zen_garden.postprocess.results.results¶

This module contains the Results class, which is used to extract and process the results of a model run.

Classes

Results(path[, enable_cache])

The Results class is used to extract and process the results of a model run.

class zen_garden.postprocess.results.results.Results(path: str, enable_cache: bool = True)¶

The Results class is used to extract and process the results of a model run.

__init__(path: str, enable_cache: bool = True)¶

Initializes the Results class.

Parameters:: path – Path to the results folder

calculate_connected_edges(node: str, direction: str, set_nodes_on_edges: dict[str, str])¶

Calculates connected edges going in (direction = ‘in’) or going out (direction = ‘out’).

Parameters:

node – current node, connected by edges
direction – direction of edges, either in or out. In: node = endnode, out: node = startnode
set_nodes_on_edges – set of nodes on edges

Returns:

list of connected edges

Return type:

set_connected_edges

edit_carrier_flows(data: DataFrame, node: str, direction: str, scenario: str) → DataFrame¶

Extracts data of carrier_flow variable as needed for the plot_energy_balance function.

Parameters:

data – pd.DataFrame containing data to extract
node – node of interest
direction – flow direction with respect to node
scenario – scenario of interest

Returns:

pd.DataFrame containing carrier_flow data desired

extract_carrier(dataframe: DataFrame, carrier: str, scenario_name: str) → DataFrame¶

Returns a dataframe that only contains the desired carrier. If carrier is not contained in the dataframe, the technologies that have the provided reference carrier are returned.

Parameters:

dataframe – pd.Dataframe containing the base data
carrier – name of the carrier
scenario_name – name of the scenario

Returns:

filtered pd.Dataframe containing only the provided carrier

get_analysis(scenario_name: str | None = None) → Analysis¶

Extract analysis configurations from a scenario.

Extracts analysis configurations from the results of a scenario. This ensures the tractability of model configurations. Analysis configurations are those specified under the analysis object in the config.json file.

Parameters:: scenario_name (str, optional) – The name of the scenario for which to extract the system configuration. If no value is given, then the first scenario is used. Default value: None.
Returns:: Analysis configuration.
Return type:: Analysis

Examples

Basic usage example:

>>> from zen_garden.postprocess.results.results import Results
>>> r = Results(path='<result_folder>')
>>> r.get_analysis() # analysis config of first scenario
>>> r.get_analysis('scenario_name') # analysis config of "scenario_name"

get_component_names(component_type: str) → list[str]¶

Returns the names of all components of a given type.

Parameters:: component_type – Type of the component
Returns:: List of component names

get_coords(scenario_name: str | None = None) → DataFrame | None¶

Extracts the coordinates of the nodes of a given Scenario. If no scenario is given, a random one is taken.

Parameters:: scenario_name – Name of the scenario
Returns:: The corresponding coordinates

Returns the raw results without any further processing.

Transforms a parameter or variable dataframe (compressed) string into an actual pandas dataframe.

Parameters:

component_name (string) – The string to decode
scenario_name – Which scenario to take. If none is specified, all are returned.
data_type – The type of data to extract. Either ‘dataframe’ or ‘units’
index – slicing index of the resulting dataframe

Returns:

The corresponding dataframe

Return type:

DataFrame

get_doc(component_name: str) → str¶

Extracts the documentation of a given Component.

Parameters:: component_name – Name of the component
Returns:: The corresponding documentation

Extracts the dual variables of a component.

Parameters:

component_name – Name of dual
scenario_name – Scenario Name
year – Year
discount_to_first_step – apply annuity to first year of interval or entire interval
keep_raw – Keep the raw values of the rolling horizon optimization
index – slicing index of the resulting dataframe

Returns:

Duals of the component

Return type:

DataFrame

get_energy_balance_dataframes(node: str, carrier: str, year: int, scenario_name: str | None = None) → dict[str, pd.Series[Any]]¶

Returns a dictionary with all dataframes that are relevant for the energy balance. The dataframes “flow_transport_in” and “flow_transport_out” contain the data of “flow_transport”, filtered for in / out flow.

Parameters:

node – Node of interest
carrier – Carrier of interest
year – Year of interest
scenario_name – Scenario name of interest

Returns:

Dictionary containing the relevant pd.Dataframes

Calculates the full timeseries.

