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Fix a typo in a log message and extend a docstring #235

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May 15, 2019
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Fix a typo in a log message and extend a docstring #235

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c8b2a33
Fix minor typo in log message
lumbric May 13, 2019
f4857f9
Improve the docstring for IamDataFrame.validate()
lumbric May 13, 2019
74e5f84
Another rewrite of docstrings for validate()
lumbric May 14, 2019
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15 changes: 12 additions & 3 deletions pyam/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -519,7 +519,11 @@ def require_variable(self, variable, unit=None, year=None,
return pd.DataFrame(index=idx).reset_index()

def validate(self, criteria={}, exclude_on_fail=False):
"""Validate scenarios using criteria on timeseries values
"""Validate scenarios using criteria on timeseries values. Returns all
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scenarios which do not match the criteria and prints a log message or
returns None, if all scenarios match the criteria.
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When called with `exclude_on_fail=True`, the object will be modified.
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Great addition, but let’s be more specific than “modified”: say “all scenarios not satisfying the criteria will be marked as exclude=True

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Hm yes, "modified" is not very specific. Modification of input objects can be dangerous in Python if users of the function are not aware of the behavior. Therefore when reading the word "modified" in the docstring, I'm warned as user, while in "marked as exclude=True" I have to think a bit more what that means. I've tried to incorporate both thoughts into the next commit for the function. I think for the method it is less important to emphasize this, letting the method modify the object is more conventional in Python, I guess.


Parameters
----------
Expand All @@ -532,7 +536,7 @@ def validate(self, criteria={}, exclude_on_fail=False):
df = _apply_criteria(self.data, criteria, in_range=False)

if not df.empty:
msg = '{} of {} data points to not satisfy the criteria'
msg = '{} of {} data points do not satisfy the criteria'
logger().info(msg.format(len(df), len(self.data)))

if exclude_on_fail and len(df) > 0:
Expand Down Expand Up @@ -1398,7 +1402,12 @@ def _make_index(df, cols=META_IDX):


def validate(df, criteria={}, exclude_on_fail=False, **kwargs):
"""Validate scenarios using criteria on timeseries values
"""Validate scenarios using criteria on timeseries values. Returns all
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scenarios which do not match the criteria and prints a log message or
returns None, if all scenarios match the criteria.
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When called with `exclude_on_fail=True`, the input object `df` will be
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modified.

Parameters
----------
Expand Down