Plot Style

Scatter Plots

Scatter

class tecplot.plot.Scatter(plot)[source]

Plot-local scatter style settings.

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

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
frame.plot_type = PlotType.Cartesian2D
plot = frame.plot()
plot.contour(0).variable = dataset.variable('T(K)')
plot.show_scatter = True

plot.scatter.variable = dataset.variable('P(N)')

for z in dataset.zones():
    scatter = plot.fieldmap(z).scatter
    scatter.symbol_type = SymbolType.Geometry
    scatter.symbol().shape = GeomShape.Circle
    scatter.fill_mode = FillMode.UseSpecificColor
    scatter.fill_color = plot.contour(0)
    scatter.color = plot.contour(0)
    scatter.size_by_variable = True

frame.add_text('Size of dots indicate relative pressure', (20, 80))

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('scatter.png')
../_images/scatter.png

Attributes

base_font Default typeface to use for text scatter symbols.
legend Scatter symbol legend.
reference_symbol Reference symbol for scatter plots.
relative_size Relative size of the reference symbol.
relative_size_units Use grid or page units for relative size.
sphere_render_quality render quality of spheres
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.base_font

Default typeface to use for text scatter symbols.

Example usage:

>>> plot.scatter.base_font.typeface = 'Times'
Type:BaseFont
Scatter.legend

Scatter symbol legend.

Example usage:

>>> plot.scatter.legend.show = True
Type:ScatterLegend
Scatter.reference_symbol

Reference symbol for scatter plots.

The reference scatter symbol is only shown when the scatter symbols are sized by a Variable in the Dataset. Example:

>>> plot.fieldmap(0).scatter.size_by_variable = True
>>> plot.scatter.variable = dataset.variable('s')
>>> plot.scatter.reference_symbol.show = True
Type:ScatterReferenceSymbol
Scatter.relative_size

Relative size of the reference symbol.

Relative size will be in cm when units are set to RelativeSizeUnits.Page. Example usage:

>>> plot.scatter.relative_size = 20
Type:float
Scatter.relative_size_units

Use grid or page units for relative size.

Relative size will be in cm when units are set to RelativeSizeUnits.Page. Example usage:

>>> from tecplot.constant import RelativeSizeUnits
>>> plot.scatter.relative_size_units = RelativeSizeUnits.Grid
>>> plot.scatter.relative_size = 2.0
Type:RelativeSizeUnits
Scatter.sphere_render_quality

render quality of spheres

Example usage:

>>> from tecplot.constant import *
>>> plot.fieldmap(0).scatter.symbol().shape = GeomShape.Sphere
>>> scatter = plot.scatter
>>> scatter.sphere_render_quality = SphereScatterRenderQuality.Low
Type:SphereScatterRenderQuality
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

ScatterReferenceSymbol

class tecplot.plot.ScatterReferenceSymbol(scatter)[source]

Reference symbol for scatter plots.

Note

The reference scatter symbol is only shown when the scatter symbols are sized by a Variable in the Dataset.

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
frame.plot_type = PlotType.Cartesian2D
plot = frame.plot()
plot.contour(0).variable = dataset.variable('T(K)')
plot.show_scatter = True

plot.scatter.variable = dataset.variable('P(N)')

plot.scatter.reference_symbol.show = True
plot.scatter.reference_symbol.symbol().shape = GeomShape.Circle
plot.scatter.reference_symbol.magnitude = plot.scatter.variable.max()
plot.scatter.reference_symbol.color = Color.Green
plot.scatter.reference_symbol.fill_color = Color.Green
plot.scatter.reference_symbol.position = (20, 81)

frame.add_text('Size of dots indicate relative pressure', (23, 80))

for z in dataset.zones():
    scatter = plot.fieldmap(z).scatter
    scatter.symbol_type = SymbolType.Geometry
    scatter.symbol().shape = GeomShape.Circle
    scatter.fill_mode = FillMode.UseSpecificColor
    scatter.fill_color = plot.contour(0)
    scatter.color = plot.contour(0)
    scatter.size_by_variable = True

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('scatter_reference_symbol.png')
../_images/scatter_reference_symbol.png

Attributes

color The Color of the reference symbol.
fill_color The fill Color of the reference symbol.
filled Fill the background area behind the reference symbol.
line_thickness Edge line thickness for geometry reference symbols.
magnitude Symbol size relative to data variable ranges.
position The \((x, y)\) position of the reference symbol.
show Display a reference scatter symbol on the plot.
symbol_type The type of symbol to display.

Methods

symbol([symbol_type]) TextSymbol or GeometrySymbol: Style control the displayed symbol.
ScatterReferenceSymbol.color

The Color of the reference symbol.

Example usage:

>>> from tecplot.constant import Color
>>> plot.scatter.reference_symbol.color = Color.Blue
Type:Color
ScatterReferenceSymbol.fill_color

The fill Color of the reference symbol.

Example usage:

>>> from tecplot.constant import Color
>>> plot.scatter.reference_symbol.fill_color = Color.Blue
Type:Color
ScatterReferenceSymbol.filled

Fill the background area behind the reference symbol.

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

>>> plot.scatter.reference_symbol.filled = True
Type:bool
ScatterReferenceSymbol.line_thickness

Edge line thickness for geometry reference symbols.

Example usage:

>>> plot.scatter.reference_symbol.line_thickness = 2.5
Type:float
ScatterReferenceSymbol.magnitude

Symbol size relative to data variable ranges.

Example usage:

>>> plot.scatter.reference_symbol.magnitude = 10.0
Type:float
ScatterReferenceSymbol.position

The \((x, y)\) position of the reference symbol.

This position is in Frame percentage units::’

>>> plot.scatter.reference_symbol.position = (50, 50)
Type:tuple
ScatterReferenceSymbol.show

Display a reference scatter symbol on the plot.

Example usage:

>>> plot.fieldmap(0).scatter.size_by_variable = True
>>> plot.scatter.variable = dataset.variable('s')
>>> plot.scatter.reference_symbol.show = True
Type:bool
ScatterReferenceSymbol.symbol(symbol_type=None)[source]

TextSymbol or GeometrySymbol: Style control the displayed symbol.

Example usage:

>>> from tecplot.constant import GeomShape
>>> reference_symbol = plot.scatter.reference_symbol
>>> reference_symbol.symbol = GeomShape.Sphere
ScatterReferenceSymbol.symbol_type

The type of symbol to display.

Example usage:

>>> from tecplot.constant import SymbolType
>>> reference_symbol = plot.scatter.reference_symbol
>>> reference_symbol.symbol_type = SymbolType.Text
Type:SymbolType

Vector Plots

Vector2D

class tecplot.plot.Vector2D(plot)[source]

Vector field style control for Cartesian 2D plots.

This object controls the style of the vectors that are plotted according to the vector properties under fieldmaps. The \((u,v)\) components are set using this class as well as attributes such as length, arrow-head size and the reference vector. This example shows how to show the vector field, adjusting the arrows color and thickness:

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', '3ElementWing.lpk')
tp.load_layout(infile)

frame = tp.active_frame()
dataset = frame.dataset
plot = frame.plot(PlotType.Cartesian2D)

frame.background_color = Color.Black
for axis in plot.axes:
    axis.show = False

plot.axes.x_axis.min = -0.2
plot.axes.x_axis.max = 0.3
plot.axes.y_axis.min = -0.2
plot.axes.y_axis.max = 0.15

vect = plot.vector
vect.u_variable = dataset.variable('U(M/S)')
vect.v_variable = dataset.variable('V(M/S)')
vect.relative_length = 0.00025
vect.size_arrowhead_by_fraction = False
vect.arrowhead_size = 4
vect.arrowhead_angle = 10

plot.show_contour = False
plot.show_streamtraces = False
plot.show_edge = True
plot.show_vector = True

cont = plot.contour(0)
cont.variable = dataset.variable('P(N/M2)')
cont.colormap_name = 'Diverging - Blue/Yellow/Red'
cont.levels.reset_levels(80000, 90000, 100000, 110000, 120000)

plot.fieldmaps().show = False

fmap = plot.fieldmap(3)
fmap.show = True
fmap.edge.color = Color.White
fmap.edge.line_thickness = 1
fmap.points.step = 5
fmap.vector.color = cont
fmap.vector.line_thickness = 0.5

tp.export.save_png('vector2d.png', 600, supersample=3)
../_images/vector2d.png

Attributes

arrowhead_angle Angle between the vector body and the head line.
arrowhead_fraction Size of the arrowhead when sizing by fraction.
arrowhead_size Size of arrowhead when sizing by frame height.
length Length of all vectors when not using relative sizing.
reference_vector Vector field reference vector.
relative_length Magnitude-varying length of the vector line.
size_arrowhead_by_fraction Base arrowhead size on length of vector line.
u_variable \(U\)-component Variable of the plotted vectors.
u_variable_index \(U\)-component Variable index of the plotted vectors.
use_grid_units Use grid-units when determining the relative length.
use_relative Use relative sizing for vector lines.
v_variable \(V\)-component Variable of the plotted vectors.
v_variable_index \(V\)-component Variable index of the plotted vectors.
Vector2D.arrowhead_angle

Angle between the vector body and the head line.

Example usage:

>>> plot.vector.arrowhead_angle = 10
Type:float (degrees)
Vector2D.arrowhead_fraction

Size of the arrowhead when sizing by fraction.

The size_arrowhead_by_fraction property must be set to True for this to take effect:

>>> plot.vector.size_arrowhead_by_fraction = True
>>> plot.vector.arrowhead_fraction = 0.4
Type:float (ratio)
Vector2D.arrowhead_size

Size of arrowhead when sizing by frame height.

The size_arrowhead_by_fraction property must be set to False for this to take effect:

>>> plot.vector.size_arrowhead_by_fraction = False
>>> plot.vector.arrowhead_size = 4
Type:float (percent of frame height)
Vector2D.length

Length of all vectors when not using relative sizing.

Example usage:

>>> plot.vector.use_relative = False
>>> plot.vector.length = 5
Type:float (percent of plot height)
Vector2D.reference_vector

Vector field reference vector.

Example usage:

>>> plot.vector.reference_vector.show = True
Type:ReferenceVector
Vector2D.relative_length

Magnitude-varying length of the vector line.

When use_relative is True, the length of the vectors will be relative to the magnitude of the velocity vector values in the data field, scaled by this parameter which is either grid-units or centimeters per unit magnitude depending on the value of use_grid_units:

>>> plot.vector.use_relative = True
>>> plot.vector.use_grid_units = True
>>> plot.vector.relative_length = 0.003
Type:float (grid units or cm per magnitude)
Vector2D.size_arrowhead_by_fraction

Base arrowhead size on length of vector line.

Example usage:

>>> plot.vector.size_arrowhead_by_fraction = True
>>> plot.vector.relative_length = 0.1
Type:bool
Vector2D.u_variable

\(U\)-component Variable of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.u_variable = dataset.variable('Pressure X')
Type:Variable
Vector2D.u_variable_index

\(U\)-component Variable index of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.u_variable_index = 3
Type:int (Zero-based index)
Vector2D.use_grid_units

Use grid-units when determining the relative length.

This takes effect only if use_relative is True. If False, relative_length will be in cm per magnitude:

>>> plot.vector.use_relative = True
>>> plot.vector.use_grid_units = False
>>> plot.vector.relative_length = 0.010
Type:bool
Vector2D.use_relative

Use relative sizing for vector lines.

This determines whether length or relative_length are used to size the arrow lines. Example usage:

>>> plot.vector.use_relative = False
>>> plot.vector.relative_length = 0.5
Type:bool
Vector2D.v_variable

\(V\)-component Variable of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.v_variable = dataset.variable('Pressure Y')
Type:Variable
Vector2D.v_variable_index

\(V\)-component Variable index of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.v_variable_index = 4
Type:int (Zero-based index)

Vector3D

class tecplot.plot.Vector3D(plot)[source]

Vector field style control for Cartesian 3D plots.

This object controls the style of the vectors that are plotted according to the vector properties under fieldmaps. The \((u,v,w)\) components are set using this class as well as attributes such as length, arrow-head size and the reference vector. See the example for 2D vector plots.

Attributes

arrowhead_angle Angle between the vector body and the head line.
arrowhead_fraction Size of the arrowhead when sizing by fraction.
arrowhead_size Size of arrowhead when sizing by frame height.
length Length of all vectors when not using relative sizing.
reference_vector Vector field reference vector.
relative_length Magnitude-varying length of the vector line.
size_arrowhead_by_fraction Base arrowhead size on length of vector line.
u_variable \(U\)-component Variable of the plotted vectors.
u_variable_index \(U\)-component Variable index of the plotted vectors.
use_grid_units Use grid-units when determining the relative length.
use_relative Use relative sizing for vector lines.
v_variable \(V\)-component Variable of the plotted vectors.
v_variable_index \(V\)-component Variable index of the plotted vectors.
w_variable \(W\)-component Variable of the plotted vectors.
w_variable_index \(W\)-component Variable index of the plotted vectors.
Vector3D.arrowhead_angle

Angle between the vector body and the head line.

Example usage:

>>> plot.vector.arrowhead_angle = 10
Type:float (degrees)
Vector3D.arrowhead_fraction

Size of the arrowhead when sizing by fraction.

The size_arrowhead_by_fraction property must be set to True for this to take effect:

>>> plot.vector.size_arrowhead_by_fraction = True
>>> plot.vector.arrowhead_fraction = 0.4
Type:float (ratio)
Vector3D.arrowhead_size

Size of arrowhead when sizing by frame height.

The size_arrowhead_by_fraction property must be set to False for this to take effect:

>>> plot.vector.size_arrowhead_by_fraction = False
>>> plot.vector.arrowhead_size = 4
Type:float (percent of frame height)
Vector3D.length

Length of all vectors when not using relative sizing.

Example usage:

>>> plot.vector.use_relative = False
>>> plot.vector.length = 5
Type:float (percent of plot height)
Vector3D.reference_vector

Vector field reference vector.

Example usage:

>>> plot.vector.reference_vector.show = True
Type:ReferenceVector
Vector3D.relative_length

Magnitude-varying length of the vector line.

When use_relative is True, the length of the vectors will be relative to the magnitude of the velocity vector values in the data field, scaled by this parameter which is either grid-units or centimeters per unit magnitude depending on the value of use_grid_units:

>>> plot.vector.use_relative = True
>>> plot.vector.use_grid_units = True
>>> plot.vector.relative_length = 0.003
Type:float (grid units or cm per magnitude)
Vector3D.size_arrowhead_by_fraction

Base arrowhead size on length of vector line.

Example usage:

>>> plot.vector.size_arrowhead_by_fraction = True
>>> plot.vector.relative_length = 0.1
Type:bool
Vector3D.u_variable

\(U\)-component Variable of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.u_variable = dataset.variable('Pressure X')
Type:Variable
Vector3D.u_variable_index

\(U\)-component Variable index of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.u_variable_index = 3
Type:int (Zero-based index)
Vector3D.use_grid_units

Use grid-units when determining the relative length.

This takes effect only if use_relative is True. If False, relative_length will be in cm per magnitude:

>>> plot.vector.use_relative = True
>>> plot.vector.use_grid_units = False
>>> plot.vector.relative_length = 0.010
Type:bool
Vector3D.use_relative

Use relative sizing for vector lines.

This determines whether length or relative_length are used to size the arrow lines. Example usage:

>>> plot.vector.use_relative = False
>>> plot.vector.relative_length = 0.5
Type:bool
Vector3D.v_variable

\(V\)-component Variable of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.v_variable = dataset.variable('Pressure Y')
Type:Variable
Vector3D.v_variable_index

\(V\)-component Variable index of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.v_variable_index = 4
Type:int (Zero-based index)
Vector3D.w_variable

\(W\)-component Variable of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.w_variable = dataset.variable('Pressure Z')
Type:Variable
Vector3D.w_variable_index

\(W\)-component Variable index of the plotted vectors.

Vectors are plotted as \((u,v,w)\). Example usage:

>>> plot.vector.w_variable_index = 5
Type:int (Zero-based index)

ReferenceVector

class tecplot.plot.ReferenceVector(vector)[source]

Vector field reference vector.

The reference vector is a single arrow with an optional label indicating the value of the shown reference length:

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'VortexShedding.plt')
tp.data.load_tecplot(infile)

frame = tp.active_frame()
dataset = frame.dataset
plot = frame.plot(PlotType.Cartesian2D)

for txt in frame.texts():
    frame.delete_text(txt)

vector_contour = plot.contour(0)
vector_contour.variable = dataset.variable('T(K)')
vector_contour.colormap_name = 'Magma'
vector_contour.colormap_filter.reversed = True
vector_contour.legend.show = False
base_contour = plot.contour(1)
base_contour.variable = dataset.variable('P(N/M2)')
base_contour.colormap_name = 'GrayScale'
base_contour.colormap_filter.reversed = True
base_contour.legend.show = False

vector = plot.vector
vector.u_variable = dataset.variable('U(M/S)')
vector.v_variable = dataset.variable('V(M/S)')
vector.relative_length = 1E-5
vector.arrowhead_size = 0.2
vector.arrowhead_angle = 16

ref_vector = vector.reference_vector
ref_vector.show = True
ref_vector.position = 50, 95
ref_vector.line_thickness = 0.4
ref_vector.label.show = True
ref_vector.label.format.format_type = NumberFormat.FixedFloat
ref_vector.label.format.precision = 1
ref_vector.magnitude = 100

fmap = plot.fieldmap(0)
fmap.contour.flood_contour_group = base_contour
fmap.vector.color = vector_contour
fmap.vector.line_thickness = 0.4

plot.show_contour = True
plot.show_streamtraces = False
plot.show_vector = True

plot.axes.y_axis.min = -0.005
plot.axes.y_axis.max = 0.005
plot.axes.x_axis.min = -0.002
plot.axes.x_axis.max = 0.008

tp.export.save_png('vector2d_reference.png', 600, supersample=3)
../_images/vector2d_reference.png

Attributes

angle Degrees counter-clockwise to rotate the reference vector.
color Color of the reference vector.
label reference vector label style control.
line_thickness reference vector line thickness.
magnitude Length of the reference vector.
position \((x,y)\) of the reference vector in percent of frame height.
show Draw the reference vector.
ReferenceVector.angle

Degrees counter-clockwise to rotate the reference vector.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.angle = 90  # vertical, up
Type:float (degrees)
ReferenceVector.color

Color of the reference vector.

