"""
Lesson Overview
~~~~~~~~~~~~~~~
"""

import pyvista as pv
from pyvista import examples

mesh = pv.Wavelet()

# %%
# ``add_mesh``
# ++++++++++++++
#
# When plotting, users must first create a :class:`pyvista.Plotter` instance (much like a Matplotlib figure). Then data are added to the plotter instance through the :func:`pyvista.Plotter.add_mesh` method. This workflow typically looks like:

pl = pv.Plotter()
pl.add_mesh(mesh)
pl.show()

# %%
# You can customize how that mesh is displayed through the parameters of the :func:`pyvista.Plotter.add_mesh` method. For example, we can change the colormap via the ``cmap`` argument:

pl = pv.Plotter()
pl.add_mesh(mesh, cmap="coolwarm")
pl.show()

# %%
# Or show the edges of the mesh with ``show_edges``:

pl = pv.Plotter()
pl.add_mesh(mesh, show_edges=True)
pl.show()

# %%
# Or adjust the opacity to be a scalar value or linear transfer function via the ``opacity`` argument:

mesh = examples.download_st_helens().warp_by_scalar()

pl = pv.Plotter()
pl.add_mesh(mesh, cmap="terrain", opacity="linear")
pl.show()

# %%
# Take a look at all of the options for `add_mesh <https://docs.pyvista.org/api/plotting/_autosummary/pyvista.Plotter.add_mesh.html>`_.
#
# The ``add_mesh`` method can be called over and over to add different data to the same ``Plotter`` scene. For example, we can create many different mesh objects and plot them together:

kinds = [
    "tetrahedron",
    "cube",
    "octahedron",
    "dodecahedron",
    "icosahedron",
]
centers = [
    (0, 1, 0),
    (0, 0, 0),
    (0, 2, 0),
    (-1, 0, 0),
    (-1, 2, 0),
]

solids = [pv.PlatonicSolid(kind, radius=0.4, center=center) for kind, center in zip(kinds, centers)]

pl = pv.Plotter(window_size=[1000, 1000])
for _ind, solid in enumerate(solids):
    pl.add_mesh(solid, color="silver", specular=1.0, specular_power=10)
pl.view_vector((5.0, 2, 3))
pl.add_floor("-z", lighting=True, color="tan", pad=1.0)
pl.enable_shadows()
pl.show()

# %%
# Subplotting
# +++++++++++
#
# Creating side-by-side comparisons of datasets is easy with PyVista's subplotting API. Get started by specifying the shape of the :class:`pyvista.Plotter` object then registering the active subplot by the :func:`pyvista.Plotter.subplot` method much like how you subplot with Matplotlib's API.
pl = pv.Plotter(shape=(1, 2))

pl.subplot(0, 0)
pl.add_mesh(pv.Sphere())

pl.subplot(0, 1)
pl.add_mesh(pv.Cube())

pl.show()

# %%
# Below is an example of side-by-side comparisons of the contours and slices of a single dataset.
#
# .. tip::
#
#    You can link the cameras of both views with the :func:`pyvista.Plotter.link_views` method
mesh = pv.Wavelet()
cntr = mesh.contour()
slices = mesh.slice_orthogonal()

pl = pv.Plotter(shape=(1, 2))

pl.add_mesh(cntr)

pl.subplot(0, 1)
pl.add_mesh(slices)

pl.link_views()
pl.view_isometric()
pl.show()

# %%
# Axes and Bounds
# +++++++++++++++
#
# Axes can be added to the scene with :func:`pyvista.Plotter.show_axes`


mesh = examples.load_random_hills()

pl = pv.Plotter()
pl.add_mesh(mesh)
pl.show_axes()
pl.show()

# %%
# And bounds similarly with :func:`pyvista.Plotter.show_bounds`
#
# .. tip::
#
#     See `Plotting Bounds <https://docs.pyvista.org/examples/02-plot/bounds.html>`_ for more details.


pl = pv.Plotter()
pl.add_mesh(mesh)
pl.show_axes()
pl.show_bounds()
pl.show()

# %%
# .. raw:: html
#
#     <center>
#       <a target="_blank" href="https://colab.research.google.com/github/pyvista/pyvista-tutorial/blob/gh-pages/notebooks/tutorial/03_figures/a_lesson_figures.ipynb">
#         <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/ width="150px">
#       </a>
#     </center>