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December 5, 2023 10:56
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| import pandas as pd | |
| from fpl_utils import fpl_utils | |
| import os | |
| def lambda_handler(event, context): | |
| try: | |
| pl_profile = event['pl_profile'] | |
| cookie = f"pl_profile={pl_profile};" | |
| update_team(event['user_id'], cookie) | |
| return event | |
| except Exception as e: | |
| sns_topic = fpl_utils.get_parameter('failure_email_sns_key') | |
| error_message = f"Error in Lambda function calculate changes: {str(e)}" | |
| fpl_utils.send_sns_notification(error_message, sns_topic) | |
| raise e # Re-raise the exception after sending the notification | |
| def update_team(user_id, cookie): | |
| team = fpl_utils.get_team_auth(user_id, cookie) | |
| gameweek = fpl_utils.get_gameweek() | |
| bucket_name = fpl_utils.get_parameter('fpl_bucket_name') | |
| player_out = fpl_utils.get_json_df(bucket_name, f'gameweek-{gameweek}/player_out.json') | |
| player_in = fpl_utils.get_json_df(bucket_name, f'gameweek-{gameweek}/player_in.json') | |
| players_df = fpl_utils.get_csv_df(bucket_name, f'players-gameweek-{gameweek}.csv') | |
| players = [x['element'] for x in team['picks']] | |
| my_original_team = players_df.loc[players_df.id.isin(players)] | |
| # Get the web_name of the player you want to drop | |
| player_name = player_out['web_name'].iloc[0] | |
| # Find the index of the player in the original team | |
| player_index = my_original_team.index[my_original_team['web_name'] == player_name][0] | |
| # Drop the player from the original team | |
| my_team = my_original_team.drop(player_index) | |
| my_team = pd.concat([my_team, player_in], ignore_index=True) | |
| my_team = calc_starting_weight(my_team) | |
| team_sheet = breakdown_team(my_team) | |
| file_name = f'gameweek-{gameweek}/full_team.json' | |
| fpl_utils.upload_json_to_s3(bucket_name, file_name, team_sheet) | |
| def calc_starting_weight(players): | |
| players['starting_weight'] = 1 | |
| players['starting_weight'] += players['diff']/2 | |
| players['starting_weight'] += players['form']*10 | |
| players.loc[players['starting_weight'] <0, 'starting_weight'] =0 | |
| return players.sort_values('starting_weight', ascending=False) | |
| def breakdown_team(my_team): | |
| outfied_players = my_team.loc[my_team.element_type>2] | |
| goalies = my_team.loc[my_team.element_type==1] | |
| defenders = my_team.loc[my_team.element_type==2] | |
| captain = outfied_players.id.iat[0] | |
| vice_captain = outfied_players.id.iat[1] | |
| starters = pd.concat([goalies.head(1), defenders.head(3), outfied_players.head(7)], ignore_index=True) | |
| subs = pd.concat([goalies.tail(1), defenders.tail(2), outfied_players.tail(1)], ignore_index=True) | |
| picks =[] | |
| count = 1 | |
| for i in range(1,5): | |
| players = starters.loc[starters.element_type==i] | |
| ids = players.id.tolist() | |
| for ide in ids: | |
| if ide == captain: | |
| player = {"element" : ide, "is_captain" : True, "is_vice_captain" : False, "position" : count} | |
| elif ide == vice_captain: | |
| player = {"element" : ide, "is_captain" : False, "is_vice_captain" : True, "position" : count} | |
| else: | |
| player = {"element" : ide, "is_captain" : False, "is_vice_captain" : False, "position" : count} | |
| picks.append(player.copy()) | |
| count+=1 | |
| ids = subs.id.tolist() | |
| for ide in ids: | |
| player = {"element" : ide, "is_captain" : False, "is_vice_captain" : False, "position" : count} | |
| picks.append(player.copy()) | |
| count+=1 | |
| team_sheet = {"picks" : picks,"chip" : None} | |
| return team_sheet |
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