how to do that, please read: How To Ask Questions The Smart Way.

Optional. Get Help With Git

We use GitHub to manage our repository. If you’re not familiar with git/GitHub, we strongly recommend following a tutorial, such as this one.

We also have a page dedicated to git commands that will help you learn the basics.

Note

You can view some of our repositories on GitLab. For more information about it, browse to our wiki page.

If anything is unclear, or you are encountering problems, feel free to contact us on gitter, and we will help you!

Step 2. Picking Up an Issue

Now it is time to pick an issue. It is the best way to familiarise yourself with the codebase. You can view all Newcomer issues on GitHub.

Important

You need to be logged in before you follow the Newcomer issues link.

See also

For more information about what bears are, please check the following link: Writing Native bears

The easy issues that will help you get started are labeled as difficulty/newcomer and are only there to give you a glimpse of what it’s like to work with us and what the workflow is like.

Now pick an issue which isn’t assigned and which you would like to fix. Leave a comment that you would like to be assigned to the issue. This way we don’t have multiple people working on the same issue at the same time. Now you can start working on it!

Caution

As stated above, you should never work on an issue without being assigned. Fortunately, corobo is here to help you! If you are interested in picking up an issue, just write the following command in gitter chat:

corobo assign <issue_link>

Be sure to copy the full link to the issue! Also take up the issue, only when you know what the problem is and how to solve it.

You can do amazing stuff using corobo.

  • Issue assigning as stated earlier.

  • File issues:

    corobo file issue <repo> <title>
<description>

  • You shouldn’t close any PR, instead mark them as work in progress:

    corobo mark wip <full url>

  • To label a PR as pending review:

    corobo mark pending <full url>

  • To see all of the corobo commands:

    corobo help

Before starting your first commit, check out this link: Writing good commits.

Important

An important part of working on issues is documenting your work in such a way that it is easy for others to read and understand. A lot of Newcomer issues involve improving documentation.

  • For more information about writing good documentation, please check the following link: Writing Documentation
  • For more information about how to style Python code according to the PEP8 code style convention, please check the following link: PEP8 Style Guide for Python code

Step 3. Creating a Fork and Testing Your Changes

This tutorial assumes you are working on your own fork. To fork the repository, go to the official repository of coala/coala-bears and click on the Fork button from the website interface. To add it locally, simply run:

$ git remote add myfork fork_link

where myfork is the name of your fork, and fork_link is a link to your fork repository.

Important

It is important that you DO NOT make your changes on the master branch of your forked repository to avoid the following cases:

  • If you make a rebase to synchronize your repository to the original, every commit that is pushed to the remote master will be pulled in your master branch. Then if you make a pull request to commit your changes to the remote, the commits that got synced from the rebase will be recommitted along with your work in the pull request.
  • You cannot have two pull requests using the same branch name. Therefore, if your fork’s master has been used in a pull request and you decide to work on a different issue you will have to branch eventually. Differently every new commit that you make on your master branch will get attached to the initial pull request and that will result in altering the purpose of that request.
  • If your fork’s master has been used in a pull request, you have to keep the change in the branch until that get’s merged to the remote master. That will lead to the complications listed above, if you decide to work on a different issue.

In order to avoid the above mentioned cases you can create a new branch where you will work on the issue. To do that run:

$ git checkout -b <branchname>

Now you need to make sure your change is actually working. For this, you will need to test it locally before pushing it to your fork and checking it with concrete examples. The first time, you will need to install some requirements. This can be done by executing the following command while in the root of the coala project directory:

$ pip3 install -r test-requirements.txt -r requirements.txt

After that, you can run coala by simply typing

$ coala

into your bash prompt. This will analyze your code and help you fix it.

See also

Executing tests

Step 4. Sending Your Changes

Caution

Before committing your changes, please check that you are indeed in a development branch created in step 3. To check if you are in a branch, type:

$ git branch

Your current branch will have an asterisk (*) next to it. Ensure that there is no asterisk next to the master branch.

Now that you’ve fixed the issue, you’ve tested it, and you think it is ready to be merged, create a commit and push it to your fork, using:

$ git push -u myfork <branchname>

where myfork is the name of your fork that you added at the previous step.

Note

You can also add a profile picture to your Github account so that you can stand out from the crowd!

Step 5. Creating a Pull Request

Now that your commit has been sent to your fork, it is time to create a Pull Request. You can do this by accessing your fork on GitHub and clicking New Pull Request.

Congratulations! You have now created your first Pull Request!

Note

Do not delete your comments on Github, because that makes it hard for other developers to follow that issue. If there is a typo or a task list to be updated, you can edit your comment instead. If you need to add new information, make a new comment.

If you know you have more work to do on this Pull Request before it is ready to be accepted, you can indicate this to other developers by starting your Pull Request title with wip (case-insensitive, stands for “Work in Progress”).

Step 6. Waiting for Review

After creating a Pull Request, your PR moves to the review process (all will be explained in the next step), and all you can do is wait. The best thing you can do at this step is review other people’s PRs. Not only will this help the maintainers with the workload, but this is one of the three core steps towards becoming a full-blown coalaian. Never close a Pull Request unless you are told to do so.

For more information about reviewing code, check out this link.

Note

Reviewing code helps you to learn from other people’s mistakes so you can avoid making those same mistakes yourself in the future! Thus, you are improving yourself in the process.

We highly encourage you to do reviews. Don’t be afraid of doing something wrong - there will always be someone looking over it before merging it to master.

Step 7. Review Process

After creating your Pull Request, it enters the review process. You can see that’s the case from the process/pending review label. Now all you have to do is wait, or let the other developers know on Gitter that you have published your changes.

Important

Do not tag the reviewers every time you push a change. They review PRs consistently whenever they have time!

Now there are two possibilities:

  • your Pull Request gets accepted, and your commits will get merged into the master branch
  • your Pull Request doesn’t get accepted, and therefore you will need to modify it as per the review comments

Caution

Wait until the reviewer has reviewed your whole Pull Request and has labeled it process/wip. If you push again before that happens, and their comments disappear, it can be considered rude.

Note

You might be wondering what those CI things on your Pull Request are. For more detailed info about them, see this page.

It’s highly unlikely that your Pull Request will be accepted on the first attempt - but don’t worry, that’s just how it works. It helps us keep coala clean and stable.

See also

Review Process.

Now, if you need to modify your code, you can simply edit it again, add it, and commit it using

$ git commit -a --amend

This will edit your last commit message. If your commit message was considered acceptable by our reviewers, you can simply send it again (without any changes). If not, edit it and send it. You have successfully edited your last commit!

Note

Don’t forget! After editing your commit, you will have to push it again. This can be done using:

$ git push --force myfork

The meaning of myfork is explained in step 3 of this guide. The Pull Request will automatically update with the newest changes.

Congratulations! Your PR just got accepted! You’re awesome. Now you should tell us about your experience and go for a low issue - they are really rewarding!

Attention

Do not delete the fork subsequent to Pull Request for review or after it is merged!

Note

Do not just fix a newcomer issue! It is highly recommended that you fix one newcomer issue to get familiar with the workflow at coala and then proceed to a difficulty/low issue.

However, those who are familiar with opensource projects can start with difficulty/low issues.

We highly encourage you to start reviewing other’s issues after you complete your newcomer issue, as reviewing helps you to learn more about coala and python.

Note

If you need help picking up an issue, you can always ask us. The community is extremely helpful, so don’t ask to ask, just ask.

If you ever have problems in finding links, you may find the solution in our useful links section.

NextGen-Core

This document provides a brief overview of coala’s NextGen-Core. coala’s NextGen-Core comes with the promise of lifting many limitations of the old core and better efficiency and performance.

What is new?

The following new features have been added as a part of the NextGen-Core:

  • Easier Interface
  • Official support for virtual files
  • Improved dependency system
  • New base bear type: DependencyBear
  • Ability to modify bear dependencies at runtime
  • Superior caching

What has changed?

  • Global Bears are now called Project Bears and Local Bears are now known as File Bears. Both the ProjectBear and the FileBear classes inherit from the new base class coalib.core.Bear.
  • There is no need to pass HiddenResults between different bears (made possible by the new dependency management system) to hide results to the result callback. Only the results that were explicitly requested by passing the needed bears are passed now. The dependency bears can pass arbitrary python objects, not just the Result objects.
  • The former run function that was inherited by all the bears to run code analysis is now replaced by the analyze function.

Easier Interface

Running a coala session with the NextGen-Core can be done by accessing only one function, core.run(bears). The run method takes the arguments bears, result_callback, cache and executor (the last two are None by default) and initiates a coala session.

  • The bears argument contains the list of bears to be run for a coala session.

  • The result_callback is a function that is called on each result as soon as it’s available. It should have the following signature:

    def result_callback(result):
    pass

  • The cache argument if provided enables caching and runs the session using the cache provided to store the bear results. The default value of this parameter is None which when provided, runs coala without a cache.

  • The executor argument is used to provide a custom executor (which is closed after the core is closed) in which the passed bears are to be run. If this argument is not provided then ProcessPoolExecutor is used using as many processes as cores available on the system.

Bears in the NextGen-Core are implemented differently as compared to the old bears. Following points must be kept in mind while writing NextGen bears:

  • Every bear has an analyze function to perform code analysis instead of the run function that was there in the old bears.

  • The new bears must be able to be constructed with section and file_dict as parameters. Default parameters are allowed but discouraged, as you have no control over them when your bear is used as a dependency.

    class TestBear(Bear):

def analyze(self, bear, section_name, file_dict):
    return "Some analysis"

More details can be found at the API Docs.

Official support for virtual files

IDEs like IntelliJ use virtual files to represent files in a filesystem (VFS) and perform operations on them. Hence NextGen-Core provides official support for virtual files. Bears have to point to the right file data objects when run, whether they are real files or virtual ones. This makes coala easier to integrate with IDEs.

Task Objects

Task objects are the representation of tasks performed by bears. Structure-wise they are a tuple containing tuples of positional arguments and dicts of keyword arguments to the execute_task function, which itself calls analyze with them caching mechanism as their hash values are stored in the cache along with the bear results and are looked up during each coala run to fetch the results.

To get a clear picture of what task objects for a bear might look like take a look at the following example FileBear:

from coalib.core.FileBear import FileBear


class SomeFileBear(FileBear):

    def analyze(self, file, filename, filename_prefix: str='',
                filename_suffix: str=''):
        yield 'Some analysis result'

Its corresponding task object would look like the one below:

[
    [(file, filename), {'filename_prefix': "", 'filename_suffix': ""}],
]

These task objects can then be offloaded by bears to be executed in a Python pool by the generate_tasks method.

Improved Dependency System

The NextGen-Core introduces a better dependency management system than the one used by the old core. It features following improvements:

  • A bear specifies its bear dependencies in BEAR_DEPS.
  • A class DependencyTracker manages dependency management. Dependencies are added and resolved by this class and it checks for circular dependencies.
  • Dependency relations between two objects are tracked using a directed graph. When two nodes are connected with a directed edge they form a dependency relation. The NextGen-Core lifts the limitation of specifying LocalBears as dependencies of GlobalBears.

The initialize_dependencies method in Core receives the bears that are to be run and processes bear dependencies using a consumer-based system so that each dependency bear has only one instance per section and file-dict. It returns a set of dependency bears along with those bears that don’t have any dependencies or whose dependencies have been resolved (these are the ones that are scheduled to be run). Before the bears are run we initialize the dependency tracking in the __init__ method of the class Session which is responsible for running coala sessions.

The bears that have no dependencies or whose dependencies have been resolved, only their tasks will be scheduled for execution. Before executing any task coala looks it up in the cache. In case of a hit, the existing results that are stored in the cache for the corresponding task arguments are called using execute_task_with_cache method. In case of a miss or if coala is run without a cache the task is executed. The bears without any running tasks are cleaned up from the state of an ongoing run by resolving its dependencies, scheduling dependent bears and removing the bear from the running_tasks dict.

Even though bears still have to pass Result instances to communicate with coala, it is now possible to pass arbitrary Python objects. Dependency bears benefit from this because now they can pass data according to their needs without being bound to Result objects only.

The dependency results lie inside self.dependecy_results and can be accessed that way. But this is highly discouraged since it bypasses caching and could yield unexpected results when the core is run multiple times in a row.

DependencyBear

Handling of bear dependencies by the old core wasn’t effective. The old core used a queuing mechanism to communicate between bear runs. The NextGen-Core improves on this.

A new bear type was introduced, DependencyBear, makes it more convenient for bear developers to write dependency bears, by passing the dependency results using task objects. This technique of handling dependencies make it possible for the DependencyBear to support caching.

This bear serves as a base class which parallelizes tasks for each dependency result. A bear dependent on other bears can specify its dependencies in BEAR_DEPS. For example, there are two bears Foo and Bar and bear Bar depends on Foo. This can be written as

class BarBear(DependencyBear):
    BEAR_DEPS = {FooBear}

This solves the dependency issues of GlobalBears on LocalBears that were there in the old core. Now that the new dependency management is in place GlobalBears won’t be stalled due to the termination of a LocalBear run. This eradicates all the synchronization problems faced by the old core.

Multiple bears can be included as a dependency of a bear in the BEAR_DEPS field. The results of the dependency bears are saved in a dictionary called _dependency_results which is initialized in the __init()__ method of the class Bear and can be accessed using the method dependency_results() also belonging to the same class.

Writing a DependencyBear

Let’s consider a bear to be dependent on a project bear Fizz and a file bear Buzz then the corresponding DependencyBear let’s call it FizzBuzz will look like the following:

class FizzBear(ProjectBear):

    def analyze(self, file, filename):
        yield 'Fizz analysis'

class BuzzyBear(FileBear):

    def analyze(self, file, filename):
        yield 'Buzz analysis'

class FizzBuzzBear(DependencyBear):
    BEAR_DEPS = {FizzBear, BuzzBear}

    def analyze(self, dependency_bear, dependency_result, a_number=100):
        yield '{} ({}) - {}'.format(
            dependency_bear.name, a_number, dependency_result)

Ability To Modify Bear Dependencies At Runtime

A bear might depend on multiple bears before its execution can begin. Bear.BEAR_DEPS is just a set of bear classes that need to be executed before that bear can run. Once all these dependencies have run, their results are appended to self.dependency_results. The results are in the form of a dictionary with the types of the bears and their corresponding results (in the form of a list) as key-value pairs. From the previous example if we try to access the BEAR_DEPS of the BarBear we will get the result {<class 'coalib.core.Bear.FooBear'>}.

