#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MERRA-2 Hyperbolic LCS
======================

Compute hyperbolic LCS using the variational theory for atmospheric flow at time of Godzilla dust
storm using MERRA-2 data which is vertically averaged over pressure surfaces
ranging from 500hPa to 800hPa..
"""

# Author: ajarvis
# Data: MERRA-2 - Global Modeling and Assimilation Office - NASA

import numpy as np
from math import copysign
from numbacs.flows import get_interp_arrays_2D, get_flow_2D
from numbacs.integration import flowmap_aux_grid_2D
from numbacs.diagnostics import C_eig_aux_2D, ftle_from_eig
from numbacs.extraction import hyperbolic_lcs
import matplotlib.pyplot as plt
# %%
# Get flow data
# --------------
# Load in atmospheric velocity data, dates, and coordinates. Set domain for 
# FTLE computation and integration span. Create interpolant and retrieve flow.
# 
# .. note::
#    Pandas is a simpler option for storing and manipulating dates but we use
#    numpy here as Pandas is not a dependency.

# load in atmospheric data
dates = np.load('../data/merra_june2020/dates.npy')
dt = (dates[1] - dates[0]).astype('timedelta64[h]').astype(int)
t = np.arange(0,len(dates)*dt,dt,np.float64)
lon = np.load('../data/merra_june2020/lon.npy')
lat = np.load('../data/merra_june2020/lat.npy')

# NumbaCS uses 'ij' indexing, most geophysical data uses 'xy'
# indexing for the spatial coordintes. We need to switch axes and
# scale by 3.6 since velocity data is in m/s and we want km/hr.
u = np.moveaxis(np.load('../data/merra_june2020/u_500_800hPa.npy'),1,2)*3.6
v = np.moveaxis(np.load('../data/merra_june2020/v_500_800hPa.npy'),1,2)*3.6
nt,nx,ny = u.shape

# set domain on which ftle will be computed
dx = 0.1
dy = 0.1
lonf = np.arange(-100,35+dx,dx)
latf = np.arange(-5,45+dy,dy)


# set integration span and integration direction
day = 16
t0_date = np.datetime64("2020-06-{:02d}".format(day))
t0 = t[np.nonzero(dates == t0_date)[0][0]]
T = -72.0
params = np.array([copysign(1,T)])

# get interpolant arrays of velocity field
grid_vel, C_eval_u, C_eval_v = get_interp_arrays_2D(t, lon, lat, u, v)

# set integration direction and retrieve flow
# set spherical = 1 since flow is on spherical domain and lon is from [-180,180)
params = np.array([copysign(1,T)])
funcptr = get_flow_2D(grid_vel, C_eval_u, C_eval_v, spherical=1)

# %%
# Integrate
# ---------
# Integrate grid of particles and auxillary grid with spacing h, return final positions

# computes final position of particle trajectories over grid + auxillary grid
# with spacing h
h = 5e-3
flowmap = flowmap_aux_grid_2D(funcptr, t0, T, lonf, latf, params,h=h)

# %%
# CG eigenvalues, eigenvectors, and FTLE
# ----------------------------------------------
# Compute eigenvalues/vectors of CG tensor from final particle positions and compute FTLE.

# compute eigenvalues/vectors of Cauchy Green tensor
eigvals,eigvecs = C_eig_aux_2D(flowmap, dx, dy, h=h)
eigval_max = eigvals[:,:,1]
eigvec_max = eigvecs[:,:,:,1]

# copmute FTLE from max eigenvalue
ftle = ftle_from_eig(eigval_max,T)
# %%
# Hyperbolic LCS
# --------------
# Compute hyperbolic LCS using the variational theory.

# set parameters for hyperbolic lcs extraction,
# see function description for more details
step_size = 5e-3
steps = 10000
lf = 0.15
lmin = 5.0
r = 2.0
nmax = 2000
dtol = 0
nlines = 20
percentile=0
ep_dist_tol=0.0
lambda_avg_min = 0
arclen_flag=False

# extract hyperbolic lcs
lcs = hyperbolic_lcs(eigval_max, eigvecs, lonf, latf, step_size, steps, lf, lmin, r, nmax, 
                     dist_tol=dtol, 
                     nlines=nlines,
                     ep_dist_tol=ep_dist_tol,
                     percentile=percentile,
                     lambda_avg_min=lambda_avg_min,
                     arclen_flag=arclen_flag)


# %%
# Plot
# ----
# Plot the results.
coastlines = np.load('../data/merra_june2020/coastlines.npy')
fig,ax = plt.subplots(dpi=200)
ax.scatter(coastlines[:,0],coastlines[:,1],1,'k',marker='.',edgecolors=None,linewidths=0)
ax.contourf(lonf,latf,ftle.T,levels=80,zorder=0)
for l in lcs:
    ax.plot(l[:,0],l[:,1],'r',lw=0.5)
    
ax.set_xlim([lonf[0],lonf[-1]])
ax.set_ylim([latf[0],latf[-1]])
ax.set_aspect('equal')
plt.show()
