"""
Quantitative Precipitation Forecast (QPF)
====================================================
This example demonstrates how to perform QPF using radar data
"""

import xarray as xr
import swirlspy.qpf.qpfrover as rov
import numpy as np
import os
from swirlspy.rad.cinrad import read_cinrad
from swirlspy.rad.xrnd import xrnd

import os.path

try:
    THIS_DIR = os.path.dirname(os.path.abspath(__file__))
except NameError:
    import sys
    THIS_DIR = os.path.dirname(os.path.abspath(sys.argv[0]))

#############################################################
# Initialising
# ---------------------------------------------------
#
# Read data from files (in this example, the files are
# in cinrad format) and storing data as xarrays.

file_type = 'cinrad-cband'
valid_time = '201805150624'
xllcenter = 113.35
yllcenter = 23.0
steps = 6
time_step_size = 5
# radar_radius = 256

os.chdir(THIS_DIR + "/../tests/test_qpf")
file_name1 = "Z_RADR_I_Z9290_20180515062400_O_DOR_CB_CAP.bin"
file_name2 = "Z_RADR_I_Z9290_20180515062900_O_DOR_CB_CAP.bin"

# Reading file data, storing as xarray
xarray1 = read_cinrad(file_name1, 113.35, 23.0,
                      480, 480, '201805150624', 5, 'aeqd', 256, 'c-band')
xarray2 = read_cinrad(file_name2, 113.35, 23.0,
                      480, 480, '201805150629', 5, 'aeqd', 256, 'c-band')
qpf_xarray = xrnd(xarray1, xarray2)
os.chdir(THIS_DIR)

####################################
# Extrapolation
# ------------------------------
# Perform rover extrapolation

# Calling rover
motion_u, motion_v, reflectivity = rov.rover_qpf(qpf_xarray, xllcenter,
                                                 yllcenter, valid_time, steps,
                                                 time_step_size, THIS_DIR)
###################################################
# Generating radar plots
# ------------------------
#
# Here we generate radar colour-images based on forecasted
# data over the next six timesteps.

rov.rover_map(reflectivity, THIS_DIR)