In the __init__() method of the class Bear the dependencies specified in the BEAR_DEPS are copied to every instance of a Bear run using which makes runtime modifications possible.

Override bears

A NextGen bear has to have the following functions to perform analysis:

  • analyze: This method contains the code that performs the actual code analysis routine that that bear is used for.
  • generate_tasks: This method is a part of the parent Bear class and returns tuples containing the positional arguments as a tuple and the keyword arguments in the form of a dict. These are actually the task objects that are scheduled and executed by the core. An absence of this method raises NotImplementedError``(one thing to be kept in mind is that you need to implement a ``generate_task only if the other bear base classes don’t offer the right parallelization level.) .

A bear inheriting from the class FileBear can parallelize tasks for each file given. A bear inheriting from the class DependencyBear can parallelize tasks for each dependency result. A bear inheriting from the class ProjectBear does not parallelize tasks for each file as it runs on the whole codebase given.

Let’s write our own bears with custom generate_tasks methods. We will call this bear PairWiseDependencyBear which will compare the results from genereted by two of its dependency bears. (This kind of bear might be useful in case of code clone detection).

# This bear provides some code analysis
class SomeDependencyBear(Bear):

    def analyze(self, bear, section_name, file_dict):
        yield 'Some analysis result'

# This bear provides some code analysis
class SomeOtherDependencyBear(Bear):

    def analyze(self, bear, section_name, file_dict):
        yield 'Some more analysis result'

# This bear depends on the above bear and performs some
# more analysis after receving its results
class PairWiseDependencyBear(Bear):
    BEAR_DEPS = {SomeDependencyBear, SomeOtherDependencyBear}

    def analyze(self, file, filename):
        return 'More analysis'

    def generate_tasks(self):
        similar_results = []
        results = [r['SomeDependencyBear'] for r in self.dependecy_results]
        other_results = [r['SomeOtherDependencyBear']
                   for r in self.dependecy_results]

        for a, b in zip(results, other_results):
            if a == b:
                similar_results = a

        # returns some kind of task object containing
        # the results common to both dependency bears
        # and their corresponding lengths
        return (((i, len(i)), {}) for i in similar_results)

Superior Caching

The NextGen-Core’s caching mechanism is based on task objects. Bears can offload tasks via generate_tasks() which get executed by a Python pool. Structure wise the cache is a dictionary-like-object with bear types and cache-tables as key value pairs. The cache-tables themselves are dictionary-like-objects that map the hash values of the task objects (generated by PersistentHash.persistent_hash) to the bear results.

At the time of scheduling the bears, the core performs a cache lookup. If the parameters to execute_tasks() are the same (in other words it looks for identical task objects in the cache, and fetches their corresponding results if found) as that of the previous run then instead of executing that bear again we get the cached results of that bear.

The NextGen-Core expects the analyze functions of each bear to provide results that only depend on the input parameters. In other words analyze shall be mapping its parameters to results. Using volatile values like time-dependent data without putting it into the task objects is prohibited since it might lead to unknown behaviour in coala.

coala settings

coala provides a common command-line interface for linting and fixing all your code, regardless of the programming languages you use.

To find out what kind of analysis coala offers for the languages you use, visit http://coala.io/languages, or run:

$ coala --show-bears --filter-by language C Python

To perform code analysis, simply specify the analysis routines (bears) and the files you want it to run on, for example:

spaceBear:

$ coala --bears SpaceConsistencyBear --files **.py

coala can also automatically fix your code:

spacePatchBear:

$ coala --bears SpaceConsistencyBear --files **.py --apply-patches

To run coala without user interaction, run the coala –non-interactive, coala –json and coala –format commands.

usage: coala [-h] [-v] [-C] [--ci] [--json] [--format [STR]] [-c FILE] [-F]
             [-I] [-s [FILE]] [--disable-caching] [--flush-cache]
             [--no-autoapply-warn] [-b NAME [NAME ...]] [-f FILE [FILE ...]]
             [-i FILE [FILE ...]] [--limit-files FILE [FILE ...]]
             [-d DIR [DIR ...]] [-V] [-L ENUM] [-m ENUM] [-N] [-B]
             [-l LANG [LANG ...]]
             [--filter-by FILTER_NAME FILTER_ARG [FILTER_ARG ...]]
             [-p LANG [LANG ...]] [-D] [--show-settings] [--show-details]
             [--log-json] [-o FILE] [-r [RELPATH]] [-S SETTING [SETTING ...]]
             [-a] [-j JOBS] [-n] [-A] [--debug]
             [TARGETS [TARGETS ...]]

Positional Arguments

TARGETS sections to be executed exclusively

Info

-v, --version show program’s version number and exit

Mode

-C, --non-interactive
 run coala in non interactive mode
--ci continuous integration run, alias for –non-interactive
--json mode in which coala will display output as json
--format output results with a custom format string, e.g. “Message: {message}”; possible placeholders: id, origin, file, line, end_line, column, end_column, severity, severity_str, message, message_base, message_arguments, affected_code, source_lines

Configuration

-c, --config configuration file to be used, defaults to .coafile
-F, --find-config
 find .coafile in ancestors of the working directory
-I, --no-config
 run without using any config file
-s, --save save used arguments to a config file to a .coafile, the given path, or at the value of -c
--disable-caching
 run on all files even if unchanged
--flush-cache rebuild the file cache
--no-autoapply-warn
 turn off warning about patches not being auto applicable

Inputs

-b, --bears names of bears to use
-f, --files files that should be checked
-i, --ignore files that should be ignored
--limit-files filter the –files argument’s matches further
-d, --bear-dirs
 additional directories which may contain bears

Outputs

-V, --verbose alias for -L DEBUG
-L, --log-level
 

Possible choices: ERROR, INFO, WARNING, DEBUG

set log output level to DEBUG/INFO/WARNING/ERROR, defaults to INFO

-m, --min-severity
 

Possible choices: INFO, NORMAL, MAJOR

set minimal result severity to INFO/NORMAL/MAJOR

-N, --no-color display output without coloring (excluding logs)
-B, --show-bears
 list all bears
-l, --filter-by-language
 filters –show-bears by the given languages
--filter-by filters –show-bears by the filter given as argument. Available filters: can_detect, can_fix, language
-p, --show-capabilities
 show what coala can fix and detect for the given languages
-D, --show-description
 show bear descriptions for –show-bears
--show-settings
 show bear settings for –show-bears
--show-details show bear details for –show-bears
--log-json output logs as json along with results (must be called with –json)
-o, --output write results to the given file (must be called with –json)
-r, --relpath return relative paths for files (must be called with –json)

Miscellaneous

-S, --settings arbitrary settings in the form of section.key=value
-a, --apply-patches
 apply all patches automatically if possible
-j, --jobs number of jobs to use in parallel
-n, --no-orig don’t create .orig backup files before patching
-A, --single-action
 apply a single action for all results
--debug run coala in debug mode, starting ipdb, which must be separately installed, on unexpected internal exceptions (implies –verbose)

Bear Installation Tool

Note

cib is currently broken. Most of the commands listed here will not work.

coala features a Bear Installation Tool that helps installing bears one by one or all of them. This tool is helpful as it also manages to install the bears’ external dependencies.

Installation

To install the tool, simply run:

$ pip3 install cib

Usage

To use the tool, you need to give it arguments.

To install bears, simply run cib install followed by names of bears, or by all. Therefore:

$ cib install all

will install all the available bears, whereas

$ cib install CPPCheckBear PEP8Bear

will install the specified bears only. cib uninstall works exactly the same way as cib install.

To see the full list of available bears, run

$ cib show

To upgrade the already installed bears, run

$ cib upgrade all

to upgrade all installed bears, or

$ cib upgrade CPPCheckBear PEP8Bear

to upgrade the specified bears. However, if they are not installed, they will not be upgraded.

cib also checks for bears’ dependencies, using:

$ cib check-deps all

For more information, run

$ cib help

How To Write a Good Commit Message

Quick reference

Example of a good commit:

setup.py: Change bears' entrypoint

This entrypoint ensures that coala discovers
the bears correctly.
It helps not writing more functions inside
``coalib`` for this.

Closes https://github.com/coala/coala/issues/5861
  • setup.py: Change bears’ entrypoint: Describe the change in
    maximum of 50 characters.
  • This entrypoint.. ..for this: Describe the reasoning of your changes
    in maximum of 72 characters per line.
  • Closes https://github.com/coala/coala/issues/5861: Mention the URL
    of the issue it closes or fixes.
configure.py: Fix docstring typo

This fixes the typo and changes
it from wether --> whether.

Fixes https://github.com/coala/coala/issues/4018
  • configure.py: Fix docstring typo: Describe the change in
    maximum of 50 characters.
  • This fixes.. ..whether.: Describe the reasoning of your changes
    in maximum of 72 characters per line.
  • Closes https://github.com/coala/coala/issues/7971: Mention the URL
    of the issue it closes or fixes.

At coala we are looking heavily at the maintainability of the code.

Note

Code is more often read than written!

We need good code and for achieving it, we ensure that every change to our code (i.e. the commits) is making it better.

What Makes a Good Commit

A good commit is atomic. It should describe one change and not more.

Why? Because we may create more bugs if we had more changes per commit.

How to Write Good Commit Messages

A commit message consists of 3 parts:

  • shortlog
  • commit body
  • issue reference

Example:

setup.py: Change bears' entrypoint

This entrypoint ensures that coala discovers the bears correctly.
It helps not writing more functions inside ``coalib`` for this.

Closes https://github.com/coala/coala/issues/5861

Shortlog

Example:

setup.py: Change bears' entrypoint
  • Maximum of 50 characters.
    Keeping subject lines at this length ensures that they are readable, and explains the change in a concise way.

  • Should describe the change - the action being done in the commit.

  • Should not include WIP prefix.

  • Should have a tag and a short description separated by a colon (:)

    • Tag
      • The file or class or package being modified.
      • Not mandatory.
    • Short Description
      • Starts with a capital letter.
      • Written in imperative present tense (i.e. Add something, not Adding something or Added something).
      • No trailing period.

Commit Body

Example:

This entrypoint ensures that coala discovers the bears correctly.
It helps not writing more functions inside ``coalib`` for this.
  • Maximum of 72 chars excluding newline for each line.
    The recommendation is to add a line break at 72 characters, so that Git has plenty of room to indent text while still keeping everything under 80 characters overall.

  • Not mandatory - but helps explain what you’re doing.

  • Should describe the reasoning for your changes. This is especially important for complex changes that are not self explanatory. This is also the right place to write about related bugs.

  • First person should not be used here.

The bot will complain if the 50/72 rule is not followed.

Issue reference

Example:

Fixes https://github.com/coala/coala/issues/5861
  • Should use the Fixes keyword if your commit fixes a bug, or Closes if it adds a feature/enhancement.
  • In some situations, e.g. bugs overcome in documents, the difference between Fixes and Closes may be very small and subjective. If a specific issue may lead to an unintended behaviour from the user or from the program it should be considered a bug, and should be addresed with Fixes. If an issue is labelled with type/bug you should always use Fixes. For all other issues use Closes.
  • Should use full URL to the issue.
  • There should be a single space between the Fixes or Closes and the URL.

Note

  • The issue reference will automatically add the link of the commit in the issue.
  • It will also automatically close the issue when the commit is accepted into coala.

See also

https://wiki.gnome.org/Git/CommitMessages

More Examples

Example 1 (fixed bug):

setup: Install .coafile via package_data

When installing the .coafile to distutils.sysconfig.get_python_lib, we
ignore that this is not the installation directory in every case. Thus
it is easier, more reliable and platform independent to let distutils
install it by itself.

Fixes https://github.com/coala/coala/issues/269

Example 2 (implemented feature):

Linter: Output command on debug

This massively helps debugging linters.

Closes https://github.com/coala/coala/issues/2060

Example 3 (fixed typo):

ConsoleInteraction.print_result: Fix docstring typo

wether --> whether.

Closes https://github.com/coala/coala/issues/4018

Editing Commit Messages

If you have previously made a commit and update it on a later date, it is advisable to also update the commit message accordingly.

In order to do this one can use the amend function as is described here.

Why Do We Need Good Commits?

  • An atomic commit is way easier to review. The reviewer thus will be able to review faster and find more bugs due to the lower complexity of the change.
  • Atomic commits are like good objects in object oriented programming - you can split up a bigger thing into many small objects. Reducing complexity is the key to developing good software and finding its bug before they occur.
  • Good commit messages make it easy to check at a glance what happened in a time range.
  • It is way easier to revert single changes without side effects. Reverting multiple commits at a time is easy, reverting a part of a commit is not.
  • git blame will be much more effective. It is the best documentation you can get. The older your code is, the more documentation it has. The better the commit messages are, the better is your hidden documentation. Your commit messages document the reason for every single change you did to any line.
  • git bisect will be much more effective. If you bisect through atomic commits to find the commit which caused a bug, you should be able to identify the real cause of the bug fastly. Good commit messages and atomicity of commits are key to that ability.

Codestyle for coala

coala follows the PEP8 codestyle with a maximum line length of 80 characters including newline. Invoke coala to let it correct your code automatically.

Additional Style Guidelines

Documentation Comments

A documentation comment consists of 2 parts split by a newline:

  • the description of what it does
  • a list of the parameters it takes in and their descriptions, the return value it gives out and the exceptions it may raise

Nothing should be written on the first and last line where the docstring begins and ends, and each message in the documentation comment must end with a full-stop. Also, the description of all arguments, return value and errors raised shall be on a newline, indented by 4 spaces.

Example:

def area(length, breadth):
    """
    Finds the area of a rectangle of the given length and breadth.

