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.__calendar, self.__finder)
if mask is None:
mask = self.__mask
dates, assets, mask_values = explode(mask)
initial_workspace.setdefault(AssetExists(), mask_values)
return engine.compute_chunk(graph, dates, assets, initial_workspace)
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.ExecutionPlan
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.
"""
def get_loader(c):
raise AssertionError("run_graph() should not require any loaders!")
engine = SimplePipelineEngine(
get_loader,
self.asset_finder,
default_domain=US_EQUITIES,
)
if mask is None:
mask = self.default_asset_exists_mask
dates, sids, mask_values = explode(mask)
initial_workspace.setdefault(AssetExists(), mask_values)
initial_workspace.setdefault(InputDates(), dates)
refcounts = graph.initial_refcounts(initial_workspace)
execution_order = graph.execution_order(initial_workspace, refcounts)
return engine.compute_chunk(
graph=graph,
dates=dates,
sids=sids,
workspace=initial_workspace,
execution_order=execution_order,
refcounts=refcounts,
hooks=NoHooks(),
)
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.ExecutionPlan
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.
"""
def get_loader(c):
raise AssertionError("run_graph() should not require any loaders!")
engine = SimplePipelineEngine(
get_loader,
self.asset_finder,
default_domain=US_EQUITIES,
)
if mask is None:
mask = self.default_asset_exists_mask
dates, sids, mask_values = explode(mask)
initial_workspace.setdefault(AssetExists(), mask_values)
initial_workspace.setdefault(InputDates(), dates)
return engine.compute_chunk(
graph=graph,
dates=dates,
sids=sids,
initial_workspace=initial_workspace,
)
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,
)
