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.
Attributes
variable
The Variable
to be used when sizing scatter symbols.variable_index
Zero-based index of the Variable
used for size of scatter symbols.
-
Scatter.
variable
¶ The
Variable
to be used when sizing scatter symbols.The variable must belong to the
Dataset
attached to theFrame
that holds thisContourGroup
. 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’sFrame
is used, and the variable itself can be obtained through it:>>> scatter = plot.scatter >>> scatter_var = dataset.variable(scatter.variable_index) >>> scatter_var.index == scatter.variable_index True
Vector Plots¶
Vector2D¶
-
class
tecplot.plot.
Vector2D
(plot)[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) for fmap in plot.fieldmaps(): fmap.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)
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.
Type: float
(degrees)Example usage:
>>> plot.vector.arrowhead_angle = 10
-
Vector2D.
arrowhead_fraction
¶ Size of the arrowhead when sizing by fraction.
Type: float
(ratio)The
size_arrowhead_by_fraction
property must be set toTrue
for this to take effect:>>> plot.vector.size_arrowhead_by_fraction = True >>> plot.vector.arrowhead_fraction = 0.4
-
Vector2D.
arrowhead_size
¶ Size of arrowhead when sizing by frame height.
Type: float
(percent of frame height)The
size_arrowhead_by_fraction
property must be set toFalse
for this to take effect:>>> plot.vector.size_arrowhead_by_fraction = False >>> plot.vector.arrowhead_size = 4
-
Vector2D.
length
¶ Length of all vectors when not using relative sizing.
Type: float
(percent of plot height)Example usage:
>>> plot.vector.use_relative = False >>> plot.vector.length = 5
-
Vector2D.
reference_vector
¶ Vector field reference vector.
Type: ReferenceVector
Example usage:
>>> plot.vector.reference_vector.show = True
-
Vector2D.
relative_length
¶ Magnitude-varying length of the vector line.
Type: float
(grid units or cm per magnitude)When
use_relative
isTrue
, 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 ofuse_grid_units
:>>> plot.vector.use_relative = True >>> plot.vector.use_grid_units = True >>> plot.vector.relative_length = 0.003
-
Vector2D.
size_arrowhead_by_fraction
¶ Base arrowhead size on length of vector line.
Type: bool
Example usage:
>>> plot.vector.size_arrowhead_by_fraction = True >>> plot.vector.relative_length = 0.1
-
Vector2D.
u_variable
¶ \(U\)-component
Variable
of the plotted vectors.Type: Variable
Vectors are plotted as \((u,v,w)\). Example usage:
>>> plot.vector.u_variable = dataset.variable('Pressure X')
-
Vector2D.
u_variable_index
¶ \(U\)-component
Variable
index of the plotted vectors.Type: integer
(Zero-based index)Vectors are plotted as \((u,v,w)\). Example usage:
>>> plot.vector.u_variable_index = 3
-
Vector2D.
use_grid_units
¶ Use grid-units when determining the relative length.
Type: bool
This takes effect only if
use_relative
isTrue
. IfFalse
,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
-
Vector2D.
use_relative
¶ Use relative sizing for vector lines.
Type: bool
This determines whether
length
orrelative_length
are used to size the arrow lines. Example usage:>>> plot.vector.use_relative = False >>> plot.vector.relative_length = 0.5
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.
Type: float
(degrees)Example usage:
>>> plot.vector.arrowhead_angle = 10
-
Vector3D.
arrowhead_fraction
¶ Size of the arrowhead when sizing by fraction.
Type: float
(ratio)The
size_arrowhead_by_fraction
property must be set toTrue
for this to take effect:>>> plot.vector.size_arrowhead_by_fraction = True >>> plot.vector.arrowhead_fraction = 0.4
-
Vector3D.
arrowhead_size
¶ Size of arrowhead when sizing by frame height.
Type: float
(percent of frame height)The
size_arrowhead_by_fraction
property must be set toFalse
for this to take effect:>>> plot.vector.size_arrowhead_by_fraction = False >>> plot.vector.arrowhead_size = 4
-
Vector3D.
length
¶ Length of all vectors when not using relative sizing.
Type: float
(percent of plot height)Example usage:
>>> plot.vector.use_relative = False >>> plot.vector.length = 5
-
Vector3D.
reference_vector
¶ Vector field reference vector.
Type: ReferenceVector
Example usage:
>>> plot.vector.reference_vector.show = True
-
Vector3D.
relative_length
¶ Magnitude-varying length of the vector line.
Type: float
(grid units or cm per magnitude)When
use_relative
isTrue
, 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 ofuse_grid_units
:>>> plot.vector.use_relative = True >>> plot.vector.use_grid_units = True >>> plot.vector.relative_length = 0.003
-
Vector3D.
size_arrowhead_by_fraction
¶ Base arrowhead size on length of vector line.
Type: bool
Example usage:
>>> plot.vector.size_arrowhead_by_fraction = True >>> plot.vector.relative_length = 0.1
-
Vector3D.
u_variable
¶ \(U\)-component
Variable
of the plotted vectors.Type: Variable
Vectors are plotted as \((u,v,w)\). Example usage:
>>> plot.vector.u_variable = dataset.variable('Pressure X')
-
Vector3D.
u_variable_index
¶ \(U\)-component
Variable
index of the plotted vectors.Type: integer
(Zero-based index)Vectors are plotted as \((u,v,w)\). Example usage:
>>> plot.vector.u_variable_index = 3
-
Vector3D.
use_grid_units
¶ Use grid-units when determining the relative length.
Type: bool
This takes effect only if
use_relative
isTrue
. IfFalse
,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
-
Vector3D.
use_relative
¶ Use relative sizing for vector lines.
Type: bool
This determines whether
length
orrelative_length
are used to size the arrow lines. Example usage:>>> plot.vector.use_relative = False >>> plot.vector.relative_length = 0.5
-
Vector3D.
v_variable
¶ \(V\)-component
Variable
of the plotted vectors.Type: Variable
Vectors are plotted as \((u,v,w)\). Example usage:
>>> plot.vector.v_variable = dataset.variable('Pressure Y')
-
Vector3D.
v_variable_index
¶ \(V\)-component
Variable
index of the plotted vectors.Type: integer
(Zero-based index)Vectors are plotted as \((u,v,w)\). Example usage:
>>> plot.vector.v_variable_index = 4
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)
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. show
Draw the reference vector.
-
ReferenceVector.
angle
¶ Degrees counter-clockwise to rotate the reference vector.
Type: float
(degrees)Example usage:
>>> ref_vector = plot.vector.reference_vector >>> ref_vector.show = True >>> ref_vector.angle = 90 # vertical, up
-
ReferenceVector.
color
¶ Color
of the reference vector.Type: Color
Example usage:
>>> from tecplot.constant import Color >>> ref_vector = plot.vector.reference_vector >>> ref_vector.show = True >>> ref_vector.color = Color.Red
-
ReferenceVector.
label
¶ reference vector label style control.
Type: ReferenceVectorLabel
Example usage:
>>> ref_vector = plot.vector.reference_vector >>> ref_vector.show = True >>> ref_vector.label.show = True
-
ReferenceVector.
line_thickness
¶ reference vector line thickness.
Type: float
(percentage of frame height)Example usage:
>>> ref_vector = plot.vector.reference_vector >>> ref_vector.show = True >>> ref_vector.line_thickness = 0.3
-
ReferenceVector.
magnitude
¶ Length of the reference vector.
Type: float
(data units)Example usage:
>>> ref_vector = plot.vector.reference_vector >>> ref_vector.show = True >>> ref_vector.magnitude = 2
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
Font
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.Type: Color
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
-
ReferenceVectorLabel.
font
¶ Font
of the reference vector label.Type: Font
Example usage:
>>> ref_vector = plot.vector.reference_vector >>> ref_vector.show = True >>> ref_vector.label.show = True >>> ref_vector.label.font.size = 6
-
ReferenceVectorLabel.
format
¶ Number formatting control for the reference vector label.
Type: LabelFormat
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
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.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)
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. include_cutoff_levels
Show color bands and labels for levels affected by color cutoff. 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 of the legend as a percentage of frame width/height. row_spacing
Spacing between rows in the legend. show
Show or hide the legend. 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.
Type: AnchorAlignment
Example usage:
>>> from tecplot.constant import PlotType, AnchorAlignment >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.anchor_alignment = AnchorAlignment.BottomCenter
-
ContourLegend.
auto_resize
¶ Automatically skip some levels to create a reasonably sized legend.
Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.auto_resize = True
-
ContourLegend.
box
¶ Legend box attributes.
Type: text.TextBox
Example usage:
>>> from tecplot.constant import PlotType, Color >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.box.color = Color.Blue
-
ContourLegend.
header_font
¶ Font used to display the name of the contour variable.
Note
The font
size_units
property may only be set toUnits.Frame
orUnits.Point
.Type: text.Font
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.header_font.italic = True
-
ContourLegend.
include_cutoff_levels
¶ Show color bands and labels for levels affected by color cutoff.
Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.include_cutoff_levels = True
-
ContourLegend.
label_format
¶ Number formatting for labels along the legend.
Type: LabelFormat
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
-
ContourLegend.
label_increment
¶ Spacing between labels along the contour legend.
Type: float
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
isContLegendLabelLocation.Increment
, labels are incremented by this value. For example, alabel_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 toContLegendLabelLocation.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
-
ContourLegend.
label_location
¶ Placement of labels on the legend.
Type: ContLegendLabelLocation
If you have selected
ColorMapDistribution.Continuous
for the contour colormap filter distribution, you have three options for placement of labels on the legend:ContLegendLabelLocation.ContourLevels
- This option places one label for each contour level. See Contour Levels and Color.ContLegendLabelLocation.Increment
- Setlabel_increment
to the increment value.ContLegendLabelLocation.ColorMapDivisions
- Places one label for each control point on the color map.
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
-
ContourLegend.
label_step
¶ Step size between labels along the legend.
Type: integer
This is an alias for
ContourLegend.contour.labels.step
:>>> contour = frame.plot().contour(0) >>> contour.legend.label_step = 3 >>> print(contour.labels.step) 3
-
ContourLegend.
number_font
¶ Font used to display numbers in the legend.
Note
The font
size_units
property may only be set toUnits.Frame
orUnits.Point
.Type: text.Font
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.number_font.italic = True
-
ContourLegend.
overlay_bar_grid
¶ Draw a line around each band in the legend color bar.
Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.overlay_bar_grid = False
-
ContourLegend.
position
¶ Position of the legend 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.
Type: 2- tuple
offloats
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
-
ContourLegend.
row_spacing
¶ Spacing between rows in the legend.
Type: float
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.row_spacing = 1.5
-
ContourLegend.
show
¶ Show or hide the legend.
Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.show = True
-
ContourLegend.
show_header
¶ Show the name of the contour variable in the legend.
Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.show_header = True
-
ContourLegend.
text_color
¶ Color of legend text.
Type: Color
Example usage:
>>> from tecplot.constant import PlotType, Color >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.text_color = Color.Blue
-
ContourLegend.
vertical
¶ Orientation of the legend.
When set to
True
, the legend is vertical. When set toFalse
, the legend is horizontal.Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend >>> legend.vertical = False # Show horizontal legend
LineLegend¶
-
class
tecplot.legend.
LineLegend
(plot, *svargs)[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 theposition
attribute. By default, all mappings are shown, but Tecplot 360 removes redundant entries.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)
Attributes
anchor_alignment
Anchor location of the legend. box
Legend box attributes. font
Legend font attributes. position
Position of the legend 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.
