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"""
.. _lookup_table_example:
Lookup Tables
~~~~~~~~~~~~~
Demonstrate the usage of a lookup table within PyVista
The :class:`pyvista.LookupTable` can be used to have fine-tuned control over
the mapping between a :class:`pyvista.DataSet`'s scalars and RGBA colors.
"""
from __future__ import annotations
import pyvista as pv
from pyvista import examples
# sphinx_gallery_start_ignore
# big figures of colormaps and widgets show better in static images
PYVISTA_GALLERY_FORCE_STATIC_IN_DOCUMENT = True
# sphinx_gallery_end_ignore
# download an example dataset
bracket = examples.download_fea_bracket().cell_data_to_point_data()
bracket
# %%
# Default Color Map - Lookup Table
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# First, let's plot using the default color map, "viridis". Internally, PyVista
# will automatically create a lookup table to map the scalars (stored here
# within `point_data`) to RGBA colors. This is shown here as a nested attribute
# to the :class:`pyvista.DataSetMapper` and it has a helpful ``repr`` method:
pl = pv.Plotter()
actor = pl.add_mesh(bracket)
actor.mapper.lookup_table
# %%
# Plot the Lookup Table
# ~~~~~~~~~~~~~~~~~~~~~
# You can also plot lookup table to see the mapping between the scalar values
# (here, between 0 and 23.3) and RGBA colors.
pl = pv.Plotter()
actor = pl.add_mesh(bracket)
actor.mapper.lookup_table.plot()
# %%
# Plot the DataSet
# ~~~~~~~~~~~~~~~~
# Let's plot the dataset using the automatically generated lookup table.
pl = pv.Plotter()
pl.add_mesh(bracket)
pl.show()
# %%
# Create a Custom Lookup Table using a Matplotlib Color Map
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Here we create a lookup table with a narrow table range (same as ``clim``)
# and color values above and below the range.
lut = pv.LookupTable(cmap='magma')
lut.scalar_range = (5, 15)
lut.below_range_color = pv.Color('grey', opacity=0.5)
lut.above_range_color = 'r'
lut.plot()
# %%
# Plot the bracket with the custom colormap
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# You can set assign the lookup table when using ``add_mesh`` with ``cmap=``.
pl = pv.Plotter()
actor = pl.add_mesh(bracket, cmap=lut, lighting=False)
pl.show()
# %%
# Create a Custom Lookup Table using VTK's Methods
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# If you want to create a completely unique color map, you can use attributes
# like :attr:`pyvista.LookupTable.hue_range` and
# :attr:`pyvista.LookupTable.value_range` to create your own lookup table.
lut = pv.LookupTable()
lut.value_range = (0.35, 1) # dark grey to white
lut.hue_range = (0.35, 0.7) # green to cyna
lut.saturation_range = (0.75, 0.5) # reduce saturation near the upper end
lut.alpha_range = (0.0, 0.9) #
lut.scalar_range = (2, 18)
lut.plot()
# %%
# Plot the bracket with the custom colormap
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Assign this custom color map to the plotter and disable lighting to improve
# the plot.
pl = pv.Plotter()
actor = pl.add_mesh(bracket, cmap=lut, lighting=False)
pl.show()
# %%
# Custom colormap with widgets
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Here we plot the scalars and dynamically change the lookup table through
# widgets. We create several overlapping single slider widgets to simulate a
# double ended slider widget.
#
# This example just controls the alpha channel.
pl = pv.Plotter()
actor = pl.add_mesh(bracket, cmap=lut, lighting=False)
pl.add_text('Alpha Range Demo')
def set_min_alpha(min_value):
max_value = lut.alpha_range[1]
if min_value > max_value:
# force the movement of the maximum value
max_value = min_value
pl.slider_widgets[1].GetRepresentation().SetValue(max_value)
lut.alpha_range = (min_value, max_value)
def set_max_alpha(max_value):
min_value = lut.alpha_range[0]
if max_value < min_value:
# force the movement of the minimum value
min_value = max_value
pl.slider_widgets[0].GetRepresentation().SetValue(min_value)
lut.alpha_range = (min_value, max_value)
# create two overlapping slider bars by hiding the tube of the second
pl.add_slider_widget(
set_min_alpha,
(0, 1),
value=lut.alpha_range[0],
interaction_event='always',
title='Alpha Range',
tube_width=0.003,
)
pl.add_slider_widget(
set_max_alpha,
(0, 1),
value=lut.alpha_range[1],
interaction_event='always',
tube_width=0.0,
)
pl.show()
# %%
# Control Several Lookup Table Attributes
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Demonstrate the use of several slider bar widgets with lookup table
# callbacks.
# Create a new lookup table with oranges
lut = pv.LookupTable()
lut.value_range = (0.3, 0.75)
lut.hue_range = (0.0, 0.095)
lut.saturation_range = (0.0, 0.67)
lut.alpha_range = (0.0, 1.0)
lut.scalar_range = (2, 18)
scalars_rng = (bracket.active_scalars.min(), bracket.active_scalars.max())
def make_double_slider(attr, idx):
"""Create a double slider for a given lookup table attribute."""
def set_min(min_value):
max_value = getattr(lut, attr)[1]
if min_value > max_value:
# force the movement of the maximum value
max_value = min_value
pl.slider_widgets[idx * 2 + 1].GetRepresentation().SetValue(max_value)
setattr(lut, attr, (min_value, max_value))
if attr == 'scalar_range':
actor.mapper.scalar_range = getattr(lut, attr)
def set_max(max_value):
min_value = getattr(lut, attr)[0]
if max_value < min_value:
# force the movement of the minimum value
min_value = max_value
pl.slider_widgets[idx * 2].GetRepresentation().SetValue(min_value)
setattr(lut, attr, (min_value, max_value))
if attr == 'scalar_range':
actor.mapper.scalar_range = getattr(lut, attr)
rng = scalars_rng if attr == 'scalar_range' else (0, 1)
# create two overlapping slider bars by hiding the tube of the second
pl.add_slider_widget(
set_min,
rng,
value=getattr(lut, attr)[0],
interaction_event='always',
title=' '.join(attr.split('_')).capitalize(),
tube_width=0.003,
pointa=(0.6, 0.9 - 0.165 * idx),
pointb=(0.9, 0.9 - 0.165 * idx),
)
pl.add_slider_widget(
set_max,
rng,
value=getattr(lut, attr)[1],
interaction_event='always',
tube_width=0.0,
pointa=(0.6, 0.9 - 0.165 * idx),
pointb=(0.9, 0.9 - 0.165 * idx),
)
pl = pv.Plotter()
actor = pl.add_mesh(bracket, cmap=lut, lighting=False)
make_double_slider('alpha_range', 0)
make_double_slider('hue_range', 1)
make_double_slider('value_range', 2)
make_double_slider('saturation_range', 3)
make_double_slider('scalar_range', 4)
pl.camera_position = [(9.021, 5.477, 7.780), (-0.679, 1.349, 0.874), (-0.498, -0.228, 0.836)]
cpos = pl.show(return_cpos=True)
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