Attributes and views¶

Attributes are arbitrary metadata on graph entities. Views expose graph state as dataframe-like tables.

In [1]:

import annnet as an

import annnet as an

Build a graph with metadata¶

In [2]:

G = an.AnnNet(directed=True)
G.add_vertices(['A', 'B', 'C', 'D', 'E'])
G.add_edges('A', 'B', edge_id='e1', weight=1.0)
G.add_edges('A', 'B', edge_id='e1_backup', weight=0.65)
G.add_edges('B', 'C', edge_id='e2', weight=2.0)
G.add_edges('C', 'D', edge_id='e3', weight=1.5)
G.add_edges('C', 'E', edge_id='complex_ce', directed=False, weight=0.8)

G.attrs.set_vertex_attrs('A', label='alpha', tier=1)
G.attrs.set_vertex_attrs_bulk(
    {
        'B': {'label': 'beta', 'tier': 1},
        'C': {'label': 'gamma', 'tier': 2},
        'D': {'label': 'delta', 'tier': 2},
        'E': {'label': 'epsilon', 'tier': 2},
    }
)
G.attrs.set_edge_attrs_bulk(
    {
        'e1': {'interaction': 'activation', 'confidence': 0.90},
        'e1_backup': {'interaction': 'activation', 'confidence': 0.65},
        'e2': {'interaction': 'inhibition', 'confidence': 0.70},
        'e3': {'interaction': 'activation', 'confidence': 0.95},
        'complex_ce': {'interaction': 'binding', 'confidence': 0.85},
    }
)
G.uns['study'] = 'demo'

G = an.AnnNet(directed=True) G.add_vertices(['A', 'B', 'C', 'D', 'E']) G.add_edges('A', 'B', edge_id='e1', weight=1.0) G.add_edges('A', 'B', edge_id='e1_backup', weight=0.65) G.add_edges('B', 'C', edge_id='e2', weight=2.0) G.add_edges('C', 'D', edge_id='e3', weight=1.5) G.add_edges('C', 'E', edge_id='complex_ce', directed=False, weight=0.8) G.attrs.set_vertex_attrs('A', label='alpha', tier=1) G.attrs.set_vertex_attrs_bulk( { 'B': {'label': 'beta', 'tier': 1}, 'C': {'label': 'gamma', 'tier': 2}, 'D': {'label': 'delta', 'tier': 2}, 'E': {'label': 'epsilon', 'tier': 2}, } ) G.attrs.set_edge_attrs_bulk( { 'e1': {'interaction': 'activation', 'confidence': 0.90}, 'e1_backup': {'interaction': 'activation', 'confidence': 0.65}, 'e2': {'interaction': 'inhibition', 'confidence': 0.70}, 'e3': {'interaction': 'activation', 'confidence': 0.95}, 'complex_ce': {'interaction': 'binding', 'confidence': 0.85}, } ) G.uns['study'] = 'demo'

Query attributes¶

In [3]:

edge_names = {
    row['edge_id']: f"{row['source']} -> {row['target']}"
    if row['directed']
    else f"{row['source']} -- {row['target']}"
    for row in G.views.edges().iter_rows(named=True)
}
activation_edges = G.attrs.get_edges_by_attr('interaction', 'activation')

print('A attrs:', G.attrs.get_vertex_attrs('A'))
print('activation edges:', [edge_names[eid] for eid in sorted(activation_edges)])
print('graph attrs:', G.attrs.get_graph_attributes())

edge_names = { row['edge_id']: f"{row['source']} -> {row['target']}" if row['directed'] else f"{row['source']} -- {row['target']}" for row in G.views.edges().iter_rows(named=True) } activation_edges = G.attrs.get_edges_by_attr('interaction', 'activation') print('A attrs:', G.attrs.get_vertex_attrs('A')) print('activation edges:', [edge_names[eid] for eid in sorted(activation_edges)]) print('graph attrs:', G.attrs.get_graph_attributes())

A attrs: {'label': 'alpha', 'tier': 1}
activation edges: ['A -> B', 'A -> B', 'C -> D']
graph attrs: {'study': 'demo'}

View tables¶

In [4]:

G.views.vertices()

G.views.vertices()

Out[4]:

shape: (5, 3)

vertex_id	label	tier
str	str	i64
"A"	"alpha"	1
"B"	"beta"	1
"C"	"gamma"	2
"D"	"delta"	2
"E"	"epsilon"	2

In [5]:

G.views.edges().select(
    ['edge_id', 'source', 'target', 'interaction', 'confidence']
)

G.views.edges().select( ['edge_id', 'source', 'target', 'interaction', 'confidence'] )

Out[5]:

shape: (5, 5)

edge_id	source	target	interaction	confidence
str	str	str	str	f64
"e1"	"A"	"B"	"activation"	0.9
"e1_backup"	"A"	"B"	"activation"	0.65
"e2"	"B"	"C"	"inhibition"	0.7
"e3"	"C"	"D"	"activation"	0.95
"complex_ce"	"C"	"E"	"binding"	0.85

Visual check¶

The drawing uses node IDs for readability and annotates edges with compact numeric weights plus the selected edge metadata.

In [6]:

from annnet.utils import plotting

plotting.plot(
    G,
    backend='graphviz',
    show_edge_labels=True,
    edge_label_keys=['interaction'],
)

from annnet.utils import plotting plotting.plot( G, backend='graphviz', show_edge_labels=True, edge_label_keys=['interaction'], )

Out[6]:

Reserved keys are protected¶

In [7]:

try:
    G.attrs.set_edge_attrs('e1', source='X')
except ValueError as err:
    print('caught:', err)

try: G.attrs.set_edge_attrs('e1', source='X') except ValueError as err: print('caught:', err)

caught: edge attributes use reserved key(s): ['source']. These names are part of the structural / dispatch contract; rename your attribute(s) to use a different key.

Views are the inspection layer for the same annotated graph object; no separate table has to be kept in sync.