Type: AnchorAlignment
Example usage:
>>> from tecplot.constant import AnchorAlignment, PlotType >>> legend = frame.plot(PlotType.XYLine).legend >>> legend.anchor_alignment = AnchorAlignment.BottomCenter
-
LineLegend.
box
¶ Legend box attributes.
Type: text.TextBox
Example usage:
>>> from tecplot.constant import PlotType, Color >>> plot = frame.plot(PlotType.XYLine) >>> plot.legend.box.color = Color.Blue
-
LineLegend.
font
¶ Legend font attributes.
Note
The font
size_units
property may only be set toUnits.Frame
orUnits.Point
.Type: text.Font
Example usage:
>>> from tecplot.constant import PlotType >>> plot = frame.plot(PlotType.XYLine) >>> plot.legend.font.italic = True
-
LineLegend.
position
¶ Position of the legend 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.
Type: 2- tuple
offloats
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.XYLine).legend >>> legend.position = (.1, .3) >>> pos = legend.position >>> pos.x # == position[0] .1 >>> pos.y # == position[1] .3
-
LineLegend.
row_spacing
¶ Spacing between rows in the legend.
Type: float
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.XYLine).legend >>> legend.row_spacing = 1.5
-
LineLegend.
show
¶ Show or hide the legend.
Type: boolean
Example usage:
>>> from tecplot.constant import PlotType >>> legend = frame.plot(PlotType.XYLine).legend >>> legend.show = True
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' # save image to file tp.export.save_png('contour_magma.png', 600, supersample=3)
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 thisContourGroup
.levels
ContourLevels
holding the list of contour levels.lines
ContourLines
object controlling contour line style.variable
The Variable
being contoured.variable_index
Zero-based index of the Variable
being contoured.
-
ContourGroup.
color_cutoff
¶ ContourColorCutoff
object controlling color cutoff min/max.Type: ContourColorCutoff
>>> cutoff = plot.contour(0).color_cutoff >>> cutoff.min = 3.14
-
ContourGroup.
colormap_filter
¶ ContourColormapFilter
object controlling colormap style properties.Type: ContourColormapFilter
>>> plot.contour(0).colormap_filter.reverse = True
-
ContourGroup.
colormap_name
¶ The name of the colormap (
str
) to be used.Type: string
Example:
>>> plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'
-
ContourGroup.
default_num_levels
¶ Default target number (
int
) of levels used when resetting.Type: integer
Example:
>>> plot.contour(0).default_num_levels = 20
-
ContourGroup.
labels
¶ ContourLabels
object controlling contour line labels.Type: ContourLabels
Lines must be turned on through the associated fieldmap object for style changes to be meaningful:
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True
-
ContourGroup.
legend
¶ ContourLegend
associated with thisContourGroup
.Type: ContourLegend
This object controls the attributes of the contour legend associated with this
ContourGroup
.Example usage:
>>> plot.contour(0).legend.show = True
-
ContourGroup.
levels
¶ ContourLevels
holding the list of contour levels.Type: ContourLevels
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)
-
ContourGroup.
lines
¶ ContourLines
object controlling contour line style.Type: ContourLines
Lines must be turned on through the associated fieldmap object for style changes to be meaningful:
>>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).lines.mode = ContourLineMode.DashNegative
-
ContourGroup.
variable
¶ The
Variable
being contoured.The variable must belong to the
Dataset
attached to theFrame
that holds thisContourGroup
. 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’sFrame
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 tp.export.save_png('contour_color_cutoff.png',600)
Attributes
include_max
Use the maximum cutoff value. include_min
Use the minimum cutoff value. inverted
Cuts values outside the range instead of inside. max
The maximum cutoff value. min
The minimum cutoff value.
-
ContourColorCutoff.
include_max
¶ Use the maximum cutoff value.
Type: boolean
Thie example turns off the maximum cutoff:
>>> plot.contour(0).color_cutoff.include_max = False
-
ContourColorCutoff.
include_min
¶ Use the minimum cutoff value.
Type: boolean
Example usage:
>>> plot.contour(0).color_cutoff.include_min = True >>> plot.contour(0).color_cutoff.min = 3.14
-
ContourColorCutoff.
inverted
¶ Cuts values outside the range instead of inside.
Type: bool
>>> plot.contour(0).color_cutoff.inverted = True
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 # save image to file tp.export.save_png('contour_filtered.png', 600, supersample=3)
Attributes
continuous_max
Upper limit for continuous colormap flooding. continuous_min
Lower limit for continuous colormap flooding. distribution
Rendering style of the colormap. fast_continuous_flood
Use a fast approximation to continuously flood the colormap. num_cycles
Number of cycles to repeat the colormap. reversed
Reverse the colormap. show_overrides
Enable the colormap overrides in this contour group. zebra_shade
Returns a ContourColormapZebraShade
filtering object.Methods
override
(index)Returns a ContourColormapOverride
object by index.
-
ContourColormapFilter.
continuous_max
¶ Upper limit for continuous colormap flooding.
Type: float
Example set the limits to the (min, max) of a variable in a specific zone:
>>> from tecplot.constant import ColorMapDistribution >>> cmap_filter = plot.contour(0).colormap_filter >>> cmap_filter.distribution = ColorMapDistribution.Continuous >>> pressure = dataset.variable('Pressure').values('My Zone') >>> cmap_filter.continuous_min = pressure.min() >>> cmap_filter.continuous_max = pressure.max()
-
ContourColormapFilter.
continuous_min
¶ Lower limit for continuous colormap flooding.
Type: float
Example usage:
>>> from tecplot.constant import ColorMapDistribution >>> cmap_filter = plot.contour(0).colormap_filter >>> cmap_filter.distribution = ColorMapDistribution.Continuous >>> cmap_filter.continuous_min = 3.1415
-
ContourColormapFilter.
distribution
¶ Rendering style of the colormap.
Type: ColorMapDistribution
Possible values:
Banded
- A solid color is assigned for all values within the band between two levels.
Continuous
- The color distribution assigns linearly varying colors to all multi-colored objects or contour flooded regions.
Example:
>>> from tecplot.constant import ColorMapDistribution >>> cmap_filter = plot.contour(0).colormap_filter >>> cmap_filter.distribution = ColorMapDistribution.Banded
-
ContourColormapFilter.
fast_continuous_flood
¶ Use a fast approximation to continuously flood the colormap.
Type: bool
Causes each cell to be flooded using interpolation between the color values at each node. When the transition from a color at one node to another node crosses over the boundary between control points in the color spectrum, fast flooding may produce colors not in the spectrum. Setting this to
False
is slower, but more accurate:>>> cmap_filter = plot.contour(0).colormap_filter >>> cmap_filter.fast_continuous_flood = True
-
ContourColormapFilter.
num_cycles
¶ Number of cycles to repeat the colormap.
Type: integer
>>> plot.contour(0).colormap_filter.num_cycles = 3
-
ContourColormapFilter.
override
(index)[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.
Type: bool
>>> plot.contour(0).colormap_filter.reversed = True
-
ContourColormapFilter.
show_overrides
¶ Enable the colormap overrides in this contour group.
Type: boolean
The overrides themselves must be turned on as well for this to have an effect on the resulting plot:
>>> contour = plot.contour(0) >>> cmap_filter = contour.colormap_filter >>> cmap_filter.show_overrides = True >>> cmap_filter.override(0).show = True
-
ContourColormapFilter.
zebra_shade
¶ Returns a
ContourColormapZebraShade
filtering object.Type: ContourColormapZebraShade
Example usage:
>>> zebra = plot.contour(0).colormap_filter.zebra_shade >>> zebra.show = True
ContourColormapOverride¶
-
class
tecplot.plot.
ContourColormapOverride
(index, colormap_filter)[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)
Attributes
color
Color which will override the colormap. end_level
Last level to override. show
Include this colormap override when filter is shown. start_level
First level to override.
-
ContourColormapOverride.
color
¶ Color which will override the colormap.
Type: Color
Example usage:
>>> from tecplot.constant import Color >>> colormap_filter = plot.contour(0).colormap_filter >>> cmap_override = colormap_filter.override(0) >>> cmap_override.color = Color.Blue
-
ContourColormapOverride.
end_level
¶ Last level to override.
Type: integer
Example usage:
>>> colormap_filter = plot.contour(0).colormap_filter >>> cmap_override = colormap_filter.override(0) >>> cmap_override.end_level = 2
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 tp.export.save_png('contour_zebra.png', 600, supersample=3)
Attributes
color
Color of the zebra shading. show
Show zebra shading in this ContourGroup
.transparent
Set the the zebra bands to be transparent.
-
ContourColormapZebraShade.
color
¶ Color of the zebra shading.
Type: Color
Example usage:
>>> from tecplot.constant import Color >>> filter = plot.contour(0).colormap_filter >>> zebra = filter.zebra_shade >>> zebra.show = True >>> zebra.color = Color.Blue
-
ContourColormapZebraShade.
show
¶ Show zebra shading in this
ContourGroup
.Type: boolean
Example usage:
>>> cmap_filter = plot.contour(0).colormap_filter >>> cmap_filter.zebra_shade.show = True
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 tp.export.save_png('contour_labels.png', 600, supersample=3)
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.
Type: bool
This causes the flow of the text to be aligned with the contour lines. Otherwise, the labels are aligned with the frame:
>>> from tecplot.constant import ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.auto_align = False
-
ContourLabels.
auto_generate
¶ Automatically generate labels along contour lines.
Type: bool
This causes a new set of contour labels to be created at each redraw:
>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.auto_generate = True
-
ContourLabels.
background_color
¶ Background fill color behind the text labels.
Type: Color
The
filled
attribute must be set toTrue
:>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.background_color = Color.Blue
-
ContourLabels.
color
¶ Text color of the labels.
Type: Color
Example:
>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.color Color.Blue
-
ContourLabels.
filled
¶ Fill the background area behind the text labels.
Type: boolean
The background can be filled with a color or disabled (made transparent) by setting this property to
False
:>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.filled = True >>> plot.contour(0).labels.background_color = Color.Blue >>> plot.contour(1).labels.show = True >>> plot.contour(1).labels.filled = False
-
ContourLabels.
font
¶ text.Font
used to show the labels.Type: text.Font
Example:
>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.font.size = 3.5
-
ContourLabels.
format
¶ Number formatting for contour labels.
Type: LabelFormat
Example usage:
>>> from tecplot.constant import NumberFormat >>> contour = plot.contour(0) >>> contour.labels.format.format_type = NumberFormat.Integer
-
ContourLabels.
label_by_level
¶ Use the contour numbers as the label instead of the data value.
Type: bool
Contour level numbers start from one when drawn. Example usage:
>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.label_by_level = True
-
ContourLabels.
margin
¶ Spacing around the text and the filled background area.
Type: float
in percentage of the text height.Contour numbers start from one when drawn. Example usage:
>>> from tecplot.constant import Color, ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.background_color = Color.Yellow >>> plot.contour(0).labels.margin = 20
-
ContourLabels.
show
¶ Show the contour line labels.
Type: bool
Contour lines must be on for this to have any effect:
>>> from tecplot.constant import ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True
-
ContourLabels.
spacing
¶ Spacing between labels along the contour lines.
Type: float
This is the distance between each label along each contour line in percentage of the frame height:
>>> from tecplot.constant import ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.spacing = 20
-
ContourLabels.
step
¶ Number of contour lines from one label to the next.
Type: int
This is the number of contour bands between lines that are to be labeled:
>>> from tecplot.constant import ContourType >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines >>> plot.contour(0).labels.show = True >>> plot.contour(0).labels.step = 4
ContourLevels¶
-
class
tecplot.plot.
