pos = nx.spring_layout(G, weight = 'weight')
nx.draw_networkx(G, pos)
pyl.savefig('bof.png')
#st.image('bof.png')
nt = Network('750px', '750px', notebook=True, heading = "")
nt.from_nx(G)
nt.force_atlas_2based()
options = {
"nodes":{
"shape":"dot",
"size":15,
"font":{
"size":16
},
"borderWidth":2
}
}
nt.options = options
nt.show('bof.html')
html_file = open('bof.html', 'r')
source_code = html_file.read()
components.html(source_code, height = 800, width = 800)
show_map(senators_plot)
st.subheader('Table comparing the similarity of tweets to all the other senators')
st.markdown("Uses 3 different metrics - Spacy similarity (using word vectors), Cosine similarity, and Euclidean Distance to compare the senator to others.")
st.dataframe(df) # Same as st.write(df)
def CreateGraph(self, graph_type, overlaps):
overlap_frame = pd.DataFrame(
columns=['Source', 'Target', 'Type', 'Weight'])
for overlap in overlaps:
for sub_lap in overlaps[overlap]:
overlap_frame = overlap_frame.append(
{
'Source': overlap,
'Target': sub_lap[0],
'Type': 'directed',
'Weight': ((1 - sub_lap[1]) / 25)
},
ignore_index=True)
net = Network(height='100%', width='100%', directed=True)
sources = overlap_frame['Source']
targets = overlap_frame['Target']
weights = overlap_frame['Weight']
edge_data = zip(sources, targets, weights)
graph = nx.DiGraph()
for index, e in enumerate(edge_data):
src = e[0]
dst = e[1]
w = e[2]
if (graph_type == 'NetworkX'):
graph.add_node(src)
graph.add_node(dst)
graph.add_edge(src, dst, weights=w)
else:
net.add_node(src,
src,
title=src,
physics=False,
group=index,
arrowStrikethrough=False)
net.add_node(dst,
dst,
title=dst,
physics=False,
group=index,
arrowStrikethrough=False)
net.add_edge(src, dst, value=w, physics=False)
if (graph_type == 'PyVis'):
options = {
'layout': {
'hierarchical': {
'enabled': True,
'levelSeparation': 50,
'treeSpacing': 75,
'nodeSpacing': 500,
'edgeMinimization': False
}
}
}
net.options = options
connections = net.get_adj_list()
for node in net.nodes:
node['size'] = len(connections[node['id']]) / 3
node['title'] += ' Neighbors: <br>' + '<br>'.join(
connections[node['id']])
node['value'] = len(connections[node['id']])
net.from_nx(graph)
net.show('SimilarityVisualizationGraph.html')
else:
degrees = [val * 10 for (node, val) in graph.degree()]
pos = nx.circular_layout(graph)
nx.draw(graph,
pos,
node_size=degrees,
with_labels=True,
font_size=8)
plt.show()
def graphMaker(filename):
graphObj = Network(height="100%",
width="100%",
bgcolor="#222222",
font_color="white",
heading="")
graphObj.barnes_hut()
graphObj.options = {
"nodes": {
"borderWidth": 2,
"borderWidthSelected": 3,
"color": {
"border": "rgba(186,182,181,1)",
"background": "rgba(247,27,0,1)",
"highlight": {
"border": "rgba(196,194,189,1)",
"background": "rgba(255,29,0,1)"
},
"hover": {
"border": "rgba(184,187,188,1)",
"background": "rgba(255,55,0,1)"
}
}
},
"edges": {
"arrowStrikethrough": False,
"color": {
"inherit": True,
"opacity": 0.35
},
"font": {
"strokeWidth": 28
},
"smooth": False
},
"physics": {
"barnesHut": {
"gravitationalConstant": -80000,
"springLength": 250,
"springConstant": 0.001
},
"minVelocity": 0.75
}
}
#Default display options for graph
data = pandas.read_csv("graphs/" + filename + ".csv")
extraDataCA = pandas.read_csv("data/CAvideos.csv")
extraDataUSA = pandas.read_csv("data/USvideos.csv")
extraDataGB = pandas.read_csv("data/GBvideos.csv")
vectorLikes = np.concatenate(
(extraDataGB['likes'], extraDataUSA['likes'], extraDataCA['likes']))
vectorDislikes = np.concatenate(
(extraDataGB['dislikes'], extraDataUSA['dislikes'],
extraDataCA['dislikes']))
vectorComments = np.concatenate(
(extraDataGB['comment_count'], extraDataUSA['comment_count'],
extraDataCA['comment_count']))
vectorViews = np.concatenate(
(extraDataGB['views'], extraDataUSA['views'], extraDataCA['views']))
vectorTitles = np.concatenate(
(extraDataGB['title'], extraDataUSA['title'], extraDataCA['title']))
vectorID = np.concatenate(
(extraDataGB['video_id'], extraDataUSA['video_id'],
extraDataCA['video_id']))
TitleMap = {}
for i in range(0, len(vectorTitles)):
TitleMap[vectorID[i]] = vectorTitles[i]
InfoMap = {}
for i in range(0, len(TitleMap)):
views = " Views: " + str(vectorViews[i])
likes = " Likes: " + str(vectorLikes[i])
dislikes = " Dislikes: " + str(vectorDislikes[i])
comments = " Number of Comments: " + str(vectorComments[i])
tempList = [views, likes, dislikes, comments]
InfoMap[TitleMap[vectorID[i]]] = tempList
#RENAME IF NECESSARY
sourceNodes = data['Source']
destNodes = data['Target']
edges = zip(sourceNodes, destNodes)
for i in edges:
fromNode = TitleMap[i[0]]
toNode = TitleMap[i[1]]
graphObj.add_node(fromNode, fromNode, title=fromNode)
graphObj.add_node(toNode, toNode, title=toNode)
graphObj.add_edge(fromNode, toNode)
for node in graphObj.nodes:
node["title"] += "\n<br>" + "<br>".join(InfoMap[node["id"]])
node["value"] = 4
graphObj.show("Visual Analysis for " + filename +
" Videos on YouTube.html")
