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, 'SimpleData', 'HeatExchanger.plt')
dataset = tp.data.load_tecplot(infile)

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

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

plot.contour(2).variable = dataset.variable('T(K)')
plot.contour(2).colormap_name = 'Sequential - Yellow/Green/Blue'

for z in dataset.zones():
    fmap = plot.fieldmap(z)
    fmap.contour.flood_contour_group = plot.contour(2)

plot.show_contour = True
plot.show_vector = True

# ensure consistent output between interactive (connected) and batch
plot.contour(2).levels.reset_to_nice()

# save image to file
tp.export.save_png('plot_field2d.png', 600, supersample=3)
../_images/plot_field2d.png

Attributes

active_fieldmap_indices

Set of active fieldmaps by index.

active_fieldmaps

Active fieldmaps in this plot.

axes

Axes style control for this plot.

data_labels

Node and cell labels.

draw_order

The order in which objects are drawn to the screen.

hoe_settings

High order element settings.

ijk_blanking

Mask off cells by \((i, j, k)\) index.

linking_between_frames

Style linking between frames.

num_fieldmaps

Number of all fieldmaps in this plot.

num_solution_times

Number of solution times for all active fieldmaps.

rgb_coloring

RGB contour flooding style control.

scatter

Plot-local Scatter style control.

show_contour

Enable contours for this plot.

show_edge

Enable zone edge lines for this plot.

show_isosurfaces

Show isosurfaces for 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_slices

Show slices for this plot.

show_streamtraces

Enable drawing Streamtraces on this plot.

show_vector

Enable drawing of vectors.

solution_time

The current solution time.

solution_times

List of active solution times.

solution_timestep

The zero-based index of the current solution time.

streamtraces

Plot-local streamtrace attributes.

transient_zone_visibility

Policy to determine transient visibility of zones.

value_blanking

Mask off cells by value.

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.

activated()

Context to ensure this plot is active.

contour(index)

ContourGroup: Plot-local ContourGroup style control.

fieldmap(key)

Returns a Cartesian2DFieldmap by Zone or index.

fieldmap_index(zone)

The index of the fieldmap associated with a Zone.

fieldmaps(*keys)

Cartesian2DFieldmapCollection by Zones or indices.

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.activated()

Context to ensure this plot is active.

Example usage:

>>> from tecplot.constant import PlotType
>>> frame = tecplot.active_frame()
>>> frame.plot_type = PlotType.XYLine  # set active plot type
>>> plot = frame.plot(PlotType.Cartesian3D)  # get inactive plot
>>> print(frame.plot_type)
PlotType.XYLine
>>> with plot.activated():
...     print(frame.plot_type)  # 3D plot temporarily active
PlotType.Cartesian3D
>>> print(frame.plot_type)  # original plot type restored
PlotType.XYLine
Cartesian2DFieldPlot.active_fieldmap_indices

Set of active fieldmaps by index.

This example sets the first three fieldmaps active, disabling all others. It then turns on scatter symbols for just these three:

>>> plot.active_fieldmap_indices = [0, 1, 2]
>>> plot.fieldmaps(0, 1, 2).scatter.show = True
Type:

set

Cartesian2DFieldPlot.active_fieldmaps

Active fieldmaps in this plot.

Example usage:

>>> plot.active_fieldmaps.vector.show = True

Note

Possible side-effect when connected to Tecplot 360.

Changing the solution times in the dataset or modifying the active fieldmaps in a frame may trigger a change in the active plot’s solution time by the Tecplot 360 interface. This is done to keep the GUI controls consistent. In batch mode, no such side-effect will take place and the user must take care to set the plot’s solution time with the plot.solution_time or plot.solution_timestep properties.

Type:

Cartesian2DFieldmapCollection

Cartesian2DFieldPlot.axes

Axes style control for this plot.

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')
Type:

Cartesian2DFieldAxes

Cartesian2DFieldPlot.contour(index)

ContourGroup: Plot-local ContourGroup style control.

Example usage:

>>> contour = frame.plot().contour(0)
>>> contour.colormap_name = 'Magma'
Cartesian2DFieldPlot.data_labels

Node and cell labels.

This object controls displaying labels for every node and/or cell in the dataset. Example usage:

>>> plot.data_labels.show_cell_labels = True
>>> plot.data_labels.step_index = 10
Type:

FieldPlotDataLabels

Cartesian2DFieldPlot.draw_order

The order in which objects are drawn to the screen.

