def daily_layout(response):
import dash_core_components as dcc
import dash_html_components as html
# import plotly.graph_objects as go
import plotly.graph_objs as go
data = [['2019-10-12', '1.00000000 USDT', '0.000 USD', '0 trade'],
['2019-10-11', '200.00000000 USDT', '0.000 USD', '0 trade'],
['2019-10-10', '-150.00000000 USDT', '0.000 USD', '0 trade'],
['2019-10-09', '25.00000000 USDT', '0.000 USD', '0 trade'],
['2019-10-08', '22.00000000 USDT', '0.000 USD', '0 trade'],
['2019-10-07', '2.00000000 USDT', '0.000 USD', '0 trade'],
['2019-10-06', '12.00000000 USDT', '0.000 USD', '0 trade']]
# name = data[0][1].split(' ')[1])
xaxis = [i[0] for i in data]
yaxis = [float(i[1].split(' ')[0]) for i in data]
layout = dcc.Graph(figure=go.Figure(
data=[go.bar(x=[2], y=[1], name='ust')]))
return layout
from data import dataset
from task1 import *
import plotly
import plotly.graph_objs as graph
data = dict()
for user_num in list(dataset.keys()):
for county, country_list in (dataset[user_num]).items():
if user_num in data:
data[user_num] += sum(country_list)
else:
data[user_num] = sum(country_list)
print(data)
diagram = graph.bar(x=list(data.keys()), y=list(data.values()))
figure = graph.Figure(data=[diagram])
plotly.ofline.plot(figure, filename='user costs.html')
def update_graph_live(n):
# Loading data from Heroku PostgreSQL
# DATABASE_URL = os.environ['DATABASE_URL']
# conn = psycopg2.connect(DATABASE_URL, sslmode='require')
# query = "SELECT id_str, text, created_at, polarity, user_location, user_followers_count FROM {}".format(
# settings.TABLE_NAME)
# df = pd.read_sql(query, con=conn)
#
# # Convert UTC into PDT
# df['created_at'] = pd.to_datetime(df['created_at']).apply(lambda x: x - datetime.timedelta(hours=7))
#
# # Clean and transform data to enable time series
# result = df.groupby([pd.Grouper(key='created_at', freq='10s'), 'polarity']).count().unstack(
# fill_value=0).stack().reset_index()
# result = result.rename(
# columns={"id_str": "Num of '{}' mentions".format(settings.TRACK_WORDS[0]), "created_at": "Time"})
# time_series = result["Time"][result['polarity'] == 0].reset_index(drop=True)
#
# min10 = datetime.datetime.now() - datetime.timedelta(hours=7, minutes=10)
# min20 = datetime.datetime.now() - datetime.timedelta(hours=7, minutes=20)
#
# neu_num = result[result['Time'] > min10]["Num of '{}' mentions".format(settings.TRACK_WORDS[0])][
# result['polarity'] == 0].sum()
# neg_num = result[result['Time'] > min10]["Num of '{}' mentions".format(settings.TRACK_WORDS[0])][
# result['polarity'] == -1].sum()
# pos_num = result[result['Time'] > min10]["Num of '{}' mentions".format(settings.TRACK_WORDS[0])][
# result['polarity'] == 1].sum()
#
# # Loading back-up summary data
# query = "SELECT daily_user_num, daily_tweets_num, impressions FROM Back_Up;"
# back_up = pd.read_sql(query, con=conn)
# daily_tweets_num = back_up['daily_tweets_num'].iloc[0] + result[-6:-3][
# "Num of '{}' mentions".format(settings.TRACK_WORDS[0])].sum()
# daily_impressions = back_up['impressions'].iloc[0] + \
# df[df['created_at'] > (datetime.datetime.now() - datetime.timedelta(hours=7, seconds=10))][
# 'user_followers_count'].sum()
# cur = conn.cursor()
#
# PDT_now = datetime.datetime.now() - datetime.timedelta(hours=7)
# if PDT_now.strftime("%H%M") == '0000':
# cur.execute("UPDATE Back_Up SET daily_tweets_num = 0, impressions = 0;")
# else:
# cur.execute(
# "UPDATE Back_Up SET daily_tweets_num = {}, impressions = {};".format(daily_tweets_num, daily_impressions))
# conn.commit()
# cur.close()
# conn.close()
#
# # Percentage Number of Tweets changed in Last 10 mins
#
# count_now = df[df['created_at'] > min10]['id_str'].count()
# count_before = df[(min20 < df['created_at']) & (df['created_at'] < min10)]['id_str'].count()
# percent = (count_now - count_before) / count_before * 100
# Create the graph
vectorizer = pickle.load(open("vector.pickel", "rb"))
