pyvista.ImageDataFilters.image_threshold#
- ImageDataFilters.image_threshold(
- threshold,
- in_value=1.0,
- out_value=0.0,
- scalars=None,
- preference='point',
- progress_bar=False,
Apply a threshold to scalar values in a uniform grid.
If a single value is given for threshold, scalar values above or equal to the threshold are
'in'
and scalar values below the threshold are'out'
. If two values are given for threshold (sequence) then values equal to or between the two values are'in'
and values outside the range are'out'
.If
None
is given forin_value
, scalars that are'in'
will not be replaced. IfNone
is given forout_value
, scalars that are'out'
will not be replaced.Warning: applying this filter to cell data will send the output to a new point array with the same name, overwriting any existing point data array with the same name.
- Parameters:
- threshold
float
or sequence[float
] Single value or (min, max) to be used for the data threshold. If a sequence, then length must be 2. Threshold(s) for deciding which cells/points are
'in'
or'out'
based on scalar data.- in_value
float
, default: 1.0 Scalars that match the threshold criteria for
'in'
will be replaced with this.- out_value
float
, default: 0.0 Scalars that match the threshold criteria for
'out'
will be replaced with this.- scalars
str
,optional
Name of scalars to process. Defaults to currently active scalars.
- preference
str
, default: “point” When scalars is specified, this is the preferred array type to search for in the dataset. Must be either
'point'
or'cell'
.- progress_barbool, default:
False
Display a progress bar to indicate progress.
- threshold
- Returns:
pyvista.ImageData
Dataset with the specified scalars thresholded.
See also
Examples
Demonstrate image threshold on an example dataset. First, plot the example dataset with the active scalars.
>>> from pyvista import examples >>> uni = examples.load_uniform() >>> uni.plot()
Now, plot the image threshold with
threshold=100
. Note how values above the threshold are 1 and below are 0.>>> ithresh = uni.image_threshold(100) >>> ithresh.plot()
See Image Data Representations for more examples using this filter.