Plot Style

Scatter Plots

Scatter

class tecplot.plot.Scatter(plot)

Plot-local scatter style settings.

This class controls the style of drawn scatter points on a specific plot.

Attributes

variable	The Variable to be used when sizing scatter symbols.
variable_index	Zero-based index of the Variable used for size of scatter symbols.

Scatter.variable

The Variable to be used when sizing scatter symbols.

The variable must belong to the Dataset attached to the Frame that holds this ContourGroup. Example usage:

>>> plot.scatter.variable = dataset.variable('P')
>>> plot.fieldmap(0).scatter.size_by_variable = True

Scatter.variable_index

Zero-based index of the Variable used for size of scatter symbols.

>>> plot.scatter.variable_index = dataset.variable('P').index
>>> plot.fieldmap(0).scatter.size_by_variable = True

The Dataset attached to this contour group’s Frame is used, and the variable itself can be obtained through it:

>>> scatter = plot.scatter
>>> scatter_var = dataset.variable(scatter.variable_index)
>>> scatter_var.index == scatter.variable_index
True

Vector Plots

Vector2D

class tecplot.plot.Vector2D(plot)

Attributes

u_variable
u_variable_index
v_variable
v_variable_index

Vector2D.u_variable

Vector2D.u_variable_index

Vector2D.v_variable

Vector2D.v_variable_index

Vector3D

class tecplot.plot.Vector3D(plot)

Attributes

u_variable
u_variable_index
v_variable
v_variable_index
w_variable
w_variable_index

Vector3D.u_variable

Vector3D.u_variable_index

Vector3D.v_variable

Vector3D.v_variable_index

Vector3D.w_variable

Vector3D.w_variable_index

Contours

ContourGroup

class tecplot.plot.ContourGroup(index, plot)

Contouring of a variable using a colormap.

This object controls the style for a specific contour group within a Frame. Contour levels, colormap and contour lines are accessed through this class.

from os import path
import tecplot as tp
from tecplot.constant import *

# load data
exdir = tp.session.tecplot_examples_directory()
datafile = path.join(exdir,'2D','polarplot.plt')
dataset = tp.data.load_tecplot(datafile)
plot = dataset.frame.plot()
plot.show_contour = True

contour = plot.contour(0)
contour.variable = dataset.variable('Mix')
contour.colormap_name = 'Magma'

# save image to file
tp.export.save_png('polarplot_magma.png', 600)

There are a fixed number of contour groups available for each plot. Others can be enabled and modified by specifying an index other than zero:

>>> contour3 = plot.contour(3)
>>> contour3.variable = dataset.variable('U')

Attributes

color_cutoff	ContourColorCutoff object controlling color cutoff min/max.
colormap_filter	ContourColormapFilter object controlling colormap style properties.
colormap_name	The name of the colormap (str) to be used.
default_num_levels	Default target number (int) of levels used when resetting.
labels	ContourLabels object controlling contour line labels.
legend	ContourLegend associated with this ContourGroup.
levels	ContourLevels holding the list of contour levels.
lines	ContourLines object controlling contour line style.
variable	The Variable being contoured.
variable_index	Zero-based index of the Variable being contoured.

ContourGroup.color_cutoff

ContourColorCutoff object controlling color cutoff min/max.

Type:ContourColorCutoff

>>> cutoff = plot.contour(0).color_cutoff
>>> cutoff.min = 3.14

ContourGroup.colormap_filter

ContourColormapFilter object controlling colormap style properties.

Type:ContourColormapFilter

>>> plot.contour(0).colormap_filter.reverse = True

ContourGroup.colormap_name

The name of the colormap (str) to be used.

Type:string

Example:

>>> plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'

ContourGroup.default_num_levels

Default target number (int) of levels used when resetting.

Type:integer

Example:

>>> plot.contour(0).default_num_levels = 20

ContourGroup.labels

ContourLabels object controlling contour line labels.

Type:ContourLabels

Lines must be turned on through the associated fieldmap object for style changes to be meaningful:

>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True

ContourGroup.legend

ContourLegend associated with this ContourGroup.

Type:ContourLegend

This object controls the values of the contour lines. Values can be added, deleted or overridden completely:

>>> plot.contour(0).levels.reset_to_nice(15)

ContourGroup.levels

ContourLevels holding the list of contour levels.

Type:ContourLevels

This object controls the values of the contour lines. Values can be added, deleted or overridden completely:

>>> plot.contour(0).levels.reset_to_nice(15)

ContourGroup.lines

ContourLines object controlling contour line style.

Type:ContourLines

Lines must be turned on through the associated fieldmap object for style changes to be meaningful:

>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).lines.mode = ContourLineMode.DashNegative

ContourGroup.variable

The Variable being contoured.

The variable must belong to the Dataset attached to the Frame that holds this ContourGroup. Example usage:

>>> plot.contour(0).variable = dataset.variable('P')

ContourGroup.variable_index

Zero-based index of the Variable being contoured.

>>> plot.contour(0).variable_index = dataset.variable('P').index

The Dataset attached to this contour group’s Frame is used:

>>> contour = plot.contour(0)
>>> contour_var = frame.dataset.variable(contour.variable_index)
>>> contour_var.index == contour.variable_index
True

ContourColorCutoff

class tecplot.plot.ContourColorCutoff(contour)

Color-mapped value limits to display.

This lets you specify a range within which contour flooding and multi-colored objects, such as scatter symbols, are displayed.

import os
import tecplot as tp
from tecplot.constant import PlotType, SurfacesToPlot

# load the data
examples_dir = tp.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir,'3D','pyramid.plt')
dataset = tp.data.load_tecplot(datafile)

# show boundary faces and contours
plot = tp.active_frame().plot()
surfaces = plot.fieldmap(0).surfaces
surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_contour = True

# cutoff contour flooding outside min/max range
cutoff = plot.contour(0).color_cutoff
cutoff.include_min = True
cutoff.min = 0.5
cutoff.include_max = True
cutoff.max = 1.0
cutoff.inverted = True

tp.export.save_png('contour_color_cutoff.png',600)

Attributes

include_max	Use the maximum cutoff value.
include_min	Use the minimum cutoff value.
inverted	Cuts values outside the range instead of inside.
max	The maximum cutoff value.
min	The minimum cutoff value.

ContourColorCutoff.include_max

Use the maximum cutoff value.

Type:boolean

Thie example turns off the maximum cutoff:

>>> plot.contour(0).color_cutoff.include_max = False

ContourColorCutoff.include_min

Use the minimum cutoff value.

Type:boolean

Example usage:

>>> plot.contour(0).color_cutoff.include_min = True
>>> plot.contour(0).color_cutoff.min = 3.14

ContourColorCutoff.inverted

Cuts values outside the range instead of inside.

Type:bool

>>> plot.contour(0).color_cutoff.inverted = True

ContourColorCutoff.max

The maximum cutoff value.

Type:float or None

The include_max must be set to True:

>>> plot.contour(0).color_cutoff.include_max = True
>>> plot.contour(0).color_cutoff.max = None

ContourColorCutoff.min

The minimum cutoff value.

Type:float or None

The include_min must be set to True:

>>> plot.contour(0).color_cutoff.include_min = True
>>> plot.contour(0).color_cutoff.min = 3.14

ContourColormapFilter

class tecplot.plot.ContourColormapFilter(contour)

Controls how the colormap is rendered for a given contour.

from os import path
import tecplot as tp
from tecplot.constant import *

# load the data
exdir = tp.session.tecplot_examples_directory()
datafile = path.join(exdir,'2D','MultiPoly2D.plt')
ds = tp.data.load_tecplot(datafile)

# set plot type to 2D field plot
frame = tp.active_frame()
frame.plot_type = PlotType.Cartesian2D
plot = frame.plot()

# show boundary faces and contours
surfaces = plot.fieldmap(0).surfaces
surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_contour = True

# by default, contour 0 is the one that's shown,
# set the contour's variable, colormap and number of levels
contour = plot.contour(0)
contour.variable = ds.variable('P')

# cycle through the colormap three times and reversed
# show a faithful (non-approximate) continuous distribution
contour_filter = contour.colormap_filter
contour_filter.num_cycles = 3
contour_filter.reversed = True
contour_filter.fast_continuous_flood = False
contour_filter.distribution = ColorMapDistribution.Continuous

# save image to file
tp.export.save_png('poly2d_filtered.png', 600)

Attributes

continuous_max	Upper limit for continuous colormap flooding.
continuous_min	Lower limit for continuous colormap flooding.
distribution	Rendering style of the colormap.
fast_continuous_flood	Use a fast approximation to continuously flood the colormap.
num_cycles	Number of cycles to repeat the colormap.
reversed	Reverse the colormap.
show_overrides	Enable the colormap overrides in this contour group.
zebra_shade	Returns a ContourColormapZebraShade filtering object.

Methods

override(index)

Returns a ContourColormapOverride object by index.

ContourColormapFilter.continuous_max

Upper limit for continuous colormap flooding.

Type:float

Example set the limits to the (min, max) of a variable in a specific zone:

>>> from tecplot.constant import ColorMapDistribution
>>> cmap_filter = plot.contour(0).colormap_filter
>>> cmap_filter.distribution = ColorMapDistribution.Continuous
>>> pressure = dataset.variable('Pressure').values('My Zone')
>>> cmap_filter.continuous_min = pressure.min
>>> cmap_filter.continuous_max = pressure.max

ContourColormapFilter.continuous_min

Lower limit for continuous colormap flooding.

Type:float

Example usage:

>>> from tecplot.constant import ColorMapDistribution
>>> cmap_filter = plot.contour(0).colormap_filter
>>> cmap_filter.distribution = ColorMapDistribution.Continuous
>>> cmap_filter.continuous_min = 3.1415

ContourColormapFilter.distribution

Rendering style of the colormap.

Type:ColorMapDistribution

Possible values:

Banded

A solid color is assigned for all values within the band between two levels.

Continuous

The color distribution assigns linearly varying colors to all multi-colored objects or contour flooded regions.

Example:

>>> from tecplot.constant import ColorMapDistribution
>>> cmap_filter = plot.contour(0).colormap_filter
>>> cmap_filter.distribution = ColorMapDistribution.Banded

ContourColormapFilter.fast_continuous_flood

Use a fast approximation to continuously flood the colormap.

Type:bool

Causes each cell to be flooded using interpolation between the color values at each node. When the transition from a color at one node to another node crosses over the boundary between control points in the color spectrum, fast flooding may produce colors not in the spectrum. Setting this to False is slower, but more accurate:

>>> cmap_filter = plot.contour(0).colormap_filter
>>> cmap_filter.fast_continuous_flood = True

ContourColormapFilter.num_cycles

Number of cycles to repeat the colormap.

Type:integer

>>> plot.contour(0).colormap_filter.num_cycles = 3

ContourColormapFilter.override(index)

Returns a ContourColormapOverride object by index.

Parameters:index (int) – The index of the colormap override object. Returns:ContourColormapOverride – The class controlling the specific contour colormap override requested by index.

Example:

>>> cmap_override = plot.contour(0).colormap_filter.override(0)
>>> cmap_override.show = True

ContourColormapFilter.reversed

Reverse the colormap.

Type:bool

>>> plot.contour(0).colormap_filter.reversed = True

ContourColormapFilter.show_overrides

Enable the colormap overrides in this contour group.

Type:boolean

The overrides themselves must be turned on as well for this to have an effect on the resulting plot:

>>> contour = plot.contour(0)
>>> cmap_filter = contour.colormap_filter
>>> cmap_filter.show_overrides = True
>>> cmap_filter.override(0).show = True

ContourColormapFilter.zebra_shade

Returns a ContourColormapZebraShade filtering object.

Type:ContourColormapZebraShade

Example usage:

>>> zebra = plot.contour(0).colormap_filter.zebra_shade
>>> zebra.show = True

ContourColormapOverride

class tecplot.plot.ContourColormapOverride(index, colormap_filter)

Assigns contour bands to specific color.

