Post-Process

Wrapping the tessif-fine post-processing.

class tessif_fine_2_2_2.post_process.FINEResultier(optimized_es, **kwargs)[source]

Bases: Resultier

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

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

class tessif_fine_2_2_2.post_process.IntegratedGlobalResultier(optimized_es, **kwargs)[source]

Bases: FINEResultier, IntegratedGlobalResultier

Global Results 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.

Note

Capacity costs calculated within the optimizing process in fine are related to the overall capacities which are installed in the energy system and not only to the new build capacity of the component. This results in differences in simulated costs compared to the post processed costs.

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

See also

For functionality documentation see the respective base class.

Examples

1. Accessing the global results, containing simulated costs and emissions, as well as post processed flow and capacity costs:

>>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> resultier = post_process_fine.IntegratedGlobalResultier(
...     coded_fine_examples.create_expansion_example())
>>> print(resultier.global_results)
{'emissions (sim)': 20.0, 'costs (sim)': 41.0, 'opex (ppcd)': 40.0, 'capex (ppcd)': 1.0}
class tessif_fine_2_2_2.post_process.ScaleResultier(optimized_es, **kwargs)[source]

Bases: FINEResultier, ScaleResultier

Extract number of constraints and store them as int.

Parameters:

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

See also

For functionality documentation see the respective base class.

Examples

1. Call and optimize a FINE energy system model. >>> # import post fine processing and example modules: >>> import tessif.examples.data.fine.py_hard as coded_fine_examples >>> import tessif.transform.es2mapping.fine as post_process_fine

>>> # optimize the energy system:
>>> fine_es = coded_fine_examples.create_mwe()

>>> # post process the capacity results:
>>> resultier = post_process_fine.ScaleResultier(fine_es)

  1. Access the number of constraints.

>>> print(resultier.number_of_constraints)
27
class tessif_fine_2_2_2.post_process.LoadResultier(optimized_es, **kwargs)[source]

Bases: FINEResultier, LoadResultier

Loads2mapping Transforming flow results into dictionairies keyed by node uid string representation.

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

See also

For functionality documentation see the respective base class.

Examples

  1. Accessing a node’s outflows as positive numbers and a node’s inflows as negative numbers: (See tessif.transform.es2mapping.base.LoadResultier.node_load for more documentation):

    >>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> resultier = post_process_fine.LoadResultier(
...     coded_fine_examples.chp_example())
>>> print(resultier.node_load['CHP'])
CHP                   Gas Grid  Heat Grid  Power Line
1990-07-13 00:00:00 -33.333333   6.666667        10.0
1990-07-13 01:00:00 -33.333333   6.666667        10.0
1990-07-13 02:00:00 -33.333333   6.666667        10.0
1990-07-13 03:00:00 -33.333333   6.666667        10.0

  2. Accessing a node’s inflows as positive numbers (See tessif.transform.es2mapping.base.LoadResultier.node_inflows for more documentation):

    >>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> resultier = post_process_fine.LoadResultier(
...     coded_fine_examples.chp_example())
>>> print(resultier.node_inflows['CHP'])
CHP                   Gas Grid
1990-07-13 00:00:00  33.333333
1990-07-13 01:00:00  33.333333
1990-07-13 02:00:00  33.333333
1990-07-13 03:00:00  33.333333

  3. Accessing a node’s outflows as positive numbers (See tessif.transform.es2mapping.base.LoadResultier.node_outflows for more documentation):

    >>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> resultier = post_process_fine.LoadResultier(
...     coded_fine_examples.chp_example())
>>> print(resultier.node_outflows['CHP'])
CHP                  Heat Grid  Power Line
1990-07-13 00:00:00   6.666667        10.0
1990-07-13 01:00:00   6.666667        10.0
1990-07-13 02:00:00   6.666667        10.0
1990-07-13 03:00:00   6.666667        10.0

  4. Accessing a node’s summed inflows as positive numbers (in case it is of component sink) or a node’s summed outflows (in case it is not a sink). (See tessif.transform.es2mapping.base.LoadResultier.node_summed_loads for more documentation):

    >>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> resultier = post_process_fine.LoadResultier(coded_fine_examples.chp_example())
>>> print(resultier.node_summed_loads['CHP'])
1990-07-13 00:00:00    16.666667
1990-07-13 01:00:00    16.666667
1990-07-13 02:00:00    16.666667
1990-07-13 03:00:00    16.666667
Freq: H, dtype: float64
class tessif_fine_2_2_2.post_process.CapacityResultier(optimized_es, **kwargs)[source]

Bases: CapacityResultier, LoadResultier

Capacities2mapping Transforming installed capacity results dictionairies keyed by node.