    :param length:
        The length of the rectangle.
    :param breadth:
        The breadth of the rectangle.
    :return:
        The area of the rectangle.
    :raises ValueError:
        Raises ValueError if the arguments are not of type
        ``float`` or ``int``.
    """

If the description for a param or other keywords exceeds 1 line, continue it in the next. Make sure that the second line is aligned below the first line.

Type Checking

If you want to assure that parameters have a certain type, you can use the enforce_signature decorator and simply annotate your function with the allowed types:

@enforce_signature
def concatenate_strings(a: str, b: str, c: (str, None)=None):
    if c is None:
        c = ""
    return a + b + c

This will raise a TypeError if a or b are not strings or c is not a string and not None.

Line Continuation

Since line continuation is not covered by PEP8 coding style guide you are supposed to keep your multiple-line lists, dicts, tuples, function definitions, function calls, and any such structures either:

  • stay on one line
  • span multiple lines that list one parameter/item each

Git Tutorial

This tutorial will help you understand how git works and how to use git to submit your commit on Github.

Note

This tutorial is about using Git in bash/cmd, which we highly recommend, as it’s cleaner. Github is a totally different thing, it is the web interface or app.

How to install Git

First step is installing Git. Supposing you are on a Debian-based distribution, this will do:

$ sudo apt-get install git

For installing Git on a Mac OS system, you can use the homebrew package manager as follows:

$ brew install git

Getting Started with coala

First of all, you have to fork the repository you are going to contribute to. This will basically give you a clone of the repository to your own repository. You can do this by opening this to fork the coala repository or this to fork the coala-bears repository and then clicking ‘Fork’ in the upper right corner.

Grabbing coala on your local machine

Now you should clone the repository to your local machine so that you can have access to all the code locally and start fixing issues! To do this, you can use these to clone the coala or coala-bears repositories:

$ git clone -o upstream https://github.com/coala/coala

or

$ git clone -o upstream https://github.com/coala/coala-bears

Note

-o upstream sets the remote name of the original coala or coala-bears repositories as upstream.

upstream is just a name we used for simplicity. You can name it however you want.

Don’t worry if you’re not familiar with what remotes are. The following section will explain more about remotes.

Now you have all your code on your local machine!

Getting to work

First let’s talk about remotes. To communicate with the outside world, git uses what are called remotes. These are repositories other than the one on your local disk which you can push your changes into (so that other people can see them) or pull from (so that you can get others changes). Now you should add a remote of your fork to your local machine so that you can pull and push your commits. This can be simply done by using the command:

$ git remote add myfork <your_fork_link>

Note

myfork is just a name we used for simplicity. You can name it however you want.

The next thing you should know before getting down to work is how to check on the changes you’ve made to your project, and your current branch. The ability to check your current branch is also extremely important, as you’ll see in the next section. The command to check this information is:

$ git status

Before we move onto the next section, you need to know about a very important branch called master. Master is the default branch that git checkouts for you when you clone a repository. It’s our policy here at coala to never develop on your fork’s master branch. This is why we create new branches, which leads us to the next section.

Creating a new branch

To start working on an issue, you first need to create a new branch where you will work. Do not change files when you are on your fork’s master branch. If you submit a Pull Request from your fork’s master branch, maintainers will assume that you didn’t read this guide. coala developers may even reject your work (even if it is a good patch), because you are showing you haven’t checked our documentation. The reason why you should never develop on your master branch is because your fork’s master branch should always be synchronized with the main repository’s master branch, which is much more challenging if it has new commits on it. This is why we create our own branch:

$ git checkout -b branchname

Note

checkout will switch to the newly created branch.

-b will create a new branch if the branch doesn’t already exist.

Some sample naming conventions for branches: + issueXXX + patchXXX + gh-XXX + A short form of the issue name (Where XXX is your issue number.)

We also recommend naming your first branch “my-first-good-pull-request”, for the purpose of this guide.

Checking your work

After the issue is fixed and you have tested it (tests are very important! never submit a change that isn’t tested), you should check your progress. Type:

$ git status

It will give you an idea about what files are currently modified and which branch you’re developing on.

Note

Tip: If there’s something you don’t find, you can always use:

$ git grep "syntax"

This will search through the whole repository and show you the files that contain the syntax.

See also

For more information about tests, check this link.

Adding the files and commiting

First, make sure you’re on the correct branch and not developing on master! If you’ve been following this guide, and this is your first pull request, you should be developing on the “my-first-good-pull-request” branch. You can check your branch with:

$ git status

Now you can add your files/folders to the current commit:

$ git add <file/folder_name>

Do this until you have added all the files needed for your commit. Then type:

$ git commit

This will lead you to a text editor. Now you need to write your commit message. We are very strict about writing commit messages as they help us maintain coala clean and stable. Commit messages usually consists of three main parts. They should have a newline between them.

  • The header

    The header should have the name of the file that you have made the change on, followed by “:”, a space, and then a short title that explains the change made.

    Example: .gitignore: Add a new Constants variable

  • The body

    The body should have a short paragraph that briefly describes the change that was made, and the reason why this change was needed in imperative. Its maximum length is 50 characters.

  • The issue that is being fixed

    This part will usually have “Fixes <issue_link>”, so the issue gets referenced on GitHub.

See also

For more information about writing commit messages, check this link.

Now that your message is written, you will have to save the file. Press escape to exit insert mode, and save the file (in Vim that is being done by pressing shift + Z twice).

Run coala

Now you can check if your commit messages and code formattings conform with the community guidelines. If something goes wrong, coala will let you know. The continuous integration (CI) will fail if coala reports errors which means that we cannot proceed with merging your fix/pull request.

$ coala

Pushing the commit

Before you push the commit, ensure that you are not developing on master again by running:

$ git status

Now you will need to push the commit to the fork. All you have to do is:

$ git push myfork

It will most likely ask for your login credentials from GitHub. Type them in, and your commit will be pushed online.

Creating a Pull Request

If you’ve made it this far, and you’re still using your ‘master’ branch, then we’re definitely going to be able to tell you have not been reading this documentation. Naughty, naughty, but there is still a way to fix your changes if you have already commited. You can run the following command, which will take you to a new branch containing all of your commited changes (Note: Some sample naming conventions can be found under the “Creating a branch” section). Then, to set your fork’s master branch back to a pristine state, check the commands in our Common Git Issues section

$ git checkout -b <branchname>

Now you would like to get your commit into the actual master branch. Making your changes available to all future users of the project. For this, you will have to create a Pull Request. To do this, you will have to go on GitHub, on your fork page. You should change the branch to the one you have worked on and submitted the commit on. Now you can create a Pull Request by clicking the New Pull Request button in the pull request tab.

Congratulations! You have just created your first Pull Request! You are awesome!

Note

If you see any error like 1 commit ahead of the master branch you need to sync your local fork with the remote repository before sending a pull request.

More information regarding syncing can be found here.

Follow-up

Now after you have created the Pull Request, there are two possibilities:

  • your PR will get accepted, and your commit will get merged into the master branch - sadly, this rarely happens on the first Pull Request

  • your PR will be rejected. There are 2 cases when a PR is rejected:

    • Test fails
    • Reviewer wants something changed (This also causes gitmate to fail)

It’s highly unlikely that your PR will be accepted on the first attempt - but don’t worry that’s just how it works. It helps us maintain coala clean and stable.

See also

Review Process.

Now if you need to modify your code, you can simply edit it again, add it and commit it using

$ git commit -a --amend

This will edit your last commit message. If your commit message was considered fine by our reviewers, you can simply send it again like this. If not, edit it and send it. Now you have successfully edited your last commit!

If you need to rebase, or want to edit an older commit from your branch, we have an amazing tutorial that you can watch to understand how it works.

Rebasing

As people work on coala new commits will be added. This will result in your local fork going out of sync with the remote repository. To sync your changes with the remote repository run the following commands in the desired branch:

Note

This assumes that the remote upstream is the original coala repository at https://github.com/coala/coala (or other, like coala/coala-bears, etc.), not your fork.

If you have followed the steps outlined in this guide and cloned the original coala repository, upstream should refer to it. You can proceed to the following section without worry.

If you’re unsure about this, run git remote -v to check which remote points to the original repository and use that instead of upstream in the following section.

$ git fetch upstream
$ git rebase upstream/master

This will fetch the commits from the remote repository and will merge it into the branch where you are currently working, and move all of the local commits that are ahead of the rebased branch to the top of the history on that branch.

Note

After following these instructions when you try to push to remote you may get fast-forwarding error. If that is the case, then you will have to force push since you are attempting to rewrite the git commit history. To do that append the --force argument in the push command:

$ git push myfork --force

Warning: Never force-push on the master branch, or any branch not owned by you.

To verify whether you have rebased correctly, go to the web page of the branch in your fork. If it says your branch is n commits behind coala:master (or whichever repo you are contributing to), then you haven’t correctly rebased yet. Otherwise, you’re good to go!

Squashing your commits

It’s possible that you have more than one commit and you want them to be squashed into a single commit. You can take your series of commits and squash them down into a single commit with the interactive rebasing tool. To squash your commits run the following command:

$ git rebase -i master

Note

master is the SHA1 hash of the commit before which you want to squash all the commits and make sure that rebase is done onto master branch.

An editor will be fired up with all the commits in your current branch (ignoring merge commits), which come after the given commit. Keep the first one as “pick” and on the second and subsequent commits with “squash”. After saving, another editor will be fired up with all the messages of commits which you want to squash. Clean up all the messages and add a new message to be displayed for the single commit.

Common Git Issues

Sometimes, you use git add -A and add files you didn’t want to your push (often after rebasing) and push it to the remote. Here ,is a short outline of, how can you remove (or revert changes in) particular files from your commit even after pushing to remote.

In your local repo, to revert the file to the state before the previous commit run the following:

$ git checkout HEAD^ /path/to/file

Now , after reverting the file(s) update your last commit, by running :

$ git commit -a --amend

To apply these changes to the remote you need to force update the branch :

$ git push -f myfork

Note

The procedure outlined above helps roll back changes by one commit only. ‘myfork’ mentioned above is your forked repository, where you push your commits.

The git checkout <revision sha> path/to/file command offers you more flexibility in reverting the changes in a file, done even from earlier than the last commit. By replacing the HEAD^ by the revision number of the particular HEAD commit, you can refer to the required revision of the file.

Might sound a little intimidating, but don’t worry, an example has been provided for you. First you can check the commit’s revision number, where the file was revised by running the following command:

$ git log /path/to/file

The revision number might look like 3cdc61015724f9965575ba954c8cd4232c8b42e4 Now, to revert the file to that revision, run the command:

$ git checkout 3cdc61015724f9965575ba954c8cd4232c8b42e4 /path/to/file.txt

Now, after the file gets reverted back to the required revision, commit the changes and (force) push to the remote.

While rebasing, you may come across mid-rebase conflicts. For information regarding how to resolve mid-rebase conflicts, please check this tutorial.

http://ohshitgit.com/ contains helpful Git snippets for recovering from various common Git issues. It is a great resource to check out when something has gone wrong.

If at any stage you are confused, or have an issue, do not close your Pull Request. Instead, contact us on gitter so that we can help you resolve your problem.

Useful Git commands

This section will briefly explain some other Git commands you will most likely use and will really make your work easier.

$ git config

The git config command lets you configure your Git installation (or an individual repository) from the command line. This command can define everything from user info to preferences to the behavior of a repository.

$ git log

The git log command displays committed snapshots. It lets you list the project history, filter it, and search for specific changes. While git status lets you inspect the working directory and the staging area, git log only operates on the committed history.

$ git push --force myfork

While we normally use git push myfork to push your commit to your fork, after further editing and work on your commit, you will need to use the --force parameter to your push to automatically update your Pull Request.

$ git reset --hard

Reset the staging area and the working directory to match the most recent commit. In addition to unstaging changes, the --hard flag tells Git to overwrite all changes in the working directory, too. Put another way: this obliterates all uncommitted changes, so make sure you really want to throw away your local developments before using it.

$ git clean

The git clean command removes untracked files from your working directory. This is really more of a convenience command, since it’s trivial to see which files are untracked with git status and remove them manually. Like an ordinary rm command, git clean is not undoable, so make sure you really want to delete the untracked files before you run it.

$ git checkout <branch>

The git checkout command is used to switch to another branch in the repository. Here <branch> is the name of the branch you want to switch to.

$ git rebase

Rebasing is the process of moving a branch to a new base commit. From a content perspective, rebasing really is just moving a branch from one commit to another. But internally, Git accomplishes this by creating new commits and applying them to the specified base—it’s literally rewriting your project history. It’s very important to understand that, even though the branch looks the same, it’s composed of entirely new commits.

$ git rebase -i

Running git rebase with the -i flag begins an interactive rebasing session. Instead of blindly moving all of the commits to the new base, interactive rebasing gives you the opportunity to alter individual commits in the process. This lets you clean up history by removing, splitting, and altering an existing series of commits. It’s like git commit --amend on steroids. Usage is $ git rebase -i <base>. Rebase the current branch onto <base>, but use an interactive rebasing session. This opens an editor where you can enter commands (described below) for each commit to be rebased. These commands determine how individual commits will be transferred to the new base. You can also reorder the commit listing to change the order of the commits themselves.

If you would like more information/commands, please use your favourite search engine to look for it. Git is widely used throughout the world and there are many good tutorials and git related Q&A threads out there.

Reviewing

This document is a guide to coala’s review process.

Am I Good Enough to Do Code Review?

Yes, if you already fixed a newcomer issue.

Reviewing can help you understand the other side of the table and learn more about coala and python. When reviewing you will get to know new people, more code and it ultimately helps you to become a better coder than you could do on your own.

You can totally help us review source code. Especially try to review source code of others and share what you have learnt with them. You can use acks and unacks like everyone else and corobo even allows you to set PRs to WIP. Check the section below for more information.

Generally follow this process:

  1. Check if the change is helping us. Sometimes people propose changes that are only helpful for specific usecases but may break others. The concept has to be good. Consider engaging in a discussion on gitter if you are unsure!
  2. Check for automatic builds. Give the contributor hints on how he can resolve them.
  3. Review the actual code. Suggest improvements and simplifications.