Specific contour bands can be assigned a unique basic color. This is useful for forcing a particular region to use blue, for example, to designate an area of water. You can define up to 16 color overrides.

from os import path
import tecplot as tp
from tecplot.constant import *

# load the data
exdir = tp.session.tecplot_examples_directory()
datafile = path.join(exdir,'2D','cstream.plt')
ds = tp.data.load_tecplot(datafile)

# set plot type to 2D field plot
frame = tp.active_frame()
frame.plot_type = PlotType.Cartesian2D
plot = frame.plot()

# show boundary faces and contours
surfaces = plot.fieldmap(0).surfaces
surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_contour = True

# by default, contour 0 is the one that's shown,
# set the contour's variable, colormap and number of levels
contour = plot.contour(0)
contour.variable = ds.variable('v3')
contour.colormap_name = 'Sequential - Yellow/Green/Blue'
contour.levels.reset(10)

# turn on colormap overrides for this contour
contour_filter = contour.colormap_filter
contour_filter.show_overrides = True

# turn on override 0, coloring the first 4 levels red
contour_override = contour_filter.override(0)
contour_override.show = True
contour_override.color = Color.Red
contour_override.start_level = 0
contour_override.end_level = 4

# save image to file
tp.export.save_png('cstream_contours.png', 600)

Attributes

color	Color which will override the colormap.
end_level	Last level to override.
show	Include this colormap override when filter is shown.
start_level	First level to override.

ContourColormapOverride.color

Color which will override the colormap.

Type:Color

Example usage:

>>> from tecplot.constant import Color
>>> colormap_filter = plot.contour(0).colormap_filter
>>> cmap_override = colormap_filter.override(0)
>>> cmap_override.color = Color.Blue

ContourColormapOverride.end_level

Last level to override.

Type:integer

Example usage:

>>> colormap_filter = plot.contour(0).colormap_filter
>>> cmap_override = colormap_filter.override(0)
>>> cmap_override.end_level = 2

ContourColormapOverride.show

Include this colormap override when filter is shown.

Type:boolean

Example usage:

>>> colormap_filter = plot.contour(0).colormap_filter
>>> cmap_override = colormap_filter.override(0)
>>> cmap_override.show = True

ContourColormapOverride.start_level

First level to override.

Type:integer

Example usage:

>>> colormap_filter = plot.contour(0).colormap_filter
>>> cmap_override = colormap_filter.override(0)
>>> cmap_override.start_level = 2

ContourColormapZebraShade

class tecplot.plot.ContourColormapZebraShade(colormap_filter)

This filter sets a uniform color for every other band.

Setting the color to None turns the bands off and makes them transparent:

from os import path
import numpy as np
import tecplot as tp
from tecplot.constant import Color, SurfacesToPlot

# load the data
examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir,'3D','pyramid.plt')
dataset = tp.data.load_tecplot(datafile)

# show boundary faces and contours
plot = tp.active_frame().plot()
surfaces = plot.fieldmap(0).surfaces
surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_contour = True
plot.show_shade = False

# set zebra filter on and make the zebra contours transparent
cont0 = plot.contour(0)
zebra = cont0.colormap_filter.zebra_shade
zebra.show = True
zebra.transparent = True

tp.export.save_png('contour_zebra.png', 600)

Attributes

color	Color of the zebra shading.
show	Show zebra shading in this ContourGroup.
transparent	Set the the zebra bands to be transparent.

ContourColormapZebraShade.color

Color of the zebra shading.

Type:Color

Example usage:

>>> from tecplot.constant import Color
>>> filter = plot.contour(0).colormap_filter
>>> zebra = filter.zebra_shade
>>> zebra.show = True
>>> zebra.color = Color.Blue

ContourColormapZebraShade.show

Show zebra shading in this ContourGroup.

Type:boolean

Example usage:

>>> cmap_filter = plot.contour(0).colormap_filter
>>> cmap_filter.zebra_shade.show = True

ContourColormapZebraShade.transparent

Set the the zebra bands to be transparent.

Type:boolean

Example usage:

>>> filter = plot.contour(0).colormap_filter
>>> zebra = filter.zebra_shade
>>> zebra.show = True
>>> zebra.transparent = True

ContourLabels

class tecplot.plot.ContourLabels(contour)

Contour line label style, position and alignment control.

These are labels that identify particular contour levels either by value or optionally, by number starting from one. The plot type must be lines or lines and flood in order to see them:

from os import path
import tecplot as tp
from tecplot.constant import Color, ContourType, SurfacesToPlot

# load the data
examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir,'3D','pyramid.plt')
dataset = tp.data.load_tecplot(datafile)

# show boundary faces and contours
plot = tp.active_frame().plot()
plot.fieldmap(0).contour.contour_type = ContourType.Lines
surfaces = plot.fieldmap(0).surfaces
surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_contour = True

# set contour label style
contour_labels = plot.contour(0).labels
contour_labels.show = True
contour_labels.auto_align = False
contour_labels.color = Color.Blue
contour_labels.background_color = Color.White
contour_labels.margin = 20

tp.export.save_png('contour_labels.png', 600)

Attributes

auto_align	Automatically align the labels with the contour lines.
auto_generate	Automatically generate labels along contour lines.
background_color	Background fill color behind the text labels.
color	Text color of the labels.
filled	Fill the background area behind the text labels.
font	Font used to show the labels.
label_by_level	Use the contour numbers as the label instead of the data value.
margin	Spacing around the text and the filled background area.
show	Show the contour line labels.
spacing	Spacing between labels along the contour lines.
step	Number of contour lines from one label to the next.

ContourLabels.auto_align

Automatically align the labels with the contour lines.

Type:bool

This causes the flow of the text to be aligned with the contour lines. Otherwise, the labels are aligned with the frame:

>>> from tecplot.constant import ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.auto_align = False

ContourLabels.auto_generate

Automatically generate labels along contour lines.

Type:bool

This causes a new set of contour labels to be created at each redraw:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.auto_generate = True

ContourLabels.background_color

Background fill color behind the text labels.

Type:Color

The filled attribute must be set to True:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.background_color = Color.Blue

ContourLabels.color

Text color of the labels.

Type:Color

Example:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.color Color.Blue

ContourLabels.filled

Fill the background area behind the text labels.

Type:boolean

The background can be filled with a color or disabled (made transparent) by setting this property to False:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.filled = True
>>> plot.contour(0).labels.background_color = Color.Blue
>>> plot.contour(1).labels.show = True
>>> plot.contour(1).labels.filled = False

ContourLabels.font

Font used to show the labels.

Type:Font

Example:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.font.size = 3.5

ContourLabels.label_by_level

Use the contour numbers as the label instead of the data value.

Type:bool

Contour level numbers start from one when drawn. Example usage:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.label_by_level = True

ContourLabels.margin

Spacing around the text and the filled background area.

Type:float in percentage of the text height.

Contour numbers start from one when drawn. Example usage:

>>> from tecplot.constant import Color, ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.background_color = Color.Yellow
>>> plot.contour(0).labels.margin = 20

ContourLabels.show

Show the contour line labels.

Type:bool

Contour lines must be on for this to have any effect:

>>> from tecplot.constant import ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True

ContourLabels.spacing

Spacing between labels along the contour lines.

Type:float

This is the distance between each label along each contour line in percentage of the frame height:

>>> from tecplot.constant import ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.spacing = 20

ContourLabels.step

Number of contour lines from one label to the next.

Type:int

This is the number of contour bands between lines that are to be labeled:

>>> from tecplot.constant import ContourType
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
>>> plot.contour(0).labels.show = True
>>> plot.contour(0).labels.step = 4

ContourLevels

class tecplot.plot.ContourLevels(contour)

List of contour level values.

A contour level is a value at which contour lines are drawn, or for banded contour flooding, the border between different colors of flooding. Initially, each contour group consists of approximately 10 levels evenly spaced over the z coordinate in the Frame‘s Dataset. These values can be manipulated with the ContourLevels object obtained via the ContourGroup.levels attribute.

from os import path
import numpy as np
import tecplot as tp

# load layout
examples_dir = tp.session.tecplot_examples_directory()
example_layout = path.join(examples_dir,'2D','3element.lpk')
tp.load_layout(example_layout)
frame = tp.active_frame()

levels = frame.plot().contour(0).levels
levels.reset_levels(np.linspace(55000,115000,61))

# save image to file
tp.export.save_png('3element_adjusted_levels.png', 600)

Note

The streamtraces in the plot above is a side-effect of settings in layout file used. For more information about streamtraces, see the plot.Streamtraces class reference.

Methods

add(*values)	Adds new levels to the existing list.
delete_nearest(value)	Removes the level closest to the specified value.
delete_range(min_value, max_value)	Inclusively, deletes all levels within a specified range.
reset([num_levels])	Resets the levels to the number specified.
reset_levels(*values)	Resets the levels to the values specified.
reset_to_nice([num_levels])	Approximately resets the levels to the number specified.

ContourLevels.add(*values)

Adds new levels to the existing list.

Parameters:*values (floats) – The level values to be added to the ContourGroup.

The values added are inserted into the list of levels in ascending order:

>>> levels = plot.contour(0).levels
>>> list(levels)
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
>>> levels.add(3.14159)
>>> list(levels)
[0.0, 1.0, 2.0, 3.0, 3.14159, 4.0, 5.0]

ContourLevels.delete_nearest(value)

Removes the level closest to the specified value.

Parameters:value (float) – Value of the level to remove.

This method deletes the contour level with the value nearest the supplied value:

>>> plot.contour(0).levels.delete_nearest(3.14)

ContourLevels.delete_range(min_value, max_value)

Inclusively, deletes all levels within a specified range.

Parameters:

• min_value (float) – Minimum value to remove.
• max_value (float) – Maximum value to remove.

This method deletes all contour levels between the specified minimum and maximum values of the contour variable (inclusive):

>>> plot.contour(0).levels.delete_range(0.5, 1.5)

ContourLevels.reset(num_levels=15)

Resets the levels to the number specified.

Parameters:num_levels (integer) – Number of levels. (default: 10)

This will reset the contour levels to a set of evenly distributed values spanning the entire range of the currently selected contouring variable:

>>> plot.contour(0).levels.reset(30)

ContourLevels.reset_levels(*values)

Resets the levels to the values specified.

Parameters:*values (floats) – The level values to be added to the ContourGroup.

This method replaces the current set of contour levels with a new set. Here, we set the levels to go from 0 to 100 in steps of 5:

>>> plot.contour(0).levels.reset_levels(*range(0,101,5))

ContourLevels.reset_to_nice(num_levels=15)

Approximately resets the levels to the number specified.

Parameters:num_levels (integer) – Approximate number of levels to create. (default: 10)

This will reset the contour levels to a set of evenly distributed values that approximately spans the range of the currently selected contouring variable. Exact range and number of levels will be adjusted to make the contour levels have “nice” values:

>>> plot.contour(0).levels.reset_to_nice(50)

ContourLines

class tecplot.plot.ContourLines(contour)

Contour line style.

This object sets the style of the contour lines once turned on.

from os import path
import tecplot as tp
from tecplot.constant import (ContourLineMode, ContourType,
                              SurfacesToPlot)

# load the data
examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir,'3D','pyramid.plt')
dataset = tp.data.load_tecplot(datafile)

# show boundary faces and contours
plot = tp.active_frame().plot()
plot.fieldmap(0).contour.contour_type = ContourType.Lines
surfaces = plot.fieldmap(0).surfaces
surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_contour = True

# set contour line style
contour_lines = plot.contour(0).lines
contour_lines.mode = ContourLineMode.SkipToSolid
contour_lines.step = 4
contour_lines.pattern_length = 2

tp.export.save_png('contour_lines.png', 600)

Attributes

mode	Type of lines to draw on the plot (ContourLineMode).
pattern_length	Length of dashed lines and space between dashes (float).
step	Number of lines to step for SkipToSolid line mode (int).

ContourLines.mode

Type of lines to draw on the plot (ContourLineMode).

Type:ContourLineMode

Possible values:

UseZoneLineType

For each zone, draw the contour lines using the line pattern and pattern length specified in the FieldmapContour for the parent Fieldmaps. If you are adding contour lines to polyhedral zones, the patterns will not be continuous from one cell to the next and the pattern will restart at every cell boundary.

SkipToSolid

Draw dashed lines between each pair of solid lines which are spaced out by the ContourLines.step property. This will override any line pattern or thickness setting in the parent Fieldmaps‘s FieldmapContour object.

DashNegative

Draw lines of positive contour variable value as solid lines and lines of negative contour variable value as dashed lines. This will override any line pattern or thickness setting in the parent Fieldmaps‘s FieldmapContour object.

Example:

>>> from tecplot.constant import ContourLineMode
>>> lines = plot.contour(0).lines
>>> lines.mode = ContourLineMode.DashNegative

ContourLines.pattern_length

Length of dashed lines and space between dashes (float).

