def get_adjustments(self,
zero_qtr_data,
requested_qtr_data,
last_per_qtr,
dates,
assets,
columns,
**kwargs):
"""
Creates an AdjustedArray from the given estimates data for the given
dates.
Parameters
----------
zero_qtr_data : pd.DataFrame
The 'time zero' data for each calendar date per sid.
requested_qtr_data : pd.DataFrame
The requested quarter data for each calendar date per sid.
last_per_qtr : pd.DataFrame
A DataFrame with a column MultiIndex of [self.estimates.columns,
normalized_quarters, sid] that allows easily getting the timeline
of estimates for a particular sid for a particular quarter.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
assets : pd.Int64Index
An index of all the assets from the raw data.
columns : list of BoundColumn
The columns for which adjustments need to be calculated.
kwargs :
Additional keyword arguments that should be forwarded to
`get_adjustments_for_sid` and to be used in computing adjustments
for each sid.
Returns
-------
col_to_all_adjustments : dict[int -> AdjustedArray]
A dictionary of all adjustments that should be applied.
"""
zero_qtr_data.sort_index(inplace=True)
# Here we want to get the LAST record from each group of records
# corresponding to a single quarter. This is to ensure that we select
# the most up-to-date event date in case the event date changes.
quarter_shifts = zero_qtr_data.groupby(
level=[SID_FIELD_NAME, NORMALIZED_QUARTERS]
).nth(-1)
col_to_all_adjustments = {}
sid_to_idx = dict(zip(assets, range(len(assets))))
quarter_shifts.groupby(level=SID_FIELD_NAME).apply(
self.get_adjustments_for_sid,
dates,
requested_qtr_data,
last_per_qtr,
sid_to_idx,
columns,
col_to_all_adjustments,
**kwargs
)
return col_to_all_adjustments
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