Be sure to not value quantity over quality! Be transparent and polite: Explain why something has to be changed and don’t just “command” the coder to obey guidelines for no reason. Reviewing always involves saying someone that their code isn’t right, be very careful not to appear rude even if you don’t mean it! Bad reviews will scare away other contributors.

Note

Commits should have a good size, every logical change should be one commit. If there are multiple changes, those make multiple commits. Don’t propose to squash commits without a reason!

When reviewing, try to be thorough: if you don’t find any issues with a pull request, you likely missed something.

If you don’t find any issues with a Pull Request and acknowledge it, a senior member will look over it and perform the merge if everything is good.

Manual Review Process

The review process for coala is as follows:

  1. Anyone can submit commits for review. These are submitted via Pull Requests on Github.

  2. The Pull Request will be labeled with a process label:

    • pending review the commit has just been pushed and is awaiting review
    • wip the Pull Request has been marked as a Work in Progress by the reviewers and has comments on it regarding how the commits shall be changed
    • approved the commits have been reviewed by the developers and they are ready to be merged into the master branch

    If you don’t have write access to coala, you can change the labels using corobo mark wip <URL> or corobo mark pending <URL>.

  3. The developers will acknowledge the commits by writing

    • In case a member is reviewing it:
    • Looks good to me better known as LGTM in case the commit is ready.
    • In case a maintainer is reviewing it:
    • ack commit_SHA or commit_SHA is ready, in case the commit is ready, or
    • unack commit_SHA or commit_SHA needs work in case it is not ready yet and needs some more work or
    • reack commit_SHA in case the commit was acknowledged before, was rebased without conflicts and the rebase did not introduce logical problems.

    Note

    Only one acknowledgment is needed per commit i.e ack commit_SHA.

  4. If the commits are not linearly mergeable into master, rebase and go to step one.

  5. All commits are acknowledged and fit linearly onto master. All continuous integration services (as described below) pass. A maintainer may leave the @gitmate-bot ff command to get the PR merged automatically.

Automated Review Process

It is only allowed to merge a pull request into master if all required GitHub states are green. This includes the GitMate review as well as the Continuous Integration systems.

Continuous integration is always done for the last commit on a pull request but should ideally pass for every commit.

For the Reviewers

  • All the pull requests waiting to be reviewed can be found at : https://coala.io/review.
  • Check the commit message.
  • Read and try to understand the code. If something looks ineffective or bug prone, leave a comment. If in doubt, let the code-writer explain their reasoning until reviewers have understood the code.
  • Generated code is not intended to be reviewed. Instead rather try to verify that the generation was done right. The commit message should expose that.
  • Every commit is reviewed independently from the other commits.
  • Tests should pass for each commit. If you suspect that tests might not pass and a commit is not checked by continuous integration, try running the tests locally.
  • Check the surroundings. In many cases people forget to remove the import when removing the use of something or similar things. It is usually good to take a look at the whole file to see if it’s still consistent.
  • Take a look at continuous integration results in the end even if they pass.
  • Coverage must not fall.
  • Be sure to assure that the tests cover all corner cases and validate the behaviour well. E.g. for bear tests just testing for a good and bad file is not sufficient. Writing Tests explains how tests should be written. Bears require special attention during testing. Testing Bears provides a guideline for how to test bears.

Note

While reviewing pull requests or patches for difficulty/low issues, make sure that the patch solves the issue and doesn’t create any further issues.

You need to thoroughly review the code, i.e. understand the functionality of the code, check whether it is efficient or not, and leave critical comments. Otherwise, don’t review! We need human reviews to find the problems which can’t be found automatically.

As you perform your review of each commit, please make comments on the relevant lines of code in the GitHub pull request. After performing your review, please comment on the pull request directly as follows:

  • If any commit passed review, make a comment that begins with “ack”, “reack”, or “ready” (all case-insensitive) and contains at least the first 6 characters of each passing commit hash delimited by spaces, commas, or forward slashes (the commit URLs from GitHub satisfy the commit hash requirements).
  • If any commit failed to pass review, make a comment that begins with “unack” or “needs work” (all case-insensitive) and contains at least the first 6 characters of each passing commit hash delimited by spaces, commas, or forward slashes (the commit URLs from GitHub satisfy the commit hash requirements).

Note

GitMate only separates by spaces and commas. If you copy and paste the SHAs they sometimes contain tabs or other whitespace, be sure to remove those!

Example:

unack 14e3ae1 823e363 342700d

If you have a large number of commits to ack, you can easily generate a list with git log --oneline master.. and write a message like this example:

reack
a8cde5b  Docs: Clarify that users may have pyvenv
5a05253  Docs: Change Developer Tutorials -> Resources
c3acb62  Docs: Create a set of notes for development setup

Rebased on top of changes that are not affected by documentation
changes.

Development Setup Notes

The following are some useful notes for setting up an environment to work on coala.

Virtualenv

We highly recommend installing coala in a virtualenv for development. This will allow you to have a contained environment in which to modify coala, separate from any other installation of coala that you may not want to break. Here we will be showing how to have a virtualenv using venv and virtualenv. We recommend using venv as it is part of the standard library and requires no extra installation. However, you can use whichever you find suitable to yourself.

Using venv

  • Make sure to have Python 3 installed in your local machine.

  • Setting up virtualenv with venv :
    $ cd working_dir # move into the dir where you want to create coala-venv
$ python3 -m venv coala-venv
# This creates an isolated Python 3 environment called coala-venv
# in your current directory.
# To activate the environment type:
$ source coala-venv/bin/activate
# To exit the environment simply type:
(coala-venv)$ deactivate

  • Now you can activate the environment and start the next part.

Using virtualenv

  • Install virtualenv using pip3 :
    $ pip3 install virtualenv
  • Create the virtualenv :
    $ cd working_dir # move into the dir where you want to create coala-venv
$ virtualenv coala-venv

NOTE: If you have both Python 3 and Python 2 installed try this command it creates an isolated Python 3 environment called coala-venv in your current directory, as coala only works for Python >= 3.4.4

$ virtualenv coala-venv -p $(which python3)
  • Run coala-venv :
    $ source coala-venv/bin/activate
(coala-venv)$ deactivate # to exit the environment

  • After this, you can start installing from git.

Repositories

If you are interested in contributing to coala, we recommend that you read our newcomers’ guide to familiarize yourself with our workflow, and perhaps with GitHub itself.

You will most likely need to work only in the coala or coala-bears repository. The former is the core of coala, and the latter contains the set of standard bears. You can fork and clone the repositories from:

https://github.com/coala/coala

https://github.com/coala/coala-bears

Beside those repositories above, package_manager and coala-utils are fundamental parts of coala which is hosted on GitLab.

https://gitlab.com/coala/package_manager/

https://gitlab.com/coala/coala-utils/

Installing from Git

We recommend installing coala and coala-bears from the master branch for latest updates and its dependencies with pip3 using the commands given below. The -e tag installs the project in the editable mode from the given path.

(coala-venv)$ git clone https://github.com/coala/coala
(coala-venv)$ cd coala
(coala-venv)$ pip3 install -e .
(coala-venv)$ cd ..
(coala-venv)$ git clone https://github.com/coala/coala-bears
(coala-venv)$ cd coala-bears
(coala-venv)$ pip3 install -e .

You will then be able to edit the repository and have the changes take effect in your virtualenv immediately. You will also be able to use pip3 to manage your installation of the package should you need to install from a different source in the future.

Building Documentation

You should run this command before trying to build the documentation:

(coala-venv)$ pip3 install -r docs-requirements.txt

Once you have done so, you can build the documentation by entering the docs directory and running python3 setup.py docs. The documentation on the coala website is in the coala (not coala-bears) repository.

Adding CI to your Fork

This tutorial will help you add the CI tools, that are used in coala repositories to test the code, to your forked repository. We recommend you to add all the CI and test everything in your own repository before doing a PR.

Before we start adding CI it’s important you have a GitHub account and know how to fork repositories. In case you don’t, you should have a look into our Git Tutorial.

Travis CI

Travis is used to confirm that the tools install and build properly. It also runs the tests and confirms all test cases pass and have 100% coverage. These are examples of travis CI checks used in coala and coala-bears repository: https://travis-ci.org/coala/coala/ and https://travis-ci.org/coala/coala-bears/. To run identical CI checks in travis you will need to configure your forked repository and to do that follow the steps mentioned below.

  1. Go to travis-ci.org and create an account. You can simply use your GitHub account for that.
  2. On the top left corner you will see a “+” icon beside “My Repositories”. Click on that, and it will take you to your travis-ci profile.
  3. Sync your account with github by clicking on the top right button saying “Sync account”.
  4. Find the forked coala repository in the list and enable builds for it.
  5. Travis CI requires a .travis.yml file containing the settings and build instructions, e.g coala’s .travis.yml. Your forked repository from coala will already have that file.
  6. Watch the builds at travis-ci.org/<username>/<repository>/builds.

AppVeyor CI

To find out how coala acts in Microsoft Windows, we use AppVeyor which runs test and build commands in a Microsoft Windows box. Here are examples of CI build in AppVeyor : https://ci.appveyor.com/project/coala/coala/ and https://ci.appveyor.com/project/coala/coala-bears/. Now to add an indentical Appveyor CI to your forked repository, follow the following instructions.

  1. Go to ci.appveyor.com and login using your GitHub account.
  2. Click on “New Project” and find forked repository from the repositories listed under your username.
  3. On the right side, you will see an “Add” button, click on it and it will add it to your projects.
  4. AppVeyor CI requires appvyor.yml file that should have the settings and instructions for windows, e.g coala’s appveyor.yml. Your forked repository already has that file.
  5. In case it has a different name or not in the root directory you have to configure it in the settings which can be found at ci.appveyor.com/project/<username>/<repository>/settings. For coala’s repository the appveyor.yml file is inside the .misc directory. So you have to go to Settings and under “Custom configuration .yml file name”, enter .misc/appveyor.yml. For coala-bears’s repository the the appveyor.yml file is in the .ci directory. So you have to enter .ci/appveyor.yml. If you have forked a different repository, enter the right .yml file path for that.
  6. In coala, the appveyor.yml sets the setting to only build from the master branch, however in your fork you may want it to build other branches as well. You can do that by configuring “Branches to Build” in Settings, so there will be no need to change the file for that.
  7. From now on appveyor will run the builds for every commit you push, which you can watch at ci.appveyor.com/project/<username>/<repository>. You can also start a build by yourself by clicking on “New Build”

Codecov

We require 100% test coverage, and to test that we use codecov.io which takes data from all other CI to confirm its coverage. Here are two example reports from coala and coala-bears repository : https://codecov.io/gh/coala/coala/ and https://codecov.io/gh/coala/coala-bears/. Once you follow the instructions here, you will have identical reports for your forked repository.

  1. Go to codecov.io and sign up using your GitHub account.
  2. Click on your username, and that will take you to a page where the repositories that use codecov are listed.
  3. Click on “Add new repository” and it will take you to a page that lists all your repositories. Choose the forked repository for which you want to enable codecov.
  4. Like other CI, this also has a configuration file, .codecov.yml file which your forked repository will already have. e.g coala’s .codecov.yml The CI uploads the test reports to codecov, which then creates an overall coverage report.
  5. You can watch the reports at codecov.io/gh/<username>/<repository>

Note

Please refrain from enabling CircleCI on your fork since they use shared build pools. You can use TravisCI instead if you need access to private builds.

Circle CI

Circle CI is also used for the same purpose as travis, to check everything installs and builds properly, and also to run the tests. Here are examples of checks in circle CI : https://circleci.com/gh/coala/coala/ and https://circleci.com/gh/coala/coala-bears/. To add these CI builds to your forked repositories follow the instructions here.

  1. Go to circleci.com and sign up using your GitHub account.
  2. After signing up it will take you to the dashboard which lists the project that already use circle and which don’t. By default it selects all the repositories, but if you want you can deselect them and only choose the forked repository.
  3. Then click the “Follow and Build” button.
  4. In project settings go to Adjust Parallelism under Build Settings and enable a second container by clicking on the box with “2x”.
  5. Using a circle.yml file it runs the builds. e.g coala’s circle.yml. Your forked repository from coala will already have that file.
  6. You can then watch the builds at circleci.com/gh/<username>/<repository>.
  7. In project settings go to Build Environments under Build Settings. You will see by default the OS used for builds is Trusty one, however we recommend using Precise as its faster.

Guide to Writing a Native Bear

Welcome. This document presents information on how to write a bear for coala. It assumes you know how to use coala. If not, please read our main tutorial

The sample sources for this tutorial lie at our coala-tutorial repository, go clone it with:

git clone https://github.com/coala/coala-tutorial

All paths and commands given here are meant to be executed from the root directory of the coala-tutorial repository.

Note

If you want to wrap an already existing tool, please refer to this tutorial instead.

What is a bear?

A bear is meant to do some analysis on source code. The source code will be provided by coala so the bear doesn’t have to care where it comes from or where it goes.

There are two kinds of bears:

  • LocalBears, which only perform analysis on each file itself
  • GlobalBears, which are project wide, like the GitCommitBear

A bear can communicate with the user via two ways:

  • Via log messages
  • Via results

Log messages will be logged according to the users settings and are usually used if something goes wrong. However you can use debug for providing development related debug information since it will not be shown to the user by default. If error/failure messages are used, the bear is expected not to continue analysis.

A Hello World Bear

Below is the code given for a simple bear that sends a debug message for each file:

import logging

from coalib.bears.LocalBear import LocalBear

class HelloWorldBear(LocalBear):
    def run(self,
            filename,
            file):
        logging.debug("Hello World! Checking file", filename, ".")

