Post-Process

Wrapping the tessif-calliope post-processing.

class tessif_calliope_0_6_6post1.post_process.CalliopeResultier(optimized_es, **kwargs)

Bases: tessif.post_process.Resultier

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

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

Note

Calliope does not support Tessif’s UID notation. To get as much as possible information the UID is stored as one string with separation via dot in the technology names. Sine Busses and Connectors are only calliope locations and no technologies they dont have a name parameter and thus do not have all UID information..

class tessif_calliope_0_6_6post1.post_process.IntegratedGlobalResultier(optimized_es, **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CalliopeResultier, 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 (Model) – An optimized calliope energy system containing its inputs as well as results.

Note

Overall costs might differ from sum of OPEX and CAPEX on expansion problems due to rounding errors on calliope specific calculation using the lifetime, which is very small on small timeframes (lifetime = timesteps/8760).

The Calliope objective differs from Tessif output if expansion problems with installed capacities are analysed. This is due to calliope not considering installed capacities by default and thus using the expansion minimum as installed capacity in tessif. The occuring costs are then erased in the mapping.

See also

For functionality documentation see the respective base class.

Examples

1. Using emission_objective() to quickly access a tessif energy system to use for doctesting, or trying out this frameworks utilities.

   >>> import tessif.examples.data.tsf.py_hard as tsf_examples
   >>> tsf_es = tsf_examples.emission_objective()
   

   Transform the energy system to an oemof energy system:

   >>> import tessif.transform.es2es.cllp as tessif_to_calliope
   >>> calliope_es = tessif_to_calliope.transform(tsf_es)
   

   Optmize the energy system:

   >>> import tessif.simulate as simulate
   >>> optimized_calliope_es = simulate.cllp_from_es(calliope_es, solver='cbc')
   

   Extract the global results:

   >>> import pprint
   >>> import tessif.transform.es2mapping.cllp as calliope_post_process
   >>> resultier = calliope_post_process.IntegratedGlobalResultier(
   ...     optimized_calliope_es)
   >>> pprint.pprint(resultier.global_results)
   {'capex (ppcd)': 0.0,
    'costs (sim)': 252.0,
    'emissions (sim)': 60.0,
    'opex (ppcd)': 252.0}
   

2. Using a native calliope model. Calling and optimize a calliope energy system model.

   >>> from tessif.frused.paths import example_dir
   >>> import calliope
   >>> calliope.set_log_verbosity('ERROR', include_solver_output=False)
   >>> calliope_es = calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
   >>> calliope_es.run()
   

   >>> import tessif.transform.es2mapping.cllp as calliope_post_process
   >>> import pprint
   >>> resultier = calliope_post_process.IntegratedGlobalResultier(calliope_es)
   >>> pprint.pprint(resultier.global_results)
   {'capex (ppcd)': 0.0,
    'costs (sim)': 105.0,
    'emissions (sim)': 53.0,
    'opex (ppcd)': 105.0}
   

class tessif_calliope_0_6_6post1.post_process.ScaleResultier(optimized_es, **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CalliopeResultier, tessif.post_process.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 calliope energy system model.

>>> from tessif.frused.paths import example_dir
>>> import calliope as native_calliope
>>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
>>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
>>> calliope_es.run()
>>> import tessif.transform.es2mapping.cllp as calliope_post_process
>>> resultier = calliope_post_process.ScaleResultier(calliope_es)

2. Access the number of constraints.

>>> print(resultier.number_of_constraints)
194

class tessif_calliope_0_6_6post1.post_process.LoadResultier(optimized_es, **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CalliopeResultier, tessif.post_process.LoadResultier

Transforming flow results into dictionairies keyed by node.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Examples

1. Calling and optimize a calliope energy system model.

>>> from tessif.frused.paths import example_dir
>>> import calliope as native_calliope
>>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
>>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
>>> calliope_es.run()
>>> import tessif.transform.es2mapping.cllp as calliope_post_process
>>> resultier = calliope_post_process.LoadResultier(calliope_es)

