import os
import argparse
import xarray as xr
import numpy as np
import pandas as pd

# --- Utility Functions (unchanged logic) ---

def generate_empty_dataframe(start_year, end_year, latitude_len, longitude_len):
    """Generates an empty DataFrame with a MultiIndex for time and grid coordinates."""
    df = pd.MultiIndex.from_product([[year for year in range(start_year, end_year + 1)],
                                     [month + 1 for month in range(12)],
                                     [latitude for latitude in range(latitude_len)],
                                     [longitude for longitude in range(longitude_len)]],
                                    names=['year', 'month', 'latitude', 'longitude']).to_frame(index=False)
    return df

def count_month(start_year, start_month, end_year, end_month, start, end):
    """Calculates the number of months before and after the data range, relative to the target range."""
    s_year, s_month = start
    e_year, e_month = end
    pre_mon = (start_year - s_year) * 12 + (start_month - s_month)
    tail_mon = (e_year - end_year) * 12 + (e_month - end_month)
    return pre_mon, tail_mon

def concat_pre_end(pre_month, tail_month, data):
    """Pads or trims the data array to fit the target time range with NaNs."""
    # Assuming a fixed spatial resolution for padding/trimming based on the original script's logic: 180*360
    spatial_size = 180 * 360
    new_data = data.copy()

    if pre_month > 0:
        prefix_len = spatial_size * pre_month
        prefix = np.full(prefix_len, np.nan)
        new_data = np.concatenate((prefix, new_data))
    elif pre_month < 0:
        del_length = abs(spatial_size * pre_month)
        new_data = new_data[del_length:]

    if tail_month > 0:
        tail_len = spatial_size * tail_month
        tail = np.full(tail_len, np.nan)
        new_data = np.concatenate((new_data, tail))
    elif tail_month < 0:
        del_length = abs(spatial_size * tail_month)
        new_data = new_data[:-del_length]
    return new_data

def get_time(pro_data):
    """Extracts the start and end year/month from the xarray Dataset time coordinate."""
    time_var = 'time'
    for var in list(pro_data.coords):
        if 'time' in var:
            time_var = var
            break

    start_time = pd.to_datetime(pro_data[time_var].values[0])
    end_time = pd.to_datetime(pro_data[time_var].values[-1])

    return start_time.year, start_time.month, end_time.year, end_time.month

def get_nc_data(key, dic, env_path, target_start, target_end):
    """Loads, processes, and time-aligns a single NetCDF variable."""
    pro_dir = os.path.join(env_path, f'{key}.nc')
    variable = dic[key.lower()]
    
    with xr.open_dataset(pro_dir) as pro_data:
        data = pro_data[variable].values.flatten()
        
        start_year, start_month, end_year, end_month = get_time(pro_data)
        
        pre_mon, tail_mon = count_month(start_year, start_month, end_year, end_month, 
                                        start=target_start, end=target_end)
        
        new_data = concat_pre_end(pre_mon, tail_mon, data)
        return new_data

# --- Main Processing Function ---

def main(env_path, gobm_dir, dgvm_dir, co2_path, output_dir, 
         env_start_year, env_start_month, env_end_year, env_end_month, nrt_dir=None):
    
    # Configuration based on original script
    LAT_LEN, LON_LEN = 180, 360
    
    DIC = {
        'sst':'sst', 'ice':'ice', 'chl':'CHL1_mean', 'mld':'somxl030', 
        'sss':'salinity', 'ssh':'sossheig', 'slp':'msl', 'wind':'wind',
    }
    
    TARGET_START = (env_start_year, env_start_month)
    TARGET_END = (env_end_year, env_end_month)

    print(f"🌊 **Starting Data Processing**")
    print(f"Target Time Range: {env_start_year}/{env_start_month} to {env_end_year}/{env_end_month}")
    print(f"Environmental Data Path: {env_path}")
    
