"""
.. _mesh_quality_example:

Computing Mesh Quality
~~~~~~~~~~~~~~~~~~~~~~

Leverage powerful VTK algorithms for computing mesh quality.

Here we will use the :func:`~pyvista.DataObjectFilters.cell_quality` filter
to compute the cell qualities. The following quality measures are available
for various cell types:

.. include:: /api/core/cell_quality/cell_quality_measures_table.rst

"""

# sphinx_gallery_thumbnail_number = 2
import pyvista as pv
from pyvista import examples

# %%
# Triangle Cell Quality
# ---------------------
# Load a :class:`~pyvista.PolyData` mesh and :meth:`~pyvista.PolyDataFilters.decimate`
# it to show coarse :attr:`~pyvista.CellType.TRIANGLE` cells for the example.
# Here we use :meth:`~pyvista.examples.downloads.download_cow`.

mesh = examples.download_cow().triangulate().decimate(0.7)

# %%
# Compute some valid measures for triangle cells.

measures = ['area', 'shape', 'min_angle', 'max_angle']
qual = mesh.cell_quality(measures)

# %%
# Plot the meshes in subplots for comparison. We define a custom method
# for adding each mesh to each subplot.


def add_mesh(plotter, mesh, *, scalars=None, cmap='bwr', show_edges=True):
    # Create a copy to avoid reusing the same mesh in different plots
    copied = mesh.copy(deep=False)
    plotter.add_mesh(copied, scalars=scalars, cmap=cmap, show_edges=show_edges)
    plotter.view_xy()


pl = pv.Plotter(shape=(2, 2))
pl.link_views()
pl.subplot(0, 0)
add_mesh(pl, qual, scalars=measures[0])
pl.subplot(0, 1)
add_mesh(pl, qual, scalars=measures[1])
pl.subplot(1, 0)
add_mesh(pl, qual, scalars=measures[2])
pl.subplot(1, 1)
add_mesh(pl, qual, scalars=measures[3])
pl.show()


# %%
# Visualize Acceptable Range
# ==========================
# The previous plots show the full range of cell quality values present in the mesh.
# However, it may be more useful to show the `acceptable` range of values instead.
# Get the acceptable range for the ``shape`` quality measure using
# :func:`~pyvista.cell_quality_info`.

info = pv.cell_quality_info('TRIANGLE', 'shape')
print(info)

# %%
# Plot the shape quality measure again but this time we color the cells based on
# the acceptable range for the measure. Cells outside of this range are saturated
# as blue or red and may be considered to be "poor" quality cells.

qual.plot(
    scalars='shape',
    clim=info.acceptable_range,
    cmap='bwr',
    below_color='blue',
    above_color='red',
    cpos='xy',
    zoom=1.5,
    show_axes=False,
)

# %%
# Use :meth:`~pyvista.DataSetFilters.extract_values` to extract the "poor" quality
# cells outside the acceptable range.

unacceptable = qual.extract_values(
    scalars='shape', ranges=info.acceptable_range, invert=True
)

# %%
# Plot the unacceptable cells along with the original mesh as wireframe for context.

pl = pv.Plotter()
pl.add_mesh(mesh, style='wireframe', color='light gray')
pl.add_mesh(unacceptable, color='lime')
pl.view_xy()
pl.camera.zoom(1.5)
pl.show()

# %%
# Tetrahedron Cell Quality
# ------------------------
# Load a mesh with :attr:`~pyvista.CellType.TETRA` cells. Here we use
# :meth:`~pyvista.examples.downloads.download_letter_a`.

mesh = examples.download_letter_a()

# %%
# Plot some valid quality measures for tetrahedral cells.

measures = ['volume', 'collapse_ratio', 'jacobian', 'scaled_jacobian']
qual = mesh.cell_quality(measures)

pl = pv.Plotter(shape=(2, 2))
pl.link_views()
pl.subplot(0, 0)
add_mesh(pl, qual, scalars=measures[0])
pl.subplot(0, 1)
add_mesh(pl, qual, scalars=measures[1])
pl.subplot(1, 0)
add_mesh(pl, qual, scalars=measures[2])
pl.subplot(1, 1)
add_mesh(pl, qual, scalars=measures[3])
pl.show()

# %%
# .. tags:: filter