Example usage:

>>> from tecplot.constant import Color
>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.color = Color.Red
Type:Color
ReferenceVector.label

reference vector label style control.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.label.show = True
Type:ReferenceVectorLabel
ReferenceVector.line_thickness

reference vector line thickness.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.line_thickness = 0.3
Type:float (percentage of frame height)
ReferenceVector.magnitude

Length of the reference vector.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.magnitude = 2
Type:float (data units)
ReferenceVector.position

\((x,y)\) of the reference vector in percent of frame height.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.position = (50, 5)  # bottom, center
Type:tuple
ReferenceVector.show

Draw the reference vector.

Example usage:

>>> plot.vector.reference_vector.show = True
Type:bool

ReferenceVectorLabel

class tecplot.plot.ReferenceVectorLabel(ref_vector)[source]

Label for the reference vector.

See the example under ReferenceVector.

Attributes

color Color of the reference vector label.
font Typeface of the reference vector label.
format Number formatting control for the reference vector label.
offset Distance from the reference vector to the associated label.
show Print a label next to the reference vector.
ReferenceVectorLabel.color

Color of the reference vector label.

Example usage:

>>> from tecplot.constant import Color
>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.label.show = True
>>> ref_vector.label.color = Color.Red
Type:Color
ReferenceVectorLabel.font

Typeface of the reference vector label.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.label.show = True
>>> ref_vector.label.font.size = 6
Type:text.Font
ReferenceVectorLabel.format

Number formatting control for the reference vector label.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.label.show = True
>>> ref_vector.label.format.format_type = NumberFormat.Exponential
Type:LabelFormat
ReferenceVectorLabel.offset

Distance from the reference vector to the associated label.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.label.show = True
>>> ref_vector.label.offset = 10
Type:float (percent of frame height)
ReferenceVectorLabel.show

Print a label next to the reference vector.

Example usage:

>>> ref_vector = plot.vector.reference_vector
>>> ref_vector.show = True
>>> ref_vector.label.show = True
Type:bool

Legends

ContourLegend

class tecplot.legend.ContourLegend(contour, *svargs)[source]

Contour legend attributes.

This class allows you to customize the appearance of the contour legend. The contour legend can be positioned anywhere inside the frame using the position attribute of this class. Example usage:

import os
import numpy as np

import tecplot
from tecplot.constant import *

# By loading a layout many style and view properties are set up already
examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'RainierElevation.lay')
tecplot.load_layout(datafile)

frame = tecplot.active_frame()
plot = frame.plot()

# Rename the elevation variable
frame.dataset.variable('E').name = "Elevation (m)"

# Set the levels to nice values
plot.contour(0).levels.reset_levels(np.linspace(200,4400,22))

legend = plot.contour(0).legend
legend.show = True
legend.vertical = False  # Horizontal
legend.auto_resize = False
legend.label_step = 5

legend.overlay_bar_grid = False
legend.position = (55, 94)  # Frame percentages

legend.box.box_type = TextBox.None_ # Remove Text box

legend.header_font.typeface = 'Courier'
legend.header_font.bold = True

legend.number_font.typeface = 'Courier'
legend.number_font.bold = True

tecplot.export.save_png('legend_contour.png', 600, supersample=3)
../_images/legend_contour.png

Attributes

anchor_alignment Anchor location of the legend.
auto_resize Automatically skip some levels to create a reasonably sized legend.
box Legend box attributes.
header_font Font used to display the name of the contour variable.
label_format Number formatting for labels along the legend.
label_increment Spacing between labels along the contour legend.
label_location Placement of labels on the legend.
label_step Step size between labels along the legend.
number_font Font used to display numbers in the legend.
overlay_bar_grid Draw a line around each band in the legend color bar.
position Position as a percentage of frame width/height.
row_spacing Spacing between rows in the legend.
show Show or hide the legend.
show_cutoff_levels Show color bands for levels affected by color cutoff.
show_header Show the name of the contour variable in the legend.
text_color Color of legend text.
vertical Orientation of the legend.
ContourLegend.anchor_alignment

Anchor location of the legend.

Example usage:

>>> from tecplot.constant import PlotType, AnchorAlignment
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.anchor_alignment = AnchorAlignment.BottomCenter
Type:AnchorAlignment
ContourLegend.auto_resize

Automatically skip some levels to create a reasonably sized legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.auto_resize = True
Type:bool
ContourLegend.box

Legend box attributes.

Example usage:

>>> from tecplot.constant import PlotType, Color
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.box.color = Color.Blue
Type:text.TextBox
ContourLegend.header_font

Font used to display the name of the contour variable.

Note

The font size_units property may only be set to Units.Frame or Units.Point.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.header_font.italic = True
Type:text.Font
ContourLegend.label_format

Number formatting for labels along the legend.

This is an alias for ContourLegend.contour.labels.format:

>>> contour = frame.plot().contour(0)
>>> contour.legend.label_format.precision = 3
>>> print(contour.labels.format.precision)
3
Type:LabelFormat
ContourLegend.label_increment

Spacing between labels along the contour legend.

Labels will be placed on the contour variable range from min to max. The smaller the increment value the more legend labels will be created. If the label_location is ContLegendLabelLocation.Increment, labels are incremented by this value. For example, a label_increment value of .5 will show labels at .5, 1.0, 1.5, etc.

Note

This value is only used if label_location is set to ContLegendLabelLocation.Increment. Otherwise it is ignored.

Example usage:

>>> from tecplot.constant import PlotType, ContLegendLabelLocation
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.label_location = ContLegendLabelLocation.Increment
>>> legend.label_increment = .5

See also

label_location

Type:float
ContourLegend.label_location

Placement of labels on the legend.

If you have selected ColorMapDistribution.Continuous for the contour colormap filter distribution, you have three options for placement of labels on the legend:

Example usage:

>>> from tecplot.constant import PlotType, ContLegendLabelLocation
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.label_location = ContourLevelLabelLocation.Increment
>>> legend.label_increment = .5

See also

label_increment

Type:ContLegendLabelLocation
ContourLegend.label_step

Step size between labels along the legend.

This is an alias for ContourLegend.contour.labels.step:

>>> contour = frame.plot().contour(0)
>>> contour.legend.label_step = 3
>>> print(contour.labels.step)
3
Type:int
ContourLegend.number_font

Font used to display numbers in the legend.

Note

The font size_units property may only be set to Units.Frame or Units.Point.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.number_font.italic = True
Type:text.Font
ContourLegend.overlay_bar_grid

Draw a line around each band in the legend color bar.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.overlay_bar_grid = False
Type:bool
ContourLegend.position

Position as a percentage of frame width/height.

The legend is automatically placed for you. You may specify the \((x,y)\) position of the legend by setting this value, where \(x\) is the percentage of frame width, and \(y\) is a percentage of frame height.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.position = (.1, .3)
>>> pos = legend.position
>>> pos.x  # == position[0]
.1
>>> pos.y  # == position[1]
.3
Type:tuple
ContourLegend.row_spacing

Spacing between rows in the legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.row_spacing = 1.5
Type:float
ContourLegend.show

Show or hide the legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.show = True
Type:bool
ContourLegend.show_cutoff_levels

Show color bands for levels affected by color cutoff.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.show_cutoff_levels = True
Type:bool
ContourLegend.show_header

Show the name of the contour variable in the legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.show_header = True
Type:bool
ContourLegend.text_color

Color of legend text.

Example usage:

>>> from tecplot.constant import PlotType, Color
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.text_color = Color.Blue
Type:Color
ContourLegend.vertical

Orientation of the legend.

When set to True, the legend is vertical. When set to False, the legend is horizontal.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
>>> legend.vertical = False  # Show horizontal legend
Type:bool

LineLegend

class tecplot.legend.LineLegend(plot)[source]

Line plot legend attributes.

The XY line legend shows the line and symbol attributes of XY mappings. In XY line plots, this legend includes the bar chart information. The legend can be positioned anywhere within the line plot frame by setting the position attribute. By default, all mappings are shown, but Tecplot 360 removes redundant entries. Example usage:

import os

import tecplot
from tecplot.constant import *

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'Rainfall.dat')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
plot = frame.plot()
frame.plot_type = tecplot.constant.PlotType.XYLine

for i in range(3):
    plot.linemap(i).show = True
    plot.linemap(i).line.line_thickness = .4

y_axis = plot.axes.y_axis(0)
y_axis.title.title_mode = AxisTitleMode.UseText
y_axis.title.text = 'Rainfall (in)'
y_axis.fit_range_to_nice()

legend = plot.legend
legend.show = True
legend.box.box_type = TextBox.Filled
legend.box.color = Color.Purple
legend.box.fill_color = Color.LightGrey
legend.box.line_thickness = .4
legend.box.margin = 5

legend.anchor_alignment = AnchorAlignment.MiddleRight
legend.row_spacing = 1.5
legend.show_text = True
legend.font.typeface = 'Arial'
legend.font.italic = True

legend.text_color = Color.Black
legend.position = (90, 88)

tecplot.export.save_png('legend_line.png', 600, supersample=3)
../_images/legend_line.png

Attributes

anchor_alignment Anchor location of the legend.
box Legend box attributes.
font Legend font attributes.
position Position as a percentage of frame width/height.
row_spacing Spacing between rows in the legend.
show Show or hide the legend.
show_text Show/hide mapping names in the legend.
text_color Color of legend text.
LineLegend.anchor_alignment

Anchor location of the legend.

Example usage:

>>> from tecplot.constant import AnchorAlignment, PlotType
>>> legend = frame.plot(PlotType.XYLine).legend
>>> legend.anchor_alignment = AnchorAlignment.BottomCenter
Type:AnchorAlignment
LineLegend.box

Legend box attributes.

Example usage:

>>> from tecplot.constant import PlotType, Color
>>> plot = frame.plot(PlotType.XYLine)
>>> plot.legend.box.color = Color.Blue
Type:text.TextBox
LineLegend.font

Legend font attributes.

Note

The font size_units property may only be set to Units.Frame or Units.Point.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.XYLine)
>>> plot.legend.font.italic = True
Type:text.Font
LineLegend.position

Position as a percentage of frame width/height.

The legend is automatically placed for you. You may specify the \((x,y)\) position of the legend by setting this value, where \(x\) is the percentage of frame width, and \(y\) is a percentage of frame height.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.XYLine).legend
>>> legend.position = (10, 30)
Type:tuple
LineLegend.row_spacing

Spacing between rows in the legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.XYLine).legend
>>> legend.row_spacing = 1.5
Type:float
LineLegend.show

Show or hide the legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> legend = frame.plot(PlotType.XYLine).legend
>>> legend.show = True
Type:bool
LineLegend.show_text

Show/hide mapping names in the legend.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.XYLine)
>>> plot.legend.show_text = True
Type:bool
LineLegend.text_color

Color of legend text.

Example usage:

>>> from tecplot.constant import PlotType, Color
>>> legend = frame.plot(PlotType.XYLine).legend
>>> legend.text_color = Color.Blue
Type:Color

RGBColoringLegend

class tecplot.legend.RGBColoringLegend(rgb_coloring)[source]

Legend for RGB coloring (multivariate contour) plots.

Note

The RGB coloring legend will only show when an active fieldmap’s contour is being flooded by RGB.

import os
import numpy as np

import tecplot as tp
from tecplot.constant import *

def normalize_variable(dataset, varname, nsigma=2):
    '''
    Normalize a variable such that the specified number of standard deviations
    are within the range [0.5, 1] and the mean is transformed to 0.5. The
    new variable will append " normalized" to the original variable's name.
    '''
    with tp.session.suspend():
        newvarname = varname + ' normalized'
        dataset.add_variable(newvarname)
        data = np.concatenate([z.values(varname).as_numpy_array()
                               for z in dataset.zones()])
        vmin = data.mean() - nsigma * data.std()
        vmax = data.mean() + nsigma * data.std()
        for z in dataset.zones():
            arr = z.values(varname).as_numpy_array()
            z.values(newvarname)[:] = (arr - vmin) / (vmax - vmin)


examples_dir = tp.session.tecplot_examples_directory()
infile = os.path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian2D)
plot.show_contour = True

# Variables must be normalized relative to each other
# to make effective use of RGB coloring.
normalize_variable(dataset, 'T(K)')
normalize_variable(dataset, 'P(N)')

plot.rgb_coloring.mode = RGBMode.SpecifyGB

# all three channel variables must be set even if
# we are only contouring on two of them.
plot.rgb_coloring.red_variable = dataset.variable(0)
plot.rgb_coloring.green_variable = dataset.variable('P(N) normalized')
plot.rgb_coloring.blue_variable = dataset.variable('T(K) normalized')

plot.rgb_coloring.legend.show = True
plot.rgb_coloring.legend.green_label = 'Pressure'
plot.rgb_coloring.legend.blue_label = 'Temperature'

plot.fieldmaps().contour.flood_contour_group = plot.rgb_coloring

tp.export.save_png('rgb_coloring_legend.png')
../_images/rgb_coloring_legend.png

Attributes

anchor_alignment Anchor location of the legend.
blue_label Label to use for the blue channel.
box Legend box attributes.
font Legend font attributes.
green_label Label to use for the green channel.
height Size of RGB coloring legend
orientation Placement of the RGB channels on the legend.
position Position as a percentage of frame width/height.
red_label Label to use for the red channel.
show Display the RGB coloring legend.
show_labels Show the RGB channel labels.
text_color Color of legend text.
use_variable_for_blue_label Use the Variable name for the blue channel.
use_variable_for_green_label Use the Variable name for the green channel.
use_variable_for_red_label Use the Variable name for the red channel.
RGBColoringLegend.anchor_alignment

Anchor location of the legend.

Example usage:

>>> from tecplot.constant import AnchorAlignment
>>> legend = plot.rgb_coloring.legend
>>> legend.anchor_alignment = AnchorAlignment.BottomCenter
Type:AnchorAlignment
RGBColoringLegend.blue_label

Label to use for the blue channel.

This can be set to a string (which may be empty) but the use_variable_for_blue_label property must be set to False for this label to be shown:

>>> plot.rgb_coloring.legend.use_variable_for_blue_label = False
>>> plot.rgb_coloring.legend.blue_label = 'water'
Type:str
RGBColoringLegend.box

Legend box attributes.

Example usage:

>>> from tecplot.constant import Color
>>> plot.rgb_coloring.legend.box.fill_color = Color.Yellow
Type:text.TextBox
RGBColoringLegend.font

Legend font attributes.

Note

The font size_units property may only be set to Units.Frame or Units.Point.

Example usage:

>>> plot.rgb_coloring.legend.font.italic = True
Type:text.Font
RGBColoringLegend.green_label

Label to use for the green channel.

This can be set to a string (which may be empty) but the use_variable_for_green_label property must be set to False for this label to be shown:

>>> plot.rgb_coloring.legend.use_variable_for_green_label = False
>>> plot.rgb_coloring.legend.green_label = 'oil'
Type:str
RGBColoringLegend.height

Size of RGB coloring legend

Example usage:

>>> plot.rgb_coloring.legend.height = 20
Type:float
RGBColoringLegend.orientation

Placement of the RGB channels on the legend.

The first color is on the bottom left, the second is on the bottom right, and the third is on top. Example usage:

>>> from tecplot.constant import RGBLegendOrientation
>>> legend = plot.rgb_coloring.legend
>>> legend.orientation = RGBLegendOrientation.RBG
Type:RGBLegendOrientation
RGBColoringLegend.position

Position as a percentage of frame width/height.

The legend is automatically placed for you. You may specify the \((x,y)\) position of the legend by setting this value, where \(x\) is the percentage of frame width, and \(y\) is a percentage of frame height.

Example usage:

>>> plot.rgb_coloring.legend.position = (20, 80)
Type:tuple
RGBColoringLegend.red_label

Label to use for the red channel.

This can be set to a string (which may be empty) but the use_variable_for_red_label property must be set to False for this label to be shown:

>>> plot.rgb_coloring.legend.use_variable_for_red_label = False
>>> plot.rgb_coloring.legend.red_label = 'gas'
Type:str
RGBColoringLegend.show

Display the RGB coloring legend.

Example usage:

>>> plot.rgb_coloring.legend.show = True
Type:bool
RGBColoringLegend.show_labels

Show the RGB channel labels.

Example usage:

>>> legend = plot.rgb_coloring.legend
>>> legend.show_labels = True
>>> legend.red_label = 'Variable A'
>>> legend.green_label = 'Variable B'
>>> legend.blue_label = 'Variable C'
Type:bool
RGBColoringLegend.text_color

Color of legend text.

Example usage:

>>> from tecplot.constant import Color
>>> legend = plot.rgb_coloring.legend
>>> legend.text_color = Color.Blue
Type:Color
RGBColoringLegend.use_variable_for_blue_label

Use the Variable name for the blue channel.

Example usage:

>>> plot.rgb_coloring.legend.use_variable_for_blue_label = False
>>> plot.rgb_coloring.legend.blue_label = 'gas'
Type:bool
RGBColoringLegend.use_variable_for_green_label

Use the Variable name for the green channel.

Example usage:

>>> plot.rgb_coloring.legend.use_variable_for_green_label = False
>>> plot.rgb_coloring.legend.green_label = 'gas'
Type:bool
RGBColoringLegend.use_variable_for_red_label

Use the Variable name for the red channel.

Example usage:

>>> plot.rgb_coloring.legend.use_variable_for_red_label = False
>>> plot.rgb_coloring.legend.red_label = 'gas'
Type:bool

ScatterLegend

class tecplot.legend.ScatterLegend(scatter)[source]

Legend style for scatter plots.

