Post-Process

Wrapping the tessif-pypsa post-processing.

class tessif_pypsa_0_19_3.post_process.PypsaResultier(optimized_es, **kwargs)[source]

Bases: tessif.post_process.Resultier

Transform nodes and edges into their name representation. Child of Resultier and mother of all pypsa Resultiers.

Parameters

optimized_es (Network) – An optimized pypsa network.

class tessif_pypsa_0_19_3.post_process.IntegratedGlobalResultier(optimized_es, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.PypsaResultier, tessif.post_process.IntegratedGlobalResultier

Extracting the integrated global results out of the energy system and conveniently aggregating them (rounded to unit place) inside a dictionairy keyed by result name.

Integrated global results (IGR) mapped by result name.

Integrated global results currently consist of meta and non-meta results. the meta results are handled by the analyze module (see tessif.analyze.Comparatier.integrated_global_results) and consist of:

  • time

  • memory

results, whereas the non-meta results usually consist of:

  • emissions

  • costs

results which are handled here. Tessif’s energy system, however, allow to formulate a number of global_constraints which then would automatically be post processed here.

The befornamed strings serve as key inside the mapping.

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.ScaleResultier(optimized_es, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.PypsaResultier, tessif.post_process.ScaleResultier

Extract number of constraints.

Parameters

optimized_es – Model specific, optimized energy system containing its results.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.LoadResultier(optimized_es, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.PypsaResultier, tessif.post_process.LoadResultier

Transforming flow results into dictionairies keyed by node uid string representation.

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.CapacityResultier(optimized_es, **kwargs)[source]

Bases: tessif.post_process.CapacityResultier, tessif_pypsa_0_19_3.post_process.LoadResultier

Transforming installed capacity results dictionairies keyed by node.

Parameters

optimized_es (Network) – An optimized pypsa network.

Raises

NotImplementedError: – Raised when postprocessing a Link that is supposed to have multiple in and outputs. Caus right now this doesn’t seem possible.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.StorageResultier(optimized_es, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.PypsaResultier, tessif.post_process.StorageResultier

Transforming storage results into dictionairies keyed by node.

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.NodeCategorizer(optimized_es, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.PypsaResultier, tessif.post_process.NodeCategorizer

Categorizing the nodes of an optimized pypsa energy system.

Categorization utilizes Uid.

Nodes are categorized by:

  • Energy sector (‘power’, ‘heat’, ‘mobility’, ‘coupled’)

  • Region (‘arbitrary label’)

  • Coordinates (latitude, longitude in degree)

  • Energy carrier (‘solar’, ‘wind’, ‘electricity’, ‘steam’ …)

  • Node type (‘arbitrary label’)

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For functionality documentation see the respective base class.

Note

Pypsa has no intrinsic way of utilizing Uids besides using its string representation as name. Therefor it is necessary to utilize tessif’s node uid styles as demonstrated in the examples below, to reap the benefits of the NodeCategorizer.

For the uid representation technique to work you either have to construct the pypsa after changing the node_uid_style.

  1. Handle the imports of the following examples and simulate the energy system:

    >>> import tessif.examples.data.pypsa.py_hard as pypsa_examples
>>> import tessif.transform.es2mapping.ppsa as post_process_pypsa
>>> import pprint

  2. Display the energy system component’s Coordinates:

    >>> # change the uid style to use coordinates
>>> configs.node_uid_style = 'coords'
>>> resultier = post_process_pypsa.NodeCategorizer(
...     pypsa_examples.create_transshipment_problem())
>>> pprint.pprint(resultier.node_coordinates)
{'bus-01_53_10': Coordinates(latitude='53', longitude='10'),
 'bus-02_53_10': Coordinates(latitude='53', longitude='10'),
 'connector-01->02_53_10': Coordinates(latitude='53', longitude='10'),
 'sink-01_53_10': Coordinates(latitude='53', longitude='10'),
 'sink-02_53_10': Coordinates(latitude='53', longitude='10'),
 'source-01_53_10': Coordinates(latitude='53', longitude='10'),
 'source-02_53_10': Coordinates(latitude='53', longitude='10')}

  3. Group energy system components by their region:

    >>> # change the uid style to use regions
>>> configs.node_uid_style = 'region'
>>> resultier = post_process_pypsa.NodeCategorizer(
...     pypsa_examples.create_transshipment_problem())
>>> pprint.pprint(resultier.node_region_grouped)
{'Germany': ['bus-01_Germany',
             'bus-02_Germany',
             'source-01_Germany',
             'source-02_Germany',
             'connector-01->02_Germany',
             'sink-01_Germany',
             'sink-02_Germany']}

  4. Group energy system components by their sector

    >>> # change the uid style to use sectors
>>> configs.node_uid_style = 'sector'
>>> resultier = post_process_pypsa.NodeCategorizer(
...     pypsa_examples.create_transshipment_problem())
>>> pprint.pprint(resultier.node_sector_grouped)
{'Power': ['bus-01_Power',
           'bus-02_Power',
           'source-01_Power',
           'source-02_Power',
           'connector-01->02_Power',
           'sink-01_Power',
           'sink-02_Power']}