Type:float

The length is in percentage of the frame height:

>>> from tecplot.constant import ContourLineMode
>>> lines = plot.contour(0).lines
>>> lines.mode = ContourLineMode.SkipToSolid
>>> lines.step = 5
>>> lines.pattern_length = 5

ContourLines.step

Number of lines to step for SkipToSolid line mode (int).

Type:int

Example:

>>> from tecplot.constant import ContourLineMode
>>> lines = plot.contour(0).lines
>>> lines.mode = ContourLineMode.SkipToSolid
>>> lines.step = 5

Isosurface

IsosurfaceGroup

class tecplot.plot.IsosurfaceGroup(index, plot)

Isosurfaces style control.

from os import path
import tecplot as tp
from tecplot.plot.isosurface import IsosurfaceGroup
from tecplot.constant import LightingEffect, Color, IsoSurfaceSelection

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).variable = dataset.variable('U(M/S)')

iso = plot.isosurface(0)  # type: IsosurfaceGroup

iso.isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
iso.isosurface_values = (135.674706817, 264.930212259, 394.185717702)

iso.shade.use_lighting_effect = True
iso.effects.lighting_effect = LightingEffect.Paneled
iso.contour.show = True

iso.mesh.show = True
iso.mesh.line_thickness = .1
iso.mesh.color = Color.Black
iso.effects.use_translucency = True
iso.effects.surface_translucency = 50

tp.export.save_png('isosurface_group.png', 600)

Attributes

contour	Contour attributes for this isosurface group.
definition_contour_group	Contour group from which isosurfaces are based.
definition_contour_group_index	Contour group index from which isosurfaces are based.
effects	Settings for isosurface effects.
isosurface_selection	Select where to draw isosurfaces.
isosurface_values	Query/Assign up to 3 values at which to draw isosurfaces.
mesh	Mesh attributes for this isosurface group.
obey_source_zone_blanking	Obey source zone blanking.
shade	Shade attributes for this isosurface group.
show	Show isosurfaces for this isosurface group.

IsosurfaceGroup.contour

Contour attributes for this isosurface group.

Type:IsosurfaceContour

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).contour.show = True

IsosurfaceGroup.definition_contour_group

Contour group from which isosurfaces are based.

Type:ContourGroup

Example usage:

>>> group = plot.contour(1)
>>> plot.isosurface(0).definition_contour_group = group

IsosurfaceGroup.definition_contour_group_index

Contour group index from which isosurfaces are based.

Type:Index

Contour group settings can be changed from plot.ContourGroup.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).definition_contour_group_index = 1

IsosurfaceGroup.effects

Settings for isosurface effects.

Type:IsosurfaceEffects

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).effects.use_translucency = True

IsosurfaceGroup.isosurface_selection

Select where to draw isosurfaces.

Type:IsoSurfaceSelection

Iso-surfaces may be drawn at:

• Contour group levels
• At specified value(s) - Specify up to three values of the contour variable at which to draw isosurfaces.

To draw isosurfaces at contour group lines:

1. Set isosurface_selection to IsoSurfaceSelection.AllContourLevels.
2. Optional: Change tecplot.plot.ContourLevels

To draw isosurfaces at up to 3 values:

1. Set isosurface_selection to one of the following: IsoSurfaceSelection.OneSpecificValue IsoSurfaceSelection.TwoSpecificValues IsoSurfaceSelection.ThreeSpecificValues
2. Set isosurface_values to a 1, 2, or 3 tuple of floats

See also isosurface_values.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.TwoSpecificValues
>>> plot.isosurface(0).isosurface_values = (.3, .8)

IsosurfaceGroup.isosurface_values

Query/Assign up to 3 values at which to draw isosurfaces.

Type:1, 2, or 3-tuple of floats, or scalar float

To draw isosurfaces at up to 3 values:

1. Set isosurface_selection to one of the following: IsoSurfaceSelection.OneSpecificValue IsoSurfaceSelection.TwoSpecificValues IsoSurfaceSelection.ThreeSpecificValues
2. Set isosurface_values to a 1, 2, or 3 tuple or list of floats, or set to a scalar float to assign the first value only.

When queried, this property will always return a 3 tuple of floats.

See also isosurface_selection.

Assign first isosurface value using a scalar float:

>>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.OneSpecificValue
>>> plot.isosurface(0).isosurface_values = 0.5
>>> plot.isosurface(0).isosurface_values[0]
>>> 0.5

Assign first isosurface value using a 1-tuple:

>>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.OneSpecificValue
>>> plot.isosurface(0).isosurface_values = (.5,) # 1-tuple
>>> plot.isosurface(0).isosurface_values
>>> 0.5

Assign all three isosurface values:

>>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
>>> plot.isosurface(0).isosurface_values = (.5, .7, 9)

Assign the third isosurface values after assigning the first two:

>>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
>>> # Assign first and second isosurface value using a tuple
>>> plot.isosurface(0).isosurface_values = (0.0, 0.1)
>>> # Assign third isosurface value
>>> plot.isosurface(0).isosurface_values[2] = .3
>>> plot.isosurface(0).isosurface_values[2]
>>> .3
>>> plot.isosurface(0).isosurface_values
>>> (0.0, 0.1, .3)

Query the three isosurface values:

>>> # isosurface_values always returns a
>>> # list-like object of 3 floats with of current
>>> # isosurface values, even if fewer than three have been set.
>>> values = plot.isosurface(0).isosurface_values
>>> values
>>> (0.1, 0.2, 0.3)
>>> values[0]
>>> 0.1
>>> values[1]
>>> 0.2
>>> values[2]
>>> 0.3
>>> len(plot.isosurface(0).isosurface_values)
>>> 3

IsosurfaceGroup.mesh

Mesh attributes for this isosurface group.

Type:IsosurfaceMesh

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).mesh.show = True

IsosurfaceGroup.obey_source_zone_blanking

Obey source zone blanking.

Type:

boolean

• When True, isosurfaces are generated for non-blanked regions only.
• When False, isosurfaces are generated for blanked and unblanked. regions.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).obey_source_zone_blanking = True

IsosurfaceGroup.shade

Shade attributes for this isosurface group.

Type:IsosurfaceShade

Example usage:

>>> plot.isosurface(0).shade.show = True

IsosurfaceGroup.show

Show isosurfaces for this isosurface group.

Type:bool

Example usage:

>>> plot.isosurface(1).show = True

IsosurfaceContour

class tecplot.plot.IsosurfaceContour(contour)

Contour attributes of the isosurface group.

import tecplot as tp
from os import path
from tecplot.plot import IsosurfaceGroup
from tecplot.constant import ContourType

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).variable = dataset.variable('U(M/S)')

iso = plot.isosurface(0)
iso.contour.use_lighting_effect = True

iso.contour.show = True
iso.contour.contour_type = ContourType.AverageCell
iso.contour.flood_contour_group_index = 5  # T(K)

tp.export.save_png('isosurface_contour.png', 600)

Attributes

contour_type	Contour display type.
flood_contour_group	Contour group to use for flooding.
flood_contour_group_index	The Index of the ContourGroup to use for flooding.
line_color	Color of contour lines.
line_contour_group	The contour group to use for contour lines.
line_contour_group_index	The Index of the ContourGroup to use for contour lines.
line_thickness	Contour line thickness as a percentage of frame width.
show	Show contours on isosurfaces.
use_lighting_effect	Enable lighting effect.

IsosurfaceContour.contour_type

Contour display type.

Type:ContourType

• ContourType.Lines - Draws lines of constant value of the specified contour variable.
• ContourType.Flood - Floods regions between contour lines with colors from a color map. The distribution of colors used for contour flooding may be banded or continuous. When banded distribution is used for flooding, a solid color is used between contour levels. If continuous color distribution is used, the flood color will vary linearly in all directions.
• ContourType.Overlay - Combines the above two options.
• ContourType.AverageCell - Floods cells or finite elements with colors from a color map according to the average value of the contour variable over the data points bounding the cell. If the variables are located at the nodes, the values at the nodes are averaged. If the variables are cell-centered, the cell-centered values are averaged to the nodes and the nodes are then averaged.
• ContourType.PrimaryValue - Floods cells or finite elements with colors from a color map according to the primary value of the contour variable for each cell. If the variable is cell centered, the primary value is the value assigned to the cell. If the variable is node located, the primary value comes from the lowest index node in the cell. If the variables are located at the nodes, the value of the lowest indexed node in the cell is used. When plotting IJK-ordered, FE-brick or FE-tetra cells, each face is considered independently of the other faces. You may get different colors on the different faces of the same cell. If the variables are cell-centered, the cell-centered value is used directly. When plotting I, J, or K-planes in 3D, the cell on the positive side of the plane supplies the value, except in the case of the last plane, where the cell on the negative side supplies the value.

Example usage:

>>> plot.isosurface(0).contour.show = True
>>> plot.isosurface(0).contour.contour_type = ContourType.Flood

IsosurfaceContour.flood_contour_group

Contour group to use for flooding.

Type:ContourGroup

Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> group = plot.contour(1)
>>> contour = plot.isosurface(1).contour
>>> contour.flood_contour_group = group

IsosurfaceContour.flood_contour_group_index

The Index of the ContourGroup to use for flooding.

Type:Index (zero-based index)

This property sets and gets, by Index, the ContourGroup used for flooding. Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> contour = plot.isosurface(0).contour
>>> contour.flood_contour_group_index = 1

IsosurfaceContour.line_color

Color of contour lines.

Type:Color or ContourGroup

Contour lines can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property.

Example usage:

>>> plot.show_isosurfaces = True
>>> plot.isosurface(0).contour.line_color = Color.Blue

IsosurfaceContour.line_contour_group

The contour group to use for contour lines.

Type:ContourGroup

Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame that use it.

Example usage:

>>> contour = plot.isosurface(0).contour
>>> group = plot.contour(1)
>>> contour.line_contour_group = group

IsosurfaceContour.line_contour_group_index

The Index of the ContourGroup to use for contour lines.

Type:integer (zero-based index)

This property sets and gets, by Index, the ContourGroup used for line placement. Although all properties of the ContourGroup can be manipulated through this object, many of them (i.e., color) will not affect the lines unless the FieldmapContour.line_color is set to the same ContourGroup. Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame that use it.

Example usage:

>>> contour = plot.isosurface(0).contour
>>> contour.line_contour_group_index = 2

IsosurfaceContour.line_thickness

Contour line thickness as a percentage of frame width.

Suggested values are one of: .02, .1, .4, .8, 1.5

Type:float

Example usage:

>>> plot.show_isosurfaces = True
>>> plot.isosurface(0).contour.line_thickness = .4

IsosurfaceContour.show

Show contours on isosurfaces.

Type:boolean

Example usage:

>>> plot.isosurface(0).contour.show = True

IsosurfaceContour.use_lighting_effect

Enable lighting effect.

Type:Boolean

When set to True, the lighting effect may be selected with the IsosurfaceEffects.lighting_effect attribute.

Note

Setting IsosurfaceContour.use_lighting_effect will also set the same value for IsosurfaceShade.use_lighting_effect, and vice-versa.

Example usage:

>>> plot.isosurface(0).shade.use_lighting_effect = True
>>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled

IsosurfaceEffects

class tecplot.plot.IsosurfaceEffects(contour)

Effects of the isosurface group.

import tecplot as tp
from os import path
from tecplot.plot import IsosurfaceGroup
from tecplot.constant import LightingEffect

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.contour(0).variable = dataset.variable('U(M/S)')

plot.show_isosurfaces = True
iso = plot.isosurface(0)
iso.shade.use_lighting_effect = True

iso.effects.lighting_effect = LightingEffect.Paneled
iso.effects.use_translucency = True
iso.effects.surface_translucency = 80

tp.export.save_png('isosurface_effects.png', 600)

Attributes

lighting_effect	Lighting effect.
surface_translucency	Surface translucency of the isosurface group.
use_translucency	Enable surface translucency for this isosurface group.

IsosurfaceEffects.lighting_effect

Lighting effect.

Type:LightingEffect

Isosurface lighting effects must be enabled by setting IsosurfaceShade.use_lighting_effect to True when setting this value.

There are two types of lighting effects: Paneled and Gouraud:

• Paneled: Within each cell, the color assigned to each area by

  shading or contour flooding is tinted by a shade constant across the cell. This shade is based on the orientation of the cell relative to your 3D light source.

