#http://guides.temple.edu/mining-twitter/scraping

#http://stackoverflow.com/questions/17905350/running-an-ipython-jupyter-notebook-non-interactively/17913858#17913858

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

data = pandas.read_csv("test.csv", header=0)

text = list(data.a)

sent=indicoio.sentiment(text)

index=np.linspace(1, len(sent),num=len(sent))

#df1 = pd.DataFrame(sent, columns=['sent'])

#df2 = pd.DataFrame(index, columns=['index'])

#df = pd.concat([df1, df2], join='outer', axis=1)

sns.tsplot(sent, err_style="boot_traces", n_boot=500)

df.set_index('index').plot()

plt.show()

indicoio.config.api_key = 'my_api_key'

# single example

indicoio.sentiment("I love writing code!")

# batch example

indicoio.sentiment([

"I love writing code!",

"Alexander and the Terrible, Horrible, No Good, Very Bad Day"

])

import indicoio

# option 1: pass configuration as a function argument

print(indicoio.sentiment('indico is so easy to use!', api_key="75b93ed62df0c77a6e58c8ebb1bb71f2", cloud="YOUR_SUBDOMAIN"))

# option 2: set module variable

indicoio.config.api_key = '75b93ed62df0c77a6e58c8ebb1bb71f2'

indicoio.config.cloud = 'YOUR_SUBDOMAIN'

print(indicoio.sentiment('indico is so easy to use!'))

import indicoio

indicoio.config.api_key = '75b93ed62df0c77a6e58c8ebb1bb71f2'

# Version >= 0.9.0

indicoio.sentiment(['indico is so easy to use!', 'Still really easy, yiss'])

# Version < 0.9.0

indicoio.batch_sentiment(['indico is so easy to use!', 'Still really easy, yiss'])

#https://indico.io/docs

#sentiment(data, [api_key], [cloud], [language])

#sentiment_hq(data, [api_key], [cloud])

#text_tags(data, [api_key], [cloud], [top_n], [threshold], [independent])

#language(data, [api_key], [cloud])

#political(data, [api_key], [cloud], [top_n], [threshold])

#keywords(data, [api_key], [version], [cloud], [top_n], [threshold], [relative]

#people(data, [api_key], [cloud], [threshold])

#places(data, [api_key], [cloud], [threshold])

#organizations(data, [api_key], [cloud], [threshold])

#twitter_engagement(data, [api_key], [cloud])

#personality(data, [api_key], [cloud])

#relevance(data, queries, [api_key], [cloud])

#text_features(data, [api_key], [cloud])

#emotion(data, [api_key], [cloud], [top_n], [threshold])

#intersections(data, apis, [api_key], [cloud])

#analyze_text(data, apis, [api_key], [cloud])

# batch example

indicoio.analyze_text(

[

"Democratic candidate Hillary Clinton is excited for the upcoming election.",

"Bill Clinton joins President Obama for a birthday golf game at Marthas Vineyard."

],

apis=['sentiment_hq', 'political']

)

# splitting

import indicoio

indicoio.sentiment('This sentence is awful. This sentence is great!', split='sentence')

#custom training

from indicoio.custom import Collection

indicoio.config.api_key = '75b93ed62df0c77a6e58c8ebb1bb71f2'

collection = Collection("collection_name")

# Add Data

collection.add_data([["text1", "label1"], ["text2", "label2"], ...])

# Training

collection.train()

# Telling Collection to block until ready

collection.wait()

# Done! Start analyzing text

collection.predict("indico is so easy to use!")

#images

import indicoio

indicoio.fer('https://IMAGE_URL')

Specifying Filepath

import indicoio

indicoio.fer('FILEPATH')

Formatting images using skimage

import skimage.io

import indicoio

pixel_array = skimage.io.imread('FILEPATH')

indicoio.fer(pixel_array)

Formatting images using PIL and numpy

from PIL import Image

import numpy as np

import indicoio

image = Image.load('FILEPATH')

pixel_array = np.array(image)

indicoio.fer(pixel_array)

#facial recognition

import indicoio

indicoio.config.api_key = '75b93ed62df0c77a6e58c8ebb1bb71f2'

# single example

indicoio.fer("<IMAGE>")

# batch example

indicoio.fer([

"<IMAGE>",

"<IMAGE>"

])

#multiple

import indicoio

indicoio.config.api_key = '75b93ed62df0c77a6e58c8ebb1bb71f2'

# single example

indicoio.analyze_image("<IMAGE>", apis=["image_features", "fer","content_filtering])

# batch example

indicoio.analyze_image([

"<IMAGE>",

"<IMAGE>"

], apis=["image_features", "fer"])