Possible values: TwoDDrawOrder.ByZone, TwoDDrawOrder.ByLayer.

The order is either by Zone or by visual layer (contour, mesh, etc.):

>>> plot.draw_order = TwoDDrawOrder.ByZone
Type:

TwoDDrawOrder

Cartesian2DFieldPlot.fieldmap(key)[source]

Returns a Cartesian2DFieldmap by Zone or index.

Parameters:

key (Zone or int) – The Zone must be in the Dataset attached to the associated frame of this plot. A negative index is interpreted as counting from the end of the available fieldmaps.

Example usage:

>>> fmap = plot.fieldmap(dataset.zone(0))
>>> fmap.scatter.show = True
Cartesian2DFieldPlot.fieldmap_index(zone)

The index of the fieldmap associated with a Zone.

Parameters:

zone (Zone) – The Zone object that belongs to the Dataset associated with this plot.

Returns:

Index

Example usage:

>>> fmap_index = plot.fieldmap_index(dataset.zone('Zone'))
>>> plot.fieldmap(fmap_index).show_mesh = True
Cartesian2DFieldPlot.fieldmaps(*keys)[source]

Cartesian2DFieldmapCollection by Zones or indices.

Parameters:

keys (list of Zones or ints) – The Zones must be in the Dataset attached to the associated frame of this plot. Negative indices are interpreted as counting from the end of the available fieldmaps.

Example usage:

>>> fmaps = plot.fieldmaps(dataset.zone(0), dataset.zone(1))
>>> fmaps.scatter.show = True

Changed in version 0.9: fieldmaps was changed from a property (0.8 and earlier) to a method requiring parentheses.

Cartesian2DFieldPlot.hoe_settings

High order element settings.

Example usage:

>>> plot.hoe_settings.num_subdivision_levels = 3
Type:

FieldPlotHOESettings

Cartesian2DFieldPlot.ijk_blanking

Mask off cells by \((i, j, k)\) index.

Example usage:

>>> plot.ijk_blanking.min_percent = (50, 50)
>>> plot.ijk_blanking.active = True
Type:

IJKBlanking

Cartesian2DFieldPlot.linking_between_frames

Style linking between frames.

Example usage:

>>> plot.linking_between_frames.group = 1
>>> plot.linking_between_frames.link_solution_time = True
Type:

Cartesian2DPlotLinkingBetweenFrames

Cartesian2DFieldPlot.num_fieldmaps

Number of all fieldmaps in this plot.

Example usage:

>>> print(frame.plot().num_fieldmaps)
3
Type:

int

Cartesian2DFieldPlot.num_solution_times

Number of solution times for all active fieldmaps.

Note

This only returns the number of active solution times. When assigning strands and solution times to zones, the zones are placed into an inactive fieldmap that must be subsequently activated. See example below.

>>> # place all zones into a single fieldmap (strand: 1)
>>> # with incrementing solution times
>>> for time, zone in enumerate(dataset.zones()):
...     zone.strand = 1
...     zone.solution_time = time
...
>>> # We must activate the fieldmap to ensure the plot's
>>> # solution times have been updated. Since we placed
>>> # all zones into a single fieldmap, we can assume the
>>> # first fieldmap (index: 0) is the one we want.
>>> plot.active_fieldmaps += [0]
>>>
>>> # now the plot's solution times are available.
>>> print(plot.num_solution_times)
10
>>> print(plot.solution_times)
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

int

Cartesian2DFieldPlot.rgb_coloring

RGB contour flooding style control.

Example usage:

>>> plot.rgb_coloring.red_variable = dataset.variable('gas')
>>> plot.rgb_coloring.green_variable = dataset.variable('oil')
>>> plot.rgb_coloring.blue_variable = dataset.variable('water')
>>> plot.show_contour = True
>>> plot.fieldmaps().contour.flood_contour_group = plot.rgb_coloring
Type:

RGBColoring

Cartesian2DFieldPlot.scatter

Plot-local Scatter style control.

Example usage:

>>> scatter = frame.plot().scatter
>>> scatter.variable = dataset.variable('P')
Type:

Scatter

Cartesian2DFieldPlot.show_contour

Enable contours for this plot.

Example usage:

>>> frame.plot().show_contour = True
Type:

bool

Cartesian2DFieldPlot.show_edge

Enable zone edge lines for this plot.

Example usage:

>>> frame.plot().show_edge = True
Type:

bool

Cartesian2DFieldPlot.show_isosurfaces

Show isosurfaces for this plot.