ContourLevels
(contour)[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
’sDataset
. These values can be manipulated with theContourLevels
object obtained via theContourGroup.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)
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 theContourGroup
.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: 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 ( integer
) – Number of levels. (default: 10)This will reset the contour levels to a set of evenly distributed values spanning the entire range of the currently selected contouring variable:
>>> plot.contour(0).levels.reset(30)
-
ContourLevels.
reset_levels
(*values)[source]¶ Resets the levels to the values specified.
Parameters: *values ( floats
) – The level values to be added to theContourGroup
.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 ( integer
) – Approximate number of levels to create. (default: 10)This will reset the contour levels to a set of evenly distributed values that approximately spans the range of the currently selected contouring variable. Exact range and number of levels will be adjusted to make the contour levels have “nice” values:
>>> plot.contour(0).levels.reset_to_nice(50)
ContourLines¶
-
class
tecplot.plot.
ContourLines
(contour)[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 tp.export.save_png('contour_lines.png', 600, supersample=3)
Attributes
mode
Type of lines to draw on the plot ( ContourLineMode
).pattern_length
Length of dashed lines and space between dashes ( float
).step
Number of lines to step for SkipToSolid
line mode (int
).
-
ContourLines.
mode
¶ Type of lines to draw on the plot (
ContourLineMode
).Type: ContourLineMode
Possible values:
UseZoneLineType
- For each zone, draw the contour lines using the line pattern
and pattern length specified in the
FieldmapContour
for the parent Fieldmaps. If you are adding contour lines to polyhedral zones, the patterns will not be continuous from one cell to the next and the pattern will restart at every cell boundary. SkipToSolid
- Draw dashed lines between each pair of solid lines which are
spaced out by the
ContourLines.step
property. This will override any line pattern or thickness setting in the parent Fieldmaps’sFieldmapContour
object. DashNegative
- Draw lines of positive contour variable value as solid lines
and lines of negative contour variable value as dashed lines.
This will override any line pattern or thickness setting in the
parent Fieldmaps’s
FieldmapContour
object.
Example:
>>> from tecplot.constant import ContourLineMode >>> lines = plot.contour(0).lines >>> lines.mode = ContourLineMode.DashNegative
-
ContourLines.
pattern_length
¶ Length of dashed lines and space between dashes (
float
).Type: float
The length is in percentage of the frame height:
>>> from tecplot.constant import ContourLineMode >>> lines = plot.contour(0).lines >>> lines.mode = ContourLineMode.SkipToSolid >>> lines.step = 5 >>> lines.pattern_length = 5
-
ContourLines.
step
¶ Number of lines to step for
SkipToSolid
line mode (int
).Type: int
Example:
>>> from tecplot.constant import ContourLineMode >>> lines = plot.contour(0).lines >>> lines.mode = ContourLineMode.SkipToSolid >>> lines.step = 5
Isosurface¶
IsosurfaceGroup¶
-
class
tecplot.plot.
IsosurfaceGroup
(index, plot)[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)
Attributes
contour
Contour attributes for this isosurface group. definition_contour_group
Contour group from which isosurfaces are based. definition_contour_group_index
Contour group index from which isosurfaces are based. effects
Settings for isosurface effects. isosurface_selection
Select where to draw isosurfaces. isosurface_values
Query/Assign up to 3 values at which to draw isosurfaces. mesh
Mesh attributes for this isosurface group. obey_source_zone_blanking
Obey source zone blanking. shade
Shade attributes for this isosurface group. show
Show isosurfaces for this isosurface group. surface_generation_method
Determines how the surface is generated. vector
Vector attributes for this isosurface group.
-
IsosurfaceGroup.
contour
¶ Contour attributes for this isosurface group.
Type: IsosurfaceContour
Example usage:
>>> plot.isosurface(0).show = True >>> plot.isosurface(0).contour.show = True
-
IsosurfaceGroup.
definition_contour_group
¶ Contour group from which isosurfaces are based.
Type: ContourGroup
Example usage:
>>> group = plot.contour(1) >>> plot.isosurface(0).definition_contour_group = group
-
IsosurfaceGroup.
definition_contour_group_index
¶ Contour group index from which isosurfaces are based.
Type: Index
Contour group settings can be changed from
plot.ContourGroup
.Example usage:
>>> plot.isosurface(0).show = True >>> plot.isosurface(0).definition_contour_group_index = 1
-
IsosurfaceGroup.
effects
¶ Settings for isosurface effects.
Type: IsosurfaceEffects
Example usage:
>>> plot.isosurface(0).show = True >>> plot.isosurface(0).effects.use_translucency = True
-
IsosurfaceGroup.
isosurface_selection
¶ Select where to draw isosurfaces.
Type: IsoSurfaceSelection
- Iso-surfaces may be drawn at:
- Contour group levels
- At specified value(s) - Specify up to three values of the contour variable at which to draw isosurfaces.
- To draw isosurfaces at contour group lines:
- Set
isosurface_selection
toIsoSurfaceSelection.AllContourLevels
. - Optional: Change
tecplot.plot.ContourLevels
- Set
- To draw isosurfaces at up to 3 values:
- Set
isosurface_selection
to one of the following:IsoSurfaceSelection.OneSpecificValue
IsoSurfaceSelection.TwoSpecificValues
IsoSurfaceSelection.ThreeSpecificValues
- Set
isosurface_values
to a 1, 2, or 3tuple
offloats
- Set
See also
isosurface_values
.Example usage:
>>> plot.isosurface(0).show = True >>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.TwoSpecificValues >>> plot.isosurface(0).isosurface_values = (.3, .8)
-
IsosurfaceGroup.
isosurface_values
¶ Query/Assign up to 3 values at which to draw isosurfaces.
Type: 1, 2, or 3- tuple
offloats
, or scalarfloat
- To draw isosurfaces at up to 3 values:
- Set
isosurface_selection
to one of the following:IsoSurfaceSelection.OneSpecificValue
IsoSurfaceSelection.TwoSpecificValues
IsoSurfaceSelection.ThreeSpecificValues
- Set
isosurface_values
to a 1, 2, or 3tuple
orlist
offloats
, or set to a scalarfloat
to assign the first value only.
- Set
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
-
IsosurfaceGroup.
mesh
¶ Mesh attributes for this isosurface group.
Type: IsosurfaceMesh
Example usage:
>>> plot.isosurface(0).show = True >>> plot.isosurface(0).mesh.show = True
-
IsosurfaceGroup.
obey_source_zone_blanking
¶ Obey source zone blanking.
Type: Example usage:
>>> plot.isosurface(0).show = True >>> plot.isosurface(0).obey_source_zone_blanking = True
-
IsosurfaceGroup.
shade
¶ Shade attributes for this isosurface group.
Type: IsosurfaceShade
Example usage:
>>> plot.isosurface(0).shade.show = True
-
IsosurfaceGroup.
show
¶ Show isosurfaces for this isosurface group.
Type: bool
Example usage:
>>> plot.isosurface(1).show = True
-
IsosurfaceGroup.
surface_generation_method
¶ Determines how the surface is generated.
Type: SurfaceGenerationMethod
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
-
IsosurfaceGroup.
vector
¶ Vector attributes for this isosurface group.
Type: IsosurfaceVector
Example usage:
>>> plot.isosurface(0).vector.show = True
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 tp.export.save_png('isosurface_contour.png', 600, supersample=3)
Attributes
contour_type
Contour display type. flood_contour_group
Contour group to use for flooding. flood_contour_group_index
The Index
of theContourGroup
to use for flooding.line_color
Color
of contour lines.line_contour_group
The contour group to use for contour lines. line_contour_group_index
The Index
of theContourGroup
to use for contour lines.line_thickness
Contour line thickness as a percentage of frame width. show
Show contours on isosurfaces. use_lighting_effect
Enable lighting effect.
-
IsosurfaceContour.
contour_type
¶ Contour display type.
Type: ContourType
ContourType.Lines
- Draws lines of constant value of the specified contour variable.ContourType.Flood
- Floods regions between contour lines with colors from a color map. The distribution of colors used for contour flooding may be banded or continuous. When banded distribution is used for flooding, a solid color is used between contour levels. If continuous color distribution is used, the flood color will vary linearly in all directions.ContourType.Overlay
- Combines the above two options.ContourType.AverageCell
- Floods cells or finite elements with colors from a color map according to the average value of the contour variable over the data points bounding the cell. If the variables are located at the nodes, the values at the nodes are averaged. If the variables are cell-centered, the cell-centered values are averaged to the nodes and the nodes are then averaged.ContourType.PrimaryValue
- Floods cells or finite elements with colors from a color map according to the primary value of the contour variable for each cell. If the variable is cell centered, the primary value is the value assigned to the cell. If the variable is node located, the primary value comes from the lowest index node in the cell. If the variables are located at the nodes, the value of the lowest indexed node in the cell is used. When plotting IJK-ordered, FE-brick or FE-tetra cells, each face is considered independently of the other faces. You may get different colors on the different faces of the same cell. If the variables are cell-centered, the cell-centered value is used directly. When plotting I, J, or K-planes in 3D, the cell on the positive side of the plane supplies the value, except in the case of the last plane, where the cell on the negative side supplies the value.
Example usage:
>>> plot.isosurface(0).contour.show = True >>> plot.isosurface(0).contour.contour_type = ContourType.Flood
-
IsosurfaceContour.
flood_contour_group
¶ Contour group to use for flooding.
Type: ContourGroup
Changing style on this
ContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> group = plot.contour(1) >>> contour = plot.isosurface(1).contour >>> contour.flood_contour_group = group
-
IsosurfaceContour.
flood_contour_group_index
¶ The
Index
of theContourGroup
to use for flooding.Type: Index
(zero-based index)This property sets and gets, by
Index
, theContourGroup
used for flooding. Changing style on thisContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> contour = plot.isosurface(0).contour >>> contour.flood_contour_group_index = 1
-
IsosurfaceContour.
line_color
¶ Color
of contour lines.Type: Color
orContourGroup
Contour lines can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property.Example usage:
>>> plot.show_isosurfaces = True >>> plot.isosurface(0).contour.line_color = Color.Blue
-
IsosurfaceContour.
line_contour_group
¶ The contour group to use for contour lines.
Type: ContourGroup
Note that changing style on this
ContourGroup
will affect all other fieldmaps on the sameFrame
that use it.Example usage:
>>> contour = plot.isosurface(0).contour >>> group = plot.contour(1) >>> contour.line_contour_group = group
-
IsosurfaceContour.
line_contour_group_index
¶ The
Index
of theContourGroup
to use for contour lines.Type: integer
(zero-based index)This property sets and gets, by
Index
, theContourGroup
used for line placement. Although all properties of theContourGroup
can be manipulated through this object, many of them (i.e., color) will not affect the lines unless theFieldmapContour.line_color
is set to the sameContourGroup
. Note that changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it.Example usage:
>>> contour = plot.isosurface(0).contour >>> contour.line_contour_group_index = 2
-
IsosurfaceContour.
line_thickness
¶ Contour line thickness as a percentage of frame width.
Suggested values are one of: .02, .1, .4, .8, 1.5
Type: float
Example usage:
>>> plot.show_isosurfaces = True >>> plot.isosurface(0).contour.line_thickness = .4
-
IsosurfaceContour.
show
¶ Show contours on isosurfaces.
Type: boolean
Example usage:
>>> plot.isosurface(0).contour.show = True
-
IsosurfaceContour.
use_lighting_effect
¶ Enable lighting effect.