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
frame.plot_type = PlotType.Cartesian2D
plot = frame.plot()
plot.show_scatter = True

# make space for the legend
plot.axes.viewport.right = 70
plot.axes.x_axis.min = 4
plot.axes.x_axis.max = 7

# assign some shape and color to each fieldmap
for i, fmap in enumerate(plot.fieldmaps()):
    for zone in fmap.zones:
        zone.name = 'Zone {}'.format(i)
    fmap.scatter.symbol().shape = GeomShape(i % 7)
    fmap.scatter.fill_mode = FillMode.UseSpecificColor
    fmap.scatter.fill_color = Color(i % 7)

plot.scatter.legend.show = True
plot.scatter.legend.row_spacing = 0.95

tp.export.save_png('scatter_legend.png')
../_images/scatter_legend.png

Attributes

anchor_alignment Anchor location of the legend.
box Legend box attributes.
font Legend font attributes.
position Position as a percentage of frame width/height.
row_spacing Spacing between rows in the legend.
show Show or hide the legend.
show_text Show/hide mapping names in the legend.
text_color Color of legend text.
ScatterLegend.anchor_alignment

Anchor location of the legend.

Example usage:

>>> from tecplot.constant import AnchorAlignment
>>> legend = plot.scatter.legend
>>> legend.anchor_alignment = AnchorAlignment.BottomCenter
Type:AnchorAlignment
ScatterLegend.box

Legend box attributes.

Example usage:

>>> from tecplot.constant import PlotType, Color
>>> plot.scatter.legend.box.color = Color.Blue
Type:text.TextBox
ScatterLegend.font

Legend font attributes.

Note

The font size_units property may only be set to Units.Frame or Units.Point.

Example usage:

>>> plot.scatter.legend.font.italic = True
Type:text.Font
ScatterLegend.position

Position as a percentage of frame width/height.

The legend is automatically placed for you. You may specify the \((x,y)\) position of the legend by setting this value, where \(x\) is the percentage of frame width, and \(y\) is a percentage of frame height.

Example usage:

>>> plot.scatter.legend.position = (10, 30)
Type:tuple
ScatterLegend.row_spacing

Spacing between rows in the legend.

Example usage:

>>> plot.scatter.legend.row_spacing = 1.5
Type:float
ScatterLegend.show

Show or hide the legend.

Example usage:

>>> plot.scatter.legend.show = True
Type:bool
ScatterLegend.show_text

Show/hide mapping names in the legend.

Example usage:

>>> plot.scatter.legend.show_text = True
Type:bool
ScatterLegend.text_color

Color of legend text.

Example usage:

>>> from tecplot.constant import Color
>>> plot.scatter.legend.text_color = Color.Blue
Type:Color

Contours

ContourGroup

class tecplot.plot.ContourGroup(index, plot)[source]

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
examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir,'SimpleData','CircularContour.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'

# ensure consistent output between interactive (connected) and batch
contour.levels.reset_to_nice()

# save image to file
tp.export.save_png('contour_magma.png', 600, supersample=3)
../_images/contour_magma.png

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.

>>> cutoff = plot.contour(0).color_cutoff
>>> cutoff.min = 3.14
Type:ContourColorCutoff
ContourGroup.colormap_filter

ContourColormapFilter object controlling colormap style properties.

>>> plot.contour(0).colormap_filter.reverse = True
Type:ContourColormapFilter
ContourGroup.colormap_name

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

Example:

>>> plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'
Type:str
ContourGroup.default_num_levels

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

Example:

>>> plot.contour(0).default_num_levels = 20
Type:int
ContourGroup.labels

ContourLabels object controlling contour line labels.

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
Type:ContourLabels
ContourGroup.legend

ContourLegend associated with this ContourGroup.

This object controls the attributes of the contour legend associated with this ContourGroup.

Example usage:

>>> plot.contour(0).legend.show = True
Type:ContourLegend
ContourGroup.levels

ContourLevels holding the list of contour levels.

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

>>> plot.contour(0).levels.reset_to_nice(15)
Type:ContourLevels
ContourGroup.lines

ContourLines object controlling contour line style.

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
Type:ContourLines
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)[source]

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,'SimpleData','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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

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

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.

Thie example turns off the maximum cutoff:

>>> plot.contour(0).color_cutoff.include_max = False
Type:bool
ContourColorCutoff.include_min

Use the minimum cutoff value.

Example usage:

>>> plot.contour(0).color_cutoff.include_min = True
>>> plot.contour(0).color_cutoff.min = 3.14
Type:bool
ContourColorCutoff.inverted

Cuts values outside the range instead of inside.

>>> plot.contour(0).color_cutoff.inverted = True
Type:bool
ContourColorCutoff.max

The maximum cutoff value.

The include_max must be set to True:

>>> plot.contour(0).color_cutoff.include_max = True
>>> plot.contour(0).color_cutoff.max = None
Type:float or None
ContourColorCutoff.min

The minimum cutoff value.

The include_min must be set to True:

>>> plot.contour(0).color_cutoff.include_min = True
>>> plot.contour(0).color_cutoff.min = 3.14
Type:float or None

ContourColormapFilter

class tecplot.plot.ContourColormapFilter(contour)[source]

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
examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir,'SimpleData','HeatExchanger.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(N)')

# 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

# ensure consistent output between interactive (connected) and batch
contour.levels.reset_to_nice()

# save image to file
tp.export.save_png('contour_filtered.png', 600, supersample=3)
../_images/contour_filtered.png

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.

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()
Type:float
ContourColormapFilter.continuous_min

Lower limit for continuous colormap flooding.

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
Type:float
ContourColormapFilter.distribution

Rendering style of the colormap.

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
Type:ColorMapDistribution
ContourColormapFilter.fast_continuous_flood

Use a fast approximation to continuously flood the colormap.

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
Type:bool
ContourColormapFilter.num_cycles

Number of cycles to repeat the colormap.

>>> plot.contour(0).colormap_filter.num_cycles = 3
Type:int
ContourColormapFilter.override(index)[source]

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.

>>> plot.contour(0).colormap_filter.reversed = True
Type:bool
ContourColormapFilter.show_overrides

Enable the colormap overrides in this contour group.

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
Type:bool
ContourColormapFilter.zebra_shade

Returns a ContourColormapZebraShade filtering object.

Example usage:

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

ContourColormapOverride

class tecplot.plot.ContourColormapOverride(index, colormap_filter)[source]

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
examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = 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 = dataset.variable('T(K)')
contour.colormap_name = 'Sequential - Yellow/Green/Blue'
contour.levels.reset(9)

# 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 = 7
contour_override.end_level = 8

# save image to file
tp.export.save_png('contour_override.png', 600, supersample=3)
../_images/contour_override.png

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.

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
Type:Color
ContourColormapOverride.end_level

Last level to override.

Example usage:

>>> colormap_filter = plot.contour(0).colormap_filter
>>> cmap_override = colormap_filter.override(0)
>>> cmap_override.end_level = 2
Type:int
ContourColormapOverride.show

Include this colormap override when filter is shown.

Example usage:

>>> colormap_filter = plot.contour(0).colormap_filter
>>> cmap_override = colormap_filter.override(0)
>>> cmap_override.show = True
Type:bool
ContourColormapOverride.start_level

First level to override.

Example usage:

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

ContourColormapZebraShade

class tecplot.plot.ContourColormapZebraShade(colormap_filter)[source]

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,'SimpleData','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

# ensure consistent output between interactive (connected) and batch
cont0.levels.reset_to_nice()

tp.export.save_png('contour_zebra.png', 600, supersample=3)
../_images/contour_zebra.png

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.

Example usage:

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

Show zebra shading in this ContourGroup.

Example usage:

>>> cmap_filter = plot.contour(0).colormap_filter
>>> cmap_filter.zebra_shade.show = True
Type:bool
ContourColormapZebraShade.transparent

Set the the zebra bands to be transparent.

Example usage:

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

ContourLabels

class tecplot.plot.ContourLabels(contour)[source]

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,'SimpleData','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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('contour_labels.png', 600, supersample=3)
../_images/contour_labels.png

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 text.Font used to show the labels.
format Number formatting for contour 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.

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
Type:bool
ContourLabels.auto_generate

Automatically generate labels along contour lines.

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
Type:bool
ContourLabels.background_color

Background fill color behind the text labels.

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
Type:Color
ContourLabels.color

Text color of the labels.

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
Type:Color
ContourLabels.filled

Fill the background area behind the text labels.

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
Type:bool
ContourLabels.font

text.Font used to show the labels.

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
Type:text.Font
ContourLabels.format

Number formatting for contour labels.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> contour = plot.contour(0)
>>> contour.labels.format.format_type = NumberFormat.Integer
Type:LabelFormat
ContourLabels.label_by_level

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

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
Type:bool
ContourLabels.margin

Spacing around the text and the filled background area.

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
Type:float in percentage of the text height.
ContourLabels.show

Show the contour line labels.

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
Type:bool
ContourLabels.spacing

Spacing between labels along the contour lines.

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
Type:float
ContourLabels.step

Number of contour lines from one label to the next.

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
Type:int

ContourLevels

class tecplot.plot.ContourLevels(contour)[source]

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,'SimpleData','3ElementWing.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('contour_adjusted_levels.png', 600, supersample=3)
../_images/contour_adjusted_levels.png

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)[source]

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)[source]

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)[source]

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)[source]

Resets the levels to the number specified.

Parameters:num_levels (int) – 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)[source]

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)[source]

Approximately resets the levels to the number specified.

Parameters:num_levels (int) – Approximate number of levels to create. (default: 15)

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)[source]

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,'SimpleData','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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('contour_lines.png', 600, supersample=3)
../_images/contour_lines.png

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).

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
Type:ContourLineMode
ContourLines.pattern_length

Length of dashed lines and space between dashes (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
Type:float
ContourLines.step

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

Example:

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

RGBColoring

class tecplot.plot.RGBColoring(plot)[source]

RGB coloring (multivariate contour) style control.

import os
import numpy as np

import tecplot as tp
from tecplot.constant import *


def normalize_variable(dataset, varname, nsigma=2):
    '''
    Normalize a variable such that the specified number of standard deviations
    are within the range [0.5, 1] and the mean is transformed to 0.5. The
    new variable will append " normalized" to the original variable's name.
    '''
    with tp.session.suspend():
        newvarname = varname + ' normalized'
        dataset.add_variable(newvarname)
        data = np.concatenate([z.values(varname).as_numpy_array()
                               for z in dataset.zones()])
        vmin = data.mean() - nsigma * data.std()
        vmax = data.mean() + nsigma * data.std()
        for z in dataset.zones():
            arr = z.values(varname).as_numpy_array()
            z.values(newvarname)[:] = (arr - vmin) / (vmax - vmin)


examples_dir = tp.session.tecplot_examples_directory()
infile = os.path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian2D)
plot.show_contour = True

# Variables must be normalized relative to each other
# to make effective use of RGB coloring.
normalize_variable(dataset, 'T(K)')
normalize_variable(dataset, 'P(N)')

plot.rgb_coloring.mode = RGBMode.SpecifyGB

# all three channel variables must be set even if
# we are only contouring on two of them.
plot.rgb_coloring.red_variable = dataset.variable(0)
plot.rgb_coloring.green_variable = dataset.variable('P(N) normalized')
plot.rgb_coloring.blue_variable = dataset.variable('T(K) normalized')

plot.rgb_coloring.legend.show = True
plot.rgb_coloring.legend.use_variable_for_green_label = False
plot.rgb_coloring.legend.green_label = 'Pressure'
plot.rgb_coloring.legend.use_variable_for_blue_label = False
plot.rgb_coloring.legend.blue_label = 'Temperature'

plot.fieldmaps().contour.flood_contour_group = plot.rgb_coloring

tp.export.save_png('rgb_coloring.png')
../_images/rgb_coloring.png

Attributes

blue_variable Variable to use for the blue channel.
blue_variable_index Variable Index to use for the blue channel.
green_variable Variable to use for the green channel.
green_variable_index Variable Index to use for the green channel.
legend Legend placement and style control.
max_intensity Variable value at maximum intensity for each channel.
min_intensity Variable value at minimum intensity for each channel.
mode Which channels to use for RGB coloring.
red_variable Variable to use for the red channel.
red_variable_index Variable Index to use for the red channel.
RGBColoring.blue_variable

Variable to use for the blue channel.

Example usage:

>>> plot.rgb_coloring.blue_variable = dataset.variable('Water')

Note

In connected mode, setting this property requires Tecplot 360 version 2018 R2 or later.

Type:Variable
RGBColoring.blue_variable_index

Variable Index to use for the blue channel.

Example usage:

>>> plot.rgb_coloring.blue_variable_index = 4

Note

In connected mode, setting this property requires Tecplot 360 version 2018 R2 or later.

Type:Index
RGBColoring.green_variable

Variable to use for the green channel.

Example usage:

>>> plot.rgb_coloring.green_variable = dataset.variable('Oil')

Note

In connected mode, setting this property requires Tecplot 360 version 2018 R2 or later.

Type:Variable
RGBColoring.green_variable_index

Variable Index to use for the green channel.

Example usage:

>>> plot.rgb_coloring.green_variable_index = 3

Note

In connected mode, setting this property requires Tecplot 360 version 2018 R2 or later.

Type:Index
RGBColoring.legend

Legend placement and style control.

Example usage:

>>> plot.rgb_coloring.legend.show = True
Type:RGBColoringLegend
RGBColoring.max_intensity

Variable value at maximum intensity for each channel.

This should typically be set to the maximum value of the data being plotted:

>>> plot.rgb_coloring.max_intensity = dataset.variable('P').max()
Type:float
RGBColoring.min_intensity

Variable value at minimum intensity for each channel.

This should typically be set to the minimum value of the data being plotted:

>>> plot.rgb_coloring.min_intensity = dataset.variable('P').min()
Type:float
RGBColoring.mode

Which channels to use for RGB coloring.

Example usage:

>>> from tecplot.constant import RGBMode
>>> plot.rgb_coloring.mode = RGBMode.SpecifyRB
Type:RGBMode
RGBColoring.red_variable

Variable to use for the red channel.

Example usage:

>>> plot.rgb_coloring.red_variable = dataset.variable('Gas')

Note

In connected mode, setting this property requires Tecplot 360 version 2018 R2 or later.

Type:Variable
RGBColoring.red_variable_index

Variable Index to use for the red channel.

Example usage:

>>> plot.rgb_coloring.red_variable_index = 5

Note

In connected mode, setting this property requires Tecplot 360 version 2018 R2 or later.

Type:Index

Isosurface

IsosurfaceGroup

class tecplot.plot.IsosurfaceGroup(index, plot)[source]

Isosurfaces style control.

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

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

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).colormap_name = 'Magma'
plot.contour(0).variable = dataset.variable('Mach')
plot.contour(0).levels.reset_levels( [.95,1.0,1.1,1.4])
plot.contour(0).legend.show = False

iso = plot.isosurface(0)
iso.show = True
iso.definition_contour_group = plot.contour(0)
iso.isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
iso.isosurface_values = [.95,1,1.1]

iso.contour.show = True
iso.contour.flood_contour_group = plot.contour(0)

iso.effects.use_translucency = True
iso.effects.surface_translucency = 80

view = plot.view
view.psi = 65.777
view.theta = 166.415
view.alpha = -1.05394
view.position = (-23.92541680486183, 101.8931504712126, 47.04269529295333)
view.width = 1.3844

tp.export.save_png('isosurface_group.png', 600, supersample=3)
../_images/isosurface_group.png

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 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.
surface_generation_method Determines how the surface is generated.
vector Vector attributes for this isosurface group.

Methods

extract([mode, assign_strand_ids]) Create new zones from this isosurface.
IsosurfaceGroup.contour

Contour attributes for this isosurface group.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).contour.show = True
Type:IsosurfaceContour
IsosurfaceGroup.definition_contour_group

Contour group from which isosurfaces are based.

Example usage:

>>> group = plot.contour(1)
>>> plot.isosurface(0).definition_contour_group = group
Type:ContourGroup
IsosurfaceGroup.definition_contour_group_index

Contour group index from which isosurfaces are based.

Contour group settings can be changed from plot.ContourGroup.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).definition_contour_group_index = 1
Type:Index
IsosurfaceGroup.effects

Settings for isosurface effects.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).effects.use_translucency = True
Type:IsosurfaceEffects
IsosurfaceGroup.extract(mode=<ExtractMode.SingleZone: 0>, assign_strand_ids=True)[source]

Create new zones from this isosurface.

Extracts the current isosurfaces represented by this group to the Dataset as one or more zones.

Parameters:
Returns:

The extracted zone if mode is ExtractMode.SingleZone, otherwise a generator of the extracted zones.

Example usage:

>>> isosurface_zone = plot.isosurface(0).extract()

New in version 2017.3: Isosurface extraction requires Tecplot 360 2017 R3 or later.

IsosurfaceGroup.isosurface_selection

Select where to draw isosurfaces.

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)
Type:IsoSurfaceSelection
IsosurfaceGroup.isosurface_values

Values at which to draw isosurfaces.

This may be a 1, 2, or 3-tuple of floats, or a single 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(values)
3
Type:tuple or float
IsosurfaceGroup.mesh

Mesh attributes for this isosurface group.

Example usage:

>>> plot.isosurface(0).show = True
>>> plot.isosurface(0).mesh.show = True
Type:IsosurfaceMesh
IsosurfaceGroup.obey_source_zone_blanking

Obey source zone blanking.

  • 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
Type:bool
IsosurfaceGroup.shade

Shade attributes for this isosurface group.

Example usage:

>>> plot.isosurface(0).shade.show = True
Type:IsosurfaceShade
IsosurfaceGroup.show

Show isosurfaces for this isosurface group.

Example usage:

>>> plot.isosurface(1).show = True
Type:bool
IsosurfaceGroup.surface_generation_method

Determines how the surface is generated.

