2023-10-28 14:40:46 +01:00
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import pandas as pd
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import argparse
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2023-10-28 19:38:42 +01:00
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# Define Player attributes
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2023-10-28 20:00:02 +01:00
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# TODO: Add roles.
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2023-10-29 09:37:24 +00:00
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gk = {
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"role_name": "gk",
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2023-10-28 19:38:42 +01:00
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"primary_multiplier": 5,
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"primary_attributes": ["Agi", "Ref"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"]
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}
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2023-10-29 09:37:24 +00:00
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fb = {
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"role_name": "fb",
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2023-10-28 20:00:02 +01:00
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"primary_multiplier": 5,
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"primary_attributes": ["Wor", "Acc", "Pac", "Sta"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Cro", "Dri", "Mar", "OtB", "Tck", "Tea"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Agi", "Ant", "Cnt", "Dec", "Fir", "Pas", "Pos", "Tec"]
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}
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2023-10-29 09:37:24 +00:00
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cd = {
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"role_name": "cd",
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"primary_multiplier": 3,
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"primary_attributes": ["Cmp", "Hea", "Jum", "Mar", "Pas", "Pos", "Str", "Tck", "Pac"],
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"secondary_multiplier": 1,
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"secondary_attributes": ["Agg", "Ant", "Bra", "Cnt", "Dec", "Fir", "Tec", "Vis"]
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}
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dm = {
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"role_name": "dm",
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"primary_multiplier": 5,
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"primary_attributes": ["Wor", "Pac", "Sta", "Pas"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Tck", "Ant", "Cnt", "Pos", "Bal", "Agi"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Tea", "Fir", "Mar", "Agg", "Cmp", "Dec", "Str"]
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}
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b2b = {
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"role_name": "b2b",
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"primary_multiplier": 5,
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"primary_attributes": ["Pas", "Wor", "Sta"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Tck", "OtB", "Tea", "Vis", "Str", "Dec", "Pos", "Pac"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Agg", "Ant", "Fin", "Lon", "Cmp", "Acc", "Bal", "Fir", "Dri", "Tec"]
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}
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w = {
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"role_name": "w",
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"primary_multiplier": 3,
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"primary_attributes": ["Acc", "Cro", "Dri", "OtB", "Pac", "Tec"],
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"secondary_multiplier": 1,
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"secondary_attributes": ["Agi", "Fir", "Pas", "Sta", "Wor"],
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}
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iw = {
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"role_name": "iw",
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"primary_multiplier": 5,
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"primary_attributes": ["Acc", "Pac", "Wor"],
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"secondary_multiplier": 3,
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"secondary_attributes": ["Dri", "Pas", "Tec", "OtB"],
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"tertiary_multiplier": 1,
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"tertiary_attributes": ["Cro", "Fir", "Cmp", "Dec", "Vis", "Agi", "Sta"]
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}
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2023-10-28 19:38:42 +01:00
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def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame:
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2023-10-28 14:40:46 +01:00
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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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player_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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player_df = player_df.replace("-", 0)
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player_df = player_df.map(lambda x: str(x).split("-")[0])
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return player_df
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2023-10-28 19:38:42 +01:00
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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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2023-10-28 14:40:46 +01:00
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# TODO: Do I even want this?
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def calc_composite_scores(player_df: pd.DataFrame) -> pd.DataFrame:
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"""Calculate Speed, Workrate and Set Piece scores
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Keyword arguments:
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player_df: Dataframe of Players and Attributes
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"""
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player_df['Spd'] = ( player_df['Pac'] + player_df['Acc'] ) / 2
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player_df['Work'] = ( player_df['Wor'] + player_df['Sta'] ) / 2
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player_df['SetP'] = ( player_df['Jum'] + player_df['Bra'] ) / 2
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return player_df
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def sum_attributes(player_df: pd.DataFrame, role: str, attribute_type: str, attributes: [str]) -> pd.DataFrame:
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"""Create a new Column containing the sum of provided attribute columns
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Keyword arguments:
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player_df: Dataframe of Players and Attributes
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role: Name of role to be used as additional column in dataframe
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attribute_type: Type of Attribute [Primary, Secondary, Tertiary]
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attributes: List of Attributes to Sum
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"""
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player_df[f'{role}_{attribute_type}'] = 0
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for attribute in attributes:
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player_df[f'{role}_{attribute_type}'] += pd.to_numeric(player_df[attribute])
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2023-10-28 19:38:42 +01:00
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player_df[f'{role}_{attribute_type}'] = round(player_df[f'{role}_{attribute_type}'] / len(attributes), 2)
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2023-10-28 14:40:46 +01:00
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return player_df
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2023-10-28 19:38:42 +01:00
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def calc_role_scores(player_df: pd.DataFrame, role: dict) -> pd.DataFrame:
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2023-10-28 14:40:46 +01:00
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"""Calculate Player Role scores based on selected attributes.
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Keyword arguments:
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player_df: Dataframe of Players and Attributes
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2023-10-28 19:38:42 +01:00
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role: Dictionary containing role name, role attributes and role attribute weightings
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2023-10-28 14:40:46 +01:00
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"""
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2023-10-28 19:38:42 +01:00
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player_df = sum_attributes(player_df, role["role_name"], "primary", role["primary_attributes"])
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player_df = sum_attributes(player_df, role["role_name"], "secondary", role["secondary_attributes"])
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2023-10-29 09:37:24 +00:00
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if "tertiary_attributes" in role:
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print("here")
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player_df = sum_attributes(player_df, role["role_name"], "tertiary", role["tertiary_attributes"])
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2023-10-28 19:38:42 +01:00
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divisor = role["primary_multiplier"] + role["secondary_multiplier"] + role["tertiary_multiplier"]
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player_df[f'{role["role_name"]}'] = round((((player_df[f'{role["role_name"]}_primary'] * 5) + (player_df[f'{role["role_name"]}_secondary'] * 3) + (player_df[f'{role["role_name"]}_tertiary'] * 1)) / divisor ), 2)
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2023-10-28 14:40:46 +01:00
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return player_df
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2023-10-28 20:00:02 +01:00
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def calc_role_scores_for_tactic_roles(player_df: pd.DataFrame, tactic_roles: [dict]):
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for role in tactic_roles:
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player_df = calc_role_scores(player_df, role)
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2023-10-28 19:38:42 +01:00
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return player_df
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2023-10-28 14:40:46 +01:00
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if __name__ == "__main__":
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# Parse Input args
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2023-10-28 14:40:46 +01:00
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parser = argparse.ArgumentParser()
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2023-10-28 20:00:02 +01:00
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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("-r", "--roles", nargs='+', type=str, help="Space seperated list of roles for Evaluation")
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2023-10-28 14:40:46 +01:00
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args = parser.parse_args()
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2023-10-28 19:38:42 +01:00
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input_filepath = args.input_filepath
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output_filepath = args.output_filepath
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2023-10-28 20:00:02 +01:00
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roles = args.roles
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# Take Role arg and convert to list of role dictionaries
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tactic_roles = []
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for role in roles:
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tactic_roles.append(globals()[role])
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2023-10-28 14:40:46 +01:00
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2023-10-28 20:00:02 +01:00
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# Inport data, calculate scores for role, export results as html
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2023-10-28 19:38:42 +01:00
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player_df = load_html_data_to_dataframe(input_filepath)
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2023-10-28 20:00:02 +01:00
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player_df = calc_role_scores_for_tactic_roles(player_df, tactic_roles)
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2023-10-28 19:38:42 +01:00
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export_html_from_dataframe(player_df, output_filepath)
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