Type: Boolean
When set to
True
, the lighting effect may be selected with theIsosurfaceEffects.lighting_effect
attribute.Example usage:
>>> plot.isosurface(0).contour.use_lighting_effect = True >>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled
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)
Attributes
lighting_effect
Lighting effect. surface_translucency
Surface translucency of the isosurface group. use_translucency
Enable surface translucency for this isosurface group.
-
IsosurfaceEffects.
lighting_effect
¶ Lighting effect.
Type: LightingEffect
Isosurface lighting effects must be enabled by setting
IsosurfaceShade.use_lighting_effect
toTrue
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 toPaneled
shading in this case.
Example usage:
>>> plot.isosurface(0).shade.use_lighting_effect = True >>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled
-
IsosurfaceEffects.
surface_translucency
¶ Surface translucency of the isosurface group.
Type: integer
Iso-surface surface translucency must be enabled by setting
IsosurfaceEffects.use_translucency
=True
when setting this value.Valid translucency values range from one (opaque) to 99 (translucent).
Example usage:
>>> plot.isosurface(0).effects.use_translucency = True >>> plot.isosurface(0).effects.surface_translucency = 20
-
IsosurfaceEffects.
use_translucency
¶ Enable surface translucency for this isosurface group.
Type: boolean
The surface translucency value can be changed by setting
IsosurfaceEffects.surface_translucency
.Example usage:
>>> plot.isosurface(0).effects.use_translucency = True >>> plot.isosurface(0).effects.surface_translucency = 20
IsosurfaceMesh¶
-
class
tecplot.plot.
IsosurfaceMesh
(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)
Attributes
color
Isosurface mesh color. line_thickness
Isosurface mesh line thickness. show
Display the mesh on isosurfaces.
-
IsosurfaceMesh.
color
¶ Isosurface mesh color.
Type: Color
orContourGroup
Iso-surface mesh lines can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property.Example usage:
>>> plot.isosurface(0).mesh.show = True >>> plot.isosurface(0).mesh.color = Color.Blue
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)
Attributes
color
Shade color. show
Show shade attributes. use_lighting_effect
Enable lighting effect.
-
IsosurfaceShade.
color
¶ Shade color.
Type: Color
Color.MultiColor
andColor.RGBColor
coloring are not available. Use flooded contours for multi-color or RGB floodingExample usage:
>>> plot.isosurface(0).shade.show = True >>> plot.isosurface(0).shade.color = Color.Blue
-
IsosurfaceShade.
show
¶ Show shade attributes.
Type: boolean
Example usage:
>>> plot.isosurface(0).shade.show = True
-
IsosurfaceShade.
use_lighting_effect
¶ Enable lighting effect.
Type: Boolean
When set to
True
, the lighting effect may be selected with theIsosurfaceEffects.lighting_effect
attribute.Example usage:
>>> plot.isosurface(0).shade.use_lighting_effect = True >>> plot.isosurface(0).effects.lighting_effect = LightingEffect.Paneled
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)
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.
Type: ArrowheadStyle
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.
-
IsosurfaceVector.
color
¶ Isosurface vector color.
Type: Color
orContourGroup
Iso-surface vectors can be a solid color or be colored by a
ContourGroup
as obtained through theplot.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.
-
IsosurfaceVector.
is_tangent
¶ Use tangent vectors for isosurfaces.
Type: boolean
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.
-
IsosurfaceVector.
line_thickness
¶ Vector line thickness as a percentage of the frame height.
Type: float
Typical values are .02, .1, .4, .8, 1.5
Example usage:
>>> plot.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.
-
IsosurfaceVector.
show
¶ Show vectors on isosurfaces.
Type: boolean
Example usage:
>>> plot.isosurface(0).vector.show = True
New in version 2017.3: Isosurface vectors requires Tecplot 360 2017 R3 or later.
-
IsosurfaceVector.
vector_type
¶ Type of vector for isosurfaces.
Type: VectorType
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.
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
. Slicecontour
, 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 tp.export.save_png('slice_example.png', 600, supersample=3)
Up to eight different slice groups can be set. Each slice group can use different slice planes or different ranges for the same slice plane.
>>> slice_3 = plot.slice(3) >>> slice_3.contour.show = True
Attributes
arbitrary_normal
Normal for arbitrary slices. contour
Contour attributes for the slice group. edge
Edge attributes for this slice group. effects
Effects attributes for this slice group. end_position
Position of the end slice. mesh
Mesh attributes for this slice group. num_intermediate_slices
Number of intermediate slicing planes. obey_source_zone_blanking
Obey source zone blanking. orientation
Select which plane the slice is drawn on (X,Y,Z, I, J, K or arbitrary). origin
Origin of the slice. shade
Shade attributes for this slice group. show
Show slices for this slice group. show_intermediate_slices
Show intermediate slices. show_primary_slice
Include the primary slice (first slice placed) in the Plot. show_start_and_end_slices
Include start and end slices. slice_source
Zones to slice through. start_position
Position of the start slice. surface_generation_method
Determines how the surface is generated. vector
Vector attributes for this slice group. Methods
set_arbitrary_from_points
(p1, p2, p3)Set an arbitrary slice from 3 points.
-
SliceGroup.
arbitrary_normal
¶ Normal for arbitrary slices.
Type: 3- tuple
of floatsExample usage:
>>> plot.slice(0).orientation = SliceSurface.Arbitrary >>> plot.slice(0).arbitrary_normal = (0.1, 0.2, 0.3) >>> plot.slice(0).arbitrary_normal.x 0.1 >>> plot.slice(0).arbitrary_normal.y 0.2 >>> plot.slice(0).arbitrary_normal.z 0.3
-
SliceGroup.
contour
¶ Contour attributes for the slice group.
Type: SliceContour
Example usage:
>>> plot.slice(0).contour.show = True
-
SliceGroup.
edge
¶ Edge attributes for this slice group.
Type: SliceEdge
Example usage:
>>> plot.slice(0).edge.show = True
-
SliceGroup.
effects
¶ Effects attributes for this slice group.
Type: SliceEffects
Example usage:
>>> plot.slice(0).effects.use_translucency = True
-
SliceGroup.
end_position
¶ Position of the end slice.
SliceGroup.show_start_and_end_slices
must beTrue
to show the end slice.Type: 3- tuple
offloat
iforientation
is X,Y,Z or zero-basedint
iforientation
is I,J,KExample usage:
>>> plot.slice(0).show_start_and_end_slices = True >>> plot.slice(0).end_position = (1, 1, 1) >>> plot.slice(0).end_position.i 1
-
SliceGroup.
mesh
¶ Mesh attributes for this slice group.
Type: SliceMesh
Example usage:
>>> plot.slice(0).mesh.show = True
-
SliceGroup.
num_intermediate_slices
¶ Number of intermediate slicing planes.
You may specify between 1 and 5,000 intermediate slices.
Type: integer
Example usage:
>>> # Show 2 intermediate slices >>> plot.slice(0).num_intermediate_slices = 2
-
SliceGroup.
obey_source_zone_blanking
¶ Obey source zone blanking.
When set to
True
, slices are subject to any blanking used for the data. When set toFalse
, slices are generated for blanked and unblanked regions.Type: boolean
Example usage:
>>> plot.slice(0).obey_source_zone_blanking = True
-
SliceGroup.
orientation
¶ Select which plane the slice is drawn on (X,Y,Z, I, J, K or arbitrary).
Type: SliceSurface
You may also choose
SliceSurface.Arbitrary
to place the slice on an arbitrary plane.To orient slices in an arbitrary direction, choose
SliceSurface.Arbitrary
. As with other slices, you may specify origin points for a primary slice and/or for start and end slices. Slices pass through the indicated origin point(s), so you can easily align the edge of a slice or group of slices along some other feature of the plot, such as an axis. If intermediate slices are activated, they are drawn equally spaced between the slices defined by the start and end origins.Example usage:
>>> plot.slice(0).orientation = SliceSurface.XPlanes
-
SliceGroup.
origin
¶ Origin of the slice.
Type: 3- tuple
offloat
iforientation
is X,Y,Z or zero-basedint
iforientation
is I,J,KFor arbitrary slice orientation, the origin can be any location. For axis orientations (
XPlanes
,YPlanes
, etc.) two of the three components are not used.Example usage:
>>> slice_0 = plot.slice(0) >>> slice_0.orientation = SliceSurface.IPlanes >>> slice_0.origin = (1, 0, 0) >>> dx = (1, 1, 1) >>> slice_0.origin += dx >>> slice_0.origin.i 2 >>> slice_0.origin.j 1 >>> slice_0.origin.k 1 >>> slice_0.orientation = SliceSurface.Arbitrary >>> slice_0.origin = (.5, .1, .1) >>> slice_0.origin += dx >>> slice_0.origin.x 1.5 >>> slice_0.origin.y .1 >>> slice_0.origin.z .1
-
SliceGroup.
set_arbitrary_from_points
(p1, p2, p3)[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.
Type: SliceShade
Example usage:
>>> plot.slice(0).shade.show = True
-
SliceGroup.
show
¶ Show slices for this slice group.
Type: bool
Example usage:
>>> plot.slice(0).show = True
-
SliceGroup.
show_intermediate_slices
¶ Show intermediate slices.
Intermediate slices are evenly distributed between the start and end slices.
Example usage:
>>> plot.slice(0).show_intermediate_slices = True
-
SliceGroup.
show_primary_slice
¶ Include the primary slice (first slice placed) in the Plot.
Type: bool
Example usage:
>>> plot.slice(0).show = True >>> plot.slice(0).show_primary_slice = True
-
SliceGroup.
show_start_and_end_slices
¶ Include start and end slices.
Type: boolean
Example usage:
>>> plot.slice(0).show_start_and_end_slices = True
-
SliceGroup.
slice_source
¶ Zones to slice through.
Choose to slice through volume Zones, surface Zones, or the surfaces of volume Zones.
Type: SliceSource
Example usage:
>>> plot.slice(0).slice_source = SliceSource.SurfaceZones
-
SliceGroup.
start_position
¶ Position of the start slice.
SliceGroup.show_start_and_end_slices
must beTrue
to show the start slice.Type: 3- tuple
offloat
iforientation
is X,Y,Z or zero-basedint
iforientation
is I,J,KExample usage:
>>> plot.slice(0).show_start_and_end_slices = True >>> plot.slice(0).start_position = (1, 1, 1) >>> plot.slice(0).start_position.i 1
-
SliceGroup.
surface_generation_method
¶ Determines how the surface is generated.
Type: SurfaceGenerationMethod
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
-
SliceGroup.
vector
¶ Vector attributes for this slice group.
Type: SliceVector
Example usage:
>>> plot.slice(0).vector.show = True
SliceContour¶
-
class
tecplot.plot.
SliceContour
(parent_slice)[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 plot.contour(2).labels.show = True 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 tp.export.save_png('slice_contour.png', 600, supersample=3)
Attributes
contour_type
Contour type for the slice contours. flood_contour_group
Contour group to use for flooding. flood_contour_group_index
The Index
of theContourGroup
to use for flooding.line_color
Color
of contour lines.line_contour_group
Contour group to use for contour lines. line_contour_group_index
The Index
of theContourGroup
to use for contour lines.line_thickness
Contour line thickness as a percentage of frame width. show
Show contours on the slice. use_lighting_effect
Enable lighting effect.
-
SliceContour.
contour_type
¶ Contour type for the slice contours.