  5. Group energy system components by their node_type:

    >>> # change the uid style to use node types
>>> configs.node_uid_style = 'node_type'
>>> resultier = post_process_pypsa.NodeCategorizer(
...     pypsa_examples.create_transshipment_problem())
>>> pprint.pprint(resultier.node_type_grouped)
{'AC-bus': ['bus-01_AC-Bus', 'bus-02_AC-Bus'],
 'AC-link': ['connector-01->02_AC-Link'],
 'AC-sink': ['sink-01_AC-Sink', 'sink-02_AC-Sink'],
 'AC-source': ['source-01_AC-Source', 'source-02_AC-Source']}
5 Group energy system components by their energy

carrier:

>>> # change the uid style to use the energy carrier
>>> configs.node_uid_style = 'carrier'
>>> resultier = post_process_pypsa.NodeCategorizer(
...     pypsa_examples.create_transshipment_problem())
>>> pprint.pprint(resultier.node_carrier_grouped)
{'Electricity': ['bus-01_Electricity',
                 'bus-02_Electricity',
                 'source-01_Electricity',
                 'source-02_Electricity',
                 'connector-01->02_Electricity',
                 'sink-01_Electricity',
                 'sink-02_Electricity']}

  1. Map the node uid representation <Labeling_Concept> of each component of the energy system to their energy carrier:

    >>> # change the uid style to use the energy carrier
>>> configs.node_uid_style = 'carrier'
>>> resultier = post_process_pypsa.NodeCategorizer(
...     pypsa_examples.create_transshipment_problem())
>>> pprint.pprint(resultier.node_energy_carriers)
{'bus-01_Electricity': 'Electricity',
 'bus-02_Electricity': 'Electricity',
 'connector-01->02_Electricity': 'Electricity',
 'sink-01_Electricity': 'Electricity',
 'sink-02_Electricity': 'Electricity',
 'source-01_Electricity': 'Electricity',
 'source-02_Electricity': 'Electricity'}

    >>> # reset the uid style
>>> configs.node_uid_style = 'name'
class tessif_pypsa_0_19_3.post_process.FlowResultier(optimized_es, **kwargs)[source]

Bases: tessif.post_process.FlowResultier, tessif_pypsa_0_19_3.post_process.LoadResultier

Transforming flow results into dictionairies keyed by edges.

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.AllResultier(optimized_es, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.CapacityResultier, tessif_pypsa_0_19_3.post_process.FlowResultier, tessif_pypsa_0_19_3.post_process.StorageResultier, tessif_pypsa_0_19_3.post_process.ScaleResultier

Transform energy system results into a dictionary keyed by attribute.

Incorporates all the functionalities from its bases.

Parameters

optimized_es (Network) – An optimized pypsa network.

Note

This class allows interfacing with ALL framework processing utilities. It extracts every bit of info the author ever needed in his postprocessing.

It is meant to be a “one fits all” solution for small energy systems. Perfectly fit for showing “proof of concepts” or debugging energy system components.

Not meant to be used with large energy systems.

class tessif_pypsa_0_19_3.post_process.LabelFormatier(optimized_es, **kwargs)[source]

Bases: tessif.post_process.LabelFormatier, tessif_pypsa_0_19_3.post_process.FlowResultier, tessif_pypsa_0_19_3.post_process.CapacityResultier

Generate component summaries as multiline label dictionairy entries.

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.NodeFormatier(optimized_es, cgrp='name', drawutil='nx', **kwargs)[source]

Bases: tessif.post_process.NodeFormatier, tessif_pypsa_0_19_3.post_process.CapacityResultier

Transforming energy system results into node visuals.

Parameters

  • optimized_es (Network) – An optimized pypsa network.

  • cgrp (str, default='name') –

    Which group of color attribute(s) to return. One of:

    {'name', 'carrer', 'sector'}

    Color related attributes are grouped by tessif.frused.namedtuples.NodeColorGroupings and are thus returned as a typing.NamedTuple. Certain api functionalities expect those attributes to be dicts. (Usually those working only on ESTransformer input). Use this parameter on Formatier creation to provide the expected dictionairy.

  • drawutil (str, default='nx') – Which drawuing utility backend to format node size, fil_size and shape to. 'dc' for plotly-dash-cytoscape or 'nx' for networkx-matplotlib.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.MplLegendFormatier(optimized_es, cgrp='all', markers='formatier', **kwargs)[source]

Bases: tessif.post_process.MplLegendFormatier, tessif_pypsa_0_19_3.post_process.CapacityResultier

Generating visually enhanced matplotlib legends for nodes and edges.

Parameters

  • optimized_es (Network) – An optimized pypsa network.

  • cgrp (str, default='name') –

    Which group of color attribute(s) to return. One of:

    {'name', 'carrer', 'sector'}

    Color related attributes are grouped by tessif.frused.namedtuples.NodeColorGroupings and are thus returned as a typing.NamedTuple. Certain api functionalities expect those attributes to be dicts. (Usually those working only on ESTransformer input). Use this parameter on Formatier creation to provide the expected dictionairy.