This bear is stored at ./bears/HelloWorldBear.py

In order to let coala execute this bear you need to let coala know where to find it. We can do that with the -d (--bear-dirs) argument:

coala -f src/*.c -d bears -b HelloWorldBear -L DEBUG --flush-cache

Note

The given bear directories must not have any glob expressions in them. Any character that could be interpreted as a part of a glob expression will be escaped. Please use comma separated values to give several such directories instead. Do not forget to flush the cache (by adding the argument --flush-cache when running coala) if you run a new bear on a file which has been previously analyzed (by coala).

You should now see an output like this on your command line:

[WARNING][15:07:39] Default coafile '.coafile' not found!
Here's what you can do:
* add `--save` to generate a config file with your current options
* add `-I` to suppress any use of config files
[DEBUG][15:07:39] Platform Linux -- Python 3.5.2, coalib
0.12.0.dev20170626132008
[DEBUG][15:07:39] The file cache was successfully flushed.
[DEBUG][15:07:39] Files that will be checked:
/home/LordVoldemort/programs/coa_dir/coala-tutorial/src/main.c
[DEBUG][15:07:40] coala is run only on changed files, bears' log
messages from previous runs may not appear. You may use the
`--flush-cache` flag to see them.
[DEBUG][15:07:40] Running bear HelloWorldBear...
[DEBUG][15:07:40] Hello World! Checking file /home/LordVoldemort/
programs/coa_dir/coala-tutorial/src/main.c .

Notice that the last ``[DEBUG]`` message is what was coded in
``HelloWorldBear.py``. All the other messages are inherited from the
``LocalBear`` class or run by the code responsible for executing the
bear.

Note

The first WARNING message is because our directory, does not contain a .coafile. If you have followed the instructions in our main tutorial, you will have a .coafile in your working directory. Its best if you delete that file before working on this tutorial, else you will see a bunch of other outputs from other bears as well.

For more detail about Python’s built-in logging facility, see https://docs.python.org/3/library/logging.html.

Communicating with the User

Now we can send messages through the queue, we can do the real work. Let’s say:

  • We want some information from the user (e.g. the tab width if we rely on indentation).
  • We’ve got some useful information for the user and want to show it to them. There might be some issue with their code or just an information like the number of lines.

So let’s extend our HelloWorldBear a bit, I’ve named the new bear with the creative name CommunicationBear:

import logging

from coalib.bears.LocalBear import LocalBear

class CommunicationBear(LocalBear):

    def run(self,
            filename,
            file,
            user_input: str):
        """
        Communicates with the user.

        :param user_input: Arbitrary user input.
        """
        logging.debug("Got '{ui}' as user input of type {type}.".format(
            ui=user_input,
            type=type(user_input)))

        yield self.new_result(message="A hello world result.",
                              file=filename)

Try executing it:

coala -f=src/\*.c -d=bears -b=CommunicationBear -L=DEBUG --flush-cache

Hey, we’ll get asked for the user_input!

[WARNING][15:20:18] Default coafile '.coafile' not found!
Here's what you can do:
* add `--save` to generate a config file with your current options
* add `-I` to suppress any use of config files
Please enter a value for the setting "user_input" (No description given.)
needed by CommunicationBear for section "cli":

Wasn’t that easy? Go ahead, enter something and observe the output.

Avada Kedavra
[DEBUG][15:22:55] Platform Linux -- Python 3.5.2, coalib
0.12.0.dev20170626132008
[DEBUG][15:22:55] The file cache was successfully flushed.
[DEBUG][15:22:55] Files that will be checked:
/home/LordVoldemort/programs/coa_dir/coala-tutorial/src/main.c
[DEBUG][15:22:55] coala is run only on changed files, bears' log messages
from previous runs may not appear. You may use the `--flush-cache` flag to
see them.
[DEBUG][15:22:55] Running bear CommunicationBear...
[DEBUG][15:22:55] Got 'Avada Kedavra' as user input of type <class 'str'>.

**** CommunicationBear [Section: cli] ****

!    ! [Severity: NORMAL]
!    ! A hello world result.
[    ] Do (N)othing
[    ] (O)pen file
[    ] Add (I)gnore comment
[    ] Enter number (Ctrl-D to exit):

So, what did coala do here?

First, coala looked at the parameters of the run method and found that we need some value named user_input. Then it parsed our documentation comment and found a description for the parameter which was shown to us to help us choose the right value. After the needed values are provided, coala converts us the value into a string because we’ve provided the str annotation for this parameter. If no annotation is given or the value isn’t convertible into the desired data type, you will get a coalib.settings.Setting.Setting.

Your docstring can also be used to tell the user what exactly your bear does.

Try executing

coala -d bears -b CommunicationBear --show-bears --show-description

This will show the user a bunch of information related to the bear like: - A description of what the bear does - The sections which uses it - The settings it uses (optional and required)

Note

The bears are not yet installed. We still have to specify the bear directory using -d or --bear-dirs flag.

Install locally Written Bears

Let’s say that we wrote a file NewBear.py that contain our NewBear and we want to run it locally. To install our NewBear:

  • Move the NewBear.py to our clone of coala-bears in coala-bear/bears/<some_directory>.
  • Update all bears from source with:
pip3 install -U <path/to/coala-bears>

Our NewBear is installed.

Try Executing:

coala --show-bears

This shows a list of all installed bears. We can find our NewBear in the list.

What Data Types are Supported?

The Setting does support some very basic types:

  • String (str)
  • Float (float)
  • Int (int)
  • Boolean (bool, will accept values like true, yes, yeah, no, nope, false)
  • List of strings (list, values will be split by comma)
  • Dict of strings (dict, values will be split by comma and colon)

You can use shortcuts for basic types, str_list for strings, int_list for ints, float_list for floats and bool_list for boolean values.

If you need another type, you can write the conversion function yourself and use this function as the annotation (if you cannot convert value, be sure to throw TypeError or ValueError). We’ve provided a few advanced conversions for you:

  • coalib.settings.Setting.path, converts to an absolute file path relative to the file/command where the setting was set
  • coalib.settings.Setting.path_list, converts to a list of absolute file paths relative to the file/command where the setting was set
  • coalib.settings.Setting.typed_list(typ), converts to a list and applies the given conversion (typ) to each element.
  • coalib.settings.Setting.typed_ordered_dict(key_type, value_type, default), converts to a dict while applying the key_type conversion to all keys, the value_type conversion to all values and uses the default value for all unset keys. Use typed_dict if the order is irrelevant for you.
  • coalib.settings.Setting.language, converts into coala Language object.

Results

In the end we’ve got a result. If a file is provided, coala will show the file, if a line is provided, coala will also show a few lines before the affecting line. There are a few parameters to the Result constructor, so you can e.g. create a result that proposes a code change to the user. If the user likes it, coala will apply it automatically - you don’t need to care.

Your function needs to return an iterable of Result objects: that means you can either return a list of Result objects or simply yield them and write the method as a generator.

Note

We are currently planning to simplify Bears for bear writers and us. In order to make your Bear future proof, we recommend writing your method in generator style.

Don’t worry: in order to migrate your Bears to our new API, you will likely only need to change two lines of code. For more information about how bears will look in the future, please read up on https://github.com/coala/coala/issues/725 or ask us on https://coala.io/chat.

Bears Depending on Other Bears

So we’ve got a result, but what if we need our Bear to depend on results from a different Bear?

Well coala has an efficient dependency management system that would run the other Bear before your Bear and get its results for you. All you need to do is to tell coala which Bear(s) you want to run before your Bear.

So let’s see how you could tell coala which Bears to run before yours:

from coalib.bears.LocalBear import LocalBear
from bears.somePathTo.OtherBear import OtherBear

class DependentBear(LocalBear):

    BEAR_DEPS = {OtherBear}

    def run(self, filename, file, dependency_results):
        results = dependency_results[OtherBear.name]

As you can see we have a BEAR_DEPS set which contains a list of bears we wish to depend on. In this case it is a set with 1 item: “OtherBear”.

Note

The BEAR_DEPS set must have classes of the bear itself, not the name as a string.

coala gets the BEAR_DEPS before executing the DependentBear and runs all the Bears in there first.

After running these bears, coala gives all the results returned by the Bears in the dependency_results dictionary, which has the Bear’s name as a key and a list of results as the value. E.g. in this case, we would have dependency_results == {'OtherBear' : [list containing results of OtherBear]]}.

Note

dependency_results is a keyword here and it cannot be called by any other name.

Hidden Results

Apart from regular Results, coala provides HiddenResults, which are used to share data between Bears as well as giving results which are not shown to the user. This feature is specifically for Bears that are dependencies of other Bears, and do not want to return Results which would be displayed when the bear is run.

Let’s see how we can use HiddenResults in our Bear:

from coalib.bears.LocalBear import LocalBear
from coalib.results.HiddenResult import HiddenResult

class OtherBear(LocalBear):

    def run(self, filename, file):
        yield HiddenResult(self, ["Some Content", "Some Other Content"])

Here we see that this Bear (unlike normal Bears) yields a HiddenResult instead of a Result. The first parameter in HiddenResult should be the instance of the Bear that yields this result (in this case self), and second argument should be the content we want to transfer between the Bears. Here we use a list of strings as content but it can be any object.

More Configuration Options

coala provides metadata to further configure your bear according to your needs. Here is the list of all the metadata you can supply:

LANGUAGES

To indicate which languages your bear supports, you need to give it a set of strings as a value:

class SomeBear(Bear):
    LANGUAGES = {'C', 'CPP','C#', 'D'}

REQUIREMENTS

To indicate the requirements of the bear, assign REQUIREMENTS a set with instances of subclass of PackageRequirement such as:

  • PipRequirement
  • NpmRequirement
  • CondaRequirement
  • DistributionRequirement
  • GemRequirement
  • GoRequirement
  • JuliaRequirement
  • RscriptRequirement
class SomeBear(Bear):
    REQUIREMENTS = {
    PipRequirement('coala_decorators', '0.2.1')}

To specify multiple requirements you can use the multiple method. This can receive both tuples of strings, in case you want a specific version, or a simple string, in case you want the latest version to be specified.

class SomeBear(Bear):
    REQUIREMENTS = PipRequirement.multiple(
        ('colorama', '0.1'),
        'coala_decorators')

INCLUDE_LOCAL_FILES

If your bear needs to include local files, then specify it by giving strings containing file paths, relative to the file containing the bear, to the INCLUDE_LOCAL_FILES.

class SomeBear(Bear):
    INCLUDE_LOCAL_FILES = {'checkstyle.jar',
        'google_checks.xml'}

CAN_DETECT and CAN_FIX

To easily keep track of what a bear can do, you can set the value of CAN_FIX and CAN_DETECT sets.

class SomeBear(Bear):
    CAN_DETECT = {'Unused Code', 'Spelling'}

    CAN_FIX = {'Syntax', 'Formatting'}

To view a full list of possible values, check this list:

  • Syntax
  • Formatting
  • Security
  • Complexity
  • Smell
  • Unused Code
  • Redundancy
  • Variable Misuse
  • Spelling
  • Memory Leak
  • Documentation
  • Duplication
  • Commented Code
  • Grammar
  • Missing Import
  • Unreachable Code
  • Undefined Element
  • Code Simplification

Specifying something to CAN_FIX makes it obvious that it can be detected too, so it may be omitted from CAN_DETECT

BEAR_DEPS

BEAR_DEPS contains bear classes that are to be executed before this bear gets executed. The results of these bears will then be passed to the run method as a dict via the dependency_results argument. The dict will have the name of the Bear as key and the list of its results as value:

class SomeOtherBear(Bear):
    BEAR_DEPS = {SomeBear}

For more detail see Bears Depending on Other Bears.

Other Metadata

Other metadata such as AUTHORS, AUTHORS_EMAILS, MAINTAINERS, MAINTAINERS_EMAILS, LICENSE, ASCIINEMA_URL, SEE_MORE can be used as follows:

class SomeBear(Bear):
    AUTHORS = {'Jon Snow'}
    AUTHORS_EMAILS = {'jon_snow@gmail.com'}
    MAINTAINERS = {'Catelyn Stark'}
    MAINTAINERS_EMAILS = {'catelyn_stark@gmail.com'}
    LICENSE = 'AGPL-3.0'
    ASCIINEMA_URL = 'https://asciinema.org/a/80761'
    SEE_MORE = 'https://www.pylint.org'

Aspect Bear

Aspect is a feature in coala that make configuring coala in project more easy and language agnostic. For more detail about aspect, see cEP-0005 in https://github.com/coala/cEPs/blob/master/cEP-0005.md.

An aspect-compliant bear MUST:

  1. Declare list of aspect it can fix and detected. Note that the aspect MUST be a leaf aspect. You can see list of supported aspect here https://github.com/coala/aspect-docs.
  2. Declare list of supported language. See list of supported language https://github.com/coala/coala/tree/master/coalib/bearlib/languages/definitions.
  3. Map setting to its equivalent aspect or taste using map_setting_to_aspect decorator.
  4. Yield result with relevant aspect.

For example, let’s make an aspect bear named SpellingCheckBear.

from coalib.bearlib.aspects import map_setting_to_aspect
from coalib.bearlib.aspects.Spelling import (
    DictionarySpelling,
    OrgSpecificWordSpelling,
)
from coalib.bears.LocalBear import LocalBear


class SpellingCheckBear(
        LocalBear,
        aspect={
            'detect': [
                DictionarySpelling,
                OrgSpecificWordSpelling,
            ],
        },
        languages=['Python']):

    @map_setting_to_aspect(
        use_standard_dictionary=DictionarySpelling,
        additional_dictionary_words=OrgSpecificWordSpelling.specific_word,
    )
    def run(self,
            filename,
            file,
            use_standard_dictionary: bool=True,
            additional_dictionary_words: list=None):
        """
        Detect wrong spelling.

        :param use_standard_dictionary:     Use standard English dictionary.
        :param additional_dictionary_words: Additional list of word.
        """
        if use_standard_dictionary:
            # Imagine this is where we save our standard dictionary.
            dictionary_words = ['lorem', 'ipsum']
        else:
            dictionary_words = []
        if additional_dictionary_words:
            dictionary_words += additional_dictionary_words

        for word in file.split():
            if word not in dictionary_words:
                yield self.new_result(
                    message='Wrong spelling in word `{}`'.format(word),
                    aspect=DictionarySpelling('py'),
                )

Linter Bears

Welcome. This tutorial aims to show you how to use the @linter (ref) decorator in order to integrate linters in your bears.