Type: ContourType
Example usage:
>>> plot.show_slices = True >>> plot.slice(0).contour.contour_type = ContourType.AverageCell
-
SliceContour.
flood_contour_group
¶ Contour group to use for flooding.
Type: ContourGroup
Changing style on this
ContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> group = plot.contour(1) >>> contour = plot.slice(1).contour >>> contour.flood_contour_group = group
-
SliceContour.
flood_contour_group_index
¶ The
Index
of theContourGroup
to use for flooding.Type: Index
(zero-based index)This property sets and gets, by
Index
, theContourGroup
used for flooding. Changing style on thisContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> plot.show_slices = True >>> contour = plot.slice(0).contour >>> contour.flood_contour_group_index = 1
-
SliceContour.
line_color
¶ Color
of contour lines.Selecting
Color.MultiColor
will color the slice contour lines based on thecontour group
variable.Type: Color
Example usage:
>>> plot.show_slices = True >>> plot.slice(0).contour.line_color = Color.Blue
-
SliceContour.
line_contour_group
¶ Contour group to use for contour lines.
Type: ContourGroup
Changing style on this
ContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> group = plot.contour(1) >>> contour = plot.slice(1).contour >>> contour.line_contour_group = group
-
SliceContour.
line_contour_group_index
¶ The
Index
of theContourGroup
to use for contour lines.Type: integer
(zero-based index)This property sets and gets, by
Index
, theContourGroup
used for line placement. Although all properties of theContourGroup
can be manipulated through this object, many of them (i.e., color) will not affect the lines unless theFieldmapContour.line_color
is set to the sameContourGroup
. Note that changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it.Example usage:
>>> plot.show_slices = True >>> contour = plot.slice(0).contour >>> contour.line_contour_group_index = 2
-
SliceContour.
line_thickness
¶ Contour line thickness as a percentage of frame width.
Suggested values are one of: .02, .1, .4, .8, 1.5
Type: float
Example usage:
>>> plot.show_slices = True >>> plot.slice(0).contour.line_thickness = .4
-
SliceContour.
show
¶ Show contours on the slice.
Type: bool
Example usage:
>>> plot.show_slices = True >>> plot.slice(1).contour.show = True
-
SliceContour.
use_lighting_effect
¶ Enable lighting effect.
Note
Setting
SliceContour.use_lighting_effect
will also set the same value forSliceShade.use_lighting_effect
, and vice-versa.The lighting effect is set with
SliceEffects.lighting_effect
, and may be one ofLightingEffect.Gouraud
orLightingEffect.Paneled
.Type: boolean
Example usage:
>>> plot.show_slices = True >>> contour = plot.slice(0).contour >>> contour.use_lighting_effect = True >>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled
SliceEdge¶
-
class
tecplot.plot.
SliceEdge
(vector)[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 slice_0 = plot.slice(0) plot.contour(0).variable = dataset.variable('U(M/S)') slice_0.edge.show = True slice_0.edge.line_thickness = 0.8 tp.export.save_png('slice_edge.png', 600, supersample=3)
Attributes
color
Edge color. edge_type
Edge type. line_thickness
Edge line thickness as a percentage of frame width. show
Show edges.
-
SliceEdge.
color
¶ Edge color.
Type: Color
Example usage:
>>> plot.slice(0).edge.show = True >>> plot.slice(0).edge.color = Color.Blue
-
SliceEdge.
edge_type
¶ Edge type.
Type: EdgeType
There are two types of edges in Tecplot 360: 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
SliceEffects¶
-
class
tecplot.plot.
SliceEffects
(effects)[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 tp.export.save_png('slice_effects.png', 600, supersample=3)
Attributes
lighting_effect
Lighting effect. surface_translucency
Surface translucency of the slice group. use_translucency
Enable surface translucency for this slice group.
-
SliceEffects.
lighting_effect
¶ Lighting effect.
Type: LightingEffect
Slice lighting effects must be enabled by setting
SliceContour.use_lighting_effect
orSliceShade.use_lighting_effect
toTrue
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 toPaneled
shading in this case.
Example usage:
>>> plot.slice(0).contour.use_lighting_effect = True >>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled
-
SliceEffects.
surface_translucency
¶ Surface translucency of the slice group.
Type: integer
Slice surface translucency must be enabled by setting
SliceEffects.use_translucency
=True
when setting this value.Valid slice translucency values range from one (opaque) to 99 (translucent).
Example usage:
>>> plot.slice(0).effects.use_translucency = True >>> plot.slice(0).effects.surface_translucency = 20
-
SliceEffects.
use_translucency
¶ Enable surface translucency for this slice group.
Type: boolean
The surface translucency value can be changed by setting
SliceEffects.surface_translucency
.Example usage:
>>> plot.slice(0).effects.use_translucency = True >>> plot.slice(0).effects.surface_translucency = 20
SliceMesh¶
-
class
tecplot.plot.
SliceMesh
(mesh)[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 tp.export.save_png('slice_mesh.png', 600, supersample=3)
Attributes
color
Slice mesh line Color
orContourGroup
.line_thickness
Mesh line thickness. show
Show mesh lines.
-
SliceMesh.
color
¶ Slice mesh line
Color
orContourGroup
.Type: Color
orContourGroup
Slice mesh lines can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property.Example usage:
>>> plot.slice(0).mesh.show = True >>> plot.slice(0).mesh.color = Color.Green
SliceShade¶
-
class
tecplot.plot.
SliceShade
(shade)[source]¶ Shade attributes of the slice group.
Show shading on the slice when
SliceContour.show
has not been selected or is set toContourType.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)
Attributes
color
Shade color. show
Show shade attributes. use_lighting_effect
Use lighting effect.
-
SliceShade.
color
¶ Shade color.
Type: Color
Color.MultiColor
andColor.RGBColor
coloring are not available. Use flooded contours for multi-color or RGB floodingExample usage:
>>> plot.slice(0).shade.show = True >>> plot.slice(0).shade.color = Color.Blue
-
SliceShade.
show
¶ Show shade attributes.
Type: boolean
Example usage:
>>> plot.slice(0).shade.show = True
-
SliceShade.
use_lighting_effect
¶ Use lighting effect.
When set to
True
, the lighting effect may be selected with theSliceEffects.lighting_effect
attribute.Note
Setting
SliceShade.use_lighting_effect
will also set the same value forSliceContour.use_lighting_effect
, and vice-versa.Type: Boolean
Example usage:
>>> plot.slice(0).shade.use_lighting_effect = True >>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled
SliceVector¶
-
class
tecplot.plot.
SliceVector
(vector)[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 tp.export.save_png('slice_vector.png', 600, supersample=3)
Attributes
arrowhead_style
Arrowhead style of slice vectors. color
Set slice vector color. is_tangent
Use tangent vectors for slices. line_thickness
Vector line thickness as a percentage of the frame height. show
Show vectors on slices. vector_type
Type of vector for slices in this slice group.
-
SliceVector.
arrowhead_style
¶ Arrowhead style of slice vectors.
Type: ArrowheadStyle
Example usage:
>>> plot.slice(0).vector.show = True >>> plot.slice(0).vector.arrowhead_style = ArrowheadStyle.Hollow
-
SliceVector.
color
¶ Set slice vector color.
Type: Color
Example usage:
>>> plot.slice(0).vector.show = True >>> plot.slice(0).vector.color = Color.Red
-
SliceVector.
is_tangent
¶ Use tangent vectors for slices.
Type: boolean
Example usage:
>>> plot.slice(0).vector.show = True >>> plot.slice(0).vector.is_tangent = True
-
SliceVector.
line_thickness
¶ Vector line thickness as a percentage of the frame height.
Type: float
Typical values are .02, .1, .4, .8, 1.5
Example usage:
>>> plot.slice(0).vector.show = True >>> plot.slice(0).vector.line_thickness = .1
-
SliceVector.
show
¶ Show vectors on slices.
Type: boolean
Example usage:
>>> plot.slice(0).vector.show = True
-
SliceVector.
vector_type
¶ Type of vector for slices in this slice group.
Type: VectorType
Example usage:
>>> plot.slice(0).vector.show = True >>> plot.slice(0).vector.vector_type = VectorType.MidAtPoint
Streamtraces¶
Streamtraces¶
-
class
tecplot.plot.
Streamtraces
(plot)[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)
Attributes
are_active
Determine if there are active streamtraces in the current plot type. arrowhead_size
Arrowhead size as a percentage of frame height. arrowhead_spacing
Distance between arrowheads in terms of Y-frame units. color
Color of streamtraces line (not rods or ribbons). count
Query the number of active streamtraces for the current plot type. dash_skip
Number of time deltas used for the “off” sections of the streamlines. has_terminating_line
Determine if the streamtraces have the terminating line. line_thickness
Streamtrace line thickness. marker_color
Color
of the streamline markers.marker_size
Size of streamline markers. marker_symbol_type
The SymbolType
to use for stream markers.max_steps
Maximum number of steps before the streamtrace is terminated. min_step_size
Smallest step size to use as a percentage of cell distance. obey_source_zone_blanking
Obey source zone blanking. rod_ribbon
Streamtrace rod/ribbon attributes. show_arrows
Display arrowheads along all streamlines. show_dashes
Display streamtrace dashes. show_markers
Display streamtrace markers. show_paths
Draw streamtrace paths (lines, ribbons, or rods). step_size
Maximum fraction of the distance across a cell that a streamtrace moves in one step. termination_line
Streamtraces termination line attributes. timing
Streamtraces timing attributes. Methods
add
(seed_point, stream_type[, direction])Add a single streamtrace to the plot of the current frame. add_on_zone_surface
(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. marker_symbol
([symbol_type])Returns a streamline symbol style object. position
(stream_number)Query the starting position of a streamtrace. set_termination_line
(line_points)Set the position of the termination line for streamtraces. streamtrace_type
(stream_number)Query the type of a streamtrace by streamtrace number.
-
Streamtraces.
add
(seed_point, stream_type, direction=<StreamDir.Both: 2>)[source]¶ Add a single streamtrace to the plot of the current frame.
The plot type must be either
Cartesian2D
orCartesian3D
.Parameters: - seed_point – (2- or 3-
tuple
offloats
): Pass a 2-tuple
offloat
for aCartesian2DFieldPlot
, or a 3-tuple
offloat
for aCartesian3DFieldPlot
. - stream_type – (
Streamtrace
): Type of streamtraces to add. - direction – (
StreamDir
, optional): Direction of propagation of the streamtraces being added.
Note
stream_type is automatically set to
Streamtrace.SurfaceLine
if the plot type isCartesian2DFieldPlot
. The only stream type available for 2D plots isStreamtrace.SurfaceLine
.Raises: TecplotSystemError
– The streamtraces could not be added.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)
- seed_point – (2- or 3-
-
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
orCartesian3D
.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
ofintegers
, optional) – Set of Zones on which to add streamtraces. IfNone
, then streamtraces will be added to the currently active zones. - num_seed_points – (
integer
, optional): Number of seed points for distributing along a rake or on defined surfaces. - direction – (
StreamDir
, optional): Direction of propagation of the streamtraces being added.
import tecplot from tecplot.constant import * import os examples_dir = tecplot.session.tecplot_examples_directory() datafile = os.path.join(examples_dir, 'OneraM6wing', 'OneraM6_SU2_RANS.plt') dataset = tecplot.data.load_tecplot(datafile) frame = tecplot.active_frame() frame.plot_type = tecplot.constant.PlotType.Cartesian3D plot = frame.plot() plot.vector.u_variable_index = 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)
- stream_type – (
-
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
orCartesian3D
.Parameters: - start_position – (2- or 3-
tuple
offloats
): Pass a 2-tuple
offloat
for aCartesian2DFieldPlot
, or a 3-tuple
offloat
for aCartesian3DFieldPlot
. - end_position – (2- or 3-
tuple
offloats
): Pass a 2-tuple
offloat
for aCartesian2DFieldPlot
, or a 3-tuple
offloat
for aCartesian3DFieldPlot
. - stream_type – (
Streamtrace
): Type of streamtraces to add. - num_seed_points – (
integer
, optional): Number of seed points for distributing along a rake or on defined surfaces. - direction – (
StreamDir
, optional): Direction of propagation of the streamtraces being added.
import tecplot from tecplot.constant import * import os examples_dir = tecplot.session.tecplot_examples_directory() datafile = os.path.join(examples_dir, '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)
- start_position – (2- or 3-
-
Streamtraces.
are_active
¶ Determine if there are active streamtraces in the current plot type.
