"""
.. _antarctica_example:

Compare Field Across Mesh Regions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Here is some velocity data from a glacier modelling simulation that is compared
across nodes in the simulation. We have simplified the mesh to have the
simulation node value already on the mesh.

This was originally posted to `pyvista/pyvista-support#83 <https://github.com/pyvista/pyvista-support/issues/83>`_.

The modeling results are courtesy of `Urruty Benoit <https://github.com/BenoitURRUTY>`_
and  are from the `Elmer/Ice <http://elmerice.elmerfem.org>`_ simulation
software.

"""

from __future__ import annotations

import numpy as np

# sphinx_gallery_thumbnail_number = 2

# sphinx_gallery_start_ignore
# labels are not supported in vtk-js
PYVISTA_GALLERY_FORCE_STATIC_IN_DOCUMENT = True
# sphinx_gallery_end_ignore

import pyvista as pv
from pyvista import examples

# Load the sample data :func:`~pyvista.examples.downloads.download_antarctica_velocity`
mesh = examples.download_antarctica_velocity()
mesh['magnitude'] = np.linalg.norm(mesh['ssavelocity'], axis=1)
mesh

# %%
# Here is a helper to extract regions of the mesh based on the simulation node.


def extract_node(node):
    idx = mesh['node_value'] == node
    return mesh.extract_points(idx)


# %%

p = pv.Plotter()
p.add_mesh(mesh, scalars='node_value')
for node in np.unique(mesh['node_value']):
    loc = extract_node(node).center
    p.add_point_labels(loc, [f'Node {node}'])
p.show(cpos='xy')


# %%

vel_dargs = dict(scalars='magnitude', clim=[1e-3, 1e4], cmap='Blues', log_scale=True)

mesh.plot(cpos='xy', **vel_dargs)

# %%

a = extract_node(12)
b = extract_node(20)

# %%

pl = pv.Plotter()
pl.add_mesh(a, **vel_dargs)
pl.add_mesh(b, **vel_dargs)
pl.show(cpos='xy')

# %%
# plot vectors without mesh

pl = pv.Plotter()
pl.add_mesh(a.glyph(orient='ssavelocity', factor=20), **vel_dargs)
pl.add_mesh(b.glyph(orient='ssavelocity', factor=20), **vel_dargs)
pl.camera_position = [
    (-1114684.6969340036, 293863.65389149904, 752186.603224546),
    (-1114684.6969340036, 293863.65389149904, 0.0),
    (0.0, 1.0, 0.0),
]
pl.show()


# %%
# Compare directions. Normalize them so we can get a reasonable direction
# comparison.

flow_a = a.point_data['ssavelocity'].copy()
flow_a /= np.linalg.norm(flow_a, axis=1).reshape(-1, 1)
flow_b = b.point_data['ssavelocity'].copy()
flow_b /= np.linalg.norm(flow_b, axis=1).reshape(-1, 1)


# plot normalized vectors
pl = pv.Plotter()
pl.add_arrows(a.points, flow_a, mag=10000, color='b', label='flow_a')
pl.add_arrows(b.points, flow_b, mag=10000, color='r', label='flow_b')
pl.add_legend()
pl.camera_position = [
    (-1044239.3240694795, 354805.0268606294, 484178.24825854995),
    (-1044239.3240694795, 354805.0268606294, 0.0),
    (0.0, 1.0, 0.0),
]
pl.show()


# %%
# flow_a that agrees with the mean flow path of flow_b
agree = flow_a.dot(flow_b.mean(0))

pl = pv.Plotter()
pl.add_mesh(a, scalars=agree, cmap='bwr', scalar_bar_args={'title': 'Flow agreement with block b'})
pl.add_mesh(b, color='w')
pl.show(cpos='xy')

# %%
agree = flow_b.dot(flow_a.mean(0))

pl = pv.Plotter()
pl.add_mesh(a, color='w')
pl.add_mesh(b, scalars=agree, cmap='bwr', scalar_bar_args={'title': 'Flow agreement with block a'})
pl.show(cpos='xy')