• Gouraud: This offers smoother, more continuous shading than

  Paneled shading, but it also results in slower plotting and larger print files. Gouraud shading is not continuous across zone boundaries unless face neighbors are specified in the data. Gouraud shading is not available for finite element volume Zone when blanking is active. The zone’s lighting effect reverts to Paneled shading in this case.

Example usage:

>>> plot.isosurface(0).shade.use_lighting_effect = True
>>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled

IsosurfaceEffects.surface_translucency

Surface translucency of the isosurface group.

Type:integer

Iso-surface surface translucency must be enabled by setting IsosurfaceEffects.use_translucency = True when setting this value.

Valid translucency values range from one (opaque) to 99 (translucent).

Example usage:

>>> plot.isosurface(0).effects.use_translucency = True
>>> plot.isosurface(0).effects.surface_translucency = 20

IsosurfaceEffects.use_translucency

Enable surface translucency for this isosurface group.

Type:boolean

The surface translucency value can be changed by setting IsosurfaceEffects.surface_translucency.

Example usage:

>>> plot.isosurface(0).effects.use_translucency = True
>>> plot.isosurface(0).effects.surface_translucency = 20

IsosurfaceMesh

class tecplot.plot.IsosurfaceMesh(mesh)

Mesh attributes of the isosurface group.

import tecplot as tp
from os import path
from tecplot.plot import IsosurfaceGroup
from tecplot.constant import Color

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.contour(0).variable = dataset.variable('U(M/S)')
plot.show_isosurfaces = True

iso = plot.isosurface(0)
iso.shade.use_lighting_effect = True
iso.contour.show = True
iso.mesh.show = True
iso.mesh.color = Color.Black
iso.mesh.line_thickness = 0.4

tp.export.save_png('isosurface_mesh.png', 600)

Attributes

color	Isosurface mesh color.
line_thickness	Isosurface mesh line thickness.
show	Display the mesh on isosurfaces.

IsosurfaceMesh.color

Isosurface mesh color.

Type:Color or ContourGroup

Iso-surface mesh lines can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property.

Example usage:

>>> plot.isosurface(0).mesh.show = True
>>> plot.isosurface(0).mesh.color = Color.Blue

IsosurfaceMesh.line_thickness

Isosurface mesh line thickness.

Type:float

Suggested values are .002, .1, .4, .8, 1.5

Example usage:

>>> plot.isosurface(0).mesh.show = True
>>> plot.isosurface(0).mesh.line_thickness = .4

IsosurfaceMesh.show

Display the mesh on isosurfaces.

Type:boolean

Example usage:

>>> plot.isosurface(0).mesh.show = True

IsosurfaceShade

class tecplot.plot.IsosurfaceShade(contour)

Shade attributes of the isosurface group.

import tecplot as tp
from os import path
from tecplot.plot import IsosurfaceGroup
from tecplot.constant import Color, LightingEffect

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).variable = dataset.variable('U(M/S)')
iso = plot.isosurface(0)

iso.contour.show = False  # Hiding the contour will reveal the shade.

iso.shade.show = True
iso.shade.color = Color.Red
iso.shade.use_lighting_effect = True

iso.effects.lighting_effect = LightingEffect.Paneled

tp.export.save_png('isosurface_shade.png', 600)

Attributes

color	Shade color.
show	Show shade attributes.
use_lighting_effect	Enable lighting effect.

IsosurfaceShade.color

Shade color.

Type:Color

Color.MultiColor and Color.RGBColor coloring are not available. Use flooded contours for multi-color or RGB flooding

Example usage:

>>> plot.isosurface(0).shade.show = True
>>> plot.isosurface(0).shade.color = Color.Blue

IsosurfaceShade.show

Show shade attributes.

Type:boolean

Example usage:

>>> plot.isosurface(0).shade.show = True

IsosurfaceShade.use_lighting_effect

Enable lighting effect.

Type:Boolean

When set to True, the lighting effect may be selected with the IsosurfaceEffects.lighting_effect attribute.

Note

Setting IsosurfaceShade.use_lighting_effect will also set the same value for IsosurfaceContour.use_lighting_effect, and vice-versa.

Example usage:

>>> plot.isosurface(0).shade.use_lighting_effect = True
>>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled

Slice

SliceGroup

class tecplot.plot.SliceGroup(index, plot)

Change attributes associated with slices.

Slices can include lighting effects, contours, meshes, and more. To customize these and other attributes of slices, use this object.

This object controls the style for a specific slice group within a Frame. Slice contour, vector, edge, effects, mesh, visibility and position information are accessed through this class.

from os import path
import tecplot as tp
from tecplot.constant import SliceSurface, ContourType

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.contour(0).variable = dataset.variable('U(M/S)')

vector_3D = tp.plot.Vector3D(plot)
vector_3D.u_variable_index = 3  # U(M/S)
vector_3D.v_variable_index = 4  # V(M/S)
vector_3D.w_variable_index = 5  # W(M/S)

plot.show_slices = True
slice_0 = plot.slice(0)

slice_0.contour.show = True
slice_0.contour.contour_type = ContourType.Overlay  # AKA "Both lines and flood"

slice_0.effects.use_translucency = True
slice_0.effects.surface_translucency = 30

slice_0.show_start_and_end_slices = True

# Show an arbitrary slice
slice_0.orientation = SliceSurface.Arbitrary
slice_0.arbitrary_normal = (1, -.25, 0)
slice_0.origin = (.19, .5, .25)  # AKA Primary Slice Position

slice_0.show_start_and_end_slices = True
slice_0.start_position = (-.21, .05, .025)
slice_0.end_position = (1.342, .95, .475)
slice_0.show_intermediate_slices = True
slice_0.num_intermediate_slices = 3

slice_0.vector.show = True

slice_0.edge.show = True
slice_0.edge.line_thickness = 0.4

tp.export.save_png('slice_example.png', 300)

Up to eight different slice groups can be set. Each slice group can use different slice planes or different ranges for the same slice plane.

>>> slice_3 = plot.slice(3)
>>> slice_3.contour.show = True

Attributes

arbitrary_normal	Normal for arbitrary slices.
contour	Contour attributes for the slice group.
edge	Edge attributes for this slice group.
effects	Effects attributes for this slice group.
end_position	Position of the end slice.
mesh	Mesh attributes for this slice group.
num_intermediate_slices	Number of intermediate slicing planes.
obey_source_zone_blanking	Obey source zone blanking.
orientation	Select which plane the slice is drawn on (X,Y,Z, I, J, K or arbitrary).
origin	Origin of the slice.
shade	Shade attributes for this slice group.
show	Show slices for this slice group.
show_intermediate_slices	Show intermediate slices.
show_primary_slice	Include the primary slice (first slice placed) in the Plot.
show_start_and_end_slices	Include start and end slices.
slice_source	Zones to slice through.
start_position	Position of the start slice.
vector	Vector attributes for this slice group.

Methods

set_arbitrary_from_points(p1, p2, p3)

Set an arbitrary slice from 3 points.

SliceGroup.arbitrary_normal

Normal for arbitrary slices.

Type:3-tuple of floats

Example usage:

>>> plot.slice(0).orientation = SliceSurface.Arbitrary
>>> plot.slice(0).arbitrary_normal = (0.1, 0.2, 0.3)
>>> plot.slice(0).arbitrary_normal.x
0.1
>>> plot.slice(0).arbitrary_normal.y
0.2
>>> plot.slice(0).arbitrary_normal.z
0.3

SliceGroup.contour

Contour attributes for the slice group.

Type:SliceContour

Example usage:

>>> plot.slice(0).contour.show = True

SliceGroup.edge

Edge attributes for this slice group.

Type:SliceEdge

Example usage:

>>> plot.slice(0).edge.show = True

SliceGroup.effects

Effects attributes for this slice group.

Type:SliceEffects

Example usage:

>>> plot.slice(0).effects.use_translucency = True

SliceGroup.end_position

Position of the end slice.

SliceGroup.show_start_and_end_slices must be True to show the end slice.

Type:3-tuple of floats

Example usage:

>>> plot.slice(0).show_start_and_end_slices = True
>>> plot.slice(0).end_position = (1, 1, 1)
>>> plot.slice(0).end_position.i
1

SliceGroup.mesh

Mesh attributes for this slice group.

Type:SliceMesh

Example usage:

>>> plot.slice(0).mesh.show = True

SliceGroup.num_intermediate_slices

Number of intermediate slicing planes.

You may specify between 1 and 5,000 intermediate slices.

Type:integer

Example usage:

>>> # Show 2 intermediate slices
>>> plot.slice(0).num_intermediate_slices = 2

SliceGroup.obey_source_zone_blanking

Obey source zone blanking.

When set to True, slices are subject to any blanking used for the data. When set to False, slices are generated for blanked and unblanked regions.

Type:boolean

Example usage:

>>> plot.slice(0).obey_source_zone_blanking = True

SliceGroup.orientation

Select which plane the slice is drawn on (X,Y,Z, I, J, K or arbitrary).

Type:SliceSurface

You may also choose SliceSurface.Arbitrary to place the slice on an arbitrary plane.

To orient slices in an arbitrary direction, choose SliceSurface.Arbitrary. As with other slices, you may specify origin points for a primary slice and/or for start and end slices. Slices pass through the indicated origin point(s), so you can easily align the edge of a slice or group of slices along some other feature of the plot, such as an axis. If intermediate slices are activated, they are drawn equally spaced between the slices defined by the start and end origins.

Example usage:

>>> plot.slice(0).orientation = SliceSurface.XPlanes

SliceGroup.origin

Origin of the slice.

Type:3-tuple of floats

For arbitrary slice orientation, the origin can be any location. For axis orientations (XPlanes, YPlanes, etc.) two of the three components are not used.

Example usage:

>>> slice_0 = plot.slice(0)
>>> slice_0.orientation = SliceSurface.IPlanes
>>> slice_0.origin = (1, 0, 0)
>>> dx = (1, 1, 1)
>>> slice_0.origin += dx
>>> slice_0.origin.i
2
>>> slice_0.origin.j
1
>>> slice_0.origin.k
1

>>> slice_0.orientation = SliceSurface.Arbitrary
>>> slice_0.origin = (.5, .1, .1)
>>> slice_0.origin += dx
>>> slice_0.origin.x
1.5
>>> slice_0.origin.y
.1
>>> slice_0.origin.z
.1

SliceGroup.set_arbitrary_from_points(p1, p2, p3)

Set an arbitrary slice from 3 points.

Set the normal and origin of an arbitrary slice using three points. The origin will be set to the 3rd point.

The three points must not be coincident or collinear. The slice’s origin is set to the third point and its normal is recalculated such that the cutting plane passes through all three points.

Parameters:

p1:  3-`tuple` of floats
p2:  3-`tuple` of floats
p3:  3-`tuple` of floats

Example usage:

>>> slice_0 = plot.slice(0)
>>> slice_0.set_arbitrary_from_points((0,0,0), (.1,.2,.3), (.1,.1,.1))

SliceGroup.shade

Shade attributes for this slice group.

Type:SliceShade

Example usage:

>>> plot.slice(0).shade.show = True

SliceGroup.show

Show slices for this slice group.

Type:bool

Example usage:

>>> plot.slice(0).show = True

SliceGroup.show_intermediate_slices

Show intermediate slices.

Intermediate slices are evenly distributed between the start and end slices.

Example usage:

>>> plot.slice(0).show_intermediate_slices = True

SliceGroup.show_primary_slice

Include the primary slice (first slice placed) in the Plot.

Type:bool

Example usage:

>>> plot.slice(0).show = True
>>> plot.slice(0).show_primary_slice = True

SliceGroup.show_start_and_end_slices

Include start and end slices.

Type:boolean

Example usage:

>>> plot.slice(0).show_start_and_end_slices = True

SliceGroup.slice_source

Zones to slice through.

Choose to slice through volume Zones, surface Zones, or the surfaces of volume Zones.

Type:SliceSource

Example usage:

>>> plot.slice(0).slice_source = SliceSource.SurfaceZones

SliceGroup.start_position

Position of the start slice.

SliceGroup.show_start_and_end_slices must be True to show the start slice.

Type:3-tuple of floats

Example usage:

>>> plot.slice(0).show_start_and_end_slices = True
>>> plot.slice(0).start_position = (1, 1, 1)
>>> plot.slice(0).start_position.i
1

SliceGroup.vector

Vector attributes for this slice group.