May be one of:

  • SurfaceGenerationMethod.Auto:
    Selects one of the surface generation algorithms best suited for the zones participating in the iso-surface generation. “All polygons” is used if one or more of the participating zones is polytope, otherwise “all triangles” are used unless the iso-surface is defined by a coordinate variable in which case “allow quads” is used.
  • SurfaceGenerationMethod.AllPolygons:
    Similar to the “All triangles” method except that all interior faces generated as a result of triangulation that are not part of the original mesh are eliminated. This preserves the original mesh of the source zones on the resulting iso-surface.
  • SurfaceGenerationMethod.AllTriangles:
    An advanced algorithm that can handle complex saddle issues and guarantees that there will be no holes in the final surface. As the surface is composed entirely of triangles, it can be delivered more efficiently to the graphics hardware.
  • SurfaceGenerationMethod.AllowQuads:
    Produces quads or triangles, and the resulting surface more closely resembles the shape of the volume cells from the source zone. Since the quads are not arbitrarily divided into triangles, no biases are introduced, and the resulting surface may appear smoother. This method is preferred when the source zone is FE-Brick or IJK-Ordered and the surface is aligned with the source cells.

Example usage:

>>> from tecplot.constant import SurfaceGenerationMethod
>>> AllowQuads = SurfaceGenerationMethod.AllowQuads
>>> plot.isosurface(0).surface_generation_method = AllowQuads
Type:SurfaceGenerationMethod
IsosurfaceGroup.vector

Vector attributes for this isosurface group.

Example usage:

>>> plot.isosurface(0).vector.show = True
Type:IsosurfaceVector

IsosurfaceContour

class tecplot.plot.IsosurfaceContour(isosurface)[source]

Contour attributes of the isosurface group.

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

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

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).colormap_name = 'Magma'
plot.contour(0).variable = dataset.variable('Mach')
plot.contour(0).legend.show = False

iso = plot.isosurface(0)
iso.show = True
iso.definition_contour_group = plot.contour(0)
iso.isosurface_selection = IsoSurfaceSelection.OneSpecificValue
iso.isosurface_values = 1

plot.contour(1).variable = dataset.variable('Density')
iso.contour.show = True
iso.contour.contour_type = ContourType.PrimaryValue
iso.contour.flood_contour_group = plot.contour(1)

view = plot.view
view.psi = 65.777
view.theta = 166.415
view.alpha = -1.05394
view.position = (-23.92541680486183, 101.8931504712126, 47.04269529295333)
view.width = 1.3844

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()
plot.contour(1).levels.reset_to_nice()

tp.export.save_png('isosurface_contour.png', 600, supersample=3)
../_images/isosurface_contour.png

Attributes

contour_type Contour display type.
flood_contour_group Contour group to use for flooding.
flood_contour_group_index The zero-based 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 zero-based 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.

  • 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
Type:ContourType
IsosurfaceContour.flood_contour_group

Contour group to use 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.isosurface(1).contour
>>> contour.flood_contour_group = group
Type:ContourGroup
IsosurfaceContour.flood_contour_group_index

The zero-based Index of the ContourGroup to use for flooding.

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
Type:Index
IsosurfaceContour.line_color

Color of contour lines.

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
Type:Color or ContourGroup
IsosurfaceContour.line_contour_group

The contour group to use for contour lines.

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
Type:ContourGroup
IsosurfaceContour.line_contour_group_index

The zero-based Index of the ContourGroup to use for contour lines.

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
Type:int
IsosurfaceContour.line_thickness

Contour line thickness as a percentage of frame width.

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

Example usage:

>>> plot.show_isosurfaces = True
>>> plot.isosurface(0).contour.line_thickness = .4
Type:float
IsosurfaceContour.show

Show contours on isosurfaces.

Example usage:

>>> plot.isosurface(0).contour.show = True
Type:bool
IsosurfaceContour.use_lighting_effect

Enable lighting effect.

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

Example usage:

>>> plot.isosurface(0).contour.use_lighting_effect = True
>>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled
Type:bool

IsosurfaceEffects

class tecplot.plot.IsosurfaceEffects(isosurface)[source]

Effects of the isosurface group.

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

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

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).colormap_name = 'Magma'
plot.contour(0).variable = dataset.variable('Mach')
plot.contour(0).legend.show = False

iso = plot.isosurface(0)
iso.show = True
iso.definition_contour_group = plot.contour(0)
iso.isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
iso.isosurface_values = [.95,1.0,1.1]

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

view = plot.view
view.psi = 65.777
view.theta = 166.415
view.alpha = -1.05394
view.position = (-23.92541680486183, 101.8931504712126, 47.04269529295333)
view.width = 1.3844

tp.export.save_png('isosurface_effects.png', 600, supersample=3)
../_images/isosurface_effects.png

Attributes

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

Surface lighting effect.

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
Type:LightingEffect
IsosurfaceEffects.surface_translucency

Surface translucency of the isosurface group.

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
Type:int
IsosurfaceEffects.use_translucency

Enable surface translucency for this isosurface group.

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
Type:bool

IsosurfaceMesh

class tecplot.plot.IsosurfaceMesh(isosurface)[source]

Mesh attributes of the isosurface group.

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

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

plot = tp.active_frame().plot()
plot.show_isosurfaces = True
plot.contour(0).colormap_name = 'Magma'
plot.contour(0).variable = dataset.variable('Mach')
plot.contour(0).legend.show = False

iso = plot.isosurface(0)
iso.show = True
iso.definition_contour_group = plot.contour(0)
iso.isosurface_selection = IsoSurfaceSelection.OneSpecificValue
iso.isosurface_values = 1
iso.mesh.show = True
iso.mesh.color = Color.Mahogany
iso.mesh.line_thickness = 0.4

view = plot.view
view.psi = 65.777
view.theta = 166.415
view.alpha = -1.05394
view.position = (-23.92541680486183, 101.8931504712126, 47.04269529295333)
view.width = 1.3844

tp.export.save_png('isosurface_mesh.png', 600, supersample=3)
../_images/isosurface_mesh.png

Attributes

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

Isosurface mesh color.

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
Type:Color or ContourGroup
IsosurfaceMesh.line_thickness

Isosurface mesh line thickness.

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

Example usage:

>>> plot.isosurface(0).mesh.show = True
>>> plot.isosurface(0).mesh.line_thickness = .4
Type:float
IsosurfaceMesh.show

Display the mesh on isosurfaces.

Example usage:

>>> plot.isosurface(0).mesh.show = True
Type:bool

IsosurfaceShade

class tecplot.plot.IsosurfaceShade(isosurface)[source]

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, 'SimpleData', '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, supersample=3)
../_images/isosurface_shade.png

Attributes

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

Shade 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
Type:Color
IsosurfaceShade.show

Show shade attributes.

Example usage:

>>> plot.isosurface(0).shade.show = True
Type:bool
IsosurfaceShade.use_lighting_effect

Enable lighting effect.

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

Example usage:

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

IsosurfaceVector

class tecplot.plot.IsosurfaceVector(isosurface)[source]

Isosurface vector field control.

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

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

examples_dir = tp.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'DuctFlow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.contour(0).variable = dataset.variable('T(K)')
plot.contour(1).variable = dataset.variable('P(N/m2)')
plot.vector.u_variable = dataset.variable('U(M/S)')
plot.vector.v_variable = dataset.variable('V(M/S)')
plot.vector.w_variable = dataset.variable('W(M/S)')

plot.show_isosurfaces = True
plot.contour(0).legend.show = False
plot.contour(1).legend.show = False

iso = plot.isosurface(0)
iso.definition_contour_group = plot.contour(0)
iso.contour.flood_contour_group = plot.contour(1)
iso.isosurface_values = 200
iso.show = True

iso.vector.show = True
iso.vector.line_thickness = 0.4
iso.vector.color = Color.Grey

view = plot.view
view.psi = 53.80
view.theta = -139.15
view.alpha = 0
view.position = (7.54498, 8.42026, 7.94559)
view.width = 0.551882

tp.export.save_png('isosurface_vector.png', 600, supersample=3)
../_images/isosurface_vector.png

Attributes

arrowhead_style Arrowhead style of isosurface vectors.
color Isosurface vector color.
is_tangent Use tangent vectors for isosurfaces.
line_thickness Vector line thickness as a percentage of the frame height.
show Show vectors on isosurfaces.
vector_type Type of vector for isosurfaces.
IsosurfaceVector.arrowhead_style

Arrowhead style of isosurface vectors.

Example usage:

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

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

Type:ArrowheadStyle
IsosurfaceVector.color

Isosurface vector color.

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

Example usage:

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

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

Type:Color or ContourGroup
IsosurfaceVector.is_tangent

Use tangent vectors for isosurfaces.

Example usage:

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

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

Type:bool
IsosurfaceVector.line_thickness

Vector line thickness as a percentage of the frame height.

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

Example usage:

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

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

Type:float
IsosurfaceVector.show

Show vectors on isosurfaces.

Example usage:

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

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

Type:bool
IsosurfaceVector.vector_type

Type of vector for isosurfaces.

Example usage:

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

New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.

Type:VectorType

Slice

SliceGroup

class tecplot.plot.SliceGroup(index, plot)[source]

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 *

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, 'SimpleData', 'DuctFlow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.contour(0).variable = dataset.variable('U(M/S)')
plot.show_edge = True
plot.fieldmap(0).edge.edge_type = EdgeType.Creases

vectors = plot.vector
vectors.u_variable = dataset.variable('U(M/S)')
vectors.v_variable = dataset.variable('V(M/S)')
vectors.w_variable = dataset.variable('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

# Show an arbitrary slice
slice_0.orientation = SliceSurface.Arbitrary
slice_0.arbitrary_normal = (1, .5, 0)

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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('slice_group.png', 600, supersample=3)
../_images/slice_group.png

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 bool; 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.
surface_generation_method Determines how the surface is generated.
vector Vector attributes for this slice group.

Methods

extract([mode, assign_strand_ids, …]) Create new zones from this slice.
set_arbitrary_from_points(p1, p2, p3) Set an arbitrary slice from 3 points.
SliceGroup.arbitrary_normal

Normal for arbitrary slices.

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
Type:tuple
SliceGroup.contour

Contour attributes for the slice group.

Example usage:

>>> plot.slice(0).contour.show = True
Type:SliceContour
SliceGroup.edge

Edge attributes for this slice group.

Example usage:

>>> plot.slice(0).edge.show = True
Type:SliceEdge
SliceGroup.effects

Effects attributes for this slice group.

Example usage:

>>> plot.slice(0).effects.use_translucency = True
Type:SliceEffects
SliceGroup.end_position

Position of the end slice.

SliceGroup.show_start_and_end_slices must be True to show the end slice. This will be a 3-tuple of float if orientation is X,Y,Z or zero-based int if orientation is I,J,K.

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
Type:tuple or int
SliceGroup.extract(mode=<ExtractMode.SingleZone: 0>, assign_strand_ids=True, resulting_1d_zone_type=<Resulting1DZoneType.IOrderedIfPossible: 0>, transient_mode=<TransientOperationMode.SingleSolutionTime: 0>)[source]

Create new zones from this slice.

Extracts the current slices represented by this group to the Dataset as one or more zones.

Parameters:
Returns:

The extracted zone is returned if mode is ExtractMode.SingleZone and transient_mode is TransientOperationMode.SingleSolutionTime, otherwise a generator of the extracted zones.

Example usage:

>>> slice_zone = plot.slice(0).extract()
SliceGroup.mesh

Mesh attributes for this slice group.

Example usage:

>>> plot.slice(0).mesh.show = True
Type:SliceMesh
SliceGroup.num_intermediate_slices

Number of intermediate slicing planes.

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

Example usage:

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

Example usage:

>>> plot.slice(0).obey_source_zone_blanking = True
Type:bool
SliceGroup.orientation

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

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
Type:SliceSurface
SliceGroup.origin

Origin of the slice.

This will be a 3-tuple of float if orientation is X,Y,Z or zero-based int if orientation is I,J,K. 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
Type:tuple or int
SliceGroup.set_arbitrary_from_points(p1, p2, p3)[source]

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.

Example usage:

>>> plot.slice(0).shade.show = True
Type:SliceShade
SliceGroup.show

Show slices for this slice group.

Example usage:

>>> plot.slice(0).show = True
Type:bool
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

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

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.

Example usage:

>>> plot.slice(0).show_start_and_end_slices = True
Type:bool
SliceGroup.slice_source

Zones to slice through.

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

Example usage:

>>> plot.slice(0).slice_source = SliceSource.SurfaceZones
Type:SliceSource
SliceGroup.start_position

Position of the start slice.

SliceGroup.show_start_and_end_slices must be True to show the start slice. This will be a 3-tuple of float if orientation is X,Y,Z or zero-based int if orientation is I,J,K.

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
Type:tuple or int
SliceGroup.surface_generation_method

Determines how the surface is generated.

May be one of:

  • SurfaceGenerationMethod.Auto:
    Selects one of the surface generation algorithms best suited for the zones participating in the slice generation. “All polygons” is used if one or more of the participating zones is polytope, otherwise “allow quads” is used.
  • SurfaceGenerationMethod.AllPolygons:
    Similar to the “All triangles” method except that all interior faces generated as a result of triangulation that are not part of the original mesh are eliminated. This preserves the original mesh of the source zones on the resulting slice.
  • SurfaceGenerationMethod.AllTriangles:
    An advanced algorithm that can handle complex saddle issues and guarantees that there will be no holes in the final surface. As the surface is composed entirely of triangles, it can be delivered more efficiently to the graphics hardware.
  • SurfaceGenerationMethod.AllowQuads:
    Produces quads or triangles, and the resulting surface more closely resembles the shape of the volume cells from the source zone. Since the quads are not arbitrarily divided into triangles, no biases are introduced, and the resulting surface may appear smoother. This method is preferred when the source zone is FE-Brick or IJK-Ordered and the surface is aligned with the source cells.

Example usage:

>>> from tecplot.constant import SurfaceGenerationMethod
>>> plot.slice(0).surface_generation_method = SurfaceGenerationMethod.AllowQuads
Type:SurfaceGenerationMethod
SliceGroup.vector

Vector attributes for this slice group.

Example usage:

>>> plot.slice(0).vector.show = True
Type:SliceVector

SliceContour

class tecplot.plot.SliceContour(slice_group)[source]

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, 'SimpleData', 'DuctFlow.plt')
dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()

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

# Use contour(0) for Flooding and contour(2) for Lines
plot.contour(0).variable = dataset.variable('P(N/m2)')
plot.contour(2).variable = dataset.variable('T(K)')
plot.contour(2).legend.show = False
slice_0.contour.show = True
slice_0.contour.flood_contour_group = plot.contour(0)
slice_0.contour.line_contour_group = plot.contour(2)
slice_0.contour.contour_type = ContourType.Overlay  # AKA "Both lines and flood"

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

# ensure consistent output between interactive (connected) and batch
slice_0.contour.flood_contour_group.levels.reset_to_nice()
slice_0.contour.line_contour_group.levels.reset_to_nice()

tp.export.save_png('slice_contour.png', 600, supersample=3)
../_images/slice_contour.png

Attributes

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

Contour type for the slice contours.

Example usage:

>>> plot.show_slices = True
>>> plot.slice(0).contour.contour_type = ContourType.AverageCell
Type:ContourType
SliceContour.flood_contour_group

Contour group to use 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.slice(1).contour
>>> contour.flood_contour_group = group
Type:ContourGroup
SliceContour.flood_contour_group_index

Zero-based Index of the flodding ContourGroup.

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
Type:Index
SliceContour.line_color

Color of contour lines.

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

Example usage:

>>> plot.show_slices = True
>>> plot.slice(0).contour.line_color = Color.Blue
Type:Color
SliceContour.line_contour_group

Contour group to use for contour lines.

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
Type:ContourGroup
SliceContour.line_contour_group_index

Zero-based Index of the ContourGroup for contour lines.

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
Type:int
SliceContour.line_thickness

Contour line thickness as a percentage of frame width.

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

Example usage:

>>> plot.show_slices = True
>>> plot.slice(0).contour.line_thickness = .4
Type:float
SliceContour.show

Show contours on the slice.

Example usage:

>>> plot.show_slices = True
>>> plot.slice(1).contour.show = True
Type:bool
SliceContour.use_lighting_effect

bool; 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.

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(slice_group)[source]

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, 'SimpleData', '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)')

slice_0 = plot.slice(0)
slice_0.edge.show = True
slice_0.edge.line_thickness = 0.8

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('slice_edge.png', 600, supersample=3)
../_images/slice_edge.png

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.

Example usage:

>>> plot.slice(0).edge.show = True
>>> plot.slice(0).edge.color = Color.Blue
Type:Color
SliceEdge.edge_type

Edge type.

There are two types of edges in Tecplot 360: 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
Type:EdgeType
SliceEdge.line_thickness

Edge line thickness as a percentage of frame width.

Example usage:

>>> plot.slice(0).edge.show = True
>>> plot.slice(0).edge.line_thickness = .8
Type:float
SliceEdge.show

Show edges.

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

Example usage:

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

SliceEffects

class tecplot.plot.SliceEffects(slice_group)[source]

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, 'SimpleData', '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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('slice_effects.png', 600, supersample=3)
../_images/slice_effects.png

Attributes

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

Surface lighting effect.

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
Type:LightingEffect
SliceEffects.surface_translucency

Surface translucency of the slice group.

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
Type:int
SliceEffects.use_translucency

Enable surface translucency for this slice group.

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
Type:bool

SliceMesh

class tecplot.plot.SliceMesh(slice_group)[source]

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, 'SimpleData', '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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('slice_mesh.png', 600, supersample=3)
../_images/slice_mesh.png

Attributes

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

Slice mesh line Color.

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
Type:Color or ContourGroup
SliceMesh.line_thickness

Mesh line thickness.

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
Type:float
SliceMesh.show

Show mesh lines.

Example usage:

>>> plot.slice(0).mesh.show = True
Type:bool

SliceShade

class tecplot.plot.SliceShade(slice_group)[source]

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, 'SimpleData', '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', 600, supersample=3)
../_images/slice_shade.png

Attributes

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

Shade 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
Type:Color
SliceShade.show

Show shade attributes.