w = pickle.load(open('train', 'rb'))
x_train = w['x_train']
vectorizer.fit(x_train)
print(vectorizer)
print(len(vectorizer.get_feature_names()))
filename = 'logR.sav'
ps = PorterStemmer()
wnl = WordNetLemmatizer()
# filename='logR.sav'
loaded_model = joblib.load(open(filename, 'rb'))
def calctime(a):
return time.time() - a
# positive = 0
# negative = 0
# neutral = 0
# compound = 0
#
# count = 0
# initime = time.time()
#
#
# plt.ion()
import test
ckey = 'WnyAgUaacX1YheRSJqwMhhZgR'
csecret = 'LzHg7GuAfJNIsHRpRXEk72TaEjcG5RL9yl85c0rbI1V1pg6rHQ'
atoken = "1125091796046843905-DNeIxEe9RNwlwzZZXwXEW3VJFlv7Az"
asecret = "n3Yc9GzA2Saa6LNPZ5465WdQNj06G6hBrqcWnpwkc4jCb"
class listener(StreamListener):
def on_data(self, data):
global initime
all_data = json.loads(data)
tweet = all_data["text"]
# username=all_data["user"]["screen_name"]
# tweet = " ".join(re.findall("[a-zA-Z]+", tweet))
#
# print(tweet)
global ps
global wnl
sequencePattern = r"(.)\1\1+"
seqReplacePattern = r"\1\1"
tweets = " ".join(filter(lambda x: x[0] != '@', tweet.split()))
tweets = re.sub(r'([^a-zA-Z0-9])', ' ', tweets)
tweets = re.sub(r'[^\w\s]', '', tweets)
# tweets=re.sub('[\s][a-z]{1,3}[\s]',' ',tweets)
# tweets=re.sub('^[a-z]{1,3}[\s]',' ',tweets)
tweets = re.sub(r'[0-9_]', '', tweets)
tweets = re.sub(r'^https?:\/\/.*[\r\n]*', '', tweets)
tweets = re.sub(r"http\S+", "", tweets)
tweets = tweets.lower()
tweets = re.sub(sequencePattern, seqReplacePattern, tweets)
# print(tweets)
tweets = tweets.split()
tweets = [word for word in tweets if not word in set(stopwords.words('english'))]
tweets = [lemma.lemmatize(word) for word in tweets]
tweets = " ".join(tweets)
# print(tweets[0])
# tweets=tweets.split(" ")
ps = PorterStemmer()
wnl = WordNetLemmatizer()
tweets = word_tokenize(tweets)
# print(tweets)
t = []
for j in tweets:
#
t.append(ps.stem(j))
# t.append(wnl.lemmatize(j))
t.append(" ")
tweets = " ".join(t)
# tweets = tweets.split()
tweets = tweets.replace('ing', '')
tweets = tweets.replace('pic', '')
tweets = tweets.replace('com', '')
# l = vectorizer.transform(tweets).toarray()
# tweets='f**k corona'
k = pd.Series(tweets)
print(k)
l = vectorizer.transform(k).toarray()
# print(l)
m = loaded_model.predict(l)
print(m[0])
t = int(calctime(initime))
# blob = TextBlob(tweet.strip())
print(t)
# print(loaded_model)
# print(vectorizer)
global positive
global negative
global neutral
global count
global compound
count = count + 1
# senti = 0
# for sen in blob.sentences:
# senti = senti + sen.sentiment.polarity
# if sen.sentiment.polarity >= 0:
# positive = positive + sen.sentiment.polarity
# else:
# negative = negative + sen.sentiment.polarity
# compound = compound + senti
# print
# count
# print
if m[0] == 1:
positive = positive + 1
elif m[0] == -1:
negative = negative + 1
else:
neutral = neutral + 1
print("pos ", positive)
print("neg", negative)
print("neu", neutral)
# u=positive+negative+neutral
sen = [positive, negative, neutral]
# print(sen)
xsen = ['positive', 'negative', 'neutral']
tweets.strip()
# print
# senti
# print
# t
# print
# str(positive) + ' ' + str(negative) + ' ' + str(neutral)
# print(len(t))
# plt.axis([ 0, 70,0,220])
# plt.xlabel('Time')
# plt.ylabel('Sentiment')
# # plt.plot([t],[positive],'go',[t] ,[negative],'ro',[t],[neutral],'bo')
# # plt.plot([t],[u])
# width = 0.35
# plt.bar(xsen, sen, width=0.35, color='r')
#
# plt.show()
# plt.pause(0.0001)
if count == 200:
return False
else:
return True
def on_error(self, status):
print(status)
auth = OAuthHandler(ckey, csecret)
auth.set_access_token(atoken, asecret)
twitterStream = Stream(auth, listener(count), lang='en', geocode="22.3511148,78.6677428,1km")
