Limit the perspective offset to 1. There is a possibility that if a
consumer of the AdjustedArrayWindow does not fetch adjustments between
the end of the data window and the vantage points beyond the end of the
window.
Until that case has a solution, e.g. having the consumer of the
AdjustedArrayWindow include the perspective offset when calculating the
query for adjustments, limit the offsets to 1.
- Refactor `test_adjusted_array` to test a range of perspective_offsets in
all tests.
- Make perspective_offset a parameter to `AdjustedArray.traverse`
instead of `AdjustedArray`.
Add a perspective offset to `AdjustedArrayWindow` and `AdjustedArray`,
so that `HistoryLoader` does not need to twiddle with offsets to support
viewing the data from the bar after end of the window, (Which is the
case when a '1d' history window is retrieved in minute mode, which is
explained in the docstring for `HistoryLoader.history`)
Presently, this simplifies the logic in
`HistoryLoader._get_adjustments_in_range`, and other incoming
AdjustmentReader's, (e.g. the roll based adjustment reader for continous
futures.) This patch should also make it easier for history and pipeline
to converge on a singular `load_adjustments` method.
MAINT: optimization - only look at assets appearing in data
TST: simplify test
DOC: add documentation for checkpoints
MAINT: explicitly cast event date field to datetime
MAINT: add back import
TST: fix indexing to remove setting wtih copy warning
TST: fix quarter normalization test
TST: change test name
BUG: remove arg
BUG: look at dict keys
TST: add test for windowing
MAINT: raise ValueError instead of asserting
TST: add assertion to check windowing
TST: parametrize test over number of quarters forward/back.
BUG: fix adjustment calculation logic for quarter crossovers.
TST: add test for previous quarter windows
BUG: fix bugs in calculating previous windows
BUG: fix missing value for datetime
TST: add test case for missing quarter
Pandas 0.18 doesn't like having null-ish values in categoricals. Fixing
this properly requires re-thinking the semantics for missing_value on
pipeline terms, so we're punting on that until after we've upgraded to
0.18.
Pandas 0.18 deprecated passing "null-ish" values to pd.categorical. The
expectation, instead, is that you use categorical's native support for
missing data, which means the user will always get NaN's for missing
entries of the categorical.
A follow-up to this change should probably drop support for custom
missing values entirely and to use LabelArray/categorical for integer
data.
They're not meaningful, and they cause warnings from numpy.
Implemented in terms of a new preprocessor, `expect_bounded`, which
takes a tuple of `upper_bound` and `lower_bound`.
Scott Sanderson