Example usage:

>>> plot.slice(0).shade.show = True
Type:bool
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.

Example usage:

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

SliceVector

class tecplot.plot.SliceVector(slice_group)[source]

Vector attributes of the slice group.

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

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir, 'SimpleData', '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('T(K)')

# Vector variables must be assigned before displaying
vectors = plot.vector
vectors.u_variable = dataset.variable('U(M/S)')
vectors.v_variable = dataset.variable('V(M/S)')
vectors.w_variable = dataset.variable('W(M/S)')

slice_vector = plot.slice(0).vector
slice_vector.show = True
slice_vector.vector_type = VectorType.MidAtPoint
slice_vector.color = Color.BluePurple

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

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('slice_vector.png', 600, supersample=3)
../_images/slice_vector.png

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.

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.arrowhead_style = ArrowheadStyle.Hollow
Type:ArrowheadStyle
SliceVector.color

Set slice vector color.

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.color = Color.Red
Type:Color
SliceVector.is_tangent

Use tangent vectors for slices.

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.is_tangent = True
Type:bool
SliceVector.line_thickness

Vector line thickness as a percentage of the frame height.

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

Example usage:

>>> plot.slice(0).vector.show = True
>>> plot.slice(0).vector.line_thickness = .1
Type:float
SliceVector.show

Show vectors on slices.

Example usage:

>>> plot.slice(0).vector.show = True
Type:bool
SliceVector.vector_type

Type of vector for slices in this slice group.

Example usage:

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

Streamtraces

Streamtraces

class tecplot.plot.Streamtraces(plot)[source]

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 *

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', '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 = 2
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,
                      num_seed_points=4)


tecplot.export.save_png('streamtrace_example.png', 600, supersample=3)
../_images/streamtrace_example.png

Attributes

active Determine if there are active streamtraces.
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(stream_type[, zones, …]) 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.
extract([concatenate, assign_strand_ids]) Create new zones from 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.active

Determine if there are active streamtraces.

Note

This property is read-only.

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

Example usage:

>>> streamtraces_are_active = plot.streamtraces.active
Type:bool
Streamtraces.add(seed_point, stream_type, direction=<StreamDir.Both: 2>)[source]

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

The plot type must be either Cartesian2D or Cartesian3D.

Parameters:

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.

import os
import tecplot
from tecplot.constant import *
import numpy as np

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

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

plot = frame.plot()
plot.contour(0).variable = dataset.variable('P(N/m2)')
plot.contour(0).levels.reset_to_nice()
plot.contour(0).legend.show = False

plot.vector.u_variable = dataset.variable('U(M/S)')
plot.vector.v_variable = dataset.variable('V(M/S)')
plot.vector.w_variable = dataset.variable('W(M/S)')

# Goal: create a grid of 12 stream trace ribbons
x_slice_location = .79
y_start = .077
y_end = .914
z_start = .052
z_end = .415

num_left_right_slices = 4  # Must be >= 2
num_top_bottom_slices = 3  # Must be >= 2

plot.show_streamtraces = True
streamtraces = plot.streamtraces
streamtraces.show_paths = True

rod = streamtraces.rod_ribbon
rod.width = .03
rod.contour.show = True

for y in np.linspace(y_start, y_end, num=num_left_right_slices):
    for z in np.linspace(z_start, z_end, num=num_top_bottom_slices):
        streamtraces.add([x_slice_location,y,z], Streamtrace.VolumeRibbon)

tecplot.export.save_png('streamtrace_add_xyz.png', 600, supersample=3)
../_images/streamtrace_add_xyz.png
Streamtraces.add_on_zone_surface(stream_type, zones=None, num_seed_points=10, direction=<StreamDir.Both: 2>)[source]

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:
  • stream_type – (Streamtrace): Type of streamtraces to add.
  • zones (set of integers, optional) – Set of Zones on which to add streamtraces. If None, then streamtraces will be added to the currently active zones.
  • num_seed_points – (int, 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 os

import tecplot
from tecplot.constant import *

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

plot.streamtraces.add_on_zone_surface(
            # To add streamtraces to the currently active zones,
            # pass zones=None
            zones=[1],  # Add streamtraces on 2nd zone only
            stream_type=Streamtrace.SurfaceLine,
            num_seed_points=200)

tecplot.export.save_png('streamtrace_add_on_zone_surface.png', 600, supersample=3)
../_images/streamtrace_add_on_zone_surface.png
Streamtraces.add_rake(start_position, end_position, stream_type, num_seed_points=10, direction=<StreamDir.Both: 2>)[source]

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

The plot type must be either Cartesian2D or Cartesian3D.

Parameters:
import os

import tecplot
from tecplot.constant import *

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', '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, supersample=3)
../_images/streamtrace_add_rake.png
Streamtraces.arrowhead_size

Arrowhead size as a percentage of frame height.

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

Example usage:

>>> plot.streamtraces.show_arrows = True
>>> plot.streamtraces.arrowhead_size = 1.0
Type:float
Streamtraces.arrowhead_spacing

Distance between arrowheads in terms of Y-frame units.

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
Type:float
Streamtraces.color

Color of streamtraces line (not rods or ribbons).

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
Type:Color or ContourGroup
Streamtraces.count

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

Returns:int

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.

Example usage:

>>> plot.streamtraces.dash_skip = 2
Type:int
Streamtraces.delete_all()[source]

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)[source]

Delete a range of streamtraces.

Parameters:
  • range_start – (int): 0-based start streamtrace number to delete.
  • range_end – (int): 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.extract(concatenate=False, assign_strand_ids=True)[source]

Create new zones from streamtraces

Extracts the current streamtraces defined in this class to the Dataset as one or more zones.

Parameters:
  • concatenate (bool, optional) – Concatenate streamtraces into a single zone for each format (Surface Line, Volume Line, Volume Ribbon, Volume Rod).
  • assign_strand_ids (bool, optional) – Automatically assign strand ID’s to the created zones. This option is ignored if concatenate if True. (default: True)
Returns:

A generator of the extracted zones.

Example usage:

>>> slice_zone = plot.streamtraces.extract()
Streamtraces.has_terminating_line

Determine if the streamtraces have the terminating line.

Note

This property is read-only.

Returns:bool. 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.

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
Type:float
Streamtraces.marker_color

Color of the streamline markers.

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
Type:Color or ContourGroup
Streamtraces.marker_size

Size of streamline markers.

Example usage:

>>> plot.streamtraces.marker_size = 1.1
Type:float
Streamtraces.marker_symbol(symbol_type=None)[source]

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: TextSymbol or GeometrySymbol, depending on marker_symbol_type

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.

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'
Type:SymbolType
Streamtraces.max_steps

Maximum number of steps before the streamtrace is terminated.

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
Type:int
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.

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
Type:float
Streamtraces.obey_source_zone_blanking

Obey source zone blanking.

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
Type:bool
Streamtraces.position(stream_number)[source]

Query the starting position of a streamtrace.

Parameters:stream_number – (int): 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.

Example usage:

>>> streamtraces.rod_ribbon.mesh.show = True
Type:StreamtraceRodRibbon
Streamtraces.set_termination_line(line_points)[source]

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.

Example usage:

>>> plot.streamtraces.show_arrows = True
Type:bool
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.

Example usage:

>>> plot.streamtraces.show_dashes = True
Type:bool
Streamtraces.show_markers

Display streamtrace markers.

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
Type:bool
Streamtraces.show_paths

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

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

Example usage:

>>> plot.streamtraces.show_paths = True

See also Streamtraces.show_markers

Type:bool
Streamtraces.step_size

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

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
Type:float
Streamtraces.streamtrace_type(stream_number)[source]

Query the type of a streamtrace by streamtrace number.

Parameters:stream_number – (int): 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_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.

Example usage:

>>> term_line = plot.streamtraces.termination_line
>>> term_line.show = True
Type:StreamtraceTerminationLine
Streamtraces.timing

Streamtraces timing attributes.

Example usage:

>>> timing = plot.streamtraces.timing
>>> timing.start = 0.01
Type:StreamtraceTiming

StreamtraceRodRibbon

class tecplot.plot.StreamtraceRodRibbon(streamtrace)[source]

Get/Set streamtrace rod/ribbon attributes.

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

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

Note

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

import os
import tecplot
from tecplot.constant import *

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

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

plot = frame.plot()
plot.show_mesh = False
plot.show_shade = False
plot.show_edge = True
plot.fieldmap(0).edge.edge_type = EdgeType.Creases
plot.contour(0).variable = dataset.variable(3)
plot.contour(0).levels.reset_to_nice()

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.contour.show = True

plot.streamtraces.add_rake(start_position=(1.5, 0.1, .4),
                           end_position=(1.5, .9, 0.1),
                           stream_type=Streamtrace.VolumeRibbon,
                           num_seed_points=3)
plot.streamtraces.add_rake(start_position=(1.5, 0.1, 0.1),
                           end_position=(1.5, .9, .4),
                           stream_type=Streamtrace.VolumeRod,
                           num_seed_points=4)

tecplot.export.save_png('streamtrace_ribbon.png', 600, supersample=3)
../_images/streamtrace_ribbon.png

Attributes

contour Streamtraces rod/ribbon contour attributes.
effects Streamtraces rod/ribbon effects.
mesh Streamtraces rod/ribbon mesh attributes.
num_rod_points Number 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.

Example usage:

>>> plot.streamtraces.rod_ribbon.contour.show = True
Type:StreamtraceRodRibbonContour
StreamtraceRodRibbon.effects

Streamtraces rod/ribbon effects.

Example usage:

>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
Type:StreamtraceRodRibbonEffects
StreamtraceRodRibbon.mesh

Streamtraces rod/ribbon mesh attributes.

Example usage:

>>> plot.streamtraces.rod_ribbon.mesh.show = True
Type:StreamtraceRodRibbonMesh
StreamtraceRodRibbon.num_rod_points

Number of rod points.

Volume rods have a polygonal cross-section; this parameter tells Tecplot 360 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
Type:int, valid range 3-100
StreamtraceRodRibbon.shade

Streamtraces rod/ribbon color and lighting attributes.

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.color = Color.Magenta
Type:StreamtraceRodRibbonShade
StreamtraceRodRibbon.width

Rod/ribbon width in grid units.

Example usage:

>>> plot.streamtraces.rod_ribbon.width = 0.01
Type:float

StreamtraceTiming

class tecplot.plot.StreamtraceTiming(streamtrace)[source]

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, 'SimpleData', 'VortexShedding.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 = dataset.variable('U(M/S)')
plot.vector.v_variable = dataset.variable('V(M/S)')
plot.show_streamtraces = True
plot.show_shade = True
plot.fieldmap(0).shade.color = Color.LightBlue


streamtraces = plot.streamtraces
streamtraces.show_markers = True
timing = streamtraces.timing
timing.anchor = 0
timing.delta = 0.0001

streamtraces.marker_size = 1.5
streamtraces.marker_symbol().shape =GeomShape.RTri
streamtraces.marker_color = Color.Mahogany

streamtraces.add_rake(start_position=(-0.003, 0.005),
                      end_position=(-0.003, -0.005),
                      stream_type=Streamtrace.TwoDLine,
                      num_seed_points=10)


plot.axes.y_axis.min = -0.02
plot.axes.y_axis.max = 0.02
plot.axes.x_axis.min = -0.008
plot.axes.x_axis.max = 0.04

tecplot.export.save_png('streamtrace_timing.png', 600, supersample=3)
../_images/streamtrace_timing.png

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.

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
Type:float
StreamtraceTiming.delta

Time between stream markers.

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
Type:float
StreamtraceTiming.end

Time after which no stream markers are drawn.

Example usage:

>>> plot.streamtraces.timing.end = 3.0
Type:float
StreamtraceTiming.reset_delta()[source]

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.

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
Type:float

StreamtraceRodRibbonContour

class tecplot.plot.StreamtraceRodRibbonContour(streamtrace)[source]

Contour flooding display for streamtrace rod/ribbons.

import os
import numpy as np
import tecplot
from tecplot.constant import *

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'DownDraft.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.contour(0).variable = dataset.variable(3)
plot.contour(0).levels.reset_levels(np.linspace(1.15,1.25,11))
plot.show_mesh = False
plot.show_shade = False
plot.show_edge = True

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

rod = plot.streamtraces.rod_ribbon
rod.width = .008
rod.contour.show = True
rod.contour.use_lighting_effect = True

plot.streamtraces.add_rake(
    start_position=(0, 0.22, 0),
    end_position=(0, 0.22, 0.1),
    stream_type=Streamtrace.VolumeRod)

plot.view.width = 0.644
plot.view.alpha = 66.4
plot.view.theta = -122.4
plot.view.psi   = 124.5
plot.view.position = (5.3, 3.56, -4.3)

tecplot.export.save_png('streamtrace_rod_contour.png', 600, supersample=3)
../_images/streamtrace_rod_contour.png

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.

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
Type:ContourGroup
StreamtraceRodRibbonContour.flood_contour_group_index

The Index of the ContourGroup to use for flooding.

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
Type:Index (zero-based index)
StreamtraceRodRibbonContour.show

Enable or disable contour flooding display.

Example usage:

>>> plot.streamtraces.rod_ribbon.contour.show = True
Type:bool
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.

Example usage:

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

StreamtraceRodRibbonEffects

class tecplot.plot.StreamtraceRodRibbonEffects(streamtrace)[source]

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

import os
import tecplot
from tecplot.constant import *

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

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

plot = frame.plot()
plot.show_mesh = False
plot.show_shade = False
plot.show_edge = True
plot.fieldmap(0).edge.edge_type = EdgeType.Creases

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=(1.5, 0, .45),
                               end_position=(1.5, 1, 0),
                               stream_type=Streamtrace.VolumeRibbon)

tecplot.export.save_png('streamtrace_ribbon_effects.png', 600, supersample=3)
../_images/streamtrace_ribbon_effects.png

Attributes

lighting_effect Get/set the lighting algorithm used when lighting
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.

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
Type:LightingEffect
StreamtraceRodRibbonEffects.surface_translucency

Surface translucency of the streamtraces ribbon.

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
Type:int
StreamtraceRodRibbonEffects.use_translucency

Enable surface translucency.

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
Type:bool

StreamtraceRodRibbonMesh

class tecplot.plot.StreamtraceRodRibbonMesh(robribbon)[source]

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 *

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'DownDraft.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 = False
plot.show_shade = False
plot.show_edge = True

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 = .008
ribbon.mesh.show = True
ribbon.mesh.line_thickness = 0.2
#Ribbon mesh color inherited from streamtrace color
plot.streamtraces.color = Color.AquaGreen

plot.streamtraces.add_rake(
    start_position=(0, 0.22, 0),
    end_position=(0, 0.22, 0.1),
    stream_type=Streamtrace.VolumeRibbon)

plot.view.width = 0.644
plot.view.alpha = 66.4
plot.view.theta = -122.4
plot.view.psi   = 124.5
plot.view.position = (5.3, 3.56, -4.3)

tecplot.export.save_png('streamtrace_ribbon_mesh.png', 600, supersample=3)
../_images/streamtrace_ribbon_mesh.png

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

Example usage:

>>> plot.streamtraces.rod_ribbon.mesh.line_thickness = 0.2
Type:float
StreamtraceRodRibbonMesh.show

Display mesh.

Note

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

Example usage:

>>> plot.streamtraces.rod_ribbon.mesh.show = True
Type:bool

StreamtraceRodRibbonShade

class tecplot.plot.StreamtraceRodRibbonShade(streamtrace)[source]

Color and lighting display for rod/ribbons.

import os
import tecplot
from tecplot.constant import *

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

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

plot = frame.plot()
plot.show_mesh = False
plot.show_shade = False
plot.show_edge = True
plot.fieldmap(0).edge.edge_type = EdgeType.Creases

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

ribbon = plot.streamtraces.rod_ribbon
ribbon.shade.show = True
ribbon.shade.color = Color.Blue
ribbon.shade.use_lighting_effect = True
ribbon.width = .03


plot.streamtraces.add_rake(start_position=(1.5, 0, .45),
                           end_position=(1.5, 1, 0),
                           stream_type=Streamtrace.VolumeRibbon)

tecplot.export.save_png('streamtrace_ribbon_shade.png', 600, supersample=3)
../_images/streamtrace_ribbon_shade.png

Attributes

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

Shade 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
Type:Color
StreamtraceRodRibbonShade.show

Show shade attributes.

Example usage:

>>> plot.streamtraces.rod_ribbon.shade.show = True
Type:bool
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.

Example usage:

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

StreamtraceTerminationLine

class tecplot.plot.StreamtraceTerminationLine(streamtrace)[source]

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 tecplot
from tecplot.constant import *
import os

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'VortexShedding.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 = dataset.variable('U(M/S)')
plot.vector.v_variable = dataset.variable('V(M/S)')
plot.show_streamtraces = True
plot.show_shade = True
plot.fieldmap(0).shade.color = Color.LightBlue

streamtraces = plot.streamtraces
streamtraces.set_termination_line([(0.03, 0.005),
                                   (0.03, -0.005), ])

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

streamtraces.add_rake(start_position=(-0.003, 0.005),
                      end_position=(-0.003, -0.005),
                      stream_type=Streamtrace.TwoDLine,
                      num_seed_points=10)

plot.axes.y_axis.min = -0.02
plot.axes.y_axis.max = 0.02
plot.axes.x_axis.min = -0.01
plot.axes.x_axis.max = 0.04

tecplot.export.save_png('streamtrace_term_line.png', 600, supersample=3)
../_images/streamtrace_term_line.png

Attributes

active Activate/disable the streamtrace termination line.
color Color of the 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.active

Activate/disable the streamtrace termination line.

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.active = True
Type:bool
StreamtraceTerminationLine.color

Color of the termination line.

Example usage:

>>> plot.streamtraces.termination_line.color = Color.Red
Type:Color
StreamtraceTerminationLine.line_pattern

Pattern of the terminating line.