Type:SliceVector

Example usage:

>>> plot.slice(0).vector.show = True

SliceContour

class tecplot.plot.SliceContour(parent_slice)

Contour attributes of the slice group.

from os import path
import tecplot as tp
from tecplot.constant import SliceSurface, ContourType

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()

plot.show_slices = True
slice_0 = plot.slice(0)

plot.contour(0).variable = dataset.variable('U(M/S)')
slice_0.contour.show = True
slice_0.contour.contour_type = ContourType.Overlay  # AKA "Both lines and flood"

slice_0.show_start_and_end_slices = True

# Show an arbitrary slice
slice_0.orientation = SliceSurface.Arbitrary
slice_0.arbitrary_normal = (1, -.25, 0)
slice_0.origin = (.19, .5, .25)  # AKA Primary Slice Position

slice_0.show_start_and_end_slices = True
slice_0.start_position = (-.21, .05, .025)
slice_0.end_position = (1.342, .95, .475)
slice_0.show_intermediate_slices = True
slice_0.num_intermediate_slices = 3

tp.export.save_png('slice_contour.png', 600)

Attributes

contour_type	Contour type for the slice contours.
flood_contour_group	Contour group to use for flooding.
flood_contour_group_index	The Index of the ContourGroup to use for flooding.
line_color	Color of contour lines.
line_contour_group	Contour group to use for contour lines.
line_contour_group_index	The Index of the ContourGroup to use for contour lines.
line_thickness	Contour line thickness as a percentage of frame width.
show	Show contours on the slice.
use_lighting_effect	Enable lighting effect.

SliceContour.contour_type

Contour type for the slice contours.

Type:ContourType

Example usage:

>>> plot.show_slices = True
>>> plot.slice(0).contour.contour_type = ContourType.AverageCell

SliceContour.flood_contour_group

Contour group to use for flooding.

Type:ContourGroup

Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> group = plot.contour(1)
>>> contour = plot.slice(1).contour
>>> contour.flood_contour_group = group

SliceContour.flood_contour_group_index

The Index of the ContourGroup to use for flooding.

Type:Index (zero-based index)

This property sets and gets, by Index, the ContourGroup used for flooding. Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> plot.show_slices = True
>>> contour = plot.slice(0).contour
>>> contour.flood_contour_group_index = 1

SliceContour.line_color

Color of contour lines.

Selecting Color.MultiColor will color the slice contour lines based on the contour group variable.

Type:Color

Example usage:

>>> plot.show_slices = True
>>> plot.slice(0).contour.line_color = Color.Blue

SliceContour.line_contour_group

Contour group to use for contour lines.

Type:ContourGroup

Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> group = plot.contour(1)
>>> contour = plot.slice(1).contour
>>> contour.line_contour_group = group

SliceContour.line_contour_group_index

The Index of the ContourGroup to use for contour lines.

Type:integer (zero-based index)

This property sets and gets, by Index, the ContourGroup used for line placement. Although all properties of the ContourGroup can be manipulated through this object, many of them (i.e., color) will not affect the lines unless the FieldmapContour.line_color is set to the same ContourGroup. Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame that use it.

Example usage:

>>> plot.show_slices = True
>>> contour = plot.slice(0).contour
>>> contour.line_contour_group_index = 2

SliceContour.line_thickness

Contour line thickness as a percentage of frame width.

Suggested values are one of: .02, .1, .4, .8, 1.5

Type:float

Example usage:

>>> plot.show_slices = True
>>> plot.slice(0).contour.line_thickness = .4

SliceContour.show

Show contours on the slice.

Type:bool

Example usage:

>>> plot.show_slices = True
>>> plot.slice(1).contour.show = True

SliceContour.use_lighting_effect

Enable lighting effect.

Note

Setting SliceContour.use_lighting_effect will also set the same value for SliceShade.use_lighting_effect, and vice-versa.

The lighting effect is set with SliceEffects.lighting_effect, and may be one of LightingEffect.Gouraud or LightingEffect.Paneled.

Type:boolean

Example usage:

>>> plot.show_slices = True
>>> contour = plot.slice(0).contour
>>> contour.use_lighting_effect = True
>>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled

SliceEdge

class tecplot.plot.SliceEdge(vector)

Edge attributes of the slice group.

When enabled, selected edge lines of all slices in this group will be shown.

from os import path
import tecplot as tp

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()

plot.show_slices = True
slice_0 = plot.slice(0)

plot.contour(0).variable = dataset.variable('U(M/S)')

slice_0.edge.show = True
slice_0.edge.line_thickness = 0.8

tp.export.save_png('slice_edge.png', 600)

Attributes

color	Edge color.
edge_type	Edge type.
line_thickness	Edge line thickness as a percentage of frame width.
show	Show edges.

SliceEdge.color

Edge color.

Type:Color

Example usage:

>>> plot.slice(0).edge.show = True
>>> plot.slice(0).edge.color = Color.Blue

SliceEdge.edge_type

Edge type.

Type:EdgeType

There are two types of edges in Tecplot 360 EX: creases and borders.

An edge border is the boundary of a Zone. An edge crease appears when the inside angle between two cells is less than a user-defined limit. The inside angle can range from 0-180 degrees (where 180 degrees indicates coplanar surfaces). The default inside angle for determining an edge crease is 135 degrees.

Example usage:

>>> plot.slice(0).edge.show = True
>>> plot.slice(0).edge.edge_type = EdgeType.BordersAndCreases

SliceEdge.line_thickness

Edge line thickness as a percentage of frame width.

Type:float

Example usage:

>>> plot.slice(0).edge.show = True
>>> plot.slice(0).edge.line_thickness = .8

SliceEdge.show

Show edges.

This property must be set to True to show any of the other edge properties.

Type:boolean

Example usage:

>>> plot.slice(0).edge.show = True
>>> plot.slice(0).edge.edge_type = EdgeType.BordersAndCreases

SliceEffects

class tecplot.plot.SliceEffects(effects)

Slice effects for this slice.

from os import path
import tecplot as tp

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()

plot.show_slices = True
slice_0 = plot.slice(0)

plot.contour(0).variable = dataset.variable('U(M/S)')
slice_0.contour.show = True

slice_0.effects.use_translucency = True
slice_0.effects.surface_translucency = 70

tp.export.save_png('slice_effects.png', 600)

Attributes

lighting_effect	Lighting effect.
surface_translucency	Surface translucency of the slice group.
use_translucency	Enable surface translucency for this slice group.

SliceEffects.lighting_effect

Lighting effect.

Type:LightingEffect

Slice lighting effects must be enabled by setting SliceContour.use_lighting_effect or SliceShade.use_lighting_effect to True when setting this value.

There are two types of lighting effects: Paneled and Gouraud:

• Paneled: Within each cell, the color assigned to each area by

  shading or contour flooding is tinted by a shade constant across the cell. This shade is based on the orientation of the cell relative to your 3D light source.

• Gouraud: This offers smoother, more continuous shading than

  Paneled shading, but it also results in slower plotting and larger print files. Gouraud shading is not continuous across zone boundaries unless face neighbors are specified in the data. Gouraud shading is not available for finite element volume Zones when blanking is active. The zone’s lighting effect reverts to Paneled shading in this case.

Example usage:

>>> plot.slice(0).contour.use_lighting_effect = True
>>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled

SliceEffects.surface_translucency

Surface translucency of the slice group.

Type:integer

Slice surface translucency must be enabled by setting SliceEffects.use_translucency = True when setting this value.

Valid slice translucency values range from one (opaque) to 99 (translucent).

Example usage:

>>> plot.slice(0).effects.use_translucency = True
>>> plot.slice(0).effects.surface_translucency = 20

SliceEffects.use_translucency

Enable surface translucency for this slice group.

Type:boolean

The surface translucency value can be changed by setting SliceEffects.surface_translucency.

Example usage:

>>> plot.slice(0).effects.use_translucency = True
>>> plot.slice(0).effects.surface_translucency = 20

SliceMesh

class tecplot.plot.SliceMesh(mesh)

Mesh attributes of the slice group.

from os import path
import tecplot as tp
from tecplot.constant import SliceSurface, ContourType

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tp.data.load_tecplot(datafile)
plot = tp.active_frame().plot()
plot.show_slices = True
plot.contour(0).variable = dataset.variable('U(M/S)')

plot.slice(0).mesh.show = True

tp.export.save_png('slice_mesh.png', 300)

Attributes

color	Slice mesh line Color or ContourGroup.
line_thickness	Mesh line thickness.
show	Show mesh lines.

SliceMesh.color

Slice mesh line Color or ContourGroup.

Type:Color or ContourGroup

Slice mesh lines can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property.

Example usage:

>>> plot.slice(0).mesh.show = True
>>> plot.slice(0).mesh.color = Color.Green

SliceMesh.line_thickness

Mesh line thickness.

Type:float

The mesh line thickness is specified as a percentage of the frame width.

Example usage:

>>> plot.slice(0).mesh.show = True
>>> plot.slice(0).mesh.line_thickness = 0.8

SliceMesh.show

Show mesh lines.

Type:boolean

Example usage:

>>> plot.slice(0).mesh.show = True

SliceShade

class tecplot.plot.SliceShade(shade)

Shade attributes of the slice group.

Show shading on the slice when SliceContour.show has not been selected or is set to ContourType.Lines.

from os import path
import tecplot as tp
from tecplot.constant import Color

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D', 'pyramid.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.show_slices = True
plot.slice(0).contour.show = False
shade = plot.slice(0).shade
shade.show = True
shade.color = Color.Red  # Slice will be colored solid red.
tp.export.save_png('slice_shade.png', 300)

Attributes

color	Shade color.
show	Show shade attributes.
use_lighting_effect	Use lighting effect.

SliceShade.color

Shade color.

Type:Color

Color.MultiColor and Color.RGBColor coloring are not available. Use flooded contours for multi-color or RGB flooding

Example usage:

>>> plot.slice(0).shade.show = True
>>> plot.slice(0).shade.color = Color.Blue

SliceShade.show

Show shade attributes.

Type:boolean

Example usage:

>>> plot.slice(0).shade.show = True

SliceShade.use_lighting_effect

Use lighting effect.

When set to True, the lighting effect may be selected with the SliceEffects.lighting_effect attribute.

Note

Setting SliceShade.use_lighting_effect will also set the same value for SliceContour.use_lighting_effect, and vice-versa.

Type:Boolean

Example usage:

>>> plot.slice(0).shade.use_lighting_effect = True
>>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled

SliceVector

class tecplot.plot.SliceVector(vector)

Vector attributes of the slice group.

from os import path
import tecplot as tp

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, '3D', 'Arrow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.contour(0).variable_index = dataset.variable('Z').index

plot.show_slices = True

# Vector variables must be assigned before displaying
vector_3D = tp.plot.Vector3D(plot)
vector_3D.u_variable_index = 1
vector_3D.v_variable_index = 2
vector_3D.w_variable_index = 3

slice_vector = plot.slice(0).vector
slice_vector.show = True

tp.export.save_png('slice_vector.png', 300)

Attributes

arrowhead_style	Arrowhead style of slice vectors.
color	Set slice vector color.
is_tangent	Use tangent vectors for slices.
line_thickness	Vector line thickness as a percentage of the frame height.
show	Show vectors on slices.
vector_type	Type of vector for slices in this slice group.

SliceVector.arrowhead_style

Arrowhead style of slice vectors.

Type:ArrowheadStyle

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.arrowhead_style = ArrowheadStyle.Hollow

SliceVector.color

Set slice vector color.

Type:Color

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.color = Color.Red

SliceVector.is_tangent

Use tangent vectors for slices.

Type:boolean

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.is_tangent = True

SliceVector.line_thickness

Vector line thickness as a percentage of the frame height.

Type:float

Typical values are .02, .1, .4, .8, 1.5

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.line_thickness = .1

SliceVector.show

Show vectors on slices.

Type:boolean

Example usage:

>>> plot.slice(0).vector.show = True

SliceVector.vector_type

Type of vector for slices in this slice group.

Type:VectorType

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.vector_type = VectorType.MidAtPoint

Streamtraces

Streamtraces

class tecplot.plot.Streamtraces(plot)

Streamtrace attributes for the plot.

A streamtrace is the path traced by a massless particle placed at an arbitrary location in a steady-state vector field. Streamtraces may be used to illustrate the nature of the vector field flow in a particular region of the Plot.