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

Examples

  1. Accessing the installed capacities and the characteristic values of the minimum working example

    (See tessif.transform.es2mapping.base.CapacityResultier.node_installed_capacity for more documentation):

    >>> # import post fine processing and example modules:
>>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine

    >>> # optimize the energy system:
>>> fine_es = coded_fine_examples.create_mwe()

    >>> # post process the capacity results:
>>> resultier = post_process_fine.CapacityResultier(fine_es)

    >>> # access the capacity results:
>>> for node, capacity in resultier.node_installed_capacity.items():
...     print(f'{node}: {capacity}')
PowerLine: None
CBET: None
Demand: 10.0
Renewable: 10.0
Gas Station: 23.81
Transformer: 10.0

    >>> # acces the characteristic value results:
>>> for node, cv in resultier.node_characteristic_value.items():
...     if cv is not None:
...         cv = round(cv, 2)
...     print(f'{node}: {cv}')
PowerLine: None
CBET: None
Demand: 1.0
Renewable: 0.75
Gas Station: 0.25
Transformer: 0.25

  2. Accessing the installed capacities and the characteristic value of the Storage Example

    >>> # import  fine postprocessing and example modules:
>>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine

    >>> # optimize the energy system:
>>> fine_es = coded_fine_examples.storage_example()

    >>> # post process the capacity results:
>>> resultier = post_process_fine.CapacityResultier(fine_es)

    >>> # access the capacity results:
>>> for node, capacity in resultier.node_installed_capacity.items():
...     print(f'{node}: {capacity}')
Power Line: None
Demand: 10.0
Generator: 20.0
Storage: 10.0

    >>> # acces the characteristic value results:
>>> for node, cv in resultier.node_characteristic_value.items():
...     if cv is not None:
...         cv = round(cv, 2)
...     print(f'{node}: {cv}')
Power Line: None
Demand: 0.94
Generator: 0.47
Storage: 0.34
property node_characteristic_value

Mapped characteristic values to node uid representations.

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:

    • Source objects

    • Sink objects

    • Transformer objects

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

    • Storage

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 the advanced system visualization 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]).

class tessif_fine_2_2_2.post_process.StorageResultier(optimized_es, **kwargs)[source]

Bases: FINEResultier, StorageResultier

Transforming storage results into dictionairies keyed by node.

Parameters:

optimized_es (fine energy system) – An optimized fine energy system containing its results

See also

For functionality documentation see the respective base class.

Examples

  1. Display a storage-node’s capacity:

    Setting spellings.get_from's logging level to debug for decluttering doctest output:

    >>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

    Actual Example:

    >>> # import post fine processing and example modules:
>>> import tessif.examples.data.fine.py_hard as coded_fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine

    >>> # optimize the energy system:
>>> fine_es = coded_fine_examples.storage_example()

    >>> # post process the capacity results:
>>> resultier = post_process_fine.StorageResultier(fine_es)

    >>> print(post_process_fine.StorageResultier(fine_es).node_soc['Storage'])
1990-07-13 00:00:00     0.0
1990-07-13 01:00:00     0.0
1990-07-13 02:00:00     7.0
1990-07-13 03:00:00     0.0
1990-07-13 04:00:00    10.0
Freq: H, Name: Storage, dtype: float64
class tessif_fine_2_2_2.post_process.NodeCategorizer(optimized_es, **kwargs)[source]

Bases: FINEResultier, NodeCategorizer

Categorizing the nodes of an optimized fine energy system.

Categorization utilizes Uid.

Nodes are categorized by:

  • Energy component (One of the ‘Bus’, ‘Sink’, etc..)

  • 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 (fine energy system model) – An optimized fine energy system containing its results

See also

For functionality documentation see the respective base class.