Example usage:

>>> frame.plot().show_isosurfaces(True)
Type:

bool

Cartesian2DFieldPlot.show_mesh

Enable mesh lines for this plot.

Example usage:

>>> frame.plot().show_mesh = True
Type:

bool

Cartesian2DFieldPlot.show_scatter

Enable scatter symbols for this plot.

Example usage:

>>> frame.plot().show_scatter = True
Type:

bool

Cartesian2DFieldPlot.show_shade

Enable surface shading effect for this plot.

Example usage:

>>> frame.plot().show_shade = True
Type:

bool

Cartesian2DFieldPlot.show_slices

Show slices for this plot.

Example usage:

>>> frame.plot().show_slices(True)
Type:

bool

Cartesian2DFieldPlot.show_streamtraces

Enable drawing Streamtraces on this plot.

Example usage:

>>> frame.plot().show_streamtraces = True
Type:

bool

Cartesian2DFieldPlot.show_vector

Enable drawing of vectors.

Example usage:

>>> frame.plot().show_vector = True
Type:

bool

Cartesian2DFieldPlot.solution_time

The current solution time.

Example usage:

>>> print(plot.solution_times)
[0.0, 1.0, 2.0]
>>> plot.solution_time = 1.0

Note

Possible side-effect when connected to Tecplot 360.

Changing the solution times in the dataset or modifying the active fieldmaps in a frame may trigger a change in the active plot’s solution time by the Tecplot 360 interface. This is done to keep the GUI controls consistent. In batch mode, no such side-effect will take place and the user must take care to set the plot’s solution time with the plot.solution_time or plot.solution_timestep properties.

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

float

Cartesian2DFieldPlot.solution_times

List of active solution times.

Note

This only returns the list of active solution times. When assigning strands and solution times to zones, the zones are placed into an inactive fieldmap that must be subsequently activated. See example below.

Example usage:

>>> print(plot.solution_times)
[0.0, 1.0, 2.0]

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

list of floats

Cartesian2DFieldPlot.solution_timestep

The zero-based index of the current solution time.

A negative index is interpreted as counting from the end of the available solution timesteps. Example usage:

>>> print(plot.solution_times)
[0.0, 1.0, 2.0]
>>> print(plot.solution_time)
0.0
>>> plot.solution_timestep += 1
>>> print(plot.solution_time)
1.0

Note

Possible side-effect when connected to Tecplot 360.

Changing the solution times in the dataset or modifying the active fieldmaps in a frame may trigger a change in the active plot’s solution time by the Tecplot 360 interface. This is done to keep the GUI controls consistent. In batch mode, no such side-effect will take place and the user must take care to set the plot’s solution time with the plot.solution_time or plot.solution_timestep properties.

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

int

Cartesian2DFieldPlot.streamtraces

Plot-local streamtrace attributes.

Example usage:

>>> streamtraces = frame.plot().streamtraces
>>> streamtraces.color = Color.Blue
Type:

Streamtraces

Cartesian2DFieldPlot.transient_zone_visibility

Policy to determine transient visibility of zones.

Possible values: ZonesAtOrBeforeSolutionTime, ZonesAtSolutionTime.

Example usage:

>>> from tecplot.constant import TransientZoneVisibility
>>> plot.transient_zone_visibility = \
...     TransientZoneVisibility.ZonesAtSolutionTime

New in version 2021.2: Transient zone visibility requires Tecplot 360 2021 R2 or later.

Type:

TransientZoneVisibility

Cartesian2DFieldPlot.value_blanking

Mask off cells by value.

Example usage:

>>> plot.value_blanking.constraint(0).comparison_value = 3.14
>>> plot.value_blanking.constraint(0).active = True
Type:

ValueBlanking

Cartesian2DFieldPlot.vector

Vector variable and style control for this plot.

Example usage:

>>> plot.vector.u_variable = dataset.variable('U')
Type:

Vector2D

Cartesian2DFieldPlot.view

Axes orientation and limits adjustments.

