gptc-news-model/analyses/constitutional_amendments.py
2023-01-08 08:45:22 -08:00

71 lines
2.1 KiB
Python

import gptc
amendments = [
("1st", "First"),
("2nd", "Second"),
("3rd", "Third"),
("4th", "Fourth"),
("5th", "Fifth"),
("6th", "Sixth"),
("7th", "Seventh"),
("8th", "Eighth"),
("9th", "Ninth"),
("10th", "Tenth"),
("11th", "Eleventh"),
("12th", "Twelfth"),
("13th", "Thirteenth"),
("14th", "Fourteenth"),
("15th", "Fifteenth"),
("16th", "Sixteenth"),
("17th", "Seventeenth"),
("18th", "Eighteenth"),
("19th", "Nineteenth"),
("20th", "Twentieth"),
("21st", "Twenty-first"),
("22nd", "Twenty-second"),
("23rd", "Twenty-third"),
("24th", "Twenty-fourth"),
("25th", "Twenty-fifth"),
("26th", "Twenty-sixth"),
("27th", "Twenty-seventh"),
]
with open("model.gptc", "rb") as f:
model = gptc.deserialize(f)
data = {}
for number, name in amendments:
number_data = model.get(number + " Amendment")
name_data = model.get(name + " Amendment")
if number_data and not name_data:
data[name] = number_data
elif name_data and not number_data:
data[name] = name_data
elif number_data and name_data:
data[name] = {
key: (number_data[key] + name_data[key]) / 2
for key in number_data.keys()
}
classified_amendments = sorted(data.items(), key=lambda x: x[1]["left"])
print("# Constitutional Amendment Analysis")
print()
print("""This is an analysis of which amendments to the U.S. Constitution are mentioned
more in right- or left-leaning American news sources. Data do not necessarily
correlate with support or opposition for the amendment among right- or
left-leaning Americans.""")
print()
print("| Amendment | Left | Right |")
print("+----------------+-------+-------+")
for amendment, data in classified_amendments:
percent_right = f"{data['right']*100:>4.1f}%"
percent_left = f"{data['left']*100:>4.1f}%"
amendment_padding = " "*(14 - len(amendment))
print(f"| {amendment}{amendment_padding} | {percent_left} | {percent_right} |")
print("+----------------+-------+-------+")
print("| Amendment | Left | Right |")