    # 1. Initialize DataFrame for Environmental Data
    print("\n## 1. Processing Environmental Variables")
    add_data = generate_empty_dataframe(start_year=env_start_year, end_year=env_end_year, 
                                        latitude_len=LAT_LEN, longitude_len=LON_LEN)
    print(f"Initialized DataFrame with shape: {add_data.shape}")

    # Process each environmental variable file
    for key_file in os.listdir(env_path): 
        key = key_file.split('.')[0]
        if key.lower() not in DIC:
            print(f"Skipping unknown file: {key_file}")
            continue
            
        print(f"Processing environmental variable: **{key}**")
        new_data = get_nc_data(key, DIC, env_path=env_path, 
                               target_start=TARGET_START, target_end=TARGET_END)
        add_data[key.lower()] = new_data

    # Handle fill values/outliers
    for i in ['sst', 'ice', 'chl']:
        err_idx = add_data[add_data[i] == -999].index
        if err_idx.shape[0] > 0:
            print(f"Found and replaced {err_idx.shape[0]} fill values (-999) in {i}")
            add_data.loc[err_idx, i] = np.nan
            
    for i in ['mld', 'ssh']:
        if add_data[i].max() > 1e36:
            err_idx = add_data[add_data[i] == add_data[i].max()].index
            if err_idx.shape[0] > 0:
                print(f"Found and replaced {err_idx.shape[0]} max-value outliers in {i}")
                add_data.loc[err_idx, i] = np.nan


    # 2. Add Coast and Region Mask Data
    print("\n## 2. Adding Region and Coast Mask Data")
    region_mask_path = os.path.join(env_path, 'Ocean_RECCAP2_mask.nc')
    if not os.path.exists(region_mask_path):
        print(f"❌ Error: Mask file not found at {region_mask_path}")
        return

    with xr.open_dataset(region_mask_path) as region_mask:
        df_spatial = pd.MultiIndex.from_product([[lat for lat in range(LAT_LEN)],
                                                 [lon for lon in range(LON_LEN)]],
                                                names=['latitude', 'longitude']).to_frame(index=False)

        map_arr = ['land', 'Atlantic', 'Pacific', 'Indian', 'Arctic', 'Southern']
        df_spatial['type'] = region_mask.variables['open_ocean'].values.flatten()
        df_spatial['type'] = df_spatial['type'].apply(lambda x: map_arr[x] if x < len(map_arr) else 'Unknown')
        
        df_encoded = pd.get_dummies(df_spatial, columns=['type'])
        df_encoded['type_ocean'] = ~df_encoded.get('type_land', False) # True if 'type_land' column doesn't exist or is False
        df_encoded['type_coast'] = region_mask.variables['coast'].values.flatten()

        cols = [c for c in df_encoded.columns if c.startswith('type_')]
        for col in cols:
            df_encoded[col] = df_encoded[col].astype(int)

        # Repeat spatial data for all months/years in the target range
        num_repetitions = add_data.shape[0] // df_encoded.shape[0]
        df_repeated = pd.concat([df_encoded] * num_repetitions, ignore_index=True)
        
        add_data[cols] = df_repeated[cols].values
        print(f"Added {len(cols)} spatial mask columns.")

    # 3. Process GOBM and Data Product (GCB)
    print("\n## 3. Processing GCB Model/Product Data")
    
    # Determine the actual model end year by scanning directories
    model_start_year = 2000
    model_end_year = 2020
    
    for dir_path in [gobm_dir, dgvm_dir]:
        if not os.path.isdir(dir_path):
            print(f"⚠️ Warning: Model directory not found: {dir_path}. Skipping.")
            continue
            
        for i in os.listdir(dir_path):
            if i.endswith('.nc'):
                try:
                    with xr.open_dataset(os.path.join(dir_path, i)) as model:
                        year = pd.to_datetime(model.time.values[-1]).year
                        if year > model_end_year:
                            model_end_year = year
                except Exception as e:
                    print(f"Error reading time from {i}: {e}")

    print(f"Model/Product Time Range: {model_start_year} to {model_end_year}")

