def init_class_fixtures(cls):
super(TestDownsampledRowwiseOperation, cls).init_class_fixtures()
cls.pipeline_engine = SimplePipelineEngine(
get_loader=lambda c: ExplodingObject(),
calendar=cls.dates,
asset_finder=cls.asset_finder,
)
def init_class_fixtures(cls):
super(TestDownsampledRowwiseOperation, cls).init_class_fixtures()
cls.pipeline_engine = SimplePipelineEngine(
get_loader=lambda c: ExplodingObject(),
asset_finder=cls.asset_finder,
default_domain=EquitySessionDomain(
cls.dates,
country_code=cls.ASSET_FINDER_COUNTRY_CODE,
),
)
def init_class_fixtures(cls):
super(WithTechnicalFactor, cls).init_class_fixtures()
cls.ndays = ndays = 24
cls.nassets = nassets = len(cls.ASSET_FINDER_EQUITY_SIDS)
cls.dates = dates = pd.date_range(cls.START_DATE, periods=ndays)
cls.assets = pd.Index(cls.asset_finder.sids)
cls.engine = SimplePipelineEngine(
lambda column: ExplodingObject(),
dates,
cls.asset_finder,
)
cls.asset_exists = exists = np.full((ndays, nassets), True, dtype=bool)
cls.asset_exists_masked = masked = exists.copy()
masked[:, -1] = False
def run_graph(self, graph, initial_workspace, mask=None):
"""
Compute the given TermGraph, seeding the workspace of our engine with
`initial_workspace`.
Parameters
----------
graph : zipline.pipeline.graph.TermGraph
Graph to run.
initial_workspace : dict
Initial workspace to forward to SimplePipelineEngine.compute_chunk.
mask : DataFrame, optional
This is a value to pass to `initial_workspace` as the mask from
`AssetExists()`. Defaults to a frame of shape `self.default_shape`
containing all True values.
Returns
-------
results : dict
Mapping from termname -> computed result.
"""
engine = SimplePipelineEngine(
lambda column: ExplodingObject(),
self.nyse_sessions,
self.asset_finder,
)
if mask is None:
mask = self.default_asset_exists_mask
dates, assets, mask_values = explode(mask)
initial_workspace.setdefault(AssetExists(), mask_values)
initial_workspace.setdefault(InputDates(), dates)
return engine.compute_chunk(
graph,
dates,
assets,
initial_workspace,
)
def test_duplicate_values(self):
UNIMPORTANT_VALUE = 57
panel = pd.Panel(
UNIMPORTANT_VALUE,
items=['a', 'b', 'b', 'a'],
major_axis=['c'],
minor_axis=['d'],
)
unused = ExplodingObject()
axis_names = ['items', 'major_axis', 'minor_axis']
for axis_order in permutations((0, 1, 2)):
transposed = panel.transpose(*axis_order)
with self.assertRaises(ValueError) as e:
PanelBarReader(unused, transposed, 'daily')
expected = (
"Duplicate entries in Panel.{name}: ['a', 'b'].".format(
name=axis_names[axis_order.index(0)], ))
self.assertEqual(str(e.exception), expected)