def update_output(value, step, arg_graph):
if self.debate_model is not None and value is not None:
if value == 'Merged':
merged_value, merged_graph = self.debate_model.get_merged_value(
step)
print("MERGED VALUE : ", merged_value)
nodes, edges = convert_to_visdcc_format(
merged_graph, self.debate_model.semantic)
title = "Merged Graph Step " + str(step)
self.agent_graph = merged_graph
return [
visdcc.Network(
id='agent-net',
selection={
'nodes': [],
'edges': []
},
data={
'nodes': nodes,
'edges': edges
},
options=dict(height='400px',
width='100%',
interaction=dict(hover=True))), title,
None
]
else:
for agent in self.debate_model.schedule.agents:
if agent.name == value:
g = agent.state[step]
self.agent_graph = g
g.view_graph()
nodes, edges = convert_to_visdcc_format(
agent.state[step], self.debate_model.semantic)
title = "Agent Graphs Step " + str(step)
strategy = "Strategy : " + str(
self.debate_model.strategies[step][
agent.name][0])
return [
visdcc.Network(id='agent-net',
selection={
'nodes': [],
'edges': []
},
data={
'nodes': nodes,
'edges': edges
},
options=dict(height='400px',
width='100%',
interaction=dict(
hover=True))),
title,
html.Div(
[html.H4('Agent Information'), strategy])
]
return None, 'Agent Graphs', None
def visualize_graph(self):
ui_elem = []
for i, use_case_exec in enumerate(self.data):
d_list = self.data[use_case_exec]
for path_cnt, d in enumerate(d_list):
ui_elem.append(html.Hr())
ui_elem.append(
html.
P(f"Use case #{i}.{path_cnt}: {use_case_exec.use_case.name}"
))
network = visdcc.Network(id=f"network_{i}_{path_cnt}",
selection={
'nodes': [],
'edges': []
},
options=BaseVisdccViz.get_options(
heigth=self.GRAPH_HEIGHT,
hierarchical_layout=True),
data=dict(nodes=d['nodes'],
edges=d['edges']))
path = d['path']
atds = path.get_filtered_meta_data_by_key(
metadata.META_DATA_ATD)
atds_info = [str(atd) for atd in atds]
ui_elem.append(html.P(f"ATDs: {atds_info}"))
container = dbc.Container(
id=f"network_{i}_container_{path_cnt}",
children=network,
style={"border": "1px solid black"})
ui_elem.append(container)
return ui_elem
def main():
"""Main program"""
args = parse_args()
ip = args.ip
port = args.port
# data loading
# initialize network object and scrape the HS3 website
network = zwiz.Network(ip=ip, port=port)
df = network.edges_df.copy()
df = df[df['type'] == 'neighbor']
# set temporary visual settings on the nodes
for node_id, node in network.nodes.items():
if node_id == 1:
node.marker_size = '10'
node.marker_shape = 'dot'
else:
node.marker_size = '7'
node.marker_shape = 'dot'
# create visdcc-friendly nodes and edges
nodes = []
for node_id, node in network.nodes.items():
if node_id not in [n['id'] for n in nodes]:
nodes.append({'id': node.node_id,
'label': node.name[0:10]+'...',
'shape': node.marker_shape,
'size': node.marker_size})
edges = []
for _, edge in network.edges.items():
if edge.id not in [e['id'] for e in edges]:
if edge.type == 'route':
edges.append({'id': edge.id,
'from': edge.source.node_id,
'to': edge.target.node_id,
'width': 2})
# create app
app = dash.Dash()
# define layout
app.layout = html.Div([
visdcc.Network(id='net', # pylint: disable=E1101
data={'nodes': nodes, 'edges': edges},
options=dict(height='800px',
width='100%',
nodes=dict(color='Grey'))),
])
# main call
if __name__ == '__main__':
app.run_server(debug=True)
def visualize_graph(self):
ui_elems = [
visdcc.Network(id=self.ui_html_id,
selection={
'nodes': [],
'edges': []
},
options=BaseVisdccViz.get_options(),
data=self.data),
]
return ui_elems
def update_output_div(input_value):
searchterm = input_value
result = youtube_search(searchterm)
just_json = result[1]
just_json[1]
url1 = "https://www.googleapis.com/youtube/v3/search?part=snippet&relatedToVideoId="
url2 = "&type=video&key="
myvideoids = []
myvideotitles = []
myrange = 3
for i in range(myrange):
myvideotitles.append(just_json[i]["snippet"]["title"])
myvideoids.append(just_json[i]["id"]["videoId"])
source, target, videotitles = youtube_spider(myvideoids, url1, url2)
source2, target2, videotitles2 = youtube_spider(target, url1, url2)
newsource = convertToNumber(source)
newsource2 = convertToNumber(source2)
newtarget = convertToNumber(target)
newtarget2 = convertToNumber(target2)
nodes = []
data = {}
for i, b in zip(videotitles + videotitles2, newsource + newsource2):
nodes.append(dict(id=b, label=i))
nodes = [i for n, i in enumerate(nodes) if i not in nodes[n + 1:]]
data["nodes"] = nodes
targetids = []
for a, b in zip(newsource + newsource2, newtarget + newtarget2):
targetids.append(str(a) + "-" + str(b))
targets = []
for a, b, c in zip(targetids, newsource + newsource2,
newtarget + newtarget2):
new_dict = dict(id=a, key=b, to=c)
new_dict["from"] = new_dict.pop("key")
targets.append(new_dict)
targets = [i for n, i in enumerate(targets) if i not in targets[n + 1:]]
data["edges"] = targets
return visdcc.Network(id='net',
data=data,
options=dict(height='500px', width='90%'))
def update_step_graph(value, final_graph):
if value is not None and self.debate_model is not None:
graph = self.debate_model.state[value]
nodes, edges = convert_to_visdcc_format(