Note

If you are planning to create a bear that does static code analysis without wrapping a tool, please refer to this link instead.

This tutorial takes you through the process of writing a local linter Bear. If you want to write a global linter Bear, for a tool that does not run once for each file, but only once for the whole project, you can still go through the steps and then read about the differences of global linter Bears at global_bears.

Why is This Useful?

A lot of programming languages already have linters implemented, so if your project uses a language that does not already have a linter Bear you might need to implement it on your own. Don’t worry, it’s easy!

What do we Need?

First of all, we need the linter executable that we are going to use. In this tutorial we will build the PylintTutorialBear so we need Pylint, a common linter for Python. It can be found here. Since it is a python package we can go ahead and install it with

$ pip3 install pylint

Writing the Bear

To write a linter bear, we need to create a class that interfaces with our linter-bear infrastructure, which is provided via the @linter decorator.

from coalib.bearlib.abstractions.Linter import linter

@linter(executable='pylint')
class PylintTutorialBear:
    pass

As you can see pylint is already provided as an executable name which gets invoked on the files you are going to lint. This is a mandatory argument for the decorator.

The linter class is only capable of processing one file at a time, for this purpose pylint or the external tool needs to be invoked every time with the appropriate parameters. This is done inside create_arguments,

@linter(executable='pylint')
class PylintTutorialBear:
    @staticmethod
    def create_arguments(filename, file, config_file):
        pass

create_arguments accepts three parameters:

  • filename: The absolute path to the file that gets processed.
  • file: The contents of the file to process, given as a list of lines (including the return character).
  • config_file: The absolute path to a config file to use. If no config file is used, this parameter is None. Processing of the config file is left to the Bear’s implementation of the method.

You can use these parameters to construct the command line arguments. The linter expects from you to return an argument sequence here. A tuple is preferred. We will do this soon for PylintTutorialBear.

Note

create_arguments doesn’t have to be a static method. In this case you also need to prepend self to the parameters in the signature. Some functionality of @linter is only available inside an instance, like logging.

def create_arguments(self, filename, file, config_file):
    self.log("Hello world")

So which are the exact command line arguments we need to provide? It depends on the output format of the linter. The @linter decorator is capable of handling different output formats:

  • regex: This parses issue messages yielded by the underlying executable.
  • corrected: Auto-generates results from a fixed/corrected file provided by the tool.
  • unified-diff: This auto-generates results from a unified-diff output provided by the executable.

In this tutorial we are going to use the regex output format. But before we continue with modifying our bear, we need to figure out how exactly output from Pylint looks like so we can parse it accordingly.

We get some promising output when invoking Pylint with

$ pylint --msg-template="L{line}C{column}: {msg_id} - {msg}" --reports=n

Sample output looks like this:

No config file found, using default configuration
************* Module coalib.bearlib.abstractions.Linter
L1C0: C0111 - Missing module docstring
L42C48: E1101 - Class 'Enum' has no 'reverse' member
L77C32: E1101 - Class 'Enum' has no 'reverse' member
L21C0: R0912 - Too many branches (16/12)
L121C28: W0613 - Unused argument 'filename'

This is something we can parse easily with a regex. So let’s implement everything we’ve found out so far:

@linter(executable='pylint',
        output_format='regex',
        output_regex=r'L(?P<line>\d+)C(?P<column>\d+): (?P<message>.*)')
class PylintTutorialBear:
    @staticmethod
    def create_arguments(filename, file, config_file):
        return ('--msg-template="L{line}C{column}: {msg_id} - {msg}"',
                '--reports=n', filename)

As you can see, the output_regex parameter consists of named groups. These are important to construct a meaningful result that contains the information that is printed out.

For the exact list of named groups @linter recognizes, see the API documentation.

Please refer to Python3 re module and Dive into python for information about regular expressions.

Let’s brush up our output_regex a bit to use even more information:

@linter(...
        output_regex=r'L(?P<line>\d+)C(?P<column>\d+): '
                     r'(?P<message>(?P<origin>.\d+) - .*)'),
        ...)

Now we use the issue identification as the origin so we are able to deactivate single rules via ignore statements inside code.

This class is already fully functional and allows to parse issues yielded by Pylint!

Using Severities

coala uses three types of severities that categorize the importance of a result:

  • INFO
  • NORMAL
  • MAJOR

which are defined in coalib.results.RESULT_SEVERITY. Pylint output contains severity information we can use:

L1C0: C0111 - Missing module docstring

The letter before the error code is the severity. In order to make use of the severity, we need to define it inside the output_regex parameter using the named group severity:

@linter(...
        output_regex=r'L(?P<line>\d+)C(?P<column>\d+): (?P<message>'
                     r'(?P<origin>(?P<severity>[WFECRI])\d+) - .*)',
        ...)

So we want to take up the severities denoted by the letters W, F, E, C, R or I. In order to use this severity value, we will first have to provide a map that takes the matched severity letter and maps it to a severity value of coalib.results.RESULT_SEVERITY so coala understands it. This is possible via the severity_map parameter of @linter:

from coalib.results.RESULT_SEVERITY import RESULT_SEVERITY

@linter(...
        severity_map={'W': RESULT_SEVERITY.NORMAL,
                      'F': RESULT_SEVERITY.MAJOR,
                      'E': RESULT_SEVERITY.MAJOR,
                      'C': RESULT_SEVERITY.NORMAL,
                      'R': RESULT_SEVERITY.NORMAL,
                      'I': RESULT_SEVERITY.INFO},
        ...)

We can test our bear like this

$ coala --bear-dirs=. --bears=PylintTutorialBear --files=sample.py

Note

In order for the above command to work we should have 2 files in our current dir: PylintTutorialBear.py and our sample.py. Naming is very important in coala. coala will look for bears by their filename and display them based on their classname.

Normally, providing a severity-map is not needed, as coala has a default severity-map which recognizes many common words used for severities. Check out the API documentation for keywords supported!

Normalize Line or Column Numbers

coala uses 1-based line & column convention, i.e. the first line and the first column are 1. However, some linters use 0-based convention. For example, pylint uses 1-based line convention and 0-based column convention. The options normalize_line_numbers and normalize_column_numbers can help us easily map linter’s convention to coala’s. They are False by default. If normalize_line_numbers is True, line numbers would be increased by one. If normalize_column_numbers is True, column numbers would be increased by one.

Note pylint uses 0-based column convention. We need to map that to coala’s convention as follows:

@linter(...
        normalize_column_numbers = True,
        ...)

Suggest Corrections Using the corrected and unified-diff Output Formats

These output formats are very simple to use and don’t require further setup from your side inside the bear:

@linter(...
        output_format='corrected')

or

@linter(...
        output_format='unified-diff')

If your underlying tool generates a corrected file or a unified-diff of the corrections, the class automatically generates patches for the changes made and yields results accordingly.

Adding Settings to our Bear

If we run

$ pylint --help

We can see that there is a --rcfile option which lets us specify a configuration file for Pylint. Let’s add that functionality to our bear.

import os

from coalib.bearlib.abstractions.Linter import linter
from coalib.results.RESULT_SEVERITY import RESULT_SEVERITY

@linter(executable='pylint',
        output_format='regex',
        output_regex=r'L(?P<line>\d+)C(?P<column>\d+): '
                     r'(?P<message>(?P<severity>[WFECRI]).*)',
        severity_map={'W': RESULT_SEVERITY.NORMAL,
                      'F': RESULT_SEVERITY.MAJOR,
                      'E': RESULT_SEVERITY.MAJOR,
                      'C': RESULT_SEVERITY.NORMAL,
                      'R': RESULT_SEVERITY.NORMAL,
                      'I': RESULT_SEVERITY.INFO})
class PylintTutorialBear:
    @staticmethod
    def create_arguments(filename, file, config_file,
                         pylint_rcfile: str=os.devnull):
        return ('--msg-template="L{line}C{column}: {msg_id} - {msg}"',
                '--reports=n', '--rcfile=' + pylint_rcfile, filename)

Just adding the needed parameter to the create_arguments signature suffices, like you would do for other bears inside run! Additional parameters are automatically queried from the coafile. Let’s also add some documentation together with the metadata attributes:

@linter(...)
class PylintTutorialBear:
    """
    Lints your Python files!

    Checks for coding standards (like well-formed variable names), detects
    semantical errors (like true implementation of declared interfaces or
    membership via type inference), duplicated code.

    See http://pylint-messages.wikidot.com/all-messages for a list of all
    checks and error codes.
    """

    @staticmethod
    def create_arguments(filename, file, config_file,
                         pylint_rcfile: str=os.devnull):
        """
        :param pylint_rcfile:
            The configuration file Pylint shall use.
        """
        ...

Note

The documentation of the param is parsed by coala and it will be used as help to the user for that specific setting.

Finished Bear

Well done, you made it this far! Now you should have built a fully functional Python linter Bear. If you followed the code from this tutorial it should look something like this

import os

from coalib.bearlib.abstractions.Linter import linter
from coalib.results.RESULT_SEVERITY import RESULT_SEVERITY

@linter(executable='pylint',
        output_format='regex',
        output_regex=r'L(?P<line>\d+)C(?P<column>\d+): '
                     r'(?P<message>(?P<severity>[WFECRI]).*)',
        severity_map={'W': RESULT_SEVERITY.NORMAL,
                      'F': RESULT_SEVERITY.MAJOR,
                      'E': RESULT_SEVERITY.MAJOR,
                      'C': RESULT_SEVERITY.NORMAL,
                      'R': RESULT_SEVERITY.NORMAL,
                      'I': RESULT_SEVERITY.INFO})
class PylintTutorialBear:
    """
    Lints your Python files!

    Checks for coding standards (like well-formed variable names), detects
    semantical errors (like true implementation of declared interfaces or
    membership via type inference), duplicated code.

    See http://pylint-messages.wikidot.com/all-messages for a list of all
    checks and error codes.

    https://pylint.org/
    """

    @staticmethod
    def create_arguments(filename, file, config_file,
                         pylint_rcfile: str=os.devnull):
        """
        :param pylint_rcfile:
            The configuration file Pylint shall use.
        """
        return ('--msg-template="L{line}C{column}: {msg_id} - {msg}"',
                '--reports=n', '--rcfile=' + pylint_rcfile, filename)

Adding Metadata Attributes

Now we need to add some more precious information to our bear. This helps by giving more information about each bear and also helps some functions gather information by using these values. Our bear now looks like:

import os

from coalib.bearlib.abstractions.Linter import linter
from dependency_management.requirements.PipRequirement import PipRequirement
from coalib.results.RESULT_SEVERITY import RESULT_SEVERITY

@linter(executable='pylint',
        output_format='regex',
        output_regex=r'L(?P<line>\d+)C(?P<column>\d+): '
                     r'(?P<message>(?P<severity>[WFECRI]).*)',
        severity_map={'W': RESULT_SEVERITY.NORMAL,
                      'F': RESULT_SEVERITY.MAJOR,
                      'E': RESULT_SEVERITY.MAJOR,
                      'C': RESULT_SEVERITY.NORMAL,
                      'R': RESULT_SEVERITY.NORMAL,
                      'I': RESULT_SEVERITY.INFO})
class PylintTutorialBear:
    """
    Lints your Python files!

    Checks for coding standards (like well-formed variable names), detects
    semantical errors (like true implementation of declared interfaces or
    membership via type inference), duplicated code.

    See http://pylint-messages.wikidot.com/all-messages for a list of all
    checks and error codes.

    https://pylint.org/
    """

    LANGUAGES = {'Python', 'Python 2', 'Python 3'}
    REQUIREMENTS = {PipRequirement('pylint', '1.*')}
    AUTHORS = {'The coala developers'}
    AUTHORS_EMAILS = {'coala-devel@googlegroups.com'}
    LICENSE = 'AGPL-3.0'
    CAN_DETECT = {'Unused Code', 'Formatting', 'Duplication', 'Security',
                  'Syntax'}
    SEE_MORE = 'https://pylint.org/'

    @staticmethod
    def create_arguments(filename, file, config_file,
                         pylint_rcfile: str=os.devnull):
      """
      :param pylint_rcfile:
          The configuration file Pylint shall use.
      """
      return ('--msg-template="L{line}C{column}: {msg_id} - {msg}"',
              '--reports=n', '--rcfile=' + pylint_rcfile, filename)

Running and Testing our Bear

By running

$ coala --bear-dirs=. --bears=PylintTutorialBear -B

We can see that our Bear setting is documented properly. To use coala with our Bear on sample.py we run

$ coala --bear-dirs=. --bears=PylintTutorialBear --files=sample.py

To use our pylint_rcfile setting we can do

$ coala --bear-dirs=. --bears=PylintTutorialBear \
> -S pylint_rcfile=my_rcfile --files=sample.py

You now know how to write a linter Bear and also how to use it in your project.

Congratulations!

Global Linter Bears

Some linting tools do not run on file level, i.e. once for each file, but on project level. They might check some properties of the directory structure or only check one specific file like the setup.py.

For these tools we need a GlobalBear and we can also use @linter to give us one, by passing the parameter global_bear=True:

from coalib.bearlib.abstractions.Linter import linter

@linter(executable='some_tool',
        global_bear=True,
        output_format='regex',
        output_regex=r'<filename>: <message>')
class SomeToolBear:
    @staticmethod
    def create_arguments(config_file):
        return []

The create_arguments method takes no filename and file in this case since there is no file context. You can still make coala aware of the file an issue was detected in, by using the filename named group in your output_regex if relevant to the wrapped tool.

As mentioned before, create_arguments doesn’t have to be a static method. In this case remember to prepend self to the parameters in the signature:

from coalib.bearlib.abstractions.Linter import linter

@linter(executable='some_tool',
        global_bear=True,
        output_format='regex',
        output_regex=r'<filename>: <message>')
class PythonTestBear:
    def create_arguments(self, config_file):
        return '--lint', self.file_dict.keys()

You can access the complete list of files using self.file_dict which return a dictionary of {filename: file contents}. Pay attention that putting the complete list of files on the command line will cause breakages when the length of the command line exceeds the OS permitted length, or the complete list of files is greater than the OS permitted number of arguments. For more information, check here.