Example usage:

>>> plot.streamtraces.termination_line.line_pattern = LinePattern.Dotted
Type:LinePattern
StreamtraceTerminationLine.line_thickness

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

Example usage:

>>> plot.streamtraces.termination_line.line_thickness = 0.1
Type:float
StreamtraceTerminationLine.pattern_length

Length of the pattern as a percentage of frame height.

Example usage:

>>> plot.streamtraces.termination_line.pattern_length = 2
Type:float
StreamtraceTerminationLine.show

Display the termination line.

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

Note

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

Example usage:

>>> plot.streamtraces.termination_line.show = True
Type:bool

Text

Font

class tecplot.text.Font(parent, sv_textshape='TEXTSHAPE')[source]

Style of text objects such as titles and labels.

This class controls the typeface and size of various text objects found in plots and axes:

from os import path
import tecplot as tp
from tecplot.constant import PlotType, Units, AxisTitleMode

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'OneraM6wing', 'OneraM6_SU2_RANS.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian2D)
plot.activate()

plot.show_contour = True

xaxis = plot.axes.x_axis
xaxis.title.title_mode = AxisTitleMode.UseText
xaxis.title.text = 'Longitudinal (m)'
xaxis.min, xaxis.max = 0, 1.2

yaxis = plot.axes.y_axis
yaxis.title.title_mode = AxisTitleMode.UseText
yaxis.title.text = 'Transverse (m)'
yaxis.min, yaxis.max = 0, 1.3

for ax in [xaxis, yaxis]:
    ax.title.font.typeface = 'Times'
    ax.title.font.bold = False
    ax.title.font.italic = True
    ax.title.font.size_units = Units.Frame
    ax.title.font.size = 7

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('font.png', 600, supersample=3)
../_images/font.png

Attributes

bold Use the bold version of the current typeface.
italic Use the italic version of the current typeface.
size Height of the font.
size_units Used by the size attribute.
typeface Specific font (or typeface) to use for text.
Font.bold

Use the bold version of the current typeface.

Example:

>>> axis.title.font.bold = True
Type:bool
Font.italic

Use the italic version of the current typeface.

Example:

>>> axis.title.font.italic = True
Type:bool
Font.size

Height of the font. in units of Font.size_units.

Example usage:

>>> axis.title.font.size = 10
Type:float
Font.size_units

Used by the size attribute.

Possible values: Units.Point, Units.Frame (percentage of frame height). This example sets the axis title to 10% of the frame height:

>>> from tecplot.constant import Units
>>> axis.title.font.size_units = Units.Frame
>>> axis.title.font.size = 10
Type:constant.Units
Font.typeface

Specific font (or typeface) to use for text.

This can be any font installed on the current system. If the font is not found, Times or Helvetica will be used when rendering the text. Example usage:

>>> axis.title.font.typeface = 'Times'
Type:str

BaseFont

class tecplot.text.BaseFont(*svargs, **kwargs)[source]

Plot-level or scatter font style fall-back settings.

Note

Base fonts are accessible directly from line plots (XYLinePlot.base_font, PolarLinePlot.base_font):

>>> frame.plot(PlotType.XYLine).base_font

and the scatter style of field plots (Scatter.base_font):

>>> frame.plot(PlotType.Cartesian3D).scatter.base_font

Attributes

bold Use the bold version of the current typeface.
italic Use the italic version of the current typeface.
typeface Specific font (or typeface) to use for text.
BaseFont.bold

Use the bold version of the current typeface.

Example:

>>> line_plot.base_font.bold = True
>>> field_plot.scatter.base_font.bold = True
Type:bool
BaseFont.italic

Use the italic version of the current typeface.

Example:

>>> line_plot.base_font.italic = True
>>> field_plot.scatter.base_font.italic = True
Type:bool
BaseFont.typeface

Specific font (or typeface) to use for text.

This can be any font installed on the current system. If the font is not found, Times or Helvetica will be used when rendering the text. Example usage:

>>> line_plot.base_font.typeface = 'Times'
>>> field_plot.scatter.base_font.typeface = 'Times'
Type:str

TextBox

class tecplot.text.TextBox(parent)[source]

Rectangular frame around a text element.

Warning

text.TextBox objects cannot be created directly. They are returned by various other read-only properties.

Attributes

box_type Type of box surrounding the text.
color Color of the box surrounding the text.
fill_color Fill color of the box surrounding the text.
line_thickness Line thickness of the box surrounding the text.
margin Margin of the box surrounding the text.
TextBox.box_type

Type of box surrounding the text.

Example usage:

>>> plot = frame.plot()
>>> plot.legend.box.box_type = constant.TextBox.None_
Type:constant.TextBox
TextBox.color

Color of the box surrounding the text.

Example usage:

>>> plot = frame.plot()
>>> plot.legend.box.color = Color.Blue
Type:Color
TextBox.fill_color

Fill color of the box surrounding the text.

Example usage:

>>> plot = frame.plot()
>>> plot.legend.box.fill_color = Color.Blue
Type:Color
TextBox.line_thickness

Line thickness of the box surrounding the text.

Example usage:

>>> plot = frame.plot()
>>> plot.legend.box.line_thickness = 0.2
Type:float
TextBox.margin

Margin of the box surrounding the text.

This property is the margin between the text inside the text box and the box as a percentage of frame height.

Example usage:

>>> plot = frame.plot()
>>> plot.legend.box.margin = 0.3
Type:float

LabelFormat

class tecplot.text.LabelFormat(labels)[source]

Formatting of numbers shown along in axes and in legends.

This example shows how to format tick label along an axis:

from datetime import datetime
import tecplot as tp
from tecplot.constant import PlotType, AxisMode, AxisAlignment, NumberFormat

tp.new_layout()
plot = tp.active_frame().plot(tp.constant.PlotType.Sketch)
plot.activate()

# setup the plot area margins
plot.axes.viewport.left = 10
plot.axes.viewport.right = 90

# show the x-axis, set the title, and alignment with the viewport
xaxis = plot.axes.x_axis
xaxis.show = True
xaxis.title.text = 'Negative numbers in parentheses'
xaxis.title.offset = 20
xaxis.line.alignment = AxisAlignment.WithViewport
xaxis.line.position = 50

# set limits, tick placement and tick label properties
xaxis.ticks.auto_spacing = False
xaxis.min, xaxis.max = -5.123e-5, 5.234e-5
xaxis.ticks.spacing = (xaxis.max - xaxis.min) / 6
xaxis.ticks.spacing_anchor = 0
xaxis.tick_labels.angle = 45
xaxis.tick_labels.offset = 3

# format the tick labels in superscript form. example: 1.234x10^5
# format negative numbers to use parentheses instead of a negative sign
xformat = xaxis.tick_labels.format
xformat.format_type = NumberFormat.SuperScript
xformat.precision = 3
xformat.show_negative_sign = False
xformat.negative_prefix = '('
xformat.negative_suffix = ')'

tp.export.save_png('label_format.png', 600, supersample=3)
../_images/label_format.png

Attributes

custom_labels_index Index of the custom label to use.
datetime_format The date/time format to be used.
format_type Type of number formatting to use.
negative_prefix Prefix string to use for negative valued tick labels.
negative_suffix Suffix string to use for negative valued tick labels.
num_custom_labels Number of custom label sets available to use.
positive_prefix Prefix string to use for positive valued tick labels.
positive_suffix Suffix string to use for positive valued tick labels.
precision Number digits after decimal for fixed floating point format.
remove_leading_zeros Strip leading zeros in the formatted number.
show_decimals_on_whole_numbers Include trailing decimal character with whole numbers.
show_negative_sign Include negative sign for negative values.
zero_prefix Prefix string to use for zero valued tick labels.
zero_suffix Suffix string to use for zero valued tick labels.

Methods

add_custom_labels(*labels) Append a list of custom labels as a new set.
custom_labels(index) List of labels for custom labels for set specified by index.
LabelFormat.add_custom_labels(*labels)[source]

Append a list of custom labels as a new set.

Example usage:

>>> labels = ['apples', 'bananas', 'carrots']
>>> axis.tick_labels.format.add_custom_labels(*labels)
>>> print(axis.tick_labels.format.custom_labels(-1))
['apples', 'bananas', 'carrots']
LabelFormat.custom_labels(index)[source]

List of labels for custom labels for set specified by index.

Example usage:

>>> axis.tick_labels.format.custom_labels(0)
['apples', 'bananas', 'carrots']
LabelFormat.custom_labels_index

Index of the custom label to use.

Example usage:

>>> axis.tick_labels.format.custom_labels_index = 0
Type:Index (zero-based)
LabelFormat.datetime_format

The date/time format to be used.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> axis.tick_labels.format.format_type = NumberFormat.TimeDate
>>> axis.tick_labels.format.datetime_format = 'mmm d, yyyy'

The format can be any combination of the following codes. Placing a backslash in front of a y, m, d, or s in the Time/Date formula will keep it from being processed as part of the formula. All characters not part of the Time/Date formula will appear as entered. For example, “\year yyyy” will appear as “year 2008”, as the backslash keeps the first y from being processed as part of the formula. If you use “m” immediately after the “h” or “hh” code or immediately before the “ss” code, the minutes instead of the month will be displayed.

Years:
yy 00-99
yyyy 1800-9999
Months:
m 1-12
mm 01-12
mmm Jan-Dec
mmmm January-December
mmmmm first letter of the month
Days:
[d] elapsed days
d 1-31
dd 01-31
ddd Sun-Sat
dddd Sunday-Saturday
ddddd S,M,T,W,T,F,S
Hours:
[h] elapsed hours
h 0-23 or 1-12
hh 00-23 or 1-12
AM/PM AM or PM
A/P AM or PM as “A” or “P”
Minutes:
[m] elapsed minutes
m 0-59
mm 00-59
Seconds:  
s 0-59
ss 00-59
.0 Tenths
.00 Hundredths
.000 Thousandths

To display the time and date on your plot as a “Sat-Jan-05-2008”, enter the following code:

"ddd-mmm-dd-yyyy"

To display the time and date on your plot as a “1-3-08”, enter the following code:

"m-d-yy"

To display the time and date on your plot as a “9:30:05 AM”, enter the following code:

"h:mm:ss AM"

To display an elapsed time, such as “3:10:15”, enter the following code:

"[d]:hh:mm"
Type:str
LabelFormat.format_type

Type of number formatting to use.

Possible values: Integer, FixedFloat, Exponential, BestFloat, SuperScript, CustomLabel, LogSuperScript, RangeBestFloat, DynamicLabel, TimeDate.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> axis.tick_labels.format.format_type = NumberFormat.BestFloat
Type:NumberFormat
LabelFormat.negative_prefix

Prefix string to use for negative valued tick labels.

This example shows how to use parentheses instead of a negative sign:

>>> axis.tick_labels.format.show_negative_sign = False
>>> axis.tick_labels.format.negative_prefix = '('
>>> axis.tick_labels.format.negative_suffix = ')'
Type:str
LabelFormat.negative_suffix

Suffix string to use for negative valued tick labels.

This example shows how to use parentheses instead of a negative sign:

>>> axis.tick_labels.format.show_negative_sign = False
>>> axis.tick_labels.format.negative_prefix = '('
>>> axis.tick_labels.format.negative_suffix = ')'
Type:str
LabelFormat.num_custom_labels

Number of custom label sets available to use.

Example usage:

>>> print(axis.tick_labels.format.num_custom_labels)
1
Type:int
LabelFormat.positive_prefix

Prefix string to use for positive valued tick labels.

Example usage:

>>> axis.tick_labels.format.positive_prefix = 'increase: '
Type:str
LabelFormat.positive_suffix

Suffix string to use for positive valued tick labels.

Example usage:

>>> axis.tick_labels.format.positive_suffix = ' (m)'
Type:str
LabelFormat.precision

Number digits after decimal for fixed floating point format.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> axis.tick_labels.format.format_type = NumberFormat.FixedFloat
>>> axis.tick_labels.format.precision = 3
Type:int
LabelFormat.remove_leading_zeros

Strip leading zeros in the formatted number.

Example usage:

>>> axis.tick_labels.format.remove_leading_zeros = True
Type:bool
LabelFormat.show_decimals_on_whole_numbers

Include trailing decimal character with whole numbers.

Example usage:

>>> axis.tick_labels.format.show_decimals_on_whole_numbers = True
Type:bool
LabelFormat.show_negative_sign

Include negative sign for negative values.

Example usage:

>>> axis.tick_labels.format.show_negative_sign = True
Type:bool
LabelFormat.zero_prefix

Prefix string to use for zero valued tick labels.

Example usage:

>>> axis.tick_labels.format.zero_prefix = 'origin: '
Type:str
LabelFormat.zero_suffix

Suffix string to use for zero valued tick labels.

Example usage:

>>> axis.tick_labels.format.zero_suffix = ' (origin)'
Type:str

Data Labels

FieldPlotDataLabels

class tecplot.plot.FieldPlotDataLabels(plot)[source]

Node and cell labels for field plots.

from os import path
import tecplot as tp
from tecplot.constant import LabelType, NumberFormat, PlotType

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'RainierElevation.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian2D)
plot.activate()
plot.show_contour = True
plot.contour(0).legend.show = False

plot.axes.x_axis.min = -8500
plot.axes.x_axis.max = 8200
plot.axes.y_axis.min = -400
plot.axes.y_axis.max = -150

plot.data_labels.show_node_labels = True
plot.data_labels.node_label_type = LabelType.VarValue
plot.data_labels.node_variable = dataset.variable('E')
plot.data_labels.index_step = 4
plot.data_labels.label_format.format_type = NumberFormat.Integer
plot.data_labels.show_box = False

tp.export.save_png('field_plot_data_labels.png')
../_images/field_plot_data_labels.png

Attributes

cell_label_type The value to be displayed for cell labels.
cell_variable Variable to use for cell labels.
cell_variable_index Index of the variable to use for cell labels.
color The Color of the data labels.
color_by_map Inherit Color from the symbol or scatter mapping style.
font Typeface control for all data labels.
index_step Step interval between labels.
label_format Floating-point number format control.
node_label_type The value to be displayed for node labels.
node_variable Variable to use for node labels.
node_variable_index Index of the variable to use for node labels.
show_box Show a box around each label.
show_cell_labels Display labels at each cell.
show_node_labels Display labels at each node.
FieldPlotDataLabels.cell_label_type

The value to be displayed for cell labels.

Possible values are LabelType.Index or LabelType.VarValue:

>>> plot.data_labels.show_cell_labels = True
>>> plot.data_labels.cell_label_type = LabelType.VarValue
Type:LabelType
FieldPlotDataLabels.cell_variable

Variable to use for cell labels.

Example usage:

>>> from tecplot.constant import LabelType
>>> plot.data_labels.show_cell_labels = True
>>> plot.data_labels.cell_label_type = LabelType.VarValue
>>> plot.data_labels.cell_variable = dataset.variable('E')
Type:Variable
FieldPlotDataLabels.cell_variable_index

Index of the variable to use for cell labels.

Example usage:

>>> from tecplot.constant import LabelType
>>> plot.data_labels.show_cell_labels = True
>>> plot.data_labels.cell_label_type = LabelType.VarValue
>>> plot.data_labels.cell_variable_index = 3
Type:Index
FieldPlotDataLabels.color

The Color of the data labels.

Example usage:

>>> from tecplot.constant import Color
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.color = Color.LightBlue
Type:Color
FieldPlotDataLabels.color_by_map

Inherit Color from the symbol or scatter mapping style.

Example usage for linemaps:

>>> from tecplot.constant import Color
>>> plot.linemap(0).symbols.color = Color.Blue
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.color_by_map = True

Example usage for fieldmaps:

>>> from tecplot.constant import Color
>>> plot.fieldmap(0).scatter.color = Color.Yellow
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.color_by_map = True
Type:bool
FieldPlotDataLabels.font

Typeface control for all data labels.

Example usage:

>>> plot.data_labels.font.typeface = 'Times'
Type:text.Font
FieldPlotDataLabels.index_step

Step interval between labels.

A value of 1 displays labels on all nodes or cells. Example usage:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.index_step = 10

See also

LinePlotDataLabels.step_mode for line plots.

Type:int
FieldPlotDataLabels.label_format

Floating-point number format control.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> labels = plot.data_labels
>>> labels.label_format.format_type = NumberFormat.Integer
Type:text.LabelFormat
FieldPlotDataLabels.node_label_type

The value to be displayed for node labels.

Possible values are LabelType.Index or LabelType.VarValue:

>>> from tecplot.constant import LabelType
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.node_label_type = LabelType.VarValue
Type:LabelType
FieldPlotDataLabels.node_variable

Variable to use for node labels.

Example usage:

>>> from tecplot.constant import LabelType
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.node_label_type = LabelType.VarValue
>>> plot.data_labels.node_variable = dataset.variable('E')
Type:Variable
FieldPlotDataLabels.node_variable_index

Index of the variable to use for node labels.

Example usage:

>>> from tecplot.constant import LabelType
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.node_label_type = LabelType.VarValue
>>> plot.data_labels.node_variable_index = 3
Type:Index
FieldPlotDataLabels.show_box

Show a box around each label.

This is True by default. Set to False to disable the box:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.show_box = False
Type:bool
FieldPlotDataLabels.show_cell_labels

Display labels at each cell.

Example usage:

>>> plot.data_labels.show_cell_labels = True
Type:bool
FieldPlotDataLabels.show_node_labels

Display labels at each node.

Example usage:

>>> plot.data_labels.show_node_labels = True
Type:bool

LinePlotDataLabels

class tecplot.plot.LinePlotDataLabels(plot)[source]

Node labels for line plots.