graph, self.debate_model.semantic)
return [
visdcc.Network(id='step-net',
selection={
'nodes': [],
'edges': []
},
data={
'nodes': nodes,
'edges': edges
},
options=dict(height='400px',
width='100%',
interaction=dict(hover=True))),
"Public Graph Step " + str(value)
]
return None, None
def visualize_graph(self):
ui_elem = []
for i, use_case_exec in enumerate(self.data):
d = self.data[use_case_exec]
ui_elem.append(html.Hr())
if use_case_exec.is_verified():
badge = dbc.Badge("verified",
color="success",
className="mr-1")
else:
badge = dbc.Badge("computed", color="dark", className="mr-1")
ui_elem.append(
html.P([
f"Use case #{i}: {use_case_exec.use_case.name} ({use_case_exec.playback_trace_id}) ",
badge
]))
network = visdcc.Network(id=f"network_{i}",
selection={
'nodes': [],
'edges': []
},
options=BaseVisdccViz.get_options(
heigth=self.GRAPH_HEIGHT,
hierarchical_layout=True),
data=d)
if use_case_exec.is_computed():
# Implicitly using the best path
path = use_case_exec.get_best_computed_path()
atds = path.get_filtered_meta_data_by_key(
metadata.META_DATA_ATD)
atds_info = [str(atd) for atd in atds]
ui_elem.append(html.P(f"ATDs: {atds_info}"))
container = dbc.Container(id=f"network_{i}_container",
children=network,
style={"border": "1px solid black"})
ui_elem.append(container)
return ui_elem
'''.replace(' ', ''),
className='eight columns offset-by-two')
],
className='row',
style={
'text-align': 'center',
'margin-bottom': '15px'
}),
dcc.Input(id='my-id', value='Global warming', type='text'),
html.Div(id='my-div',
style={
'text-align': 'center',
'margin-bottom': '15px'
}),
visdcc.Network(id='net',
data=data,
options=dict(height='500px', width='90%')),
dcc.RadioItems(id='color',
options=[{
'label': 'Red',
'value': '#ff0000'
}, {
'label': 'Green',
'value': '#00ff00'
}, {
'label': 'Blue',
'value': '#0000ff'
}],
value='Red'),
])
}),
html.Div(id='kol_from_id', hidden=True),
html.Div(id='clicks', hidden=True),
dcc.Loading(
visdcc.Network(id='net',
data={
'nodes': nodes,
'edges': edges
},
options={
'height': '700px',
'width': '100%',
'nodes': {
'chosen': True,
'shape': 'dot',
'color': {
'highlight': {
'border': '#2B7CE9',
'background': '#D2E5FF'
},
'hover': {
'border': '#2B7CE9',
'background': '#D2E5FF'
}
}
}
}))
],
style={
"border": "10px white solid",
'font-size': '12px'
def get_app_layout(graph_data, color_legends=[], directed=False):
"""Create and return the layout of the app
Parameters
--------------
graph_data: dict{nodes, edges}
network data in format of visdcc
"""
# Step 1: find categorical features of nodes
node_df = pd.DataFrame(graph_data['nodes'])
cat_node_features = ['None'] + node_df.columns[(node_df.dtypes == 'object') & (node_df.apply(pd.Series.nunique) <= 20)].tolist()
try: # remove irrelevant cols
cat_node_features.remove('shape')
cat_node_features.remove('label')
cat_node_features.remove('id')
except:
pass
# Step 2: find categorical features of edges
edge_df = pd.DataFrame(graph_data['edges']).drop(columns=['color'])
cat_edge_features = ['None'] + edge_df.columns[(edge_df.dtypes == 'object') & (edge_df.apply(pd.Series.nunique) <= 20)].tolist()
try: # remove irrelevant cols
cat_edge_features.remove('from')
cat_edge_features.remove('to')
cat_edge_features.remove('id')
except:
pass
# Step 3: create and return the layout
# resolve path
this_dir, _ = os.path.split(__file__)
image_filename = os.path.join(this_dir, "assest", "logo.png")
encoded_image = base64.b64encode(open(image_filename, 'rb').read())
return html.Div([
# create_row(html.H2(children="Jaal")), # Title
create_row(html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()), width="80px")),
create_row([
dbc.Col([
# setting panel
dbc.Form([
# ---- search section ----
html.H6("Search"),
html.Hr(className="my-2"),
search_form,
# ---- filter section ----
html.H6("Filter"),
html.Hr(className="my-2"),
filter_node_form,
filter_edge_form,
# ---- color section ----
create_row([
html.H6("Color"), # heading
dbc.Button("Legends", id="color-legend-toggle", outline=True, color="secondary", size="sm"), # legend
# add the legends popup
dbc.Popover(
children=color_legends,
id="color-legend-popup", is_open=False, target="color-legend-toggle",
),
], {**fetch_flex_row_style(), 'margin-left': 0, 'margin-right':0, 'justify-content': 'space-between'}),
html.Hr(className="my-2"),
get_select_form_layout(
id='color_nodes',
options=[{'label': opt, 'value': opt} for opt in cat_node_features],
label='Color nodes by',
description='Select the categorical node property to color nodes by'
),
get_select_form_layout(
id='color_edges',
options=[{'label': opt, 'value': opt} for opt in cat_edge_features],
label='Color edges by',
description='Select the categorical edge property to color edges by'
),
], className="card", style={'padding': '5px', 'background': '#e5e5e5'}),
],width=3, style={'display': 'flex', 'justify-content': 'center', 'align-items': 'center'}),
# graph
dbc.Col(
visdcc.Network(id = 'graph',
data = graph_data,
options = dict(height= '600px', width= '100%',
interaction={'hover': True},
edges={'arrows':{'to': directed}},
physics={'stabilization': {'iterations': 100}}
