# Volume Rendering
# ================

# Volume render uniform mesh types like `pyvista.ImageData` or 3D
# NumPy arrays.
# This also explores how to extract a volume of interest (VOI) from a
# `pyvista.ImageData` using the
# `pyvista.ImageDataFilters.extract_subset()` filter.
import numpy as np
import pyvista as pv
from pyvista import examples


# Download a volumetric dataset
vol = examples.download_knee_full()
vol

# Simple Volume Render
# --------------------

# A nice camera position
cpos = pv.CameraPosition(
    position=(-381.74, -46.02, 216.54),
    focal_point=(74.8305, 89.2905, 100.0),
    viewup=(0.23, 0.072, 0.97),
)

vol.plot(volume=True, cmap='bone', cpos=cpos)

# Opacity Mappings
# ----------------

# Or use the `pyvista.Plotter.add_volume()` method like below.
# Note that here we use a non-default opacity mapping to a sigmoid:
pl = pv.Plotter()
pl.add_volume(vol, cmap='bone', opacity='sigmoid')
pl.camera_position = cpos
pl.show()

# You can also use a custom opacity mapping
opacity = [0, 0, 0, 0.1, 0.3, 0.6, 1]

pl = pv.Plotter()
pl.add_volume(vol, cmap='viridis', opacity=opacity)
pl.camera_position = cpos
pl.show()

# We can also use a shading technique when volume rendering with the `shade`
# option
pl = pv.Plotter(shape=(1, 2))
pl.add_volume(vol, cmap='viridis', opacity=opacity, shade=False)
pl.add_text('No shading')
pl.camera_position = cpos
pl.subplot(0, 1)
pl.add_volume(vol, cmap='viridis', opacity=opacity, shade=True)
pl.add_text('Shading')
pl.link_views()
pl.show()

# Cool Volume Examples
# --------------------

# Here are a few more cool volume rendering examples.

# Head Dataset
# ~~~~~~~~~~~~

head = examples.download_head()

pl = pv.Plotter()
pl.add_volume(head, cmap='cool', opacity='sigmoid_6', show_scalar_bar=False)
pl.camera_position = pv.CameraPosition(
    position=(-228.0, -418.0, -158.0),
    focal_point=(94.0, 122.0, 82.0),
    viewup=(-0.2, -0.3, 0.9),
)
pl.camera.zoom(1.5)
pl.show()

# Bolt-Nut MultiBlock Dataset
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~

# NOTE:
#     See how we set interpolation to `'linear'` here to smooth out scalars of
#     each individual cell to make a more appealing plot. Two actor are returned
#     by `add_volume` because `bolt_nut` is a `pyvista.MultiBlock`
#     dataset.

bolt_nut = examples.download_bolt_nut()

pl = pv.Plotter()
actors = pl.add_volume(
    bolt_nut, cmap='coolwarm', opacity='sigmoid_5', show_scalar_bar=False
)
actors[0].prop.interpolation_type = 'linear'
actors[1].prop.interpolation_type = 'linear'
pl.camera_position = pv.CameraPosition(
    position=(127.4, -68.3, 88.2),
    focal_point=(30.3, 54.3, 26.0),
    viewup=(-0.25, 0.28, 0.93),
)
cpos = pl.show(return_cpos=True)

# Frog Dataset
# ~~~~~~~~~~~~

frog = examples.download_frog()

pl = pv.Plotter()
pl.add_volume(frog, cmap='viridis', opacity='sigmoid_6', show_scalar_bar=False)
pl.camera_position = pv.CameraPosition(
    position=(929.0, 1067.0, -278.9),
    focal_point=(249.5, 234.5, 101.25),
    viewup=(-0.2048, -0.2632, -0.9427),
)
pl.camera.zoom(1.5)
pl.show()

# Extracting a VOI
# ----------------

# Use the `pyvista.ImageDataFilters.extract_subset()` filter to extract
# a volume of interest/subset volume to volume render. This is ideal when
# dealing with particularly large volumes and you want to volume render only
# a specific region.
# Load a particularly large volume
large_vol = examples.download_damavand_volcano()
large_vol

opacity = [0, 0.75, 0, 0.75, 1.0]
clim = [0, 100]

pl = pv.Plotter()
pl.add_volume(
    large_vol,
    cmap='magma',
    clim=clim,
    opacity=opacity,
    opacity_unit_distance=6000,
)
pl.show()

# Woah, that's a big volume. We probably don't want to volume render the
# whole thing. So let's extract a region of interest under the volcano.
# The region we will extract will be between nodes 175 and 200 on the x-axis,
# between nodes 105 and 132 on the y-axis, and between nodes 98 and 170 on
# the z-axis.
voi = large_vol.extract_subset([175, 200, 105, 132, 98, 170])

pl = pv.Plotter()
pl.add_mesh(large_vol.outline(), color='k')
pl.add_mesh(voi, cmap='magma')
pl.show()

# Ah, much better. Let's now volume render that region of interest.
pl = pv.Plotter()
pl.add_volume(voi, cmap='magma', clim=clim, opacity=opacity, opacity_unit_distance=2000)
pl.camera_position = pv.CameraPosition(
    position=(531554.5542909054, 3944331.800171338, 26563.04809259223),
    focal_point=(599088.1433822059, 3982089.287834022, -11965.14728669936),
    viewup=(0.3738545892415734, 0.244312810377319, 0.8947312427698892),
)
pl.show()

# Volume With Segmentation Mask
# -----------------------------

# Visualize a medical image with a corresponding binary segmentation mask.
# For this example, we use `download_whole_body_ct_male()`
# though `download_whole_body_ct_female()`, or any
# other dataset with a corresponding label or mask may be used.
# Load the dataset and get the ct image and a mask image. Here, a mask of the heart is
# used.
dataset = examples.download_whole_body_ct_male()
ct_image = dataset['ct']
heart_mask = dataset['segmentations']['heart']

# Use the segmentation mask to isolate the heart in the CT image.
# Initialize a new array and image with CT background values. Here, we set the scalar
# values to `-1000` which typically corresponds to air (low density).
heart_array = np.full_like(ct_image.active_scalars, -1000)

# Extract the intensities for the heart segment. We use heart mask's array
# to mask the CT image to only extract the intensities of interest.
ct_image_array = ct_image.active_scalars
heart_mask_array = heart_mask.active_scalars
heart_array[heart_mask_array == np.True_] = ct_image_array[heart_mask_array == np.True_]

# Add the masked array to the CT image as a new set of scalar values.
ct_image['heart'] = heart_array

# Create the plot.
# For the CT image, the opacity is set to a sigmoid function to show the
# subject's skeleton. Since different images have different intensity
# distributions, you may need to experiment with different sigmoid functions.
# See `add_volume()` for details.
pl = pv.Plotter()

# Add the CT image.
pl.add_volume(
    ct_image,
    scalars='NIFTI',
    cmap='bone',
    opacity='sigmoid_15',
    show_scalar_bar=False,
)

# Add masked CT image of the heart and use a contrasting color map.
_ = pl.add_volume(
    ct_image,
    scalars='heart',
    cmap='gist_heat',
    opacity='linear',
    opacity_unit_distance=np.mean(ct_image.spacing),
)

# Orient the camera to provide a latero-anterior view.
pl.view_yz()
pl.camera.azimuth = 70
pl.camera.up = (0, 0, 1)
pl.camera.zoom(1.5)
pl.show()

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