Crawto-Quality

A Collection of Tools for improving code quality

Crawto-Doc

A command-line-tool to add numpy style docstrings to your python projects.
From reading just your code, its a docstring with a description, parameters, and and attributes & examples where applicable.
While it cannot totally complete your documentation, Crawto-Doc can fill in as much information you give it. Using mypy will fill in some missing pieces.

Why

Because documentation is a oft boring or an after-thought. Many programs exist to turn documentation into websites or otherwise shareable text. The CrawtoDoc package exists to turn code into documentation.

From

class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
    @_deprecate_positional_args
    def __init__(self, *, strategy="warn", random_state=None, constant=None):  
        self.strategy = strategy
        self.random_state = random_state
        self.constant = constant

To

class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):  
    """<#TODO Description>

    Parameters
    ----------
    strategy : <#TODO type definition>, default=warn
        <#TODO Description>

    random_state : <#TODO type definition>, default=None
        <#TODO Description>

    constant : <#TODO type definition>, default=None
        <#TODO Description>

    Attributes
    ----------
    sparse_output_ : <#TODO type definition>
        <#TODO Attribute Description>

    n_outputs_ : <#TODO type definition>
        <#TODO Attribute Description>

    n_features_in_ : <#TODO type definition>
        <#TODO Attribute Description>

    outputs_2d_ : <#TODO type definition>
        <#TODO Attribute Description>

    Examples
    --------
    >>> from crawto-quality import crawto_doc
    >>> example = DummyClassifier(strategy='warn', random_state=None, constant=None)
    >>> example.fit(X=<#TODO Example Value>, y=<#TODO Example Value>, sample_weight=None)
    <#TODO Method Return Value>
    >>> example.predict(X=<#TODO Example Value>)
    <#TODO Method Return Value>
    >>> example.predict_proba(X=<#TODO Example Value>)
    <#TODO Method Return Value>
    >>> example.predict_log_proba(X=<#TODO Example Value>)
    <#TODO Method Return Value>
    >>> example.score(X=<#TODO Example Value>, y=<#TODO Example Value>, sample_weight=None)
    <#TODO Method Return Value>
    """