Examples

  1. Display the energy system component’s Coordinates:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.create_mwe())
>>> pprint.pprint(resultier.node_coordinates)
{'CBET': Coordinates(latitude=53, longitude=10),
 'Demand': Coordinates(latitude=53, longitude=10),
 'Gas Station': Coordinates(latitude=53, longitude=10),
 'PowerLine': Coordinates(latitude=53, longitude=10),
 'Renewable': Coordinates(latitude=53, longitude=10),
 'Transformer': Coordinates(latitude=53, longitude=10)}

  2. Group energy system components by their region:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.create_mwe())
>>> pprint.pprint(resultier.node_region_grouped)
{'Germany': ['PowerLine',
             'CBET',
             'Demand',
             'Renewable',
             'Gas Station',
             'Transformer']}

  3. Group energy system components by their sector

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.create_mwe())
>>> pprint.pprint(resultier.node_sector_grouped)
{'Power': ['PowerLine',
           'CBET',
           'Demand',
           'Renewable',
           'Gas Station',
           'Transformer']}

  4. Group energy system components by their node_type:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.create_mwe())
>>> pprint.pprint(resultier.node_type_grouped)
{'AC-bus': ['PowerLine'],
 'AC-source': ['Renewable'],
 'Gas import': ['Gas Station'],
 'Gas-bus': ['CBET'],
 'Gas-powerplant': ['Transformer'],
 'Sink': ['Demand']}

  5. Group energy system components by their energy carrier:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.create_mwe())
>>> pprint.pprint(resultier.node_carrier_grouped)
{'Electricity': ['PowerLine', 'Demand', 'Renewable'],
 'Gas': ['CBET', 'Gas Station', 'Transformer']}

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

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.create_mwe())
>>> pprint.pprint(resultier.node_energy_carriers)
{'CBET': 'gas',
 'Demand': 'Electricity',
 'Gas Station': 'gas',
 'PowerLine': 'Electricity',
 'Renewable': 'Electricity',
 'Transformer': 'gas'}

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

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.NodeCategorizer(fine_examples.emission_objective())
>>> pprint.pprint(resultier.node_components)
{'Bus': ['Power Line', 'CBET'],
 'Sink': ['Demand'],
 'Source': ['CBE', 'Renewable'],
 'Transformer': ['Transformer']}
class tessif_fine_2_2_2.post_process.FlowResultier(optimized_es, **kwargs)[source]

Bases: FlowResultier, LoadResultier

Flows2mapping Transforming flow results into dictionairies keyed by edges.

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

See also

For functionality documentation see the respective base class.

Examples

  1. Display the net energy flows of a small energy system:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())
>>> pprint.pprint(resultier.edge_net_energy_flow)
{Edge(source='CBE', target='CBET'): 30.0,
 Edge(source='CBET', target='Transformer'): 30.0,
 Edge(source='Power Line', target='Demand'): 40.0,
 Edge(source='Renewable', target='Power Line'): 27.4,
 Edge(source='Transformer', target='Power Line'): 12.6}

  2. Display the total costs incurred sorted by edge/flow:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())
>>> pprint.pprint(resultier.edge_total_costs_incurred)
{Edge(source='CBE', target='CBET'): 0.0,
 Edge(source='CBET', target='Transformer'): 0.0,
 Edge(source='Power Line', target='Demand'): 0.0,
 Edge(source='Renewable', target='Power Line'): 137.0,
 Edge(source='Transformer', target='Power Line'): 60.0}

  3. Display the total emissions caused sorted by edge/flow:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())
>>> pprint.pprint(resultier.edge_total_emissions_caused)
{Edge(source='CBE', target='CBET'): 0.0,
 Edge(source='CBET', target='Transformer'): 0.0,
 Edge(source='Power Line', target='Demand'): 0.0,
 Edge(source='Renewable', target='Power Line'): 0.0,
 Edge(source='Transformer', target='Power Line'): 5.292}

  4. Display the specific flow costs of this energy system:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())
>>> pprint.pprint(resultier.edge_specific_flow_costs)
{Edge(source='CBE', target='CBET'): 0.0,
 Edge(source='CBET', target='Transformer'): 0.0,
 Edge(source='Power Line', target='Demand'): 0.0,
 Edge(source='Renewable', target='Power Line'): 5.0,
 Edge(source='Transformer', target='Power Line'): 4.761904761904762}

  5. Display the specific emission of this energy system:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())
>>> pprint.pprint(resultier.edge_specific_emissions)
{Edge(source='CBE', target='CBET'): 0.0,
 Edge(source='CBET', target='Transformer'): 0.0,
 Edge(source='Power Line', target='Demand'): 0.0,
 Edge(source='Renewable', target='Power Line'): 0.0,
 Edge(source='Transformer', target='Power Line'): 0.42}

  6. Show the caluclated edge weights of this energy system:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())
>>> pprint.pprint(resultier.edge_weight)
{Edge(source='CBE', target='CBET'): 0.1,
 Edge(source='CBET', target='Transformer'): 0.1,
 Edge(source='Power Line', target='Demand'): 0.1,
 Edge(source='Renewable', target='Power Line'): 1.0,
 Edge(source='Transformer', target='Power Line'): 0.9523809523809523}

  7. Access the reference emissions and net energy flow:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> resultier = fine.FlowResultier(fine_examples.emission_objective())

    >>> print(resultier.edge_reference_emissions)
0.42

    >>> print(resultier.edge_reference_net_energy_flow)
40.0
class tessif_fine_2_2_2.post_process.AllResultier(optimized_es, **kwargs)[source]

Bases: CapacityResultier, FlowResultier, StorageResultier, ScaleResultier

Transform energy system results into a dictionary keyed by attribute.

