#!/usr/bin/env python3
"""
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(f"2020-06-{day:02d}")
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()