import pandas
import pyarrow.parquet
import pickle



# Gabe requested that the splits defined in ThermoMPNN of the MegaScale dataset be the default splits

# ThermoMPNN/datsets.py
#   class MegaScaleDataset
#     def __init__(csg, split):
#         fname = self.cfg.data_loc.megascale_csv
#         df = pd.read_csv(fname, usecols=["ddG_ML", "mut_type", "WT_name", "aa_seq", "dG_ML"])
#
#         # remove unreliable data and more complicated mutations
#         df = df.loc[df.ddG_ML != '-', :].reset_index(drop=True)
#         df = df.loc[
#            ~df.mut_type.str.contains("ins") &
#            ~df.mut_type.str.contains("del") &
#            ~df.mut_type.str.contains(":"), :].reset_index(drop=True)
#
#         splits = <load from self.cfg.data_loc.megascale_splits>
# 
#         if self.split != 'all' and (cfg.reduce != 'prot' or self.split != 'train'):
#           self.wt_names = splits[split]
#
#
#

# local.yaml
#   data_loc:
#     megascale_csv: "<truncated>/Processed_K50_dG_datasets/Tsuboyama2023_Dataset2_Dataset3_20230416.csv"








with open("data/ThermoMPNN/dataset_splits/mega_splits.pkl", "rb") as f:
    mega_splits  = pickle.load(f)

splits = []
for split_name, split_ids in mega_splits.items():
    splits.append(
        pandas.DataFrame({
            'split_name': split_name,
            'id': split_ids}))

splits = pandas.concat(splits)
splits.reset_index(drop=True, inplace=True)

pyarrow.parquet.write_table(
    pyarrow.Table.from_pandas(splits),
    where = "intermediate/ThermoMPNN_splits.parquet")


parquet_file = pyarrow.parquet.ParquetFile('intermediate/ThermoMPNN_splits.parquet')
parquet_file.metadata
# <pyarrow._parquet.FileMetaData object at 0x149f5d2667a0>
#   created_by: parquet-cpp-arrow version 17.0.0
#   num_columns: 2
#   num_rows: 2020
#   num_row_groups: 1
#   format_version: 2.6
#   serialized_size: 1881