mirror of
https://github.com/karl0ss/football-manager-squad-and-recruitment.git
synced 2025-05-12 09:48:13 +01:00
103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
import argparse
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import pandas as pd
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def load_xlsx_data_to_dataframe(filepath: str) -> pd.DataFrame:
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"""Read XLSX file into a Dataframe.
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Keyword arguments:
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filepath -- path to xlsx file
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"""
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df = pd.read_excel(filepath, engine="openpyxl", nrows=12)
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return df
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def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame:
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"""Read HTML file exported by FM into a Dataframe.
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Keyword arguments:
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filepath -- path to fm player html file
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"""
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df = pd.read_html(filepath, header=0, encoding="utf-8", keep_default_na=False)[0]
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# Clean Dataframe to get rid of unknown values and ability ranges (takes the lowest value)
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# This casts to a string to be able to split, so we have to cast back to an int later.
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df = df.replace("-", 0)
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df = df.map(lambda x: str(x).split("-")[0])
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return df
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def export_html_from_dataframe(player_df: pd.DataFrame, filepath: str) -> str:
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"""Export Dataframe as html with jQuery Data Tables.
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Taken from: https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python.
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Keyword arguments:
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filepath -- path to fm player html file
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"""
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table_html = player_df.to_html(table_id="table", index=False)
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html = f"""
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<html>
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<header>
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<link href="https://cdn.datatables.net/1.11.5/css/jquery.dataTables.min.css" rel="stylesheet">
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</header>
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<body>
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{table_html}
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<script src="https://code.jquery.com/jquery-3.6.0.slim.min.js" integrity="sha256-u7e5khyithlIdTpu22PHhENmPcRdFiHRjhAuHcs05RI=" crossorigin="anonymous"></script>
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<script type="text/javascript" src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script>
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<script>
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$(document).ready( function () {{
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$('#table').DataTable({{
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paging: false,
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order: [[12, 'desc']],
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// scrollY: 400,
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}});
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}});
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</script>
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</body>
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</html>
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"""
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open(filepath, "w", encoding="utf-8").write(html)
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def calc_role_scores(player_df: pd.DataFrame, attribute_df: pd.DataFrame) -> pd.DataFrame:
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"""Calculate Player position scores based on selected attribute weightings.
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Keyword arguments:
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player_df: Dataframe of Players and their Attributes
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attribute_df: Dataframe of Attributes and their Weightings
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"""
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for _, weightings in attribute_df.iterrows():
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role = weightings["Ratings Weights"]
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player_df[role] = 0
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for attribute in weightings.index[1:]:
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weighting = weightings[attribute]
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try:
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player_df[role] += round(pd.to_numeric(player_df[attribute]) * weighting / 20, 2)
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except Exception as e: # Used to Nat being used twice (Nationality and Natural Fitness)
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print(e)
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continue
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player_df.loc[player_df[role] < 10, role] = 0
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return player_df
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if __name__ == "__main__":
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# Parse Input args
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--input-filepath", type=str, help="Path to Input Html file")
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parser.add_argument("-o", "--output-filepath", type=str, help="Path to Export resultant Html file")
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parser.add_argument("-a", "--attribute-filepath", type=str, help="Path to Attribute XLSX file", default="./attribute_ratings.xlsx")
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args = parser.parse_args()
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input_filepath = args.input_filepath
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output_filepath = args.output_filepath
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attribute_filepath = args.attribute_filepath
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# Inport data, calculate scores for role,
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attribute_df = load_xlsx_data_to_dataframe(attribute_filepath)
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player_df = load_html_data_to_dataframe(input_filepath)
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player_df = calc_role_scores(player_df, attribute_df)
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# trim attributes from final output
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player_df = player_df.drop(player_df.columns[15:-11], axis=1)
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# export results as html
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export_html_from_dataframe(player_df, output_filepath)
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