Note
This property is read-only.
Returns: boolean
.True
if there are active streamtraces in the current plot type.Example usage:
>>> streamtraces_are_active = plot.streamtraces.are_active
-
Streamtraces.
arrowhead_size
¶ Arrowhead size as a percentage of frame height.
Type: float
Recommend values are one of 1, 3, 5, 8, or 12.
Example usage:
>>> plot.streamtraces.show_arrows = True >>> plot.streamtraces.arrowhead_size = 1.0
-
Streamtraces.
arrowhead_spacing
¶ Distance between arrowheads in terms of Y-frame units.
Type: float
For example, a value of 10 will space arrowheads approximately ten percent of the frame height apart from each other along each streamline.
Example usage:
>>> plot.streamtraces.show_arrows = True >>> plot.streamtraces.arrowhead_spacing = 10
-
Streamtraces.
color
¶ Color of streamtraces line (not rods or ribbons).
Type: Color
orContourGroup
Streamtraces can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property.Example usage:
>>> plot.streamtraces.color = Color.Red
-
Streamtraces.
count
¶ Query the number of active streamtraces for the current plot type.
Returns: integer
Note
This property is read-only.
>>> num_active_streamtraces = plot.streamtraces.count
-
Streamtraces.
dash_skip
¶ Number of time deltas used for the “off” sections of the streamlines.
Note
The
dash_skip
value must be greater than 0.Type: integer
Example usage:
>>> plot.streamtraces.dash_skip = 2
-
Streamtraces.
delete_all
()[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 isCartesian3D
, 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: Raises: TecplotSystemError
– The streamtraces in the range could not be deleted.Example usage:
>>> # Delete the first 100 streamtraces >>> plot.streamtraces.delete_range(0, 99)
-
Streamtraces.
has_terminating_line
¶ Determine if the streamtraces have the terminating line.
Note
This property is read-only.
Returns: boolean
.True
if the streamtraces have the terminating line,False
otherwise.Example usage:
>>> has_terminating_line = plot.streamtraces.has_terminating_line
-
Streamtraces.
line_thickness
¶ Streamtrace line thickness.
Type: float
Line thickness as a percentage of the frame height for 2D lines, or a percentage of the median axis length for 3D surface lines and volume lines.
Suggested values are .02, .1, .4, .8, 1.5
Example usage:
>>> plot.streamtraces.line_thickness = 1.1
-
Streamtraces.
marker_color
¶ Color
of the streamline markers.Type: Color
orContourGroup
Streamtrace markers can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property.Example usage:
>>> plot.streamtraces.marker_color = Color.Blue
-
Streamtraces.
marker_size
¶ Size of streamline markers.
Type: float
Example usage:
>>> plot.streamtraces.marker_size = 1.1
-
Streamtraces.
marker_symbol
(symbol_type=None)[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 fromStreamtraces.marker_symbol_type
.Returns:
TextSymbol
orGeometrySymbol
, depending onmarker_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.Type: SymbolType
This sets the active symbol type for streamtrace markers. Use
Streamtraces.marker_symbol
to access the symbol:>>> from tecplot.constant import SymbolType >>> streamtrace = plot.streamtraces >>> streamtraces.marker_symbol_type = SymbolType.Text >>> symbol = streamtraces.marker_symbol(SymbolType.Text) >>> symbol.text = 'a'
-
Streamtraces.
max_steps
¶ Maximum number of steps before the streamtrace is terminated.
Type: integer
max_steps
prevents streamtraces from spinning forever in a vortex, or from wandering into a region where the vector components are very small, very random, or both.If a small
step_size
is selected, themax_steps
should be a large value.Example usage:
>>> plot.streamtraces.max_steps = 5000
-
Streamtraces.
min_step_size
¶ Smallest step size to use as a percentage of cell distance.
A typical minimum step size value is 0.00001, which is the default.
Type: float
Warning
Setting this too small results in integration problems. Setting this greater than or equal to the
step_size
results in a constant step size.Example usage:
>>> plot.streamtraces.min_step_size = .0002
-
Streamtraces.
obey_source_zone_blanking
¶ Obey source zone blanking.
Type: boolean
When
True
, streamtraces are generated for non-blanked regions only. WhenFalse
, streamtraces are generated for both blanked and unblanked regions.Example usage:
>>> plot.streamtraces.obey_source_zone_blanking = True
-
Streamtraces.
position
(stream_number)[source]¶ Query the starting position of a streamtrace.
Parameters: stream_number – ( integer
): 0-based stream number to query.Returns: tuple
offloats
Get the position of streamtrace number 3:
>>> position = plot.streamtraces.position(2) # Note: 0-based >>> position.x # == position[0] 0.1 >>> position.y # == position[1] 0.2 >>> position.z # == position[2] 0.3
-
Streamtraces.
rod_ribbon
¶ Streamtrace rod/ribbon attributes.
Type: StreamtraceRodRibbon
Example usage:
>>> streamtraces.rod_ribbon.mesh.show = True
-
Streamtraces.
set_termination_line
(line_points)[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.
Type: boolean
Example usage:
>>> plot.streamtraces.show_arrows = True
-
Streamtraces.
show_dashes
¶ Display streamtrace dashes.
The lengths of the dashes and the spaces between the dashes are controlled by the value of
StreamtraceTiming.delta
. Set theStreamtraces.dash_skip
attribute to control the number of time deltas used for the “off” sections of the streamtraces.Type: boolean
Example usage:
>>> plot.streamtraces.show_dashes = True
-
Streamtraces.
show_markers
¶ Display streamtrace markers.
Type: boolean
Stream markers are only available for surface and volume type streamlines.
You may also specify the
size
,color
, andshape
of the markers.Example usage:
>>> plot.streamtraces.show_markers = True
-
Streamtraces.
show_paths
¶ Draw streamtrace paths (lines, ribbons, or rods).
Type: boolean
A streamtrace path may be a line, ribbon or rod.
Example usage:
>>> plot.streamtraces.show_paths = True
See also
Streamtraces.show_markers
-
Streamtraces.
step_size
¶ Maximum fraction of the distance across a cell that a streamtrace moves in one step.
Type: float
The step size is the maximum fraction of the distance across a cell that a streamtrace moves in one step. A streamtrace adjusts its step size between
step_size
andmin_step_size
, depending on local curvature of the streamtrace.A typical value (and the default) is 0.25, which results in four integration steps through each cell or element. The value for Step Size affects the accuracy of the integration.
Warning
Setting step size too small can result in round-off errors, while setting it too large can result in truncation errors and missed cells.
Example usage:
>>> plot.streamtraces.step_size = .25
-
Streamtraces.
streamtrace_type
(stream_number)[source]¶ Query the type of a streamtrace by streamtrace number.
Parameters: stream_number – ( integer
): 0-based stream number to query.Returns: Streamtrace
Get the type of streamtrace 3. Note 0-based stream number:
>>> streamtrace_type = plot.streamtraces.streamtrace_type(2) # streamtrace 3 >>> streamtrace_type <Streamtrace.VolumeLine: 2>
-
Streamtraces.
termination_line
¶ Streamtraces termination line attributes.
A streamtrace termination line is a polyline that terminates any streamtraces that cross it. The termination line is useful for stopping streamtraces before they spiral or stall.
Type: StreamtraceTerminationLine
Example usage:
>>> term_line = plot.streamtraces.termination_line >>> term_line.show = True
-
Streamtraces.
timing
¶ Streamtraces timing attributes.
Type: StreamtraceTiming
Example usage:
>>> timing = plot.streamtraces.timing >>> timing.start = 0.01
StreamtraceRodRibbon¶
-
class
tecplot.plot.
StreamtraceRodRibbon
(streamtrace)[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)
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.
Type: StreamtraceRodRibbonContour
Example usage:
>>> plot.streamtraces.rod_ribbon.contour.show = True
-
StreamtraceRodRibbon.
effects
¶ Streamtraces rod/ribbon effects.
Type: StreamtraceRodRibbonEffects
Example usage:
>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
-
StreamtraceRodRibbon.
mesh
¶ Streamtraces rod/ribbon mesh attributes.
Type: StreamtraceRodRibbonMesh
Example usage:
>>> plot.streamtraces.rod_ribbon.mesh.show = True
-
StreamtraceRodRibbon.
num_rod_points
¶ Number of rod points.
Type: integer
, valid range 3-100Volume 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
-
StreamtraceRodRibbon.
shade
¶ Streamtraces rod/ribbon color and lighting attributes.
Type: StreamtraceRodRibbonShade
Example usage:
>>> plot.streamtraces.rod_ribbon.shade.color = Color.Magenta
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)
Attributes
anchor
Time that a dash is guaranteed to start. delta
Time between stream markers. end
Time after which no stream markers are drawn. start
Time at which the first marker should be drawn. Methods
reset_delta
()Reset the time delta for dashed streamtraces.
-
StreamtraceTiming.
anchor
¶ Time that a dash is guaranteed to start.
Type: float
A dash is guaranteed to start at
anchor
, provided the start and end time surround the dash.Example usage:
>>> plot.streamtraces.timing.anchor = 1.1
-
StreamtraceTiming.
delta
¶ Time between stream markers.
Type: float
delta
is the time interval that measures the time between stream markers. The actual distance between markers is the product of this number and the localVector
magnitude.Call
StreamtraceTiming.reset_delta()
to reset this to the default.Example usage:
>>> plot.streamtraces.timing.delta = 0.1
-
StreamtraceTiming.
end
¶ Time after which no stream markers are drawn.
Type: float
Example usage:
>>> plot.streamtraces.timing.end = 3.0
-
StreamtraceTiming.
reset_delta
()[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.
Type: float
A start time of zero means that the first marker is drawn at the starting point. A start time of 2.5 means that the first stream marker is drawn 2.5 time units downstream of the starting point.
Example usage:
>>> plot.streamtraces.timing.start = 2.5
StreamtraceRodRibbonContour¶
-
class
tecplot.plot.
StreamtraceRodRibbonContour
(streamtrace)[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)
Attributes
flood_contour_group
Contour group to use for flooding. flood_contour_group_index
The Index
of theContourGroup
to use for flooding.show
Enable or disable contour flooding display. use_lighting_effect
Enable lighting effect for streamtrace rod/ribbons.
-
StreamtraceRodRibbonContour.
flood_contour_group
¶ Contour group to use for flooding.