2. Accessing a node’s outflows as positive numbers and a node’s inflows as negative numbers:

   >>> print(resultier.node_load['Powerline'])
   Powerline            Battery  Generator  Solar Panel  Battery  Demand
   1990-07-13 00:00:00     -0.0       -0.0        -12.0      1.0    11.0
   1990-07-13 01:00:00     -8.0       -0.0         -3.0      0.0    11.0
   1990-07-13 02:00:00     -0.9       -3.1         -7.0      0.0    11.0
   

3. Accessing a node’s inflows as positive numbers:

   >>> print(resultier.node_inflows['Powerline'])
   Powerline            Battery  Generator  Solar Panel
   1990-07-13 00:00:00      0.0        0.0         12.0
   1990-07-13 01:00:00      8.0        0.0          3.0
   1990-07-13 02:00:00      0.9        3.1          7.0
   

4. Accessing a node’s outflows as positive numbers:

   >>> print(resultier.node_outflows['Powerline'])
   Powerline            Battery  Demand
   1990-07-13 00:00:00      1.0    11.0
   1990-07-13 01:00:00      0.0    11.0
   1990-07-13 02:00:00      0.0    11.0
   

5. Accessing the sum of a node’s inflow (in case it’s a Sink) or the sum of a node’s outflows (in case it’s NOT a Sink):

   >>> print(resultier.node_summed_loads['Powerline'])
   1990-07-13 00:00:00    12.0
   1990-07-13 01:00:00    11.0
   1990-07-13 02:00:00    11.0
   dtype: float64
   

class tessif_calliope_0_6_6post1.post_process.CapacityResultier(optimized_es, **kwargs)

Bases: tessif.post_process.CapacityResultier, tessif_calliope_0_6_6post1.post_process.LoadResultier

Transforming installed capacity results dictionairies keyed by node.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Note

Expansion costs on CHP’s will look different than they have been set in model. This is due to the fact, that calliope only considers one cost parameter, thus the different CHP parameters are put together to one value and later on cannot be separated again.

Examples

1. Transforming and optimize a tessif energy system using calliope.

>>> import tessif.examples.data.tsf.py_hard as tsf_examples
>>> import tessif.transform.es2es.cllp as tessif_to_calliope
>>> import tessif.simulate as simulate
>>> tsf_es = tsf_examples.create_mwe()
>>> calliope_es = tessif_to_calliope.transform(tsf_es)
>>> optimized_calliope_es = simulate.cllp_from_es(calliope_es, solver='cbc')
>>> import tessif.transform.es2mapping.cllp as calliope_post_process
>>> import pprint

2. Display a small energy system’s installed capacities after optimization as well as their original capacities:

   >>> resultier = calliope_post_process.CapacityResultier(optimized_calliope_es)
   >>> pprint.pprint(resultier.node_installed_capacity)
   {'Battery': 20.0,
    'Demand': 10.0,
    'Gas Station': 23.809524,
    'Generator': 10.0,
    'Pipeline': None,
    'Powerline': None}
   >>> pprint.pprint(resultier.node_original_capacity)
   {'Battery': 20.0,
    'Demand': 10.0,
    'Gas Station': 0.0,
    'Generator': 0.0,
    'Pipeline': None,
    'Powerline': None}
   

3. Display a small energy system’s characteristic values after optimization:

   >>> pprint.pprint(resultier.node_characteristic_value)
   {'Battery': 0.0,
    'Demand': 1.0,
    'Gas Station': 0.75,
    'Generator': 0.75,
    'Pipeline': None,
    'Powerline': None}
   

4. Display a small energy system’s reference capacity:

   >>> pprint.pprint(resultier.node_reference_capacity)
   23.809524
   

5. Display capacity data for a multi output transformer:

   >>> tsf_es = tsf_examples.create_component_es(expansion_problem=True)
   >>> calliope_es = tessif_to_calliope.transform(tsf_es)
   >>> optimized_calliope_es = simulate.cllp_from_es(calliope_es, solver='cbc')
   >>> resultier = calliope_post_process.CapacityResultier(optimized_calliope_es)
   >>> print(resultier.node_installed_capacity['Biogas CHP'])
   Heatline     250.0
   Powerline    200.0
   dtype: float64
   >>> print(resultier.node_expansion_costs['Biogas CHP'])
   Heatline           0.0
   Powerline    3828125.0
   dtype: float64
   

