86 lines
1.9 KiB
Python
86 lines
1.9 KiB
Python
import gptc
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states = [
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"Alabama",
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"Alaska",
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"Arizona",
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"Arkansas",
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"California",
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"Colorado",
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"Connecticut",
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"Delaware",
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"Florida",
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"Georgia",
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"Hawaii",
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"Idaho",
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"Illinois",
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"Indiana",
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"Iowa",
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"Kansas",
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"Kentucky",
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"Louisiana",
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"Maine",
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"Maryland",
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"Massachusetts",
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"Michigan",
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"Minnesota",
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"Mississippi",
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"Missouri",
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"Montana",
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"Nebraska",
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"Nevada",
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"New Hampshire",
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"New Jersey",
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"New Mexico",
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"New York",
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"North Carolina",
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"North Dakota",
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"Ohio",
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"Oklahoma",
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"Oregon",
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"Pennsylvania",
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"Rhode Island",
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"South Carolina",
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"South Dakota",
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"Tennessee",
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"Texas",
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"Utah",
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"Vermont",
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"Virginia",
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"Washington",
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"West Virginia",
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"Wisconsin",
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"Wyoming",
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]
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with open("model.gptc", "rb") as f:
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model = gptc.deserialize(f)
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classified_states = []
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for state in states:
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classified_states.append((state, model.get(state),))
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classified_states.sort(key=lambda x: x[1]["left"])
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longest = max([len(state) for state in states])
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print("# State Analysis")
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print()
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print("""This is an analysis of which states are mentioned more in right- or left-
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leaning American news sources. Results do not necessarily correlate with the
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political views of residents of the states; for example, the predominantly
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liberal state of Oregon is mentioned more in right-leaning sources than in
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left-leaning ones.""")
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print()
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print("| State | Left | Right |")
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print("+----------------+-------+-------+")
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for state, data in classified_states:
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percent_right = f"{round(data['right']*1000)/10}%"
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percent_left = f"{round(data['left']*1000)/10}%"
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state_padding = " "*(longest - len(state))
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print(f"| {state}{state_padding} | {percent_left} | {percent_right} |")
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print("+----------------+-------+-------+")
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print("| State | Left | Right |")
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