    # Prepare DataFrame for GCB data
    df_gcb = generate_empty_dataframe(start_year=model_start_year, end_year=model_end_year, 
                                      latitude_len=LAT_LEN, longitude_len=LON_LEN)
    # var_name = 'fgco2'  # Assuming 'fgco2' is the variable of interest in GCB files
    # var_name = 'sfco2'
    # Load and flatten GCB data
    for dir_path in [gobm_dir, dgvm_dir]:
        if not os.path.isdir(dir_path): continue
            
        for i in os.listdir(dir_path):
            if i.endswith('.nc'):
                name = i.split('.')[0]
                print(f"Loading GCB data: **{name}**")
                try:
                    with xr.open_dataset(os.path.join(dir_path, i)) as model:
                        # Select relevant time slice
                        value_var = 'fgco2'
                        for var in list(model.variables):
                            if 'co2' in var:
                                value_var = var
                                break

                        print(f"Identified variable for GCB data: {value_var}")
                        model_sel = model.sel(time=slice(str(model_start_year), str(model_end_year)))
                        df_gcb[name] = model_sel[value_var].values.flatten()
                except Exception as e:
                    print(f"Error processing GCB file {i}: {e}")

    # Merge GCB data into the main DataFrame (add_data)
    # Get index range in add_data that matches GCB time range
    target_idx = add_data[(add_data['year'] >= model_start_year) & 
                          (add_data['year'] <= model_end_year)].index
    target_cols = df_gcb.columns[4:]
    
    if len(target_idx) == df_gcb.shape[0]:
        add_data.loc[target_idx, target_cols] = df_gcb[target_cols].values
        print("Merged GCB data successfully.")
    else:
        print("❌ GCB data size mismatch. Not merged.")


    # 4. Add CO2 data
    print("\n## 4. Adding CO2 Data")
    try:
        co2 = pd.read_csv(co2_path)
    except Exception as e:
        print(f"❌ Error reading CO2 file: {e}. Skipping CO2 data.")
        co2 = None
        
    if co2 is not None and 'co2' in co2.columns:
        co2_start_year = max(int(co2.head(1)['year'].values[0]), env_start_year)
        co2_end_year = min(int(co2.tail(1)['year'].values[0]), env_end_year)

        target_idx = co2[(co2['year'] >= co2_start_year) & 
                         (co2['year'] <= co2_end_year)].index
        src_idx = add_data[(add_data['year'] >= co2_start_year) & 
                           (add_data['year'] <= co2_end_year)].index
        
        target_col = 'co2'
        if len(src_idx) == len(target_idx):
            add_data.loc[src_idx, target_col] = co2.loc[target_idx, target_col].values
            print(f"Merged CO2 data for years {co2_start_year} to {co2_end_year}.")
        else:
            print("❌ CO2 data size mismatch for the overlapping period. Not merged.")
            
    # 5. Final Save
    print("\n## 5. Saving Final Data")
    os.makedirs(output_dir, exist_ok=True)
    output_filename = f'new_{env_end_year}{env_end_month:02d}.feather'
    # output_filename = f'new_{env_end_year}{env_end_month:02d}_64.feather'
    output_filepath = os.path.join(output_dir, output_filename)
    
    # Convert float64 to float32 to save space (as per original file's logic)
    float64_cols = add_data.select_dtypes(include=['float64']).columns
    add_data[float64_cols] = add_data[float64_cols].astype('float32')
    
    add_data.to_feather(output_filepath)
    print(f"✅ Successfully saved final DataFrame to: **{output_filepath}**")
    print(f"Final DataFrame shape: {add_data.shape}")


    # process nrt data
    if nrt_dir:
        
        nrt_start_year = 2000
        add_data = add_data[add_data.year >= nrt_start_year]
        nrt_filepath = os.path.join(nrt_dir, f'new_{env_end_year}{env_end_month:02d}.feather')
        add_data.to_feather(nrt_filepath)

        print(f"✅ Successfully saved NRT DataFrame to: **{nrt_filepath}**")



if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Process and merge environmental and model data into a single feather file.")
    
