"""
QPF (Thailand)
===================================================
This example demonstrates how to perform
operational deterministic QPF up to three hours
from hourly composite rainfall data, using data from Thailand.
"""

###########################################################
# Definitions
# --------------------------------------------------------
#

########################################################################
# Import all required modules and methods:

# Python package to allow system command line functions
import os
# Python package to manage warning message
import warnings
# Python package for time calculations
import pandas as pd
# Python package for numerical calculations
import numpy as np
# Python package for xarrays to read and handle netcdf data
import xarray as xr
# Python package for projection
import cartopy.crs as ccrs
# Python package for land/sea features
import cartopy.feature as cfeature
# Python package for reading map shape file
import cartopy.io.shapereader as shpreader
# Python package for creating plots
from matplotlib import pyplot as plt
# Python package for projection conversion
from utm.conversion import from_latlon
# Python package for colorbars
from matplotlib.colors import BoundaryNorm, ListedColormap
# Python package for area definition
from pyresample import get_area_def

# Python com-swirls package to calculate motion field (rover) and semi-lagrangian advection
from swirlspy.qpf import rover, sla
# swirlspy Thailand netcdf file parser function
from swirlspy.rad import read_netcdf_th_refl
# swirlspy regrid function
from swirlspy.core.resample import grid_resample
# swirlspy standardize data function
from swirlspy.utils import standardize_attr, FrameType
# swirlspy data convertion function
from swirlspy.utils.conversion import to_rainfall_depth, acc_rainfall_depth
# directory constants
from swirlspy.tests.samples import DATA_DIR
from swirlspy.tests.outputs import OUTPUT_DIR

warnings.filterwarnings("ignore")
plt.switch_backend('agg')

start_time = pd.Timestamp.now()

#############################################################
# Initialising
# ---------------------------------------------------
#
# This section demonstrates extracting
# radar reflectivity data.
#
# Step 1: Define a basetime.
#

# Supply basetime
baseT = "202010161000"
basetime = pd.Timestamp(baseT)


##############################################################################
# Step 2: Using basetime, generate timestamps of desired radar files.

# Obtain radar files
located_files = []
interval = pd.Timedelta(15, 'm')
duration = pd.Timedelta(60, 'm')

while duration >= pd.Timedelta(0):
    located_files.append(
        os.path.abspath(os.path.join(
            DATA_DIR,
            'netcdf_tmd',
            (basetime - duration).strftime('RADAR_Thailand2_%Y%m%dT%H%M%S000Z.nc')
        )
        ))
    duration -= interval

##############################################################################
# Step 3: Read data from radar files into xarray.DataArray
# using read_netcdf_th_refl().
#

# defining the a and b value of the Z-R relationship
ZRa = 300
ZRb = 1.4


reflect_list = []  # stores reflectivity from read_netcdf_th-refl()
for filename in located_files:
    reflec = read_netcdf_th_refl(
        filename, ZRa, ZRb
    )
    reflect_list.append(reflec)


##############################################################################
# Step 4 : Define the target grid
#
# Defining target grid

area_id = "Thai1975"
description = ("Covering whole territory of Thailand")
proj_id = 'Thai'
projection = "+proj=utm +zone=47 +a=6377276.345 +b=6356075.41314024 +towgs84=210,814,289,0,0,0,0 +units=m +no_defs "
x_size = len(reflect_list[0][0][0])
y_size = len(reflect_list[0][0][:])

lon = reflect_list[0][0][0].coords['lon']
lat = reflect_list[0][0].coords['lat']

# Using utm to calculate the coordinate system
ll = from_latlon(float(lat[-1]), float(lon[0]), 47)
ur = from_latlon(float(lat[0]), float(lon[-1]), 47)

area_extent = (ll[0], ll[1], ur[0], ur[1])

area_def_tgt = get_area_def(
    area_id, description, proj_id, projection, x_size, y_size, area_extent
)


##############################################################################
# Step 5: Reproject the radar data from read_netcdf_th_refl()
# (source) projection to Thai (target) projection.

# Extracting the AreaDefinition of the source projection

area_def_src = reflect_list[0].attrs['area_def']

reproj_reflect_list = []
for reflect in reflect_list:
    reproj_reflect = grid_resample(
        reflect, area_def_src, area_def_tgt,
        coord_label=['easting', 'northing']
    )
    reproj_reflect_list.append(reproj_reflect)


############################################################################
# Step 6: Assigning reflectivity xarrays at the last two timestamps to
# variables for use during ROVER QPF.

initialising_time = pd.Timestamp.now()


###########################################################
# Running ROVER and Semi-Lagrangian Advection
# -------------------------------------------
#
# 1. Concatenate two reflectivity xarrays along time dimension.
# 2. Run ROVER, with the concatenated xarray as the input.
# 3. Perform Semi-Lagrangian Advection using the motion fields from rover.

