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DOC: Change docstrings to make documentation easier to read

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Ana Ruelas
2017-06-05 16:01:33 -04:00
parent 2178ad1081
commit 4aeef2d533
2 changed files with 65 additions and 47 deletions
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zipline/pipeline/engine.py
+40 -25
View File
@@ -37,7 +37,8 @@ class PipelineEngine(with_metaclass(ABCMeta)):
@abstractmethod
def run_pipeline(self, pipeline, start_date, end_date):
"""
Compute values for `pipeline` between `start_date` and `end_date`.
Compute values for ``pipeline`` between ``start_date`` and
``end_date``.
Returns a DataFrame with a MultiIndex of (date, asset) pairs.
@@ -55,14 +56,14 @@ class PipelineEngine(with_metaclass(ABCMeta)):
result : pd.DataFrame
A frame of computed results.
The columns `result` correspond to the entries of
The ``result`` columns correspond to the entries of
`pipeline.columns`, which should be a dictionary mapping strings to
instances of `zipline.pipeline.term.Term`.
instances of :class:`zipline.pipeline.term.Term`.
For each date between `start_date` and `end_date`, `result` will
contain a row for each asset that passed `pipeline.screen`. A
screen of None indicates that a row should be returned for each
asset that existed each day.
For each date between ``start_date`` and ``end_date``, ``result``
will contain a row for each asset that passed `pipeline.screen`.
A screen of ``None`` indicates that a row should be returned for
each asset that existed each day.
"""
raise NotImplementedError("run_pipeline")
@@ -90,18 +91,18 @@ class PipelineEngine(with_metaclass(ABCMeta)):
result : pd.DataFrame
A frame of computed results.
The columns `result` correspond to the entries of
The ``result`` columns correspond to the entries of
`pipeline.columns`, which should be a dictionary mapping strings to
instances of `zipline.pipeline.term.Term`.
instances of :class:`zipline.pipeline.term.Term`.
For each date between `start_date` and `end_date`, `result` will
contain a row for each asset that passed `pipeline.screen`. A
screen of None indicates that a row should be returned for each
asset that existed each day.
For each date between ``start_date`` and ``end_date``, ``result``
will contain a row for each asset that passed `pipeline.screen`.
A screen of ``None`` indicates that a row should be returned for
each asset that existed each day.
See Also
--------
:meth:`PipelineEngine.run_pipeline`
:meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
"""
raise NotImplementedError("run_chunked_pipeline")
@@ -217,15 +218,6 @@ class SimplePipelineEngine(PipelineEngine):
"""
Compute a pipeline.
Parameters
----------
pipeline : zipline.pipeline.Pipeline
The pipeline to run.
start_date : pd.Timestamp
Start date of the computed matrix.
end_date : pd.Timestamp
End date of the computed matrix.
The algorithm implemented here can be broken down into the following
stages:
@@ -256,10 +248,33 @@ class SimplePipelineEngine(PipelineEngine):
Step 2 is performed in ``SimplePipelineEngine.compute_chunk``.
Steps 3, 4, and 5 are performed in ``SimplePiplineEngine._to_narrow``.
Parameters
----------
pipeline : zipline.pipeline.Pipeline
The pipeline to run.
start_date : pd.Timestamp
Start date of the computed matrix.
end_date : pd.Timestamp
End date of the computed matrix.
Returns
-------
result : pd.DataFrame
A frame of computed results.
The ``result`` columns correspond to the entries of
`pipeline.columns`, which should be a dictionary mapping strings to
instances of :class:`zipline.pipeline.term.Term`.
For each date between ``start_date`` and ``end_date``, ``result``
will contain a row for each asset that passed `pipeline.screen`.
A screen of ``None`` indicates that a row should be returned for
each asset that existed each day.
See Also
--------
:meth:`PipelineEngine.run_pipeline`
:meth:`PipelineEngine.run_chunked_pipeline`
:meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
:meth:`zipline.pipeline.engine.PipelineEngine.run_chunked_pipeline`
"""
if end_date < start_date:
raise ValueError(
zipline/pipeline/factors/factor.py
+25 -22
View File
@@ -881,34 +881,37 @@ class Factor(RestrictedDTypeMixin, ComputableTerm):
winsorized : zipline.pipeline.Factor
A Factor producing a winsorized version of self.
Example
-------
Examples
--------
.. code-block:: python
price = USEquityPricing.close.latest
columns={
'PRICE': price,
'WINSOR_1: price.winsorize(
min_percentile=0.25, max_percentile=0.75
),
'WINSOR_2': price.winsorize(
min_percentile=0.50, max_percentile=1.0
),
'WINSOR_3': price.winsorize(
min_percentile=0.0, max_percentile=0.5
),
price = USEquityPricing.close.latest
columns={
'PRICE': price,
'WINSOR_1: price.winsorize(
min_percentile=0.25, max_percentile=0.75
),
'WINSOR_2': price.winsorize(
min_percentile=0.50, max_percentile=1.0
),
'WINSOR_3': price.winsorize(
min_percentile=0.0, max_percentile=0.5
),
}
}
Given a pipeline with columns, defined above, the result for a
given day could look like:
'PRICE' 'WINSOR_1' 'WINSOR_2' 'WINSOR_3'
Asset_1 1 2 4 3
Asset_2 2 2 4 3
Asset_3 3 3 4 3
Asset_4 4 4 4 4
Asset_5 5 5 5 4
Asset_6 6 5 5 4
::
'PRICE' 'WINSOR_1' 'WINSOR_2' 'WINSOR_3'
Asset_1 1 2 4 3
Asset_2 2 2 4 3
Asset_3 3 3 4 3
Asset_4 4 4 4 4
Asset_5 5 5 5 4
Asset_6 6 5 5 4
See Also
--------