import polars as pl
import polars.selectors as cs
from great_tables import GT, md


def create_bar(prop_fill: float, max_width: int, height: int) -> str:
    """Create divs to represent prop_fill as a bar."""
    width = round(max_width * prop_fill, 2)
    px_width = f"{width}px"
    return f"""\
    <div style="width: {max_width}px; background-color: lightgrey;">\
        <div style="height:{height}px;width:{px_width};background-color:green;"></div>\
    </div>\
    """


df = pl.read_csv("./sports_earnings.csv")

res = (
    df.with_columns(
        (pl.col("Off-the-Field Earnings") / pl.col("Total Earnings")).alias("raw_perc"),
        (pl.col("Sport").str.to_lowercase() + ".png").alias("icon"),
    )
    .head(9)
    .with_columns(
        pl.col("raw_perc")
          .map_elements(lambda x: create_bar(x, max_width=75, height=20))
          .alias("Off-the-Field Earnings Perc")
    )
    .select("Rank", "Name", "icon", "Sport", "Total Earnings", "Off-the-Field Earnings", "Off-the-Field Earnings Perc")
)

(
    GT(res, rowname_col="Rank")
    .tab_header("Highest Paid Athletes in 2023")
    .tab_spanner("Earnings", cs.contains("Earnings"))
    #.fmt_number(cs.starts_with("Total"), scale_by = 1/1_000_000, decimals=1)
    .cols_label(**{
        "Total Earnings": "Total $M",
        "Off-the-Field Earnings": "Off field $M",
        "Off-the-Field Earnings Perc": "Off field %"
    })
    .fmt_number(["Total Earnings", "Off-the-Field Earnings"], scale_by = 1/1_000_000, decimals=1)
    .fmt_image("icon", path="./")
    .tab_source_note(
        md(
            '<br><div style="text-align: center;">'
            "Original table: [@LisaHornung_](https://twitter.com/LisaHornung_/status/1752981867769266231)"
            " | Sports icons: [Firza Alamsyah](https://thenounproject.com/browse/collection-icon/sports-96427)"
            " | Data: Forbes"
            "</div>"
            "<br>"
        )
    )
)

Highest Paid Athletes in 2023
	Name	icon	Sport	Earnings
				Total $M	Off field $M	Off field %
1	Cristiano Ronaldo		Soccer	136.0	90.0
2	Lionel Messi		Soccer	130.0	65.0
3	Kylian Mbappé		Soccer	120.0	20.0
4	LeBron James		Basketball	119.5	75.0
5	Canelo Alvarez		Boxing	110.0	10.0
6	Dustin Johnson		Golf	107.0	5.0
7	Phil Mickelson		Golf	106.0	2.0
8	Stephen Curry		Basketball	100.4	52.0
9	Roger Federer		Tennis	95.1	95.0
Original table: @LisaHornung_ | Sports icons: Firza Alamsyah | Data: Forbes