Type: ContourGroup
This property sets and gets the ContourGroup used for flooding. Changing style on this
ContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> group = plot.contour(1) >>> contour = plot.streamtraces.rod_ribbon.contour >>> contour.flood_contour_group = group
-
StreamtraceRodRibbonContour.
flood_contour_group_index
¶ The
Index
of theContourGroup
to use for flooding.Type: Index
(zero-based index)This property sets and gets, by
Index
, theContourGroup
used for flooding. Changing style on thisContourGroup
will affect all fieldmaps on the sameFrame
that use it.Example usage:
>>> contour = plot.streamtraces.rod_ribbon.contour >>> contour.flood_contour_group_index = 0 # First contour group
-
StreamtraceRodRibbonContour.
show
¶ Enable or disable contour flooding display.
Type: boolean
Example usage:
>>> plot.streamtraces.rod_ribbon.contour.show = True
-
StreamtraceRodRibbonContour.
use_lighting_effect
¶ Enable lighting effect for streamtrace rod/ribbons.
Note
Setting
StreamtraceRodRibbonContour.use_lighting_effect
will also set the same value forStreamtraceRodRibbonShade.use_lighting_effect
, and vice-versa.The lighting effect is set with
StreamtraceRodRibbonEffects.lighting_effect
, and may be one ofLightingEffect.Gouraud
orLightingEffect.Paneled
.Type: boolean
Example usage:
>>> ribbon = plot.streamtraces.rod_ribbon >>> contour = ribbon.contour >>> contour.use_lighting_effect = True >>> ribbon.effects.lighting_effect = LightingEffect.Paneled
StreamtraceRodRibbonEffects¶
-
class
tecplot.plot.
StreamtraceRodRibbonEffects
(streamtrace)[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)
Attributes
lighting_effect
Get/set the lighting algorithm used when lighting streamtrace rods and ribbons. surface_translucency
Surface translucency of the streamtraces ribbon. use_translucency
Enable surface translucency.
-
StreamtraceRodRibbonEffects.
lighting_effect
¶ - Get/set the lighting algorithm used when lighting
- streamtrace rods and ribbons.
Type: LightingEffect
Ribbon lighting effects must be enabled by setting
StreamtraceRodRibbonShade.use_lighting_effect
toTrue
when setting this value.Note that setting
StreamtraceRodRibbonShade.use_lighting_effect
will also set this value forribbon 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 toPaneled
shading in this case.
Example usage:
>>> plot.streamtraces.rod_ribbon.shade.use_lighting_effect = True >>> plot.streamtraces.rod_ribbon.effects.lighting_effect = LightingEffect.Paneled
-
StreamtraceRodRibbonEffects.
surface_translucency
¶ Surface translucency of the streamtraces ribbon.
Type: integer
Surface translucency must be enabled by setting
StreamtraceRodRibbonEffects.use_translucency
=True
when setting this value.Valid translucency values range from one (opaque) to 99 (translucent).
Example usage:
>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True >>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20
-
StreamtraceRodRibbonEffects.
use_translucency
¶ Enable surface translucency.
Type: boolean
The surface translucency value can be changed by setting
StreamtraceRodRibbonEffects.surface_translucency
.Example usage:
>>> plot.streamtraces.rod_ribbon.effects.use_translucency = True >>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20
StreamtraceRodRibbonMesh¶
-
class
tecplot.plot.
StreamtraceRodRibbonMesh
(streamtrace)[source]¶ Streamtraces rod/ribbon mesh attributes.
Note
To set the mesh color or line thickness, see
Streamtraces.color
andStreamtraces.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)
Attributes
line_thickness
Get/Set streamtrace rod/ribbon mesh line thickness as a percentage of frame height. show
Display mesh.
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)
Attributes
color
Shade color. show
Show shade attributes. use_lighting_effect
Use lighting effect.
-
StreamtraceRodRibbonShade.
color
¶ Shade color.
Type: Color
Color.MultiColor
andColor.RGBColor
coloring are not available. Use flooded contours for multi-color or RGB flooding.Example usage:
>>> plot.streamtraces.rod_ribbon.shade.show = True >>> plot.streamtraces.rod_ribbon.shade.color = Color.Blue
-
StreamtraceRodRibbonShade.
show
¶ Show shade attributes.
Type: boolean
Example usage:
>>> plot.streamtraces.rod_ribbon.shade.show = True
-
StreamtraceRodRibbonShade.
use_lighting_effect
¶ Use lighting effect.
When set to
True
, the lighting effect may be selected with theSliceEffects.lighting_effect
attribute.Note
Setting
SliceShade.use_lighting_effect
will also set the same value forSliceContour.use_lighting_effect
, and vice-versa.Type: Boolean
Example usage:
>>> plot.streamtraces.rod_ribbon.shade.use_lighting_effect = True >>> plot.streamtraces.rod_ribbon.effects.lighting_effect = LightingEffect.Paneled
StreamtraceTerminationLine¶
-
class
tecplot.plot.
StreamtraceTerminationLine
(streamtrace)[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 mustadd 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.is_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)
Attributes
color
Color of the termination line. is_active
Activate/disable the streamtrace termination line. line_pattern
Pattern of the terminating line. line_thickness
Thickness of the termination line as a percentage of frame height. pattern_length
Length of the pattern as a percentage of frame height. show
Display the termination line.
-
StreamtraceTerminationLine.
color
¶ Color of the termination line.
Type: Color
Example usage:
>>> plot.streamtraces.termination_line.color = Color.Red
-
StreamtraceTerminationLine.
is_active
¶ Activate/disable the streamtrace termination line.
Type: boolean
Set to
True
to activate the termination line and terminate any streamtraces that cross it. Set toFalse
and redraw the plot with unterminated streamtraces.Example usage:
>>> plot.streamtraces.termination_line.is_active = True
-
StreamtraceTerminationLine.
line_pattern
¶ Pattern of the terminating line.
Type: LinePattern
Example usage:
>>> plot.streamtraces.termination_line.line_pattern = LinePattern.Dotted
-
StreamtraceTerminationLine.
line_thickness
¶ Thickness of the termination line as a percentage of frame height.
Type: float
Example usage:
>>> plot.streamtraces.termination_line.line_thickness = 0.1
-
StreamtraceTerminationLine.
pattern_length
¶ Length of the pattern as a percentage of frame height.
Type: float
Example usage:
>>> plot.streamtraces.termination_line.pattern_length = 2
-
StreamtraceTerminationLine.
show
¶ Display the termination line.
Type: boolean
Set to
True
to display the termination line. Set toFalse
and redraw the plot to display terminated streamlines (ifis_active
is set toTrue
), but not the termination line itself.Example usage:
>>> plot.streamtraces.termination_line.show = True
Text¶
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.
Type: constant.TextBox
Example usage:
>>> plot = frame.plot() >>> plot.legend.box.box_type = constant.TextBox.None_
-
TextBox.
color
¶ Color of the box surrounding the text.
Type: Color
Example usage:
>>> plot = frame.plot() >>> plot.legend.box.color = Color.Blue
-
TextBox.
fill_color
¶ Fill color of the box surrounding the text.
Type: Color
Example usage:
>>> plot = frame.plot() >>> plot.legend.box.fill_color = Color.Blue
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 tp.export.save_png('font.png', 600, supersample=3)
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
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.
Type: boolean
Example:
>>> axis.title.font.bold = True
-
Font.
italic
¶ Use the italic version of the current typeface.
Type: boolean
Example:
>>> axis.title.font.italic = True
-
Font.
size
¶ Height of the font.
Type: float
in units ofFont.size_units
Example usage:
>>> axis.title.font.size = 10
-
Font.
size_units
¶ Units used by the size attribute.
Type: Units
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
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)
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.
Type: Index
(zero-based)Example usage:
>>> axis.tick_labels.format.custom_labels_index = 0
-
LabelFormat.
datetime_format
¶ The date/time format to be used.
Type: string
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"
-
LabelFormat.
format_type
¶ Type of number formatting to use.
Type: NumberFormat
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
-
LabelFormat.
negative_prefix
¶ Prefix string to use for negative valued tick labels.
Type: string
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 = ')'
-
LabelFormat.
negative_suffix
¶ Suffix string to use for negative valued tick labels.
Type: string
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 = ')'
-
LabelFormat.
num_custom_labels
¶ Number of custom label sets available to use.
Type: int
Example usage:
>>> print(axis.tick_labels.format.num_custom_labels) 1
-
LabelFormat.
positive_prefix
¶ Prefix string to use for positive valued tick labels.
Type: string
Example usage:
>>> axis.tick_labels.format.positive_prefix = 'increase: '
-
LabelFormat.
positive_suffix
¶ Suffix string to use for positive valued tick labels.
Type: string
Example usage:
>>> axis.tick_labels.format.positive_suffix = ' (m)'
-
LabelFormat.
precision
¶ Number digits after decimal for fixed floating point format.
Type: integer
Example usage:
>>> from tecplot.constant import NumberFormat >>> axis.tick_labels.format.format_type = NumberFormat.FixedFloat >>> axis.tick_labels.format.precision = 3
-
LabelFormat.
remove_leading_zeros
¶ Strip leading zeros in the formatted number.
Type: boolean
Example usage:
>>> axis.tick_labels.format.remove_leading_zeros = True
-
LabelFormat.
show_decimals_on_whole_numbers
¶ Include trailing decimal character with whole numbers.
Type: boolean
Example usage:
>>> axis.tick_labels.format.show_decimals_on_whole_numbers = True
-
LabelFormat.
show_negative_sign
¶ Include negative sign for negative values.
Type: boolean
Example usage:
>>> axis.tick_labels.format.show_negative_sign = True
Viewport¶
ReadOnlyViewport¶
-
class
tecplot.plot.
ReadOnlyViewport
(axes)[source]¶ Attributes
bottom
( float
) Bottom position of viewport relative to theFrame
.left
( float
) Left position of viewport relative to theFrame
.right
( float
) Right position of viewport relative to theFrame
.top
( float
) Top position of viewport relative to theFrame
.
-
ReadOnlyViewport.
bottom
¶ (
float
) Bottom position of viewport relative to theFrame
.Type: float
in percentage of frame height from the bottom of the frame.Example usage:
>>> print(plot.axes.viewport.bottom) 10.0
-
ReadOnlyViewport.
left
¶ (
float
) Left position of viewport relative to theFrame
.Type: float
in percentage of frame width from the left of the frame.Example usage:
>>> print(plot.axes.viewport.left) 10.0
Viewport¶
-
class
tecplot.plot.
Viewport
(axes)[source]¶ Attributes
bottom
( float
) Bottom position of viewport relative to theFrame
.left
( float
) Left position of viewport relative to theFrame
.right
( float
) Right position of viewport relative to theFrame
.top
( float
) Top position of viewport relative to theFrame
.
-
Viewport.
bottom
¶ (
float
) Bottom position of viewport relative to theFrame
.Type: float
in percentage of frame height from the bottom of the frame.Example usage:
>>> print(plot.axes.viewport.bottom) 10.0
-
Viewport.
left
¶ (
float
) Left position of viewport relative to theFrame
.Type: float
in percentage of frame width from the left of the frame.Example usage:
>>> print(plot.axes.viewport.left) 10.0
Cartesian2DViewport¶
-
class
tecplot.plot.
Cartesian2DViewport
(axes)[source]¶ Attributes
bottom
( float
) Bottom position of viewport relative to theFrame
.left
( float
) Left position of viewport relative to theFrame
.nice_fit_buffer
Tolerance for viewport/frame fit niceness. right
( float
) Right position of viewport relative to theFrame
.top
( float
) Top position of viewport relative to theFrame
.top_snap_target
Target value for top when being adjusted or dragged. top_snap_tolerance
Tolerance for snapping to target value for top.
