[+] Class attributes

src/collect_twitter.py

@@ -4,6 +4,7 @@ It contains functions related scraping users/tweets, including:
 - getting the tweets of a user
 - downloading many users by checking their followers and follower's followers, etc.
 """
+
 import json
 import math
 import os

src/processing.py

@@ -27,14 +27,16 @@ class ProcessedUser(NamedTuple):
     example, using dataclass, the json for one UserPopularity object will be:
     {"username": "a", "popularity": 1, "num_postings": 1}, while using NamedTuple, the json will be:
     ["a", 1, 1], which saves an entire 42 bytes for each user.
+
+    Attributes:
+        - username: The Twitter user's screen name
+        - popularity: A measurement of a user's popularity, such as followers count
+        - num_postings: Number of tweets
+        - language: Language code in Twitter's language code format
     """
-    # Username
     username: str
-    # A measurement of a user's popularity, such as followers count
     popularity: int
-    # Number of tweets
     num_postings: int
-    # Language
     lang: str
 
 
@@ -107,6 +109,11 @@ class UserSample:
     """
     This is a data class storing our different samples.
 
+    Attributes:
+        - most_popular: Our sample of the most popular users on Twitter
+        - random: Our sample of random users on Twitter
+        - english_news: Our sample of news media accounts on Twitter
+
     Representation Invariants:
         - all(news != '' for news in self.english_news)
 
@@ -224,20 +231,21 @@ def load_user_sample() -> UserSample:
 
 class Posting(NamedTuple):
     """
-    Posting data stores the processed tweets data, and it contains info such as whether or not a
+    Posting data stores the processed tweets data, and it contains info such as whether a tweet is
-    tweet is covid-related
+    covid-related
+
+    Attributes:
+        - covid_related: True if the post is determined to be covid-related
+        - popularity: A measure of tweet popularity measured by comments + likes
+        - repost: Whether the post is a repost
+        - date: Posting date and time in ISO format ("YYYY-MM-DDThh-mm-ss")
 
     Representation Invariants:
         - popularity >= 0
-
     """
-    # Full text of the post's content
     covid_related: bool
-    # Popularity of the post
     popularity: int
-    # Is it a repost
     repost: bool
-    # Date in ISO format
     date: str
 
 

src/report.py

@@ -20,6 +20,9 @@ def generate_report() -> str:
     """
     Compile the report document and generate a markdown report
 
+    Preconditions:
+        - RES_DIR exists, and contains the necessary resources used in this project.
+
     :return: Markdown report
     """
     # Load markdown

src/visualization.py

@@ -30,9 +30,12 @@ class UserFloat:
     This is used for both COVID tweet frequency and popularity ratio data, because both of these
     are floating point data.
 
+    Attributes:
+        - name: Twitter user's screen name
+        - data: The float data that's associated with this user
+
     Representation Invariants:
         - self.name != ''
-
     """
     name: str
     data: float
@@ -42,23 +45,33 @@ class Sample:
     """
     A sample of many users, containing statistical data that will be used in graphs.
 
+    Attributes:
+        - name: Sample name
+        - users: List of user screen names in this sample
+        - user_freqs: Total frequencies of all posts for each user across all dates (sorted)
+        - user_pops: Total popularity ratios of all posts for each user across all dates (sorted)
+        - user_all_pop_avg: Average popularity of all u's posts
+        - user_date_covid_pop_avg: Average popularity of COVID tweets by a specific user on a date
+        (user_covid_tweets_pop[user][date] = Average popularity of COVID-posts by {user} on {date})
+        - date_covid_freq: Total COVID-tweets frequency on a specific date for all users.
+        - dates: dates[i] = The i-th day since the first tweet
+        - date_freqs: date_freqs[i] = COVID frequency of all posts from all sampled users on date[i]
+        - date_pops: date_pops[i] = Average pop-ratio of all posts from all sampled users on date[i]
+
     Representation Invariants:
         - self.name != ''
         - all(name != '' for name in self.users)
-
     """
     name: str
     users: list[str]
-    # Total frequencies of all posts for each user across all dates (sorted)
+
     user_freqs: list[UserFloat]
-    # Total popularity ratios of all posts for each user across all dates (sorted)
     user_pops: list[UserFloat]
-    # Average popularity of all u's posts
     user_all_pop_avg: dict[str, float]
-    # Average popularity of COVID tweets by a specific user on a specific date
+
     # user_covid_tweets_pop[user][date] = Average popularity of COVID-posts by {user} on {date}
     user_date_covid_pop_avg: dict[str, dict[str, float]]
-    # Total COVID-tweets frequency on a specific date for all users.
+
     date_covid_freq: dict[str, float]
     # dates[i] = The i-th day since the first tweet
     dates: list[datetime]