#!/usr/bin/env python3
# Copyright (c) 2021, GeoVista Contributors.
#
# This file is part of GeoVista and is distributed under the 3-Clause BSD license.
# See the LICENSE file in the package root directory for licensing details.

"""
True Winds (Foreign Grid)
-------------------------

This example demonstrates how to render "true" winds, when attached to data points
given in a non-standard Coordinate Reference System (CRS).

📋 Summary
^^^^^^^^^^

The example constructs sample points on a North Polar Stereographic grid, and assigns
synthetic vector values to them, representing wind data. These data are presented as
"true" winds, i.e., eastward (``U``) and northward (``V``) vector components, therefore
not related to the foreign sample points grid. This is a fairly typical real world
use case.

A synthetic eastward wind with a constant ``4.0 ms-1`` is generated everywhere,
so the final effect is clearly visible.

We use the :meth:`geovista.bridge.Transform.from_points` method, passing the
winds to the ``vectors`` keyword, and specifying the different CRS of both the
sample points (``crs``) and the vectors (``vectors_crs``).

.. tags::

    experimental 🧪,
    component: graticule, component: texture, component: vectors,
    domain: meteorology,
    filter: glyph,
    version: 0.6.0

----

"""  # noqa: D205,D212,D400

from __future__ import annotations

import cartopy.crs as ccrs
import numpy as np
import pyvista as pv

import geovista as gv


def main() -> None:
    """Plot vectors on different CRS from points.

    Notes
    -----
    .. versionadded:: 0.6.0

    """
    # Create a stereographic CRS (foreign grid) for the sample points.
    crs_polar = ccrs.NorthPolarStereo()

    # Create a geographic (latitude/longitude) CRS for the vectors.
    crs_latlon = ccrs.Geodetic()

    # Create the polar grid points.
    x = np.linspace(-5.0e6, 5.0e6, 20)
    y = np.linspace(-4.0e6, 2.0e6, 20)
    xs, ys = np.meshgrid(x, y)

    # Create the eastward (us) and northward (vs) wind vector components.
    xs, ys = xs.flatten(), ys.flatten()
    us, vs = 4.0 * np.ones_like(xs), np.zeros_like(ys)

    # Create the point cloud mesh with attached wind vectors.
    mesh = gv.Transform.from_points(
        xs, ys, vectors=(us, vs), crs=crs_polar, vectors_crs=crs_latlon
    )

    # Generate a mesh containing arrow glyphs from the wind vectors.
    arrows = mesh.glyph(factor=0.02)

    # Now render the plotter scene.
    p = gv.GeoPlotter()
    p.add_mesh(arrows, color="red")
    p.add_base_layer(texture=gv.natural_earth_hypsometric())
    p.add_graticule()

    # Define a specific camera position and orientation.
    cpos = pv.CameraPosition(
        position=(0.60371, -0.01103, 3.06958),
        focal_point=(0.0, 0.0, 0.00289),
        viewup=(-0.98099, -0.01998, 0.19304),
    )

    p.add_axes()
    p.show(cpos=cpos)


if __name__ == "__main__":
    main()