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, )