# -*- coding: utf-8 -*-
"""
Created on Tue Jun 30 13:32:43 2020
@author: ninjaac
"""
from node_red_data_collection import sentiment_data
import json
import plotly.express as px
import plotly.graph_objects as go
tweet_df,sentiment_df,emotion_df=sentiment_data.get_sentiment_data()
fig_pie = px.pie(sentiment_df, values='sentiment',
title='Sentiment of peoples',
)
fig_pie.update_traces(textposition='inside', textinfo='percent+label')
fig_pie.update_layout(
margin={'l':5 ,'r':5 ,'b':5 ,'t':10,'pad':10},
plot_bgcolor='black',
paper_bgcolor='black',
height=150,
)
fig_pie.show()
# -*- coding: utf-8 -*-
"""
from tweetwrangling import tweets_analyse
from node_red_data_collection import sentiment_data
import json
import plotly.express as px
import plotly.graph_objects as go
#data collection part__________________________________________________________
total_confirmed, sorted_confirmed, total_deaths, sorted_death, total_recovered, sorted_recovered, top_25_cities = tweets_analyse.get_data(
)
time_series_df = tweets_analyse.time_series_creation()
#read sentiment data
sentiment_values, sentimet_names, emotion_value, emotion_name, future_sentiment_value, future_sentimet_names, total = sentiment_data.get_sentiment_data(
)
colors = {
'background': 'black',
}
#total count of deatha and confirmed cases
total = [total_confirmed, total_deaths, total_recovered]
total_name = [
' - Global confirmed', ' - Global Death',
' - Global Recovery'
]
#sorted cases among the country
#generate scrolabel data for death and confirm cases
def update_bar_graph(n):
colors = {
'backgroud': "black",
'text': 'gray',
}
print('hello bar emotion updated')
sentiment_values, sentimet_names, emotion_value, emotion_name, future_sentiment_value, future_sentimet_names, total = sentiment_data.get_sentiment_data(
)
return generate_bar_graph(emotion_value, emotion_name, colors)
def update_pie_graph(n):
colors = {
'backgroud': "black",
'text': 'gray',
}
print("hello")
sentiment_values, sentimet_names, emotion_value, emotion_name, future_sentiment_value, future_sentimet_names, total = sentiment_data.get_sentiment_data(
)
return generate_pie_sentimnet_graph(sentiment_values, sentimet_names,
colors)