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'SunSpots.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.XYLine)
plot.activate()
plot.data_labels.show_node_labels = True
plot.data_labels.index_step = 3

tp.export.save_png('line_plot_data_labels.png')
../_images/line_plot_data_labels.png

Attributes

color The Color of the data labels.
color_by_map Inherit Color from the symbol or scatter mapping style.
font Typeface control for all data labels.
index_step Step interval between labels.
label_format Floating-point number format control.
node_label_type The value to be displayed for node labels.
show_box Show a box around each label.
show_node_labels Display labels at each node.
step_distance Distance between labels when stepping by frame units.
step_mode The scale to use when stepping through elements.
LinePlotDataLabels.color

The Color of the data labels.

Example usage:

>>> from tecplot.constant import Color
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.color = Color.LightBlue
Type:Color
LinePlotDataLabels.color_by_map

Inherit Color from the symbol or scatter mapping style.

Example usage for linemaps:

>>> from tecplot.constant import Color
>>> plot.linemap(0).symbols.color = Color.Blue
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.color_by_map = True

Example usage for fieldmaps:

>>> from tecplot.constant import Color
>>> plot.fieldmap(0).scatter.color = Color.Yellow
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.color_by_map = True
Type:bool
LinePlotDataLabels.font

Typeface control for all data labels.

Example usage:

>>> plot.data_labels.font.typeface = 'Times'
Type:text.Font
LinePlotDataLabels.index_step

Step interval between labels.

A value of 1 displays labels on all nodes or cells. Example usage:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.index_step = 10

See also

LinePlotDataLabels.step_mode for line plots.

Type:int
LinePlotDataLabels.label_format

Floating-point number format control.

Example usage:

>>> from tecplot.constant import NumberFormat
>>> labels = plot.data_labels
>>> labels.label_format.format_type = NumberFormat.Integer
Type:text.LabelFormat
LinePlotDataLabels.node_label_type

The value to be displayed for node labels.

Possible values are LabelType.Index or LabelType.VarValue:

>>> from tecplot.constant import LabelType
>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.node_label_type = LabelType.VarValue
Type:LabelType
LinePlotDataLabels.show_box

Show a box around each label.

This is True by default. Set to False to disable the box:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.show_box = False
Type:bool
LinePlotDataLabels.show_node_labels

Display labels at each node.

Example usage:

>>> plot.data_labels.show_node_labels = True
Type:bool
LinePlotDataLabels.step_distance

Distance between labels when stepping by frame units.

Example usage:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.step_mode = StepMode.ByFrameUnits
>>> plot.data_labels.step_distance = 10.0
Type:float
LinePlotDataLabels.step_mode

The scale to use when stepping through elements.

Possible values are: StepMode.ByIndex and StepMode.ByFrameUnits. Example usage:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.step_mode = StepMode.ByFrameUnits
>>> plot.data_labels.step_distance = 10.0
Type:StepMode

Viewport

ReadOnlyViewport

class tecplot.plot.ReadOnlyViewport(axes)[source]

Attributes

bottom float in percentage of frame height from the bottom of frame
left float in percentage of frame width from the left of the frame.
right float in percentage of frame width from the left of the frame.
top float in percentage of frame height from the bottom of the frame.
ReadOnlyViewport.bottom

float in percentage of frame height from the bottom of frame

Example usage:

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

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

Example usage:

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

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

Example usage:

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

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)[source]

Attributes

bottom float in percentage of frame height from the bottom of frame
left float in percentage of frame width from the left of the frame.
right float in percentage of frame width from the left of the frame.
top float in percentage of frame height from the bottom of the frame.
Viewport.bottom

float in percentage of frame height from the bottom of frame

Example usage:

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

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

Example usage:

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

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

Example usage:

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

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)[source]

Attributes

bottom float in percentage of frame height from the bottom of frame
left float in percentage of frame width from the left of the frame.
nice_fit_buffer Tolerance for viewport/frame fit niceness.
right float in percentage of frame width from the left of the frame.
top float in percentage of frame height from the bottom of 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 in percentage of frame height from the bottom of frame

Example usage:

>>> print(plot.axes.viewport.bottom)
10.0
Cartesian2DViewport.left

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.

Example usage:

>>> plot.axes.viewport.nice_fit_buffer = 20
Type:float
Cartesian2DViewport.right

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

Example usage:

>>> print(plot.axes.viewport.right)
90.0
Cartesian2DViewport.top

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.

Example usage:

>>> plot.axes.viewport.top_snap_target = 90
Type:float
Cartesian2DViewport.top_snap_tolerance

Tolerance for snapping to target value for top.

Example usage:

>>> plot.axes.viewport.top_snap_tolerance = 8
Type:float

PolarViewport

class tecplot.plot.PolarViewport(axes)[source]

Attributes

border_color Border line color around the viewport.
border_thickness Border line thickness around the viewport.
bottom float in percentage of frame height from the bottom of frame
fill_color Background fill color of the entire viewport.
left float in percentage of frame width from the left of the frame.
right float in percentage of frame width from the left of the frame.
show_border Draw a border line around the viewport.
top float in percentage of frame height from the bottom of the frame.
PolarViewport.border_color

Border line color around the viewport.

Example usage:

>>> from tecplot.constant import Color
>>> plot.axes.viewport.show_border = True
>>> plot.axes.viewport.border_thickness = 0.8
>>> plot.axes.viewport.border_color = Color.Red
Type:Color
PolarViewport.border_thickness

Border line thickness around the viewport.

Example usage:

>>> plot.axes.viewport.show_border = True
>>> plot.axes.viewport.border_thickness = 0.8
Type:float
PolarViewport.bottom

float in percentage of frame height from the bottom of frame

Example usage:

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

Background fill color of the entire viewport.

Example usage:

>>> from tecplot.constant import Color
>>> plot.axes.viewport.fill_color = Color.Blue
Type:Color
PolarViewport.left

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

Example usage:

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

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

Example usage:

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

Draw a border line around the viewport.

Example usage:

>>> plot.axes.viewport.show_border = True
Type:bool
PolarViewport.top

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

Example usage:

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

View and Lighting

Cartesian2DFieldView

class tecplot.plot.Cartesian2DFieldView(plot, *svargs)[source]

Adjust the way cartesian 2D data is displayed.

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian2D)
plot.activate()
plot.show_contour = True
plot.contour(0).variable = dataset.variable('P(N)')
plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'

plot.view.fit_to_nice()

# ensure consistent output between interactive (connected) and batch
plot.contour(0).levels.reset_to_nice()

tp.export.save_png('view_2D.png', 600, supersample=3)
../_images/view_2D.png

Attributes

magnification Magnification for the data being plotted.

Methods

adjust_to_nice() Shifts axes to make axis-line values “nice”
center([consider_blanking]) Center the data within the axis grid area.
fit([consider_blanking]) Fit the data being plotted within the axis grid area.
fit_data([consider_blanking]) Fit data zones or line mappings within the grid area.
fit_to_nice([consider_blanking]) Set axis range to begin/end on major axis increments.
translate([x, y]) Shift the data being plotted in the X and/or Y direction.
zoom(xmin, ymin, xmax, ymax) Zoom the view to a rectangular region of the plot.
Cartesian2DFieldView.adjust_to_nice()

Shifts axes to make axis-line values “nice”

Modifies the axis range to fit the minimum and maximum of the variable assigned to that axis, then snaps the major tick marks to the ends of the axis. If axis dependency is not independent, this may affect the range on another axis.

In other words, given an existing range of values RMin, RMax and an initial delta, D (such as axis ranges with grid spacing or contour levels), determine a new delta (ND) that:

  • Is 1,2, or 5 times 10 to some power that is the “best” alternative to D.
  • Produces new range min and max values that are some multiple of ND that are nearest the original RMin and RMax

Axes are shifted without changing the extents of the window.

Cartesian2DFieldView.center(consider_blanking=True)

Center the data within the axis grid area.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)
Cartesian2DFieldView.fit(consider_blanking=True)

Fit the data being plotted within the axis grid area.

Note

This also takes into consideration text and geometries that are plotted using the grid coordinate system.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)
Cartesian2DFieldView.fit_data(consider_blanking=True)

Fit data zones or line mappings within the grid area.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)

Note

This does not take into consideration text and geometries that are plotted using the grid coordinate system.

Cartesian2DFieldView.fit_to_nice(consider_blanking=True)[source]

Set axis range to begin/end on major axis increments.

Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)
Cartesian2DFieldView.magnification

Magnification for the data being plotted.

The magnification value is a decimal percent and must be greater than 0. A magnification size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

>>> view.magnification = 0.10
Type:float
Cartesian2DFieldView.translate(x=0.0, y=0.0)

Shift the data being plotted in the X and/or Y direction.

Note

The amount translated is in frame units.

Parameters:
  • x (float, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0)
  • y (float, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

Translate the view 10 percent of the frame width to the right:

>>> view.translate(x=10)

Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

>>> view.translate(x=5, y=-20)
Cartesian2DFieldView.zoom(xmin, ymin, xmax, ymax)

Zoom the view to a rectangular region of the plot.

Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (xmin, ymin) and (xmax, ymax).

Note

All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

Parameters:
  • xmin – (float) X min corner of the rectangle to be viewed.
  • ymin – (float) Y min corner of the rectangle to be viewed.
  • xmax – (float) X max corner of the rectangle to be viewed.
  • ymax – (float) Y max corner of the rectangle to be viewed.

Zoom so the rectangular region with corners at (1, 0) and (7, 9) is in view:

>>> view.zoom(1, 7, 0, 9)

Cartesian3DView

class tecplot.plot.Cartesian3DView(plot)[source]

Adjust the way cartesian 3D data is displayed.

import tecplot
import os
from tecplot.constant import *
examples_dir = tecplot.session.tecplot_examples_directory()
infile = os.path.join(examples_dir, 'SimpleData', 'F18.plt')
ds = tecplot.data.load_tecplot(infile)
plot = tecplot.active_frame().plot(PlotType.Cartesian3D)
plot.activate()
plot.view.width = 17.5
plot.view.alpha = 0
plot.view.theta = 125
plot.view.psi   = 65
plot.view.position = (-100, 80, 65)

tecplot.export.save_jpeg('view_3D.jpeg', 600, supersample=3)
../_images/view_3D.jpeg

Attributes

alpha Eye origin view Alpha angle in degrees.
distance Get or set the view distance.
field_of_view Amount of the plot which is displayed.
magnification Magnification for the data being plotted.
position 3D viewer position.
projection Projection type (Perspective or Orthographic).
psi Eye origin view Psi angle in degrees.
rotation_origin Center of rotation for rotate_axes and rotate_viewer.
theta Eye origin view Theta angle in degrees.
width 3D view width.

Methods

center([consider_blanking]) Center the data within the axis grid area.
fit([consider_blanking]) Fit the data being plotted within the axis grid area.
fit_data([consider_blanking]) Fit data zones or line mappings within the grid area.
fit_surfaces([consider_blanking]) Fit 3D plot surfaces to the grid area.
rotate_axes(angle[, normal]) Adjust the view so the axes are rotated about some normal vector.
rotate_to_angles(psi, theta, alpha) Rotate the plot to specific angles.
rotate_viewer(angle[, normal]) Rotate the camera or viewer about some normal vector.
translate([x, y]) Shift the data being plotted in the X and/or Y direction.
zoom(xmin, ymin, xmax, ymax) Zoom the view to a rectangular region of the plot.
Cartesian3DView.alpha

Eye origin view Alpha angle in degrees.

The Alpha angle is the twist about the eye origin ray:

>>> plot.view.alpha = 95.0
Type:float
Cartesian3DView.center(consider_blanking=True)

Center the data within the axis grid area.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)
Cartesian3DView.distance

Get or set the view distance.

The view distance is the distance from the viewer to the plane that is parallel to the screen and passes through the 3-D rotation origin.

Note

Changing this value will also change the viewer position.

See also

position

Example usage:

>>> plot.view.distance
13.5
>>> plot.view.distance = 10.0
>>> plot.view.distance
10.0
Type:float
Cartesian3DView.field_of_view

Amount of the plot which is displayed.

Get or set the amount of the plot (in terms of spherical arc) in front of the viewer that may be seen.

Warning

field_of_view cannot be set if projection is Projection.Orthographic.

Example usage:

>>> from tecplot.constant import Projection
>>> plot.view.projection = Projection.Perspective
>>> plot.view.field_of_view = 9.6
Type:float
Cartesian3DView.fit(consider_blanking=True)

Fit the data being plotted within the axis grid area.

Note

This also takes into consideration text and geometries that are plotted using the grid coordinate system.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)
Cartesian3DView.fit_data(consider_blanking=True)

Fit data zones or line mappings within the grid area.

Parameters:consider_blanking (Boolean, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)

Note

This does not take into consideration text and geometries that are plotted using the grid coordinate system.

Cartesian3DView.fit_surfaces(consider_blanking=True)[source]

Fit 3D plot surfaces to the grid area.

Parameters:consider_blanking (bool, optional) – If True and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: True)

Note

3D volume zones are excluded when surfaces_to_plot is SurfacesToPlot.None_.

Cartesian3DView.magnification

Magnification for the data being plotted.

The magnification value is a decimal percent and must be greater than 0. A magnification size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

>>> view.magnification = 0.10
Type:float
Cartesian3DView.position

3D viewer position.

The viewer position is the viewer’s relation to the image:

>>> plot.view.position
(1.25, 3.2, 0.74)
>>> plot.view.position.x
1.25
>>> plot.view.position = (2.5, 0.0, 1.0)
>>> plot.view.position.y
0.0
>>> plot.view.position.z
1.0

See also

distance

Type:tuple
Cartesian3DView.projection

Projection type (Perspective or Orthographic).

When set to Perspective, Tecplot 360 draws the plot in perspective. When set to Orthographic, the plot is drawn with orthographic projection where the shape of the object does not change with distance.

Note

Requires Tecplot version 2017.2 or later.

Example usage:

>>> from tecplot.constant import Projection
>>> plot.view.projection = Projection.Orthographic
Type:Projection
Cartesian3DView.psi

Eye origin view Psi angle in degrees.

The Psi angle is the tilt of the eye origin ray away from the Z-axis:

>>> plot.view.psi = 90.0
Type:float
Cartesian3DView.rotate_axes(angle, normal=(0, 0, 1))[source]

Adjust the view so the axes are rotated about some normal vector.

This effectively rotates the axes around the axes origin about some normal using the right-hand rule by the specified angle in degrees. The rotation is performed about the position: Cartesian3DView.rotation_origin.

Parameters:
  • angle (float) – The angle in degrees to rotate.
  • normal (tuple, optional) – The direction vector \((x, y, z)\) around which the rotation will be done. (default: (0, 0, 1))

Example of rotating 30 degrees about the \(x\)-axis and around the data’s origin:

>>> plot.view.rotation_origin = (0, 0, 0)
>>> plot.view.rotate_axes(30, (1, 0, 0))
Cartesian3DView.rotate_to_angles(psi, theta, alpha)[source]

Rotate the plot to specific angles.

Parameters:
  • psi – (float): Tilt, in degrees, of the eye origin ray away from the Z-axis.
  • theta – (float): Rotation, in degrees, of the eye origin ray about the Z-axis.
  • alpha – (float): Twist, in degrees, about the eye origin ray.
Cartesian3DView.rotate_viewer(angle, normal=(0, 0, 1))[source]

Rotate the camera or viewer about some normal vector.

This rotates the viewer about some normal using the right-hand rule by the specified angle in degrees. The rotation is performed about the position: Cartesian3DView.rotation_origin.

Parameters:
  • angle (float) – The angle in degrees to rotate.
  • normal (tuple, optional) – The direction vector \((x, y, z)\) around which the rotation will be done. (default: (0, 0, 1))

Example of rotating 2 degrees about the \(x\)-axis and around the data’s origin:

>>> plot.view.rotation_origin = (0, 0, 0)
>>> plot.view.rotate_viewer(2, (1, 0, 0))
Cartesian3DView.rotation_origin

Center of rotation for rotate_axes and rotate_viewer.

Example of rotating 30 degrees about the \(y\)-axis and around the data-position \((x, y, z) = (1, 2, 3)\):

>>> plot.view.rotation_origin = (1, 2, 3)
>>> plot.view.rotate_axes(30, (0, 1, 0))
Type:tuple
Cartesian3DView.theta

Eye origin view Theta angle in degrees.

The Theta angle is the rotation of the eye origin ray about the Z-axis:

>>> plot.view.theta = 24.3
Type:float
Cartesian3DView.translate(x=0.0, y=0.0)

Shift the data being plotted in the X and/or Y direction.

Note

The amount translated is in frame units.

Parameters:
  • x (float, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0)
  • y (float, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

Translate the view 10 percent of the frame width to the right:

>>> view.translate(x=10)

Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

>>> view.translate(x=5, y=-20)
Cartesian3DView.width

3D view width.

The 3D view width is the amount of the plot (in X-axis units) in front of the viewer that may be seen.

Warning

width cannot be set if projection is Perspective.

Example usage:

>>> plot.view.width = 1.5
Type:float
Cartesian3DView.zoom(xmin, ymin, xmax, ymax)

Zoom the view to a rectangular region of the plot.

Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (xmin, ymin) and (xmax, ymax).

Note

All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

Parameters:
  • xmin – (float) X min corner of the rectangle to be viewed.
  • ymin – (float) Y min corner of the rectangle to be viewed.
  • xmax – (float) X max corner of the rectangle to be viewed.
  • ymax – (float) Y max corner of the rectangle to be viewed.

Zoom so the rectangular region with corners at (1, 0) and (7, 9) is in view:

>>> view.zoom(1, 7, 0, 9)

XYLineView

class tecplot.plot.XYLineView(plot)[source]

Adjust the way XY Line data is displayed.

import os
import tecplot
from tecplot.constant import *

examples_dir = tecplot.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'Rainfall.dat')
dataset = tecplot.data.load_tecplot(datafile)

frame = tecplot.active_frame()
plot = frame.plot()
frame.plot_type = tecplot.constant.PlotType.XYLine

for i in range(3):
    plot.linemap(i).show = True
    plot.linemap(i).line.line_thickness = .4

y_axis = plot.axes.y_axis(0)
y_axis.title.title_mode = AxisTitleMode.UseText
y_axis.title.text = 'Rainfall (in)'
plot.view.fit_to_nice()

tecplot.export.save_png('view_line.png', 600, supersample=3)
../_images/view_line.png

Attributes

extents Viewport extents in data units.
magnification Magnification for the data being plotted.

