import pandas as pd import argparse # Define Player attributes # TODO: Add roles. gk = { "role_name": "gk", "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"] } fb = { "role_name": "fb", "primary_multiplier": 5, "primary_attributes": ["Wor", "Acc", "Pac", "Sta"], "secondary_multiplier": 3, "secondary_attributes": ["Cro", "Dri", "Mar", "OtB", "Tck", "Tea"], "tertiary_multiplier": 1, "tertiary_attributes": ["Agi", "Ant", "Cnt", "Dec", "Fir", "Pas", "Pos", "Tec"] } cd = { "role_name": "cd", "primary_multiplier": 3, "primary_attributes": ["Cmp", "Hea", "Jum", "Mar", "Pas", "Pos", "Str", "Tck", "Pac"], "secondary_multiplier": 1, "secondary_attributes": ["Agg", "Ant", "Bra", "Cnt", "Dec", "Fir", "Tec", "Vis"] } dm = { "role_name": "dm", "primary_multiplier": 5, "primary_attributes": ["Wor", "Pac", "Sta", "Pas"], "secondary_multiplier": 3, "secondary_attributes": ["Tck", "Ant", "Cnt", "Pos", "Bal", "Agi"], "tertiary_multiplier": 1, "tertiary_attributes": ["Tea", "Fir", "Mar", "Agg", "Cmp", "Dec", "Str"] } b2b = { "role_name": "b2b", "primary_multiplier": 5, "primary_attributes": ["Pas", "Wor", "Sta"], "secondary_multiplier": 3, "secondary_attributes": ["Tck", "OtB", "Tea", "Vis", "Str", "Dec", "Pos", "Pac"], "tertiary_multiplier": 1, "tertiary_attributes": ["Agg", "Ant", "Fin", "Lon", "Cmp", "Acc", "Bal", "Fir", "Dri", "Tec"] } w = { "role_name": "w", "primary_multiplier": 3, "primary_attributes": ["Acc", "Cro", "Dri", "OtB", "Pac", "Tec"], "secondary_multiplier": 1, "secondary_attributes": ["Agi", "Fir", "Pas", "Sta", "Wor"], } iw = { "role_name": "iw", "primary_multiplier": 5, "primary_attributes": ["Acc", "Pac", "Wor"], "secondary_multiplier": 3, "secondary_attributes": ["Dri", "Pas", "Tec", "OtB"], "tertiary_multiplier": 1, "tertiary_attributes": ["Cro", "Fir", "Cmp", "Dec", "Vis", "Agi", "Sta"] } def load_html_data_to_dataframe(filepath: str) -> pd.DataFrame: """Read HTML file exported by FM into a Dataframe Keyword arguments: filepath -- path to fm player html file """ player_df = pd.read_html(filepath, header=0, encoding="utf-8", keep_default_na=False)[0] # Clean Dataframe to get rid of unknown values and ability ranges (takes the lowest value) # This casts to a string to be able to split, so we have to cast back to an int later. player_df = player_df.replace("-", 0) 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"""

{table_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 Keyword arguments: player_df: Dataframe of Players and Attributes """ player_df['Spd'] = ( player_df['Pac'] + player_df['Acc'] ) / 2 player_df['Work'] = ( player_df['Wor'] + player_df['Sta'] ) / 2 player_df['SetP'] = ( player_df['Jum'] + player_df['Bra'] ) / 2 return player_df def sum_attributes(player_df: pd.DataFrame, role: str, attribute_type: str, attributes: [str]) -> pd.DataFrame: """Create a new Column containing the sum of provided attribute columns Keyword arguments: player_df: Dataframe of Players and Attributes role: Name of role to be used as additional column in dataframe attribute_type: Type of Attribute [Primary, Secondary, Tertiary] attributes: List of Attributes to Sum """ 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: dict) -> pd.DataFrame: """Calculate Player Role scores based on selected attributes. Keyword arguments: player_df: Dataframe of Players and Attributes role: Dictionary containing role name, role attributes and role attribute weightings """ 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"]) if "tertiary_attributes" in role: print("here") 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_role_scores_for_tactic_roles(player_df: pd.DataFrame, tactic_roles: [dict]): for role in tactic_roles: player_df = calc_role_scores(player_df, role) return player_df if __name__ == "__main__": # Parse Input args parser = argparse.ArgumentParser() parser.add_argument("-i", "--input-filepath", type=str, help="Path to Input Html file") parser.add_argument("-o", "--output-filepath", type=str, help="Path to Export resultant Html file") parser.add_argument("-r", "--roles", nargs='+', type=str, help="Space seperated list of roles for Evaluation") args = parser.parse_args() input_filepath = args.input_filepath output_filepath = args.output_filepath roles = args.roles # Take Role arg and convert to list of role dictionaries tactic_roles = [] for role in roles: tactic_roles.append(globals()[role]) # Inport data, calculate scores for role, export results as html player_df = load_html_data_to_dataframe(input_filepath) player_df = calc_role_scores_for_tactic_roles(player_df, tactic_roles) export_html_from_dataframe(player_df, output_filepath)