"""
Plot with Opacity
~~~~~~~~~~~~~~~~~
Plot a mesh's scalar array with an opacity transfer funciton or opacity mapping
based on a scalar array.
"""
# sphinx_gallery_thumbnail_number = 2
import pyvista as pv
from pyvista import examples
# Load St Helens DEM and warp the topography
image = examples.download_st_helens()
mesh = image.warp_by_scalar()
###############################################################################
# Global Value
# ++++++++++++
#
# You can also apply a global opacity value to the mesh by passing a single
# float between 0 and 1 which would enable you to see objects behind the mesh:
p = pv.Plotter()
p.add_mesh(
image.contour(),
line_width=5,
)
p.add_mesh(mesh, opacity=0.85, color=True)
p.show()
###############################################################################
# # See `Matplotlib's complete list of available colormaps`_, # `Colorcet's complete list`_, and `cmocean's complete list`_. # # .. _Matplotlib's complete list of available colormaps: https://matplotlib.org/tutorials/colors/colormaps.html # .. _Colorcet's complete list: https://colorcet.holoviz.org/user_guide/index.html # .. _cmocean's complete list: https://matplotlib.org/cmocean/ ############################################################################### # Custom Made Colormaps # +++++++++++++++++++++ # # To get started using a custom colormap, download some data with scalar values to # plot. mesh = examples.download_st_helens().warp_by_scalar() # Add scalar array with range (0, 100) that correlates with elevation mesh['values'] = pv.plotting.normalize(mesh['Elevation']) * 100 ############################################################################### # Build a custom colormap - here we make a colormap with 5 discrete colors # and we specify the ranges where those colors fall: # Define the colors we want to use blue = np.array([12/256, 238/256, 246/256, 1]) black = np.array([11/256, 11/256, 11/256, 1]) grey = np.array([189/256, 189/256, 189/256, 1]) yellow = np.array([255/256, 247/256, 0/256, 1]) red = np.array([1, 0, 0, 1]) mapping = np.linspace(mesh['values'].min(), mesh['values'].max(), 256)
def test_download_st_helens():
data = examples.download_st_helens()
assert data.n_points
""" Extract Array to Table ~~~~~~~~~~~~~~~~~~~~~~ This example will demonstrate how to extract an array from any input data set to make a :class:`pyvista.Table` of that single data array. Aftwards, we plot a histogram of that data array. This example demos :class:`PVGeo.filters.ExtractArray` """ from PVGeo.filters import ExtractArray from pyvista import examples import matplotlib.pyplot as plt ############################################################################### # Create input data dataset = examples.download_st_helens() ############################################################################### # Construct the filter filt = ExtractArray() # Define the array to extract # Apply the filter on the input table = filt.apply(dataset, 'Elevation') print(table) ############################################################################### plt.hist(table['Elevation'])
p = pv.Plotter()
p.add_mesh(mesh, style="wireframe", color="w")
p.add_mesh(line, color="b")
p.show()
###############################################################################
# Run the filter and produce a line plot
mesh.plot_over_line(a, b, resolution=100)
###############################################################################
# Flat Surface
# ++++++++++++
#
# We could also plot the values of a mesh that lies on a flat surface
mesh = examples.download_st_helens()
# Make two points to construct the line between
a = [mesh.center[0], mesh.bounds[2], mesh.bounds[5]]
b = [mesh.center[0], mesh.bounds[3], mesh.bounds[5]]
# Preview how this line intersects this mesh
line = pv.Line(a, b)
p = pv.Plotter()
p.add_mesh(mesh)
p.add_mesh(line, color="white", line_width=10)
p.add_point_labels([a, b], ["A", "B"],
font_size=48,
point_color="red",
text_color="red")
def __init__(self):
self._example_data = examples.download_st_helens()
_ExampleLoader.__init__(self)