   Note how the CHP expansion costs are only one value in Calliope even if they are splitted for each output in the Tessif energy system:

   >>> for transformer in tsf_es.transformers:
   ...     if transformer.uid.name == 'Biogas CHP':
   ...         print(transformer.expansion_costs)
   {'biogas': 0, 'electricity': 3500000, 'hot_water': 262500}
   

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_calliope_0_6_6post1.post_process.StorageResultier(optimized_es, **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CalliopeResultier, tessif.post_process.StorageResultier

Transforming storage results into dictionaries keyed by node.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Examples

1. Transforming and optimize a tessif energy system using calliope.

>>> import tessif.examples.data.tsf.py_hard as tsf_examples
>>> import tessif.transform.es2es.cllp as tessif_to_calliope
>>> import tessif.simulate as simulate
>>> tsf_es = tsf_examples.create_storage_example()
>>> calliope_es = tessif_to_calliope.transform(tsf_es)
>>> optimized_calliope_es = simulate.cllp_from_es(calliope_es, solver='cbc')
>>> import tessif.transform.es2mapping.cllp as calliope_post_process
>>> import pprint

2. Display a storage-node’s capacity:

   >>> resultier = calliope_post_process.StorageResultier(optimized_calliope_es)
   >>> print(resultier.node_soc['Storage'])
   1990-07-13 00:00:00     8.100000
   1990-07-13 01:00:00    16.200000
   1990-07-13 02:00:00    27.000000
   1990-07-13 03:00:00    15.888889
   1990-07-13 04:00:00     4.777778
   Freq: H, Name: Storage, dtype: float64
   

class tessif_calliope_0_6_6post1.post_process.NodeCategorizer(optimized_es, **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CalliopeResultier, tessif.post_process.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 (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Note

Calliope does not fully support tessif’s uid notation. The UID is given inside the calliope technology name. Busses and connector are only locations and no techs, so they don’t have any UID information besides the coordinates and the Carrier which information can be recreated using the in and output. The tessif component type can be identified since each calliope technology inherits from a parent technology which can be linked to a tessif component. Busses can be identified by the fact of not having a parent, due to not being a tech.

Examples

1. Calling and optimize a calliope energy system model.

>>> from tessif.frused.paths import example_dir
>>> import calliope as native_calliope
>>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
>>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
>>> calliope_es.run()
>>> import tessif.transform.es2mapping.cllp as calliope_post_process
>>> import pprint

2. Group energy system components by their Coordinates:

   >>> resultier = calliope_post_process.NodeCategorizer(calliope_es)
   >>> pprint.pprint(resultier.node_coordinates)
   {'Battery': Coordinates(latitude=42.0, longitude=42.0),
    'Demand': Coordinates(latitude=42.0, longitude=42.0),
    'Gas Station': Coordinates(latitude=42.0, longitude=42.0),
    'Generator': Coordinates(latitude=42.0, longitude=42.0),
    'Pipeline': Coordinates(latitude=42.0, longitude=42.0),
    'Powerline': Coordinates(latitude=42.0, longitude=42.0),
    'Solar Panel': Coordinates(latitude=42.0, longitude=42.0)}
   

3. Group energy system components by their region:

   >>> pprint.pprint(resultier.node_region_grouped)
   {'Germany': ['Battery', 'Demand', 'Gas Station', 'Generator', 'Solar Panel'],
    'Unspecified': ['Pipeline', 'Powerline']}
   

4. Group energy system components by their sector

   >>> pprint.pprint(resultier.node_sector_grouped)
   {'Power': ['Battery', 'Demand', 'Gas Station', 'Generator', 'Solar Panel'],
    'Unspecified': ['Pipeline', 'Powerline']}
   

