[M] Move constants to constants.py

This commit is contained in:
Hykilpikonna
2021-11-24 10:58:15 -05:00
parent 815eab8cf1
commit eda17f2ad0
4 changed files with 16 additions and 11 deletions
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@@ -0,0 +1,6 @@
# Constants (The instructors said that we can use global constants here:
# https://piazza.com/class/ksovzjrlsye72f?cid=1664
# They should not end with "/"
DATA_DIR = './data'
TWEETS_DIR = f'{DATA_DIR}/twitter/user-tweets'
USER_DIR = f'{DATA_DIR}/twitter/user'
+8 -9
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@@ -4,13 +4,6 @@ from process.twitter_process import *
from raw_collect.twitter import *
from utils import *
# Constants (The instructors said that we can use global constants here:
# https://piazza.com/class/ksovzjrlsye72f?cid=1664
# They should not end with "/"
DATA_DIR = './data'
TWEETS_DIR = f'{DATA_DIR}/twitter/user-tweets'
USER_DIR = f'{DATA_DIR}/twitter/user'
if __name__ == '__main__':
# Load config and create API
@@ -18,7 +11,7 @@ if __name__ == '__main__':
api = tweepy_login(conf)
#####################
# Data collection - Step C1
# Data collection - Step C1.1
# Download a wide range of users from Twitter using follow-chaining starting from a single user.
# download_users_start(api, 'voxdotcom')
@@ -26,6 +19,11 @@ if __name__ == '__main__':
# you want to stop the process, you can resume it later using the following line:
# download_users_resume_progress(api)
####################
# Data collection - Step C1.2
# Download all tweets from twitternews
download_all_tweets(api, 'twitternews')
#####################
# Data processing - Step P1
# (After step C1) Process the downloaded twitter users, extract screen name, popularity, and
@@ -35,7 +33,7 @@ if __name__ == '__main__':
#####################
# Data processing - Step P2
# (After step P1) Select 500 most popular users and 500 random users who meet a particular
# criteria as our sample.
# criteria as our sample, also find news channels
# select_user_sample()
# Just curious, who are the 20 most popular individuals on twitter?
@@ -66,6 +64,7 @@ if __name__ == '__main__':
# Who posted the most covid tweets? (covid vs non-covid ratio)
# - Graph histogram of this ratio
# Who has the most covid tweet popularity (popularity of covid vs non-covid tweets ratio)
# - Graph histogram of this ratio
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@@ -7,7 +7,7 @@ from dataclasses import dataclass
from py7zr import SevenZipFile
from main import DATA_DIR, TWEETS_DIR, USER_DIR
from constants import DATA_DIR, TWEETS_DIR, USER_DIR
from utils import *
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@@ -9,7 +9,7 @@ from typing import List
import tweepy
from tweepy import API, TooManyRequests, User, Tweet, Unauthorized
from main import TWEETS_DIR, USER_DIR
from constants import TWEETS_DIR, USER_DIR
from utils import *