Reference¶
Scenario¶
Scenario class¶
deflex.DeflexScenario([meta, input_data, …]) |
The Deflex Scenario is the center of a deflex energy model. |
Read/Write a scenario¶
deflex.DeflexScenario.read_xlsx(filename) |
Load scenario data from an xlsx file. |
deflex.DeflexScenario.read_csv(path) |
Load scenario from a csv-collection. |
deflex.create_scenario(path[, file_type]) |
Create a deflex scenario object from file. |
deflex.search_input_scenarios(path[, csv, …]) |
Search for files with an .xlsx extension or directories ending with ‘_csv’. |
deflex.DeflexScenario.to_xlsx(filename) |
Store the input data into an xlsx-file. |
deflex.DeflexScenario.to_csv(path) |
Store the input data as a csv-collection. |
deflex.DeflexScenario.dump(filename) |
Store a solved scenario class into the binary pickle format. |
deflex.DeflexScenario.store_graph(filename, …) |
Store the EnergySystem graph into a .graphml file. |
Compute scenario¶
deflex.DeflexScenario.compute([solver, …]) |
Create a solph.Model from the input data and optimise it using an external solver. |
Advanced scenario methods¶
deflex.DeflexScenario.check_input_data() |
Check the input data for NaN values. |
deflex.DeflexScenario.table2es() |
Create a populated solph.EnergySystem from the input data. |
deflex.DeflexScenario.create_model() |
Create a solph model from an EnergySystem object. |
deflex.DeflexScenario.create_nodes() |
Creates solph components and buses from the input data and store them in a dictionary with unique IDs as keys. |
deflex.DeflexScenario.solve(model[, solver, …]) |
Solve the solph.Model. |
deflex.DeflexScenario.initialise_energy_system() |
Create a solph.EnergySystem and store it in the es attribute. |
deflex.DeflexScenario.add_nodes_to_es(nodes) |
Add nodes to an existing solph.EnergySystem. |
Scripts¶
Python scripts¶
deflex.model_scenario([path, file_type, …]) |
Compute a deflex scenario with the full work flow: |
deflex.batch_model_scenario(path[, …]) |
Model a single scenario in batch mode. |
deflex.model_multi_scenarios(scenarios[, …]) |
Model multi scenarios in parallel. |
Console scripts¶
deflex.console_scripts.main() |
deflex-compute [-h] [–version] [–results [RESULTS]] [–dump [DUMP]] [–solver [SOLVER]] path |
deflex.console_scripts.result() |
deflex-results [-h] [–version] [–filetype [FILETYPE]] function in_path out_path |
Postprocessing¶
Restore dumped scenarios¶
deflex.search_dumped_scenarios(path[, extension]) |
Filter results by extension and meta data. |
deflex.restore_scenario(filename[, …]) |
Restore a full Scenario from a dump file (.dflx). |
deflex.restore_results(file_names[, …]) |
Restore only the result dictionary from a dumped scenario or a list of dumped scenarios. |
Analyse and draw graph¶
deflex.postprocessing.graph.Edge(**kwargs) |
An edge of a DeflexGraph |
deflex.DeflexGraph(results, **kwargs) |
The deflex model graph with a networkx representation. |
deflex.DeflexGraph.nxgraph(**kwargs) |
Get a networkx.DiGraph() from the deflex results. |
deflex.DeflexGraph.write(filename, **kwargs) |
Write the graph into a .graphml file. |
deflex.DeflexGraph.color_edges_by_weight([…]) |
Color all edges by their weight using a matplotlib color map (cmap). |
deflex.DeflexGraph.color_nodes_by_type(colors) |
Color all nodes in a specific color according to their class. |
deflex.DeflexGraph.color_nodes_by_substring(colors) |
Color all nodes in a specific color according to a given substring. |
deflex.DeflexGraph.group_nodes_by_type([…]) |
Group all nodes by types returning a dictionary with the types or the name of the types as keys and the list of nodes as value. |
Analyse cycles¶
deflex.Cycles(results[, storages, lines, digits]) |
Detect all simple cycles in the directed graph. |
deflex.Cycles.cycles |
Get all cycles of the model. |
deflex.Cycles.used_cycles |
Get all cycles from a list of cycles that are used. |
deflex.Cycles.suspicious_cycles |
Get all cycles from a list of cycles that are suspicious. |
deflex.Cycles.get_suspicious_time_steps() |
Detect the time steps of a cycle in which all flows are non-zero. |
deflex.Cycles.print() |
Print an overview of the cycles. |
deflex.Cycles.details() |
Print out a more detailed overview over the existing cycles. |
Basic results processing¶
deflex.get_all_results(results) |
Get all results from a computed deflex scenario. |
deflex.nodes2table(results[, no_sums]) |
Get a table with all nodes as a MultiIndex with the sum of their in an out flows. |
deflex.solver_results2series(results) |
Get the meta results from the solver. |
deflex.fetch_dual_results(results[, bus, …]) |
Collect all the results of the dual variables. |
deflex.meta_results2series(results) |
Get meta results as a pandas.Series |
deflex.get_time_index(results) |
Get the time index of the model. |
deflex.calculate_key_values(results[, …]) |
Get time series of typical key values. |
Advanced results processing¶
deflex.group_buses(buses, fields) |
Group buses by parts of the label. |
deflex.fetch_converter_parameters(results, …) |
Fetch relevant parameters of every Transformer of the energy system. |
deflex.fetch_attributes_of_commodity_sources(results) |
Get the attributes of the commodity sources. |
deflex.get_combined_bus_balance(results[, …]) |
Combine different buses of the same type. |
deflex.get_converter_balance(results[, cat, …]) |
Get the balance around the converters of the system. |
Tools for Electricity models¶
deflex.merit_order_from_scenario(scenario[, …]) |
Create a merit order from a deflex scenario. |
deflex.merit_order_from_results(result) |
Create a merit order from deflex results. |
Geometry examples for plotting¶
deflex.deflex_geo(rmap) |
Fetch default deflex geometries as a named tuple with the following fields: |
deflex.divide_off_and_onshore(regions) |
Sort regions into onshore and offshore regions (Germany). |
General tools¶
deflex.dict2file(tables, path[, filetype, …]) |
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deflex.use_logging(**kwargs) |
CHP allocation tools¶
deflex.allocate_fuel_deflex(method, eta_e, …) |
Allocate the fuel input of chp plants to the two output flows. |
deflex.allocate_fuel(method, eta_e, eta_th, …) |
Allocate the fuel input of chp plants to the two output flows: heat and electricity. |
deflex.efficiency_method(eta_e, eta_th) |
Efficiency Method - a method to allocate the fuel input of chp plants to the two output flows: heat and electricity |
deflex.exergy_method(eta_e, eta_th, eta_c) |
Exergy Method or Carnot Method- a method to allocate the fuel input of chp plants to the two output flows: heat and electricity |
deflex.finnish_method(eta_e, eta_th, …) |
Alternative Generation or Finnish Method - a method to allocate the fuel input of chp plants to the two output flows: heat and electricity |
deflex.iea_method(eta_e, eta_th) |
IEA Method (International Energy Association - a method to allocate the fuel input of chp plants to the two output flows: heat and electricity |