"""
Visualizing 3D model data
=========================

For visualizing 3D data it might make sense to plot multiple views / slices.
To create them we use ``pyvista`` as it provides all the necessary functionality.
If we want to plot multiple meshes, they have to be in a 2D ``numpy`` array.
The shape of this array determines the number of rows and columns in our plot.
First, let's load 3D example data from ``pyvista`` and plot the only available
data: "facies" with a native pyvista plot.
"""

# %%
import numpy as np
from pyvista import examples

import ogstools as ot

mesh = examples.load_channels()
data = ot.variables.Scalar("facies", categoric=True, cmap="tab20")
section = mesh.clip("y", (0, 20, 0))

# %%
#
# .. code-block:: python
#
#    ot.plot.contourf(
#        section, data, show_edges=False, opacities={0: 0.2}, interactive=False
#    )
#

# only plotting a part of the example, as using opacities is expensive
# performance-wise and the example is big.
# sphinx_gallery_start_ignore
ot.plot.contourf(
    section, data, show_edges=False, opacities={0: 0.2}
).screenshot(return_img=False)
# sphinx_gallery_end_ignore


# %% [markdown]
# It might make sense to plot multiple views / slices of 3D data.
# To create them we use the slicing functions of ``pyvista``.
# If we want to plot multiple meshes, they have to be in a 2D ``numpy`` array.
# The shape of this array determines the number of rows and columns in our plot.

# %%
# Let's create multiple slices along the z axis and plot them in a 2 by 2 grid.

slices = np.reshape(list(mesh.slice_along_axis(n=4, axis="z")), (2, 2))
fig = ot.plot.contourf(slices, data, figsize=[8, 6], fontsize=12)
for ax, slice in zip(fig.axes, np.ravel(slices), strict=False):
    ax.set_title(f"z = {slice.center[2]:.1f}")

# %%
# We can also slice along the y-axis and plot the meshes in one row.

slices = np.reshape(mesh.slice_along_axis(n=3, axis="y"), (1, -1))
fig = ot.plot.contourf(slices, data, figsize=[8, 6], fontsize=12)
for ax, slice in zip(fig.axes, np.ravel(slices), strict=False):
    ax.set_title(f"y = {slice.center[1]:.1f}")

# %%
# Arbitrary oriented slices are also possible. They get projected to the
# cardinal plane, from which they have the least rotational offset.

fig = ot.plot.contourf(mesh.slice([1, -2, 0]), data)

# %%