Example usage:

>>> plot.view.fit()
Type:

Cartesian2DFieldView

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, 'SimpleData', 'SpaceShip.lpk')
dataset = tp.load_layout(infile)

frame = tp.active_frame()
plot = frame.plot(PlotType.Cartesian3D)
plot.activate()
plot.use_lighting_effect = False
plot.show_streamtraces = False
plot.use_translucency = True

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

# save image to file
tp.export.save_png('plot_field3d.png', 600, supersample=3)
../_images/plot_field3d.png

Attributes

active_fieldmap_indices

Set of active fieldmaps by index.

active_fieldmaps

Active fieldmaps in this plot.

axes

Axes style control for this plot.

data_labels

Node and cell labels.

hoe_settings

High order element settings.

ijk_blanking

Mask off cells by \((i, j, k)\) index.

light_source

Control the direction and effects of lighting.

line_lift_fraction

Lift lines above plot by percentage distance to the eye.

linking_between_frames

Style linking between frames.

near_plane_fraction

position of the "near plane".

num_fieldmaps

Number of all fieldmaps in this plot.

num_solution_times

Number of solution times for all active fieldmaps.

perform_extra_sorting

Use a more robust depth sorting algorithm for display.

rgb_coloring

RGB contour flooding style control.

scatter

Plot-local Scatter style control.

show_contour

Enable contours for this plot.

show_edge

Enable zone edge lines for this plot.

show_isosurfaces

Show isosurfaces for 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_slices

Show slices for this plot.

show_streamtraces

Enable drawing Streamtraces on this plot.

show_vector

Enable drawing of vectors.

solution_time

The current solution time.

solution_times

List of active solution times.

solution_timestep

The zero-based index of the current solution time.

streamtraces

Plot-local streamtrace attributes.

symbol_lift_fraction

Lift symbols above plot by percentage distance to the eye.

transient_zone_visibility

Policy to determine transient visibility of zones.

use_lighting_effect

Enable lighting effect for all objects within this plot.

use_translucency

Enable translucent effect for all objects within this plot.

value_blanking

Mask off cells by value.

vector

Vector variable and style control for this plot.

vector_lift_fraction

Lift vectors above plot by percentage distance to the eye.

view

Viewport, axes orientation and limits adjustments.

Methods

activate()

Make this the active plot type on the parent frame.

activated()

Context to ensure this plot is active.

contour(index)

ContourGroup: Plot-local ContourGroup style control.

fieldmap(key)

Returns a Cartesian3DFieldmap by Zone or index.

fieldmap_index(zone)

The index of the fieldmap associated with a Zone.

fieldmaps(*keys)

Cartesian3DFieldmapCollection by Zones or indices.

isosurface(index)

IsosurfaceGroup: Plot-local isosurface settings.

slice(index)

SliceGroup: Plot-local slice style control.

slices(*indices)

SliceGroupCollection: Plot-local slice style control.

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.activated()

Context to ensure this plot is active.

Example usage:

>>> from tecplot.constant import PlotType
>>> frame = tecplot.active_frame()
>>> frame.plot_type = PlotType.XYLine  # set active plot type
>>> plot = frame.plot(PlotType.Cartesian3D)  # get inactive plot
>>> print(frame.plot_type)
PlotType.XYLine
>>> with plot.activated():
...     print(frame.plot_type)  # 3D plot temporarily active
PlotType.Cartesian3D
>>> print(frame.plot_type)  # original plot type restored
PlotType.XYLine
Cartesian3DFieldPlot.active_fieldmap_indices

Set of active fieldmaps by index.

This example sets the first three fieldmaps active, disabling all others. It then turns on scatter symbols for just these three:

>>> plot.active_fieldmap_indices = [0, 1, 2]
>>> plot.fieldmaps(0, 1, 2).scatter.show = True
Type:

set

Cartesian3DFieldPlot.active_fieldmaps

Active fieldmaps in this plot.

Example usage:

>>> plot.active_fieldmaps.vector.show = True

Note

Possible side-effect when connected to Tecplot 360.

Changing the solution times in the dataset or modifying the active fieldmaps in a frame may trigger a change in the active plot’s solution time by the Tecplot 360 interface. This is done to keep the GUI controls consistent. In batch mode, no such side-effect will take place and the user must take care to set the plot’s solution time with the plot.solution_time or plot.solution_timestep properties.

Type:

Cartesian3DFieldmapCollection

Cartesian3DFieldPlot.axes

Axes style control for this plot.

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')
Type:

Cartesian3DFieldAxes

Cartesian3DFieldPlot.contour(index)

ContourGroup: Plot-local ContourGroup style control.

Example usage:

>>> contour = frame.plot().contour(0)
>>> contour.colormap_name = 'Magma'
Cartesian3DFieldPlot.data_labels

Node and cell labels.

