topotherm.simultaneity

Calculate the simultaneity factor of a given graph of the optimal district heating network.

Functions

`calculate`(graph)	Calculate the simultaneity factor of a given graph of the optimal district
`compute_laplacian`(graph)	Compute the Laplacian matrix of a given graph.
`resolve_loops`(graph, laplace)	Resolve loops in the graph and update the Laplacian matrix accordingly.
`resolve_multi_connections`(G_copy, laplace)	Resolves multiple incoming connections for nodes in a directed graph by propagating 'connections' attributes
`get_n_end_consumers`(graph, laplace)	Identify and mark end consumers in the graph based on the Laplacian matrix.
`calculate_simultaneity_factor`(graph)	Calculate the simultaneity factor for edges in the graph.
`update_data`(df_nodes, df_edges, network)	Update the edge and node data with simultaneity attributes.

Module Contents

topotherm.simultaneity.calculate(graph)[source]

Calculate the simultaneity factor of a given graph of the optimal district heating network.

Args:: graph: A NetworkX graph object representing the network.
Returns:: nx.DiGraph: A graph containing the simultaneity factors of the graph in the attribute ‘simultaneity’.

Parameters:: graph (networkx.DiGraph)
Return type:: networkx.DiGraph

topotherm.simultaneity.compute_laplacian(graph)[source]

Compute the Laplacian matrix of a given graph.

Args:: graph: A NetworkX graph object of the solved network connections.
Returns:: pd.DataFrame: A pandas DataFrame representing the Laplacian matrix.

Parameters:: graph (networkx.DiGraph)
Return type:: numpy.array

topotherm.simultaneity.resolve_loops(graph, laplace)[source]

Resolve loops in the graph and update the Laplacian matrix accordingly.

Args:: graph: A NetworkX graph object. laplace: The Laplacian matrix of the graph.
Returns:: A tuple containing the updated graph and Laplacian matrix.

Parameters:

graph (networkx.DiGraph)
laplace (pandas.DataFrame)

Return type:

tuple[networkx.DiGraph, numpy.array]

topotherm.simultaneity.resolve_multi_connections(G_copy, laplace)[source]

Resolves multiple incoming connections for nodes in a directed graph by propagating ‘connections’ attributes and updating the Laplacian matrix accordingly.

Parameters: G_copy: A copy of the directed graph from the previous step. laplace: The Laplacian matrix to be updated.

Returns: laplace: Updated Laplacian matrix with modified diagonal elements.

Parameters:

G_copy (networkx.DiGraph)
laplace (numpy.array)

Return type:

numpy.array

topotherm.simultaneity.get_n_end_consumers(graph, laplace)[source]

Identify and mark end consumers in the graph based on the Laplacian matrix.

Args:: graph: A NetworkX graph object. laplace: The Laplacian matrix of the graph.
Returns:: The graph with ‘n_consumers’ attributes added to nodes.

Parameters:

graph (networkx.DiGraph)
laplace (numpy.array)

Return type:

networkx.DiGraph

topotherm.simultaneity.calculate_simultaneity_factor(graph)[source]

Calculate the simultaneity factor for edges in the graph.

Args:: graph: A NetworkX graph object with number of end consumers marked as n_consumers attributes.
Returns:: nx.DiGraph: A graph that has edge names and values for the simultaneity factor

Parameters:: graph (networkx.DiGraph)
Return type:: networkx.DiGraph

topotherm.simultaneity.update_data(df_nodes, df_edges, network)[source]

Update the edge and node data with simultaneity attributes.

Args:: node_data: A DataFrame containing edge data. edge_data: A DataFrame containing node data. network_simultaneity: A NetworkX graph object with simultaneity factors, needs to contain power, simultaneity attributes
Returns:: tuple[pd.DataFrame, pd.DataFrame]: Updated edge and node data DataFrames.

Parameters:

df_nodes (pandas.DataFrame)
df_edges (pandas.DataFrame)
network (networkx.DiGraph)

Return type:

tuple[pandas.DataFrame, pandas.DataFrame]