Parameters:

component_name – Name of the component
scenario_name – The scenario for with the component should be extracted (only if needed)
discount_to_first_step – apply annuity to first year of interval or entire interval
year – year of which full time series is selected
keep_raw – Keep the raw values of the rolling horizon optimization
index – slicing index of the resulting dataframe

Returns:

Full timeseries

get_full_ts_per_scenario(scenario: Scenario, component: Component, year: int | None = None, discount_to_first_step: bool = True, keep_raw: bool | None = False, index: tuple[str] = None) → DataFrame¶

Calculates the full timeseries per scenario.

Parameters:

scenario – The scenario for with the component should be extracted (only if needed)
component – Component for the Series
discount_to_first_step – apply annuity to first year of interval or entire interval
year – year of which full time series is selected
keep_raw – Keep the raw values of the rolling horizon optimization
index – slicing index of the resulting dataframe

Returns:

Full timeseries

get_index_names(component_name: str, scenario_name: str | None = None) → list[str]¶

Docstring for get_index_names.

Parameters:

self – Description
component_name (str) – Description
scenario_name (Optional[str]) – Description

Returns:

Description

Return type:

list[str]

get_solver(scenario_name: str | None = None) → Solver¶

Extract solver configurations from a scenario.

Extracts solver configurations from the results of a scenario. This ensures the tractability of model configurations. Solver configurations are those specified under the solver object in the config.json file.

Parameters:: scenario_name (str, optional) – The name of the scenario for which to extract the system configuration. If no value is given, then the first scenario is used. Default value: None.
Returns:: Solver configuration.
Return type:: Solver

Examples

Basic usage example:

>>> from zen_garden.postprocess.results.results import Results
>>> r = Results(path='<result_folder>')
>>> r.get_solver() # solver configurations of first scenario
>>> r.get_solver('scenario_name') # solver configuration of "scenario_name"

get_system(scenario_name: str | None = None) → System¶

Extract system configurations from a scenario.

Extracts system configurations from the results of a scenario. This ensures the tractability of model configurations. System configurations are those specified in the system.json file of a given model.

Parameters:: scenario_name (str, optional) – The name of the scenario for which to extract the system configuration. If no value is given, then the first scenario is used. Default value: None.
Returns:: System configuration.
Return type:: System

Examples

Basic usage example:

>>> from zen_garden.postprocess.results.results import Results
>>> r = Results(path='<result_folder>')
>>> r.get_system() # system configurations of first scenario
>>> r.get_system('scenario_name') # system configuration of "scenario_name"

Calculates the total values of a component for a all scenarios.

Parameters:

component_name – Name of the component. Should not be used for dual variables!
year – Filter the results by a given year
scenario_name – Filter the results by a given scenario
keep_raw – Keep the raw values of the rolling horizon optimization
index – slicing index of the resulting dataframe

Returns:

Total values of the component

Return type:

DataFrame

get_total_per_scenario(scenario: Scenario, component: Component, year: int | None = None, keep_raw: bool | None = False, index: tuple[str] = None) → pd.DataFrame | pd.Series[Any]¶

Calculates the total values of a component for a specific scenario.

Parameters:

scenario – Scenario
component – Component
year – Filter the results by a given year
keep_raw – Keep the raw values of the rolling horizon optimization
index – slicing index of the resulting dataframe

Returns:

Total values of the component

Extracts the unit of a given Component. If no scenario is given, a random one is taken.

Parameters:

component_name – Name of the component
scenario_name – Name of the scenario
index – slicing index of the resulting dataframe
droplevel – Drop the location and time levels of the multiindex
convert_to_yearly_unit – If True, the unit is converted to a yearly unit, i.e., for components with an operational time step type, the unit is multiplied by hours.

Returns:

The corresponding unit

Return type:

DataFrame

get_years(scenario_name: str | None = None) → list[int]¶

Extracts the years of a given Scenario. If no scenario is given, a random one is taken.

Parameters:: scenario_name – Name of the scenario
Returns:: List of years

has_MF(scenario_name: str | None = None) → bool¶

Extracts the System config of a given Scenario. If no scenario is given, a random one is taken.

Parameters:: scenario_name – Name of the scenario
Returns:: The corresponding System config