# %%
import pyreadstat
import json
import re
import datetime

# %%
df_sav, meta = pyreadstat.read_sav('./data/ZA8841_v1-0-0.sav')
print(df_sav.shape)

# %%
df_sav.head(1).to_dict(orient='records')

# %%
def safe_json(obj):
    if isinstance(obj, (datetime.datetime, datetime.date)):
        return obj.isoformat()
    if isinstance(obj, set):
        return list(obj)
    if hasattr(obj, '__dict__'):
        return str(obj)
    return obj

meta_dict = vars(meta)
meta_json = json.dumps(meta_dict, default=safe_json, indent=2)

# %%
with open('./data/za8841_meta.json', 'w') as f:
    f.write(meta_json)

# %%
# Load your mapping dictionary (from JSON file or directly)
with open("./data/za8841_meta.json") as f:
    meta = json.load(f)
value_labels = meta["variable_value_labels"]

# %%
# Assume df_sav is your loaded SPSS dataframe
# For each column in the mapping, map values if the column exists in df_sav
for col, mapping in value_labels.items():
    if col in df_sav.columns:
        # Convert keys to float if needed (SPSS values often are float)
        mapping_float = {float(k): v for k, v in mapping.items()}
        df_sav[col] = df_sav[col].map(mapping_float).fillna(df_sav[col])

# Now all mapped columns have human-readable values
print(df_sav.head())

# %%
labels_map = meta["column_names_to_labels"]
def make_pandas_friendly(col):
    col = labels_map.get(col, col)
    col = re.sub(r'[.\s]+', '_', col)
    col = re.sub(r'[^0-9a-zA-Z_]', '', col)
    col = col.lower()
    col = re.sub(r'__+', '_', col)      # Replace double (or more) underscores with single
    col = col.strip('_')                # Remove leading/trailing underscores
    return col

df_sav.columns = [make_pandas_friendly(col) for col in df_sav.columns]

# %%
df_sav.head(1).to_dict(orient='records')

# %%