def update_output_div(graph_json, n_clicks_modal,
layout_name, model_name, graphGenType, actions, tracer, graphGenInput, selected, clickData, hoverData):
ctx = dash.callback_context
if not ctx.triggered:
graph = nx.jit_graph(graph_json)
return graph_json, generateFigure(graph, model_name, dropdown_model, tracer), ''
source = ctx.triggered[0]['prop_id'].split('.')[0]
if source == 'dropdown-layout':
print(f'Changing layout to {layout_name}')
graph = nx.jit_graph(graph_json)
updateLayout(graph, layout_name, layouts)
return graph_json, generateFigure(graph, model_name, dropdown_model, tracer), ''
elif source == 'dropdown-model':
print(f'Changing model type to {model_name}')
graph = nx.jit_graph(graph_json)
updateLayout(graph, layout_name, layouts)
return graph_json, generateFigure(graph, model_name, dropdown_model, tracer), ''
elif source == 'session-actions':
if len(actions) == 0:
raise PreventUpdate()
for action in actions:
executor = actions_exec.get(action[0])
if executor is not None:
function = executor.get(action[1])
if function is not None:
args = action[2] if len(action) > 2 else {}
args['selected'] = selected
args['hover'] = hoverData
args['click'] = clickData
graph = nx.jit_graph(graph_json)
newData = function({'graph': graph},
action[2] if len(action) > 2 else [])
graph = newData['graph']
updateLayout(graph, layout_name, layouts)
return (json.loads(nx.jit_data(graph)),
generateFigure(graph, model_name, dropdown_model, tracer), '')
else:
print(f'Could not find function {action[1]}')
else:
print(f'Could not find executor {action[0]}')
elif source == 'session-tracer':
graph = nx.jit_graph(graph_json)
return graph_json, generateFigure(graph, model_name, dropdown_model, tracer), ''
elif source == 'modal-gen-generate':
graph_gen = graph_gens.get(graphGenType)
if graph_gen is None:
print(f'Could not find graph type {graph_gen}')
else:
inputs = [value if value is not None else graph_gen['argvals'][idx]
for idx, value in enumerate(graphGenInput)]
print(f'Generating new graph with layout {graphGenType} with input {inputs}')
graph = addMinRequirements(graph_gen['gen'](*inputs))
return json.loads(nx.jit_data(graph)), generateFigure(graph, model_name, dropdown_model, tracer), ''
print(f'Could not trigger source: {ctx.triggered}')
raise PreventUpdate
def _parse_ai2d_rst_json(data):
""""
Creates NetworkX graphs from dictionaries loaded from AI2D-RST JSON.
Parameters:
data: A dictionary loaded from AI2D-RST JSON.
Returns:
Grouping, connectivity and discourse graphs as NetworkX graphs
"""
# Separate dictionaries for each layer from the JSON dictionary
grouping_dict_from_json = data['grouping']
conn_dict_from_json = data['connectivity']
rst_dict_from_json = data['rst']
# Create the grouping graph using the nx.jit_graph function
grouping_graph = nx.jit_graph(grouping_dict_from_json,
create_using=nx.DiGraph())
# Check if connectivity annotation exists
if conn_dict_from_json is not None:
# Create connectivity graph manually
connectivity_graph = nx.DiGraph()
# Load nodes and edges
nodes = conn_dict_from_json['nodes']
edges = conn_dict_from_json['edges']
# Add nodes manually to the connectivity graph
for node in nodes:
connectivity_graph.add_node(node[0], kind=node[1]['kind'])
# Add edges manually to the connectivity graph
for e in edges:
connectivity_graph.add_edge(e[0], e[1], kind=e[2]['kind'])
else:
connectivity_graph = None
# Create the RST graph using the nx.jit_graph function
rst_graph = nx.jit_graph(rst_dict_from_json, create_using=nx.DiGraph())
# Return all three graphs
return grouping_graph, connectivity_graph, rst_graph
def send_task(host, n, m, seed, customer):
host = host
user = '******'
port = 22
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
client.connect(hostname=host, username=user, port=port) # connecting
stdin, stdout, stderr = client.exec_command(
'sudo /usr/bin/python3 /home/azureuser/www/Grid_solver.py {} {} {} {}'.
format(n, m, seed, customer)) # sending task and recieving results
print(stderr.read())
#stdin.read()
graph, colors = str(stdout.read()).split('\\n')[0:2] # parsing results
stdin.flush()
graph = json.loads(graph[2:])
colors = json.loads(colors)
print(colors)
G = nx.jit_graph(graph)
nodes = list(G.nodes)
colors = list(map(lambda x: colors[nodes[x]],
range(len(colors)))) # setting colors in right order
nx.draw(G,
node_color=colors,
pos=nx.drawing.layout.kamada_kawai_layout(G),
with_labels=True)
plt.savefig("static/{}_{}_{}.png".format(n, m, seed))
client.close()
)
cur2 = con.cursor()
cur.execute(
"Select cansmiles, Count(atomid) cc From graphs Where iteration > 0 Group By cansmiles Having cc > 1"
)
for row in cur:
cansmi = row[0]
graphs = []
graphid = []
cur2.execute(
"Select graphid, jit_graph From graphs Where atomid Is Not Null and cansmiles=?",
[cansmi])
for grow in cur2:
graphid.append(grow[0])
graphs.append(nx.jit_graph(grow[1]))
ngraphs = len(graphs)
if ngraphs > 1:
nmax = ngraphs * (ngraphs - 1) / 2
niso = 0
for i in range(ngraphs):
g1 = graphs[i]
for j in range(0, i):
g2 = graphs[j]
if is_isomorphic(g1, g2):
cur2.execute(
"Insert Into isomorphs (a_graphid, b_graphid) Values (?,?)",
(graphid[i], graphid[j]))
cur2.execute(
"Insert Into isomorphs (a_graphid, b_graphid) Values (?,?)",
def load_graph_file(options):
with open(options['output'] + '.g', 'r') as file:
content = json.load(file)
d = nx.jit_graph(content, create_using=nx.DiGraph())
return d
See the JIT documentation and examples at https://philogb.github.io/jit/
"""
import json
import matplotlib.pyplot as plt
import networkx as nx
# add some nodes to a graph
G = nx.Graph()
G.add_node("one", type="normal")
G.add_node("two", type="special")
G.add_node("solo")
# add edges
G.add_edge("one", "two")
G.add_edge("two", 3, type="extra special")
# convert to JIT JSON
jit_json = nx.jit_data(G, indent=4)
print(jit_json)
X = nx.jit_graph(json.loads(jit_json))
print(f"Nodes: {list(X.nodes(data=True))}")
print(f"Edges: {list(X.edges(data=True))}")
nx.draw(G, pos=nx.planar_layout(G), with_labels=True)
plt.show()
def fromJsonStr(jsonStr):
return addConvenienceAttributes(nx.jit_graph(json.loads(jsonStr)))
def load() -> nx.DiGraph:
try:
with open(GRAPH_FILE, 'r') as f:
return nx.jit_graph(f.read(), create_using=nx.DiGraph())
except:
return nx.DiGraph()