Where to Find More…

If you need more information about the @linter decorator, refer to the API documentation.

Linter Bears - Advanced Feature Reference

Often linters are no easy tools. To squeeze out the last bit of functionality and efficiency, @linter provides some advanced features ready for use.

Supplying Configuration Files with generate_config

Sometimes tools require a configuration file to run. @linter supports that easily by overriding generate_config().

@linter(executable='...')
class MyBear:
    @staticmethod
    def generate_config(filename, file):
        config_file = ("value1 = 1\n"
                       "value2 = 2")
        return config_file

The string returned by this method is written into a temporary file before invoking create_arguments().

The path of the temporary configuration file can be accessed inside create_arguments() via the config_file parameter:

@linter(executable='...')
class MyBear:
    @staticmethod
    def generate_config(filename, file):
        config_file = ("value1 = 1\n"
                       "value2 = 2")
        return config_file

    @staticmethod
    def create_arguments(filename, file, config_file):
        return "--use-config", config_file

If you return None, no configuration file is generated. A common case where to explicitly return None is when you want to expose a setting for the user to use his own tool-specific config. In case the user specifies such a config file, we can avoid generating one again with generate_config to reduce I/O load.

@linter(executable='...')
class MyBear:
    @staticmethod
    def generate_config(filename, file,
                        user_config: str='',
                        setting_a: bool=False):
        if user_config:
            return None
        else:
            return 'A={}'.format(setting_a)

    def create_arguments(filename, file, config_file,
                         user_config: str=''):
        return (filename, '--use-config',
                user_config if user_config else config_file)

Note

By default, no configuration file is generated.

Custom Processing Functions with process_output

Inside @linter only a few output formats are supported. And they can’t be combined for different output streams. To specify an own output parsing/processing behaviour, process_output can be overridden.

@linter(executable='my_tool')
class MyBear:
    def process_output(self, output, filename, file):
        pass

The output variable contains the string output from the executable. Depending on how you use the use_stdout and use_stderr parameters from @linter, output can contain either a tuple or a plain string: If use_stdout and use_stderr are both True, a tuple is placed with (stdout, stderr). If only one of them is True, a string is passed (containing the output stream chosen).

Inside process_output you need to yield results according to the executable output. It is also possible to combine the built-in capabilities. There are several functions accessible with the naming scheme process_output_<output-format>.

  • process_output_regex: Extracts results using a regex.

    @linter(executable='my_tool',
        use_stdout=False,
        use_stderr=True)
class MyBear:
    # Assuming the tool puts some issue messages into stderr.
    def process_output(self, output, filename, file):
        # output is a string, as we activated just ``use_stderr``
        map = {'info': RESULT_SEVERITY.INFO,
               'warn': RESULT_SEVERITY.NORMAL,
               'error': RESULT_SEVERITY.MAJOR}
        regex = "(?P<line>\d+):(?P<message>(?P<severity>[WEI]).*)"
        yield from self.process_output_regex(stderr,
                                             filename,
                                             file,
                                             regex,
                                             map)

    A static message to use for results instead of grabbing it from the executable output (via the message named regex group) can also be provided using the result_message parameter.

  • process_output_corrected: Extracts results (with patches) by using a corrected version of the file processed.

    @linter(executable='my_tool',
        use_stdout=True,
        use_stderr=False)
class MyBear:
    # Assuming the tool puts a corrected version of the file into stdout.
    def process_output(self, output, filename, file):
        # output is a string, as we activated just ``use_stdout``
        yield from self.process_output_corrected(
                            stdout,
                            filename,
                            file,
                            diff_severity=RESULT_SEVERITY.NORMAL,
                            diff_distance=2)

    The diff_distance parameter takes the number of unchanged lines allowed in between two changed lines so they get yielded as a single diff. If -1 is given, every change will be yielded as an own diff.

  • process_output_unified_diff: Extracts results (with patches) by processing a unified diff.

    @linter(executable='my_tool',
        use_stdout=True,
        use_stderr=True)
class MyBear:
    # Assuming the tool puts a unified diff into stdout
    # and additional issue messages (that can't be fixed automatically)
    # into stderr, let's combine both streams!
    def process_output(self, output, filename, file):
        # output is now a tuple, as we activated both, ``use_stdout`` and
        # ``use_stderr``.
        stdout, stderr = output
        yield from self.process_output_unified_diff(stdout,
                                                    filename,
                                                    file)
        regex = "(?P<message>.*)"
        yield from self.process_output_regex(stderr,
                                             filename,
                                             file,
                                             regex)

JSON output is also very common:

@linter(executable='my_tool')
class MyBear:
    def process_output(self, output, filename, file):
        for issue in json.loads(output):
            yield Result.from_values(origin=self,
                                     message=issue["message"],
                                     file=filename)

Additional Prerequisite Check

@linter supports doing an additional executable check before running the bear, together with the normal one (checking if the executable exists). For example, this is useful to test for the existence of external modules (like Java modules).

To enable this additional check with your commands, use the prerequisite_check_command parameter of @linter.

@linter(executable='...'
        prerequisite_check_command=('python3', '-c', 'import my_module'))
class MyBear:
    pass

If the default error message does not suit you, you can also supply prerequisite_check_fail_message together with prerequisite_check_command.

@linter(executable='...'
        prerequisite_check_command=('python3', '-c', 'import my_module'),
        prerequisite_check_fail_message='my_module does not exist.')
class MyBear:
    pass

External Bears

Welcome. This tutorial will teach you how to use the @external_bear_wrap decorator in order to write Bears in languages other than Python.

Note

This tutorial assumes that you already know the basics. If you are new, please refer to the Writing Native Bears section.

If you are planning to create a bear that uses an already existing tool (aka linter), please refer to the Linter Bears section.

Why is This Useful?

coala is a great language independent static analysis tool, where users can write their own static analysis routines.

Enabling users to write external bears means that they can write their own static analysis routine in their favourite language.

How Does This Work?

By using the @external_bear_wrap decorator you will have all the necessary data sent to your external executable (filename, lines, settings) as a JSON string via stdin. Afterwards, the analysis takes place in your executable that can be written in literally any language. In the end, you will have to provide the Results in a JSON string via stdout.

In this tutorial, we will go through 2 examples where we create a very simple bear. The first example will use a compiled language, C++, that creates a standalone binary whilst in the second example we will take a look at JS that needs node in order to run out of the browser.

External Bear Generation Tool

If you really do not want to write any Python code, there is a tool here, coala-bears-create, that will create the wrapper for you. We will be using

$ coala-bears-create -ext

in order to generate the wrapper for the bear.

Writing a Bear in C++

The bear that will be created with this tutorial will check whether there is any coala spelled with a capital C since that is a horrible mistake for one to make.

  1. Create a new directory and make it your current working directory.

  2. Run coala-bears-create as mentioned above in order to create the wrapper for our C++ bear. Answer the first question with a path to your created directory (since it should be the current one you can choose the default value and just hit Enter).

  3. The most important questions are the ones regarding the executable name and the bear name. Use coalaCheckBear for the bear name and coalaCheck_cpp for the executable name.

  4. The rest of the questions are not important (languages, developer name and contact info, license, etc) to the tutorial and you can go with the defaults. When you are prompted about settings answer no (default). After the script is finished running there should be 2 files in your current directory: coalaCheckBear.py (the wrapper) and coalaCheckBearTest.py.

  5. This tutorial will not focus on testing so ignore the second file for now. The wrapper should look similar to the code block presented below. Some code has been cleaned for convenience of explanation.

    Note

    The LICENSE specified applies only to the python code. You can license your executable however you see fit.

    import os

from coalib.bearlib.abstractions.ExternalBearWrap import (
    external_bear_wrap)


@external_bear_wrap(executable='coalaCheck_cpp',
                 settings={})
class coalaCheckBear:
    """
    Checks for coala written with uppercase 'C'
    """
    LANGUAGES = {'All'}
    REQUIREMENTS = {''}
    AUTHORS = {'Me'}
    AUTHORS_EMAILS = {'me@mail.com'}
    LICENSE = 'AGPL'

    @staticmethod
    def create_arguments():
        return ()

  6. Since the input will be a JSON string some kind of JSON class is needed. nlohmann’s JSON library ( https://github.com/nlohmann/json) is a great choice because it is easy to integrate and is used in this tutorial.

  7. Create coalaCheck.cpp and start by testing the input. The best thing about nlohmann’s JSON library is that you can parse JSON directly from stdin like this:

    #include <iostream>

#include "json.hpp"

using json = nlohmann::json;
using namespace std;

json in;

int main() {

    cin >> in;

    cout << in;

    return 0;
}

  8. Create a Makefile. The JSON library requires C++11 so a sample Makefile would look like this:

    build: coalaCheck.cpp
    g++ -std=c++11 -o coalaCheck_cpp coalaCheck.cpp

  9. Compile and test the binary by giving it a JSON string. It should print the JSON string back at stdout.

  10. Read about the JSON Spec that the input uses (The JSON Spec). The filename is found in in["filename"] and the list of lines is found in in["file"].

  11. Create a result adding function, also an init function proves quite useful for initializing the output json.

    #include <iostream>
#include <string>

#include "json.hpp"

using json = nlohmann::json;
using namespace std;

json in;
json out;
string origin;

void init_results(string bear_name) {
    origin = bear_name;
    out["results"] = json::array({});
}

void add_result(string message, int line, int column, int severity) {
    json result = {
        {"origin", origin},
        {"message", message},
        {"affected_code", json::array({{
            {"file", in["filename"]},
            {"start", {
                {"column", column},
                {"file", in["filename"]},
                {"line", line}
            }},
            {"end", {
                {"column", column+6},
                {"file", in["filename"]},
                {"line", line}
            }}
        }})},
        {"severity", severity}
    };
    out["results"] += result;
}

int main() {

    cin >> in;

    init_results("coalaCheckBear");

    cout << out;
    return 0;
}

    Note

    The C++ operators and syntax are not well suited for JSON manipulation but nlohmann’s JSON lib makes it as easy as possible.

  12. Iterate over the lines and check for "coala" with an uppercase "C". Use string’s find function like so:

    #include <iostream>
#include <string>

#include "json.hpp"

using json = nlohmann::json;
using namespace std;

json in;
json out;
string origin;

void init_results(string bear_name) {
    origin = bear_name;
    out["results"] = json::array({});
}

void add_result(string message, int line, int column, int severity) {
    json result = {
        {"origin", origin},
        {"message", message},
        {"affected_code", json::array({{
            {"file", in["filename"]},
            {"start", {
                {"column", column},
                {"file", in["filename"]},
                {"line", line}
            }},
            {"end", {
                {"column", column+6},
                {"file", in["filename"]},
                {"line", line}
            }}
        }})},
        {"severity", severity}
    };
    out["results"] += result;
}

int main() {

    cin >> in;

    init_results("coalaCheckBear");

    int i = 0;
    for (auto it=in["file"].begin(); it !=in["file"].end(); it++) {
        i++;
        string line = *it;
        size_t found = line.find("Coala");
        while (found != string::npos) {
            add_result("Did you mean 'coala'?", i, found, 2);
            found = line.find("Coala", found+1);
        }
    }

    cout << out;

    return 0;
}

  13. After building the executable it has to be added to the PATH env variable. It is possible to modify the wrapper and give it the full path. Add the current directory to the PATH like so:

    $ export PATH=$PATH:$PWD

    The last step is to test if everything is working properly. This is the testfile used in this tutorial ( testfile).

  14. Execute the Bear by running:

    $ coala -d . -b coalaCheckBear -f testfile

Note

If you have ran coala over a file more than once without modifying it, coala will try to cache it. In order to avoid such behavior add --flush-cache at the end of the command.

Writing a Bear With Javascript and Node

This part of the tutorial will demonstrate how to make an External Bear that uses a script that needs another binary to run (e.g. python, bash, node).

  1. Run coala-bears-create -ext but supply node as the executable name.

    Note

    This tutorial uses node v6.2.2. It should work with older versions too but we suggest that you update.

    When another binary is needed to run the source code, the create_arguments method comes in handy.

  2. Add the source code file as an argument to the create_arguments method (so that the command becomes node coalaCheck.js).

    The create_arguments method returns a tuple so if only one argument is added then a comma has to be used at the end (e.g. (one_item,)).

