Plot¶
Plots¶
Cartesian2DFieldPlot¶
-
class
tecplot.plot.
Cartesian2DFieldPlot
(frame)[source]¶ 2D plot containing field data associated with style through fieldmaps.
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, '2D', 'intflow.lpk') dataset = tp.load_layout(infile) frame = tp.active_frame() plot = frame.plot(PlotType.Cartesian2D) plot.activate() plot.show_edge = False plot.show_mesh = False plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue' # save image to file tp.export.save_png('plot_field2d.png', 600)
Attributes
active_fieldmap_indices
Set of active fieldmaps by index. active_fieldmaps
Active fieldmaps in this plot. axes
Axes style control for this plot. draw_order
The order in which objects are drawn to the screen. fieldmaps
All fieldmaps in this plot. num_fieldmaps
Number of all fieldmaps in this plot. scatter
Plot-local Scatter
style control.show_contour
Enable contours for this plot. show_edge
Enable zone edge lines for this plot. show_lighting_effect
Enable lighting effect for all objects within this plot. show_mesh
Enable mesh lines for this plot. show_scatter
Enable scatter symbols for this plot. show_shade
Enable surface shading effect for this plot. show_streamtraces
Enable drawing streamtraces on this plot. show_translucency
Enable translucent effect for all objects within this plot. show_vector
Enable drawing of vectors. vector
Vector variable and style control for this plot. view
Axes orientation and limits adjustments. Methods
activate
()Make this the active plot type on the parent frame. contour
(index)Plot-local ContourGroup
style control.fieldmap
(key)Returns a Cartesian2DFieldmap
byZone
or index.fieldmap_index
(zone)Returns the index of the fieldmap associated with a Zone
.
-
Cartesian2DFieldPlot.
activate
()[source]¶ Make this the active plot type on the parent frame.
Example usage:
>>> from tecplot.constant import PlotType >>> plot = frame.plot(PlotType.Cartesian2D) >>> plot.activate()
-
Cartesian2DFieldPlot.
active_fieldmap_indices
¶ Set of active fieldmaps by index.
Type: Set displays
Example usage:
>>> for i in plot.active_fieldmap_indices: ... plot.fieldmap(i).scatter.show = True
-
Cartesian2DFieldPlot.
active_fieldmaps
¶ Active fieldmaps in this plot.
Yields:
Cartesian2DFieldmap
orCartesian3DFieldmap
Example usage:
>>> for fmap in plot.active_fieldmaps: ... fmap.vector.show = True
-
Cartesian2DFieldPlot.
axes
¶ Axes style control for this plot.
Type: Cartesian2DFieldAxes
Example usage:
>>> from tecplot.constant import PlotType >>> frame.plot_type = PlotType.Cartesian2D >>> axes = frame.plot().axes >>> axes.x_axis.variable = dataset.variable('U') >>> axes.y_axis.variable = dataset.variable('V')
-
Cartesian2DFieldPlot.
contour
(index)¶ Plot-local
ContourGroup
style control.Type: ContourGroup
Example usage:
>>> contour = frame.plot().contour(0) >>> contour.colormap_name = 'Magma'
-
Cartesian2DFieldPlot.
draw_order
¶ The order in which objects are drawn to the screen.
Type: TwoDDrawOrder
Possible values:
TwoDDrawOrder.ByZone
,TwoDDrawOrder.ByLayer
.The order is either by
Zone
or by visual layer (contour, mesh, etc.):>>> plot.draw_order = TwoDDrawOrder.ByZone
-
Cartesian2DFieldPlot.
fieldmap
(key)[source]¶ Returns a
Cartesian2DFieldmap
byZone
or index.- Parameter:
Example usage:
>>> fmap = plot.fieldmap(dataset.zone(0)) >>> fmap.scatter.show = True
-
Cartesian2DFieldPlot.
fieldmap_index
(zone)¶ Returns the index of the fieldmap associated with a
Zone
.Returns: Index
Example usage:
>>> fmap_index = plot.fieldmap_index(dataset.zone('Zone')) >>> plot.fieldmap(fmap_index).show_mesh = True
-
Cartesian2DFieldPlot.
fieldmaps
¶ All fieldmaps in this plot.
Yields:
Cartesian2DFieldmap
orCartesian3DFieldmap
Example usage:
>>> for fmap in plot.fieldmaps: ... fmap.mesh.show = True
-
Cartesian2DFieldPlot.
num_fieldmaps
¶ Number of all fieldmaps in this plot.
Type: integer
Example usage:
>>> print(frame.plot().num_fieldmaps) 3
-
Cartesian2DFieldPlot.
scatter
¶ Plot-local
Scatter
style control.Type: Scatter
Example usage:
>>> scatter = frame.plot().scatter >>> scatter.variable = dataset.variable('P')
-
Cartesian2DFieldPlot.
show_contour
¶ Enable contours for this plot.
Type: bool
Example usage:
>>> frame.plot().show_contour = True
-
Cartesian2DFieldPlot.
show_edge
¶ Enable zone edge lines for this plot.
Type: bool
Example usage:
>>> frame.plot().show_edge = True
-
Cartesian2DFieldPlot.
show_lighting_effect
¶ Enable lighting effect for all objects within this plot.
Type: bool
Example usage:
>>> frame.plot().show_lighting_effect = True
-
Cartesian2DFieldPlot.
show_mesh
¶ Enable mesh lines for this plot.
Type: bool
Example usage:
>>> frame.plot().show_mesh = True
-
Cartesian2DFieldPlot.
show_scatter
¶ Enable scatter symbols for this plot.
Type: bool
Example usage:
>>> frame.plot().show_scatter = True
-
Cartesian2DFieldPlot.
show_shade
¶ Enable surface shading effect for this plot.
Type: bool
Example usage:
>>> frame.plot().show_shade = True
-
Cartesian2DFieldPlot.
show_streamtraces
¶ Enable drawing streamtraces on this plot.
Type: bool
Example usage:
>>> frame.plot().show_streamtraces = True
-
Cartesian2DFieldPlot.
show_translucency
¶ Enable translucent effect for all objects within this plot.
Type: bool
Example usage:
>>> frame.plot().show_translucency = True
-
Cartesian2DFieldPlot.
show_vector
¶ Enable drawing of vectors.
Type: bool
Example usage:
>>> frame.plot().show_vector = True
-
Cartesian2DFieldPlot.
vector
¶ Vector variable and style control for this plot.
Type: Vector2D
Example usage:
>>> plot.vector.u_variable = dataset.variable('U')
-
Cartesian2DFieldPlot.
view
¶ Axes orientation and limits adjustments.
Type: Cartesian2DView
Example usage:
>>> plot.view.fit()
Cartesian3DFieldPlot¶
-
class
tecplot.plot.
Cartesian3DFieldPlot
(frame)[source]¶ 3D plot containing field data associated with style through fieldmaps.
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, '3D', 'spaceship.lpk') dataset = tp.load_layout(infile) frame = tp.active_frame() plot = frame.plot(PlotType.Cartesian3D) plot.activate() plot.show_lighting_effect = False plot.show_streamtraces = False # save image to file tp.export.save_png('plot_field3d.png', 600)
Attributes
active_fieldmap_indices
Set of active fieldmaps by index. active_fieldmaps
Active fieldmaps in this plot. axes
Axes style control for this plot. fieldmaps
All fieldmaps in this plot. num_fieldmaps
Number of all fieldmaps in this plot. scatter
Plot-local Scatter
style control.show_contour
Enable contours for this plot. show_edge
Enable zone edge lines for this plot. show_lighting_effect
Enable lighting effect for all objects within this plot. show_mesh
Enable mesh lines for this plot. show_scatter
Enable scatter symbols for this plot. show_shade
Enable surface shading effect for this plot. show_streamtraces
Enable drawing streamtraces on this plot. show_translucency
Enable translucent effect for all objects within this plot. show_vector
Enable drawing of vectors. vector
Vector variable and style control for this plot. view
Viewport, axes orientation and limits adjustments. Methods
activate
()Make this the active plot type on the parent frame. contour
(index)Plot-local ContourGroup
style control.fieldmap
(key)Returns a Cartesian3DFieldmap
byZone
or index.fieldmap_index
(zone)Returns the index of the fieldmap associated with a Zone
.
-
Cartesian3DFieldPlot.
activate
()[source]¶ Make this the active plot type on the parent frame.
Example usage:
>>> from tecplot.constant import PlotType >>> plot = frame.plot(PlotType.Cartesian3D) >>> plot.activate()
-
Cartesian3DFieldPlot.
active_fieldmap_indices
¶ Set of active fieldmaps by index.
Type: Set displays
Example usage:
>>> for i in plot.active_fieldmap_indices: ... plot.fieldmap(i).scatter.show = True
-
Cartesian3DFieldPlot.
active_fieldmaps
¶ Active fieldmaps in this plot.
Yields:
Cartesian2DFieldmap
orCartesian3DFieldmap
Example usage:
>>> for fmap in plot.active_fieldmaps: ... fmap.vector.show = True
-
Cartesian3DFieldPlot.
axes
¶ Axes style control for this plot.
Type: Cartesian3DFieldAxes
Example usage:
>>> from tecplot.constant import PlotType >>> frame.plot_type = PlotType.Cartesian3D >>> axes = frame.plot().axes >>> axes.x_axis.variable = dataset.variable('U') >>> axes.y_axis.variable = dataset.variable('V') >>> axes.z_axis.variable = dataset.variable('W')
-
Cartesian3DFieldPlot.
contour
(index)¶ Plot-local
ContourGroup
style control.Type: ContourGroup
Example usage:
>>> contour = frame.plot().contour(0) >>> contour.colormap_name = 'Magma'
-
Cartesian3DFieldPlot.
fieldmap
(key)[source]¶ Returns a
Cartesian3DFieldmap
byZone
or index.- Parameter:
Example usage:
>>> fmap = plot.fieldmap(dataset.zone(0)) >>> fmap.scatter.show = True
-
Cartesian3DFieldPlot.
fieldmap_index
(zone)¶ Returns the index of the fieldmap associated with a
Zone
.Returns: Index
Example usage:
>>> fmap_index = plot.fieldmap_index(dataset.zone('Zone')) >>> plot.fieldmap(fmap_index).show_mesh = True
-
Cartesian3DFieldPlot.
fieldmaps
¶ All fieldmaps in this plot.
Yields:
Cartesian2DFieldmap
orCartesian3DFieldmap
Example usage:
>>> for fmap in plot.fieldmaps: ... fmap.mesh.show = True
-
Cartesian3DFieldPlot.
num_fieldmaps
¶ Number of all fieldmaps in this plot.
Type: integer
Example usage:
>>> print(frame.plot().num_fieldmaps) 3
-
Cartesian3DFieldPlot.
scatter
¶ Plot-local
Scatter
style control.Type: Scatter
Example usage:
>>> scatter = frame.plot().scatter >>> scatter.variable = dataset.variable('P')
-
Cartesian3DFieldPlot.
show_contour
¶ Enable contours for this plot.
Type: bool
Example usage:
>>> frame.plot().show_contour = True
-
Cartesian3DFieldPlot.
show_edge
¶ Enable zone edge lines for this plot.
Type: bool
Example usage:
>>> frame.plot().show_edge = True
-
Cartesian3DFieldPlot.
show_lighting_effect
¶ Enable lighting effect for all objects within this plot.
Type: bool
Example usage:
>>> frame.plot().show_lighting_effect = True
-
Cartesian3DFieldPlot.
show_mesh
¶ Enable mesh lines for this plot.
Type: bool
Example usage:
>>> frame.plot().show_mesh = True
-
Cartesian3DFieldPlot.
show_scatter
¶ Enable scatter symbols for this plot.
Type: bool
Example usage:
>>> frame.plot().show_scatter = True
-
Cartesian3DFieldPlot.
show_shade
¶ Enable surface shading effect for this plot.
Type: bool
Example usage:
>>> frame.plot().show_shade = True
-
Cartesian3DFieldPlot.
show_streamtraces
¶ Enable drawing streamtraces on this plot.
Type: bool
Example usage:
>>> frame.plot().show_streamtraces = True
-
Cartesian3DFieldPlot.
show_translucency
¶ Enable translucent effect for all objects within this plot.
Type: bool
Example usage:
>>> frame.plot().show_translucency = True
-
Cartesian3DFieldPlot.
show_vector
¶ Enable drawing of vectors.
Type: bool
Example usage:
>>> frame.plot().show_vector = True
-
Cartesian3DFieldPlot.
vector
¶ Vector variable and style control for this plot.
Type: Vector3D
Example usage:
>>> plot.vector.u_variable = dataset.variable('U')
-
Cartesian3DFieldPlot.
view
¶ Viewport, axes orientation and limits adjustments.
Type: Cartesian3DView
Example usage:
>>> plot.view.fit()
PolarLinePlot¶
-
class
tecplot.plot.