))
, width=9)]),
# stats cards
# dbc.Row([
# dbc.Col(create_card(id="nodes_count", value="NA", description='Nodes'),width={'offset':3}),
# dbc.Col(create_card(id="edges_count", value="NA", description='Edges'),width={'offset':6})
# ])
])
nodes = [{'id': node_name, 'label': node_name, 'shape': 'dot', 'size': 7} for i, node_name in enumerate(node_list)]
# create edges from df
edges = []
for row in df.to_dict(orient='records'):
source, target = row['Source'], row['Target']
edges.append({
'id': source + "__" + target,
'from': source,
'to': target,
'width': 2,
})
# define layout
app.layout = html.Div([
visdcc.Network(id = 'net',
data = {'nodes': nodes, 'edges': edges},
options = dict(height= '600px', width= '100%')),
dcc.RadioItems(id = 'color',
options=[{'label': 'Red' , 'value': '#ff0000'},
{'label': 'Green', 'value': '#00ff00'},
{'label': 'Blue' , 'value': '#0000ff'} ],
value='Red' )
])
# define callback
@app.callback(
Output('net', 'options'),
[Input('color', 'value')])
def myfun(x):
return {'nodes':{'color': x}}
nodes_list.append({'id': X, 'label': str(X), 'color': '#A9E2F3'})
edge_list = [{
'id': f'{X}-{i}',
'from': X,
'to': i,
'label': str(round(score, 3))
} for i, score in zip(sim_id, sim_score)]
data = {
'nodes': nodes_list,
'edges': edge_list,
}
return data
element1 = visdcc.Network(id='sim-net',
options=dict(height='800px', width='80%'))
element2 = html.Div(id='test-diff-output')
graph_vis = html.Div(children=[
html.Div(element1, style={
'width': '80%',
'margin-left': '250px'
}), element2
],
style={
'margin-left': '200px',
'margin-right': '100px',
'width': '80%'
})
def frontend_layout_with_stats():
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import visdcc
from app import app
from apps import app1, app2
app.layout = html.Div([
dcc.Location(id='url', refresh=False),
html.Div(id='page-content'),
html.Div(visdcc.Network(id='net',
options=dict(
height='600px',
width='100%',
physics={'barnesHut': {
'avoidOverlap': 0
}},
)),
style={'display': 'none'})
])
@app.callback(Output('page-content', 'children'), [Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/apps/app1':
return app1.layout
elif pathname == '/apps/app2':
return app2.layout
else:
return app1.layout
value = 'off' )],
style={'width': '15%', 'padding': '20px 0px 0px 20px', 'display':'inline-block'} ), html.Br(), html.Br(),
html.B(id = 'time-value', style={'font-size':18, 'padding': '20px 0px 0px 30px'}),
html.Div(dcc.RangeSlider(id = 'time-slider',
count = 1,
min = time.mktime(mint),
max = time.mktime(maxt),
step = 400,
value = [time.mktime(mint), time.mktime(mint) + 7000],
pushable = 3600,
marks = labelt
),
style={'padding': '20px 20px 70px 20px', 'width': '65%'} ),
visdcc.Network(id = 'net',
data = { 'nodes' : [{'id':g[i], 'x':li_x[i], 'y':li_y[i] } for i in range(len(g))],
'edges' : [] } ,
selection = {'nodes':[], 'edges':[]},
style = {'display':'inline-block', 'height':'600px', 'width':'65%', 'padding':'20px', 'vertical-align':'top'}),
html.Div([
html.Img(src='/static/legend.png', style={'width': '300px'}), html.Br(),
html.B('可能影響的事件：',
style={'font-size':20, 'display':'inline-block', 'padding':'20px', 'margin-right':'15px', 'width': '170px'} ),
html.B('可能的異常因子：',
style={'font-size':20, 'display':'inline-block', 'padding':'20px', 'width': '170px'} ), html.Br(),
html.Div(id = 'impact',
style = {'background-color':'rgb(255, 239, 225)', 'padding':'20px', 'width': '170px',
'vertical-align':'top', 'margin-right':'15px', 'display':'inline-block'}),
html.Div(id = 'factor',
style = {'background-color':'rgb(240, 255, 225)', 'padding':'20px', 'width': '170px',
'vertical-align':'top', 'display':'inline-block'}),
], style = {'display':'inline-block', 'text-align': 'center'})
@author: jimmybow
"""
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import visdcc
app = dash.Dash()
app.layout = html.Div([
visdcc.Network(id='net',
selection={
'nodes': [],
'edges': []
},
options=dict(height='600px', width='100%')),
html.Div(id='nodes'),
html.Div(id='edges')
])
@app.callback(Output('nodes', 'children'), [Input('net', 'selection')])
def myfun(x):
s = 'Selected nodes : '
if len(x['nodes']) > 0: s += str(x['nodes'][0])
return s
@app.callback(Output('edges', 'children'), [Input('net', 'selection')])
'height': '200px',
}
}
# styles = {
# 'pre': {
# 'border': 'thin lightgrey solid',
# 'overflowX': 'scroll',
# 'width': '70%',
# }
# }
app.layout = dhc.Div([
dhc.Div([
visdcc.Network(id = 'net',
options = dict(height= '600px', width= '100%',
# physics={'barnesHut': {'avoidOverlap': 1,"damping": 0.4,"centralGravity": 0.3,"springConstant": 0.1,"springLength": 100,}}
physics={'barnesHut': {'avoidOverlap': 1,"damping": 0.4,"centralGravity": 0.1,"springConstant": 0.04,}}
)),
], className='column', style={'float':'left','height': '600px', 'width': '67%'}),
# dcc.Input(id = 'label',
# placeholder = 'Enter a label ...',
# type = 'text',
# value = '' ),
dcc.Interval(
id='interval-component',
interval=3*1000, # in milliseconds
n_intervals=0
),
dhc.Div([
visdcc.Network(
id='net',
data={
'nodes': [{
'id': 23,
'label': 'Russia',
'title': 'Class:Country<br>Name:Russia<br>Confidence:high',