This object controls displaying labels for every node and/or cell in the dataset. Example usage:

>>> plot.data_labels.show_cell_labels = True
>>> plot.data_labels.step_index = 10
Type:

FieldPlotDataLabels

Cartesian3DFieldPlot.fieldmap(key)[source]

Returns a Cartesian3DFieldmap by Zone or index.

Parameters:

key (Zone or int) – The Zone must be in the Dataset attached to the associated frame of this plot. A negative index is interpreted as counting from the end of the available fieldmaps.

Example usage:

>>> fmap = plot.fieldmap(dataset.zone(0))
>>> fmap.scatter.show = True
Cartesian3DFieldPlot.fieldmap_index(zone)

The index of the fieldmap associated with a Zone.

Parameters:

zone (Zone) – The Zone object that belongs to the Dataset associated with this plot.

Returns:

Index

Example usage:

>>> fmap_index = plot.fieldmap_index(dataset.zone('Zone'))
>>> plot.fieldmap(fmap_index).show_mesh = True
Cartesian3DFieldPlot.fieldmaps(*keys)[source]

Cartesian3DFieldmapCollection by Zones or indices.

Parameters:

keys (list of Zones or integers) – The Zones must be in the Dataset attached to the associated frame of this plot. Negative indices are interpreted as counting from the end of the available fieldmaps.

Example usage:

>>> fmaps = plot.fieldmaps(dataset.zone(0), dataset.zone(1))
>>> fmaps.scatter.show = True

Changed in version 0.9: fieldmaps was changed from a property (0.8 and earlier) to a method requiring parentheses.

Cartesian3DFieldPlot.hoe_settings

High order element settings.

Example usage:

>>> plot.hoe_settings.num_subdivision_levels = 3
Type:

FieldPlotHOESettings

Cartesian3DFieldPlot.ijk_blanking

Mask off cells by \((i, j, k)\) index.

Example usage:

>>> plot.ijk_blanking.min_percent = (50, 50)
>>> plot.ijk_blanking.active = True
Type:

IJKBlanking

Cartesian3DFieldPlot.isosurface(index)[source]

IsosurfaceGroup: Plot-local isosurface settings.

Example usage:

>>> isosurface_0 = frame.plot().isosurface(0)
>>> isosurface_0.mesh.color = Color.Blue
Cartesian3DFieldPlot.light_source

Control the direction and effects of lighting.

Example usage:

>>> plot.light_source.intensity = 70.0
Type:

LightSource

Cartesian3DFieldPlot.line_lift_fraction

Lift lines above plot by percentage distance to the eye.

Example usage:

>>> plot.line_lift_fraction = 0.6
Type:

float

Cartesian3DFieldPlot.linking_between_frames

Style linking between frames.

Example usage:

>>> plot.linking_between_frames.group = 1
>>> plot.linking_between_frames.link_solution_time = True
Type:

Cartesian3DPlotLinkingBetweenFrames

Cartesian3DFieldPlot.near_plane_fraction

position of the “near plane”.

In a 3D plot, the “near plane” acts as a windshield. Anything in front of this plane does not display. Example usage:

>>> plot.near_plane_fraction = 0.1
Type:

float

Cartesian3DFieldPlot.num_fieldmaps

Number of all fieldmaps in this plot.

Example usage:

>>> print(frame.plot().num_fieldmaps)
3
Type:

int

Cartesian3DFieldPlot.num_solution_times

Number of solution times for all active fieldmaps.

Note

This only returns the number of active solution times. When assigning strands and solution times to zones, the zones are placed into an inactive fieldmap that must be subsequently activated. See example below.

>>> # place all zones into a single fieldmap (strand: 1)
>>> # with incrementing solution times
>>> for time, zone in enumerate(dataset.zones()):
...     zone.strand = 1
...     zone.solution_time = time
...
>>> # We must activate the fieldmap to ensure the plot's
>>> # solution times have been updated. Since we placed
>>> # all zones into a single fieldmap, we can assume the
>>> # first fieldmap (index: 0) is the one we want.
>>> plot.active_fieldmaps += [0]
>>>
>>> # now the plot's solution times are available.
>>> print(plot.num_solution_times)
10
>>> print(plot.solution_times)
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

int

Cartesian3DFieldPlot.perform_extra_sorting

Use a more robust depth sorting algorithm for display.