PolarLinePlot
(frame)[source]¶ Polar plot with line data and associated style through linemaps.
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.variable('R')[:] = r zone.variable('Theta')[:] = theta plot = frame.plot(PlotType.PolarLine) plot.activate() plot.axes.r_axis.max = r.max() plot.axes.theta_axis.mode = ThetaMode.Radians plot.delete_linemaps() lmap = plot.add_linemap('Linemap', zone, dataset.variable('R'), dataset.variable('Theta')) lmap.line.line_thickness = 0.8 plot.view.fit() tp.export.save_png('plot_polar.png', 600)
Attributes
active_linemap_indices
Set displays
of all active linemaps by index.active_linemaps
Yields all active linemaps. axes
Axes style control for this plot. legend
Line legend style and placement control. num_linemaps
Number of linemaps held by this plot. show_lines
Enable lines for this plot. show_symbols
Enable symbols at line vertices for this plot. view
View control of the plot relative to the frame. Methods
activate
()Make this the active plot type on the parent frame. add_linemap
(name, zone, r, theta[, show])Add a linemap using the specified zone and variables. delete_linemaps
(*linemaps)Clear all linemaps within this plot. linemap
(pattern)Returns a specific linemap within this plot. linemaps
([pattern])Yields linemaps matching the given pattern
-
PolarLinePlot.
activate
()[source]¶ Make this the active plot type on the parent frame.
Example usage:
>>> from tecplot.constant import PlotType >>> plot = frame.plot(PlotType.PolarLine) >>> plot.activate()
-
PolarLinePlot.
active_linemap_indices
¶ Set displays
of all active linemaps by index.Type: Set displays
ofintegers
Numbers are zero-based indices to the linemaps:
>>> active_indices = plot.active_linemap_indices >>> active_lmaps = [plot.linemap(i) for i in active_indices]
-
PolarLinePlot.
active_linemaps
¶ Yields all active linemaps.
Yields:
XYLinemap
orPolarLinemap
Example usage:
>>> from tecplot.constant import Color >>> for lmap in plot.active_linemaps: ... lmap.line.color = Color.Blue
-
PolarLinePlot.
add_linemap
(name, zone, r, theta, show=True)[source]¶ Add a linemap using the specified zone and variables.
Parameters: - name (
string
) – Name of the linemap which can be used for retrieving withPolarLinePlot.linemap
. - zone (
Zone
) – The data to be used when drawing this linemap. - r (
Variable
) – Ther
variable which must be from the sameDataset
astheta
andzone
. - theta (
Variable
) – Thetheta
variable which must be from the sameDataset
asr
andzone
. - show (
boolean
, optional) – Enable this linemap as soon as it’s added. (default:True
)
Returns: Example usage:
>>> lmap = plot.add_linemap('Line 1', dataset.zone('Zone'), ... dataset.variable('R'), ... dataset.variable('Theta')) >>> lmap.line.line_thickness = 0.8
- name (
-
PolarLinePlot.
axes
¶ Axes style control for this plot.
Type: PolarLineAxes
Example usage:
>>> from tecplot.constant import PlotType, ThetaMode >>> frame.plot_type = PlotType.PolarLine >>> axes = frame.plot().axes >>> axes.theta_mode = ThetaMode.Radians
-
PolarLinePlot.
delete_linemaps
(*linemaps)¶ Clear all linemaps within this plot.
- Parameter:
Example usage:
>>> plot.delete_linemaps() >>> print(plot.num_linemaps) 0
-
PolarLinePlot.
legend
¶ Line legend style and placement control.
Type: LineLegend
Example usage:
>>> plot.legend.show = True
-
PolarLinePlot.
linemap
(pattern)[source]¶ Returns a specific linemap within this plot.
- Parameter:
Returns: PolarLinemap
Example usage:
>>> plot.linemap(0).error_bar.show = True
-
PolarLinePlot.
linemaps
(pattern=None)¶ Yields linemaps matching the given pattern
- Parameter:
- pattern (
string
, optional): A name pattern to match. If no - pattern is given, all linemaps are yielded.
- pattern (
Yields: XYLinemap
orPolarLine
objects.Example usage:
>>> for lmap in plot.linemaps(): ... lmap.show = True
-
PolarLinePlot.
num_linemaps
¶ Number of linemaps held by this plot.
Type: integer
Example usage:
>>> print(plot.num_linemaps) 3
-
PolarLinePlot.
show_lines
¶ Enable lines for this plot.
Type: boolean
Example usage:
>>> plot.show_lines = True
XYLinePlot¶
-
class
tecplot.plot.
XYLinePlot
(frame)[source]¶ Cartesian plot with line data and associated style through linemaps.
from os import path import tecplot as tp from tecplot.constant import PlotType, FillMode examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, '2D', 'random.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() plot = frame.plot(PlotType.XYLine) plot.activate() plot.show_symbols = True plot.linemap(0).line.show = False plot.linemap(0).symbols.fill_color = FillMode.UseLineColor # save image to file tp.export.save_png('plot_xyline.png', 600)
Attributes
active_linemap_indices
Set displays
of all active linemaps by index.active_linemaps
Yields all active linemaps. axes
Axes style control for this plot. legend
Line legend style and placement control. num_linemaps
Number of linemaps held by this plot. show_bars
Enable bar chart drawing mode for this plot. show_error_bars
Enable error bars for this plot. show_lines
Enable lines for this plot. show_symbols
Enable symbols at line vertices for this plot. view
View control of the plot relative to the frame. Methods
activate
()Make this the active plot type on the parent frame. add_linemap
(name, zone, x, y[, show])Add a linemap using the specified zone and variables. delete_linemaps
(*linemaps)Clear all linemaps within this plot. linemap
(pattern)Returns a specific linemap within this plot. linemaps
([pattern])Yields linemaps matching the given pattern
-
XYLinePlot.
activate
()[source]¶ Make this the active plot type on the parent frame.
Example usage:
>>> from tecplot.constant import PlotType >>> plot = frame.plot(PlotType.XYLine) >>> plot.activate()
-
XYLinePlot.
active_linemap_indices
¶ Set displays
of all active linemaps by index.Type: Set displays
ofintegers
Numbers are zero-based indices to the linemaps:
>>> active_indices = plot.active_linemap_indices >>> active_lmaps = [plot.linemap(i) for i in active_indices]
-
XYLinePlot.
active_linemaps
¶ Yields all active linemaps.
Yields:
XYLinemap
orPolarLinemap
Example usage:
>>> from tecplot.constant import Color >>> for lmap in plot.active_linemaps: ... lmap.line.color = Color.Blue
-
XYLinePlot.
add_linemap
(name, zone, x, y, show=True)[source]¶ Add a linemap using the specified zone and variables.
Parameters: - name (
string
) – Name of the linemap which can be used for retrieving withXYLinePlot.linemap
. - zone (
Zone
) – The data to be used when drawing this linemap. - x (
Variable
) – Thex
variable which must be from the sameDataset
asy
andzone
. - y (
Variable
) – They
variable which must be from the sameDataset
asx
andzone
. - show (
boolean
, optional) – Enable this linemap as soon as it’s added. (default:True
)
Returns: Example usage:
>>> lmap = plot.add_linemap('Line 1', dataset.zone('Zone'), ... dataset.variable('X'), ... dataset.variable('Y')) >>> lmap.line.line_thickness = 0.8
- name (
-
XYLinePlot.
axes
¶ Axes style control for this plot.
Type: XYLineAxes
Example usage:
>>> from tecplot.constant import PlotType, AxisMode >>> frame.plot_type = PlotType.XYLine >>> axes = frame.plot().axes >>> axes.axis_mode = AxisMode.XYDependent >>> axes.xy_ratio = 2
-
XYLinePlot.
delete_linemaps
(*linemaps)¶ Clear all linemaps within this plot.
- Parameter:
Example usage:
>>> plot.delete_linemaps() >>> print(plot.num_linemaps) 0
-
XYLinePlot.
legend
¶ Line legend style and placement control.
Type: LineLegend
Example usage:
>>> plot.legend.show = True
-
XYLinePlot.
linemap
(pattern)[source]¶ Returns a specific linemap within this plot.
- Parameter:
Returns: XYLinemap
Example usage:
>>> plot.linemap(0).error_bar.show = True
-
XYLinePlot.
linemaps
(pattern=None)¶ Yields linemaps matching the given pattern
- Parameter:
- pattern (
string
, optional): A name pattern to match. If no - pattern is given, all linemaps are yielded.
- pattern (
Yields: XYLinemap
orPolarLine
objects.Example usage:
>>> for lmap in plot.linemaps(): ... lmap.show = True
-
XYLinePlot.
num_linemaps
¶ Number of linemaps held by this plot.
Type: integer
Example usage:
>>> print(plot.num_linemaps) 3
-
XYLinePlot.
show_bars
¶ Enable bar chart drawing mode for this plot.
Type: boolean
Example usage:
>>> plot.show_bars = True
-
XYLinePlot.
show_error_bars
¶ Enable error bars for this plot.
Type: boolean
The variable to be used for error bars must be set first on at least one linemap within this plot:
>>> plot.linemap(0).error_bars.variable = dataset.variable('E') >>> plot.show_error_bars = True
-
XYLinePlot.
show_lines
¶ Enable lines for this plot.
Type: boolean
Example usage:
>>> plot.show_lines = True
SketchPlot¶
-
class
tecplot.plot.
SketchPlot
(frame, *svargs)[source]¶ A plot space with no data attached.
import tecplot as tp from tecplot.constant import PlotType frame = tp.active_frame() plot = frame.plot(PlotType.Sketch) frame.add_text('Hello, World!', (36, 50), size=34) plot.axes.x_axis.show = True plot.axes.y_axis.show = True tp.export.save_png('plot_sketch.png', 600)
Attributes
axes
Axes (x and y) for the sketch plot. Methods
activate
()Make this the active plot type on the parent frame.
-
SketchPlot.
activate
()[source]¶ Make this the active plot type on the parent frame.
Example usage:
>>> from tecplot.constant import PlotType >>> plot = frame.plot(PlotType.Sketch) >>> plot.activate()
-
SketchPlot.
axes
¶ Axes (x and y) for the sketch plot.
Type: SketchAxes
Example usage:
>>> from tecplot.constant import PlotType >>> frame.plot_type = PlotType.Sketch >>> frame.plot().axes.x_axis.show = True
Fieldmaps¶
Cartesian2DFieldmap¶
-
class
tecplot.plot.
Cartesian2DFieldmap
(index, plot)[source]¶ Attributes
contour
FieldmapContour
style including flooding, lines and line coloring.edge
FieldmapEdge
style control for boundary lines.effects
FieldmapEffects
style control for clipping and blanking effects.group
Zero-based group number for this Fieldmaps. mesh
FieldmapMesh
style lines connecting neighboring data points.points
FieldmapPoints
object to control which points to draw.scatter
FieldmapScatter
style for scatter plots.shade
FieldmapShade
style control for surface shading.show
Display this fieldmap on the plot. show_iso_surfaces
( bool
) Enable drawing of Iso-surfaces.show_slices
( bool
) Enable drawing of slice surfaces.show_streamtraces
( bool
) Enable drawing of streamtraces.surfaces
FieldmapSurfaces
object to control which surfaces to draw.vector
FieldmapVector
style for vector field plots using arrows.zones
List of zones used by this fieldmap.
-
Cartesian2DFieldmap.
contour
¶ FieldmapContour
style including flooding, lines and line coloring.Type: FieldmapContour
-
Cartesian2DFieldmap.
edge
¶ FieldmapEdge
style control for boundary lines.Type: FieldmapEdge
-
Cartesian2DFieldmap.
effects
¶ FieldmapEffects
style control for clipping and blanking effects.Type: FieldmapEffects
-
Cartesian2DFieldmap.
group
¶ Zero-based group number for this Fieldmaps.
Type: integer
This is a piece of auxiliary data and can be useful for identifying a subset of fieldmaps. For example, to loop over all fieldmaps that have group set to 4:
>>> plot.fieldmap(0).group = 4 >>> plot.fieldmap(3).group = 4 >>> for fmap in filter(lambda f: f.group == 4, plot.fieldmaps()): ... print(fmap.index) 0 3
-
Cartesian2DFieldmap.
mesh
¶ FieldmapMesh
style lines connecting neighboring data points.Type: FieldmapMesh
-
Cartesian2DFieldmap.
points
¶ FieldmapPoints
object to control which points to draw.Type: FieldmapPoints
-
Cartesian2DFieldmap.
scatter
¶ FieldmapScatter
style for scatter plots.Type: FieldmapScatter
-
Cartesian2DFieldmap.
shade
¶ FieldmapShade
style control for surface shading.Type: FieldmapShade
-
Cartesian2DFieldmap.
show
¶ Display this fieldmap on the plot.
