How to visualize provenance#
Note
This tutorial can be downloaded and run as a Jupyter Notebook: visualising_graphs.ipynb
The provenance graph of a database can be visually inspected, via graphviz, using both the python API and command-line interface.
See also
verdi graph generate -h
We first load a profile, containing the provenance graph (in this case we load an archive as the profile).
from aiida import load_profile
from aiida.common import LinkType
from aiida.orm import LinkPair
from aiida.storage.sqlite_zip import SqliteZipBackend
from aiida.tools.visualization import Graph, pstate_node_styles
profile = load_profile(SqliteZipBackend.create_profile('include/graph1.aiida'))
/home/docs/checkouts/readthedocs.org/user_builds/aiida-core/envs/v2.0.0/lib/python3.8/site-packages/paramiko/transport.py:236: CryptographyDeprecationWarning: Blowfish has been deprecated
"class": algorithms.Blowfish,
dict1_uuid = '0ea79a16-501f-408a-8c84-a2704a778e4b'
calc1_uuid = 'b23e692e-4e01-48dd-b515-4c63877d73a4'
The Graph class is used to store visual representations of the nodes and edges, which can be added separately or cumulatively by one of the graph traversal methods.
The graphviz attribute returns a graphviz.Digraph instance, which will auto-magically render the graph in the notebook, or can be used to save the graph to file.
graph = Graph()
graph.add_node(dict1_uuid)
graph.add_node(calc1_uuid)
graph.graphviz
graph.add_edge(
dict1_uuid, calc1_uuid,
link_pair=LinkPair(LinkType.INPUT_CALC, "input1"))
graph.graphviz
graph.add_incoming(calc1_uuid)
graph.add_outgoing(calc1_uuid)
graph.graphviz
The Graph can also be initialized with global style attributes,
as outlined in the graphviz attributes table.
graph = Graph(node_id_type="uuid",
global_node_style={"penwidth": 1},
global_edge_style={"color": "blue"},
graph_attr={"size": "8!,8!", "rankdir": "LR"})
graph.add_incoming(calc1_uuid)
graph.add_outgoing(calc1_uuid)
graph.graphviz
Additionally functions can be parsed to the Graph initializer, to specify exactly how each node will be represented. For example, the pstate_node_styles() function colors process nodes by their process state.
def link_style(link_pair, **kwargs):
return {"color": "blue"}
graph = Graph(node_style_fn=pstate_node_styles,
link_style_fn=link_style,
graph_attr={"size": "8!,8!", "rankdir": "LR"})
graph.add_incoming(calc1_uuid)
graph.add_outgoing(calc1_uuid)
graph.graphviz
Edges can be annotated by one or both of their edge label and link type.
graph = Graph(graph_attr={"size": "8!,8!", "rankdir": "LR"})
graph.add_incoming(calc1_uuid,
annotate_links="both")
graph.add_outgoing(calc1_uuid,
annotate_links="both")
graph.graphviz
The recurse_descendants() and recurse_ancestors() methods can be used to construct a full provenance graph.
graph = Graph(graph_attr={"size": "8!,8!", "rankdir": "LR"})
graph.recurse_descendants(
dict1_uuid,
origin_style=None,
include_process_inputs=True,
annotate_links="both"
)
graph.graphviz
The link types can also be filtered, to view only the ‘data’ or ‘logical’ provenance.
graph = Graph(graph_attr={"size": "8,8!", "rankdir": "LR"})
graph.recurse_descendants(
dict1_uuid,
origin_style=None,
include_process_inputs=True,
annotate_links="both",
link_types=("input_calc", "create")
)
graph.graphviz
graph = Graph(graph_attr={"size": "8,8!", "rankdir": "LR"})
graph.recurse_descendants(
dict1_uuid,
origin_style=None,
include_process_inputs=True,
annotate_links="both",
link_types=("input_work", "return")
)
graph.graphviz
If you wish to highlight specific node classes,
then the highlight_classes option can be used
to only color specified nodes:
graph = Graph(graph_attr={"size": "20,20", "rankdir": "LR"})
graph.recurse_descendants(
dict1_uuid,
highlight_classes=['Dict']
)
graph.graphviz