# Combining the two reflectivity DataArrays
# the order of the coordinate keys is now ['y', 'x', 'time']
# as opposed to ['time', 'x', 'y']

reflect_concat = xr.concat(reproj_reflect_list, dim='time')
standardize_attr(reflect_concat, frame_type=FrameType.dBZ, zero_value=9999.)


# Rover
motion = rover(reflect_concat)

rover_time = pd.Timestamp.now()


# Semi Lagrangian Advection
reflectivity = sla(reflect_concat, motion, 12)

sla_time = pd.Timestamp.now()


##############################################################
# Concatenating observed and forecasted reflectivities
# -------------------------------------------------------
#
# 1. Add forecasted reflectivity to reproj_reflectivity_list.
# 2. Concatenate observed and forecasted reflectivity
#    xarray.DataArrays along the time dimension.


reflectivity = xr.concat([reflect_concat[:-1, ...], reflectivity], dim='time')
reflectivity.attrs['long_name'] = '2 km radar composite reflectivity'
standardize_attr(reflectivity)

concat_time = pd.Timestamp.now()


#############################################
# Generating radar reflectivity maps
# -----------------------------------
#
# Define the color scale and format of the plots
# and plot using xarray.plot().
#
# In this example, only hourly images will be plotted.
#

# Defining colour scale and format
levels = [
    -32768,
    10, 15, 20, 24, 28, 32,
    34, 38, 41, 44, 47, 50,
    53, 56, 58, 60, 62
]
cmap = ListedColormap([
    '#FFFFFF', '#08C5F5', '#0091F3', '#3898FF', '#008243', '#00A433',
    '#00D100', '#01F508', '#77FF00', '#E0D100', '#FFDC01', '#EEB200',
    '#F08100', '#F00101', '#E20200', '#B40466', '#ED02F0'
])


norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

# Defining the crs
crs = area_def_tgt.to_cartopy_crs()

# Defining coastlines
map_shape_file = os.path.join(DATA_DIR, "shape/rsmc")
ocean_color = np.array([[[178, 208, 254]]], dtype=np.uint8)
land_color = cfeature.COLORS['land']
coastline = cfeature.ShapelyFeature(
    list(shpreader.Reader(map_shape_file).geometries()),
    ccrs.PlateCarree()
)

# Generating a timelist for every hour
timelist = [
    (basetime + pd.Timedelta(minutes=60*i-15)) for i in range(4)
]


# Obtaining the slice of the xarray to be plotted
da_plot = reflectivity.sel(time=timelist)


# Defining motion quivers
qx = motion.coords['easting'].values[::50]
qy = motion.coords['northing'].values[::50]
qu = motion.values[0, ::50, ::50]
qv = motion.values[1, ::50, ::50]

# Plotting
p = da_plot.plot(
    col='time', col_wrap=2,
    subplot_kws={'projection': crs},
    cbar_kwargs={
        'extend': 'max',
        'ticks': levels[1:],
        'format': '%.3g'
    },
    cmap=cmap,
    norm=norm
)


for idx, ax in enumerate(p.axes.flat):
    # ocean
    ax.imshow(np.tile(ocean_color, [2, 2, 1]),
              origin='upper',
              transform=ccrs.PlateCarree(),
              extent=[-180, 180, -180, 180],
              zorder=-1)
    # coastline, color
    ax.add_feature(coastline,
                   facecolor=land_color, edgecolor='none', zorder=0)
    # overlay coastline without color
    ax.add_feature(coastline, facecolor='none',
                   edgecolor='gray', linestyle=':', linewidth=0.5)
    # precipitation
    ax.quiver(qx, qy, qu, qv, pivot='mid', scale=20, scale_units='inches')
    ax.gridlines()
    ax.set_title(
        "Precipitation\n"
        f"Based @ {basetime.strftime('%H:%MZ')}",
        loc='left',
        fontsize=7
    )
    ax.set_title(
        ''
    )
    ax.set_title(
        f"{basetime.strftime('%Y-%m-%d')} \n"
        f"Valid @ {timelist[idx].strftime('%H:%MZ')} ",
        loc='right',
        fontsize=7
    )


plt.savefig(
    os.path.join(OUTPUT_DIR, f"rover-output-map-mn{baseT}.png"),
    dpi=300
)

radar_image_time = pd.Timestamp.now()


##########################################################################
# Accumulating hourly rainfall for 3-hour forecast
# ------------------------------------------------
#
# Hourly accumulated rainfall is calculated every 30 minutes, the first
# endtime is the basetime i.e. T+0min.
#
# #. Convert reflectivity in dBZ to rainfalls in 6 mins with to_rainfall_depth().
# #. Changing time coordinates of xarray from start time to endtime.
# #. Accumulate hourly rainfall every 30 minutes using multiple_acc().