Type: bool
Example usage for turning on all fieldmaps:
>>> for fmap in plot.fieldmaps(): ... fmap.show = True
-
Cartesian2DFieldmap.
surfaces
¶ FieldmapSurfaces
object to control which surfaces to draw.Type: FieldmapSurfaces
-
Cartesian2DFieldmap.
vector
¶ FieldmapVector
style for vector field plots using arrows.Type: FieldmapVector
Cartesian3DFieldmap¶
-
class
tecplot.plot.
Cartesian3DFieldmap
(index, plot)[source]¶ Attributes
contour
FieldmapContour
style including flooding, lines and line coloring.edge
FieldmapEdge
style control for boundary lines.effects
FieldmapEffects3D
style control for blanking and lighting effects.group
Zero-based group number for this Fieldmaps. mesh
FieldmapMesh
style lines connecting neighboring data points.points
FieldmapPoints
object to control which points to draw.scatter
FieldmapScatter
style for scatter plots.shade
FieldmapShade3D
style control for surface shading.show
Display this fieldmap on the plot. show_iso_surfaces
( bool
) Enable drawing of Iso-surfaces.show_slices
( bool
) Enable drawing of slice surfaces.show_streamtraces
( bool
) Enable drawing of streamtraces.surfaces
FieldmapSurfaces
object to control which surfaces to draw.vector
FieldmapVector
style for vector field plots using arrows.zones
List of zones used by this fieldmap.
-
Cartesian3DFieldmap.
contour
¶ FieldmapContour
style including flooding, lines and line coloring.Type: FieldmapContour
-
Cartesian3DFieldmap.
edge
¶ FieldmapEdge
style control for boundary lines.Type: FieldmapEdge
-
Cartesian3DFieldmap.
effects
¶ FieldmapEffects3D
style control for blanking and lighting effects.Type: FieldmapEffects3D
-
Cartesian3DFieldmap.
group
¶ Zero-based group number for this Fieldmaps.
Type: integer
This is a piece of auxiliary data and can be useful for identifying a subset of fieldmaps. For example, to loop over all fieldmaps that have group set to 4:
>>> plot.fieldmap(0).group = 4 >>> plot.fieldmap(3).group = 4 >>> for fmap in filter(lambda f: f.group == 4, plot.fieldmaps()): ... print(fmap.index) 0 3
-
Cartesian3DFieldmap.
mesh
¶ FieldmapMesh
style lines connecting neighboring data points.Type: FieldmapMesh
-
Cartesian3DFieldmap.
points
¶ FieldmapPoints
object to control which points to draw.Type: FieldmapPoints
-
Cartesian3DFieldmap.
scatter
¶ FieldmapScatter
style for scatter plots.Type: FieldmapScatter
-
Cartesian3DFieldmap.
shade
¶ FieldmapShade3D
style control for surface shading.Type: FieldmapShade3D
-
Cartesian3DFieldmap.
show
¶ Display this fieldmap on the plot.
Type: bool
Example usage for turning on all fieldmaps:
>>> for fmap in plot.fieldmaps(): ... fmap.show = True
-
Cartesian3DFieldmap.
surfaces
¶ FieldmapSurfaces
object to control which surfaces to draw.Type: FieldmapSurfaces
-
Cartesian3DFieldmap.
vector
¶ FieldmapVector
style for vector field plots using arrows.Type: FieldmapVector
FieldmapContour¶
-
class
tecplot.plot.
FieldmapContour
(fieldmap)[source]¶ Style control for flooding and contour lines.
This object controls which contour groups are associated with flooding, line placement and line coloring. Three different contour groups may be used though there are eight total groups that can be configured in a single plot. In this example, we flood by the first contour group (index: 0), place contour lines by the second contour group (index: 1) and color the lines by the third contour group (index: 2):
import numpy as np import tecplot as tp from tecplot.constant import * from tecplot.data.operate import execute_equation # Get the active frame, setup a grid (30x30x30) # where each dimension ranges from 0 to 30. # Add variables P,Q,R to the dataset and give # values to the data. frame = tp.active_frame() dataset = frame.dataset for v in ['X','Y','Z','P','Q','R']: dataset.add_variable(v) zone = dataset.add_ordered_zone('Zone', (30,30,30)) xx = np.linspace(0,30,30) for v,arr in zip(['X','Y','Z'],np.meshgrid(xx,xx,xx)): zone.variable(v)[:] = arr.ravel() execute_equation('{P} = -10 * {X} + {Y}**2 + {Z}**2') execute_equation('{Q} = {X} - 10 * {Y} - {Z}**2') execute_equation('{R} = {X}**2 + {Y}**2 - {Z} ') # Enable 3D field plot and turn on contouring # with boundary faces frame.plot_type = PlotType.Cartesian3D plot = frame.plot() srf = plot.fieldmap(0).surfaces srf.surfaces_to_plot = SurfacesToPlot.BoundaryFaces plot.show_contour = True # get the contour group associated with the # newly created zone contour = plot.fieldmap(dataset.zone('Zone')).contour # turn on flooding and lines, increase line thickness contour.contour_type = ContourType.Overlay contour.line_thickness = 0.7 # assign flooding to the first contour group contour.flood_group = plot.contour(0) contour.flood_group.variable = dataset.variable('P') contour.flood_group.colormap_name = 'Sequential - Yellow/Green/Blue' contour.flood_group.legend.show = False # assign line placement to the second contour group contour.line_group = plot.contour(1) contour.line_group.legend.show = False contour.line_group.variable = dataset.variable('Q') contour.line_group.default_num_levels = 4 # add labels to the lines based on placement contour.line_group.labels.show = True contour.line_group.labels.font.size = 3.5 contour.line_group.labels.font.bold = True # assign line coloring to the third contour group contour.line_color = plot.contour(2) contour.line_color.legend.show = False contour.line_color.variable = dataset.variable('R') # save image to PNG file tp.export.save_png('fieldmap_contour.png', 600)
Attributes
contour_type
ContourType
to plot.flood_group
The ContourGroup
to use for flooding.flood_group_index
The Index
of theContourGroup
to use for flooding.lighting_effect
Enable lighting effect on this contour. line_color
The Color
orContourGroup
to use when coloring the lines.line_group
The ContourGroup
to use for line placement and style.line_group_index
The Index
of theContourGroup
to use for contour lines.line_pattern
LinePattern
type to use for contour lines.line_thickness
Thickness ( float
) of the drawn lines.pattern_length
Length ( float
) of the pattern segment for non-solid lines.show
Enable drawing the contours.
-
FieldmapContour.
contour_type
¶ ContourType
to plot.Type: ContourType
Possible values are:
ContourType.Flood
(default)- Filled color between the contour levels.
ContourType.Lines
- Lines only.
ContourType.Overlay
- Lines overlayed on flood.
ContourType.AverageCell
- Filled color by the average value within cells.
ContourType.PrimaryValue
- Filled color by the value at the primary corner of the cells.
In this example, we enable both flooding and contour lines:
>>> from tecplot.constant import ContourType >>> contour = plot.fieldmap(0).contour >>> contour.contour_type = ContourType.Overlay
-
FieldmapContour.
flood_group
¶ The
ContourGroup
to use for flooding.Type: ContourGroup
This property sets and gets the
ContourGroup
used for flooding. Changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it. Example usage:>>> cmap_name = 'Sequential - Yellow/Green/Blue' >>> contour = plot.fieldmap(0).contour >>> contour.flood_group = plot.contour(1) >>> contour.flood_group.variable = dataset.variable('P') >>> contour.flood_group.colormap_name = cmap_name
-
FieldmapContour.
flood_group_index
¶ The
Index
of theContourGroup
to use for flooding.Type: integer
(zero-based index)This property sets and gets, by
Index
, theContourGroup
used for flooding. Changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it. Example usage:>>> contour = plot.fieldmap(0).contour >>> contour.flood_group_index = 1 >>> contour.flood_group.variable = dataset.variable('P')
-
FieldmapContour.
lighting_effect
¶ Enable lighting effect on this contour.
Type: bool
Example usage:
>>> contour = plot.fieldmap(0).contour >>> contour.lighting_effect = False
-
FieldmapContour.
line_color
¶ The
Color
orContourGroup
to use when coloring the lines.Type: Color
orContourGroup
FieldmapContour lines can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property. Note that changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it. Example usage:>>> from tecplot.constant import Color >>> contour = plot.fieldmap(1).contour >>> contour.line_color = Color.Blue
Example of setting the color from a
ContourGroup
:>>> contour = plot.fieldmap(0).contour >>> contour.line_color = plot.contour(1) >>> contour.line_color.variable = dataset.variable('P')
-
FieldmapContour.
line_group
¶ The
ContourGroup
to use for line placement and style.Type: ContourGroup
This property sets and gets the
ContourGroup
used for line placement and though all properties of theContourGroup
can be manipulated through this object, many of them such as color will not effect 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.fieldmap(0).contour >>> contour.line_group = plot.contour(2) >>> contour.line_group.variable = dataset.variable('Z')
-
FieldmapContour.
line_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 and though all properties of theContourGroup
can be manipulated through this object, many of them such as color will not effect 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.fieldmap(0).contour >>> contour.line_group_index = 2 >>> contour.line_group.variable = dataset.variable('Z')
-
FieldmapContour.
line_pattern
¶ LinePattern
type to use for contour lines.Type: LinePattern
Possible values:
Solid
,Dashed
,DashDot
,Dotted
,LongDash
,DashDotDot
.Example usage:
>>> from tecplot.constant import LinePattern >>> contour = plot.fieldmap(0).contour >>> contour.line_pattern = LinePattern.DashDotDot
-
FieldmapContour.
line_thickness
¶ Thickness (
float
) of the drawn lines.Type: float
in percentage of theFrame
‘s height.Example usage:
>>> contour = plot.fieldmap(0).contour >>> contour.line_thickness = 0.7
FieldmapEdge¶
-
class
tecplot.plot.
FieldmapEdge
(fieldmap)[source]¶ Volume boundary lines.
An edge plot layer displays the connections of the outer lines (
IJ
-ordered zones), finite element surface zones, or planes (IJK
-ordered zones). The FieldmapEdge layer allows you to display the edges (creases and borders) of your data. Zone edges exist only for ordered zones or 2D finite element zones. Three-dimensional finite element zones do not have boundaries.import os import tecplot as tp from tecplot.constant import Color, EdgeType, PlotType, SurfacesToPlot examples_dir = tp.session.tecplot_examples_directory() datafile = os.path.join(examples_dir, '3D', 'jet_surface.plt') dataset = tp.data.load_tecplot(datafile) frame = dataset.frame # Enable 3D field plot, turn on contouring and translucency frame.plot_type = PlotType.Cartesian3D plot = frame.plot() plot.show_contour = True plot.show_edge = True plot.contour(0).colormap_name = 'Sequential - Blue' # adjust effects for every fieldmap in this dataset for zone in dataset.zones(): fmap = plot.fieldmap(zone) fmap.contour.flood_group.variable = dataset.variable('S') fmap.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces edge = fmap.edge edge.edge_type = EdgeType.Creases edge.color = Color.Blue edge.line_thickness = 0.7 # save image to file tp.export.save_png('fieldmap_edge.png', 600)
Attributes
color
Line Color
.edge_type
Where to draw edge lines. i_border
Which border lines to draw in the I
-dimension.j_border
Which border lines to draw in the J
-dimension.k_border
Which border lines to draw in the K
-dimension.line_thickness
Thickness of the edge lines drawn. show
Draw the mesh for this fieldmap.
-
FieldmapEdge.
edge_type
¶ Where to draw edge lines.
Type: EdgeType
Possible values:
Borders
,Creases
,BordersAndCreases
.Example usage:
>>> from tecplot.constant import EdgeType >>> plot.show_edge = True >>> plot.fieldmap(0).edge.edge_type = EdgeType.Creases
-
FieldmapEdge.
i_border
¶ Which border lines to draw in the
I
-dimension.Type: BorderLocation
Possible values:
None
,Min
,Max
,Both
.Example usage:
>>> from tecplot.constant import BorderLocation >>> plot.show_edge = True >>> plot.fieldmap(0).edge.i_border = BorderLocation.Min
-
FieldmapEdge.
j_border
¶ Which border lines to draw in the
J
-dimension.Type: BorderLocation
Possible values:
None
,Min
,Max
,Both
.Example usage:
>>> from tecplot.constant import BorderLocation >>> plot.show_edge = True >>> plot.fieldmap(0).edge.j_border = BorderLocation.Both
-
FieldmapEdge.
k_border
¶ Which border lines to draw in the
K
-dimension.Type: BorderLocation
Possible values:
None
,Min
,Max
,Both
.Example usage:
>>> from tecplot.constant import BorderLocation >>> plot.show_edge = True >>> plot.fieldmap(0).edge.k_border = None
FieldmapEffects¶
-
class
tecplot.plot.
FieldmapEffects
(fieldmap)[source]¶ Clipping and blanking style control.
This object controls value blanking and clipping from plane slices for this fieldmap.
