hadar.optimizer package

Submodules

hadar.optimizer.input module

class hadar.optimizer.input.Consumption(quantity: Union[List[T], numpy.ndarray, float], cost: Union[List[T], numpy.ndarray, float], name: str = '')

Bases: hadar.optimizer.input.JSON

Consumption element.

static from_json(dict)

Bases: hadar.optimizer.input.JSON

Link element

static from_json(dict)
class hadar.optimizer.input.Production(quantity: Union[List[T], numpy.ndarray, float], cost: Union[List[T], numpy.ndarray, float], name: str = 'in')

Bases: hadar.optimizer.input.JSON

Production element

static from_json(dict)
class hadar.optimizer.input.Storage(name, capacity: int, flow_in: float, flow_out: float, cost: float = 0, init_capacity: int = 0, eff: float = 0.99)

Bases: hadar.optimizer.input.JSON

Storage element

static from_json(dict)
class hadar.optimizer.input.Converter(name: str, src_ratios: Dict[Tuple[str, str], float], dest_network: str, dest_node: str, cost: float, max: float)

Bases: hadar.optimizer.input.JSON

Converter element

static from_json(dict: dict)
to_json() → dict
class hadar.optimizer.input.InputNetwork(nodes: Dict[str, hadar.optimizer.input.InputNode] = None)

Bases: hadar.optimizer.input.JSON

Network element

static from_json(dict)
class hadar.optimizer.input.InputNode(consumptions: List[hadar.optimizer.input.Consumption], productions: List[hadar.optimizer.input.Production], storages: List[hadar.optimizer.input.Storage], links: List[hadar.optimizer.input.Link])

Bases: hadar.optimizer.input.JSON

Node element

static from_json(dict)
class hadar.optimizer.input.Study(horizon: int, nb_scn: int = 1, version: str = None)

Bases: hadar.optimizer.input.JSON

Main object to facilitate to build a study

Add a link inside network.

Parameters:
  • network – network where nodes belong
  • src – source node name
  • dest – destination node name
  • cost – cost of use
  • quantity – transfer capacity
Returns:
add_network(network: str)
add_node(network: str, node: str)
static from_json(dict)
network(name='default')

Entry point to create study with the fluent api.

Returns:
class hadar.optimizer.input.NetworkFluentAPISelector(study, selector)

Bases: object

Network level of Fluent API Selector.

build()

Build study.

Returns:return study
converter(name: str, to_network: str, to_node: str, max: float, cost: float = 0)

Add a converter element.

Parameters:
  • name – converter name
  • to_network – converter output network
  • to_node – converter output node on network
  • max – maximum quantity from converter
  • cost – cost for each quantity produce by converter
Returns:

Add a link on network.

Parameters:
  • src – node source
  • dest – node destination
  • cost – unit cost transfer
  • quantity – available capacity
Returns:

NetworkAPISelector with new link.
network(name='default')

Go to network level.

Parameters:name – network level, ‘default’ as default name
Returns:NetworkAPISelector with selector set to ‘default’
node(name)

Go to node level.

Parameters:name – node to select when changing level
Returns:NodeFluentAPISelector initialized
class hadar.optimizer.input.NodeFluentAPISelector(study, selector)

Bases: object

Node level of Fluent API Selector

build()

Build study.

Returns:study
consumption(name: str, cost: int, quantity: Union[List[T], numpy.ndarray, float])

Add consumption on node.

Parameters:
  • name – consumption name
  • cost – cost of unsuitability
  • quantity – consumption to sustain
Returns:

NodeFluentAPISelector with new consumption
converter(name: str, to_network: str, to_node: str, max: float, cost: float = 0)

Add a converter element.

Parameters:
  • name – converter name
  • to_network – converter output network
  • to_node – converter output node on network
  • max – maximum quantity from converter
  • cost – cost for each quantity produce by converter
Returns:

Add a link on network.

Parameters:
  • src – node source
  • dest – node destination
  • cost – unit cost transfer
  • quantity – available capacity
Returns:

NetworkAPISelector with new link.
network(name='default')

Go to network level.