'group': 'Country',
'color': '#00ffff'
}, {
'id': 24,
'label': 'Ukraine',
'title':
'Class:Country<br>Name:Ukraine<br>Confidence:high',
'group': 'Country'
}, {
'id': 32,
'label': 'U.S.',
'title': 'Class:Country<br>Name:U.S.<br>Confidence:medium',
'group': 'Country'
}, {
'id': 51,
'label': 'Russian',
'title': 'Class:Country<br>Name:Russian<br>Confidence:low',
'group': 'Country'
}, {
'id': 52,
'label': 'British',
'title': 'Class:Country<br>Name:British<br>Confidence:low',
'group': 'Country'
}, {
'id': 33,
'label': 'British',
'title':
'Class:Nationality<br>Name:British<br>Confidence:medium',
'group': 'Nationality'
}, {
'id': 34,
'label': 'Russian',
'title':
'Class:Nationality<br>Name:Russian<br>Confidence:medium',
'group': 'Nationality'
}, {
'id': 35,
'label': 'Ukrainian',
'title':
'Class:Nationality<br>Name:Ukrainian<br>Confidence:medium',
'group': 'Nationality'
}, {
'id': 25,
'label': 'Washington',
'title':
'Class:StatesOrProvinces<br>Name:Washington<br>Confidence:high',
'group': 'StatesOrProvinces'
}, {
'id': 40,
'label': 'consultant',
'title':
'Class:Title<br>Name:consultant<br>Confidence:low',
'group': 'Title'
}, {
'id': 41,
'label': 'candidate',
'title': 'Class:Title<br>Name:candidate<br>Confidence:low',
'group': 'Title'
}, {
'id': 42,
'label': 'actress',
'title': 'Class:Title<br>Name:actress<br>Confidence:low',
'group': 'Title'
}, {
'id': 43,
'label': 'lawmaker',
'title': 'Class:Title<br>Name:lawmaker<br>Confidence:low',
'group': 'Title'
}, {
'id': 44,
'label': 'president',
'title': 'Class:Title<br>Name:president<br>Confidence:low',
'group': 'Title'
}, {
'id': 45,
'label': 'businessman',
'title':
'Class:Title<br>Name:businessman<br>Confidence:low',
'group': 'Title'
}, {
'id': 46,
'label': 'attorney',
'title': 'Class:Title<br>Name:attorney<br>Confidence:low',
'group': 'Title'
}, {
'id': 47,
'label': 'spokesman',
'title': 'Class:Title<br>Name:spokesman<br>Confidence:low',
'group': 'Title'
}, {
'id': 48,
'label': 'security adviser',
'title':
'Class:Title<br>Name:security adviser<br>Confidence:low',
'group': 'Title'
}, {
'id': 49,
'label': 'chairman',
'title': 'Class:Title<br>Name:chairman<br>Confidence:low',
'group': 'Title'
}, {
'id': 50,
'label': 'intelligence officer',
'title':
'Class:Title<br>Name:intelligence officer<br>Confidence:low',
'group': 'Title'
}, {
'id': 37,
'label': 'Trump',
'title': 'Class:Facility<br>Name:Trump<br>Confidence:low',
'group': 'Facility'
}, {
'id': 38,
'label': 'Trump Organization',
'title':
'Class:Facility<br>Name:Trump Organization<br>Confidence:low',
'group': 'Facility'
}, {
'id': 2,
'label': 'Christopher Steele',
'title':
'Class:Person<br>Name:Christopher Steele<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 3,
'label': 'Cohen',
'title':
'Class:Person<br>Name:Cohen<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 4,
'label': 'Flynn',
'title':
'Class:Person<br>Name:Flynn<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 5,
'label': 'Michael Cohen',
'title':
'Class:Person<br>Name:Michael Cohen<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 6,
'label': 'Michael Flynn',
'title':
'Class:Person<br>Name:Michael Flynn<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 7,
'label': 'Mueller',
'title':
'Class:Person<br>Name:Mueller<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 8,
'label': 'Paul Manafort',
'title':
'Class:Person<br>Name:Paul Manafort<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 9,
'label': 'Stormy Daniels',
'title':
'Class:Person<br>Name:Stormy Daniels<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 10,
'label': 'Trump',
'title':
'Class:Person<br>Name:Trump<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 11,
'label': 'Vladimir Putin',
'title':
'Class:Person<br>Name:Vladimir Putin<br>Confidence:very_high',
'group': 'Person'
}, {
'id': 15,
'label': 'Manafort',
'title':
'Class:Person<br>Name:Manafort<br>Confidence:high',
'group': 'Person'
}, {
'id': 16,
'label': 'Daniels',
'title': 'Class:Person<br>Name:Daniels<br>Confidence:high',
'group': 'Person'
}, {
'id': 17,
'label': 'Michael',
'title': 'Class:Person<br>Name:Michael<br>Confidence:high',
'group': 'Person'
}, {
'id': 18,
'label': 'Robert S. Mueller III',
'title':
'Class:Person<br>Name:Robert S. Mueller III<br>Confidence:high',
'group': 'Person'
}, {
'id': 26,
'label': 'Andrew Harnik/AP',
'title':
'Class:Person<br>Name:Andrew Harnik/AP<br>Confidence:medium',
'group': 'Person'
}, {
'id': 27,
'label': 'Democrat',
'title':
'Class:Person<br>Name:Democrat<br>Confidence:medium',
'group': 'Person'
}, {
'id': 28,
'label': 'Steele',
'title':
'Class:Person<br>Name:Steele<br>Confidence:medium',
'group': 'Person'
}, {
'id': 36,
'label': '130,000',
'title':
'Class:Money<br>Name:130,000<br>Confidence:medium',
'group': 'Money'
}, {
'id': 39,
'label': '$130,000',
'title': 'Class:Money<br>Name:$130,000<br>Confidence:low',
'group': 'Money'
}, {
'id': 12,
'label': 'BBC',
'title':
'Class:Organization<br>Name:BBC<br>Confidence:very_high',
'group': 'Organization'
}, {
'id': 13,
'label': 'Trump',
'title':
'Class:Organization<br>Name:Trump<br>Confidence:very_high',