Attributes
clip_planes
Clip planes to use when drawing this fieldmap. value_blanking
Enable value blanking effect for this fieldmap.
FieldmapEffects3D¶
-
class
tecplot.plot.
FieldmapEffects3D
(fieldmap)[source]¶ Clipping, blanking, lighting and translucency style control.
import os import tecplot as tp from tecplot.constant import LightingEffect, PlotType, SurfacesToPlot examples_dir = tp.session.tecplot_examples_directory() datafile = os.path.join(examples_dir, '3D', 'jet_surface.plt') dataset = tp.data.load_tecplot(datafile) frame = dataset.frame # Enable 3D field plot, turn on contouring and translucency frame.plot_type = PlotType.Cartesian3D plot = frame.plot() plot.show_contour = True plot.show_translucency = True # adjust effects for every fieldmap in this dataset for zone in dataset.zones(): fmap = plot.fieldmap(zone) fmap.contour.flood_group.variable = dataset.variable('S') fmap.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces eff = fmap.effects eff.lighting_effect = LightingEffect.Paneled eff.surface_translucency = 30 # save image to file tp.export.save_png('fieldmap_effects3d.png', 600)
Attributes
clip_planes
Clip planes to use when drawing this fieldmap. lighting_effect
The type of lighting effect to render. surface_translucency
Translucency of all drawn surfaces for this fieldmap. value_blanking
Enable value blanking effect for this fieldmap.
-
FieldmapEffects3D.
clip_planes
¶ Clip planes to use when drawing this fieldmap.
Type: list
ofintegers
[0-5] orNone
Example usage:
>>> plot.fieldmap(0).effects.clip_planes = [0,1,2]
-
FieldmapEffects3D.
lighting_effect
¶ The type of lighting effect to render.
Type: LightingEffect
Possible values:
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 plot type offers smoother, more continuous shading than
Paneled
shading, but it results in slower plotting and larger vector images.Gouraud
shading is not continuous across zone boundaries unless face neighbors are specified in the data and is not available for finite element volume zones when blanking is active in which case, the zone’s lighting effect reverts toPaneled
shading in this case.
If
IJK
-ordered data withFieldmapSurfaces.surfaces_to_plot
is set toSurfacesToPlot.ExposedCellFaces
, faces exposed by blanking will revert toPaneled
shading.Example usage:
>>> from tecplot.constant import LightingEffect >>> effects = plot.fieldmap(0).effects >>> effects.lighting_effect = LightingEffect.Paneled
FieldmapMesh¶
-
class
tecplot.plot.
FieldmapMesh
(fieldmap)[source]¶ Lines connecting neighboring data points.
The mesh plot layer displays the lines connecting neighboring data points within a
Zone
. ForI
-ordered data, the mesh is a single line connecting all of the points in order of increasingI
-index. ForIJ
-ordered data, the mesh consists of two families of lines connecting adjacent data points of increasingI
-index and increasingJ
-index. ForIJK
-ordered data, the mesh consists of three families of lines, one connecting points of increasingI
-index, one connecting points of increasingJ
-index, and one connecting points of increasingK
-index. For finite element zones, the mesh is a plot of every edge of all of the elements that are defined by the connectivity list for the node points.from os import path import numpy as np import tecplot as tp from tecplot.constant import PlotType, MeshType examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, '3D', 'jet_surface.plt') dataset = tp.data.load_tecplot(infile) # Enable 3D field plot and turn on contouring frame = tp.active_frame() frame.plot_type = PlotType.Cartesian3D plot = frame.plot() plot.show_mesh = True contour = plot.contour(0) contour.variable = dataset.variable('S') contour.colormap_name = 'Doppler' contour.levels.reset_levels(np.linspace(0.02,0.12,11)) # set the mesh type and color for all zones for zone in dataset.zones(): mesh = plot.fieldmap(zone).mesh mesh.mesh_type = MeshType.HiddenLine mesh.color = contour # save image to file tp.export.save_png('fieldmap_mesh.png', 600)
Attributes
color
The Color
orContourGroup
to use when drawing the lines.line_pattern
LinePattern
type to use for mesh lines.line_thickness
Thickness ( float
) of the drawn lines.mesh_type
MeshType
to show.pattern_length
Length ( float
) of the pattern segment for non-solid lines.show
Draw the mesh for this fieldmap.
-
FieldmapMesh.
color
¶ The
Color
orContourGroup
to use when drawing the lines.Type: Color
orContourGroup
FieldmapContour lines can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property. Note that changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it. Example usage:>>> from tecplot.constant import Color >>> plot.fieldmap(1).mesh.color = Color.Blue
Example of setting the color from a
ContourGroup
:>>> plot.fieldmap(1).mesh.color = plot.contour(1)
-
FieldmapMesh.
line_pattern
¶ LinePattern
type to use for mesh lines.Type: LinePattern
Possible values:
Solid
,Dashed
,DashDot
,Dotted
,LongDash
,DashDotDot
.Example usage:
>>> from tecplot.constant import LinePattern >>> mesh = plot.fieldmap(0).mesh >>> mesh.line_pattern = LinePattern.Dashed
-
FieldmapMesh.
line_thickness
¶ Thickness (
float
) of the drawn lines.Type: float
in percentage of theFrame
‘s height.Example usage:
>>> plot.fieldmap(0).mesh.line_thickness = 0.7
-
FieldmapMesh.
mesh_type
¶ MeshType
to show.Type: MeshType
Possible values:
Wireframe
Wire frame meshes are drawn below any other zone layers on the same zone. In 3D Cartesian plots, no hidden lines are removed. For 3D volume zones (finite element volume or IJK-ordered), the full 3D mesh (consisting of all the connecting lines between data points) is not generally drawn because the sheer number of lines would make it confusing. The mesh drawn will depend on
FieldmapSurfaces
which can be obtained through the parent fieldmap withmesh.fieldmap.surfaces
:from tecplot.constant import MeshType, SurfacesToPlot mesh = plot.fieldmap(0).mesh mesh.mesh_type = MeshType.Wireframe surfaces = mesh.fieldmap.surfaces surfaces.surfaces_to_plot = SurfacesToPlot.IPlanes
By default, only the mesh on exposed cell faces is shown.
Overlay
- Similar to Wire Frame, mesh lines are drawn over all other zone
layers except for vectors and scatter symbols. In 3D Cartesian
plots, the area behind the cells of the plot is still visible
(unless another plot type such as contour flooding prevents this).
As with Wire Frame, the mesh drawn will depend on
FieldmapSurfaces
which can be obtained through the parent fieldmap withmesh.fieldmap.surfaces
. HiddenLine
Similar to Overlay, except hidden lines are removed from behind the mesh. In effect, the cells (elements) of the mesh are opaque. FieldmapSurfaces and lines that are hidden behind another surface are removed from the plot. For 3D volume zones, using this plot type obscures everything inside the zone. If you choose this option for 3D volume zones, then choosing to plot every surface with:
from tecplot.constant import HiddenLine, SurfacesToPlot mesh = plot.fieldmap(0).mesh mesh.mesh_type = MeshType.HiddenLine surfaces = mesh.fieldmap.surfaces surfaces.surfaces_to_plot = SurfacesToPlot.All
has the same effect as plotting only exposed cell faces with:
surfaces.surfaces_to_plot = SurfacesToPlot.ExposedCellFaces
but is much slower.
FieldmapPoints¶
-
class
tecplot.plot.
FieldmapPoints
(fieldmap)[source]¶ Type and density of the points used for vector and scatter plots.
This object controls the location of the points for
FieldmapVector
andFieldmapScatter
plots relative to the cells.from os import path import tecplot as tp from tecplot.constant import PlotType, PointsToPlot examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, '2D', 'exchng.plt') dataset = tp.data.load_tecplot(infile) # Enable 3D field plot and turn on contouring frame = tp.active_frame() frame.plot_type = 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_vector = True for zone in dataset.zones(): points = plot.fieldmap(zone).points points.points_to_plot = PointsToPlot.SurfaceCellCenters points.step = (2,2) # save image to file tp.export.save_png('fieldmap_points.png', 600)
Attributes
points_to_plot
Location of the points to show. step
Step along the dimensions ( I
,J
,K
).
-
FieldmapPoints.
points_to_plot
¶ Location of the points to show.
Type: PointsToPlot
Possible values:
SurfaceNodes
- Draws only the nodes that are on the surface of the
Zone
. AllNodes
- Draws all nodes in the
Zone
. SurfaceCellCenters
- Draws points at the cell centers which are on or near the surface
of the
Zone
. AllCellCenters
- Draws points at all cell centers in the
Zone
. AllConnected
- Draws all the nodes that are connected by the node map. Nodes without any connectivity are not drawn.
Example usage:
>>> from tecplot.constant import PointsToPlot >>> pts = plot.fieldmap(0).points >>> sts.points_to_plot = PointsToPlot.SurfaceCellCenters
-
FieldmapPoints.
step
¶ Step along the dimensions (
I
,J
,K
).Type: tuple
ofintegers
This property specifies the
I
,J
, andK
-step intervals. For irregular and finite element data, only the first parameter orI
-Step has an effect. This steps through the nodes in the order they are listed in the data file. In this case, a single number can be given, but note that the return type is always a 3-tuple
for both ordered and irregular data.Example for
IJK
ordered data:>>> plot.fieldmap(0).points.step = (10,10,None) >>> print(plot.fieldmap(0).points.step) (10, 10, 1)
Example for irregular data:
>>> plot.fieldmap(0).points.step = 10 >>> print(plot.fieldmap(0).points.step) (10, 1, 1)
FieldmapScatter¶
-
class
tecplot.plot.
FieldmapScatter
(fieldmap)[source]¶ Plot of nodes using symbols.
FieldmapScatter
plots display symbols at the data points in a field. The symbols may be sized according to the values of a specified variable, colored by the values of the contour variable, or may be uniformly sized or colored. Unlike contour plots, scatter plots do not require any mesh structure connecting the points, allowing scatter plots of irregular data.from os import path import tecplot as tp from tecplot.constant import PlotType, SymbolType examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, '2D', 'random.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.Cartesian2D plot = frame.plot() plot.show_scatter = True scatter = plot.fieldmap(0).scatter scatter.symbol_type = SymbolType.Geometry scatter.fill_color = plot.contour(0) tp.export.save_png('fieldmap_scatter.png', 600)
Attributes
color
Line Color
orContourGroup
of the drawn symbols.fill_color
Fill or background color. line_thickness
Width of the lines when drawing symbols. show
Show the scatter symbols. size
Size of the symbols to draw. size_by_variable
Use a variable to determine relative size of symbols. symbol_type
The SymbolType
to use for this scatter plot.Methods
symbol
([symbol_type])Returns a scatter symbol style object.
-
FieldmapScatter.
color
¶ Line
Color
orContourGroup
of the drawn symbols.Type: Color
orContourGroup
This can be a solid color or a
ContourGroup
as obtained through theplot.contour
property. Note that changing style on theContourGroup
will affect all other fieldmaps in the sameFrame
that use it. Example usage:>>> from tecplot.constant import Color >>> plot.fieldmap(1).scatter.color = Color.Blue
Example of setting the color from a
ContourGroup
:>>> plot.fieldmap(1).scatter.color = plot.contour(1)
-
FieldmapScatter.
fill_color
¶ Fill or background color.
Type: Color
,ContourGroup
,FillMode.UseLineColor
,FillMode.UseBackgroundColor
orFillMode.None_
.Example usage:
>>> from tecplot.constant import Color, SymbolType >>> scatter = plot.fieldmap(0).scatter >>> scatter.symbol_type = SymbolType.Geometry >>> scatter.fill_color = Color.Red
-
FieldmapScatter.
line_thickness
¶ Width of the lines when drawing symbols.
Type: float
Example usage:
>>> from tecplot.constant import SymbolType >>> scatter = plot.fieldmap(0).scatter >>> scatter.symbol_type = SymbolType.Geometry >>> scatter.line_thickness = 1
-
FieldmapScatter.
show
¶ Show the scatter symbols.
Type: boolean
Example usage:
>>> plot.show_scatter = True >>> plot.fieldmap(2).scatter.show = True
-
FieldmapScatter.
size
¶ Size of the symbols to draw.
Type: float
Example usage:
>>> plot.fieldmap(0).scatter.size = 4
-
FieldmapScatter.
size_by_variable
¶ Use a variable to determine relative size of symbols.
Type: bool
Example usage:
>>> plot.scatter.variable = dataset.variable('P') >>> plot.fieldmap(0).scatter.size_by_variable = True
-
FieldmapScatter.
symbol
(symbol_type=None)[source]¶ Returns a scatter symbol style object.
