"""
prep_input.py

This script reads in MERRA2 SLV,LND data in combination with 
flux station data together with HLS chips to produce a CSV with 
aggregate data values that can be used to train for GPP flux prediction.

Author: Besart Mujeci, Srija Chakraborty, Christopher Phillips

Usage:
    python prep_input.py
"""
import rclone
from pathlib import Path
import shutil
import os
import pandas as pd
import netCDF4 as nc
import numpy as np
import pandas as pd


# --- --- ---
def convert_HLS_date(chip_name):
    """
    Extracts date string from HLS tile name and returns date object

    Args:
        chip_name (string): name of hls file

    Returns:
        datetime: datetime object of time string
    """
    hls_date = chip_name.split('.')[3][:7]
    year = int(hls_date[:4])
    day = int(hls_date[4:])    
    date = datetime(year, 1, 1)+timedelta(days=day-1)
        
    return date
# --- --- ---


# --- --- --- Set up rclone and get chips and merra files
rawdir = ''
merradir = ''
cfg = ""
result = rclone.with_config(cfg).run_cmd("ls", extra_args=[f"{rawdir}/"])
output_lines = result['out'].decode('utf-8').splitlines()
file_list = [line.split(maxsplit=1)[1] for line in output_lines if line]
result = rclone.with_config(cfg).run_cmd("ls", extra_args=[f"{merradir}/"])
output_lines = result['out'].decode('utf-8').splitlines()
merras = [line.split(maxsplit=1)[1] for line in output_lines if line]
# --- --- ---


# --- --- --- Set up paths
# Location of station tile list
station_file = './TILED_filtered_flux_sites_2018_2021.csv'
# Location to save the input file
spath = './all_inputs.csv'
odir = ''
# --- --- ---


# --- --- --- Get station information
stations = {}
fn = open(station_file, 'r')
for line in list(fn)[1:]:
    dummy = line.split(',')
    stations[dummy[1].strip()] = (dummy[1], float(dummy[9]), float(dummy[8]), dummy[3], dummy[4])
fn.close()
flux_nets = os.listdir("./fluxnets/flux_sites_2018_2021/")
# --- --- ---

# Locate all HLS chips
chips = sorted(file_list)
skipped = []

# Make the input file to which to save the data
out_fn = open(spath, 'w')
out_fn.write(f'Chip,Station,T2MIN,T2MAX,T2MEAN,TSMDEWMEAN,GWETROOT,LHLAND,SHLAND,SWLAND,PARDFLAND,PRECTOTLAND,GPP')

# And loop over them
for chip in chips:
    rclone.with_config(cfg).copy(f"{rawdir}/{chip}", f"./{chip}")

    # Match to an Ameriflux station
    chip_name = chip.split('/')[-1]
    tile = chip_name.split('.')[2][1:]
    station_name = chip_name.split('.')[6].split("_")[0]
    try: # Skip tiles for which no station exists
        station = stations[station_name]
    except:
        print(f"exception - {('station dict indexing', station_name, tile)}")
        continue
    date = helpers.convert_HLS_date(chip_name)

    # Locate station from tile and pull in the daily reference value
    try: # Skip tiles for which no station data is available
        station_file = [fluxnet for fluxnet in flux_nets if station[0] in fluxnet][0]
        flux_df = pd.read_csv(f"genai-usra-east/impact/fluxnets/flux_sites_2018_2021/{station_file}")
    except:
        print(f"exception - {('station exception', station_name, tile)}")
        continue

    flux_times = np.array(flux_df.TIMESTAMP, dtype='str')
    flux_gpp = np.array(flux_df.GPP_NT_VUT_REF)
    try: # Skip if cannot find CO2 data
        quality_flag = np.array(flux_df.NEE_VUT_REF_QC)
        if quality_flag[flux_times==date.strftime("%Y%m%d")][0] >= 0.6:
            co2 = flux_gpp[flux_times==date.strftime("%Y%m%d")][0]
        else: # Quality not met, skip
            print(f"co2 quality not met for - {('co2', station_name, tile)}")
            continue
    except:
        print(f"co2 quality not met for - {('co2 exception', station_name, tile)}")
        skipped.append(('co2 exception', station_name, tile))
        continue


    # Pull MERRA-2 data for temperature and dew
    merra_file = [file for file in merras if "slv" in file and str(date.strftime("%Y%m%d")) in file][0]
    rclone.with_config(cfg).copy(f"{merradir}/{merra_file}", f"./merra/")
    merra_fn = nc.Dataset(f'./merra/{merra_file}')

    # Pull in the MERRA-2 grid and find closest point
    mlons = merra_fn.variables['lon'][:]
    mlats = merra_fn.variables['lat'][:]
    xind = np.argmin((mlons-station[1])**2)
    yind = np.argmin((mlats-station[2])**2)
    
    # Read the variables and collect stats based on time dimension
    tmax = np.max(merra_fn.variables['T2M'], keepdims=True, axis=0)
    tmin = np.min(merra_fn.variables['T2M'], keepdims=True, axis=0)
    tmean = np.nanmean(merra_fn.variables['T2M'][:,yind, xind])
    tmax = tmax[0,yind,xind]
    tmin = tmin[0,yind,xind]
    tdewmean = np.nanmean(merra_fn.variables['T2MDEW'][:,yind, xind])
    

    shutil.rmtree(Path(f"./merra"))
    
    # Pull MERRA-2 data for surface data
    merra_file = [file for file in merras if "lnd" in file and str(date.strftime("%Y%m%d")) in file][0]
    rclone.with_config(cfg).copy(f"{merradir}/{merra_file}", f"./merra/")
    merra_fn = nc.Dataset(f'./merra/{merra_file}')
    
    # Pull in the MERRA-2 grid and find closest point
    mlons = merra_fn.variables['lon'][:]
    mlats = merra_fn.variables['lat'][:]
    xind = np.argmin((mlons-station[1])**2)
    yind = np.argmin((mlats-station[2])**2)
    
    # Read the variables and collect stats based on time dimension
    GWETROOT = np.nanmean(merra_fn.variables['GWETROOT'][:,yind,xind])
    LHLAND = np.nanmean(merra_fn.variables['LHLAND'][:,yind,xind])
    SHLAND = np.nanmean(merra_fn.variables['SHLAND'][:,yind,xind])
    PARDFLAND = np.nanmean(merra_fn.variables['PARDFLAND'][:,yind,xind])
    PRECTOTLAND = np.nanmean(merra_fn.variables['PRECTOTLAND'][:,yind,xind])
    SWLAND = np.nanmean(merra_fn.variables['SWLAND'][:,yind,xind])

    shutil.rmtree(Path(f"./merra"))
    shutil.rmtree(Path(f"./{tile}"))
    # Save chip name, MERRA-2 values, and Ameriflux measurement to data file
    out_fn.write(f'\n{chip_name},{station[0]},{tmin:.2f},{tmax:.2f},{tmean:.2f},{tdewmean:.2f},{GWETROOT:.2f},{LHLAND:.2f},{SHLAND:.2f},{SWLAND:.2f},{PARDFLAND:2f},{PRECTOTLAND:2f},{co2}')
# Close the file
out_fn.close()
rclone.with_config(cfg).copy(f"{spath}", f"{odir}")
print("DONE")