5. Group energy system components by their node_type:

   >>> pprint.pprint(resultier.node_type_grouped)
   {'Demand': ['Demand'],
    'Renewable': ['Solar Panel'],
    'Source': ['Gas Station'],
    'Storage': ['Battery'],
    'Transformer': ['Generator'],
    'Unspecified': ['Pipeline', 'Powerline']}
   

6. Group energy system components by their energy carrier:

   >>> pprint.pprint(resultier.node_carrier_grouped)
   {'Electricity': ['Battery', 'Demand', 'Generator', 'Powerline', 'Solar Panel'],
    'Fuel': ['Pipeline'],
    'Gas': ['Gas Station']}
   

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

   >>> pprint.pprint(resultier.node_energy_carriers)
   {'Battery': 'electricity',
    'Demand': 'electricity',
    'Gas Station': 'Gas',
    'Generator': 'electricity',
    'Pipeline': 'fuel',
    'Powerline': 'electricity',
    'Solar Panel': 'electricity'}
   

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

   >>> pprint.pprint(resultier.node_components)
   {'Bus': ['Pipeline', 'Powerline'],
    'Sink': ['Demand'],
    'Source': ['Gas Station', 'Solar Panel'],
    'Storage': ['Battery'],
    'Transformer': ['Generator']}
   

Note how the many of the uid’s data from busses (same for connectors) are unspecified due to their implementation in calliope not as a technologie but a combination of empty location and transmissions between each location.

class tessif_calliope_0_6_6post1.post_process.FlowResultier(optimized_es, **kwargs)

Bases: tessif.post_process.FlowResultier, tessif_calliope_0_6_6post1.post_process.LoadResultier

Transforming flow results into dictionairies keyed by edges.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Note

Calliope can not assign different costs and emissions for each output of a CHP. To take those costs and emissions into account they are added to the input costs and emissions.

Examples

1. Calling and optimize a calliope energy system model.

>>> from tessif.frused.paths import example_dir
>>> import calliope
>>> calliope.set_log_verbosity('ERROR', include_solver_output=False)
>>> calliope_es = calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
>>> calliope_es.run()
>>> import tessif.transform.es2mapping.cllp as calliope_post_process
>>> import pprint

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

>>> resultier = calliope_post_process.FlowResultier(calliope_es)
>>> pprint.pprint(resultier.edge_net_energy_flow)
{Edge(source='Battery', target='Powerline'): 8.9,
 Edge(source='Gas Station', target='Pipeline'): 7.38,
 Edge(source='Generator', target='Powerline'): 3.1,
 Edge(source='Pipeline', target='Generator'): 7.38,
 Edge(source='Powerline', target='Battery'): 1.0,
 Edge(source='Powerline', target='Demand'): 33.0,
 Edge(source='Solar Panel', target='Powerline'): 22.0}

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

>>> pprint.pprint(resultier.edge_total_costs_incurred)
{Edge(source='Battery', target='Powerline'): 0.0,
 Edge(source='Gas Station', target='Pipeline'): 73.8,
 Edge(source='Generator', target='Powerline'): 31.0,
 Edge(source='Pipeline', target='Generator'): 0.0,
 Edge(source='Powerline', target='Battery'): 0.0,
 Edge(source='Powerline', target='Demand'): 0.0,
 Edge(source='Solar Panel', target='Powerline'): 0.0}

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

>>> pprint.pprint(resultier.edge_total_emissions_caused)
{Edge(source='Battery', target='Powerline'): 0.0,
 Edge(source='Gas Station', target='Pipeline'): 22.14,
 Edge(source='Generator', target='Powerline'): 31.0,
 Edge(source='Pipeline', target='Generator'): 0.0,
 Edge(source='Powerline', target='Battery'): 0.0,
 Edge(source='Powerline', target='Demand'): 0.0,
 Edge(source='Solar Panel', target='Powerline'): 0.0}