Parameters: symbol_type ( SymbolType
, optional) – The type of symbol to return. By default, this will return the active symbol type which is obtained fromFieldmapScatter.symbol_type
.Returns:
TextScatterSymbol
orGeometryScatterSymbol
Example usage:
>>> from tecplot.constant import SymbolType >>> plot.fieldmap(0).scatter.symbol_type = SymbolType.Text >>> symbol = plot.fieldmap(0).scatter.symbol() >>> symbol.text = 'a'
-
FieldmapScatter.
symbol_type
¶ The
SymbolType
to use for this scatter plot.Type: SymbolType
Possible values are:
Geometry
,Text
.Example usage:
>>> from tecplot.constant import SymbolType >>> plot.fieldmap(0).scatter.symbol_type = SymbolType.Text
GeometryScatterSymbol¶
-
class
tecplot.plot.
GeometryScatterSymbol
(parent)[source]¶ Geometric shape for scatter plots.
from os import path import tecplot as tp from tecplot.constant import (Color, PlotType, PointsToPlot, SymbolType, GeomShape) examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, '2D', 'exchng.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.Cartesian2D plot = frame.plot() plot.show_scatter = True zones = list(dataset.zones()) for i,zone in enumerate(zones): points = plot.fieldmap(zone).points points.points_to_plot = PointsToPlot.SurfaceCellCenters points.step = (2,2) scatter = plot.fieldmap(zone).scatter scatter.color = Color(i) scatter.fill_color = Color(i+len(zones)) scatter.size = 2 scatter.line_thickness = 0.5 scatter.symbol_type = SymbolType.Geometry scatter.symbol().shape = GeomShape(i%7) tp.export.save_png('fieldmap_scatter_geometry.png', 600)
Attributes
shape
Geometric shape to use when plotting scatter points.
-
GeometryScatterSymbol.
shape
¶ Geometric shape to use when plotting scatter points.
Type: GeomShape
Possible values:
Square
,Del
,Grad
,RTri
,LTri
,Diamond
,Circle
,Cube
,Sphere
,Octahedron
,Point
.Example usage:
>>> from tecplot.constant import SymbolType, GeomShape >>> scatter = plot.fieldmap(0).scatter >>> scatter.symbol_type = SymbolType.Geometry >>> scatter.symbol().shape = GeomShape.Diamond
TextScatterSymbol¶
-
class
tecplot.plot.
TextScatterSymbol
(parent)[source]¶ Text character for scatter plots.
Only a single character can be used.
from os import path import tecplot as tp from tecplot.constant import (Color, PlotType, PointsToPlot, SymbolType, GeomShape) examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, '2D', 'exchng.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.Cartesian2D plot = frame.plot() plot.show_scatter = True zones = list(dataset.zones()) for i,zone in enumerate(zones): points = plot.fieldmap(zone).points points.points_to_plot = PointsToPlot.SurfaceCellCenters points.step = (4,4) scatter = plot.fieldmap(zone).scatter scatter.color = Color((i%4)+13) scatter.fill_color = Color.Yellow scatter.size = 3 scatter.symbol_type = SymbolType.Text symbol = scatter.symbol() symbol.text = hex(i)[-1] tp.export.save_png('fieldmap_scatter_text.png', 600)
Attributes
text
The ASCII character to use as the symbol to show typeface
Type face to use when rendering text-based scatter.
-
TextScatterSymbol.
text
¶ The ASCII character to use as the symbol to show
Note
This is limited to a single character.
Example usage:
>>> from tecplot.constant import SymbolType >>> scatter = plot.fieldmap(0).scatter >>> scatter.symbol_type = SymbolType.Text >>> scatter.symbol().text = 'X'
-
TextScatterSymbol.
typeface
¶ Type face to use when rendering text-based scatter.
Type: Font.Greek
,Font.Math
,Font.UserDefined
orNone
(default).If
None
(the default), the plot’s top-level scatter settings will determine the font used.Example usage:
>>> from tecplot.constant import SymbolType, Font >>> scatter = plot.fieldmap(0).scatter >>> scatter.symbol_type = SymbolType.Text >>> scatter.symbol().typeface = Font.Greek
FieldmapShade¶
-
class
tecplot.plot.
FieldmapShade
(fieldmap)[source]¶ Fill color for displayed surfaces on 2D field plots.
Although most commonly used with 3D surfaces (see
FieldmapShade3D
), shade plots can be used to flood 2D plots with solid colors.import os import random import tecplot from tecplot.constant import Color, PlotType random.seed(1) examples_dir = tecplot.session.tecplot_examples_directory() datafile = os.path.join(examples_dir, '3D', 'jet_surface.plt') dataset = tecplot.data.load_tecplot(datafile) frame = dataset.frame frame.plot_type = PlotType.Cartesian2D plot = frame.plot() for zone in dataset.zones(): color = Color(random.randint(0,63)) while color == Color.White: color = Color(random.randint(0,63)) fmap = plot.fieldmap(zone) fmap.shade.color = color tecplot.export.save_png('jet_surface_shade2d.png', 600)
Attributes
color
Fill Color
of the shade.show
FieldmapShade the drawn surfaces.
FieldmapShade3D¶
-
class
tecplot.plot.
FieldmapShade3D
(fieldmap)[source]¶ Fill color for displayed surfaces on 3D field plots.
This class inherits all functionality and purpose from
FieldmapShade
and adds the ability to turn on or off the lighting effect. In 3D plots, fieldmap effects (translucency and lighting) cause color variation (shading) throughout the zones. Shading can can be useful in discerning the shape of the data:import os import random import tecplot from tecplot.constant import Color, PlotType, SurfacesToPlot random.seed(1) examples_dir = tecplot.session.tecplot_examples_directory() datafile = os.path.join(examples_dir, '3D', 'jet_surface.plt') dataset = tecplot.data.load_tecplot(datafile) frame = dataset.frame frame.plot_type = PlotType.Cartesian3D plot = frame.plot() for zone in dataset.zones(): color = Color(random.randint(0,63)) while color == Color.White: color = Color(random.randint(0,63)) fmap = plot.fieldmap(zone) fmap.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces fmap.shade.color = color fmap.shade.lighting_effect = False tecplot.export.save_png('jet_surface_shade3d.png', 600)
Attributes
color
Fill Color
of the shade.lighting_effect
Draw a lighting effect on the shaded surfaces. show
FieldmapShade the drawn surfaces.
-
FieldmapShade3D.
color
¶ Fill
Color
of the shade.Type: Color
Example usage:
>>> from tecplot.constant import Color >>> plot.fieldmap(0).shade.color = Color.Blue
FieldmapSurfaces¶
-
class
tecplot.plot.
FieldmapSurfaces
(fieldmap)[source]¶ Plot surfaces from volume data.
This class controls viewing volume data as surfaces, either via a boundary surface or one or more planes along the
I
,J
,K
dimensions for ordered data.import numpy as np import tecplot as tp from tecplot.constant import * from tecplot.data.operate import execute_equation # Get the active frame, setup a grid (30x30x30) # where each dimension ranges from 0 to 30. # Add variable P to the dataset and give # values to the data. frame = tp.active_frame() dataset = frame.dataset for v in ['X','Y','Z','P']: dataset.add_variable(v) zone = dataset.add_ordered_zone('Zone', (30,30,30)) xx = np.linspace(0,30,30) for v,arr in zip(['X','Y','Z'],np.meshgrid(xx,xx,xx)): zone.variable(v)[:] = arr.ravel() execute_equation('{P} = -10*{X} + {Y}**2 + {Z}**2') # Enable 3D field plot and turn on contouring frame.plot_type = PlotType.Cartesian3D plot = frame.plot() plot.show_contour = True # get a handle of the fieldmap for this zone fmap = plot.fieldmap(dataset.zone('Zone')) # set the active contour group to flood by variable P fmap.contour.flood_group.variable = dataset.variable('P') # show I and J-planes through the surface fmap.surfaces.surfaces_to_plot = SurfacesToPlot.IJPlanes # show only the first and last I-planes # min defaults to 0, max defaults to -1 # we set step to -1 which is equivalent # to the I-dimensions's max fmap.surfaces.i_range = None,None,-1 # show J-planes at indices: [5, 15, 25] fmap.surfaces.j_range = 5,25,10 # save image to file tp.export.save_png('fieldmap_surfaces_ij.png', 600)
Attributes
i_range
IndexRange
for the I dimension of ordered data.j_range
IndexRange
for the J dimension of ordered data.k_range
IndexRange
for the K dimension of ordered data.surfaces_to_plot
The surfaces to show.
-
FieldmapSurfaces.
i_range
¶ IndexRange
for the I dimension of ordered data.Type: tuple
ofintegers
(min, max, step)This example shows
I
-planes ati = [0, 2, 4, 6, 8, 10]
:>>> from tecplot.constant import SurfacesToPlot >>> srf = frame.plot().fieldmap(0).surfaces >>> srf.surfaces_to_plot = SurfacesToPlot.IPlanes >>> srf.i_range = 0, 10, 2
-
FieldmapSurfaces.
j_range
¶ IndexRange
for the J dimension of ordered data.Type: tuple
ofintegers
(min, max, step)This example shows all
J
-planes starting withj = 10
up to the maximumJ
-plane of the associatedZone
:>>> from tecplot.constant import SurfacesToPlot >>> srf = frame.plot().fieldmap(0).surfaces >>> srf.surfaces_to_plot = SurfacesToPlot.JPlanes >>> srf.j_range = 10, None, 1
-
FieldmapSurfaces.
k_range
¶ IndexRange
for the K dimension of ordered data.Type: tuple
ofintegers
(min, max, step)This example shows all
K
-planes starting with the first up to 5 from the lastK
-plane of the associatedZone
:>>> from tecplot.constant import SurfacesToPlot >>> srf = frame.plot().fieldmap(0).surfaces >>> srf.surfaces_to_plot = SurfacesToPlot.KPlanes >>> srf.k_range = None, -5
-
FieldmapSurfaces.
surfaces_to_plot
¶ The surfaces to show.
Type: SurfacesToPlot
Possible values:
BoundaryFaces
,ExposedCellFaces
,IPlanes
,JPlanes
,KPlanes
,IJPlanes
,JKPlanes
,IKPlanes
,IJKPlanes
,All
, the python built-inNone
.Options such as
IJKPlanes
show planes from multiple dimensions. For example, theIJPlanes
value shows both theI
-planes and theJ
-planes. The following example shows a 3D field plot using faces on the boundary:>>> from tecplot.constant import SurfacesToPlot >>> frame.plot_type = PlotType.Cartesian3D >>> srf = frame.plot().fieldmap(0).surfaces >>> srf.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
FieldmapVector¶
-
class
tecplot.plot.
FieldmapVector
(fieldmap)[source]¶ Field plot of arrows.
Before doing anything with vector plots, one must set the variables to be used for the
(U, V, W)
coordinates. This is done through the plot object. Once set, the vectors can be displayed and manipulated using this class:import numpy as np import tecplot as tp from tecplot.data.operate import execute_equation from tecplot.constant import (PlotType, PointsToPlot, VectorType, ArrowheadStyle) frame = tp.active_frame() dataset = frame.dataset for v in ['X','Y','Z','P','Q','R']: dataset.add_variable(v) zone = dataset.add_ordered_zone('Zone', (30,30,30)) xx = np.linspace(0,30,30) for v,arr in zip(['X','Y','Z'],np.meshgrid(xx,xx,xx)): zone.variable(v)[:] = arr.ravel() execute_equation('{P} = -10 * {X} + {Y}**2 + {Z}**2') execute_equation('{Q} = {X} - 10 * {Y} - {Z}**2') execute_equation('{R} = {X}**2 + {Y}**2 - {Z} ') frame.plot_type = PlotType.Cartesian3D plot = frame.plot() plot.contour(0).variable = dataset.variable('P') plot.contour(0).colormap_name = 'Two Color' plot.vector.u_variable = dataset.variable('P') plot.vector.v_variable = dataset.variable('Q') plot.vector.w_variable = dataset.variable('R') plot.show_vector = True points = plot.fieldmap(0).points points.points_to_plot = PointsToPlot.AllNodes points.step = (5,3,2) vector = plot.fieldmap(0).vector vector.show = True vector.vector_type = VectorType.MidAtPoint vector.arrowhead_style = ArrowheadStyle.Filled vector.color = plot.contour(0) vector.line_thickness = 0.4 # save image to file tp.export.save_png('fieldmap_vector.png', 600)
Attributes
arrowhead_style
The ArrowheadStyle
drawn.color
The Color
orContourGroup
to use when drawing vectors.line_pattern
The LinePattern
used to draw the arrow line.line_thickness
The width of the arrow line. pattern_length
Length of the pattern used when drawing vector lines. show
Enable drawing vectors on the plot. tangent_only
Show only tangent vectors. vector_type
Anchor point of the drawn vectors.
