Quickstart¶

Build your first AnnNet in five minutes. By the end you'll have:

• A directed graph with four vertices and three edges
• A look at how to inspect it
• A save-and-reload round-trip

Nothing else. Subsequent lessons add metadata, views, slices, etc.

1. Create a graph¶

AnnNet is the central class. Pass directed=True/False at construction.

In [1]:

from annnet import AnnNet

G = AnnNet(directed=True)
G

from annnet import AnnNet G = AnnNet(directed=True) G

Out[1]:

AnnNet object with n_vertices × n_edges = 0 × 0
    directed: True
    slices: ['default']

2. Add vertices¶

Several input shapes are accepted; pass a list of ids for the common case.

In [2]:

G.add_vertices(['A', 'B', 'C', 'D'])
print('vertex count:', G.nv)
print('vertex ids:  ', sorted(G.vertices()))

G.add_vertices(['A', 'B', 'C', 'D']) print('vertex count:', G.nv) print('vertex ids: ', sorted(G.vertices()))

vertex count: 4
vertex ids:   ['A', 'B', 'C', 'D']

3. Add edges¶

Each edge gets an explicit edge_id and an optional weight. The first two positional args are source and target.

In [3]:

G.add_edges('A', 'B', edge_id='e1', weight=1.0)
G.add_edges('B', 'C', edge_id='e2', weight=2.0)
G.add_edges('C', 'D', edge_id='e3', weight=1.5)

print('edge count:', G.ne)
print('shape (nv, ne):', G.shape)

G.add_edges('A', 'B', edge_id='e1', weight=1.0) G.add_edges('B', 'C', edge_id='e2', weight=2.0) G.add_edges('C', 'D', edge_id='e3', weight=1.5) print('edge count:', G.ne) print('shape (nv, ne):', G.shape)

edge count: 3
shape (nv, ne): (4, 3)

4. Inspect¶

The fastest tabular view is G.views.edges(). It produces a dataframe with source/target/weight columns plus any attributes you've attached.

In [4]:

G.views.edges().head()

G.views.edges().head()

Out[4]:

shape: (3, 11)

edge_id	kind	source	target	edge_type	head	tail	members	directed	global_weight	effective_weight
str	str	str	str	str	null	null	null	bool	f64	f64
"e1"	"binary"	"A"	"B"	"binary"	null	null	null	true	1.0	1.0
"e2"	"binary"	"B"	"C"	"binary"	null	null	null	true	2.0	2.0
"e3"	"binary"	"C"	"D"	"binary"	null	null	null	true	1.5	1.5

5. Save and reload¶

.annnet is the native format. Lossless, manifest-aware.

In [5]:

import tempfile
from pathlib import Path
from annnet import read, write

path = Path(tempfile.mkdtemp()) / 'demo.annnet'
write(G, path)

G2 = read(path)
print('reloaded:', G2.shape)

import tempfile from pathlib import Path from annnet import read, write path = Path(tempfile.mkdtemp()) / 'demo.annnet' write(G, path) G2 = read(path) print('reloaded:', G2.shape)

reloaded: (4, 3)

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Next: 02 — Attributes & views.