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

>>> pprint.pprint(resultier.edge_specific_flow_costs)
{Edge(source='Battery', target='Powerline'): 0.0,
 Edge(source='Gas Station', target='Pipeline'): 10.0,
 Edge(source='Generator', target='Powerline'): 10.0,
 Edge(source='Pipeline', target='Generator'): 0.0,
 Edge(source='Powerline', target='Battery'): 0,
 Edge(source='Powerline', target='Demand'): 0.0,
 Edge(source='Solar Panel', target='Powerline'): 0.0}

6. Display the specific emission of this energy system:

>>> pprint.pprint(resultier.edge_specific_emissions)
{Edge(source='Battery', target='Powerline'): 0.0,
 Edge(source='Gas Station', target='Pipeline'): 3.0,
 Edge(source='Generator', target='Powerline'): 10.0,
 Edge(source='Pipeline', target='Generator'): 0.0,
 Edge(source='Powerline', target='Battery'): 0,
 Edge(source='Powerline', target='Demand'): 0.0,
 Edge(source='Solar Panel', target='Powerline'): 0.0}

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

>>> pprint.pprint(resultier.edge_weight)
{Edge(source='Battery', target='Powerline'): 0.1,
 Edge(source='Gas Station', target='Pipeline'): 1.0,
 Edge(source='Generator', target='Powerline'): 1.0,
 Edge(source='Pipeline', target='Generator'): 0.1,
 Edge(source='Powerline', target='Battery'): 0.1,
 Edge(source='Powerline', target='Demand'): 0.1,
 Edge(source='Solar Panel', target='Powerline'): 0.1}

8. Access the reference emissions and net energy flow:

>>> print(resultier.edge_reference_emissions)
10.0

>>> print(resultier.edge_reference_net_energy_flow)
33.0

class tessif_calliope_0_6_6post1.post_process.AllResultier(optimized_es, **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CapacityResultier, tessif_calliope_0_6_6post1.post_process.FlowResultier, tessif_calliope_0_6_6post1.post_process.StorageResultier, tessif_calliope_0_6_6post1.post_process.ScaleResultier

Transform energy system results into a dictionary keyed by attribute.

Incorporates all the functionalities from its bases.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as 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_calliope_0_6_6post1.post_process.LabelFormatier(optimized_es, **kwargs)

Bases: tessif.post_process.LabelFormatier, tessif_calliope_0_6_6post1.post_process.FlowResultier, tessif_calliope_0_6_6post1.post_process.CapacityResultier

Generate component summaries as multiline label dictionairy entries.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Examples

1. Calling and optimize a calliope energy system model.

   >>> from tessif.frused.paths import example_dir
   >>> import calliope as native_calliope
   >>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
   >>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
   >>> calliope_es.run()
   

2. Compile and display a node’s summary:

   >>> import tessif.transform.es2mapping.cllp as calliope_post_process
   >>> formatier = calliope_post_process.LabelFormatier(calliope_es)
   >>> print(formatier.node_summaries['Demand'])
   {'Demand': 'Demand\n11 MW\ncf: 1.0'}
   

2. Compile and display an edge’s summary:

   >>> print(formatier.edge_summaries[('Solar Panel', 'Powerline')])
   {('Solar Panel', 'Powerline'): '22 MWh\n0.0 €/MWh\n0.0 t/MWh'}
   

class tessif_calliope_0_6_6post1.post_process.NodeFormatier(optimized_es, cgrp='name', **kwargs)

Bases: tessif.post_process.NodeFormatier, tessif_calliope_0_6_6post1.post_process.CapacityResultier

Transforming energy system results into node visuals.

Parameters

• optimized_es (Model) – An optimized calliope energy system containing its inputs as well as 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.

See also

For functionality documentation see the respective base class.