Note

Because streamtraces are dependent upon a vector field, you must define vector components before creating streamtraces. However, it is not necessary to activate the Vector zone layer to use streamtraces.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'eddy.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_mesh = True
plot.show_shade = False

plot.vector.u_variable_index = 4
plot.vector.v_variable_index = 5
plot.vector.w_variable_index = 6
plot.show_streamtraces = True

streamtraces = plot.streamtraces
streamtraces.color = Color.Blue

streamtraces.show_arrows = True
streamtraces.arrowhead_size = 3
streamtraces.step_size = .25
streamtraces.line_thickness = .2
streamtraces.max_steps = 100

streamtraces.add_rake(start_position=(45.49, 15.32, 59.1),
                      end_position=(48.89, 53.2, 47.6),
                      stream_type=Streamtrace.SurfaceLine)


tecplot.export.save_png('streamtrace_example.png', 600)

Attributes

are_active	Determine if there are active streamtraces in the current plot type.
arrowhead_size	Arrowhead size as a percentage of frame height.
arrowhead_spacing	Distance between arrowheads in terms of Y-frame units.
color	Color of streamtraces line (not rods or ribbons).
count	Query the number of active streamtraces for the current plot type.
dash_skip	Number of time deltas used for the “off” sections of the streamlines.
has_terminating_line	Determine if the streamtraces have the terminating line.
line_thickness	Streamtrace line thickness.
marker_color	Color of the streamline markers.
marker_size	Size of streamline markers.
marker_symbol_type	The SymbolType to use for stream markers.
max_steps	Maximum number of steps before the streamtrace is terminated.
min_step_size	Smallest step size to use as a percentage of cell distance.
obey_source_zone_blanking	Obey source zone blanking.
rod_ribbon	Streamtrace rod/ribbon attributes.
show_arrows	Display arrowheads along all streamlines.
show_dashes	Display streamtrace dashes.
show_markers	Display streamtrace markers.
show_paths	Draw streamtrace paths (lines, ribbons, or rods).
step_size	Maximum fraction of the distance across a cell that a streamtrace moves in one step.
termination_line	Streamtraces termination line attributes.
timing	Streamtraces timing attributes.

Methods

add(seed_point, stream_type[, direction])	Add a single streamtrace to the plot of the current frame.
add_on_zone_surface(zones, stream_type[, ...])	Add streamtraces to one or more zones in a plot.
add_rake(start_position, end_position, ...)	Add a rake of streamtraces to the plot of the current frame.
delete_all()	Delete all streamtraces for the current plot type.
delete_range(range_start, range_end)	Delete a range of streamtraces.
marker_symbol([symbol_type])	Returns a streamline symbol style object.
position(stream_number)	Query the starting position of a streamtrace.
set_termination_line(line_points)	Set the position of the termination line for streamtraces.
streamtrace_type(stream_number)	Query the type of a streamtrace by streamtrace number.

Streamtraces.add(seed_point, stream_type, direction=<StreamDir.Both: 2>)

Add a single streamtrace to the plot of the current frame.

The plot type must be either Cartesian2D or Cartesian3D.

Parameters:

• seed_point – (2- or 3- tuple of floats): Pass a 2-tuple of float for a Cartesian2DFieldPlot, or a 3-tuple of float for a Cartesian3DFieldPlot.
• stream_type – (Streamtrace): Type of streamtraces to add.
• direction – (StreamDir, optional): Direction of propagation of the streamtraces being added.

Note

stream_type is automatically set to Streamtrace.SurfaceLine if the plot type is Cartesian2DFieldPlot. The only stream type available for 2D plots is Streamtrace.SurfaceLine.

Raises:TecplotSystemError – The streamtraces could not be added.

import tecplot
from tecplot.constant import *
import os

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'eddy.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_mesh = True
plot.show_shade = False

plot.vector.u_variable_index = 4
plot.vector.v_variable_index = 5
plot.vector.w_variable_index = 6
plot.show_streamtraces = True
plot.streamtraces.add([3, 4, 5], Streamtrace.VolumeLine)
tecplot.export.save_png('streamtrace_add_xyz.png', 600)

Streamtraces.add_on_zone_surface(zones, stream_type, num_seed_points=10, direction=<StreamDir.Both: 2>)

Add streamtraces to one or more zones in a plot.

The plot type must be either Cartesian2D or Cartesian3D.

Note

For volume zones the streamtraces are propagated from the surfaces of the volume.

Parameters:

• zones (Set displays of integers) – Set of Zones on which to add streamtraces.
• stream_type – (Streamtrace): Type of streamtraces to add.
• num_seed_points – (integer, optional): Number of seed points for distributing along a rake or on defined surfaces.
• direction – (StreamDir, optional): Direction of propagation of the streamtraces being added.

import tecplot
from tecplot.constant import *
import os

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'OneraM6wing', 'OneraM6_SU2_RANS.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()

plot.vector.u_variable_index = 3
plot.vector.v_variable_index = 4
plot.vector.w_variable_index = 5
plot.show_streamtraces = True

plot.streamtraces.add_on_zone_surface(
    zones=[1],  # Add streamtraces on 2nd zone only
    stream_type=Streamtrace.VolumeLine)
tecplot.export.save_png('streamtrace_add_on_zone_surface.png', 600)

Streamtraces.add_rake(start_position, end_position, stream_type, num_seed_points=10, direction=<StreamDir.Both: 2>)

Add a rake of streamtraces to the plot of the current frame.

The plot type must be either Cartesian2D or Cartesian3D.

Parameters:

• start_position – (2- or 3- tuple of floats): Pass a 2-tuple of float for a Cartesian2DFieldPlot, or a 3-tuple of float for a Cartesian3DFieldPlot.
• end_position – (2- or 3- tuple of floats): Pass a 2-tuple of float for a Cartesian2DFieldPlot, or a 3-tuple of float for a Cartesian3DFieldPlot.
• stream_type – (Streamtrace): Type of streamtraces to add.
• num_seed_points – (integer, optional): Number of seed points for distributing along a rake or on defined surfaces.
• direction – (StreamDir, optional): Direction of propagation of the streamtraces being added.

import tecplot
from tecplot.constant import *
import os

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'eddy.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_mesh = True
plot.show_shade = False

plot.vector.u_variable_index = 4
plot.vector.v_variable_index = 5
plot.vector.w_variable_index = 6
plot.show_streamtraces = True

streamtraces = plot.streamtraces
streamtraces.add_rake(start_position=[.5, .5, .5],
                      end_position=[20, 20, 20],
                      stream_type=Streamtrace.VolumeLine)
tecplot.export.save_png('streamtrace_add_rake.png', 600)

Streamtraces.are_active

Determine if there are active streamtraces in the current plot type.

Note

This property is read-only.

Returns:boolean. True if there are active streamtraces in the current plot type.

Example usage:

>>> streamtraces_are_active = plot.streamtraces.are_active

Streamtraces.arrowhead_size

Arrowhead size as a percentage of frame height.

Type:float

Recommend values are one of 1, 3, 5, 8, or 12.

Example usage:

>>> plot.streamtraces.show_arrows = True
>>> plot.streamtraces.arrowhead_size = 1.0

Streamtraces.arrowhead_spacing

Distance between arrowheads in terms of Y-frame units.

Type:float

For example, a value of 10 will space arrowheads approximately ten percent of the frame height apart from each other along each streamline.

Example usage:

>>> plot.streamtraces.show_arrows = True
>>> plot.streamtraces.arrowhead_spacing = 10

Streamtraces.color

Color of streamtraces line (not rods or ribbons).

Type:Color or ContourGroup

Streamtraces can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property.

Example usage:

>>> plot.streamtraces.color = Color.Red

Streamtraces.count

Query the number of active streamtraces for the current plot type.

Returns:integer

Note

This property is read-only.

>>> num_active_streamtraces = plot.streamtraces.count

Streamtraces.dash_skip

Number of time deltas used for the “off” sections of the streamlines.

Note

The dash_skip value must be greater than 0.

Type:integer

Example usage:

>>> plot.streamtraces.dash_skip = 2

Streamtraces.delete_all()

Delete all streamtraces for the current plot type.

2D and 3D streamtraces are independent of each other.

If the plot type is Cartesian2D, all 2D streamtraces are deleted. If the plot type is Cartesian3D, all 3D streamtraces are deleted.

Raises:TecplotSystemError – The streamtraces could not be deleted.

Example usage:

>>> plot.streamtraces.delete_all()

Streamtraces.delete_range(range_start, range_end)

Delete a range of streamtraces.

Parameters:

• range_start – (integer): 0-based start streamtrace number to delete.
• range_end – (integer): 0-based end streamtrace number to delete.

Raises:

TecplotSystemError – The streamtraces in the range could not be deleted.

Example usage:

>>> # Delete the first 100 streamtraces
>>> plot.streamtraces.delete_range(0, 99)

Streamtraces.has_terminating_line

Determine if the streamtraces have the terminating line.

Note

This property is read-only.

Returns:boolean. True if the streamtraces have the terminating line, False otherwise.

Example usage:

>>> has_terminating_line = plot.streamtraces.has_terminating_line

Streamtraces.line_thickness

Streamtrace line thickness.

Type:float

Line thickness as a percentage of the frame height for 2D lines, or a percentage of the median axis length for 3D surface lines and volume lines.

Suggested values are .02, .1, .4, .8, 1.5

Example usage:

>>> plot.streamtraces.line_thickness = 1.1

Streamtraces.marker_color

Color of the streamline markers.

Type:Color or ContourGroup

Streamtrace markers can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property.

Example usage:

>>> plot.streamtraces.marker_color = Color.Blue

Streamtraces.marker_size

Size of streamline markers.

Type:float

Example usage:

>>> plot.streamtraces.marker_size = 1.1

Streamtraces.marker_symbol(symbol_type=None)

Returns a streamline symbol style object.

Parameters:symbol_type (SymbolType, optional) – The type of symbol to return. By default, this will return the active marker symbol type which is obtained from Streamtraces.marker_symbol_type.

Returns: SymbolType

Example usage:

>>> from tecplot.constant import SymbolType
>>> streamtrace = plot.streamtraces
>>> streamtraces.marker_symbol_type = SymbolType.Text
>>> symbol = streamtraces.marker_symbol(SymbolType.Text)
>>> symbol.text = 'a'

Streamtraces.marker_symbol_type

The SymbolType to use for stream markers.

Type:SymbolType

This sets the active symbol type for streamtrace markers. Use Streamtraces.marker_symbol to access the symbol:

>>> from tecplot.constant import SymbolType
>>> streamtrace = plot.streamtraces
>>> streamtraces.marker_symbol_type = SymbolType.Text
>>> symbol = streamtraces.marker_symbol(SymbolType.Text)
>>> symbol.text = 'a'

Streamtraces.max_steps

Maximum number of steps before the streamtrace is terminated.

Type:integer

max_steps prevents streamtraces from spinning forever in a vortex, or from wandering into a region where the vector components are very small, very random, or both.

If a small step_size is selected, the max_steps should be a large value.

Example usage:

>>> plot.streamtraces.max_steps = 5000

Streamtraces.min_step_size

Smallest step size to use as a percentage of cell distance.

A typical minimum step size value is 0.00001, which is the default.

Type:float

Warning

Setting this too small results in integration problems. Setting this greater than or equal to the step_size results in a constant step size.

Example usage:

>>> plot.streamtraces.min_step_size = .0002

Streamtraces.obey_source_zone_blanking

Obey source zone blanking.

Type:boolean

When True, streamtraces are generated for non-blanked regions only. When False, streamtraces are generated for both blanked and unblanked regions.

Example usage:

>>> plot.streamtraces.obey_source_zone_blanking = True

Streamtraces.position(stream_number)

Query the starting position of a streamtrace.

Parameters:stream_number – (integer): 0-based stream number to query. Returns:tuple of floats

Get the position of streamtrace number 3:

>>> position = plot.streamtraces.position(2) # Note: 0-based
>>> position.x  # == position[0]
0.1
>>> position.y  # == position[1]
0.2
>>> position.z  # == position[2]
0.3

Streamtraces.rod_ribbon

Streamtrace rod/ribbon attributes.

Type:StreamtraceRodRibbon

Example usage::

>>> streamtraces.rod_ribbon.mesh.show = True

Streamtraces.set_termination_line(line_points)

Set the position of the termination line for streamtraces.

Parameters:line_points – (array of float tuple) Points of the termination line. Raises:TecplotSystemError – Termination line could not be set.

Example usage:

>>> # Multi-segment line between points (0,0)-(5,8)-(3,6)
>>> line_points = [(0, 0), (5, 8), (3,6)]
>>> plot.streamtraces.set_termination_line(line_points)

Streamtraces.show_arrows

Display arrowheads along all streamlines.

