"""
Convert to RGB format (vector data)
===========================
This example demonstrates convertion from wind data to uv rgb format (with HKO earth format).
"""

###########################################################
# 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 image generation
from PIL import Image
# Python package for image preview
import matplotlib.pyplot as plt

# swirlspy iris parser function
from swirlspy.rad.iris import read_iris_grid
# swirlspy regrid function
from swirlspy.preprocess import grid_align
# swirlspy rgb convertion function
from swirlspy.utils.conversion import to_rgb_data, to_hko_earth_format
# directory constants
from swirlspy.tests.samples import DATA_DIR
from swirlspy.tests.outputs import OUTPUT_DIR

warnings.filterwarnings("ignore")

# Logging
start_time = pd.Timestamp.now()

#########################################################################
# Loading radar data
# -----------------------------------------------------------------------
#

# Specify the basetime
basetime = pd.Timestamp('201902190800')

# Reading the wind data
reflec = read_iris_grid(
    os.path.join(
        DATA_DIR,
        basetime.strftime("iris/ppi/TMS%y%m%d%H%M02.PPIMK3B")
    )
)

initialising_time = pd.Timestamp.now()


#########################################################################
# Reproject to WGS 84, required for HKO earth format
# -----------------------------------------------------------------------
#

# calculate x, y step size
y_step = -0.025
x_step = 0.025
y = np.arange(24.5, 20.5, y_step)
x = np.arange(112, 116, x_step)

reflec_wgs84 = grid_align(
    reflec, reflec.attrs['area_def'].proj_str,
    x, y, '+proj=longlat +datum=WGS84 +no_defs'
)

preparation_time = pd.Timestamp.now()

#########################################################################
# Convert image data into rgb format
# -----------------------------------------------------------------------
#

# only shape with (y, x) is allowed
data = reflec_wgs84.sel(time=basetime)

# only allow positive values
data = data.where(data >= 0)

# this step is not necessary, depends on any meta data and preprocess is required by your platform
earth_data = to_hko_earth_format(data, coords_dp=3, y_step=y_step, x_step=x_step)
rgb = to_rgb_data(earth_data)

convertion_time = pd.Timestamp.now()

#########################################################################
# Visualisation
# -----------------------------------------------------------------------
#

path = os.path.join(OUTPUT_DIR, "rgb_wind.png")
with Image.fromarray(rgb, 'RGBA') as img:
    img.save(path, 'png')

# preview
with Image.open(path) as image:
    plt.axis('off')
    plt.imshow(image)
    plt.plot()
    plt.show()

visualise_time = pd.Timestamp.now()

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

print(f"Start time: {start_time}")
print(f"Initialising time: {initialising_time}")
print(f"Preparation time: {preparation_time}")
print(f"Convertion time: {convertion_time}")
print(f"Visualise time: {visualise_time}")

print(f"Time to initialise: {initialising_time - start_time}")
print(f"Time to prepare information: {preparation_time - initialising_time}")
print(f"Time to convertion: {convertion_time - preparation_time}")
print(f"Time to visualise: {visualise_time - convertion_time}")

print(f"Total: {visualise_time - start_time}")