'group': 'Organization'
}, {
'id': 14,
'label': 'Trump Organization',
'title':
'Class:Organization<br>Name:Trump Organization<br>Confidence:very_high',
'group': 'Organization'
}, {
'id': 19,
'label': 'New York Times',
'title':
'Class:Organization<br>Name:New York Times<br>Confidence:high',
'group': 'Organization'
}, {
'id': 29,
'label': 'Democrat',
'title':
'Class:Organization<br>Name:Democrat<br>Confidence:medium',
'group': 'Organization'
}, {
'id': 30,
'label': 'Senate Intelligence Committee',
'title':
'Class:Organization<br>Name:Senate Intelligence Committee<br>Confidence:medium',
'group': 'Organization'
}, {
'id': 20,
'label': 'Manhattan',
'title': 'Class:City<br>Name:Manhattan<br>Confidence:high',
'group': 'City'
}, {
'id': 21,
'label': 'Prague',
'title': 'Class:City<br>Name:Prague<br>Confidence:high',
'group': 'City'
}, {
'id': 22,
'label': 'Moscow',
'title': 'Class:City<br>Name:Moscow<br>Confidence:high',
'group': 'City'
}, {
'id': 31,
'label': 'New York City',
'title':
'Class:City<br>Name:New York City<br>Confidence:medium',
'group': 'City'
}, {
'id': 1,
'label': 'Article:Why Michael Cohen is poss',
'title':
'Class:Reports<br>Title:Why Michael Cohen is possibly the biggest threat to Trump',
'group': 'Reports'
}, {
'id': 0,
'label': 'Provider:washingtonpost',
'title': 'Class:Sources<br>Source:washingtonpost',
'group': 'Sources'
}],
'edges': [{
'from': 0,
'to': 1,
'title': 'created'
}, {
'from': 10,
'to': 44,
'title': 'title'
}, {
'from': 5,
'to': 46,
'title': 'title'
}, {
'from': 10,
'to': 13,
'title': 'employee_or_member_of'
}, {
'from': 5,
'to': 13,
'title': 'employee_or_member_of'
}, {
'from': 11,
'to': 34,
'title': 'origin'
}, {
'from': 11,
'to': 51,
'title': 'origin'
}, {
'from': 11,
'to': 44,
'title': 'title'
}, {
'from': 9,
'to': 42,
'title': 'title'
}, {
'from': 5,
'to': 42,
'title': 'title'
}, {
'from': 2,
'to': 33,
'title': 'origin'
}, {
'from': 2,
'to': 52,
'title': 'origin'
}, {
'from': 2,
'to': 50,
'title': 'title'
}, {
'from': 6,
'to': 48,
'title': 'title'
}, {
'from': 5,
'to': 44,
'title': 'title'
}, {
'from': 5,
'to': 31,
'title': 'cities_of_residence'
}, {
'from': 5,
'to': 32,
'title': 'countries_of_residence'
}, {
'from': 3,
'to': 32,
'title': 'countries_of_residence'
}, {
'from': 5,
'to': 45,
'title': 'title'
}, {
'from': 8,
'to': 49,
'title': 'title'
}, {
'from': 5,
'to': 49,
'title': 'title'
}, {
'from': 5,
'to': 41,
'title': 'title'
}]
},
options=dict(height='600px', width='100%')),
def dissertation(app):
#Set lists about available menus:
node_list = ['theory', 'testing', 'about']
about_list = ['about me', 'about project']
program_list = ['chord', 'score']
testing_list = ['field test 1', 'opera']
math_list = ['fourier', 'mfcc', 'critical band', 'spreading function']
theory_list = ['mathematics', 'masking', 'blending', 'orchestration']
initial = ['Orchestration Analyzer']
theory_list_without_math = theory_list.copy()
theory_list_without_math.remove(
'mathematics') #Pop out math from theory list, because it has subnodes
#initial_data = {'nodes':[{'id': initial[0], 'label': initial[0], 'shape': 'box', 'font':{'size':100}}], 'edges':[]}
#List of pages with text in them:
def flatten_list(l):
return [item for sublist in l for item in sublist]
text_pages = flatten_list(
[about_list, testing_list, theory_list_without_math])
#Define function to add menu elements
def add_nodes(label):
return {
'id': label,
'label': label,
'shape': 'text',
'font': {
'size': 20,
'color': 'grey',
'face': 'Arial Black'
},
'color': {
'background': 'black',
'border': 'grey'
},
}
def add_edges(parent, child):
return {'id': parent + "-" + child, 'from': parent, 'to': child}
#The starting data for menus:
initial_data = {
'nodes': [{
'id': initial[0],
'label': 'Click here',
'shape': 'text',
'font': {
'size': 24,
'color': 'grey',
'face': 'Arial Black'
}
}],
'edges': []
}
#Unusable function, don't have energy to debug:
def not_exist(data, id, nodeid):
for val in data:
if val[id] == nodeid:
return False
else:
return True
#Add menuitems and connect them to clicked node:
def append_nodes(data, nodelist, parent):
for i in range(len(nodelist)):
val = nodelist[i]
data['nodes'].append(add_nodes(val))
data['edges'].append(add_edges(val, parent))
return data
#When items are clicked, this is the function
def act_on_click(data, node_id):
text = ''
if node_id == 'about':
data = append_nodes(data, about_list, node_id)
if node_id == initial[0]:
data = append_nodes(data, node_list, node_id)
if node_id == 'program':
data = append_nodes(data, program_list, node_id)
if node_id == 'testing':
data = append_nodes(data, testing_list, node_id)
if node_id == 'theory':
data = append_nodes(data, theory_list, node_id)
if node_id == 'mathematics':
data = append_nodes(data, math_list, node_id)
return data, text
#This function strips the nodes if node is clicked again
def strip_on_click(node_data, node_id):
new_nodelist = node_data['nodes'].copy()
new_edgelist = node_data['edges'].copy()
if node_id == 'about': lista = about_list
if node_id == 'testing': lista = testing_list
if node_id == initial[0]:
return initial_data # If logo is clicked, back to squeare one :)
if node_id == 'program': lista = program_list
if node_id == 'theory': lista = theory_list
if node_id == 'mathematics': lista = math_list
for nod in lista:
for val in node_data['nodes']:
if val['id'] == nod:
new_nodelist.remove(val)
for val in node_data['edges']:
if val['to'] == nod:
new_edgelist.remove(val)
node_data['nodes'] = []
node_data['edges'] = []
node_data['nodes'] = new_nodelist
node_data['edges'] = new_edgelist
return node_data
import dissertation.about_text
import dissertation.field_test_text
import dissertation.masking_text
import dissertation.history_of_orchestration
def text_on_click(node_id):
text = html.Div('Sorry, not yet implemented')
if node_id == 'about me':
text = dissertation.about_text.aboutText()
if node_id == 'field test 1':
text = dissertation.field_test_text.field_test_text()
if node_id == 'masking':
text = dissertation.masking_text.masking_text()
if node_id == 'orchestration':
text = dissertation.history_of_orchestration.history_text()
text = dbc.Modal([
dbc.ModalBody(
html.Div(children=[html.Div(text, style={'height': '100vh'})],
className='fadein'))
],
scrollable=True,
is_open=True,
autoFocus=True,
size='xl')
return text
##############################
# Disseration menu callbacks #
##############################
@app.callback(
[
Output('testing', 'children'), #The main container for content
Output('net', 'data'), #Menu items data
Output('nodestore', 'children'), #Hidden place to store data
Output('hidden4', 'style'), #To show/hide menuitems
Output('back', 'style'),
Output('net', 'focus'), #To focus a node, not really working :(
Output('hidden2', 'style'), #To show/hide program
Output('hidden3', 'style'),
],
[
Input('net', 'selection'), #Selected nodes
Input('back', 'n_clicks')
], #Backbutton
[
State('net', 'data'),
State('nodestore', 'children'),
])
def node_function(sel, back_button, data, nodestore):
chord = {'display': 'none'}
score = {'display': 'none'}
nodestore = json.loads(nodestore) #Load things from data store
hideshow = {'display': 'block'}
back = {'display': 'none'}
focus = True
# Intro when page loads:
intro = html.Div([
html.Div('Orchestration Analyzer',
className='fadein',
style={
'fontSize': 80,
'fontFace': 'Arial',
'color': 'grey',
'textAlign': 'center'
}),
html.Div(
'Artificial intelligence in orchestration by Uljas Pulkkis',
className='fadein',
style={
'fontSize': 20,
'fontFace': 'Arial',
'color': 'grey',
'textAlign': 'center',
'margin': '0px'
})
],
style={'marginBottom': -120})
intro = ''
#Launch back button and set it to none
if back_button > 0 and nodestore['back']: #Back button to switch views
nodestore['back'] = False
score = chord = {'display': 'none'}
chord = chord = {'display': 'none'}
return [
intro, data,
json.dumps(nodestore), {
'display': 'block'
}, {
'display': 'none'
}, focus, chord, score
]
nodestore['back'] = False
#data = dict()
#data, fit, focus, id, moveTo, options, selection, style
if sel != None and 'nodes' in sel: #If clicked for the first time
if sel['nodes'] == []: #If empty screen is clicked
return [
intro, data,
json.dumps(nodestore), hideshow, back, focus, chord, score
]
node_id = sel['nodes'][0]
focus = node_id #Set focus on clicked node
if node_id in text_pages: #node_id == 'about me': #About me text will be shown
text = text_on_click(node_id)
nodestore['back'] = True
hideshow = {'display': 'none'}
back = {'display': 'none', 'color': '#404040'}
intro = text
return [
text, data,
json.dumps(nodestore), hideshow, back, focus, chord, score
]
if node_id in program_list:
nodestore['back'] = True
if node_id == 'chord':
hideshow = {'display': 'none'}
back = {'display': 'none', 'color': '#404040'}
score = chord = {'display': 'none'}
chord = chord = {'display': 'block'}
if node_id == 'score':
hideshow = {'display': 'none'}
back = {'display': 'none', 'color': '#404040'}
score = chord = {'display': 'block'}
chord = chord = {'display': 'none'}
return [
'', data,
json.dumps(nodestore), hideshow, back, focus, chord, score
]
elif not node_id in nodestore: #If node is never clicked
nodestore[node_id] = True
if nodestore[node_id]: #If certain node is selected
# data['nodes'][0] ={'id': initial[0], 'label': '', 'shape': 'image', 'size': 100,
# 'image': 'data:image/png;base64,{}'.format(encoded_image.decode()), 'font': {'size': 100}}
data, msg = act_on_click(data, node_id)
nodestore[node_id] = False
intro = ''
#return [msg, data, json.dumps(nodestore), hideshow, back]
elif not nodestore[
node_id]: #If node is clicked for the second time
data = strip_on_click(data, node_id)
nodestore[node_id] = True
intro = ''
#return ['', data, json.dumps(nodestore), hideshow, back]
#node_index=data['nodes'].index({'id': node_number})
#return json.dumps(data['nodes'][node_index]) #[sel['nodes']] ##[sel['nodes'][0]]
#When program starts, this is returned:
return [
intro, data,
json.dumps(nodestore), hideshow, back, focus, chord, score
]
net_nodes = html.Div([
html.Div(id='nodestore',
children=json.dumps({initial[0]: True}),
style={'display': 'none'}),
html.Button(id='back',
n_clicks=0,
children='BACK TO MASKING SKULL',