-
FieldmapVector.
arrowhead_style
¶ The
ArrowheadStyle
drawn.Type: ArrowheadStyle
Possible values:
Plain
,Filled
,Hollow
.Example usage:
>>> from tecplot.constant import ArrowheadStyle >>> plot.fieldmap(0).vector.arrowhead_style = ArrowheadStyle.Filled
-
FieldmapVector.
color
¶ The
Color
orContourGroup
to use when drawing vectors.Type: Color
orContourGroup
FieldmapVectors can be a solid color or be colored by a
ContourGroup
as obtained through theplot.contour
property. Note that changing style on thisContourGroup
will affect all other fieldmaps on the sameFrame
that use it. Example usage:>>> from tecplot.constant import Color >>> plot.fieldmap(1).vector.color = Color.Blue
Example of setting the color from a
ContourGroup
:>>> plot.fieldmap(1).vector.color = plot.contour(1)
-
FieldmapVector.
line_pattern
¶ The
LinePattern
used to draw the arrow line.Type: LinePattern
Possible values:
Solid
,Dashed
,DashDot
,Dotted
,LongDash
,DashDotDot
.Example usage:
>>> from tecplot.constant import LinePattern >>> vector = plot.fieldmap(0).vector >>> vector.line_pattern = LinePattern.DashDot
-
FieldmapVector.
line_thickness
¶ The width of the arrow line.
Type: float
(percentage ofFrame
height)Example usage:
>>> from tecplot.constant import LinePattern >>> vector = plot.fieldmap(0).vector.line_thickness = 0.7
-
FieldmapVector.
pattern_length
¶ Length of the pattern used when drawing vector lines.
Type: float
(percentage ofFrame
height)Example usage:
>>> from tecplot.constant import LinePattern >>> vector = plot.fieldmap(0).vector >>> vector.line_pattern = LinePattern.Dashed >>> vector.pattern_length = 3.5
-
FieldmapVector.
show
¶ Enable drawing vectors on the plot.
Type: bool
Example usage:
>>> plot.show_vector = True >>> plot.fieldmap(0).vector.show = True
-
FieldmapVector.
tangent_only
¶ Show only tangent vectors.
Set to
True
to display only the tangent component of vectors. Tangent vectors are drawn on 3D surfaces only where it is possible to determine a vector normal to the surface. A plot where multiple surfaces intersect each other using common nodes is a case where tangent vectors are not drawn because there is more than one normal to choose from. An example of this would be a volumeIJK
-ordered zone where both theI
andJ
-planes are shown. If tangent vectors cannot be drawn, then regular vectors are plotted instead.Type: bool
Example usage:
>>> plot.fieldmap(0).vector.tangent_only = True
-
FieldmapVector.
vector_type
¶ Anchor point of the drawn vectors.
Type: VectorType
Possible values:
TailAtPoint
,HeadAtPoint
,MidAtPoint
,HeadOnly
.Example usage:
>>> from tecplot.constant import VectorType >>> plot.fieldmap(0).vector.vector_type = VectorType.MidAtPoint
Linemaps¶
PolarLinemap¶
-
class
tecplot.plot.
PolarLinemap
(uid, plot)[source]¶ Attributes
curve
LinemapCurve
style and fitting-method control for lines.function_dependency
The independent variable for function evalulation. index
Zero-based integer identifier for this Linemaps. indices
LinemapIndices
object controlling which lines are shown.line
LinemapLine
style for lines to be drawn.name
Name identifier of this Linemaps. r_axis
Radial axis used by this linemap. r_variable
r_variable_index
show
Display this linemap on the plot. show_in_legend
Show this Linemaps in the legend. sort_by
Variable
be used when listing lines shown.symbols
LinemapSymbols
style for markers at points along the lines.theta_axis
Angular axis used by this linemap. theta_variable
theta_variable_index
zone
Zone
this Linemaps will draw.zone_index
Zero-based index of the Zone
this Linemaps will draw.
-
PolarLinemap.
curve
¶ LinemapCurve
style and fitting-method control for lines.Type: LinemapCurve
-
PolarLinemap.
function_dependency
¶ The independent variable for function evalulation.
Type: FunctionDependency
Possible values:
RIndependent
,ThetaIndependent
.
-
PolarLinemap.
index
¶ Zero-based integer identifier for this Linemaps.
Type: integer
Example:
>>> lmap = plot.linemap(1) >>> print(lmap.index) 1
-
PolarLinemap.
indices
¶ LinemapIndices
object controlling which lines are shown.Type: LinemapIndices
-
PolarLinemap.
line
¶ LinemapLine
style for lines to be drawn.Type: LinemapLine
-
PolarLinemap.
name
¶ Name identifier of this Linemaps.
Type: string
Names are automatically assigned to each mapping. The nature of the name depends on the type of data used to create the mapping. If your data has only one dependent variable, the default is to use the zone name for the mapping. If your data has multiple dependent variables, then the default is to use the dependent variable name for the mapping. In either case each mapping is assigned a special name (
&ZN&
or&DN&
) that is replaced with the zone or variable name when the name is displayed.Selecting variables in a 3D finite element zone may require significant time, since the variable must be loaded over the entire zone. XY and Polar line plots are best used with linear or ordered data, or with two-dimensional finite element data.
Certain placeholder text will be replaced with values based on elements within the plot. By combining static text with these placeholders, you can construct a name in any format you like:
>>> plot.linemap(2).name = 'Zone: &ZN&'
The placeholders available are:
- Zone name (
&ZN&
) - This will be replaced with the actual name of the zone assigned to that mapping.
- Zone number (
&Z#&
) - This will be replaced with the actual number of the zone assigned to the mapping.
- Independent variable name (
&IV&
) - This will be replaced with the actual name of the independent variable assigned to that mapping.
- Independent variable number (
&I#&
) - This will be replaced with the actual number of the independent variable assigned to the mapping.
- Dependent variable name (
&DV&
) - This will be replaced with the actual name of the dependent variable assigned to that mapping.
- Dependent variable number (
&D#&
) - This will be replaced with the actual number of the dependent variable assigned to the mapping.
- Map number (
&M#&
) - This will be replaced with the actual number of the mapping.
- X-Axis number (
&X#&
) - This will be replaced with the actual number of the X-axis assigned to that mapping for XY Line plots. This option is not available for Polar Line plots.
- Y-Axis number (
&Y#&
) - This will be replaced with the actual number of the Y-axis assigned to that mapping for XY Line plots. This option is not available for Polar Line plots.
- Zone name (
-
PolarLinemap.
r_axis
¶ Radial axis used by this linemap.
Type: RadialLineAxis
-
PolarLinemap.
r_variable
¶
-
PolarLinemap.
r_variable_index
¶
-
PolarLinemap.
show
¶ Display this linemap on the plot.
Type: bool
Example usage for turning on all linemaps:
>>> for lmap in plot.linemaps(): ... lmap.show = True
-
PolarLinemap.
show_in_legend
¶ Show this Linemaps in the legend.
Type: LegendShow
Possible values:
LegendShow.Yes
- The mapping appears in the legend even if the mapping is turned off (deactivated) or its entry in the table looks exactly like another mapping’s entry.
LegendShow.No
- The mapping never appears in the legend.
LegendShow.Auto
(default)- The mapping appears in the legend only when the mapping is turned on. If two mappings would result in the same entry in the legend, only one entry is shown.
-
PolarLinemap.
sort_by
¶ Variable
be used when listing lines shown.Type: Variable
orLineMapSort
.Possible values: a
Variable
that is part of the associatedDataset
,LineMapSort.IndependentVar
,LineMapSort.DependentVar
, orLineMapSort.None_
.A specific variable may be used:
>>> plot.linemap(0).sort_by = dataset.variable('P')
or the
LineMapSort
enumeration:>>> from tecplot.constant import LineMapSort >>> plot.linemap(0).sort_by = LineMapSort.DependentVar
-
PolarLinemap.
symbols
¶ LinemapSymbols
style for markers at points along the lines.Type: LinemapSymbols
-
PolarLinemap.
theta_axis
¶ Angular axis used by this linemap.
Type: PolarAngleLineAxis
-
PolarLinemap.
theta_variable
¶
-
PolarLinemap.
theta_variable_index
¶
XYLinemap¶
-
class
tecplot.plot.
XYLinemap
(uid, plot)[source]¶ Data mapping for 2D Cartesian line plots.
Linemaps connect a specific
Zone
/Variable
combination to a line or set of lines, depending on the dimension of the data if ordered. Linemaps can share any of the axes available in the plot and orientation can be verical or horizontal by setting the independent variable withXYLinemap.function_dependency
:from os import path import tecplot as tp from tecplot.constant import PlotType, Color examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() lmap = plot.linemap(0) lmap.line.line_thickness = 0.8 lmap.line.color = Color.DeepRed lmap.y_axis.title.color = Color.DeepRed lmap = plot.linemap(1) lmap.show = True lmap.y_axis_index = 1 lmap.line.line_thickness = 0.8 lmap.line.color = Color.Blue lmap.y_axis.title.color = Color.Blue tp.export.save_png('linemap_xy.png', 600)
Attributes
bars
LinemapBars
style for bar charts.curve
LinemapCurve
style and fitting-method control for lines.error_bars
LinemapErrorBars
style for error bars.function_dependency
The independent variable for function evalulation. index
Zero-based integer identifier for this Linemaps. indices
LinemapIndices
object controlling which lines are shown.line
LinemapLine
style for lines to be drawn.name
Name identifier of this Linemaps. show
Display this linemap on the plot. show_in_legend
Show this Linemaps in the legend. sort_by
Variable
be used when listing lines shown.symbols
LinemapSymbols
style for markers at points along the lines.x_axis
X-axis used by this linemap. x_axis_index
Zero-based index of the x-axis used by this linemap. x_variable
Variable
used for x-positions of this linemap.x_variable_index
Zero-based index of the Variable
used for x-positions.y_axis
Y-axis used by this linemap. y_axis_index
Zero-based index of the y-axis used by this linemap. y_variable
Variable
used for y-positions of this linemap.y_variable_index
Zero-based index of the Variable
used for y-positions.zone
Zone
this Linemaps will draw.zone_index
Zero-based index of the Zone
this Linemaps will draw.
-
XYLinemap.
bars
¶ LinemapBars
style for bar charts.Type: LinemapBars
-
XYLinemap.
curve
¶ LinemapCurve
style and fitting-method control for lines.Type: LinemapCurve
-
XYLinemap.
error_bars
¶ LinemapErrorBars
style for error bars.Type: LinemapErrorBars
-
XYLinemap.
function_dependency
¶ The independent variable for function evalulation.
Type: FunctionDependency
Possible values:
XIndependent
,YIndependent
.Example usage:
>>> from tecplot.constant import FunctionDependency >>> lmap = plot.linemap(0) >>> lmap.function_dependency = FunctionDependency.YIndependent
-
XYLinemap.
index
¶ Zero-based integer identifier for this Linemaps.
Type: integer
Example:
>>> lmap = plot.linemap(1) >>> print(lmap.index) 1
-
XYLinemap.
indices
¶ LinemapIndices
object controlling which lines are shown.Type: LinemapIndices
-
XYLinemap.
line
¶ LinemapLine
style for lines to be drawn.Type: LinemapLine
-
XYLinemap.
name
¶ Name identifier of this Linemaps.
Type: string
Names are automatically assigned to each mapping. The nature of the name depends on the type of data used to create the mapping. If your data has only one dependent variable, the default is to use the zone name for the mapping. If your data has multiple dependent variables, then the default is to use the dependent variable name for the mapping. In either case each mapping is assigned a special name (
&ZN&
or&DN&
) that is replaced with the zone or variable name when the name is displayed.Selecting variables in a 3D finite element zone may require significant time, since the variable must be loaded over the entire zone. XY and Polar line plots are best used with linear or ordered data, or with two-dimensional finite element data.
Certain placeholder text will be replaced with values based on elements within the plot. By combining static text with these placeholders, you can construct a name in any format you like:
>>> plot.linemap(2).name = 'Zone: &ZN&'
The placeholders available are:
- Zone name (
&ZN&
) - This will be replaced with the actual name of the zone assigned to that mapping.
- Zone number (
&Z#&
) - This will be replaced with the actual number of the zone assigned to the mapping.
- Independent variable name (
&IV&
) - This will be replaced with the actual name of the independent variable assigned to that mapping.
- Independent variable number (
&I#&
) - This will be replaced with the actual number of the independent variable assigned to the mapping.
- Dependent variable name (
&DV&
) - This will be replaced with the actual name of the dependent variable assigned to that mapping.
- Dependent variable number (
&D#&
) - This will be replaced with the actual number of the dependent variable assigned to the mapping.
- Map number (
&M#&
) - This will be replaced with the actual number of the mapping.
- X-Axis number (
&X#&
) - This will be replaced with the actual number of the X-axis assigned to that mapping for XY Line plots. This option is not available for Polar Line plots.
- Y-Axis number (
&Y#&
) - This will be replaced with the actual number of the Y-axis assigned to that mapping for XY Line plots. This option is not available for Polar Line plots.
- Zone name (
-
XYLinemap.
show
¶ Display this linemap on the plot.