Examples

1. Calling and optimize a calliope energy system model.

   >>> from tessif.frused.paths import example_dir
   >>> import calliope as native_calliope
   >>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
   >>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
   >>> calliope_es.run()
   

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

   >>> import tessif.transform.es2mapping.cllp as calliope_post_process
   >>> import pprint
   >>> formatier = calliope_post_process.NodeFormatier(calliope_es)
   >>> pprint.pprint(formatier.node_shape)
   {'Battery': 's',
    'Demand': '8',
    'Gas Station': 'o',
    'Generator': '8',
    'Pipeline': 'o',
    'Powerline': 'o',
    'Solar Panel': 's'}
   

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

   >>> pprint.pprint(formatier.node_size)
   {'Battery': 300,
    'Demand': 330,
    'Gas Station': 3000,
    'Generator': 450,
    'Pipeline': 'variable',
    'Powerline': 'variable',
    'Solar Panel': 600}
   

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

   >>> pprint.pprint(formatier.node_fill_size)
   {'Battery': 110.0,
    'Demand': 330.0,
    'Gas Station': 74.0,
    'Generator': 31.0,
    'Pipeline': None,
    'Powerline': None,
    'Solar Panel': 220.0}
   

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

   >>> formatier = calliope_post_process.NodeFormatier(calliope_es, cgrp='all')
   >>> pprint.pprint(formatier.node_color.name)
   {'Battery': '#ccff00',
    'Demand': '#330099',
    'Gas Station': '#336666',
    'Generator': '#ff6600',
    'Pipeline': '#336666',
    'Powerline': '#ffcc00',
    'Solar Panel': '#ff9900'}
   

   >>> pprint.pprint(formatier.node_color.carrier)
   {'Battery': '#FFD700',
    'Demand': '#FFD700',
    'Gas Station': '#336666',
    'Generator': '#FFD700',
    'Pipeline': '#AFAFAF',
    'Powerline': '#FFD700',
    'Solar Panel': '#FFD700'}
   

   >>> pprint.pprint(formatier.node_color.sector)
   {'Battery': '#ffff33',
    'Demand': '#ffff33',
    'Gas Station': '#ffff33',
    'Generator': '#ffff33',
    'Pipeline': '#AFAFAF',
    'Powerline': '#AFAFAF',
    'Solar Panel': '#ffff33'}
   

6. 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:

   >>> pprint.pprint({k: type(v)
   ...     for k,v in formatier.node_color_maps.name.items()})
   {'Battery': <class 'str'>,
    'Demand': <class 'str'>,
    'Gas Station': <class 'str'>,
    'Generator': <class 'str'>,
    'Pipeline': <class 'str'>,
    'Powerline': <class 'str'>,
    'Solar Panel': <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_calliope_0_6_6post1.post_process.MplLegendFormatier(optimized_es, cgrp='all', markers='formatier', **kwargs)

Bases: tessif.post_process.MplLegendFormatier, tessif_calliope_0_6_6post1.post_process.CapacityResultier

Generating visually enhanced matplotlib legends for nodes and edges.

Parameters

• optimized_es (Model) – An optimized calliope energy system containing its inputs as well as 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.

• 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. Calling and optimize a calliope energy system model.

   >>> from tessif.frused.paths import example_dir
   >>> import calliope as native_calliope
   >>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
   >>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
   >>> calliope_es.run()
   

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

   >>> import tessif.transform.es2mapping.cllp as calliope_post_process
   >>> import pprint
   >>> formatier = calliope_post_process.MplLegendFormatier(calliope_es)
   >>> pprint.pprint(formatier.node_legend.name['legend_labels'])
   ['Battery',
    'Demand',
    'Gas Station',
    'Generator',
    'Pipeline',
    'Powerline',
    'Solar Panel']
   

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

   >>> 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_calliope_0_6_6post1.post_process.EdgeFormatier(optimized_es, **kwargs)

Bases: tessif.post_process.EdgeFormatier, tessif_calliope_0_6_6post1.post_process.FlowResultier

Transforming energy system results into edge visuals.

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as results.

See also

For functionality documentation see the respective base class.