twitterStream.filter(track=["#IndiaFightsCorona", "covid19 india", "corona india", "#covid19#india", "corona warriors","#cluelessbjp"])
children = [
html.Div([
dcc.Graph(
id='crossfilter-indicator-scatter',
figure={
'data': [
go.bar(
x=t,
y=xsen,
name="Neutrals",
opacity=0.8,
mode='lines',
line=dict(width=0.5, color='rgb(131, 90, 241)'),
stackgroup='one'
)
]
}
)
], style={'width': '73%', 'display': 'inline-block', 'padding': '0 0 0 20'})
# html.Div([
# dcc.Graph(
# id='pie-chart',
# figure={
# 'data': [
# go.Pie(
# labels=['Positives', 'Negatives', 'Neutrals'],
# values=[positive, negative, neutral],
# name="View Metrics",
# marker_colors=['rgba(184, 247, 212, 0.6)', 'rgba(255, 50, 50, 0.6)',
# 'rgba(131, 90, 241, 0.6)'],
# textinfo='value',
# hole=.65)
# ],
# 'layout': {
# 'showlegend': False,
# 'title': 'Tweets In Last 10 Mins',
# 'annotations': [
# dict(
# text='{0:.1f}K'.format((positive + negative + neutral) / 1000),
# font=dict(
# size=40
# ),
# showarrow=False
# )
# ]
# }
#
# }
# )
# ], style={'width': '27%', 'display': 'inline-block'})
# ]),
# html.Div(
# className='row',
# children=[
# html.Div(
# children=[
# html.P('Tweets/10 Mins Changed By',
# style={
# 'fontSize': 17
# }
# ),
# html.P('{0:.2f}%'.format(percent) if percent <= 0 else '+{0:.2f}%'.format(percent),
# style={
# 'fontSize': 40
# }
# )
# ],
# style={
# 'width': '20%',
# 'display': 'inline-block'
# }
#
# ),
# html.Div(
# children=[
# html.P('Potential Impressions Today',
# style={
# 'fontSize': 17
# }
# ),
# html.P('{0:.1f}K'.format(daily_impressions / 1000) \
# if daily_impressions < 1000000 else \
# ('{0:.1f}M'.format(daily_impressions / 1000000) if daily_impressions < 1000000000 \
# else '{0:.1f}B'.format(daily_impressions / 1000000000)),
# style={
# 'fontSize': 40
# }
# )
# ],
# style={
# 'width': '20%',
# 'display': 'inline-block'
# }
# ),
# html.Div(
# children=[
# html.P('Tweets Posted Today',
# style={
# 'fontSize': 17
# }
# ),
# html.P('{0:.1f}K'.format(daily_tweets_num / 1000),
# style={
# 'fontSize': 40
# }
# )
# ],
# style={
# 'width': '20%',
# 'display': 'inline-block'
# }
# ),
#
# html.Div(
# children=[
# html.P(
# "Currently tracking \"Facebook\" brand (NASDAQ: FB) on Twitter in Pacific Daylight Time (PDT).",
# style={
# 'fontSize': 25
# }
# ),
# ],
# style={
# 'width': '40%',
# 'display': 'inline-block'
# }
# ),
#
# ],
# style={'marginLeft': 70}
# )
]
return children
#
# if __name__ == '__main__':
# app.run_server(debug=True)
'text': '#7FDBFF'
}
df = pd.read_csv('tweet_ana.csv')
with open('Tweet_topic_sentiment.json','r') as f:
data = json.load(f)
sa2 = '206041122'
#app.layout = html.Div([
# 横向柱形图
dcc.Graph(
className='city_graph',
figure=go.Figure(
data = [go.bar(
x = list(data[sa2]['hashtag'].values())[:10][::-1],
y = list(data[sa2]['hashtag'].keys())[:10][::-1]
#type = 'bar'
)],
layout=go.layout(
orientation= 'h',
ylabel='hashtags',
xlabel= 'frequency',
title=data[sa2]['sa2_name']+'\'s hot topics on Social Media',
showlegend = True,
legend = go.layout.Legend(x=0,y=1.0),
yaxis = go.yaxis(hoverformat='.0f'),
margin=go.layout.Margin(l = 80, r= 35, t=50, b=30)
)),
id='graph-bar-horizontal',
style={'height':300}
)
fig = go.Figure(data=go.Scatter(x=x, y=y))
fig.show()
#
# CHART 3 (HORIZONTAL BAR)
# #
# bar_data = [
# {"genre": "Thriller", "viewers": 123456},
# {"genre": "Mystery", "viewers": 234567},
# {"genre": "Sci-Fi", "viewers": 987654},
# {"genre": "Fantasy", "viewers": 876543},
# {"genre": "Documentary", "viewers": 283105},
# {"genre": "Action", "viewers": 544099},
# {"genre": "Romantic Comedy", "viewers": 121212}
# ]
print("----------------")
print("GENERATING BAR CHART...")
print(bar_data) # TODO: create a horizontal bar chart based on the bar_data
x = []
y = []
for d in bar_data:
x.append(d["genre"])
y.append(d["viewers"])
fig = go.figure([go.bar(x=x, y=y)])
fig.show()