Incorporates all the functionalities from its bases.

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

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_fine_2_2_2.post_process.LabelFormatier(optimized_es, **kwargs)[source]

Bases: LabelFormatier, FlowResultier, CapacityResultier

Generate component summaries as multiline label dictionairy entries.

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

See also

For functionality documentation see the respective base class.

Examples

  1. Compile and display a node’s summary:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.LabelFormatier(fine_examples.create_mwe())
>>> pprint.pprint(formatier.node_summaries['Demand'])
{'Demand': 'Demand\n10 MW\ncf: 1.0'}

  2. Compile and display an edge’s summary:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.LabelFormatier(fine_examples.create_mwe())
>>> pprint.pprint(formatier.edge_summaries[
...     ('Renewable', 'PowerLine')])
{('Renewable', 'PowerLine'): '30 MWh\n1.0 €/MWh\n0.0 t/MWh'}
class tessif_fine_2_2_2.post_process.NodeFormatier(optimized_es, cgrp='name', drawutil='nx', **kwargs)[source]

Bases: NodeFormatier, CapacityResultier

Transforming energy system results into node visuals.

Parameters:

  • optimized_es (fine energy system model) – An optimized fine energy system containing its results.`

  • 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.

Examples

  1. Generate and display a small energy system’s node shape mapping:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.NodeFormatier(fine_examples.create_mwe())
>>> pprint.pprint(formatier.node_shape)
{'CBET': 'o',
 'Demand': '8',
 'Gas Station': 'o',
 'PowerLine': 'o',
 'Renewable': 'o',
 'Transformer': '8'}

    Compare these to the dcgrph ready shapes:

    >>> formatier = fine.NodeFormatier(
...     fine_examples.create_mwe(),
...     drawutil='dc')
>>> pprint.pprint(formatier.node_shape)
{'CBET': 'ellipse',
 'Demand': 'ellipse',
 'Gas Station': 'round-rectangle',
 'PowerLine': 'ellipse',
 'Renewable': 'round-rectangle',
 'Transformer': 'round-octagon'}

  2. Generate and display a small energy system’s node size mapping:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.NodeFormatier(fine_examples.create_mwe())
>>> pprint.pprint(formatier.node_size)
{'CBET': 'variable',
 'Demand': 1260,
 'Gas Station': 3000,
 'PowerLine': 'variable',
 'Renewable': 1260,
 'Transformer': 1260}

    Compare these to the dcgrph ready sizes scaled to a different default node size:

    >>> formatier = fine.NodeFormatier(
...     fine_examples.create_mwe(),
...     drawutil='dc')
>>> pprint.pprint(formatier.node_size)
{'CBET': 'variable',
 'Demand': 38,
 'Gas Station': 90,
 'PowerLine': 'variable',
 'Renewable': 38,
 'Transformer': 38}

  3. Generate and display a small energy system’s node fill size mapping:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.NodeFormatier(fine_examples.create_mwe())
>>> pprint.pprint(formatier.node_fill_size)
{'CBET': None,
 'Demand': 1260.0,
 'Gas Station': 750.0,
 'PowerLine': None,
 'Renewable': 945.0,
 'Transformer': 315.0}

    Compare these to the dcgrph ready sizes scaled to a different default node size:

    >>> formatier = fine.NodeFormatier(
...     fine_examples.create_mwe(),
...     drawutil='dc')
>>> pprint.pprint(formatier.node_fill_size)
{'CBET': None,
 'Demand': 38.0,
 'Gas Station': 22.0,
 'PowerLine': None,
 'Renewable': 28.0,
 'Transformer': 10.0}

  4. Generate and display a small energy system’s node color mapping basd on the grouping:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.NodeFormatier(
...     fine_examples.create_mwe(), cgrp='all')
>>> pprint.pprint(formatier.node_color.name)
{'CBET': '#AFAFAF',
 'Demand': '#330099',
 'Gas Station': '#336666',
 'PowerLine': '#ffff33',
 'Renewable': '#AFAFAF',
 'Transformer': '#9999ff'}