  • markers (str, default='formatier') –

    What marker to use for legend entries. Either 'formatier' or one of the matplotlib.markers.

    If 'formatier' is used, markers will be inferred from NodeFormatier.node_shape.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.EdgeFormatier(optimized_es, drawutil='nx', cls=None, **kwargs)[source]

Bases: tessif.post_process.EdgeFormatier, tessif_pypsa_0_19_3.post_process.FlowResultier

Transforming energy system results into edge visuals.

Parameters

  • optimized_es (Network) – An optimized pypsa network.

  • drawutil (str, default='nx') – Which drawuing utility backend to format node size, fil_size and shape to. 'dc' for plotly-dash-cytoscape or 'nx' for networkx-matplotlib.

  • cls (tuple, default=None) –

    2-Tuple / CLS namedtuple defining the relative flow cost thresholds and the respective style specifications. Used to map specific flow costs to edge line style representations.

    If None, default implementation is used based on drawutil.

    For drawutil='nx' Networkx-Matplotlib:

    cls = ([0, .33, .66], ['dotted', 'dashed', 'solid'])

    For drawutil='dc' Dash-Cytoscape styles are used:

    cls = ([0, .33, .66], ['dotted', 'dashed', 'solid'])

    Translating to all edges of relative specific flows costs, between 0 and .33 are correlated to have a ':'/'dotted' linestyle.

See also

For functionality documentation see the respective base class.

class tessif_pypsa_0_19_3.post_process.AllFormatier(optimized_es, cgrp='all', markers='formatier', drawutil='nx', cls=None, **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.LabelFormatier, tessif_pypsa_0_19_3.post_process.NodeFormatier, tessif_pypsa_0_19_3.post_process.MplLegendFormatier, tessif_pypsa_0_19_3.post_process.EdgeFormatier

Transforming ES results into visual expression dicts keyed by attribute. Incorperates all the functionalities from its parents.

Parameters

  • optimized_es (Network) – An optimized pypsa network.

  • cgrp (str, default='name') –

    Which group of color attribute(s) to return. One of:

    {'name', 'carrer', 'sector'}

    Color related attributes are grouped by tessif.frused.namedtuples.NodeColorGroupings and are thus returned as a typing.NamedTuple. Certain api functionalities expect those attributes to be dicts. (Usually those working only on ESTransformer input). Use this parameter on Formatier creation to provide the expected dictionairy.

    Used by NodeFormatier and MplLegendFormatier

  • markers (str, default='formatier') –

    What marker to use for legend entries. Either 'formatier' or one of the matplotlib.markers.

    If 'formatier' is used, markers will be inferred from NodeFormatier.node_shape.

    Used by MplLegendFormatier

  • drawutil (str, default='nx') – Which drawuing utility backend to format node size, fil_size and shape to. 'dc' for plotly-dash-cytoscape or 'nx' for networkx-matplotlib.

  • cls (tuple, default=None) –

    2-Tuple / CLS namedtuple defining the relative flow cost thresholds and the respective style specifications. Used to map specific flow costs to edge line style representations.

    If None, default implementation is used based on drawutil.

    For drawutil='nx' Networkx-Matplotlib:

    cls = ([0, .33, .66], ['dotted', 'dashed', 'solid'])

    For drawutil='dc' Dash-Cytoscape styles are used:

    cls = ([0, .33, .66], ['dotted', 'dashed', 'solid'])

    Translating to all edges of relative specific flows costs, between 0 and .33 are correlated to have a ':'/'dotted' linestyle.

Note

This class allows interfacing with ALL framework processing utilities. It extracts every bit of info the author ever needed in his postprocessing.

It is meant to be a “one fits all” solution for small energy systems. Perfectly fit for showing “proof of concepts” or debugging energy system components.

Not meant to be used with large energy systems.

class tessif_pypsa_0_19_3.post_process.ICRHybridier(optimized_es, colored_by='name', **kwargs)[source]

Bases: tessif_pypsa_0_19_3.post_process.PypsaResultier, tessif.post_process.ICRHybridier

Aggregate numerical and visual information for visualizing the Integrated_Component_Results (ICR).

Parameters

optimized_es (Network) – An optimized pypsa network.

See also

For non model specific attributes see the respective base class.

property node_characteristic_value

Characteristic values of the energy system components mapped to their node uid representation.

Components of variable size or have a characteristic value as stated in tessif.frused.defaults.energy_system_nodes.

Characteristic value in this context means:

  • \(cv = \frac{\text{characteristic flow}} {\text{installed capacity}}\) for:

  • \(cv = \frac{\text{mean}\left(\text{SOC}\right)} {\text{capacity}}\) for:

Characteristic flow in this context means:

  • mean( LoadResultier.node_summed_loads )

    • Source objects

    • Sink objects

  • mean(0th outflow) for:

    • Transformer objects

The node fillsize of integrated component results graphs scales with the characteristic value. If no capacity is defined (i.e for nodes of variable size, like busses or excess sources and sinks, node size is set to it’s default ( nxgrph_visualize_defaults[node_fill_size]).