Parameters:name – network level, ‘default’ as default name
Returns:NetworkAPISelector with selector set to ‘default’
node(name)

Go to different node level.

Parameters:name – new node level
Returns:NodeFluentAPISelector
production(name: str, cost: int, quantity: Union[List[T], numpy.ndarray, float])

Add production on node.

Parameters:
  • name – production name
  • cost – unit cost of use
  • quantity – available capacities
Returns:

NodeFluentAPISelector with new production
storage(name, capacity: int, flow_in: float, flow_out: float, cost: float = 0, init_capacity: int = 0, eff: int = 0.99)

Create storage.

Parameters:
  • capacity – maximum storage capacity (like of many quantity to use inside storage)
  • flow_in – max flow into storage during on time step
  • flow_out – max flow out storage during on time step
  • cost – unit cost of storage at each time-step. default 0
  • init_capacity – initial capacity level. default 0
  • eff – storage efficient (applied on input flow stored). default 0.99
to_converter(name: str, ratio: float = 1)

Add an ouptput to converter.

Parameters:
  • name – converter name
  • ratio – ratio for output
Returns:

hadar.optimizer.optimizer module

class hadar.optimizer.optimizer.LPOptimizer

Bases: hadar.optimizer.optimizer.Optimizer

Basic Optimizer works with linear programming.

solve(study: hadar.optimizer.input.Study) → hadar.optimizer.output.Result

Solve adequacy study.

Parameters:study – study to resolve
Returns:study’s result
class hadar.optimizer.optimizer.RemoteOptimizer(url: str, token: str = '')

Bases: hadar.optimizer.optimizer.Optimizer

Use a remote optimizer to compute on cloud.

solve(study: hadar.optimizer.input.Study) → hadar.optimizer.output.Result

Solve adequacy study.

Parameters:study – study to resolve
Returns:study’s result

hadar.optimizer.output module

class hadar.optimizer.output.OutputProduction(quantity: Union[numpy.ndarray, list], cost: Union[numpy.ndarray, list], name: str = 'in')

Bases: hadar.optimizer.input.JSON

Production element

static from_json(dict)
class hadar.optimizer.output.OutputNode(consumptions: List[hadar.optimizer.output.OutputConsumption], productions: List[hadar.optimizer.output.OutputProduction], storages: List[hadar.optimizer.output.OutputStorage], links: List[hadar.optimizer.output.OutputLink])

Bases: hadar.optimizer.input.JSON

Node element

static build_like_input(input: hadar.optimizer.input.InputNode, fill: numpy.ndarray)

Use an input node to create an output node. Keep list elements fill quantity by zeros.

Parameters:
  • input – InputNode to copy
  • fill – array to use to fill data
Returns:

OutputNode like InputNode with all quantity at zero
static from_json(dict)
class hadar.optimizer.output.OutputStorage(name: str, capacity: Union[numpy.ndarray, list], flow_in: Union[numpy.ndarray, list], flow_out: Union[numpy.ndarray, list])

Bases: hadar.optimizer.input.JSON

Storage element

static from_json(dict)

Bases: hadar.optimizer.input.JSON

Link element

static from_json(dict)
class hadar.optimizer.output.OutputConsumption(quantity: Union[numpy.ndarray, list], cost: Union[numpy.ndarray, list], name: str = '')

Bases: hadar.optimizer.input.JSON

Consumption element

static from_json(dict)
class hadar.optimizer.output.OutputNetwork(nodes: Dict[str, hadar.optimizer.output.OutputNode])

Bases: hadar.optimizer.input.JSON

Network element

static from_json(dict)
class hadar.optimizer.output.OutputConverter(name: str, flow_src: Dict[Tuple[str, str], Union[numpy.ndarray, List[T]]], flow_dest: Union[numpy.ndarray, List[T]])

Bases: hadar.optimizer.input.JSON

Converter element

static from_json(dict: dict)
to_json() → dict
class hadar.optimizer.output.Result(networks: Dict[str, hadar.optimizer.output.OutputNetwork], converters: Dict[str, hadar.optimizer.output.OutputConverter])

Bases: hadar.optimizer.input.JSON

Result of study

static from_json(dict)

Module contents