    # Directory/File Paths
    parser.add_argument('--env_path', type=str, 
                        default='/data/angcb/home/kpiyu/data/Environmental_variables/2025/Environmental_variables',
                        help='Path to the directory containing environmental variable NetCDF files and the mask file.')
    parser.add_argument('--gobm_dir', type=str, 
                        default='/data/angcb/home/kpiyu/data/Model/GCB2025/GCB2025/',
                        help='Path to the directory containing GOBM model NetCDF files.')
    parser.add_argument('--dgvm_dir', type=str, 
                        default='/data/angcb/home/kpiyu/data/Data_product/GCB2025/GCB2025',
                        help='Path to the directory containing DGVM data product NetCDF files.')
    parser.add_argument('--co2_path', type=str, 
                        default='/data/angcb/home/kpiyu/ANGCB/dataset/co2_output/co2_full_grid_2025_8.csv',
                        help='Path to the CO2 CSV file.')
    parser.add_argument('--output_dir', type=str, 
                        default='/data/angcb/home/kpiyu/ANGCB/dataset/preprocessed_data',
                        help='Directory where the final feather file will be saved.')
    parser.add_argument('--nrt_dir', type=str, 
                        default=None,
                        help='Directory where the final NRT feather file will be saved.')
    # Time Parameters
    parser.add_argument('--env_start_year', type=int, default=1959,
                        help='Start year for the main environmental data processing range.')
    parser.add_argument('--env_start_month', type=int, default=1, choices=range(1, 13),
                        help='Start month for the main environmental data processing range.')
    parser.add_argument('--env_end_year', type=int, default=2025,
                        help='End year for the main environmental data processing range.')
    parser.add_argument('--env_end_month', type=int, default=12, choices=range(1, 13),
                        help='End month for the main environmental data processing range.')


    args = parser.parse_args()
    
    main(env_path=args.env_path, gobm_dir=args.gobm_dir, dgvm_dir=args.dgvm_dir, 
         co2_path=args.co2_path, output_dir=args.output_dir,
         env_start_year=args.env_start_year, env_start_month=args.env_start_month, 
         env_end_year=args.env_end_year, env_end_month=args.env_end_month,
         nrt_dir=args.nrt_dir)


# python data_preprocess.py \
# --env_path /data/angcb/home/kpiyu/data/Environmental_variables/2025/Environmental_variables \
# --gobm_dir /data/angcb/home/kpiyu/data/Model/GCB2025/GCB2025/ \
# --dgvm_dir /data/angcb/home/kpiyu/data/Data_product/GCB2025/GCB2025 \
# --co2_path /data/angcb/home/kpiyu/ANGCB/dataset/co2_output/co2_full_grid_2025_8.csv \
# --output_dir /data/angcb/home/kpiyu/ANGCB/dataset/preprocessed_data \
# --env_start_year 1959 \
# --env_start_month 1 \
# --env_end_year 2025 \
# --env_end_month 12

# python data_preprocess.py \
# --env_path /data/angcb/home/kpiyu/data/Environmental_variables/2025/Environmental_variables \
# --gobm_dir /data/angcb/home/kpiyu/data/Model/GCB2025/fCO2/ \
# --dgvm_dir /data/angcb/home/kpiyu/data/Data_product/GCB2025/fCO2/ \
# --co2_path /data/angcb/home/kpiyu/ANGCB/dataset/co2_output/co2_full_grid_2025_8.csv \
# --output_dir /data/angcb/home/kpiyu/ANGCB/dataset/flux/ \
# --env_start_year 1959 \
# --env_start_month 1 \
# --env_end_year 2025 \
# --env_end_month 12