Link and Node Dictionaries

After defining a network in RivGraph with rivgraph.classes.rivnetwork.compute_network(), two dictionaries will be created: links and nodes. This page of the documentation will describe the keys present in these dictionaries.

Note

Not all dictionary key:value pairs are the same length as the number of links or nodes. Some dictionary key:value pairs contain meta-information about the network, or information that only applies to a subset of links/nodes. When the number of key:value pairs matches the number of links or nodes, then they are aligned such that the i’th index of any key:value pair refers to the same link or node.

• The Links Dictionary

• The Nodes Dictionary

The Links Dictionary

Links Key Values

N represents the number of links.

Generic Link Keys

key	type	length	description
id	orderedSet	N	unique link id assigned to each link not in a particular order and not guaranteed to skip some id’s
conn	list of 2-element lists	N	node ids at link endpoints. After running directionality these are in order i.e. [us_nodeid ds_nodeid]
idx	list of lists of ints	N	list of the pixel coordinates in index format (via np.ravel_index using the shape of the input binary image)
n_networks	int	1	number of networks identified
parallels	list of lists	varies	each sublist contains all link ids of links forming a parallel set; i.e. links that start and end at the same node.
wid_pix	list of np.arrays	N	width at each pixel of each link (pixels defined by idx)
len	list	N	length of each link
wid	list	N	average width of each link
len_adj	list	N	length of each link adjusted for accuracy
wid_adj	list	N	width of each link adjusted for accuracy

Directionality-specific Keys

key	type	length	description
certain	np.array	N	1 if the link’s direction has been set else 0
certain_order	np.array	N	describes the order in which links were set. Lower certain_order corresponds to earlier-set links.
certain_alg	np.array	N	algorithm id of the algorithm ultimately used to set the link direction
guess	list of lists	N	guesses of the upstream node id for each link; corresponds to guess_alg
guess_alg	list of lists	N	algorithm id used to compute guess; corresponds to guess

Braided River Exclusive Keys

key	type	length	description
maxang	np.array	N	???
cldists	np.array	N	number of mesh-generated transects each link intersects
clangs	np.array	N	angle of the link with respect to the valley centerline in radians
wid_pctdiff	np.array	N	percent difference between the widest and narrowest pixels in a link

Delta Exclusive Keys

key	type	length	description
slope	list	N	slope computed from synthetic DEM; note that this has no physical basis and should not be interpreted as actual link slope

Accessing Link Values

For example, if you know the link_id of the link you are interested in, you can get its index with links['id'].index(link_id).

The Nodes Dictionary

Nodes Key Values

M represents the number of nodes.

Generic Node Keys

key	type	length	description
id	orderedSet	M	unique node id assigned to each node not in a particular order and not guaranteed to skip some id’s
conn	list of lists	M	link ids of all links connected to this node
idx	list of lists of ints	M	list of the pixel coordinates in index format (via np.ravel_index using the shape of the input binary image)
inlets	list of ints	varies	node ids of all nodes identified as inlets
outlets	list of ints	varies	node ids of all nodes identified as outlets
int_angle	np.array	M	most interior angle of links connected to each node in degrees; computed via rivgraph.ln_utils.junction_angles()
jtype	np.array	M	junction type; either ‘c’ (confluence) or ‘b’ (bifurcation); computed via rivgraph.ln_utils.junction_angles()
width_ratio	np.array	M	ratio of wider link/narrower link for the two links used to compute int_angle; computed via rivgraph.ln_utils.junction_angles()

Accessing Node Values

For example, if you wish to find the links connected to node_id == 66, you can use nodes['conn'][nodes['id'].index(66)].