"""
Eddy detection : Med
====================

Script will detect eddies on adt field, and compute u,v with method add_uv(which could use, only if equator is avoid)

Figures will show different step to detect eddies.

"""
from datetime import datetime

from matplotlib import pyplot as plt
from numpy import arange

from py_eddy_tracker import data
from py_eddy_tracker.dataset.grid import RegularGridDataset


# %%
def start_axes(title):
    fig = plt.figure(figsize=(13, 5))
    ax = fig.add_axes([0.03, 0.03, 0.90, 0.94])
    ax.set_xlim(-6, 36.5), ax.set_ylim(30, 46)
    ax.set_aspect("equal")
    ax.set_title(title, weight="bold")
    return ax


def update_axes(ax, mappable=None):
    ax.grid()
    if mappable:
        plt.colorbar(mappable, cax=ax.figure.add_axes([0.94, 0.05, 0.01, 0.9]))


# %%
# Load Input grid, ADT is used to detect eddies
g = RegularGridDataset(
    data.get_demo_path("dt_med_allsat_phy_l4_20160515_20190101.nc"),
    "longitude",
    "latitude",
)

ax = start_axes("ADT (m)")
m = g.display(ax, "adt", vmin=-0.15, vmax=0.15, cmap="RdBu_r")
update_axes(ax, m)

# %%
# Get geostrophic speed u,v
# -------------------------
# U/V are deduced from ADT, this algortihm is not ok near the equator (~+- 2Â°)
g.add_uv("adt")
ax = start_axes("U/V deduce from ADT (m)")
ax.set_xlim(2.5, 9), ax.set_ylim(37.5, 40)
m = g.display(ax, "adt", vmin=-0.15, vmax=0.15, cmap="RdBu_r")
u, v = g.grid("u").T, g.grid("v").T
ax.quiver(g.x_c, g.y_c, u, v, scale=10)
update_axes(ax, m)

# %%
# Pre-processings
# ---------------
# Apply a high-pass filter to remove the large scale and highlight the mesoscale
g.bessel_high_filter("adt", 500)
ax = start_axes("ADT (m) filtered (500km)")
m = g.display(ax, "adt", vmin=-0.15, vmax=0.15, cmap="RdBu_r")
update_axes(ax, m)

# %%
# Identification
# --------------
# Run the identification step with slices of 2 mm
date = datetime(2016, 5, 15)
a, c = g.eddy_identification("adt", "u", "v", date, 0.002, shape_error=55)

# %%
# Display of all closed contours found in the grid (only 1 contour every 4)
ax = start_axes("ADT closed contours (only 1 / 4 levels)")
g.contours.display(ax, step=4)
update_axes(ax)

# %%
# Contours included in eddies
ax = start_axes("ADT contours used as eddies")
g.contours.display(ax, only_used=True)
update_axes(ax)

# %%
# Post analysis
# -------------
# Contours can be rejected for several reasons (shape error to high, several extremum in contour, ...)
ax = start_axes("ADT rejected contours")
g.contours.display(ax, only_unused=True)
update_axes(ax)

# %%
# Criteria for rejecting a contour:
#  0. - Accepted (green)
#  1. - Rejection for shape error (red)
#  2. - Masked value within contour (blue)
#  3. - Under or over the pixel limit bounds (black)
#  4. - Amplitude criterion (yellow)
ax = start_axes("Contours' rejection criteria")
g.contours.display(ax, only_unused=True, lw=0.5, display_criterion=True)
update_axes(ax)

# %%
# Display the shape error of each tested contour, the limit of shape error is set to 55 %
ax = start_axes("Contour shape error")
m = g.contours.display(
    ax, lw=0.5, field="shape_error", bins=arange(20, 90.1, 5), cmap="PRGn_r"
)
update_axes(ax, m)

# %%
# Some closed contours contains several eddies (aka, more than one extremum)
ax = start_axes("ADT rejected contours containing eddies")
g.contours.label_contour_unused_which_contain_eddies(a)
g.contours.label_contour_unused_which_contain_eddies(c)
g.contours.display(
    ax,
    only_contain_eddies=True,
    color="k",
    lw=1,
    label="Could be a contour of interaction",
)
a.display(ax, color="r", linewidth=0.75, label="Anticyclonic", ref=-10)
c.display(ax, color="b", linewidth=0.75, label="Cyclonic", ref=-10)
ax.legend()
update_axes(ax)

# %%
# Output
# ------
# When displaying the detected eddies, dashed lines are for effective contour, solide lines for the contour of
# the maximum mean speed. See figure 1 of https://doi.org/10.1175/JTECH-D-14-00019.1

ax = start_axes("Detected Eddies")
a.display(
    ax, color="r", linewidth=0.75, label="Anticyclonic ({nb_obs} eddies)", ref=-10
)
c.display(ax, color="b", linewidth=0.75, label="Cyclonic ({nb_obs} eddies)", ref=-10)
ax.legend()
update_axes(ax)

# %%
# Display the speed radius of the detected eddies
ax = start_axes("Speed Radius (km)")
kwargs = dict(vmin=10, vmax=50, s=80, ref=-10, cmap="magma_r", factor=0.001)
a.scatter(ax, "radius_s", **kwargs)
m = c.scatter(ax, "radius_s", **kwargs)
update_axes(ax, m)

# %%
# Filling the effective radius contours with the effective radius values
ax = start_axes("Effective Radius (km)")
kwargs = dict(vmin=10, vmax=80, cmap="magma_r", factor=0.001, lut=14, ref=-10)
a.filled(ax, "effective_radius", **kwargs)
m = c.filled(ax, "radius_e", **kwargs)
update_axes(ax, m)