Type: bool
Example usage for turning on all linemaps:
>>> for lmap in plot.linemaps(): ... lmap.show = True
-
XYLinemap.
show_in_legend
¶ Show this Linemaps in the legend.
Type: LegendShow
Possible values:
LegendShow.Yes
- The mapping appears in the legend even if the mapping is turned off (deactivated) or its entry in the table looks exactly like another mapping’s entry.
LegendShow.No
- The mapping never appears in the legend.
LegendShow.Auto
(default)- The mapping appears in the legend only when the mapping is turned on. If two mappings would result in the same entry in the legend, only one entry is shown.
-
XYLinemap.
sort_by
¶ Variable
be used when listing lines shown.Type: Variable
orLineMapSort
.Possible values: a
Variable
that is part of the associatedDataset
,LineMapSort.IndependentVar
,LineMapSort.DependentVar
, orLineMapSort.None_
.A specific variable may be used:
>>> plot.linemap(0).sort_by = dataset.variable('P')
or the
LineMapSort
enumeration:>>> from tecplot.constant import LineMapSort >>> plot.linemap(0).sort_by = LineMapSort.DependentVar
-
XYLinemap.
symbols
¶ LinemapSymbols
style for markers at points along the lines.Type: LinemapSymbols
-
XYLinemap.
x_axis
¶ X-axis used by this linemap.
Type: Line Axes Example usage:
>>> plot.linemap(0).x_axis = plot.axes.x_axis(2)
-
XYLinemap.
x_axis_index
¶ Zero-based index of the x-axis used by this linemap.
Type: integer
Example usage:
>>> plot.linemap(0).x_axis_index = 2
-
XYLinemap.
x_variable
¶ Variable
used for x-positions of this linemap.Type: Variable
Example usage:
>>> plot.linemap(0).x_variable = dataset.variable('P')
-
XYLinemap.
x_variable_index
¶ Zero-based index of the
Variable
used for x-positions.Type: integer
Example usage:
>>> plot.linemap(0).x_variable_index = 2
-
XYLinemap.
y_axis
¶ Y-axis used by this linemap.
Type: Line Axes Example usage:
>>> plot.linemap(0).x_axis = plot.axes.y_axis(2)
-
XYLinemap.
y_axis_index
¶ Zero-based index of the y-axis used by this linemap.
Type: integer
Example usage:
>>> plot.linemap(0).y_axis_index = 2
-
XYLinemap.
y_variable
¶ Variable
used for y-positions of this linemap.Type: Variable
Example usage:
>>> plot.linemap(0).y_variable = dataset.variable('Q')
-
XYLinemap.
y_variable_index
¶ Zero-based index of the
Variable
used for y-positions.Type: integer
Example usage:
>>> plot.linemap(0).y_variable_index = 2
LinemapLine¶
-
class
tecplot.plot.
LinemapLine
(linemap)[source]¶ Style control for the line to be drawn.
This controls the style of the lines plotted for a given
XYLinemap
.from os import path import tecplot as tp from tecplot.constant import PlotType, Color, LinePattern examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() lmap = plot.linemap(0) line = lmap.line line.color = Color.Blue line.line_thickness = 1 line.line_pattern = LinePattern.LongDash line.pattern_length = 2 # save image to file tp.export.save_png('linemap_line.png', 600)
Attributes
color
Color
of the line to be drawn.line_pattern
Pattern style of the line to be drawn. line_thickness
Width of the line to be drawn. pattern_length
Segment length of the repeated line pattern. show
Display this point-to-point line on the plot.
-
LinemapLine.
color
¶ Color
of the line to be drawn.Type: Color
Example usage:
>>> from tecplot.constant import Color >>> plot.linemap(0).line.color = Color.Blue
-
LinemapLine.
line_pattern
¶ Pattern style of the line to be drawn.
Type: LinePattern
Possible values:
Solid
,Dashed
,DashDot
,Dotted
,LongDash
,DashDotDot
.Example usage:
>>> from tecplot.constant import LinePattern >>> lmap = plot.linemap(0) >>> lmap.line.line_pattern = LinePattern.LongDash
-
LinemapLine.
line_thickness
¶ Width of the line to be drawn.
Type: float
Example usage:
>>> plot.linemap(0).line.line_thickness = 0.5
LinemapCurve¶
-
class
tecplot.plot.
LinemapCurve
(linemap)[source]¶ Curve-fitting of the line.
This class controls how the line is to be drawn between data points. By default, the
CurveType.LineSeg
option is used and straight lines are used. Settingcurve_type
to a fit type or spline type will replace the line segments with a smooth curve:import numpy as np from os import path import tecplot as tp from tecplot.constant import PlotType, CurveType examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) dataset.add_variable('Weight') # convert error to weighting to be used for fitting below # This converts the error to (1 / error) # and normalizes to the range [1,100] zone = dataset.zone('ZONE 1') err1 = zone.variable('Error 1') wvar = zone.variable('Weight') err = err1.as_numpy_array() sigma = 1. / err dsigma = sigma.max() - sigma.min() sigma = (99 * (sigma - sigma.min()) / dsigma) + 1 wvar[:] = sigma frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() xvar = dataset.variable(0) for lmap,var in zip(plot.linemaps(),list(dataset.variables())[1:4]): lmap.x_variable = xvar lmap.y_variable = var lmap.show = True curves = [lmap.curve for lmap in plot.linemaps()] curves[0].curve_type = CurveType.PolynomialFit curves[0].num_points = 1000 curves[0].polynomial_order = 10 curves[1].curve_type = CurveType.PowerFit curves[1].fit_range = 4,8 curves[1].weight_variable = dataset.variable('Weight') curves[2].curve_type = CurveType.Spline curves[2].spline_derivative_at_ends = 0,0 # save image to file tp.export.save_png('linemap_curve.png', 600)
Attributes
curve_type
Type of curve to draw or fit. fit_range
The range to fit and display a fitted curve. num_points
Number of points to use when drawing a fitted curve. polynomial_order
Order of the fit when set to polynomial. spline_derivative_at_ends
Clamp the derivative of the spline fit at the edges of the range. weight_variable
Variable to use for curve-fit weighting. weight_variable_index
Zero-based index of the variable to use for curve-fit weighting.
-
LinemapCurve.
curve_type
¶ Type of curve to draw or fit.
Type: CurveType
Possible values:
LineSeg
,PolynomialFit
,EToRFit
,PowerFit
,Spline
,ParaSpline
.CurveType.LineSeg
(line segment, no curve-fit)- A series of linear segments connect adjacent data points. In XY Line plots, these will be line segments.
CurveType.PolynomialFit
- A polynomial of order
LinemapCurve.polynomial_order
is fit to the data points where \(1 <= N <= 10\). \(N = 1\) is a straight-line fit. CurveType.EToRFit
(exponential curve-fit)- An exponential curve-fit that finds the best curve of the form \(Y = e^{b\cdot X+c}\) which is equivalent to \(Y = a\cdot e^{b\cdot X}\), where \(a = e^c\). To use this curve type, Y-values for this variable must be all positive or all negative. If the function dependency is set to \(X = f(Y)\) all X-values must be all positive or all negative.
CurveType.PowerFit
- A power curve fit that finds the best curve of the form \(Y = e^{b \cdot \ln X + c}\) which is equivalent to \(Y = a\cdot X^b\) , where \(a = e^c\). To use this curve type, Y-values for this variable must be all positive or all negative; X-values must be all positive. If the function dependency is set to \(X = f(Y)\), X-values must be all positive or all negative, and the Y-values must all be positive.
CurveType.Spline
- A smooth curve is generated that goes through every point. The spline is drawn through the data points after sorting the points into increasing values of the independent variable, resulting in a single-valued function of the independent variable. The spline may be clamped or free. With a clamped spline, you supply the derivative of the function at each end point; with a non-clamped (natural or free) spline, these derivatives are determined for you. In xy-line plots, specifying the derivative gives you control over the initial and final slopes of the curve.
CurveType.ParaSpline
(parametric spline)- Creates a smooth curve as with a spline, except the assumption is
that both variables are functions of the index of the data points.
For example in xy-line plot,
ParaSpline
fits \(x = f(i)\) and \(y=g(i)\) where \(f()\) and \(g()\) are both smooth. No additional sorting of the points is performed. This spline may result in a multi-valued function of either or both axis variables.
Example usage:
>>> from tecplot.constant import CurveType >>> plot.linemap(0).curve.curve_type = CurveType.PolynomialFit
-
LinemapCurve.
fit_range
¶ The range to fit and display a fitted curve.
Type: 2- tuple
offloats
orNone
Example showing how to set the limits of a polynomial fit to [5,10]:
>>> from tecplot.constant import CurveType >>> curve = plot.linemap(0).curve >>> curve.curve_type = CurveType.PolynomialFit >>> curve.fit_range = 5, 10
Use
None
to use the whole range of independent variable:>>> curve.fit_range = None
-
LinemapCurve.
num_points
¶ Number of points to use when drawing a fitted curve.
Type: integer
Example usage:
>>> from tecplot.constant import CurveType >>> curve = plot.linemap(0).curve >>> curve.curve_type = CurveType.PolynomialFit >>> curve.num_points = 100
-
LinemapCurve.
polynomial_order
¶ Order of the fit when set to polynomial.
Type: integer
(1 to 10)A value of 1 will fit the data to a straight line. Example usage:
>>> from tecplot.constant import CurveType >>> curve = plot.linemap(0).curve >>> curve.curve_type = CurveType.PolynomialFit >>> curve.polynomial_order = 4
-
LinemapCurve.
spline_derivative_at_ends
¶ Clamp the derivative of the spline fit at the edges of the range.
Type: 2- tuple
offloats
orNone
Example showing how to set the derivative at the limits of a spline curve to zero:
>>> from tecplot.constant import CurveType >>> curve = plot.linemap(0).curve >>> curve.curve_type = CurveType.Spline >>> curve.spline_derivative_at_ends = 0, 0
Use
None
to allow the derivatives to float with fitting:>>> curve.spline_derivative_at_ends = None
-
LinemapCurve.
weight_variable
¶ Variable to use for curve-fit weighting.
Type: Variable
orNone
Example usage:
>>> from tecplot.constant import CurveType >>> curve = plot.linemap(0).curve >>> curve.curve_type = CurveType.PolynomialFit >>> curve.weight_variable = dataset.variable('P')
Use
None
to disable weighting by a variable when fitting:>>> curve.weight_variable = None
-
LinemapCurve.
weight_variable_index
¶ Zero-based index of the variable to use for curve-fit weighting.
Type: integer
orNone
Example usage:
>>> from tecplot.constant import CurveType >>> curve = plot.linemap(0).curve >>> curve.curve_type = CurveType.PolynomialFit >>> curve.weight_variable_index = 3
Use
None
to disable weighting by a variable when fitting:>>> curve.weight_variable_index = None
LinemapBars¶
-
class
tecplot.plot.
LinemapBars
(linemap)[source]¶ Bar chart style control.
A bar chart is an XY Line plot that uses vertical or horizontal bars placed along an axis to represent data points. Changing the function dependency of the linemap with
XYLinemap.function_dependency
controls the direction of the bars. By default, all mappings use \(y = f(x)\) and appear as vertical bar charts. Setting y to be the independent variable will cause the bars to be horizontal.from os import path import tecplot as tp from tecplot.constant import PlotType, Color examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() plot.show_bars = True lmap = plot.linemap(0) bars = lmap.bars bars.show = True bars.size = 0.6*(100 / dataset.zone(0).shape[0]) bars.fill_color = Color.Red bars.line_color = Color.Red bars.line_thickness = 0.01 tp.export.save_png('linemap_bars.png', 600)
Attributes
fill_color
Fill color of the bars. line_color
Edge line color of the bars. line_thickness
Edge line thickness of the bars. show
Display bars on the plot for this Linemaps. size
Width of the bars.
-
LinemapBars.
fill_color
¶ Fill color of the bars.
Type: Color
,FillMode.UseLineColor
,FillMode.UseBackgroundColor
, orFillMode.None_
.Example usage:
>>> from tecplot.constant import Color, SymbolType >>> bars = plot.linemap(0).bars >>> bars.fill_color = Color.Red
-
LinemapBars.
line_color
¶ Edge line color of the bars.
Type: Color
Example usage:
>>> from tecplot.constant import Color >>> plot.linemap(0).bars.line_color = Color.Red
-
LinemapBars.
line_thickness
¶ Edge line thickness of the bars.
Type: float
Example usage:
>>> plot.linemap(0).bars.line_thickness = 0.1
LinemapErrorBars¶
-
class
tecplot.plot.
LinemapErrorBars
(linemap)[source]¶ Error bar style and variable assignment control.
