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Scalar Quantities

Visualize scalar (real or integer)-valued data at the points of a point cloud.

Example: 

import numpy as np
import polyscope as ps
ps.init()

# register a point cloud
N = 100
points = np.random.rand(N, 3)
ps_cloud = ps.register_point_cloud("my points", points)

# generate some random data per-point
vals = np.random.rand(N)

# basic visualization
ps_cloud.add_scalar_quantity("rand vals", vals)

# manually specify a range for colormapping
ps_cloud.add_scalar_quantity("rand vals with range", vals, vminmax=(-5., 5.), enabled=True)

# use a different colormap
ps_cloud.add_scalar_quantity("rand vals with range", vals, cmap='blues')

# use the 'datatype' to specify default visualization semantics
vals_gaussian = np.random.normal(size=N)
ps_cloud.add_scalar_quantity("gaussian vals symmetric", vals_gaussian, datatype='symmetric')

# view the point cloud with all of these quantities
ps.show()

PointCloud.add_scalar_quantity(name, values, enabled=None, datatype="standard", vminmax=None, cmap=None)

Add a scalar quantity to the point cloud.

  • name string, a name for the quantity
  • values a length N numpy array, scalars at points

This function also accepts optional keyword arguments listed below, which customize the appearance and behavior of the quantity.

Categorical Scalars

Scalar quantities can also be used to visualize integer-valued labels such as categories, classes, segmentations, flags, etc.

Add the labels as a scalar quantity where the values just happen to be integers (each integer represents a particular class or label), and set datatype='categorical'. This will change the visualization to a different set of defaults, adjust some shading rules, and use a distinct color from the colormap for each label.

Scalar Quantity Options

When adding a scalar quantity, the following keyword options can be set. These are available for all kinds of scalar quantities on all structures.

Keyword arguments:

  • enabled boolean, whether the quantity is initially enabled (note that generally only one quantitiy can be shown at a time; the most recent will be used)
  • datatype, one of "standard", "symmetric", "magnitude", or "categorical", affects default colormap and map range, and the categorical policies mentioned above
  • vminmax, a 2-tuple of floats, specifying the min and max range for colormap limits; the default is None, which computes the min and max of the data
  • cmap, which colormap to use
  • isoline keywords (darker-shaded stripes showing isocontours of the scalar field):
    • isolines_enabled are isolines enabled (default: False)
    • isoline_style one of stripe, 'contour (default: stripe)
    • isoline_period how wide should the darkend stripes be, in data units (default: dynamically estimated)
    • isoline_period_relative if true, interpret the width value as relative to the world coordinate length scale (default: False)
    • isoline_darkness how much darker should the alternating stripes be (default: 0.7)
    • isoline_contour_thickness how thick should the contour lines be (default: 0.3)

If not specified, these optional parameters will assume a reasonable default value, or a persistent value if previously set.