    >>> pprint.pprint(formatier.node_color.carrier)
{'CBET': '#336666',
 'Demand': '#FFD700',
 'Gas Station': '#336666',
 'PowerLine': '#FFD700',
 'Renewable': '#FFD700',
 'Transformer': '#336666'}

    >>> pprint.pprint(formatier.node_color.sector)
{'CBET': '#ffff33',
 'Demand': '#ffff33',
 'Gas Station': '#ffff33',
 'PowerLine': '#ffff33',
 'Renewable': '#ffff33',
 'Transformer': '#ffff33'}

  5. Generate and display a small energy system’s node color map (colors that are cycled through for each component type) mapping basd on the grouping:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> from tessif.transform.es2mapping import fine
>>> import pprint
>>> formatier = fine.NodeFormatier(
...     fine_examples.create_mwe(), cgrp='all')
>>> pprint.pprint({k: type(v)
...     for k,v in formatier.node_color_maps.name.items()})
{'CBET': <class 'str'>,
 'Demand': <class 'str'>,
 'Gas Station': <class 'str'>,
 'PowerLine': <class 'str'>,
 'Renewable': <class 'str'>,
 'Transformer': <class 'str'>}
>>> pprint.pprint({k: type(v)
...     for k,v in formatier.node_color_maps.carrier.items()})
{'CBET': <class 'str'>,
 'Demand': <class 'str'>,
 'Gas Station': <class 'str'>,
 'PowerLine': <class 'str'>,
 'Renewable': <class 'str'>,
 'Transformer': <class 'str'>}
>>> pprint.pprint({k: type(v)
...     for k,v in formatier.node_color_maps.sector.items()})
{'CBET': <class 'str'>,
 'Demand': <class 'str'>,
 'Gas Station': <class 'str'>,
 'PowerLine': <class 'str'>,
 'Renewable': <class 'str'>,
 'Transformer': <class 'str'>}

    (Hint for devs: Which color string is mapped depends on order of mapping. Therefore only the type is printed and not its string value since it may differ from doctest to doctest.)

class tessif_fine_2_2_2.post_process.MplLegendFormatier(optimized_es, cgrp='all', markers='formatier', **kwargs)[source]

Bases: MplLegendFormatier, CapacityResultier

Generating visually enhanced matplotlib legends for nodes and edges.

Parameters:

  • optimized_es (fine energy system) – An optimized fine energy system containing its results.`

  • cgrp (str, default='name') –

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

    {'name', 'carrier', '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.

Examples

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

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> import pprint

  2. Generate and display name grouped legend labels of a small energy system:

    >>> formatier = post_process_fine.MplLegendFormatier(
...     fine_examples.create_mwe(), cgrp='all')
>>> pprint.pprint(formatier.node_legend.name['legend_labels'])
['CBET', 'Demand', 'Gas Station', 'PowerLine', 'Renewable', 'Transformer']

  3. Generate and display matplotlib legend attributes to describe fency node styles. Fency as in:

    • variable node size being outer fading circles

    • cycle filling being proportional capacity factors

    • outer diameter being proportional installed capacities

    >>> formatier = post_process_fine.MplLegendFormatier(
...     fine_examples.create_mwe(), cgrp='all')
>>> pprint.pprint({k: type(v)
...     for k,v in formatier.node_style_legend.items()})
{'legend_bbox_to_anchor': <class 'tuple'>,
 'legend_borderaxespad': <class 'int'>,
 'legend_handles': <class 'list'>,
 'legend_labels': <class 'list'>,
 'legend_labelspacing': <class 'int'>,
 'legend_loc': <class 'str'>,
 'legend_title': <class 'str'>}
class tessif_fine_2_2_2.post_process.EdgeFormatier(optimized_es, drawutil='nx', cls=None, **kwargs)[source]

Bases: EdgeFormatier, FlowResultier

Transforming energy system results into edge visuals.

Parameters:

  • optimized_es (fine energy system model) – An optimized fine energy system containing its results

  • 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.

