From bd06ff14edb19b04dd9b60d8c030ae95ba1dfd77 Mon Sep 17 00:00:00 2001
From: Maya Tydykov <mtydykov@quantopian.com>
Date: Tue, 24 Jan 2017 12:48:56 -0500
Subject: [PATCH] MAINT: optionally apply deltas adjustments based on info from
 dataset

---
 zipline/pipeline/loaders/blaze/core.py | 40 ++++++++++++++++++++------
 1 file changed, 31 insertions(+), 9 deletions(-)

diff --git a/zipline/pipeline/loaders/blaze/core.py b/zipline/pipeline/loaders/blaze/core.py
index 00755078..0052029d 100644
--- a/zipline/pipeline/loaders/blaze/core.py
+++ b/zipline/pipeline/loaders/blaze/core.py
@@ -206,8 +206,13 @@ is_invalid_deltas_node = complement(flip(isinstance, valid_deltas_node_types))
 get__name__ = op.attrgetter('__name__')
 
 
-class ExprData(namedtuple('ExprData', 'expr deltas checkpoints odo_kwargs')):
-    """A pair of expressions and data resources. The expresions will be
+class ExprData(
+    namedtuple(
+        'ExprData',
+        'expr deltas checkpoints odo_kwargs apply_deltas_adjustments'
+    )
+):
+    """A pair of expressions and data resources. The expressions will be
     computed using the resources as the starting scope.
 
     Parameters
@@ -221,13 +226,19 @@ class ExprData(namedtuple('ExprData', 'expr deltas checkpoints odo_kwargs')):
     odo_kwargs : dict, optional
         The keyword arguments to forward to the odo calls internally.
     """
-    def __new__(cls, expr, deltas=None, checkpoints=None, odo_kwargs=None):
+    def __new__(cls,
+                expr,
+                deltas=None,
+                checkpoints=None,
+                odo_kwargs=None,
+                apply_deltas_adjustments=True):
         return super(ExprData, cls).__new__(
             cls,
             expr,
             deltas,
             checkpoints,
             odo_kwargs or {},
+            apply_deltas_adjustments,
         )
 
     def __repr__(self):
@@ -239,6 +250,7 @@ class ExprData(namedtuple('ExprData', 'expr deltas checkpoints odo_kwargs')):
             str(self.deltas),
             str(self.checkpoints),
             self.odo_kwargs,
+            self.apply_deltas_adjustments,
         ))
 
 
@@ -547,20 +559,25 @@ def from_blaze(expr,
                odo_kwargs=None,
                missing_values=None,
                no_deltas_rule='warn',
-               no_checkpoints_rule='warn'):
+               no_checkpoints_rule='warn',
+               apply_deltas_adjustments=True,):
     """Create a Pipeline API object from a blaze expression.
 
     Parameters
     ----------
     expr : Expr
         The blaze expression to use.
+    apply_deltas_adjustments : bool, optional
+        Whether or not deltas adjustments should be applied for this dataset.
+        True by default because not applying deltas adjustments is an exception
+        rather than the rule.
     deltas : Expr, 'auto' or None, optional
         The expression to use for the point in time adjustments.
         If the string 'auto' is passed, a deltas expr will be looked up
         by stepping up the expression tree and looking for another field
         with the name of ``expr._name`` + '_deltas'. If None is passed, no
         deltas will be used.
-    deltas : Expr, 'auto' or None, optional
+    checkpoints : Expr, 'auto' or None, optional
         The expression to use for the forward fill checkpoints.
         If the string 'auto' is passed, a checkpoints expr will be looked up
         by stepping up the expression tree and looking for another field
@@ -714,6 +731,7 @@ def from_blaze(expr,
         if checkpoints is not None else
         None,
         odo_kwargs=odo_kwargs,
+        apply_deltas_adjustments=apply_deltas_adjustments
     )
     if single_column is not None:
         # We were passed a single column, extract and return it.
@@ -984,7 +1002,9 @@ class BlazeLoader(dict):
         except ValueError:
             raise AssertionError('all columns must come from the same dataset')
 
-        expr, deltas, checkpoints, odo_kwargs = self[dataset]
+        expr, deltas, checkpoints, odo_kwargs, apply_deltas_adjustments = self[
+            dataset
+        ]
         have_sids = (dataset.ndim == 2)
         asset_idx = pd.Series(index=assets, data=np.arange(len(assets)))
         assets = list(map(int, assets))  # coerce from numpy.int64
@@ -1094,15 +1114,17 @@ class BlazeLoader(dict):
                                           have_sids=have_sids)
         ffill_across_cols(dense_output, columns, {c.name: c.name
                                                   for c in columns})
+        adjustments_from_deltas = lambda *args: {}
         if have_sids:
-            adjustments_from_deltas = adjustments_from_deltas_with_sids
+            if apply_deltas_adjustments:
+                adjustments_from_deltas = adjustments_from_deltas_with_sids
             column_view = identity
         else:
             # If we do not have sids, use the column view to make a single
             # column vector which is unassociated with any assets.
             column_view = op.itemgetter(np.s_[:, np.newaxis])
-
-            adjustments_from_deltas = adjustments_from_deltas_no_sids
+            if apply_deltas_adjustments:
+                adjustments_from_deltas = adjustments_from_deltas_no_sids
             mask = np.full(
                 shape=(len(mask), 1), fill_value=True, dtype=bool_dtype,
             )