Karl/football-manager-squad-and-recruitment
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added export of dataframe, rounded score calculation, use dict for roles

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jake-fawcett 2023-10-28 19:38:42 +01:00
parent ab1dd8e39d
commit db51b113d4
1 changed files with 61 additions and 19 deletions
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80
position_score_calculator.py
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@ -1,7 +1,19 @@
import pandas as pd
import argparse
def load_data(filepath: str) -> pd.DataFrame:
# Define Player attributes
goalkeeper = {
"role_name": "goalkeeper",
"primary_multiplier": 5,
"primary_attributes": ["Agi", "Ref"],
"secondary_multiplier": 3,
"secondary_attributes": ["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"],
"tertiary_multiplier": 1,
"tertiary_attributes": ["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"]
}
def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame:
"""Read HTML file exported by FM into a Dataframe
Keyword arguments:
@ -14,6 +26,37 @@ def load_data(filepath: str) -> pd.DataFrame:
player_df = player_df.map(lambda x: str(x).split("-")[0])
return player_df
def export_html_from_dataframe(player_df: pd.DataFrame, filepath: str) -> str:
"""Export Dataframe as html with jQuery Data Tables
Taken from: https://www.thepythoncode.com/article/convert-pandas-dataframe-to-html-table-python.
Keyword arguments:
filepath -- path to fm player html file
"""
table_html = player_df.to_html(table_id="table", index=False)
html = f"""
<html>
<header>
<link href="https://cdn.datatables.net/1.11.5/css/jquery.dataTables.min.css" rel="stylesheet">
</header>
<body>
{table_html}
<script src="https://code.jquery.com/jquery-3.6.0.slim.min.js" integrity="sha256-u7e5khyithlIdTpu22PHhENmPcRdFiHRjhAuHcs05RI=" crossorigin="anonymous"></script>
<script type="text/javascript" src="https://cdn.datatables.net/1.11.5/js/jquery.dataTables.min.js"></script>
<script>
$(document).ready( function () {{
$('#table').DataTable({{
paging: false,
order: [[12, 'desc']],
// scrollY: 400,
}});
}});
</script>
</body>
</html>
"""
open(filepath, "w", encoding="utf-8").write(html)
# TODO: Do I even want this?
def calc_composite_scores(player_df: pd.DataFrame) -> pd.DataFrame:
"""Calculate Speed, Workrate and Set Piece scores
@ -38,38 +81,37 @@ def sum_attributes(player_df: pd.DataFrame, role: str, attribute_type: str, attr
player_df[f'{role}_{attribute_type}'] = 0
for attribute in attributes:
player_df[f'{role}_{attribute_type}'] += pd.to_numeric(player_df[attribute])
player_df[f'{role}_{attribute_type}'] = round(player_df[f'{role}_{attribute_type}'] / len(attributes), 2)
return player_df
def calc_role_scores(player_df: pd.DataFrame, role: str, primary_attributes: [str], secondary_attributes: [str], tertiary_attributes: [str]) -> pd.DataFrame:
def calc_role_scores(player_df: pd.DataFrame, role: dict) -> pd.DataFrame:
"""Calculate Player Role scores based on selected attributes.
Keyword arguments:
player_df: Dataframe of Players and Attributes
role: Name of role to be used as additional column in dataframe
primary_attributes: List of Most important attributes for a role
secondary_attributes: List of Most secondary attributes for a role
tertiary_attributes: List of Most tertiary attributes for a role
role: Dictionary containing role name, role attributes and role attribute weightings
"""
player_df = sum_attributes(player_df, role, "primary", primary_attributes)
player_df = sum_attributes(player_df, role, "secondary", secondary_attributes)
player_df = sum_attributes(player_df, role, "tertiary", tertiary_attributes)
divisor = (len(primary_attributes) * 5) + (len(secondary_attributes) * 3) + (len(tertiary_attributes) * 1)
player_df[f'{role}'] = (((player_df[f'{role}_primary'] * 5) + (player_df[f'{role}_secondary'] * 3) + (player_df[f'{role}_tertiary'] * 1)) / divisor )
player_df = sum_attributes(player_df, role["role_name"], "primary", role["primary_attributes"])
player_df = sum_attributes(player_df, role["role_name"], "secondary", role["secondary_attributes"])
player_df = sum_attributes(player_df, role["role_name"], "tertiary", role["tertiary_attributes"])
divisor = role["primary_multiplier"] + role["secondary_multiplier"] + role["tertiary_multiplier"]
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)
return player_df
def calc_player_scores(player_df: pd.DataFrame):
# TODO: Create objects for each role that can be used here.
player_df = calc_role_scores(player_df, "goalkeeper", primary_attributes=["Agi", "Ref"],
secondary_attributes=["1v1", "Ant", "Cmd", "Cnt", "Kic", "Pos"],
tertiary_attributes=["Acc", "Aer", "Cmp", "Dec", "Fir", "Han", "Pas", "Thr", "Vis"])
player_df = calc_role_scores(player_df, goalkeeper)
# TODO: Add roles.
print(player_df)
return player_df
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--filepath", type=str)
parser.add_argument("-i", "--input-filepath", type=str)
parser.add_argument("-o", "--output-filepath", type=str)
args = parser.parse_args()
filepath = args.filepath
input_filepath = args.input_filepath
output_filepath = args.output_filepath
player_df = load_data(filepath)
calc_player_scores(player_df)
player_df = load_html_data_to_dataframe(input_filepath)
player_df = calc_player_scores(player_df)
export_html_from_dataframe(player_df, output_filepath)