    Note

    The LICENSE specified applies only to the python code. You can license your executable however you see fit.

    import os

from coalib.bearlib.abstractions.ExternalBearWrap import (
    external_bear_wrap)


@external_bear_wrap(executable='node',
                    settings={})
class coalaCheckBear:
    """
    Checks for coala written with uppercase 'C'
    """
    LANGUAGES = {'All'}
    REQUIREMENTS = {'node'}
    AUTHORS = {'Me'}
    AUTHORS_EMAILS = {'me@mail.com'}
    LICENSE = 'AGPL'

    @staticmethod
    def create_arguments():
        return ('coalaCheck.js',)

  3. Create coalaCheck.js and add basic I/O handling.

    var input = "";

console.log = (msg) => {
    process.stdout.write(`${msg}\n`);
};

process.stdin.setEncoding('utf8');

process.stdin.on('readable', () => {
    var chunk = process.stdin.read();
    if (chunk !== null) {
        input += chunk;
    }
});

process.stdin.on('end', () => {
    input = JSON.parse(input);
    console.log(JSON.stringify(input));
});

  4. The I/O can be tested by running node coalaCheck.js and supplying a valid JSON string in the stdin.

  5. Add the init and the add result functions.

    var out = {};
var origin;

init_results = (bear_name) => {
    origin = bear_name;
    out["results"] = [];
};

add_result = (message, line, column, severity) => {
    var result = {
        "origin": origin,
        "message": message,
        "affected_code": [{
             "file": input["filename"],
             "start": {
                 "column": column,
                 "file": input["filename"],
                 "line": line
             },
             "end": {
                 "column": column+6,
                 "file": input["filename"],
                 "line": line
             }
        }],
        "severity": severity
    };
    out["results"].push(result)
};

  6. Iterate over the lines and check for "coala" spelled with a capital "C". The final source should look like this:

    var input = "";
var out = {};
var origin;

console.log = (msg) => {
    process.stdout.write(`${msg}\n`);
};

init_results = (bear_name) => {
    origin = bear_name;
    out["results"] = [];
};

add_result = (message, line, column, severity) => {
    var result = {
        "origin": origin,
        "message": message,
        "affected_code": [{
             "file": input["filename"],
             "start": {
                 "column": column,
                 "file": input["filename"],
                 "line": line
             },
             "end": {
                 "column": column+6,
                 "file": input["filename"],
                 "line": line
             }
        }],
        "severity": severity
    };
    out["results"].push(result)
};

process.stdin.setEncoding('utf8');

process.stdin.on('readable', () => {
    var chunk = process.stdin.read();
    if (chunk !== null) {
        input += chunk;
    }
});

process.stdin.on('end', () => {
    input = JSON.parse(input);
    init_results("coalaCheckBear");
    for (i in input["file"]) {
        var line = input["file"][i];
        var found = line.indexOf("Coala");
        while (found != -1) {
            add_result("Did you mean 'coala'?",
                       parseInt(i)+1, found+1, 2);
            found = line.indexOf("Coala", found+1)
        }
    }
    console.log(JSON.stringify(out));
});

In order to run this Bear there is no need to add the source code to the path because the binary being run is node. Although there is a problem: the argument supplied will be looked up only in the current directory. To fix this you can add the full path of the .js file in the argument list. In this case just run the bear from the same directory as coalaCheck.js. The code for this example can be found here.

The JSON Spec

coala will send you data in a JSON string via stdin and the executable has to provide a JSON string via stdout. The specs are the following:

  • input JSON spec
Tree Type Description
filename str the name of the file being analysed
file list file contents as a list of lines
settings obj settings as key:value pairs
  • output JSON spec
Tree Type Description
results list list of results
  origin str usually the name of the bear
  message str message to be displayed to the user
  affected_code list contains SourceRange objects
    file str the name of the file
    start obj start position of affected code
      file str the name of the file
      line int line number
      column int column number
    end obj end position of affected code
      file str the name of the file
      line int line number
      column int column number
  severity int severity of the result (0-2)
  debug_msg str message to be shown in DEBUG log
  additional_info str additional info to be displayed

Note

The output JSON spec is the same as the one that coala --json uses. If you ever get lost you can run coala --json over a file and check the results.

How to use LocalBearTestHelper to test your bears

coala has an awesome testing framework to write tests for bears with ease.

You can use the following to test your bears:

  • LocalBearTestHelper.check_validity
  • LocalBearTestHelper.check_results
  • verify_local_bears

Understanding through examples

Let us understand how to write tests for TooManyLinesBear in some_dir. TooManyLinesBear checks if a file has less than or equal to max_number_of_lines lines. max_number_of_lines by default is 10.

from coalib.results.Result import Result
from coalib.bears.LocalBear import LocalBear


class TooManyLinesBear(LocalBear):

    def run(file,
            filename,
            max_number_of_lines: int=10):
        """
        Detects if a file has more than ``max_number_of_lines`` lines.

        :param max_number_of_lines:
            Maximum number of lines to be allowed for a file. Default is 10.
        """

        if len(file) > max_number_of_lines:
            yield Result(self, 'Too many lines')

EXAMPLE 1 using verify_local_bear

from bears.some_dir.TooManyLinesBear import TooManyLinesBear
from coalib.testing.LocalBearTestHelper import verify_local_bear

good_file = '1\n2\n3\n4\n'.splitlines()
bad_file = '1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n'.splitlines()

TooManyLinesBearTest = verify_local_bear(TooManyLinesBear,
                                         valid_files=(good_file,),
                                         invalid_files=(bad_file,))

good_file is a file which your bear considers as non-style-violating and a bad_file is one which has at least one error/warning/info. We need to write a good_file which has less than or equal to max_number_of_lines lines and a bad_file which has more than max_number_of_lines lines and feed them to verify_local_bear as input along with your bear (TooManyLinesBear in this case) and a few additional arguments.

Note

good_file and bad_file are sequences just like file. A file is a sequence of an input file.

EXAMPLE 2 using LocalBearTestHelper.check_validity

from queue import Queue
from bears.some_dir.TooManyLinesBear import TooManyLinesBear

from coalib.testing.LocalBearTestHelper import LocalBearTestHelper
from coalib.settings.Section import Section
from coalib.settings.Setting import Setting


class TooManyLinesBearTest(LocalBearTestHelper):

    def setUp(self):
        self.section = Section('name')
        self.section.append(Setting('max_number_of_lines', '10'))
        self.uut = TooManyLinesBear(self.section, Queue())

    def test_valid(self):
        self.check_validity(self.uut, ["import os"])

    def test_invalid(self):
        self.check_validity(self.uut, bad_file, valid=False)

Note

bad_file here is same as bad_file in the above example.

check_validity asserts if your bear does not yield any results for a particular check with a list of strings. First a Section and your Bear (in this case TooManyLinesBear) is setUp. Now your Section consists by default Settings. You can append any Setting depending on your test. Validate a check by passing your bear, lines to check as parameters (pass a few other parameters if necessary) to check_validity. The method self.check_validity(self.uut, ["import os"]) asserts if your bear self.uut does not yield a result when a list of strings ["import os"] is passed. The method self.check_validity(self.uut, bad_file, valid=False) asserts if your bear self.uut yields a result when bad_file is passed.

EXAMPLE 3 using LocalBearTestHelper.check_results

from queue import Queue

from bears.some_dir.TooManyLinesBear import TooManyLinesBear
from coalib.testing.LocalBearTestHelper import LocalBearTestHelper
from coalib.results.Result import Result
from coalib.settings.Section import Section


class TooManyLinesBearTest(LocalBearTestHelper):

    def setUp(self):
        self.uut = TooManyLinesBear(Section('name'), Queue())

    def test_run(self):
        self.check_results(
            self.uut,
            file,
            [Result.from_values('TooManyLinesBear',
                                'Too many lines',
                                'filename')],
            settings={'max_number_of_lines': 20})

check_results asserts if your bear results match the actual results on execution on CLI. Just like the above example, we need to setUp a Section and your Bear with some Settings. check_results validates your results by giving your local bear, lines to check and expected results as input. check_results asserts if your bear’s results on checking the file match with Results.from_values(...).

A Final Note

LocalBearTestHelper is written to ease off testing for bears. Make sure that your tests have 100% coverage and zero redundancy. Use check_results as much as possible to test your bears.

Glossary

  • uut - Unit Under Test

Introduction

Tests are an essential element to check if your written components in coala really do work like they should. Even when you think “I really looked over my code, no need for tests” you are wrong! Bugs introduced when not writing tests are often the most horrible ones, they have the characteristic to be undiscoverable (or only discoverable after dozens of hours of searching). Try to test as much as possible! The more tests you write the more you can be sure you did everything correctly. Especially if someone else modifies your component, they can be sure with your tests that they don’t introduce a bug. Keep these points in your mind when you’re writing a test:

  • 100% test-coverage
  • zero redundancy

A patch will not be accepted unless there is a 100% branch coverage. Redundant tests are a waste of effort because you are testing the same piece of code again and again, which is unnecessary.

Actually Writing a Test

So how do you implement a test in coala? First up, tests are placed into the coala-bears/tests (if you want to write a test for a bear) or coala/tests (if you test a component written for the coalib) directory. They are also written in Python (version 3) and get automatically executed by running:

$ pytest

There’s only one constraint: The name of the test file has to end with Test.py (for example MyCustomTest.py, but not MyCustomTestSuite.py).

Note

If pytest seems to give errors, try running python3 -m pytest instead.

Often you don’t want to run all available tests. To run your specific one, type (in the coala root folder):

$ pytest -k <your-test>

You can even give partial names or queries like “not MyCustomTest” to not run a specific test. More information is shown with pytest -h

Coming to the test file structure. Every test script starts with your imports. According to the coala code style (and pep8 style) we first do system imports (like re or subprocessing), followed by first party imports (like coalib.result.Result).

Then the actual test suite class follows, that contains the tests. Each test suite is made up of test cases, where the test suite checks the overall functionality of your component by invoking each test case.

The basic declaration for a test suite class is as follows:

class YourComponentTest(unittest.TestCase):
    # Your test cases.
    pass

You should derive your test suite from unittest.TestCase to have access to the setUp() and tearDown() functions (covered in section below: ``setUp()`` and ``tearDown()``) and also to the assertion functions.

Now to the test cases: To implement a test case, just declare a class member function without parameters, starting with test_. Easy, isn’t it?

class YourComponentTest(unittest.TestCase):
    # Tests somethin'.
    def test_case1(self):
        pass

    # Doesn't test, this is just a member function, since the function name
    # does not start with 'test_'.
    def not_testing(self):
        pass

But how do you actually test if your component is correct? For that purpose you have asserts. Asserts check whether a condition is fulfilled and pass the result to the overall test-suite-invoking-instance, that manages all tests in coala. The result is processed and you get a message if something went wrong in your test.

See also

unittest assert-methods
Documentation on the assert functions from python’s inbuilt unittest.

So an example test that succeeds would be:

# The sys import and setup is not needed here because this example doesn't
# use coala components.
import unittest


class YourComponentTest(unittest.TestCase):
    # Tests somethin'.
    def test_case1(self):
        # Does '1' equal '1'? Interestingly it does... mysterious...
        self.assertEqual(1, 1)
        # Hm yeah, True is True.
        self.assertTrue(True)

Note

Tests in coala are evaluated against their coverage, means how many statements will be executed from your component when invoking your test cases. A branch coverage of 100% is needed for any commit in order to be pushed to master - please ask us on gitter if you need help raising your coverage!

The branch coverage can be measured locally with the pytest --cov command.

See also

Module Executing Tests
Documentation of running Tests with coverage

As our coverage is measured across builds against several python versions (we need version specific branches here and there) you will not get the full coverage locally! Simply make a pull request to get the coverage measured automatically.

If some code is untestable, you need to mark your component code with # pragma: no cover. Important: Provide a reason why your code is untestable. Code coverage is measured using python 3.4.4 and 3.5 on linux.

# Reason why this function is untestable.
def untestable_func(): # pragma: no cover
    # Untestable code.
    pass

setUp() and tearDown()

Often you reuse components or need to make an inital setup for your tests. For that purpose the function setUp() exists. Just declare it inside your test suite and it is invoked automatically once at test suite startup:

class YourComponentTest(unittest.TestCase):
    def setUp(self):
        # Your initialization of constants, operating system API calls etc.
        pass

The opposite from this is the tearDown() function. It gets invoked when the test suite finished running all test cases. Declare it like setUp() before:

class YourComponentTest(unittest.TestCase):
    def tearDown(self):
        # Deinitialization, release calls etc.
        pass

Kickstart

This section contains a concluding and simple example that you can use as a kickstart for test-writing.

Put the code under the desired folder inside tests, modify it to let it test your stuff and run the test from the coala root folder using pytest.

# Import here your needed system components.
import sys
import unittest

# Import here your needed coala components.


# Your test unit. The name of this class is displayed in the test
# evaluation.
class YourTest(unittest.TestCase):
    def setUp(self):
        # Here you can set up your stuff. For example constant values,
        # initializations etc.
        pass

    def tearDown(self):
        # Here you clean up your stuff initialized in setUp(). For example
        # deleting arrays, call operating system API etc.
        pass

    def test_case1(self):
        # A test method. Put your test code here.
        pass

Writing Documentation

This document gives a short introduction on how to write documentation for the coala project.

Documentation is written in reStructuredText and rendered by Read the Docs to our lovely users. You can view the current user documentation on http://docs.coala.io.

To familiarize yourself with the reStructuredText syntax please see this guide.

After getting the coala source code (see Installation Instructions), you can start hacking on existent documentation files. They reside in a separate repository that can be found here.

If you want to add new pages, you need to alter the index.rst file in the root of the repository. Please read http://www.sphinx-doc.org/en/stable/markup/toctree.html#toctree-directive for an explanation of the syntax.

You should run this command before trying to build the documentation:

pip3 install -r docs-requirements.txt

You can test the documentation locally through simply running make html in the root directory. This generates _build\html\index.html that you can view on your browser.

Testing

You can help us test coala in several ways.

Executing our Tests

coala has a big test suite. It is meant to work on every platform on every PC. If you just execute our tests you are doing us a favor.

To run tests, You first need to install some dependencies. This can be done by following these steps:

If you have not already, clone the repository (or a fork of it) by running:

$ git clone https://github.com/coala/coala

Navigate to the directory where coala is located.

Next you need to install some requirements. This can be done by executing the following command while in the root of the coala project directory.

$ pip3 install -r test-requirements.txt -r requirements.txt

You can then execute our tests with

$ pytest

Note

If pytest seems to give errors, try running python3 -m pytest instead.

and report any errors you get!

To run our tests, you can also use python3 setup.py test

Note

If you need to customize test running, you can get more options about allowing skipped tests, getting code coverage displayed or omitting/selecting tests using pytest directly.

$ pytest --help

Note

You may not get a test coverage of 100% locally. The coverage published on codecov.io (GitHub Projects) and codecov.io (GitLab Projects) are actually merged results for several python versions. The results are merged from different OS. Appveyor results provide coverage of Windows specific lines, and Travis/Circle provide coverage of Unix specific lines. Also, the lack of tests that developers often forget to write is typically why one will see test coverage not reach 100%. Thus, your test coverage can ‘pass’ without reaching 100%. If you make changes to the code, then you should concentrate on getting 100% coverage on the changes made rather than worrying about the coverage of the whole project.

Using test coverage

To get coverage information, you can run:

$ pytest --cov

You can view the coverage report as html by running:

$ pytest --cov --cov-report html

The html report will be saved .htmlreport inside the coala repository.