Examples

  1. Handle the imports:

    >>> import tessif.examples.data.fine.py_hard as fine_examples
>>> import tessif.transform.es2mapping.fine as post_process_fine
>>> import pprint

  2. Generate and display a small energy system’s edge withs:

    >>> formatier = post_process_fine.EdgeFormatier(
...     fine_examples.create_mwe())
>>> pprint.pprint(list(sorted(formatier.edge_width.items())))
[(Edge(source='CBET', target='Transformer'), 0.6),
 (Edge(source='Gas Station', target='CBET'), 0.6),
 (Edge(source='PowerLine', target='Demand'), 1.0),
 (Edge(source='Renewable', target='PowerLine'), 0.75),
 (Edge(source='Transformer', target='PowerLine'), 0.25)]

    Compare these to the dcgrph ready widths, scaled to a different default edge width:

    >>> formatier = post_process_fine.EdgeFormatier(
...     fine_examples.create_mwe(),
...     drawutil='dc')
>>> pprint.pprint(list(sorted(formatier.edge_width.items())))
[(Edge(source='CBET', target='Transformer'), 4.17),
 (Edge(source='Gas Station', target='CBET'), 4.17),
 (Edge(source='PowerLine', target='Demand'), 7.0),
 (Edge(source='Renewable', target='PowerLine'), 5.25),
 (Edge(source='Transformer', target='PowerLine'), 1.75)]

  3. Generate and display a small energy system’s edge colors:

    >>> formatier = post_process_fine.EdgeFormatier(
...     fine_examples.create_mwe())
>>> pprint.pprint(list(sorted(formatier.edge_color.items())))
[(Edge(source='CBET', target='Transformer'), [0.15]),
 (Edge(source='Gas Station', target='CBET'), [0.15]),
 (Edge(source='PowerLine', target='Demand'), [0.15]),
 (Edge(source='Renewable', target='PowerLine'), [0.15]),
 (Edge(source='Transformer', target='PowerLine'), [0.15])]

    (draw_networkx_edges() expects iterable of floats when using a colormap)

    Compare these to the dcgrph ready colors, directly scaling the relativ specific emissions to a hex-color value.

    >>> formatier = post_process_fine.EdgeFormatier(
...     fine_examples.create_mwe(),
...     drawutil='dc')
>>> pprint.pprint(list(sorted(formatier.edge_color.items())))
[(Edge(source='CBET', target='Transformer'), '#d8d8d8'),
 (Edge(source='Gas Station', target='CBET'), '#d8d8d8'),
 (Edge(source='PowerLine', target='Demand'), '#d8d8d8'),
 (Edge(source='Renewable', target='PowerLine'), '#d8d8d8'),
 (Edge(source='Transformer', target='PowerLine'), '#d8d8d8')]

  4. Generate and display a small energy system’s edge line styles:

    >>> formatier = post_process_fine.EdgeFormatier(
...     fine_examples.create_mwe())
>>> pprint.pprint(list(sorted(formatier.edge_linestyle.items())))
[(Edge(source='CBET', target='Transformer'), ':'),
 (Edge(source='Gas Station', target='CBET'), ':'),
 (Edge(source='PowerLine', target='Demand'), ':'),
 (Edge(source='Renewable', target='PowerLine'), ':'),
 (Edge(source='Transformer', target='PowerLine'), '-')]

    Compare these to the dcgrph ready styles, using different specifiers:

    >>> formatier = post_process_fine.EdgeFormatier(
...     fine_examples.create_mwe(),
...     drawutil='dc')
>>> pprint.pprint(list(sorted(formatier.edge_linestyle.items())))
[(Edge(source='CBET', target='Transformer'), 'dotted'),
 (Edge(source='Gas Station', target='CBET'), 'dotted'),
 (Edge(source='PowerLine', target='Demand'), 'dotted'),
 (Edge(source='Renewable', target='PowerLine'), 'dotted'),
 (Edge(source='Transformer', target='PowerLine'), 'solid')]
class tessif_fine_2_2_2.post_process.AllFormatier(optimized_es, cgrp='all', markers='formatier', drawutil='nx', cls=None, **kwargs)[source]

Bases: LabelFormatier, MplLegendFormatier, NodeFormatier, EdgeFormatier

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

Parameters:

  • optimized_es (fine energy system model) – An optimized fine energy system containing its results

  • 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_fine_2_2_2.post_process.ICRHybridier(optimized_es, colored_by='name', **kwargs)[source]

Bases: FINEResultier, ICRHybridier

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

Parameters:

optimized_es (fine energy system model) – An optimized fine energy system containing its results

See also

For non model specific attributes see the respective base class.

property node_characteristic_value

Map node label to characteristic value.

Components of variable size have a characteristic value of None.

The node fillsize of integrated compnent 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]).