Source code for

The module includes methods to generate network layers
(information that is not stored in the water network model or the graph).

.. rubric:: Contents

.. autosummary::

import numpy as np
import pandas as pd

[docs]def generate_valve_layer(wn, placement_type='strategic', n=1, seed=None): """ Generate valve layer data, which can be used in valve segmentation analysis. Parameters ----------- wn : wntr WaterNetworkModel A WaterNetworkModel object placement_type : string Options include 'strategic' and 'random'. - If 'strategic', n is the number of pipes from each node that do not contain a valve. In this case, n is generally 0, 1 or 2 (i.e. N, N-1, N-2 valve placement). - If 'random', then n randomly placed valves are used to define the valve layer. n : int - If 'strategic', n is the number of pipes from each node that do not contain a valve. - If 'random', n is the number of number of randomly placed valves. seed : int or float Random seed Returns --------- valve_layer : pandas DataFrame Valve layer, defined by node and link pairs (for example, valve 0 is on link A and protects node B). The valve_layer DataFrame is indexed by valve number, with columns named 'node' and 'link'. """ if seed is not None: np.random.seed(seed) valve_layer = [] if placement_type=='random': for pipe_name in np.random.choice(wn.pipe_name_list, n): pipe = wn.get_link(pipe_name) valve_layer.append([pipe_name, pipe.start_node_name]) elif placement_type == 'strategic': for node_name, node in wn.nodes(): links = wn.get_links_for_node(node_name) for l in np.random.choice(links, max(len(links)-n,0), replace=False): valve_layer.append([l, node_name]) valve_layer = pd.DataFrame(valve_layer, columns=['link', 'node']) return valve_layer