-
Cartesian2DViewport.
bottom
¶ (
float
) Bottom position of viewport relative to theFrame
.Type: float
in percentage of frame height from the bottom of the frame.Example usage:
>>> print(plot.axes.viewport.bottom) 10.0
-
Cartesian2DViewport.
left
¶ (
float
) Left position of viewport relative to theFrame
.Type: float
in percentage of frame width from the left of the frame.Example usage:
>>> print(plot.axes.viewport.left) 10.0
-
Cartesian2DViewport.
nice_fit_buffer
¶ Tolerance for viewport/frame fit niceness.
Type: float
Example usage:
>>> plot.axes.viewport.nice_fit_buffer = 20
-
Cartesian2DViewport.
right
¶ (
float
) Right position of viewport relative to theFrame
.Type: float
in percentage of frame width from the left of the frame.Example usage:
>>> print(plot.axes.viewport.right) 90.0
-
Cartesian2DViewport.
top
¶ (
float
) Top position of viewport relative to theFrame
.Type: float
in percentage of frame height from the bottom of the frame.Example usage:
>>> print(plot.axes.viewport.top) 90.0
PolarViewport¶
-
class
tecplot.plot.
PolarViewport
(axes)[source]¶ Attributes
border_color
border_thickness
bottom
( float
) Bottom position of viewport relative to theFrame
.fill_color
left
( float
) Left position of viewport relative to theFrame
.right
( float
) Right position of viewport relative to theFrame
.show_border
top
( float
) Top position of viewport relative to theFrame
.
-
PolarViewport.
border_color
¶
-
PolarViewport.
border_thickness
¶
-
PolarViewport.
bottom
¶ (
float
) Bottom position of viewport relative to theFrame
.Type: float
in percentage of frame height from the bottom of the frame.Example usage:
>>> print(plot.axes.viewport.bottom) 10.0
-
PolarViewport.
fill_color
¶
-
PolarViewport.
left
¶ (
float
) Left position of viewport relative to theFrame
.Type: float
in percentage of frame width from the left of the frame.Example usage:
>>> print(plot.axes.viewport.left) 10.0
-
PolarViewport.
right
¶ (
float
) Right position of viewport relative to theFrame
.Type: float
in percentage of frame width from the left of the frame.Example usage:
>>> print(plot.axes.viewport.right) 90.0
-
PolarViewport.
show_border
¶
View¶
Cartesian2DView¶
-
class
tecplot.plot.
Cartesian2DView
(plot)[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() tp.export.save_png('view_2D.png', 600, supersample=3)
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, xmax, ymin, ymax)Zoom the view to a rectangular region of the plot.
-
Cartesian2DView.
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.
Raises: TecplotSystemError
– Internal error.
-
Cartesian2DView.
center
(consider_blanking=True)¶ Center the data within the axis grid area.
Parameters: consider_blanking ( Boolean
, optional) – IfTrue
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
)Raises: TecplotSystemError
– View could not be centered.
-
Cartesian2DView.
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) – IfTrue
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
)Raises: TecplotSystemError
– Internal error.
-
Cartesian2DView.
fit_data
(consider_blanking=True)¶ Fit data zones or line mappings within the grid area.
Parameters: consider_blanking ( Boolean
, optional) – IfTrue
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.
Raises: TecplotSystemError
– Internal error.
-
Cartesian2DView.
fit_to_nice
(consider_blanking=True)¶ 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) – IfTrue
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
)Raises: TecplotSystemError
– Internal error.
-
Cartesian2DView.
magnification
¶ Magnification for the data being plotted.
Type: float
The
magnification
value is a decimal percent and must be greater than 0. Amagnification
size of 1.0 (100%) will size the plot so that it can fit within the grid area.Raises: TecplotSystemError
– Magnification could not be queried or set. Possible cases whereTecplotSystemError
is raised include XY plots where no mappings are active or floating point out of range errorScale the view to ten percent of the size at which the data would fit the full frame:
>>> view.magnification = 0.10 >>> view.magnification 0.10
-
Cartesian2DView.
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)
Raises: TecplotSystemError
– View could not be translated.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)
- x (
-
Cartesian2DView.
zoom
(xmin, xmax, ymin, 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: Raises: TecplotSystemError
– The view could not be zoomed.Zoom so the rectangular region with corners at (xmin, ymin)=(1,0) and (xmax, ymax)=(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)
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
orOrthographic
).psi
Eye origin view Psi angle in degrees. 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. fit_to_nice
([consider_blanking])Set axis range to begin/end on major axis increments. rotate_to_angles
(psi, theta, alpha)Rotate the plot to specific angles. translate
([x, y])Shift the data being plotted in the X and/or Y direction. zoom
(xmin, xmax, ymin, 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.
Type: float
Example usage:
>>> plot.view.alpha = 95.0
-
Cartesian3DView.
center
(consider_blanking=True)¶ Center the data within the axis grid area.
Parameters: consider_blanking ( Boolean
, optional) – IfTrue
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
)Raises: TecplotSystemError
– View could not be centered.
-
Cartesian3DView.
distance
¶ Get or set the view distance.
Type: float
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
Example usage:
>>> plot.view.distance 13.5 >>> plot.view.distance = 10.0 >>> plot.view.distance 10.0
-
Cartesian3DView.
field_of_view
¶ Amount of the plot which is displayed.
Type: float
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 ifprojection
isProjection.Orthographic
.Example usage:
>>> from tecplot.constant import Projection >>> plot.view.projection = Projection.Perspective >>> plot.view.field_of_view = 9.6
-
Cartesian3DView.
fit
(consider_blanking=True)[source]¶ 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. Axes are also included.
Parameters: consider_blanking ( Boolean
, optional) – IfTrue
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
)Raises: TecplotSystemError
– Internal error.
-
Cartesian3DView.
fit_data
(consider_blanking=True)¶ Fit data zones or line mappings within the grid area.
Parameters: consider_blanking ( Boolean
, optional) – IfTrue
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.
Raises: TecplotSystemError
– Internal error.
-
Cartesian3DView.
fit_surfaces
(consider_blanking=True)[source]¶ Fit 3D plot surfaces to the grid area.
Parameters: consider_blanking ( Boolean
, optional) – IfTrue
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
isSurfacesToPlot.None_
.Raises: TecplotSystemError
– Internal error.
-
Cartesian3DView.
fit_to_nice
(consider_blanking=True)¶ 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) – IfTrue
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
)Raises: TecplotSystemError
– Internal error.
-
Cartesian3DView.
magnification
¶ Magnification for the data being plotted.
Type: float
The
magnification
value is a decimal percent and must be greater than 0. Amagnification
size of 1.0 (100%) will size the plot so that it can fit within the grid area.Raises: TecplotSystemError
– Magnification could not be queried or set. Possible cases whereTecplotSystemError
is raised include XY plots where no mappings are active or floating point out of range errorScale the view to ten percent of the size at which the data would fit the full frame:
>>> view.magnification = 0.10 >>> view.magnification 0.10
-
Cartesian3DView.
position
¶ 3D viewer position.
The viewer position is the viewer’s relation to the image.
Type: 3- tuple
offloats
Example usage:
>>> 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
-
Cartesian3DView.
projection
¶ Projection type (
Perspective
orOrthographic
).Type: Projection
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
-
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.
Type: float
Example usage:
>>> plot.view.psi = 90.0
-
Cartesian3DView.
rotate_to_angles
(psi, theta, alpha)[source]¶ Rotate the plot to specific angles.
Parameters:
-
Cartesian3DView.
theta
¶ Eye origin view Theta angle in degrees.
The Theta angle is the rotation of the eye origin ray about the Z-axis.
Type: float
Example usage:
>>> plot.view.theta = 24.3
-
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)
Raises: TecplotSystemError
– View could not be translated.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)
- x (
-
Cartesian3DView.
width
¶ 3D view width.
Type: float
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 ifprojection
isPerspective
.Example usage:
>>> plot.view.width = 1.5
-
Cartesian3DView.
zoom
(xmin, xmax, ymin, ymax)[source]¶ 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: Raises: TecplotSystemError
– The view could not be zoomed.Zoom so the rectangular region with corners at (xmin, ymin)=(1,0) and (xmax, ymax)=(7,9) is in view:
>>> view.zoom(1, 7, 0, 9)
LineView¶
-
class
tecplot.plot.
LineView
(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)
Attributes
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, xmax, ymin, ymax)Zoom the view to a rectangular region of the plot.
-
LineView.
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.
Raises: TecplotSystemError
– Internal error.
-
LineView.
center
()¶ Center the data within the axis grid area.
Raises: TecplotSystemError
– View could not be centered.
-
LineView.
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.
Raises: TecplotSystemError
– Internal error.
-
LineView.
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.
Raises: TecplotSystemError
– Internal error.
-
LineView.
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.
Raises: TecplotSystemError
– Internal error.
-
LineView.
magnification
¶ Magnification for the data being plotted.
Type: float
The
magnification
value is a decimal percent and must be greater than 0. Amagnification
size of 1.0 (100%) will size the plot so that it can fit within the grid area.Raises: TecplotSystemError
– Magnification could not be queried or set. Possible cases whereTecplotSystemError
is raised include XY plots where no mappings are active or floating point out of range errorScale the view to ten percent of the size at which the data would fit the full frame:
>>> view.magnification = 0.10 >>> view.magnification 0.10
-
LineView.
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)
Raises: TecplotSystemError
– View could not be translated.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)
- x (
-
LineView.
zoom
(xmin, xmax, ymin, 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: Raises: TecplotSystemError
– The view could not be zoomed.Zoom so the rectangular region with corners at (xmin, ymin)=(1,0) and (xmax, ymax)=(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.reset_to_entire_circle() plot.view.fit() tp.export.save_png('view_polar.png', 600, supersample=3)
Attributes
extents
View extents in grid units of transformed X & Y. 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. fit_to_nice
()Set axis range to begin/end on major axis increments. reset_to_entire_circle
()Set the range of Theta to encompass an entire circle. translate
([x, y])Shift the data being plotted in the X and/or Y direction.
-
PolarView.
center
()¶ Center the data within the axis grid area.
Raises: TecplotSystemError
– View could not be centered.
-
PolarView.
extents
¶ View extents in grid units of transformed X & Y.
Type: 4- tuple
offloat
(x1, y1, x2, y2)- x1, y1: Upper left corner of the extents.
- x2, y2: Lower right corner of the extents.
Set the view of the polar plot to view the full extents of the plot area:
>>> plot.view.extents = (10, 10, 90, 90) >>> plot.view.extents.x1 10.0 >>> plot.view.extents.y1 10.0 >>> plot.view.extents.x2 90.0 >>> plot.view.extents.y2 90.0
-
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.
Raises: TecplotSystemError
– Internal error.
-
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.
Raises: TecplotSystemError
– Internal error.
-
PolarView.
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.
Raises: TecplotSystemError
– Internal error.
-
PolarView.
magnification
¶ Magnification for the data being plotted.
Type: float
The
magnification
value is a decimal percent and must be greater than 0. Amagnification
size of 1.0 (100%) will size the plot so that it can fit within the grid area.Raises: TecplotSystemError
– Magnification could not be queried or set. Possible cases whereTecplotSystemError
is raised include XY plots where no mappings are active or floating point out of range errorScale the view to ten percent of the size at which the data would fit the full frame:
>>> view.magnification = 0.10 >>> view.magnification 0.10
-
PolarView.
reset_to_entire_circle
()[source]¶ Set the range of Theta to encompass an entire circle.
>>> plot.view.reset_to_entire_circle()
Raises: TecplotSystemError
– Internal error.
-
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)
Raises: TecplotSystemError
– View could not be translated.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)
- x (