Type:boolean

Example usage:

>>> plot.streamtraces.show_arrows = True

Streamtraces.show_dashes

Display streamtrace dashes.

The lengths of the dashes and the spaces between the dashes are controlled by the value of StreamtraceTiming.delta. Set the Streamtraces.dash_skip attribute to control the number of time deltas used for the “off” sections of the streamtraces.

Type:boolean

Example usage:

>>> plot.streamtraces.show_dashes = True

Streamtraces.show_markers

Display streamtrace markers.

Type:boolean

Stream markers are only available for surface and volume type streamlines.

You may also specify the size, color, and shape of the markers.

Example usage:

>>> plot.streamtraces.show_markers = True

Streamtraces.show_paths

Draw streamtrace paths (lines, ribbons, or rods).

Type:boolean

A streamtrace path may be a line, ribbon or rod.

Example usage:

>>> plot.streamtraces.show_paths = True

See also Streamtraces.show_markers

Streamtraces.step_size

Maximum fraction of the distance across a cell that a streamtrace moves in one step.

Type:float

The step size is the maximum fraction of the distance across a cell that a streamtrace moves in one step. A streamtrace adjusts its step size between step_size and min_step_size, depending on local curvature of the streamtrace.

A typical value (and the default) is 0.25, which results in four integration steps through each cell or element. The value for Step Size affects the accuracy of the integration.

Warning

Setting step size too small can result in round-off errors, while setting it too large can result in truncation errors and missed cells.

Example usage:

>>> plot.streamtraces.step_size = .25

Streamtraces.streamtrace_type(stream_number)

Query the type of a streamtrace by streamtrace number.

Parameters:stream_number – (integer): 0-based stream number to query. Returns:Streamtrace

Get the type of streamtrace 3. Note 0-based stream number:

>>> streamtrace_type = plot.streamtraces.streamtrace_type(2) # streamtrace 3
>>> streamtrace_type
<Streamtrace.VolumeLine: 2>

Streamtraces.termination_line

Streamtraces termination line attributes.

A streamtrace termination line is a polyline that terminates any streamtraces that cross it. The termination line is useful for stopping streamtraces before they spiral or stall.

Type:StreamtraceTerminationLine

Example usage:

>>> term_line = plot.streamtraces.termination_line
>>> term_line.show = True

Streamtraces.timing

Streamtraces timing attributes.

Type:StreamtraceTiming

Example usage:

>>> timing = plot.streamtraces.timing
>>> timing.start = 0.01

StreamtraceRodRibbon

class tecplot.plot.StreamtraceRodRibbon(streamtrace)

Get/Set streamtrace rod/ribbon attributes.

The StreamtraceRodRibbon class allows you to query and set attributes of streamtrace rod/ribbon types:

• Streamtrace.SurfaceRibbon
• Streamtrace.VolumeRibbon
• Streamtrace.VolumeRod

In addition to attributes common to all rod/ribbon streamtrace types such as width, some attributes are further divided into subcategories:

• Rod/ribbon mesh
• Rod/ribbon contour
• Rod/ribbon shade
• Rod/ribbon effects

Note

To change the color of streamtrace rods/ribbons, set StreamtraceRodRibbonShade.color.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()  # type: Cartesian3DFieldPlot
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.contour(0).variable = dataset.variable(3)
plot.contour(0).levels.reset_to_nice()

plot.show_mesh = True
plot.show_shade = False

plot.vector.u_variable_index = 3
plot.vector.v_variable_index = 4
plot.vector.w_variable_index = 5

plot.show_streamtraces = True
plot.streamtraces.rod_ribbon.width = .05
plot.streamtraces.rod_ribbon.contour.show = True

plot.streamtraces.add_rake(start_position=(0, 0, 0),
                           end_position=(.05, .05, .175),
                           stream_type=Streamtrace.VolumeRibbon,
                           num_seed_points=26)

tecplot.export.save_png('streamtrace_ribbon.png', 600)

Attributes

contour	Streamtraces rod/ribbon contour attributes.
effects	Streamtraces rod/ribbon effects.
mesh	Streamtraces rod/ribbon mesh attributes.
num_rod_points	Numer of rod points.
shade	Streamtraces rod/ribbon color and lighting attributes.
width	Rod/ribbon width in grid units.

StreamtraceRodRibbon.contour

Streamtraces rod/ribbon contour attributes.

Type:StreamtraceRodRibbonContour

Example usage:

>>> plot.streamtraces.rod_ribbon.contour.show = True

StreamtraceRodRibbon.effects

Streamtraces rod/ribbon effects.

Type:StreamtraceRodRibbonEffects

Example usage:

>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True

StreamtraceRodRibbon.mesh

Streamtraces rod/ribbon mesh attributes.

Type:StreamtraceRodRibbonMesh

Example usage:

>>> plot.streamtraces.rod_ribbon.mesh.show = True

StreamtraceRodRibbon.num_rod_points

Numer of rod points.

Type:integer, valid range 3-100

Volume rods have a polygonal cross-section; this parameter tells Tecplot 360 EX what that cross-section should be. (Three is an equilateral triangle, four is a square, five is a regular pentagon, and so on.) If you want two sets of volume rods with different cross-sections, you must create one set and then extract the set as a zone, then configure a new set of streamtraces with the second cross-section.

Example usage:

>>> plot.streamtraces.rod_ribbon.num_rod_points = 10

StreamtraceRodRibbon.shade

Streamtraces rod/ribbon color and lighting attributes.

Type:StreamtraceRodRibbonShade

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.color = Color.Magenta

StreamtraceRodRibbon.width

Rod/ribbon width in grid units.

Type:float

Example usage:

>>> plot.streamtraces.rod_ribbon.width = 0.01

StreamtraceTiming

class tecplot.plot.StreamtraceTiming(streamtrace)

Timed markers for streamlines.

Use StreamtraceTiming to control timed markers for streamlines, and timed dashes for all types of streamtraces. Stream markers are drawn at time locations along streamlines. The spacing between stream markers is proportional to the magnitude of the local vector field.

import tecplot
from tecplot.constant import *
import os

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '2D', 'cylinder.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian2D

plot = frame.plot()
plot.vector.u_variable_index = 3
plot.vector.v_variable_index = 4
plot.show_streamtraces = True

streamtraces = plot.streamtraces
streamtraces.show_markers = True
timing = streamtraces.timing
timing.anchor = 0
timing.start = 0.01
timing.end = 0.2
timing.delta = 0.1

streamtraces.add(seed_point=[1, 1], stream_type=Streamtrace.TwoDLine)

tecplot.export.save_png('streamtrace_timing.png', 600)

Attributes

anchor	Time that a dash is guaranteed to start.
delta	Time between stream markers.
end	Time after which no stream markers are drawn.
start	Time at which the first marker should be drawn.

Methods

reset_delta()

Reset the time delta for dashed streamtraces.

StreamtraceTiming.anchor

Time that a dash is guaranteed to start.

Type:float

A dash is guaranteed to start at anchor, provided the start and end time surround the dash.

Example usage:

>>> plot.streamtraces.timing.anchor = 1.1

StreamtraceTiming.delta

Time between stream markers.

Type:float

delta is the time interval that measures the time between stream markers. The actual distance between markers is the product of this number and the local Vector magnitude.

Call StreamtraceTiming.reset_delta() to reset this to the default.

Example usage:

>>> plot.streamtraces.timing.delta = 0.1

StreamtraceTiming.end

Time after which no stream markers are drawn.

Type:float

Example usage:

>>> plot.streamtraces.timing.end = 3.0

StreamtraceTiming.reset_delta()

Reset the time delta for dashed streamtraces.

The delta time is reset such that a stream dash in the vicinity of the maximum vector magnitude will have a length approximately equal to 10 percent of the frame width.

Raises:TecplotSystemError – Streamtraces time delta could not be reset.

Example usage:

>>> plot.streamtraces.timing.reset_delta()

StreamtraceTiming.start

Time at which the first marker should be drawn.

Type:float

A start time of zero means that the first marker is drawn at the starting point. A start time of 2.5 means that the first stream marker is drawn 2.5 time units downstream of the starting point.

Example usage:

>>> plot.streamtraces.timing.start = 2.5

StreamtraceRodRibbonContour

class tecplot.plot.StreamtraceRodRibbonContour(streamtrace)

Contour flooding display for streamtrace rod/ribbons.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()  # type: Cartesian3DFieldPlot
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.contour(0).variable = dataset.variable(3)
plot.contour(0).levels.reset_to_nice()

plot.show_mesh = False
plot.show_shade = False

plot.vector.u_variable_index = 3
plot.vector.v_variable_index = 4
plot.vector.w_variable_index = 5
plot.show_streamtraces = True

rod_ribbon = plot.streamtraces.rod_ribbon
rod_ribbon.contour.show = True
rod_ribbon.contour.use_lighting_effect = True

plot.streamtraces.add(seed_point=[0, 0, 0],
                      stream_type=Streamtrace.VolumeRibbon)

plot.streamtraces.rod_ribbon.width = .5

tecplot.export.save_png('streamtrace_ribbon_contour.png', 600)

Attributes

flood_contour_group	Contour group to use for flooding.
flood_contour_group_index	The Index of the ContourGroup to use for flooding.
show	Enable or disable contour flooding display.
use_lighting_effect	Enable lighting effect for streamtrace rod/ribbons.

StreamtraceRodRibbonContour.flood_contour_group

Contour group to use for flooding.

Type:ContourGroup

This property sets and gets the ContourGroup used for flooding. Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> group = plot.contour(1)
>>> contour = plot.streamtraces.rod_ribbon.contour
>>> contour.flood_contour_group = group

StreamtraceRodRibbonContour.flood_contour_group_index

The Index of the ContourGroup to use for flooding.

Type:Index (zero-based index)

This property sets and gets, by Index, the ContourGroup used for flooding. Changing style on this ContourGroup will affect all fieldmaps on the same Frame that use it.

Example usage:

>>> contour = plot.streamtraces.rod_ribbon.contour
>>> contour.flood_contour_group_index = 0  # First contour group

StreamtraceRodRibbonContour.show

Enable or disable contour flooding display.

Type:boolean

Example usage:

>>> plot.streamtraces.rod_ribbon.contour.show = True

StreamtraceRodRibbonContour.use_lighting_effect

Enable lighting effect for streamtrace rod/ribbons.

Note

Setting StreamtraceRodRibbonContour.use_lighting_effect will also set the same value for StreamtraceRodRibbonShade.use_lighting_effect, and vice-versa.

The lighting effect is set with StreamtraceRodRibbonEffects.lighting_effect, and may be one of LightingEffect.Gouraud or LightingEffect.Paneled.

Type:boolean

Example usage:

>>> ribbon = plot.streamtraces.rod_ribbon
>>> contour = ribbon.contour
>>> contour.use_lighting_effect = True
>>> ribbon.effects.lighting_effect = LightingEffect.Paneled

StreamtraceRodRibbonEffects

class tecplot.plot.StreamtraceRodRibbonEffects(streamtrace)

Controls how lighting and translucency interacts with streamtrace rods and ribbons.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()  # type: Cartesian3DFieldPlot
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.contour(0).variable = dataset.variable(3)
plot.contour(0).levels.reset_to_nice()

plot.show_mesh = False
plot.show_shade = False

plot.vector.u_variable_index = 3
plot.vector.v_variable_index = 4
plot.vector.w_variable_index = 5
plot.show_streamtraces = True
plot.streamtraces.rod_ribbon.width = .03
plot.streamtraces.rod_ribbon.shade.color = Color.Green

plot.streamtraces.rod_ribbon.effects.use_translucency = True
plot.streamtraces.rod_ribbon.effects.surface_translucency = 80

plot.streamtraces.add_rake(start_position=(0, 0, 0),
                           end_position=(.05, .05, .175),
                           stream_type=Streamtrace.VolumeRibbon)

tecplot.export.save_png('streamtrace_ribbon_effects.png', 600)

Attributes

lighting_effect	Get/set the lighting algorithm used when lighting streamtrace rods and ribbons.
surface_translucency	Surface translucency of the streamtraces ribbon.
use_translucency	Enable surface translucency.

StreamtraceRodRibbonEffects.lighting_effect

Get/set the lighting algorithm used when lighting

streamtrace rods and ribbons.

