The SISEPUEDE Database
Each instantiation of SISEPUEDE relies on a unique AnalysisID, which is used to define an output database. The use of a unique ID ensures that a SISEPUEDE session can be restored easily.
Describe database concepts, structure, etc.
Recommended merges
Discuss the AnalysisID - code on instantiating using an existing analysis id
Note
AnalysisIDs from file names require the replacement of semicolons with colons (add example, clarify)
Default Tables
ANALYSIS_METADATA
The ANALYSIS_METADATA table contains the configuration parameters used to setup SISEPUEDE for the session. These parameters include information on time periods, output units (for emissions, length, area, etc.), assumptions about historical data in Circular Economy and Energy, and more. For more information on configuration parameters, see Analytical Parameters.
ATTRIBUTE_DESIGN
The ATTRIBUTE_DESIGN table contains the attribute table describing the design_id dimension of analysis, which characterizes the magnitudes of lever effects and uncertainty fans.
ATTRIBUTE_LHC_SAMPLES_EXOGENOUS_UNCERTAINTIES
The ATTRIBUTE_LHC_SAMPLES_EXOGENOUS_UNCERTAINTIES table contains raw LHC samples for each exogenous uncertainty sample group (fields). Each row contains the LHC trials used to generate exogenous uncertainties (future_id) in SISEPUEDE. These LHC trials are separate from those used to develop uncertainties in lever effects (see below).
ATTRIBUTE_LHC_SAMPLES_LEVER_EFFECTS
The ATTRIBUTE_LHC_SAMPLES_EXOGENOUS_UNCERTAINTIES table contains raw LHC samples associated with levers (wide by strategy sample group). Each row contains the LHC trials used to generate lever effect uncertainties (future_id) in SISEPUEDE.
ATTRIBUTE_PRIMARY
The ATTRIBUTE_PRIMARY table defines the primary key (primary_id) for the runs contained in the database.
Note
To save memory and disk space, SISEPUEDE stores all primary key information in an OrderedDirectProductTable class, which does not explicitly store all rows of the table. Instead, SISEPUEDE only stores rows associated with `primary_id` values that are contained within the output database. The following OrderedDirectProductTable methods can be used to find more information on the primary key indexing.
OrderedDirectProductTable.get_dims_from_key(): Get dimensional values associated with a key (inverse ofget_key_value)OrderedDirectProductTable.get_key_value(): Get a key value associated with dimensional values (inverse ofget_dims_from_key)OrderedDirectProductTable.get_indexing_dataframe(): Get a data frame associated with select dimensional values or with key values.
ATTRIBUTE_STRATEGY
The ATTRIBUTE_STRATEGY table contains all information used to define strategies in SISEPUEDE, including the name of the strategy, baseline specification (should only exist for one strategy–by convention, it is recommended that this strategy is set to 0), and the strategy_id.
MODEL_BASE_INPUT_DATABASE
The MODEL_BASE_INPUT_DATABASE explicitly stores all information used to define input data for baseline futures for all regions; it is long by region, strategy, and time_period and wide by input variable.
MODEL_INPUT
The MODEL_INPUT table stores explicit model inputs for every scenario that is available in the database. It is long by primary_id, region, and time_period. The MODEL_INPUT table is optional; by default, it is not stored to save space. NOTE: ADD MECHANISM FOR ALLOWING INPUT TABLE TO BE WRITTEN
Note
Individual futures (and the table itself) can be reproduced quickly using SISEPUEDE’s internal functions in combination with LHS tables, which are saved by default. To generate an input table associated with a primary key, use SISEPUEDE.generate_scenario_database_from_primary_key().
MODEL_OUTPUT
The MODEL_OUTPUT table stores explicit model outputs for every scenario (indexed by primary_id) that is run under the given AnalysisID. It is long by primary_id, region, and time_period.
Optional and Derivative Tables
SISEPUEDE contains a mechanism for storing derivative tables using specified functions. DESCRIBE LATER