A single
XYLinemap
holds a singleVariable
assignment for error bars. Therefore, if you wish to have separate error bars for x and y, two linemaps are required:from math import sqrt from os import path import tecplot as tp from tecplot.constant import PlotType, Color, ErrorBar # setup dataset frame = tp.active_frame() ds = frame.create_dataset('Dataset') for v in ['x', 'y', 'xerr', 'yerr']: ds.add_variable(v) zone = ds.add_ordered_zone('Zone', 5) # create some data (x, y) zone.variable('x')[:] = [0,1,2,3,4] zone.variable('y')[:] = [1,2,4,8,10] # error in x is a constant zone.variable('xerr')[:] = [0.2]*5 # error in y is the square-root of the value zone.variable('yerr')[:] = [sqrt(y) for y in zone.variable('y')[:]] frame.plot_type = PlotType.XYLine plot = frame.plot() plot.delete_linemaps() xerr_lmap = plot.add_linemap('xerr', zone, ds.variable('x'), ds.variable('y')) yerr_lmap = plot.add_linemap('yerr', zone, ds.variable('x'), ds.variable('y')) xerr_lmap.error_bars.variable = ds.variable('xerr') xerr_lmap.error_bars.bar_type = ErrorBar.Horz xerr_lmap.error_bars.color = Color.Blue xerr_lmap.error_bars.line_thickness = 0.8 xerr_lmap.error_bars.show = True yerr_lmap.error_bars.variable = ds.variable('yerr') yerr_lmap.error_bars.bar_type = ErrorBar.Vert yerr_lmap.error_bars.color = Color.Blue yerr_lmap.error_bars.line_thickness = 0.8 yerr_lmap.error_bars.show = True plot.show_lines = False plot.show_error_bars = True plot.view.fit() tp.export.save_png('linemap_error_bars.png', 600)
Attributes
bar_type
Style of the error bar to draw. color
Color
of the error bars.endcap_size
Length of the endcaps of the error bars. line_thickness
Width of the error bar lines. show
Display error bars on the plot for this Linemaps. step
Space between points to show error bars. step_mode
Space the error bars by index or frame height. variable
Variable
to use for error bar sizes.variable_index
Zero-based variable index to use for error bar sizes.
-
LinemapErrorBars.
bar_type
¶ Style of the error bar to draw.
Type: ErrorBar
Possible values:
Up
,Down
,Left
,Right
,Horz
,Vert
,Cross
.Example usage:
>>> from tecplot.constant import ErrorBar >>> plot.linemap(0).bar_type = ErrorBar.Cross
-
LinemapErrorBars.
color
¶ Color
of the error bars.Type: Color
Example usage:
>>> from tecplot.constant import Color >>> plot.linemap(0).color = Color.Red
-
LinemapErrorBars.
endcap_size
¶ Length of the endcaps of the error bars.
Type: float
Example usage:
>>> plot.linemap(0).endcap_size = 2.5
-
LinemapErrorBars.
line_thickness
¶ Width of the error bar lines.
Type: float
Example usage:
>>> plot.linemap(0).line_thickness = 0.8
-
LinemapErrorBars.
show
¶ Display error bars on the plot for this Linemaps.
Type: bool
The parent plot object must have error bars enables as well which will require a variable to be set:
>>> plot.linemap(0).error_bars.variable = dataset.variable('E') >>> plot.show_error_bars = True >>> plot.linemap(0).error_bars.show = True
-
LinemapErrorBars.
step
¶ Space between points to show error bars.
Type: float
The step is specified either as a percentage of the frame height or as a number of indices to skip depending on the value of
LinemapErrorBars.step_mode
. This example will add error bars to every third point:>>> plot.linemap(0).error_bars.step = 3
-
LinemapErrorBars.
step_mode
¶ Space the error bars by index or frame height.
Type: StepMode
This example will make sure all error bars are no closer than 10% of the frame height to each other:
>>> from tecplot.constant import StepMode >>> ebars = plot.linemap(0).error_bars >>> ebars.step_mode = StepMode.ByFrameUnits >>> ebars.step = 10
LinemapIndices¶
-
class
tecplot.plot.
LinemapIndices
(linemap)[source]¶ Ordering and spacing of points to be drawn.
Each mapping can show either I, J, or K-varying families of lines. By default, the I-varying family of lines are displayed. You can also choose which members of the family are drawn (and using which data points), by specifying index ranges for each of I, J, and K. The index range for the varying index says which points to include in each line, and the index ranges for the other indices determine which lines in the family to include.
from os import path import tecplot as tp from tecplot.constant import PlotType, IJKLines examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() for lmap in list(plot.linemaps())[:3]: lmap.show = True lmap.indices.varying_index = IJKLines.I lmap.indices.i_range = 0,0,3 # save image to file tp.export.save_png('linemap_indices.png', 600)
Attributes
i_range
IndexRange
for the I dimension of ordered data.j_range
IndexRange
for the J dimension of ordered data.k_range
IndexRange
for the K dimension of ordered data.varying_index
Family of lines to be drawn.
-
LinemapIndices.
i_range
¶ IndexRange
for the I dimension of ordered data.Type: tuple
ofintegers
(min, max, step)This example shows
I
-lines ati = [0, 2, 4, 6, 8, 10]
:>>> plot.linemap(0).indices.i_range = 0, 10, 2
-
LinemapIndices.
j_range
¶ IndexRange
for the J dimension of ordered data.Type: tuple
ofintegers
(min, max, step)This example shows all
J
-lines starting withj = 10
up to the maximumJ
-line of the associatedZone
:>>> plot.linemap(0).indices.j_range = 10, None, 1
-
LinemapIndices.
k_range
¶ IndexRange
for the K dimension of ordered data.Type: tuple
ofintegers
(min, max, step)This example shows all
K
-lines starting with the first up to 5 from the lastK
-line of the associatedZone
:>>> plot.linemap(0).indices.k_range = None, -5
LinemapSymbols¶
-
class
tecplot.plot.
LinemapSymbols
(linemap)[source]¶ Style control for markers placed along lines.
This class allows the user to set the style of the symbols to be shown including setting a geometric shape, text character, line and fill colors and spacing. The plot-level
show_symbols
attribute must be enabled to show symbols in any specific linemap within the plot:from os import path import tecplot as tp from tecplot.constant import PlotType, Color examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() plot.show_symbols = True for lmap in list(plot.linemaps())[:3]: lmap.symbols.show = True lmap.symbols.size = 2.5 lmap.symbols.color = Color.Blue lmap.symbols.line_thickness = 0.4 lmap.symbols.fill_color = Color.Azure # save image to file tp.export.save_png('linemap_symbols.png', 600)
Attributes
color
Edge or text Color
of the drawn symbols.fill_color
The fill or background color. line_thickness
Width of the lines when drawing geometry symbols. show
Display symbols along the lines to be drawn. size
Size of the symbols to draw. step
Space between symbols to be shown. step_mode
Space the symbols by index or frame height. symbol_type
The SymbolType
to use for this linemap.Methods
symbol
([symbol_type])Returns a linemap symbol style object.
-
LinemapSymbols.
color
¶ Edge or text
Color
of the drawn symbols.Type: Color
Example usage:
>>> from tecplot.constant import Color >>> plot.linemap(1).symbols.color = Color.Blue
-
LinemapSymbols.
fill_color
¶ The fill or background color.
Type: Color
,FillMode.UseLineColor
,FillMode.UseBackgroundColor
, orFillMode.None_
.Example usage:
>>> from tecplot.constant import Color >>> symbols = plot.linemap(0).symbols >>> symbols.fill_color = Color.Yellow
-
LinemapSymbols.
line_thickness
¶ Width of the lines when drawing geometry symbols.
Type: float
Example usage:
>>> from tecplot.constant import SymbolType >>> symbols = plot.linemap(0).symbols >>> symbols.symbol_type = SymbolType.Geometry >>> symbols.line_thickness = 0.8
-
LinemapSymbols.
show
¶ Display symbols along the lines to be drawn.
Type: bool
The parent plot object must have symbols enabled as well:
>>> plot.show_symbols = True >>> plot.linemap(0).symbols.show = True
-
LinemapSymbols.
size
¶ Size of the symbols to draw.
Type: float
Example usage:
>>> plot.linemap(0).symbols.size = 3.5
-
LinemapSymbols.
step
¶ Space between symbols to be shown.
Type: float
The step is specified either as a percentage of the frame height or as a number of indices to skip depending on the value of
LinemapSymbols.step_mode
. This example will add symbols to every third point:>>> plot.linemap(0).symbols.step = 3
-
LinemapSymbols.
step_mode
¶ Space the symbols by index or frame height.
Type: StepMode
This example will make sure all symbols are no closer than 10% of the frame height to each other:
>>> from tecplot.constant import StepMode >>> sym = plot.linemap(0).symbols >>> sym.step_mode = StepMode.ByFrameUnits >>> sym.step = 10
-
LinemapSymbols.
symbol
(symbol_type=None)[source]¶ Returns a linemap symbol style object.
Parameters: symbol_type ( SymbolType
, optional) – The type of symbol to return. By default, this will return the active symbol type which is obtained fromLinemapSymbols.symbol_type
.Returns:
TextSymbol
orGeometrySymbol
Example usage:
>>> from tecplot.constant import SymbolType >>> plot.linemap(0).symbols.symbol_type = SymbolType.Text >>> symbol = plot.linemap(0).symbols.symbol() >>> symbol.text = 'a'
-
LinemapSymbols.
symbol_type
¶ The
SymbolType
to use for this linemap.Type: SymbolType
Possible values are:
SymbolType.Geometry
,SymbolType.Text
.This sets the active symbol type. Use LinemapSymbols.symbol` to access the symbol:
>>> from tecplot.constant import SymbolType >>> linemap = plot.linemap(0) >>> linemap.symbols.symbol_type = SymbolType.Text >>> symbol = linemap.symbols.symbol(SymbolType.Text) >>> symbol.text = 'a'
GeometrySymbol¶
-
class
tecplot.plot.
GeometrySymbol
(parent)[source]¶ Geometric shape for linemap symbols.
from os import path import tecplot as tp from tecplot.constant import PlotType, Color, GeomShape, SymbolType examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() plot.show_symbols = True cols = [Color.DeepRed, Color.Blue, Color.Fern] shapes = [GeomShape.Square, GeomShape.Circle, GeomShape.Del] for lmap,color,shape in zip(plot.linemaps(), cols, shapes): lmap.show = True lmap.line.color = color symbols = lmap.symbols symbols.show = True symbols.size = 4.5 symbols.color = color symbols.fill_color = color symbols.symbol_type = SymbolType.Geometry symbols.symbol().shape = shape plot.view.fit() # save image to file tp.export.save_png('linemap_symbols_geometry.png', 600)
Attributes
shape
Geometric shape to use when plotting linemap symbols.
-
GeometrySymbol.
shape
¶ Geometric shape to use when plotting linemap symbols.
Type: GeomShape
Possible values:
Square
,Del
,Grad
,RTri
,LTri
,Diamond
,Circle
,Cube
,Sphere
,Octahedron
,Point
.Example usage:
>>> from tecplot.constant import SymbolType, GeomShape >>> symbols = plot.linemap(0).symbols >>> symbols.symbol_type = SymbolType.Geometry >>> symbols.symbol().shape = GeomShape.Diamond
TextSymbol¶
-
class
tecplot.plot.
TextSymbol
(parent)[source]¶ Text character for linemap symbols.
Only a single character can be used.
from os import path import tecplot as tp from tecplot.constant import PlotType, Color, SymbolType examples_dir = tp.session.tecplot_examples_directory() infile = path.join(examples_dir, 'XY', 'rainfall.plt') dataset = tp.data.load_tecplot(infile) frame = tp.active_frame() frame.plot_type = PlotType.XYLine plot = frame.plot() plot.show_symbols = True cols = [Color.DeepRed, Color.Blue, Color.Fern] chars = ['S','D','M'] for lmap,color,character in zip(plot.linemaps(), cols, chars): lmap.show = True lmap.line.color = color syms = lmap.symbols syms.show = True syms.symbol_type = SymbolType.Text syms.size = 2.5 syms.color = Color.White syms.fill_color = color syms.symbol().text = character plot.view.fit() # save image to file tp.export.save_png('linemap_symbols.png', 600)
Attributes
text
The ASCII character to use as the symbol to show typeface
Type face to use when rendering text-based symbols.
-
TextSymbol.
text
¶ The ASCII character to use as the symbol to show
Note
This is limited to a single character.
Example usage:
>>> from tecplot.constant import SymbolType >>> symbols = plot.linemap(0).symbols >>> symbols.symbol_type = SymbolType.Text >>> symbols.symbol().text = 'X'
-
TextSymbol.
typeface
¶ Type face to use when rendering text-based symbols.
Type: Font.Greek
,Font.Math
, Font.UserDefined` or Python’s built-inNone
(default).If
None
(the default), the plot’s top-level scatter settings will determine the font used.Example usage:
>>> from tecplot.constant import SymbolType, Font >>> symbols = plot.linemap(0).symbols >>> symbols.symbol_type = SymbolType.Text >>> symbols.symbol().typeface = Font.Greek