MAINT: Rename ConstantLoader to PrecomputedLoader.

tests/pipeline/test_engine.py

@@ -41,7 +41,7 @@ from zipline.data.us_equity_pricing import BcolzDailyBarReader
 from zipline.finance.trading import TradingEnvironment
 from zipline.lib.adjustment import MULTIPLY
 from zipline.pipeline.loaders.synthetic import (
-    ConstantLoader,
+    PrecomputedLoader,
     NullAdjustmentReader,
     SyntheticDailyBarWriter,
 )
@@ -126,16 +126,16 @@ class ColumnArgs(tuple):
         return hash(frozenset(self))
 
 
-class RecordingConstantLoader(ConstantLoader):
+class RecordingPrecomputedLoader(PrecomputedLoader):
     def __init__(self, *args, **kwargs):
-        super(RecordingConstantLoader, self).__init__(*args, **kwargs)
+        super(RecordingPrecomputedLoader, self).__init__(*args, **kwargs)
 
         self.load_calls = []
 
     def load_adjusted_array(self, columns, dates, assets, mask):
         self.load_calls.append(ColumnArgs(*columns))
 
-        return super(RecordingConstantLoader, self).load_adjusted_array(
+        return super(RecordingPrecomputedLoader, self).load_adjusted_array(
             columns, dates, assets, mask,
         )
 
@@ -159,7 +159,7 @@ class ConstantInputTestCase(TestCase):
         }
         self.asset_ids = [1, 2, 3]
         self.dates = date_range('2014-01', '2014-03', freq='D', tz='UTC')
-        self.loader = ConstantLoader(
+        self.loader = PrecomputedLoader(
             constants=self.constants,
             dates=self.dates,
             assets=self.asset_ids,
@@ -364,7 +364,7 @@ class ConstantInputTestCase(TestCase):
         dates_to_test = self.dates[-30:]
 
         constants = {open_: 1, close: 2, volume: 3}
-        loader = ConstantLoader(
+        loader = PrecomputedLoader(
             constants=constants,
             dates=self.dates,
             assets=self.asset_ids,
@@ -430,14 +430,15 @@ class ConstantInputTestCase(TestCase):
                       Loader1DataSet1.col2: 2,
                       Loader1DataSet2.col1: 3,
                       Loader1DataSet2.col2: 4}
-        loader1 = RecordingConstantLoader(constants=constants1,
-                                          dates=self.dates,
-                                          assets=self.asset_ids)
+
+        loader1 = RecordingPrecomputedLoader(constants=constants1,
+                                             dates=self.dates,
+                                             assets=self.assets)
         constants2 = {Loader2DataSet.col1: 5,
                       Loader2DataSet.col2: 6}
-        loader2 = RecordingConstantLoader(constants=constants2,
-                                          dates=self.dates,
-                                          assets=self.asset_ids)
+        loader2 = RecordingPrecomputedLoader(constants=constants2,
+                                             dates=self.dates,
+                                             assets=self.assets)
 
         engine = SimplePipelineEngine(
             lambda column:

tests/pipeline/test_term.py

@@ -19,7 +19,6 @@ from zipline.pipeline.data.testing import TestingDataSet
 from zipline.pipeline.term import AssetExists, NotSpecified
 from zipline.pipeline.expression import NUMEXPR_MATH_FUNCS
 from zipline.utils.numpy_utils import (
-    bool_dtype,
     datetime64ns_dtype,
     float64_dtype,
 )

zipline/pipeline/loaders/synthetic.py

@@ -32,31 +32,34 @@ def nanos_to_seconds(nanos):
     return nanos / (1000 * 1000 * 1000)
 
 
-class ConstantLoader(PipelineLoader):
+class PrecomputedLoader(PipelineLoader):
     """
-    Synthetic PipelineLoader that returns a constant value for each column.
+    Synthetic PipelineLoader that uses a pre-computed array for each column.
 
     Parameters
     ----------
-    constants : dict
-        Map from column to value(s) to use for that column.
+    values : dict
+        Map from column to values to use for that column.
         Values can be anything that can be passed as the first positional
-        argument to a DataFrame of the same shape as `mask`.
-    mask : pandas.DataFrame
-        Mask indicating when assets existed.
-        Indices of this frame are used to align input queries.
+        argument to a DataFrame whose indices are ``dates`` and ``sids``
+    dates : iterable[datetime-like]
+        Row labels for input data.  Can be anything that pd.DataFrame will
+        coerce to a DatetimeIndex.
+    sids : iterable[int-like]
+        Column labels for input data.  Can be anything that pd.DataFrame will
+        coerce to an Int64Index.
 
     Notes
     -----
-    Adjustments are unsupported with ConstantLoader.
+    Adjustments are unsupported by this loader.
     """
-    def __init__(self, constants, dates, assets):
+    def __init__(self, constants, dates, sids):
         loaders = {}
         for column, const in iteritems(constants):
             frame = DataFrame(
                 const,
                 index=dates,
-                columns=assets,
+                columns=sids,
                 dtype=column.dtype,
             )
             loaders[column] = DataFrameLoader(