# Convert reflectivity to rainrates
rainfalls = to_rainfall_depth(reflectivity, a=ZRa, b=ZRb)

# Converting the coordinates of xarray from start to endtime
rainfalls.coords['time'] = [
    pd.Timestamp(t) + pd.Timedelta(15, 'm')
    for t in rainfalls.coords['time'].values
]


intervalstep = 30
result_step_size = pd.Timedelta(minutes=intervalstep)


# Accumulate hourly rainfall every 30 minutes
acc_rf = acc_rainfall_depth(
    rainfalls,
    basetime,
    basetime + pd.Timedelta(hours=3),
    result_step_size=result_step_size
)
acc_rf.attrs['long_name'] = 'Rainfall accumulated over the past 60 minutes'


acc_time = pd.Timestamp.now()


# Defining times for plotting
timelist = [basetime + pd.Timedelta(i, 'h') for i in range(4)]

#######################################################################
# Plotting rainfall maps
# ---------------------------------------
#
# Define the colour scheme and format and plot using xarray.plot().
#
# In this example, only hourly images will be plotted.
#

# Defining the colour scheme
levels2 = [
    0.,  0.5,   2.,   5.,  10.,  20.,
    30.,  40.,  50.,  70., 100., 150.,
    200., 300., 400., 500., 600., 700.
]

cmap2 = ListedColormap([
    '#ffffff', '#9bf7f7', '#00ffff', '#00d5cc', '#00bd3d', '#2fd646',
    '#9de843', '#ffdd41', '#ffac33', '#ff621e', '#d23211', '#9d0063',
    '#e300ae', '#ff00ce', '#ff57da', '#ff8de6', '#ffe4fd'
])

norm2 = BoundaryNorm(levels2, ncolors=cmap2.N, clip=True)

# Defining times for plotting
timelist = [basetime + pd.Timedelta(i, 'h') for i in range(4)]

# Obtaining xarray slice to be plotted
da_plot = acc_rf.sel(
    time=timelist
)

# Plotting
p = da_plot.plot(
    col='time', col_wrap=2,
    subplot_kws={'projection': crs},
    cbar_kwargs={
        'extend': 'max',
        'ticks': levels2,
        'format': '%.3g'
    },
    cmap=cmap2,
    norm=norm2
)
for idx, ax in enumerate(p.axes.flat):
    # ocean
    ax.imshow(np.tile(ocean_color, [2, 2, 1]),
              origin='upper',
              transform=ccrs.PlateCarree(),
              extent=[-180, 180, -180, 180],
              zorder=-1)
    # coastline, color
    ax.add_feature(coastline,
                   facecolor=land_color, edgecolor='none', zorder=0)
    # overlay coastline without color
    ax.add_feature(coastline, facecolor='none',
                   edgecolor='gray', linestyle=':', linewidth=0.5)
    ax.gridlines()
    ax.set_title(
        "Past Hour Rainfall\n"
        f"Based @ {basetime.strftime('%H:%MZ')}",
        loc='left',
        fontsize=7
    )
    ax.set_title(
        ''
    )
    ax.set_title(
        f"{timelist[idx].strftime('%Y-%m-%d')} \n"
        f"Valid @ {timelist[idx].strftime('%H:%MZ')} ",
        loc='right',
        fontsize=7
    )


plt.savefig(
    os.path.join(OUTPUT_DIR, f"rainfall_{baseT}.png"),
    dpi=300
)

rf_image_time = pd.Timestamp.now()


######################################################################
# Checking run time of each component
# --------------------------------------------------------------------
#

print(f"Start time: {start_time}")
print(f"Initialising time: {initialising_time}")
print(f"Rover time: {rover_time}")
print(f"SLA time: {sla_time}")
print(f"Concatenating time: {concat_time}")
print(f"Plotting radar image time: {radar_image_time}")
print(f"Accumulating rainfall time: {acc_time}")
print(f"Plotting rainfall map time: {rf_image_time}")

print(f"Time to initialise: {initialising_time-start_time}")
print(f"Time to run rover: {rover_time-initialising_time}")
print(f"Time to perform SLA: {sla_time-rover_time}")
print(f"Time to concatenate xarrays: {concat_time - sla_time}")
print(f"Time to plot radar image: {radar_image_time - concat_time}")
print(f"Time to accumulate rainfall: {acc_time - radar_image_time}")
print(f"Time to plot rainfall maps: {rf_image_time - acc_time}")
print(f"Total Running time: {rf_image_time - start_time}")