# Computing Mesh Quality
# ======================

# Leverage powerful VTK algorithms for computing mesh quality.
# Here we will use the `cell_quality()` filter
# to compute the cell qualities. The following quality measures are available
# for various cell types:

# Cell Quality Measures
#
# =======================
# Measure
# =======================
# `area`
# `aspect_frobenius`
# `aspect_gamma`
# `aspect_ratio`
# `collapse_ratio`
# `condition`
# `diagonal`
# `dimension`
# `distortion`
# `jacobian`
# `max_angle`
# `max_aspect_frobenius`
# `max_edge_ratio`
# `med_aspect_frobenius`
# `min_angle`
# `oddy`
# `radius_ratio`
# `relative_size_squared`
# `scaled_jacobian`
# `shape`
# `shape_and_size`
# `shear`
# `shear_and_size`
# `skew`
# `stretch`
# `taper`
# `volume`
# `warpage`
# =======================

import pyvista as pv
from pyvista import examples

# Triangle Cell Quality
# ---------------------

# Load a `PolyData` mesh and `decimate()`
# it to show coarse `TRIANGLE` cells for the example.
# Here we use `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 with `plot_compare()`.
# Every subplot is drawn with the same arguments, so we compute each measure
# separately: `cell_quality()` makes the measure
# it computes the active scalars.
plot_kwargs = dict(cmap='bwr', show_edges=True)

datasets = {measure: mesh.cell_quality(measure) for measure in measures}

pv.plot_compare(datasets, cpos='xy', **plot_kwargs)

# 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
# `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 `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 `TETRA` cells. Here we use
# `download_letter_a()`.
mesh = examples.download_letter_a()

# Plot some valid quality measures for tetrahedral cells.
measures = ['volume', 'collapse_ratio', 'jacobian', 'scaled_jacobian']

datasets = {measure: mesh.cell_quality(measure) for measure in measures}

pv.plot_compare(datasets, cpos='xy', **plot_kwargs)

# ----------------------------------------------------------------------
# Generated by sphinx-examples-as-code https://github.com/pyvista/sphinx-examples-as-code