from __future__ import print_function

from netCDF4 import Dataset
import numpy as np
import datetime as dt
import os
import sys
import math

###########################################

   ##
   ##  input file specified on the command line
   ##  load the data into the numpy array
   ##

if len(sys.argv) == 3:
  # Read the input file as the first argument
  input_file = os.path.expandvars(sys.argv[1])
  var_name   = sys.argv[2]
  try:
    # Print some output to verify that this script ran
    print("Input File: " + repr(input_file))
    print("Variable Name : " + repr(var_name))

    # Read input file
    f = Dataset(input_file, 'r')

    # Read the requested variable
    data = np.float64(f.variables[var_name][:])

    # Expect that dimensions are ordered (lat, lon)
    # If (lon, lat), transpose the data
    if(f.variables[var_name].dimensions[0] == 'lon'):
       data = data.transpose()

    # Reset any negative values to missing data (-9999 in MET)
    data[data<0] = -9999

    # Flip data along the equator
    data = data[::-1]

    # Store a deep copy of the data for MET
    met_data = data.copy()

    print("Data Shape: " + repr(met_data.shape))
    print("Data Type:  " + repr(met_data.dtype))
  except NameError:
    print("Trouble reading input file: " . input_file)
else:
    print("Must specify exactly one input file and the variable name.")
    sys.exit(1)

###########################################

# Function to parse time strings with or without microseconds
def parse_dtg(time_str):
   try:
      time_val = dt.datetime.strptime(time_str, "%Y-%m-%d %H:%M:%S.%fZ")
   except:
      time_val = dt.datetime.strptime(time_str, "%Y-%m-%d %H:%M:%SZ")
   return time_val

###########################################

# Determine timing information from global attributes
beg_time   = parse_dtg(f.getncattr('BeginDate') + ' ' + f.getncattr('BeginTime'))
end_time   = parse_dtg(f.getncattr('EndDate')   + ' ' + f.getncattr('EndTime'))
valid_time = end_time
init_time  = valid_time
accum_sec  = math.ceil((end_time - beg_time).total_seconds())
accum_time = "%02d%02d%02d" % ((accum_sec/3600), (accum_sec%3600)/60, (accum_sec%60))

print("Begin Time =", beg_time)
print("End Time =", end_time)
print("Accum Time =",accum_time)

# Determine LatLon grid information
lat = np.float64(f.variables['lat'][:])
lon = np.float64(f.variables['lon'][:])

###########################################

   ##
   ##  create the metadata dictionary
   ##

attrs = {
   'valid': valid_time.strftime("%Y%m%d_%H%M%S"),
   'init':  valid_time.strftime("%Y%m%d_%H%M%S"),
   'lead':  '00',
   'accum': accum_time,

   'name':      var_name,
   'long_name': var_name,
   'level':     'Surface',
   'units':     'mm',

   'grid': {
       'name':        '3B42-Grid',
       'type' :          'LatLon',
       'lat_ll' :        min(lat),
       'lon_ll' :        min(lon),
       'delta_lat' : lat[1]-lat[0],
       'delta_lon' : lon[1]-lon[0],
       'Nlat' :           len(lat),
       'Nlon' :           len(lon),
   }
}

print("Attributes: " + repr(attrs))