Type:LightingEffect

Ribbon lighting effects must be enabled by setting StreamtraceRodRibbonShade.use_lighting_effect to True when setting this value.

Note that setting StreamtraceRodRibbonShade.use_lighting_effect will also set this value for ribbon contours.

There are two types of lighting effects: Paneled and Gouraud:

• Paneled: Within each cell, the color assigned to each area by

  shading or contour flooding is tinted by a shade constant across the cell. This shade is based on the orientation of the cell relative to your 3D light source.

• Gouraud: This offers smoother, more continuous shading than

  Paneled shading, but it also results in slower plotting and larger print files. Gouraud shading is not continuous across zone boundaries unless face neighbors are specified in the data. Gouraud shading is not available for finite element volume Zone when blanking is active. The zone’s lighting effect reverts to Paneled shading in this case.

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.use_lighting_effect = True
>>> plot.streamtraces.rod_ribbon.effects.lighting_effect = LightingEffect.Paneled

StreamtraceRodRibbonEffects.surface_translucency

Surface translucency of the streamtraces ribbon.

Type:integer

Surface translucency must be enabled by setting StreamtraceRodRibbonEffects.use_translucency = True when setting this value.

Valid translucency values range from one (opaque) to 99 (translucent).

Example usage:

>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
>>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20

StreamtraceRodRibbonEffects.use_translucency

Enable surface translucency.

Type:boolean

The surface translucency value can be changed by setting StreamtraceRodRibbonEffects.surface_translucency.

Example usage:

>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
>>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20

StreamtraceRodRibbonMesh

class tecplot.plot.StreamtraceRodRibbonMesh(streamtrace)

Streamtraces rod/ribbon mesh attributes.

Note

To set the mesh color or line thickness, see Streamtraces.color and Streamtraces.line_thickness.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'jetflow.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()  # type: Cartesian3DFieldPlot
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_mesh = False
plot.show_shade = False

plot.vector.u_variable_index = 4
plot.vector.v_variable_index = 5
plot.vector.w_variable_index = 6
plot.show_streamtraces = True

ribbon = plot.streamtraces.rod_ribbon
ribbon.width = .018
ribbon.mesh.show = True

plot.streamtraces.add_rake(
    start_position=(0.323, 0.103, 0.491),
    end_position=(-0.367, 0.229, 0.116),
    stream_type=Streamtrace.VolumeRibbon)

tecplot.export.save_png('streamtrace_ribbon_mesh.png', 600)

Attributes

line_thickness	Get/Set streamtrace rod/ribbon mesh line thickness as a percentage of frame height.
show	Display mesh.

StreamtraceRodRibbonMesh.line_thickness

Get/Set streamtrace rod/ribbon mesh line thickness as a percentage of frame height.

Typical values are .02, .1, .4, .8, 1.5

Type:float

Example usage:

>>> plot.streamtraces.rod_ribbon.mesh.line_thickness = 0.2

StreamtraceRodRibbonMesh.show

Display mesh.

Note

The mesh color for streamtraces is determined by the line color.

Type:boolean

Example usage:

>>> plot.streamtraces.rod_ribbon.mesh.show = True

StreamtraceRodRibbonShade

class tecplot.plot.StreamtraceRodRibbonShade(streamtrace)

Color and lighting display for rod/ribbons.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()  # type: Cartesian3DFieldPlot
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.contour(0).variable = dataset.variable(3)
plot.contour(0).levels.reset_to_nice()

plot.show_mesh = False
plot.show_shade = False

plot.vector.u_variable_index = 3
plot.vector.v_variable_index = 4
plot.vector.w_variable_index = 5
plot.show_streamtraces = True
plot.streamtraces.show_paths = True

plot.streamtraces.rod_ribbon.shade.show = True
plot.streamtraces.rod_ribbon.shade.color = Color.Blue
plot.streamtraces.rod_ribbon.shade.use_lighting_effect = False

plot.streamtraces.add_rake(start_position=(0, 0, 0),
                           end_position=(.05, .05, .175),
                           stream_type=Streamtrace.VolumeRibbon)

tecplot.export.save_png('streamtrace_ribbon_shade.png', 600)

Attributes

color	Shade color.
show	Show shade attributes.
use_lighting_effect	Use lighting effect.

StreamtraceRodRibbonShade.color

Shade color.

Type:Color

Color.MultiColor and Color.RGBColor coloring are not available. Use flooded contours for multi-color or RGB flooding.

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.show = True
>>> plot.streamtraces.rod_ribbon.shade.color = Color.Blue

StreamtraceRodRibbonShade.show

Show shade attributes.

Type:boolean

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.show = True

StreamtraceRodRibbonShade.use_lighting_effect

Use lighting effect.

When set to True, the lighting effect may be selected with the SliceEffects.lighting_effect attribute.

Note

Setting SliceShade.use_lighting_effect will also set the same value for SliceContour.use_lighting_effect, and vice-versa.

Type:Boolean

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.use_lighting_effect = True
>>> plot.streamtraces.rod_ribbon.effects.lighting_effect = LightingEffect.Paneled

StreamtraceTerminationLine

class tecplot.plot.StreamtraceTerminationLine(streamtrace)

Streamtraces termination line attributes.

A streamtrace termination line is a polyline that terminates any streamtraces that cross it. The termination line is useful for stopping streamtraces before they spiral or stall.

Note

Before setting any StreamtraceTerminationLine properties, you must add a termination line.

Streamtraces are terminated whenever any of the following occur:

• The maximum number of integration steps is reached.
• Any point where a streamtrace passes outside the available data.
• The streamtrace reaches a point where the velocity magnitude is zero.

import os
import tecplot
from tecplot.constant import *
from tecplot.plot import Cartesian3DFieldPlot

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, '3D_Volume', 'eddy.plt')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
frame.plot_type = tecplot.constant.PlotType.Cartesian3D

plot = frame.plot()  # type: Cartesian3DFieldPlot
plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
plot.show_mesh = True
plot.show_shade = False

plot.vector.u_variable_index = 4
plot.vector.v_variable_index = 5
plot.vector.w_variable_index = 6
plot.show_streamtraces = True

streamtraces = plot.streamtraces
streamtraces.set_termination_line([(18.2293706294, 50.5561212121),
                                (51.7714125874, -19.9307785548)])

term_line = streamtraces.termination_line
term_line.is_active = True
term_line.show = True
term_line.color = Color.Red
term_line.line_pattern = LinePattern.Dashed
term_line.pattern_length = 3
term_line.line_thickness = .5

plot.streamtraces.add_rake(
    start_position=(45.49, 15.32, 59.1),
    end_position=(48.89, 53.2, 47.6),
    stream_type=Streamtrace.VolumeLine)

tecplot.export.save_png('streamtrace_term_line.png', 600)

Attributes

color	Color of the termination line.
is_active	Activate/disable the streamtrace termination line.
line_pattern	Pattern of the terminating line.
line_thickness	Thickness of the termination line as a percentage of frame height.
pattern_length	Length of the pattern as a percentage of frame height.
show	Display the termination line.

StreamtraceTerminationLine.color

Color of the termination line.

Type:Color

Example usage:

>>> plot.streamtraces.termination_line.color = Color.Red

StreamtraceTerminationLine.is_active

Activate/disable the streamtrace termination line.

Type:boolean

Set to True to activate the termination line and terminate any streamtraces that cross it. Set to False and redraw the plot with unterminated streamtraces.

Note

To display the termination line itself, set show to True.

Example usage:

>>> plot.streamtraces.termination_line.is_active = True

StreamtraceTerminationLine.line_pattern

Pattern of the terminating line.

Type:LinePattern

Example usage:

>>> plot.streamtraces.termination_line.line_pattern = LinePattern.Dotted

StreamtraceTerminationLine.line_thickness

Thickness of the termination line as a percentage of frame height.

Type:float

Example usage:

>>> plot.streamtraces.termination_line.line_thickness = 0.1

StreamtraceTerminationLine.pattern_length

Length of the pattern as a percentage of frame height.

Type:float

Example usage:

>>> plot.streamtraces.termination_line.pattern_length = 2

StreamtraceTerminationLine.show

Display the termination line.

Type:boolean

Set to True to display the termination line. Set to False and redraw the plot to display terminated streamlines (if is_active is set to True), but not the termination line itself.

Note

To display terminated streamtraces, is_active must be set to True.

Example usage:

>>> plot.streamtraces.termination_line.show = True

Viewport

ReadOnlyViewport

class tecplot.plot.ReadOnlyViewport(axes)

Attributes

bottom	(float) Bottom position of viewport relative to the Frame.
left	(float) Left position of viewport relative to the Frame.
right	(float) Right position of viewport relative to the Frame.
top	(float) Top position of viewport relative to the Frame.

ReadOnlyViewport.bottom

(float) Bottom position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.bottom)
10.0

ReadOnlyViewport.left

(float) Left position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.left)
10.0

ReadOnlyViewport.right

(float) Right position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.right)
90.0

ReadOnlyViewport.top

(float) Top position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.top)
90.0

Viewport

class tecplot.plot.Viewport(axes)

Attributes

bottom	(float) Bottom position of viewport relative to the Frame.
left	(float) Left position of viewport relative to the Frame.
right	(float) Right position of viewport relative to the Frame.
top	(float) Top position of viewport relative to the Frame.

Viewport.bottom

(float) Bottom position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.bottom)
10.0

Viewport.left

(float) Left position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.left)
10.0

Viewport.right

(float) Right position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.right)
90.0

Viewport.top

(float) Top position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.top)
90.0

Cartesian2DViewport

class tecplot.plot.Cartesian2DViewport(axes)

Attributes

bottom	(float) Bottom position of viewport relative to the Frame.
left	(float) Left position of viewport relative to the Frame.
nice_fit_buffer	Tolerance for viewport/frame fit niceness.
right	(float) Right position of viewport relative to the Frame.
top	(float) Top position of viewport relative to the Frame.
top_snap_target	Target value for top when being adjusted or dragged.
top_snap_tolerance	Tolerance for snapping to target value for top.

Cartesian2DViewport.bottom

(float) Bottom position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.bottom)
10.0

Cartesian2DViewport.left

(float) Left position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.left)
10.0

Cartesian2DViewport.nice_fit_buffer

Tolerance for viewport/frame fit niceness.

Type:float

Example usage:

>>> plot.axes.viewport.nice_fit_buffer = 20

Cartesian2DViewport.right

(float) Right position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.right)
90.0

Cartesian2DViewport.top

(float) Top position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.top)
90.0

Cartesian2DViewport.top_snap_target

Target value for top when being adjusted or dragged.

Type:float

Example usage:

>>> plot.axes.viewport.top_snap_target = 90

Cartesian2DViewport.top_snap_tolerance

Tolerance for snapping to target value for top.

Type:float

Example usage:

>>> plot.axes.viewport.top_snap_tolerance = 8

PolarViewport

class tecplot.plot.PolarViewport(axes)

Attributes

border_color
border_thickness
bottom	(float) Bottom position of viewport relative to the Frame.
fill_color
left	(float) Left position of viewport relative to the Frame.
right	(float) Right position of viewport relative to the Frame.
show_border
top	(float) Top position of viewport relative to the Frame.

PolarViewport.border_color

PolarViewport.border_thickness

PolarViewport.bottom

(float) Bottom position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.bottom)
10.0

PolarViewport.fill_color

PolarViewport.left

(float) Left position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.left)
10.0

PolarViewport.right

(float) Right position of viewport relative to the Frame.

Type:float in percentage of frame width from the left of the frame.

Example usage:

>>> print(plot.axes.viewport.right)
90.0

PolarViewport.show_border

PolarViewport.top

(float) Top position of viewport relative to the Frame.

Type:float in percentage of frame height from the bottom of the frame.

Example usage:

>>> print(plot.axes.viewport.top)
90.0

View

Cartesian2DView

class tecplot.plot.Cartesian2DView(plot)

Methods

fit()

Cartesian2DView.fit()

Cartesian3DView

class tecplot.plot.Cartesian3DView(plot)

Attributes

alpha
distance
position
psi
theta
width

Methods

fit()

Cartesian3DView.alpha

Cartesian3DView.distance

Cartesian3DView.fit()

Cartesian3DView.position

Cartesian3DView.psi

Cartesian3DView.theta

Cartesian3DView.width

LineView

class tecplot.plot.LineView(plot)

Methods

fit()

LineView.fit()

PolarView

class tecplot.plot.PolarView(plot)

Methods

fit()

PolarView.fit()