className='button',
style={'display': 'none'}),
html.Div(id='testing'),
html.Div(
id='nethide',
children=visdcc.Network(
id='net',
options=dict(minHeight='900px',
animation=True,
height='900px',
width='100%',
clickToUse=False,
physics={
'solver': 'repulsion',
'repulsion': {
'nodeDistance': 150
}
}),
data=initial_data
), # data=append_nodes(data, node_list, initial[0])),
style={
'width': '100vw',
'display': 'block'
}),
html.Div(id='diss-hidden-container', style={'display': 'none'}),
html.Div(id='hidden2', style={'display': 'none'}),
html.Div(id='hidden3', style={'display': 'none'}),
html.Div(id='hidden4', style={'display': 'none'}),
])
return net_nodes
row_selectable=False,
editable=False,
filterable=True,
sortable=True,
id='edges-data')
],
style={
'width': '19%',
'float': 'left'
}),
html.Div(
[
visdcc.Network(id='net',
data={
'nodes': stations,
'edges': edges
},
options=dict(height='{}px'.format(MAP_HEIGHT),
width='{}px'.format(MAP_WIDTH),
physics={'enabled': False}))
],
style={
'width': '80%',
'float': 'right',
'border-style': 'solid',
'border-width': "0.2px"
})
]),
html.Div([
html.H4(' ', style={'clear': 'both'}),
html.Article('Undeliverable',
style={
"""
Created on Sat Feb 3 20:50:16 2018
@author: jimmybow
"""
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import visdcc
app = dash.Dash()
app.layout = html.Div([
visdcc.Network(id='net', options=dict(height='600px', width='100%')),
dcc.Input(id='label',
placeholder='Enter a label ...',
type='text',
value=''),
html.Br(),
html.Br(),
dcc.RadioItems(id='color',
options=[{
'label': 'Red',
'value': '#ff0000'
}, {
'label': 'Green',
'value': '#00ff00'
}, {
'label': 'Blue',
def run_simulation(nb_agents, nb_args, nb_clones, comfort_limit,
p_favor_bias, p_against_bias, nb_clicks, id,
protocol):
if nb_clicks is not None:
changed_id = [
p['prop_id'] for p in dash.callback_context.triggered
][0]
if 'run-button' in changed_id:
if len(protocol) != 0:
protocol = protocol[0]
else:
protocol = 'complete'
print(protocol)
if id is not None and len(id) > 0:
print(id)
self.debate_model, self.game_stats = run_debate_id(
id, protocol=protocol)
else:
try:
nb_args = int(nb_args)
nb_agents = int(nb_agents)
nb_clones = int(nb_clones)
comfort_limit = float(comfort_limit)
p_favor_bias = float(p_favor_bias)
p_against_bias = float(p_against_bias)
except:
raise ("Invalid Arguments")
self.debate_model, self.game_stats = run_debate(
nb_args,
nb_agents,
lightmode=False,
comfort_limit=comfort_limit,
p_favor_bias=p_favor_bias,
p_against_bias=p_against_bias,
protocol=protocol,
number_of_clones=nb_clones)
nodes, edges = convert_to_visdcc_format(
self.debate_model.argument_graph,
self.debate_model.semantic)
final_nodes, final_edges = convert_to_visdcc_format(
self.debate_model.public_graph,
self.debate_model.semantic)
# building the options for the agent dropdown
dropdown_options = [{
'label': 'Merged Graph',
'value': 'Merged'
}]
for agent in self.debate_model.schedule.agents:
dropdown_options += [{
'label':
'Agent ' + str(agent.name),
'value':
agent.name
}]
return [
visdcc.Network(id='net',
selection={
'nodes': [],
'edges': []
},
data={
'nodes': nodes,
'edges': edges
},
options=dict(
height='400px',
width='100%',
interaction=dict(hover=True))),
visdcc.Network(id='f-graph',
selection={
'nodes': [],
'edges': []
},
data={
'nodes': final_nodes,
'edges': final_edges
},
options=dict(
height='350px',
width='100%',
interaction=dict(hover=True))),
generate_table(self.game_stats), dropdown_options,
self.game_stats['Steps'] - 1,
self.game_stats['Steps'] - 1
]
return (None, None, None, [], 0, 0)
def get_app_layout(graph_data):
"""Create and return the layout of the app
Parameters
--------------
graph_data: dict{nodes, edges}
network data in format of visdcc
"""
# Step 1: find categorical features of nodes
node_df = pd.DataFrame(graph_data['nodes'])
cat_node_features = ['None'] + node_df.columns[
(node_df.dtypes == 'object')
& (node_df.apply(pd.Series.nunique) <= 20)].tolist()
try: # remove irrelevant cols
cat_node_features.remove('shape')
cat_node_features.remove('label')
cat_node_features.remove('id')
except:
pass
# Step 2: find categorical features of edges
edge_df = pd.DataFrame(graph_data['edges']).drop(columns=['color'])
cat_edge_features = ['None'] + edge_df.columns[
(edge_df.dtypes == 'object')
& (edge_df.apply(pd.Series.nunique) <= 20)].tolist()
try: # remove irrelevant cols
cat_edge_features.remove('from')
cat_edge_features.remove('to')
cat_edge_features.remove('id')
except:
pass
# Step 3: create and return the layout
# resolve path
this_dir, _ = os.path.split(__file__)
image_filename = os.path.join(this_dir, "assest", "logo.png")
encoded_image = base64.b64encode(open(image_filename, 'rb').read())
return html.Div([
# create_row(html.H2(children="Jaal")), # Title
create_row(
html.Img(src='data:image/png;base64,{}'.format(
encoded_image.decode()),
width="80px")),
create_row([
dbc.Col(
[
# setting panel
dbc.Form(
[
# html.H5("Setting Panel"),
# html.Hr(className="my-2"),
html.H6("Search"),
html.Hr(className="my-2"),
search_form,
html.H6("Filter"),
html.Hr(className="my-2"),
