netflow.checks#

Functions

`check_connected_graph`(G)	Raises an AssertionError if the graph is not connected
`check_distance_matrix`(A)	Raises AssertionError if the distance matrix is not non-negative and symmetric.
`check_edges_in_graph`(G, edges)	Raises AssertionError if not all edges are in the graph.
`check_graph_no_self_loops`(G)	Raises an AssertionError if the graph has self-loops
`check_graph_weights`(G, component, weight[, tol])	Raises an AssertionError if there are weights below the tolerance
`check_matrix_no_nan`(A)	Raises AssertionError if the matrix has any missing values.
`check_matrix_nonnegative`(A)	Raises AssertionError if the matrix has negative values.
`check_symmetric`(A)	Raises AssertionError if the matrix is not symmetric.

netflow.checks.check_connected_graph( G, )[source]#: Raises an AssertionError if the graph is not connected

netflow.checks.check_distance_matrix( A, )[source]#

Raises AssertionError if the distance matrix is not non-negative and symmetric.

Note: This does not require the diagonal to be zeros.

netflow.checks.check_edges_in_graph( G, edges, )[source]#: Raises AssertionError if not all edges are in the graph.

netflow.checks.check_graph_no_self_loops( G, )[source]#: Raises an AssertionError if the graph has self-loops

netflow.checks.check_graph_weights( G, component, weight, tol=1e-06, )[source]#

Raises an AssertionError if there are weights below the tolerance

Parameters:

G (networkx graph) – The graph.
component ({'node', 'edge'}) – Component of graph the weights are attributed to.
weight (str) – Graph attribute of the weight.
tol (float, default = TOL) – The tolerance to check that no weights are below tol.

netflow.checks.check_matrix_no_nan( A, )[source]#: Raises AssertionError if the matrix has any missing values.

netflow.checks.check_matrix_nonnegative( A, )[source]#: Raises AssertionError if the matrix has negative values.

netflow.checks.check_symmetric( A, )[source]#: Raises AssertionError if the matrix is not symmetric.