Examples

1. Calling and optimize a calliope energy system model.

   >>> from tessif.frused.paths import example_dir
   >>> import calliope as native_calliope
   >>> native_calliope.set_log_verbosity('ERROR', include_solver_output=False)
   >>> calliope_es = native_calliope.Model(f'{example_dir}/data/calliope/fpwe/model.yaml')
   >>> calliope_es.run()
   

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

   >>> import tessif.transform.es2mapping.cllp as calliope_post_process
   >>> import pprint
   >>> formatier = calliope_post_process.EdgeFormatier(calliope_es)
   >>> pprint.pprint(list(sorted(formatier.edge_width.items())))
   [(Edge(source='Battery', target='Powerline'), 0.27),
    (Edge(source='Gas Station', target='Pipeline'), 0.22),
    (Edge(source='Generator', target='Powerline'), 0.1),
    (Edge(source='Pipeline', target='Generator'), 0.22),
    (Edge(source='Powerline', target='Battery'), 0.1),
    (Edge(source='Powerline', target='Demand'), 1.0),
    (Edge(source='Solar Panel', target='Powerline'), 0.67)]
   

   >>> dc_formatier = calliope_post_process.EdgeFormatier(
   ...     calliope_es,
   ...     drawutil="dc")
   >>> pprint.pprint(list(sorted(dc_formatier.edge_width.items())))
   [(Edge(source='Battery', target='Powerline'), 1.89),
    (Edge(source='Gas Station', target='Pipeline'), 1.57),
    (Edge(source='Generator', target='Powerline'), 0.66),
    (Edge(source='Pipeline', target='Generator'), 1.57),
    (Edge(source='Powerline', target='Battery'), 0.5),
    (Edge(source='Powerline', target='Demand'), 7.0),
    (Edge(source='Solar Panel', target='Powerline'), 4.67)]
   

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

   >>> pprint.pprint(list(sorted(formatier.edge_color.items())))
   [(Edge(source='Battery', target='Powerline'), [0.15]),
    (Edge(source='Gas Station', target='Pipeline'), [0.3]),
    (Edge(source='Generator', target='Powerline'), [1.0]),
    (Edge(source='Pipeline', target='Generator'), [0.15]),
    (Edge(source='Powerline', target='Battery'), [0.15]),
    (Edge(source='Powerline', target='Demand'), [0.15]),
    (Edge(source='Solar Panel', target='Powerline'), [0.15])]
   

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

   >>> pprint.pprint(list(sorted(dc_formatier.edge_color.items())))
   [(Edge(source='Battery', target='Powerline'), '#d8d8d8'),
    (Edge(source='Gas Station', target='Pipeline'), '#b2b2b2'),
    (Edge(source='Generator', target='Powerline'), '#000000'),
    (Edge(source='Pipeline', target='Generator'), '#d8d8d8'),
    (Edge(source='Powerline', target='Battery'), '#d8d8d8'),
    (Edge(source='Powerline', target='Demand'), '#d8d8d8'),
    (Edge(source='Solar Panel', target='Powerline'), '#d8d8d8')]
   

class tessif_calliope_0_6_6post1.post_process.AllFormatier(optimized_es, cgrp='all', markers='formatier', **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.LabelFormatier, tessif_calliope_0_6_6post1.post_process.NodeFormatier, tessif_calliope_0_6_6post1.post_process.MplLegendFormatier, tessif_calliope_0_6_6post1.post_process.EdgeFormatier

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

Parameters

• optimized_es (Model) – An optimized calliope energy system containing its inputs as well as 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 dictionary.

  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

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_calliope_0_6_6post1.post_process.ICRHybridier(optimized_es, colored_by='name', **kwargs)

Bases: tessif_calliope_0_6_6post1.post_process.CalliopeResultier, tessif.post_process.ICRHybridier

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

Parameters

optimized_es (Model) – An optimized calliope energy system containing its inputs as well as 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.

Characteristic value in this context means:

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

  • Source objects ( generator in pypsa)

  • Sink objects ( load in pypsa)

  • Transformer objects ( generator in pypsa)

  • Connector objects ( link or transformer in pypsa)

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

  • Storage ( generator in pypsa)

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]).