@@ -345,7 +345,7 @@ however the need usually indicates an issue with the package itself.
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#### _ Optional dependencies are not that optional_
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If your package contains optional dependencies, doctests do not know about them
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- being optional. So you either guard the imports in doctests (yikes!), or
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+ being optional. So you either guard the imports in doctests themselves (yikes!), or
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the collections fails if dependencies are not available.
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The solution is to explicitly ` --ignore ` the paths to modules with optionals.
@@ -371,17 +371,17 @@ objects will be collected.
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The decision on what is public is as follows: an object is public iff
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- - it is included into the ` __all__ ` list of a public module;
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- - the name of the object does not have a leading underscore;
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- - the name of the module from which the object is collected does not have
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+ - it is included into the ` __all__ ` list of a public module;
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+ - the name of the object does not have a leading underscore;
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+ - the name of the module from which the object is collected does not have
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a leading underscore.
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Consider an example: ` scipy.linalg.det ` is defined in ` scipy/linalg/_basic.py ` ,
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so it is collected twice, from ` _basic.py ` and from ` __init__.py ` . The rule above
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leads to
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- - ` scipy.linalg._basic.det ` , collected from ` scipy/linalg/_basic.py ` , is private.
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- - ` scipy.linalg.det ` , collected from ` scipy/linalg/__init__.py ` , is public.
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+ - ` scipy.linalg._basic.det ` , collected from ` scipy/linalg/_basic.py ` , is private.
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+ - ` scipy.linalg.det ` , collected from ` scipy/linalg/__init__.py ` , is public.
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#### _ ` pytest ` 's assertion rewriting_
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@@ -398,7 +398,7 @@ adding `--assert=plain` is reasonable.
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#### _ Mixing strings and numbers_
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Generally, we aim to handle mixtures of strings and numeric data. Deeply nested data
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- structures however may cause the checker to fall back to the vanilla ` doctest ` literal
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+ structures, however, may cause the checker to fall back to the vanilla ` doctest ` literal
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checking. For instance, ` ["value", 1/3] ` will use the floating-point aware checker, and
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so will ` {"value": 1/3, "other value": 2/3} ` or ` [(1, 2), (3, 4)] ` ; Meanwhile, nested
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dictionaries, ` {"a": dict(value=1/3)} ` , or lists of tuples with mixed entries,
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