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

Compute the hyperbolic OECS saddles 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 numbacs.flows import get_interp_arrays_2D, get_callable_2D
from numbacs.diagnostics import S_eig_2D_func
from numbacs.extraction import hyperbolic_oecs
import matplotlib.pyplot as plt

# %%
# Get flow data
# --------------
# Load in atmospheric velocity data, dates, and coordinates. Set domain for 
# iLE computation, set time, and retrieve jit-callable function for velocity data.
# 
# .. 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 more refined domain on which iLE will be computed
dx = 0.15
dy = 0.15
lonf = np.arange(-35,25+dx,dx)
latf = np.arange(-5,40+dy,dy)


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

# get jit-callable interpolant of velocity data
vel_func = get_callable_2D(grid_vel, C_eval_u, C_eval_v, spherical=1)

# set time at which hyperbolic OECS will be computed
day = 20
t0_date = np.datetime64("2020-06-{:02d}".format(day))
t0 = t[np.nonzero(dates == t0_date)[0][0]]
# %%
# S eigenvalues, eigenvectors
# ---------------------------
# Compute eigenvalues/vectors of S tensor from velocity field at time t = t0.

# compute eigenvalues/vectors of Eulerian rate of strain tensor
eigvals,eigvecs = S_eig_2D_func(vel_func,lonf,latf,h=1e-3,t0=t0)
s2 = eigvals[:,:,1]

# %%
# Hyperbolic OECS saddles
# -----------------------
# Compute generalized saddle points and hyperbolic oecs.

# set parameters for hyperbolic_oecs function
r = 5
h = 1e-3
steps = 4000
maxlen = 1.5
minval = np.percentile(s2,50)
n = 10

# compute hyperbolic_oecs
oecs = hyperbolic_oecs(s2,eigvecs,lonf,latf,r,h,steps,maxlen,minval,n=n)

# %%
# Plot all OECS
# -------------
# Plot the OECS overlaid on iLE. 
# 
# .. note::
#    Cartopy is a useful package for geophysical plotting but it is not
#    a dependency so we use matplotlib here.

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,zorder=1)
ax.contourf(lonf,latf,s2.T,levels=np.linspace(0,np.percentile(s2,99.5),51),
            extend='both',zorder=0)
 
for k in range(len(oecs)):
    ax.plot(oecs[k][0][:,0],oecs[k][0][:,1],'r',lw=1)
    ax.plot(oecs[k][1][:,0],oecs[k][1][:,1],'b',lw=1)
ax.set_xlim([lonf[0],lonf[-1]])
ax.set_ylim([latf[0],latf[-1]])
ax.set_aspect('equal')  
plt.show()
# %%
# Advect OECS
# -----------
# Advect OECS and a circle centered at the generalized saddle point.

# import necessary functions
from numbacs.flows import get_flow_2D
from numbacs.utils import gen_filled_circ
from numbacs.integration import flowmap_n

# get funcptr, set parameters for integration, and integrate
funcptr = get_flow_2D(grid_vel, C_eval_u, C_eval_v, spherical = 1)
nc = 1000
nT = 4
T = 24.
t_eval = np.linspace(0,T,nT)
adv_circ = []
adv_rep = []
adv_att = []

# advect the top 3 (in strength) OECS 
for k in range(len(oecs[:3])):
    circ1 = gen_filled_circ(r-3.5,nc,c=oecs[k][2])
    adv_circ.append(flowmap_n(funcptr, t0, T, circ1, np.array([1.0]), n = nT)[0])
    adv_rep.append(flowmap_n(funcptr, t0, T, oecs[k][0], np.array([1.0]), n = nT)[0])
    adv_att.append(flowmap_n(funcptr, t0, T, oecs[k][1], np.array([1.0]), n = nT)[0])
    
# %%
# Plot advected OECS
# ------------------
# Plot advected OECS at 0, 8, 16, and 24 hours after t0.
fig,axs = plt.subplots(nrows=2,ncols=2,sharex=True,sharey=True,dpi=200)
axs = axs.flat
nax = len(axs) 
for i in range(nax):
    axs[i].scatter(coastlines[:,0],coastlines[:,1],1,'k',marker='.',
                   edgecolors=None,linewidths=0,zorder=1)
    kt = i
    axs[i].set_title('t0 + {:02d}hrs'.format(round(t_eval[i])))
    for k in range(len(adv_rep)):
        axs[i].scatter(adv_rep[k][:,kt,0],adv_rep[k][:,kt,1],1,'r',marker='.',
                       edgecolors=None,linewidths=0)
        axs[i].scatter(adv_att[k][:,kt,0],adv_att[k][:,kt,1],1,'b',marker='.',
                       edgecolors=None,linewidths=0)
        axs[i].scatter(adv_circ[k][:,kt,0],adv_circ[k][:,kt,1],0.5,'g',zorder=0)
    axs[i].set_xlim([lonf[0],lonf[-1]+10])
    axs[i].set_ylim([latf[0],latf[-1]])
    axs[i].set_aspect('equal')    
plt.show()