def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Only load requested columns.
requested_column_names = [
self._columns[column.name] for column in columns
]
requested_spilt_adjusted_columns = [
column_name for column_name in self._split_adjusted_column_names
if column_name in requested_column_names
]
raw = load_raw_data(
sids,
domain.data_query_cutoff_for_sessions(dates),
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name]
for column in columns},
self._split_adjustments,
requested_spilt_adjusted_columns,
self._split_adjusted_asof,
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Only load requested columns.
requested_column_names = [
self._columns[column.name] for column in columns
]
raw = load_raw_data(
sids,
dates,
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name]
for column in columns},
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
def load_adjusted_array(self, columns, dates, assets, mask):
# Only load requested columns.
requested_column_names = [
self._columns[column.name] for column in columns
]
requested_spilt_adjusted_columns = [
column_name for column_name in self._split_adjusted_column_names
if column_name in requested_column_names
]
raw = load_raw_data(
assets,
dates,
self._data_query_time,
self._data_query_tz,
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name]
for column in columns},
self._split_adjustments,
requested_spilt_adjusted_columns,
self._split_adjusted_asof,
).load_adjusted_array(
columns,
dates,
assets,
mask,
)
def load_adjusted_array(self, columns, dates, assets, mask):
# Only load requested columns.
requested_column_names = [self._columns[column.name]
for column in columns]
requested_spilt_adjusted_columns = [
column_name
for column_name in self._split_adjusted_column_names
if column_name in requested_column_names
]
raw = load_raw_data(
assets,
dates,
self._data_query_time,
self._data_query_tz,
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name] for column in columns},
self._split_adjustments,
requested_spilt_adjusted_columns,
self._split_adjusted_asof,
).load_adjusted_array(
columns,
dates,
assets,
mask,
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Only load requested columns.
requested_column_names = [self._columns[column.name]
for column in columns]
requested_spilt_adjusted_columns = [
column_name
for column_name in self._split_adjusted_column_names
if column_name in requested_column_names
]
raw = load_raw_data(
sids,
domain.data_query_cutoff_for_sessions(dates),
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name] for column in columns},
self._split_adjustments,
requested_spilt_adjusted_columns,
self._split_adjusted_asof,
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
def load_adjusted_array(self, columns, dates, assets, mask):
raw = load_raw_data(assets, dates, self._data_query_time,
self._data_query_tz, self._expr, self._odo_kwargs)
return EventsLoader(
events=raw,
next_value_columns=self._next_value_columns,
previous_value_columns=self._previous_value_columns,
).load_adjusted_array(
columns,
dates,
assets,
mask,
)
def load_adjusted_array(self, columns, dates, assets, mask):
raw = load_raw_data(assets,
dates,
self._data_query_time,
self._data_query_tz,
self._expr,
self._odo_kwargs)
return EventsLoader(
events=raw,
next_value_columns=self._next_value_columns,
previous_value_columns=self._previous_value_columns,
).load_adjusted_array(
columns,
dates,
assets,
mask,
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
raw = load_raw_data(
sids,
domain.data_query_cutoff_for_sessions(dates),
self._expr,
self._odo_kwargs,
)
return EventsLoader(
events=raw,
next_value_columns=self._next_value_columns,
previous_value_columns=self._previous_value_columns,
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Only load requested columns.
requested_column_names = [self._columns[column.name]
for column in columns]
raw = load_raw_data(
sids,
dates,
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name] for column in columns},
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
