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patched for compatability with new data logging structure

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peregrinates 2025-08-15 12:02:13 -04:00
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commit 975c4f59b9
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5 changed files with 407 additions and 186 deletions
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analysis/make_all_analysis_data.py
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@ -19,7 +19,7 @@ python analysis/make_all_analysis_data.py --selected_game game1 --game_data_fold
python analysis/make_all_analysis_data.py --game_data_folder "/path/to/Game Data" --output_folder "/path/to/Game Data - Analysis"
"""
import argparse
import os
from pathlib import Path
import pandas as pd
from tqdm import tqdm
@ -27,49 +27,67 @@ from tqdm import tqdm
from analysis.p1_make_longform_orders_data import make_longform_order_data
from analysis.p2_make_convo_data import make_conversation_data
from analysis.p3_make_phase_data import make_phase_data
from analysis.analysis_helpers import process_standard_game_inputs, process_game_in_zip
from analysis.analysis_helpers import get_country_to_model_mapping, process_standard_game_inputs, process_game_inputs_in_zip, COUNTRIES
from typing import Dict
def process_game_data_from_folders(game_name : str, game_path : Path) -> Dict[str, pd.DataFrame]:
def process_game_data_from_folders(game_path : Path) -> Dict[str, pd.DataFrame]:
"""Reads log data from folder and makes analytic data sets"""
game_data : dict[str, pd.DataFrame] = process_standard_game_inputs(game_data_folder=game_path, selected_game=game_name)
game_data : dict[str, pd.DataFrame] = process_standard_game_inputs(path_to_folder=game_path)
orders_data : pd.DataFrame = make_longform_order_data(overview=game_data["overview"],
country_to_model = get_country_to_model_mapping(game_data["overview"], game_data["all_responses"])
orders_data : pd.DataFrame = make_longform_order_data(country_to_model=country_to_model,
lmvs_data=game_data["lmvs_data"],
all_responses=game_data["all_responses"])
conversations_data : pd.DataFrame = make_conversation_data(overview=game_data["overview"], lmvs_data=game_data["lmvs_data"])
phase_data : pd.DataFrame = make_phase_data(overview=game_data["overview"],
lmvs_data=game_data["lmvs_data"],
conversations_data=conversations_data,
orders_data=orders_data)
return {"orders_data": orders_data, "conversations_data": conversations_data, "phase_data": phase_data}
conversations_data : pd.DataFrame = make_conversation_data(country_to_model=country_to_model, lmvs_data=game_data["lmvs_data"])
def process_game_data_from_zip(zip_path : Path, game_name : str) -> Dict[str, pd.DataFrame]:
"""Reads log data from zip and makes analytic data sets"""
game_data : dict[str, pd.DataFrame] = process_game_in_zip(zip_path=zip_path, selected_game=game_name)
orders_data : pd.DataFrame = make_longform_order_data(overview=game_data["overview"],
lmvs_data=game_data["lmvs_data"],
all_responses=game_data["all_responses"])
conversations_data : pd.DataFrame = make_conversation_data(overview=game_data["overview"], lmvs_data=game_data["lmvs_data"])
phase_data : pd.DataFrame = make_phase_data(overview=game_data["overview"],
phase_data : pd.DataFrame = make_phase_data(country_to_model=country_to_model,
lmvs_data=game_data["lmvs_data"],
conversations_data=conversations_data,
orders_data=orders_data)
return {"orders_data": orders_data, "conversations_data": conversations_data, "phase_data": phase_data}
return {"orders_data": orders_data,
"conversations_data": conversations_data,
"phase_data": phase_data}
def process_game_data_from_zip(zip_path: Path, game_name: str) -> Dict[str, pd.DataFrame]:
"""Reads log data from zip and makes analytic data sets"""
game_data: dict[str, pd.DataFrame] = process_game_inputs_in_zip(zip_path=zip_path, selected_game=game_name)
country_to_model = get_country_to_model_mapping(game_data["overview"], game_data["all_responses"])
orders_data: pd.DataFrame = make_longform_order_data(
country_to_model=country_to_model,
lmvs_data=game_data["lmvs_data"],
all_responses=game_data["all_responses"],
)
conversations_data: pd.DataFrame = make_conversation_data(
country_to_model=country_to_model, lmvs_data=game_data["lmvs_data"]
)
phase_data: pd.DataFrame = make_phase_data(
country_to_model=country_to_model,
lmvs_data=game_data["lmvs_data"],
conversations_data=conversations_data,
orders_data=orders_data,
)
return {
"orders_data": orders_data,
"conversations_data": conversations_data,
"phase_data": phase_data,
}
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run all three analysis scripts in sequence with the same arguments.")
parser.add_argument(
"--selected_game",
type=str,
@ -98,16 +116,18 @@ if __name__ == "__main__":
games_to_process = args.selected_game
if not games_to_process:
games_to_process = os.listdir(args_dict["game_data_folder"])
games_to_process = [p.name for p in args_dict["game_data_folder"].iterdir() if p.is_dir()]
for game in tqdm(games_to_process, desc="Processing games"):
game_path = args_dict["game_data_folder"] / game
if not game_path.is_dir():
continue
try:
results = process_game_data_from_folders(game_name=game, game_path=args_dict["game_data_folder"])
results = process_game_data_from_folders(game_path=game_path)
for data_set, df in results.items():
output_path = args_dict["analysis_folder"] / data_set / f"{game}_{data_set}.csv"
output_dir = args_dict["analysis_folder"] / data_set
output_dir.mkdir(parents=True, exist_ok=True)
output_path = output_dir / f"{game}_{data_set}.csv"
df.to_csv(output_path, index=False)
except Exception as e:
print(f"Error processing game {game}: {e}")