filter_node_form,
filter_edge_form,
html.H6("Color"),
html.Hr(className="my-2"),
get_select_form_layout(
id='color_nodes',
options=[{
'label': opt,
'value': opt
} for opt in cat_node_features],
label='Color nodes by',
description=
'Select the categorical node property to color nodes by'
),
get_select_form_layout(
id='color_edges',
options=[{
'label': opt,
'value': opt
} for opt in cat_edge_features],
label='Color edges by',
description=
'Select the categorical edge property to color edges by'
),
],
className="card",
style={
'padding': '5px',
'background': '#e5e5e5'
}),
],
width=3,
style={
'display': 'flex',
'justify-content': 'center',
'align-items': 'center'
}),
# graph
dbc.Col(visdcc.Network(id='graph',
data=graph_data,
options=dict(height='600px',
width='100%',
interaction={'hover': True})),
width=9)
]),
# stats cards
# dbc.Row([
# dbc.Col(create_card(id="nodes_count", value="NA", description='Nodes'),width={'offset':3}),
# dbc.Col(create_card(id="edges_count", value="NA", description='Edges'),width={'offset':6})
# ])
])
app.layout = html.Div([
visdcc.Network(id='net',
data={
'nodes': [{
'id': 1,
'label': 'Node 1',
'color': '#00ffff'
}, {
'id': 2,
'label': 'Node 2'
}, {
'id': 4,
'label': 'Node 4'
}, {
'id': 5,
'label': 'Node 5'
}, {
'id': 6,
'label': 'Node 6'
}],
'edges': [{
'id': '1-3',
'from': 1,
'to': 3
}, {
'id': '1-2',
'from': 1,
'to': 2
}]
},
options=dict(height='600px', width='100%')),
dcc.RadioItems(id='color',
html.H1(children='Language percent',
style={
'textAlign': 'center',
'color': colors['text']
}),
dcc.Graph(id='pie-bar'),
html.Div([
html.Hr(),
html.H2(children='Network',
style={
'textAlign': 'center'
}),
visdcc.Network(id='net',
data=get_network_data(),
options=dict(height='400px', width='100%'))
]),
html.Iframe(srcDoc=f.read(),height="900",width="1300", id='iframe'),
html.Div([
html.Div([
html.H3(children='sentiment data analysis ® 2018')
], className='center-wrap-content'),
], className='rs-footer'),
], className='main-content')
])
@app.callback(Output('intermediate-value', 'children'), [Input('dropdown', 'value')],
events=[Event('interval-component', 'interval')])
app.layout = html.Div([
html.Div(json.dumps(data), id = 'data', style = {'display':'none'}),
html.Div(id = 'x_input', style = {'display':'none'}),
html.Div('{"b1_e": null, "b2_e": null}', id = 'nclick', style = {'display':'none'}),
html.H1('Iris 鳶尾花品種預測',
style = {'color': '#ae6c0d', 'display':'inline-block', 'width': '80%',
'text-align': 'center', 'vertical-align':'top'}),
html.Div([dcc.Dropdown(id = 'choose',
options=[{'label': '模型評估', 'value': 'model'},
{'label': '預測', 'value': 'pre'} ],
value = 'model' )],
style={'width': '15%', 'margin': '21.44px', 'display':'inline-block', 'font-size': 20} ), html.Br(),
html.Div([
visdcc.Network(id = 'net', data = data, options = options,
selection = {'nodes':[], 'edges':[]},
style = {'width': '82%', 'display':'inline-block', 'vertical-align':'top'}),
visdcc.Network(id = 'legend', data = data_legend, options = options_legend,
selection = {'nodes':[], 'edges':[]},
style = {'width': '18%', 'display':'inline-block', 'vertical-align':'top'})],
style = {'width': '60%', 'display':'inline-block', 'vertical-align':'top'}),
html.Div(id = 'right-hand', style = {'width': '40%', 'display':'inline-block', 'text-align': 'center'})
])
@app.callback(
Output('net', 'options'),
[Input('net', 'id')])
def myfun(x):
return({'layout': {'hierarchical': {'enabled': False} }} )
@app.callback(
html.Button(id='back',
n_clicks=0,
children='..-- T A K E M E B A C K --..',
className='button',
style={'display': 'none'}),
html.Div(id='chord', style={'display': 'none'}),
html.Div(id='score', style={'display': 'none'}),
html.Div(id='testing'),
#html.Div(children=[
visdcc.Network(id='net',
options=dict(minHeight='900px',
animation=True,
height='900px',
width='100%',
clickToUse=False,
physics={
'solver': 'repulsion',
'repulsion': {
'nodeDistance': 150
}
}),
data=initial_data
) #data=append_nodes(data, node_list, initial[0])),
#],style={'height':'100%'}),
# dcc.Input(id = 'label',
# placeholder = 'Enter a label ...',
# type = 'text',
# value = '' ),
# html.Br(),html.Br(),
# dcc.RadioItems(id = 'color',
# options=[{'label': 'Red' , 'value': '#ff0000'},
# {'label': 'Green', 'value': '#00ff00'},
# uncomment to run cypher query directly from Neo4j
# Neo4j should be running
#from cypher import title
app.title = 'SAGE'
app.layout = html.Div([
# represents the URL bar, doesn't render anything
dcc.Location(id='url', refresh=False),
# content will be rendered in this element
html.Div(id='page-content'),
# workaround to render table component in sage_table app
html.Div(dt.DataTable(rows=[{}]), style={'display': 'none'}),
# workaround to render network viz component in app
html.Div(visdcc.Network(id='net', data={
'nodes': [],
'edges': []
}))
])
index_page = layout
@app.callback(Output('page-content', 'children'), [Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/':
return index_page
if pathname == '/apps/app1':
return app1.layout
elif pathname == '/apps/app2':
return app2.layout
elif pathname == '/apps/sql':
return sql.layout
