oemof.outputlib package

Submodules

oemof.outputlib.processing module

Modules for providing a convenient data structure for solph results.

Information about the possible usage is provided within the examples.

This file is part of project oemof (github.com/oemof/oemof). It’s copyrighted by the contributors recorded in the version control history of the file, available from its original location oemof/oemof/outputlib/processing.py

SPDX-License-Identifier: GPL-3.0-or-later

oemof.outputlib.processing.convert_keys_to_strings(result, keep_none_type=False)[source]

Convert the dictionary keys to strings.

All (tuple) keys of the result object e.g. results[(pp1, bus1)] are converted into strings that represent the object labels e.g. results[(‘pp1’,’bus1’)].

oemof.outputlib.processing.create_dataframe(om)[source]

Create a result dataframe with all optimization data.

Results from Pyomo are written into pandas DataFrame where separate columns are created for the variable index e.g. for tuples of the flows and components or the timesteps.

oemof.outputlib.processing.get_timestep(x)[source]

Get the timestep from oemof tuples.

The timestep from tuples (n, n, int), (n, n), (n, int) and (n,) is fetched as the last element. For time-independent data (scalars) zero ist returned.

oemof.outputlib.processing.get_tuple(x)[source]

Get oemof tuple within iterable or create it.

Tuples from Pyomo are of type (n, n, int), (n, n) and (n, int). For single nodes n a tuple with one object (n,) is created.

oemof.outputlib.processing.meta_results(om, undefined=False)[source]

Fetch some meta data from the Solver. Feel free to add more keys.

Valid keys of the resulting dictionary are: ‘objective’, ‘problem’, ‘solver’.

om : oemof.solph.Model
A solved Model.
undefined : bool
By default (False) only defined keys can be found in the dictionary. Set to True to get also the undefined keys.
Returns:
Return type:dict
oemof.outputlib.processing.parameter_as_dict(system, exclude_none=True)[source]

Create a result dictionary containing node parameters.

Results are written into a dictionary of pandas objects where a Series holds all scalar values and a dataframe all sequences for nodes and flows. The dictionary is keyed by flows (n, n) and nodes (n, None), e.g. parameter[(n, n)][‘sequences’] or parameter[(n, n)][‘scalars’].

Parameters:
  • system (energy_system.EnergySystem) – A populated energy system.
  • exclude_none (bool) – If True, all scalars and sequences containing None values are excluded
Returns:

dict

Return type:

Parameters for all nodes and flows

oemof.outputlib.processing.remove_timestep(x)[source]

Remove the timestep from oemof tuples.

The timestep is removed from tuples of type (n, n, int) and (n, int).

oemof.outputlib.processing.results(om)[source]

Create a result dictionary from the result DataFrame.

Results from Pyomo are written into a dictionary of pandas objects where a Series holds all scalar values and a dataframe all sequences for nodes and flows. The dictionary is keyed by the nodes e.g. results[idx][‘scalars’] and flows e.g. results[n, n][‘sequences’].

oemof.outputlib.views module

Modules for providing convenient views for solph results.

Information about the possible usage is provided within the examples.

This file is part of project oemof (github.com/oemof/oemof). It’s copyrighted by the contributors recorded in the version control history of the file, available from its original location oemof/oemof/outputlib/views.py

SPDX-License-Identifier: GPL-3.0-or-later

class oemof.outputlib.views.NodeOption[source]

Bases: str, enum.Enum

An enumeration.

All = 'all'
HasInputs = 'has_inputs'
HasOnlyInputs = 'has_only_inputs'
HasOnlyOutputs = 'has_only_outputs'
HasOutputs = 'has_outputs'
oemof.outputlib.views.convert_to_multiindex(group, index_names=None, droplevel=None)[source]

Convert dict to pandas DataFrame with multiindex

Parameters:
  • group (dict) – Sequences of the oemof.solph.Model.results dictionary
  • index_names (arraylike) – Array with names of the MultiIndex
  • droplevel (arraylike) – List containing levels to be dropped from the dataframe
oemof.outputlib.views.filter_nodes(results, option=<NodeOption.All: 'all'>, exclude_busses=False)[source]

Get set of nodes from results-dict for given node option.

This function filters nodes from results for special needs. At the moment, the following options are available:

Additionally, busses can be excluded by setting exclude_busses to True.

Parameters:
  • results (dict) –
  • option (NodeOption) –
  • exclude_busses (bool) – If set, all bus nodes are excluded from the resulting node set.
Returns:

A set of Nodes.

Return type:

set

oemof.outputlib.views.get_node_by_name(results, *names)[source]

Searches results for nodes

Names are looked up in nodes from results and either returned single node (in case only one name is given) or as list of nodes. If name is not found, None is returned.

oemof.outputlib.views.net_storage_flow(results, node_type)[source]

Calculates the net storage flow for storage models that have one input edge and one output edge both with flows within the domain of non-negative reals.

results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type for which (storage) type net flows are calculated
Returns:
  • pandas.DataFrame object with multiindex colums. Names of levels of columns
  • are (from, to, net_flow.)

Examples

import oemof.solph as solph from oemof.outputlib import views

# solve oemof solph model ‘m’ # Then collect node weights views.net_storage_flow(m.results(), node_type=solph.GenericStorage)

oemof.outputlib.views.node(results, node, multiindex=False, keep_none_type=False)[source]

Obtain results for a single node e.g. a Bus or Component.

Either a node or its label string can be passed. Results are written into a dictionary which is keyed by ‘scalars’ and ‘sequences’ holding respective data in a pandas Series and DataFrame.

oemof.outputlib.views.node_input_by_type(results, node_type, droplevel=None)[source]

Gets all inputs for all nodes of the type node_type and returns a dataframe.

results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type of the node for that inputs are selected

import oemof.solph as solph from oemof.outputlib import views

# solve oemof solph model ‘m’ # Then collect node weights views.node_input_by_type(m.results(), node_type=solph.Sink)

oemof.outputlib.views.node_output_by_type(results, node_type, droplevel=None)[source]

Gets all outputs for all nodes of the type node_type and returns a dataframe.

results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type of the node for that outputs are selected

import oemof.solph as solph from oemof.outputlib import views

# solve oemof solph model ‘m’ # Then collect node weights views.node_output_by_type(m.results(), node_type=solph.Transformer)

oemof.outputlib.views.node_weight_by_type(results, node_type)[source]

Extracts node weights (if exist) of all components of the specified node_type.

Node weight are endogenous optimzation variables associated with the node and not the edge between two node, foxample the variable representing the storage level.

Parameters:
  • results (dict) – A result dictionary from a solved oemof.solph.Model object
  • node_type (oemof.solph class) – Specifies the type for which node weights should be collected

Example

from oemof.outputlib import views

# solve oemof model ‘m’ # Then collect node weights views.node_weight_by_type(m.results(), node_type=solph.GenericStorage)

Module contents