def get_pattern_data(search_param):
twitter = Twitter(language='en')
for tweet in twitter.search(search_param, cached=True):
print(plaintext(tweet.text).encode('ascii', 'ignore').decode('utf-8'))
g = Graph()
for i in range(10):
for result in twitter.search(search_param, start=i + 1, count=50):
s = result.text.lower()
s = plaintext(s)
s = parsetree(s)
p = '{NP} (VP) ' + search_param + ' {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0, 0, 0, 0.75)) # R,G,B,A
#if len(g)>0:
# g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('data', directed=False, weighted=0.6)
def compare_visualization(product_sku, compare_phrase):
all_reviews = ReviewInfo.objects.all().filter(sku=product_sku)
g = Graph()
count = 0.0
for e in all_reviews :
s = e.comment.lower()
s = plaintext(s)
s = parsetree(s)
#p = '{NP} (VP) faster than {NP}'
p = '{NP} (VP) ' + compare_phrase + ' {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
count += 1.0
print count/len(all_reviews), '\r'
if len(g) > 0:
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:80]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('static/compare_visualization', directed=True, weighted=2.0)
return True
else:
return False
def get_pattern_data(search_param):
twitter = Twitter(language='en')
for tweet in twitter.search(search_param, cached=True):
print(plaintext(tweet.text).encode('ascii', 'ignore').decode('utf-8'))
g = Graph()
for i in range(10):
for result in twitter.search(search_param, start=i+1,count=50):
s = result.text.lower()
s = plaintext(s)
s = parsetree(s)
p = '{NP} (VP) ' +search_param+ ' {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
#if len(g)>0:
# g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('data', directed=False, weighted=0.6)
# This example demonstrates how a graph visualization can be exported to HTML,
# using the HTML5 <canvas> tag and Javascript.
# All properties (e.g. stroke color) of nodes and edges are ported.
g = Graph()
# Random nodes.
for i in range(50):
g.add_node(id=str(i + 1), radius=5, stroke=(0, 0, 0, 1), text=(0, 0, 0, 1))
# Random edges.
for i in range(75):
node1 = choice(g.nodes)
node2 = choice(g.nodes)
g.add_edge(node1, node2, length=1.0, weight=random(), stroke=(0, 0, 0, 1))
for node in g.sorted()[:20]:
# More blue = more important.
node.fill = (0.6, 0.8, 1.0, 0.8 * node.weight)
# This node's label is different from its id.
# We'll also make it a link, see the href attribute at the bottom.
g["1"].text.string = "home"
# The export() command generates a folder with an index.html,
# that displays the graph using an interactive, force-based spring layout.
# You can drag the nodes around - open index.html in a browser and try it out!
# The layout can be tweaked in many ways:
export(
g,
"test",
import os, sys; sys.path.insert(0, os.path.join("..", "..", ".."))
from pattern.graph import Graph, CENTRALITY
# Simple Graph demonstration.
g = Graph()
for n in ("tree", "nest", "bird", "fly", "insect", "ant"):
g.add_node(n)
g.add_edge("tree", "nest")
g.add_edge("nest", "bird")
g.add_edge("bird", "fly")
g.add_edge("fly", "insect")
g.add_edge("insect", "ant")
g.add_edge("ant", "tree")
g.add_edge("ant", "bird")
print g.shortest_path(g.node("tree"), g.node("fly"))
print g.shortest_path(g.node("nest"), g.node("ant"))
print
# Which nodes get the most traffic?
print g.sorted(order=CENTRALITY)
# Random nodes.
for i in range(50):
g.add_node(id=str(i + 1),
radius=5,
stroke=(0, 0, 0, 1),
text = (0, 0, 0, 1))
# Random edges.
for i in range(75):
node1 = choice(g.nodes)
node2 = choice(g.nodes)
g.add_edge(node1, node2,
length=1.0,
weight=random(),
stroke=(0, 0, 0, 1))
for node in g.sorted()[:20]:
# More blue = more important.
node.fill = (0.6, 0.8, 1.0, 0.8 * node.weight)
g.prune(0)
# This node's label is different from its id.
# We'll make it a hyperlink, see the href attribute at the bottom.
# FIXME this fails if the 1 has been pruned
# g[1].text.string = "home"
# The export() command generates a folder with an index.html,
# that displays the graph using an interactive, force-based spring layout.
# You can drag the nodes around - open index.html in a browser and try it out!
# The layout can be tweaked in many ways:
from pattern.web import Bing, plaintext
from pattern.en import parsetree
from pattern.search import search
from pattern.graph import Graph
g = Graph()
for i in range(10):
# for result in Bing().search('"more important than"', start=i+1,
for result in Bing().search('"is less important than"', start=i+1,
count=50):
s = result.text.lower()
s = plaintext(s)
s = parsetree(s)
#p = '{NP} (VP) more important than {NP}'
p = '{NP} (VP) is less important than {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('test', directed=True, weighted=0.6)
import os, sys
sys.path.append(os.path.join("..", "..", ".."))
from pattern.graph import Graph, CENTRALITY
# Simple Graph demonstration.
g = Graph()
for n in ("tree", "nest", "bird", "fly", "insect", "ant"):
g.add_node(n)
g.add_edge("tree", "nest")
g.add_edge("nest", "bird")
g.add_edge("bird", "fly")
g.add_edge("fly", "insect")
g.add_edge("insect", "ant")
g.add_edge("ant", "tree")
g.add_edge("ant", "bird")
print g.shortest_path(g.node("tree"), g.node("fly"))
print g.shortest_path(g.node("nest"), g.node("ant"))
print
# Which nodes get the most traffic?
print g.sorted(order=CENTRALITY)
p = Pattern.fromstring('NP (VP) more important than NP')
for m in p.search(s):
a = m.constituents(p[+0])[-1] # Left NP.
b = m.constituents(p[-1])[+0] # Right NP.
a = (isinstance(a, Chunk) and a.head or a).string
b = (isinstance(b, Chunk) and b.head or b).string
if a and b:
if a not in g:
g.add_node(a, radius=5, stroke=(0, 0, 0, 0.8))
if b not in g:
g.add_node(b, radius=5, stroke=(0, 0, 0, 0.8))
g.add_edge(g[b], g[a], stroke=(0, 0, 0, 0.6))
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sorted by Node.weight.
n.fill = (0.0, 0.5, 1.0, 0.7 * n.weight)
export(g,
'test',
directed=True,
weighted=0.6,
distance=6,
force=0.05,
repulsion=150)
import os
os.system('ls -lR test/')
# Example of pattern: http://www.clips.ua.ac.be/pages/pattern
from pattern.web import Bing, plaintext