Methods

adjust_to_nice() Shifts axes to make axis-line values “nice”
center() Center the data within the axis grid area.
fit() Fit the data being plotted within the axis grid area.
fit_data() Fit data zones or line mappings within the grid area.
fit_to_nice() Set axis range to begin/end on major axis increments.
translate([x, y]) Shift the data being plotted in the X and/or Y direction.
zoom(xmin, ymin, xmax, ymax) Zoom the view to a rectangular region of the plot.
XYLineView.adjust_to_nice()

Shifts axes to make axis-line values “nice”

Modifies the axis range to fit the minimum and maximum of the variable assigned to that axis, then snaps the major tick marks to the ends of the axis. If axis dependency is not independent, this may affect the range on another axis.

In other words, given an existing range of values RMin, RMax and an initial delta, D (such as axis ranges with grid spacing or contour levels), determine a new delta (ND) that:

  • Is 1,2, or 5 times 10 to some power that is the “best” alternative to D.
  • Produces new range min and max values that are some multiple of ND that are nearest the original RMin and RMax

Axes are shifted without changing the extents of the window.

XYLineView.center()

Center the data within the axis grid area.

Raises:TecplotSystemError – View could not be centered.
XYLineView.extents

Viewport extents in data units.

Extents are represented by the tuple: (x1, y1, x2, y2) and setting this effectively zooms the view of the data within the viewport. The values (x1, y1) and (x2, y2) are the lower-left and upper-right edges respectively and are in data units. The following example will set the bottom and left edges of the viewport to the value of -3 and the top and right edges to the value of 5:

>>> plot.view.extents = -3, -3, 5, 5
Type:tuple
XYLineView.fit()

Fit the data being plotted within the axis grid area.

Note

This also takes into consideration text and geometries that are plotted using the grid coordinate system.

XYLineView.fit_data()

Fit data zones or line mappings within the grid area.

Note

This does not take into consideration text and geometries that are plotted using the grid coordinate system.

XYLineView.fit_to_nice()

Set axis range to begin/end on major axis increments.

Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

XYLineView.magnification

Magnification for the data being plotted.

The magnification value is a decimal percent and must be greater than 0. A magnification size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

>>> view.magnification = 0.10
Type:float
XYLineView.translate(x=0.0, y=0.0)

Shift the data being plotted in the X and/or Y direction.

Note

The amount translated is in frame units.

Parameters:
  • x (float, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0)
  • y (float, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

Translate the view 10 percent of the frame width to the right:

>>> view.translate(x=10)

Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

>>> view.translate(x=5, y=-20)
XYLineView.zoom(xmin, ymin, xmax, ymax)

Zoom the view to a rectangular region of the plot.

Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (xmin, ymin) and (xmax, ymax).

Note

All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

Parameters:
  • xmin – (float) X min corner of the rectangle to be viewed.
  • ymin – (float) Y min corner of the rectangle to be viewed.
  • xmax – (float) X max corner of the rectangle to be viewed.
  • ymax – (float) Y max corner of the rectangle to be viewed.

Zoom so the rectangular region with corners at (1, 0) and (7, 9) is in view:

>>> view.zoom(1, 7, 0, 9)

PolarView

class tecplot.plot.PolarView(plot)[source]

Adjust the way polar data is displayed.

import numpy as np
import tecplot as tp
from tecplot.constant import PlotType, ThetaMode

frame = tp.active_frame()

npoints = 300
r = np.linspace(0, 2000, npoints)
theta = np.linspace(0, 10, npoints)

dataset = frame.create_dataset('Data', ['R', 'Theta'])
zone = dataset.add_ordered_zone('Zone', (300,))
zone.values('R')[:] = r
zone.values('Theta')[:] = theta

plot = frame.plot(PlotType.PolarLine)
plot.activate()

plot.axes.r_axis.max = np.max(r)
plot.axes.theta_axis.mode = ThetaMode.Radians

plot.delete_linemaps()
lmap = plot.add_linemap('Linemap', zone, dataset.variable('R'),
                        dataset.variable('Theta'))
lmap.line.line_thickness = 0.8

plot.view.fit()

tp.export.save_png('view_polar.png', 600, supersample=3)
../_images/view_polar.png

Attributes

extents Viewport extents in radial data units.
magnification Magnification for the data being plotted.

Methods

center() Center the data within the axis grid area.
fit() Fit the data being plotted within the axis grid area.
fit_data() Fit data zones or line mappings within the grid area.
translate([x, y]) Shift the data being plotted in the X and/or Y direction.
zoom(xmin, ymin, xmax, ymax) Zoom the view to a rectangular region of the plot.
PolarView.center()

Center the data within the axis grid area.

Raises:TecplotSystemError – View could not be centered.
PolarView.extents

Viewport extents in radial data units.

Extents are represented by the tuple: (x1, y1, x2, y2) and setting this effectively zooms the view of the data within the viewport. The values (x1, y1) and (x2, y2) are the lower-left and upper-right edges respectively and are in radial (data) units. The following example will set the bottom and left edges of the viewport to the radial value of -3 and the top and right edges to the radial value of 5:

>>> plot.view.extents = -3, -3, 5, 5
Type:tuple
PolarView.fit()

Fit the data being plotted within the axis grid area.

Note

This also takes into consideration text and geometries that are plotted using the grid coordinate system.

PolarView.fit_data()

Fit data zones or line mappings within the grid area.

Note

This does not take into consideration text and geometries that are plotted using the grid coordinate system.

PolarView.magnification

Magnification for the data being plotted.

The magnification value is a decimal percent and must be greater than 0. A magnification size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

>>> view.magnification = 0.10
Type:float
PolarView.translate(x=0.0, y=0.0)

Shift the data being plotted in the X and/or Y direction.

Note

The amount translated is in frame units.

Parameters:
  • x (float, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0)
  • y (float, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

Translate the view 10 percent of the frame width to the right:

>>> view.translate(x=10)

Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

>>> view.translate(x=5, y=-20)
PolarView.zoom(xmin, ymin, xmax, ymax)

Zoom the view to a rectangular region of the plot.

Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (xmin, ymin) and (xmax, ymax).

Note

All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

Parameters:
  • xmin – (float) X min corner of the rectangle to be viewed.
  • ymin – (float) Y min corner of the rectangle to be viewed.
  • xmax – (float) X max corner of the rectangle to be viewed.
  • ymax – (float) Y max corner of the rectangle to be viewed.

Zoom so the rectangular region with corners at (1, 0) and (7, 9) is in view:

>>> view.zoom(1, 7, 0, 9)

LightSource

class tecplot.plot.LightSource(plot)[source]

Three-dimensional light source style control.

The light source is a point of light infinitely far from the drawing area.

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

examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'F18.plt')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian3D)
plot.activate()

plot.light_source.direction = (0., -0.7, 0.9)
plot.light_source.intensity = 70
plot.light_source.specular_intensity = 80
plot.light_source.specular_shininess = 50

tp.export.save_png('light_source.png')
../_images/light_source.png

Attributes

background_light Percentage intensity of the omni-directional fill light.
direction \((x, y, z)\) direction of the light rays.
force_gouraud_for_contour_flood Force gouraud effects for shaded continuous flooding.
force_paneled_for_cell_flood Force paneled effects for shaded cell flooding.
intensity Percentage intensity of the light source.
specular_intensity Percentage intensity of specular highlights.
specular_shininess Percentage of shininess for specular highlights.
surface_color_contrast Percentage of contrast for surface colors.
LightSource.background_light

Percentage intensity of the omni-directional fill light.

Example usage:

>>> plot.light_source.background_light = 70.0
Type:float
LightSource.direction

\((x, y, z)\) direction of the light rays.

The direction is in the view coordinate system where \(z\) goes into the page and the origin of \((x, y)\) is in the lower left corner. The default is \((-0.2, -0.2, 0.959)\):

>>> plot.light_source.direction = (0, -0.7, 0.9)
Type:tuple
LightSource.force_gouraud_for_contour_flood

Force gouraud effects for shaded continuous flooding.

Example usage:

>>> plot.light_source.force_gouraud_for_contour_flood = True
Type:bool
LightSource.force_paneled_for_cell_flood

Force paneled effects for shaded cell flooding.

Example usage:

>>> plot.light_source.force_paneled_for_cell_flood = True
Type:bool
LightSource.intensity

Percentage intensity of the light source.

Example usage:

>>> plot.light_source.intensity = 50.0
Type:float
LightSource.specular_intensity

Percentage intensity of specular highlights.

Set this to zero to turn off specular effects:

>>> plot.light_source.specular_intensity = 0
Type:float
LightSource.specular_shininess

Percentage of shininess for specular highlights.

Example usage:

>>> plot.light_source.specular_shininess = 80.0
Type:float
LightSource.surface_color_contrast

Percentage of contrast for surface colors.

Example usage:

>>> plot.light_source.surface_color_contrast = 80.0
Type:float

Frame Linking

SketchPlotLinkingBetweenFrames

class tecplot.plot.SketchPlotLinkingBetweenFrames(frame)[source]

SketchPlot Frame style linking control.

See also

Cartesian3DPlotLinkingBetweenFrames for details on how to link style across multiple frames.

Attributes

group Group number (1-32).
link_frame_size_and_position Match frame geometry.
link_solution_time Match current solution time.
SketchPlotLinkingBetweenFrames.group

Group number (1-32).

Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 5
>>> frame_linking.link_frame_size_and_position = True
>>> frame_linking.link_solution_time = True
Type:int

Match frame geometry.

Keeps the same geometry across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_frame_size_and_position = True
Type:bool

Match current solution time.

Keeps the same active solution time across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_solution_time = True
Type:bool

Cartesian2DPlotLinkingBetweenFrames

class tecplot.plot.Cartesian2DPlotLinkingBetweenFrames(frame)[source]

Cartesian2DFieldPlot Frame style linking control.

See also

Cartesian3DPlotLinkingBetweenFrames for details on how to link style across multiple frames.

Attributes

group Group number (1-32).
link_axis_position Match axis position within the frames.
link_contour_levels Match all contour levels.
link_frame_size_and_position Match frame geometry.
link_solution_time Match current solution time.
link_value_blanking Match all value blanking style settings.
link_x_axis_range Match x-axis range.
link_y_axis_range Match y-axis range.
Cartesian2DPlotLinkingBetweenFrames.group

Group number (1-32).

Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 5
>>> frame_linking.link_frame_size_and_position = True
>>> frame_linking.link_solution_time = True
Type:int

Match axis position within the frames.

Keeps the same axis position across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_axis_position = True
Type:bool

Match all contour levels.

Keeps the same contour levels across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_contour_levels = True
Type:bool

Match frame geometry.

Keeps the same geometry across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_frame_size_and_position = True
Type:bool

Match current solution time.

Keeps the same active solution time across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_solution_time = True
Type:bool

Match all value blanking style settings.

Keeps the same value blanking across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_value_blanking = True
Type:bool

Match x-axis range.

Keeps the same x-axis range across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_x_axis_range = True
Type:bool

Match y-axis range.

Keeps the same y-axis range across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_y_axis_range = True
Type:bool

Cartesian3DPlotLinkingBetweenFrames

class tecplot.plot.Cartesian3DPlotLinkingBetweenFrames(frame)[source]

Cartesian3DFieldPlot Frame style linking control.

The following example shows how to set up a series of transparent overlay frames where each overlay shows one component of the \((U, V, W)\) vector from the Dataset. All four frames are linked to each other (group 1) so they have the same size, position and view.

import os

import tecplot as tp
from tecplot.constant import *

examples_dir = tp.session.tecplot_examples_directory()
infile = os.path.join(examples_dir, 'SimpleData', 'DuctFlow.plt')
dataset = tp.data.load_tecplot(infile)

# Create a "blank" (zeroed-out) variable to use when plotting
# only one component of the (U, V, W) vectors
tp.data.operate.execute_equation(r'{blank} = 0')

# Setup the background frame and plot style
frame = tp.active_frame()
frame.background_color = Color.Black

plot = frame.plot(PlotType.Cartesian3D)
plot.activate()

contour = plot.contour(0)
contour.variable = dataset.variable('P(N/m2)')
contour.legend.show = False

plot.use_translucency = True
plot.show_contour = True
plot.show_edge = True
plot.axes.orientation_axis.color = Color.White
plot.view.width = 2.43

fmap = plot.fieldmap(0)
fmap.edge.edge_type = EdgeType.Creases
fmap.edge.color = Color.White
fmap.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces

frame_linking = plot.linking_between_frames
frame_linking.group = 1
frame_linking.link_view = True
frame_linking.link_frame_size_and_position = True

def add_transparent_overlay(frame):
    '''Creates a transparent frame overlay with "blank" vector variables.'''
    overlay_frame = frame.page.add_frame()
    overlay_frame.transparent = True

    plot = overlay_frame.plot(frame.plot_type)
    plot.activate()
    plot.show_shade = False
    plot.axes.orientation_axis.show = False

    blank_var = overlay_frame.dataset.variable('blank')
    plot.vector.u_variable = blank_var
    plot.vector.v_variable = blank_var
    plot.vector.w_variable = blank_var
    plot.show_vector = True

    fmap = plot.fieldmap(0)
    fmap.vector.line_thickness = 0.35
    fmap.points.step = 80
    fmap.points.points_to_plot = PointsToPlot.AllCellCenters

    frame_linking = plot.linking_between_frames
    frame_linking.group = 1
    frame_linking.link_view = True
    frame_linking.link_frame_size_and_position = True

    return plot

# Create three overlays - one for each vector component we want to show
u_plot = add_transparent_overlay(frame)
u_plot.vector.u_variable = dataset.variable('U(M/S)')
u_plot.fieldmap(0).vector.color = Color.Red

v_plot = add_transparent_overlay(frame)
v_plot.vector.v_variable = dataset.variable('V(M/S)')
v_plot.fieldmap(0).vector.color = Color.Green

w_plot = add_transparent_overlay(frame)
w_plot.vector.w_variable = dataset.variable('W(M/S)')
w_plot.fieldmap(0).vector.color = Color.Blue

# Now that all plots have been linked,
# movement in one will affect all three plots.
u_plot.view.translate(x=5)

tp.export.save_png('plot3d_linking_between_frames.png')
../_images/plot3d_linking_between_frames.png

Attributes

group Group number (1-32).
link_contour_levels Match all contour levels.
link_frame_size_and_position Match frame geometry.
link_isosurface_values Match isosurface values.
link_slice_positions Match slice positions.
link_solution_time Match current solution time.
link_value_blanking Match all value blanking style settings.
link_view Match the view orientation and position.
Cartesian3DPlotLinkingBetweenFrames.group

Group number (1-32).

Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 5
>>> frame_linking.link_frame_size_and_position = True
>>> frame_linking.link_solution_time = True
Type:int

Match all contour levels.

Keeps the same contour levels across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_contour_levels = True
Type:bool

Match frame geometry.

Keeps the same geometry across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_frame_size_and_position = True
Type:bool

Match isosurface values.

Keeps the same isosurfaces across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_isosurface_values = True
Type:bool

Match slice positions.

Keeps the same slice positions across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_slice_positions = True
Type:bool

Match current solution time.

Keeps the same active solution time across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_solution_time = True
Type:bool

Match all value blanking style settings.

Keeps the same value blanking across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_value_blanking = True
Type:bool

Match the view orientation and position.

Keeps the same view across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_view = True
Type:bool

XYLinePlotLinkingBetweenFrames

class tecplot.plot.XYLinePlotLinkingBetweenFrames(frame)[source]

XYLinePlot Frame style linking control.

See also

Cartesian3DPlotLinkingBetweenFrames for details on how to link style across multiple frames.

Attributes

group Group number (1-32).
link_axis_position Match axis position within the frames.
link_frame_size_and_position Match frame geometry.
link_solution_time Match current solution time.
link_value_blanking Match all value blanking style settings.
link_x_axis_range Match x-axis range.
link_y_axis_range Match y-axis range.
XYLinePlotLinkingBetweenFrames.group

Group number (1-32).

Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 5
>>> frame_linking.link_frame_size_and_position = True
>>> frame_linking.link_solution_time = True
Type:int

Match axis position within the frames.

Keeps the same axis position across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_axis_position = True
Type:bool

Match frame geometry.

Keeps the same geometry across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_frame_size_and_position = True
Type:bool

Match current solution time.

Keeps the same active solution time across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_solution_time = True
Type:bool

Match all value blanking style settings.

Keeps the same value blanking across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_value_blanking = True
Type:bool

Match x-axis range.

Keeps the same x-axis range across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_x_axis_range = True
Type:bool

Match y-axis range.

Keeps the same y-axis range across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_y_axis_range = True
Type:bool

PolarPlotLinkingBetweenFrames

class tecplot.plot.PolarPlotLinkingBetweenFrames(frame)[source]

PolarLinePlot Frame style linking control.

See also

Cartesian3DPlotLinkingBetweenFrames for details on how to link style across multiple frames.

Attributes

group Group number (1-32).
link_frame_size_and_position Match frame geometry.
link_solution_time Match current solution time.
link_value_blanking Match all value blanking style settings.
link_view Match polar view settings.
PolarPlotLinkingBetweenFrames.group

Group number (1-32).

Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 5
>>> frame_linking.link_frame_size_and_position = True
>>> frame_linking.link_solution_time = True
Type:int

Match frame geometry.

Keeps the same geometry across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_frame_size_and_position = True
Type:bool

Match current solution time.

Keeps the same active solution time across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_solution_time = True
Type:bool

Match all value blanking style settings.

Keeps the same value blanking across all frames in the specified group. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_value_blanking = True
Type:bool

Match polar view settings.

Keeps the same view across all frames in the specified group showing a polar plot. Example usage:

>>> frame_linking = plot.linking_between_frames
>>> frame_linking.group = 1
>>> frame_linking.link_view = True
Type:bool