When printing 3D plots in a vector graphics format, Tecplot 360 must sort the objects so that it can draw those farthest from the screen first and those closest to the screen last. By default, Tecplot 360 uses a quick sorting algorithm. This is not always accurate and does not detect problems such as intersecting objects. When perform_extra_sorting set to True, Tecplot 360 uses a slower, more accurate approach that detects and resolves such problems. Example usage:

>>> plot.perform_extra_sorting = True
Type:

bool

Cartesian3DFieldPlot.rgb_coloring

RGB contour flooding style control.

Example usage:

>>> plot.rgb_coloring.red_variable = dataset.variable('gas')
>>> plot.rgb_coloring.green_variable = dataset.variable('oil')
>>> plot.rgb_coloring.blue_variable = dataset.variable('water')
>>> plot.show_contour = True
>>> plot.fieldmaps().contour.flood_contour_group = plot.rgb_coloring
Type:

RGBColoring

Cartesian3DFieldPlot.scatter

Plot-local Scatter style control.

Example usage:

>>> scatter = frame.plot().scatter
>>> scatter.variable = dataset.variable('P')
Type:

Scatter

Cartesian3DFieldPlot.show_contour

Enable contours for this plot.

Example usage:

>>> frame.plot().show_contour = True
Type:

bool

Cartesian3DFieldPlot.show_edge

Enable zone edge lines for this plot.

Example usage:

>>> frame.plot().show_edge = True
Type:

bool

Cartesian3DFieldPlot.show_isosurfaces

Show isosurfaces for this plot.

Example usage:

>>> frame.plot().show_isosurfaces(True)
Type:

bool

Cartesian3DFieldPlot.show_mesh

Enable mesh lines for this plot.

Example usage:

>>> frame.plot().show_mesh = True
Type:

bool

Cartesian3DFieldPlot.show_scatter

Enable scatter symbols for this plot.

Example usage:

>>> frame.plot().show_scatter = True
Type:

bool

Cartesian3DFieldPlot.show_shade

Enable surface shading effect for this plot.

Example usage:

>>> frame.plot().show_shade = True
Type:

bool

Cartesian3DFieldPlot.show_slices

Show slices for this plot.

Example usage:

>>> frame.plot().show_slices(True)
Type:

bool

Cartesian3DFieldPlot.show_streamtraces

Enable drawing Streamtraces on this plot.

Example usage:

>>> frame.plot().show_streamtraces = True
Type:

bool

Cartesian3DFieldPlot.show_vector

Enable drawing of vectors.

Example usage:

>>> frame.plot().show_vector = True
Type:

bool

Cartesian3DFieldPlot.slice(index)[source]

SliceGroup: Plot-local slice style control.

Example usage:

>>> from tecplot.constant import Color
>>> slice0 = frame.plot().slice(0)
>>> slice0.mesh.show = True
>>> slice0.mesh.color = Color.Blue
Cartesian3DFieldPlot.slices(*indices)[source]

SliceGroupCollection: Plot-local slice style control.

Example setting setting mesh color of all slices to blue:

>>> from tecplot.constant import Color
>>> slices = frame.plot().slices()
>>> slices.mesh.show = True
>>> slices.mesh.color = Color.Blue

Example turning on the first two slice’s contour layer:

>>> slices = frame.plot().slices(0, 1)
>>> slices.contour.show = True
Cartesian3DFieldPlot.solution_time

The current solution time.

Example usage:

>>> print(plot.solution_times)
[0.0, 1.0, 2.0]
>>> plot.solution_time = 1.0

Note

Possible side-effect when connected to Tecplot 360.

Changing the solution times in the dataset or modifying the active fieldmaps in a frame may trigger a change in the active plot’s solution time by the Tecplot 360 interface. This is done to keep the GUI controls consistent. In batch mode, no such side-effect will take place and the user must take care to set the plot’s solution time with the plot.solution_time or plot.solution_timestep properties.

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

float

Cartesian3DFieldPlot.solution_times

List of active solution times.

Note

This only returns the list of active solution times. When assigning strands and solution times to zones, the zones are placed into an inactive fieldmap that must be subsequently activated. See example below.

Example usage:

>>> print(plot.solution_times)
[0.0, 1.0, 2.0]

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

list of floats

Cartesian3DFieldPlot.solution_timestep

The zero-based index of the current solution time.

A negative index is interpreted as counting from the end of the available solution timesteps. Example usage:

>>> print(plot.solution_times)
[0.0, 1.0, 2.0]
>>> print(plot.solution_time)
0.0
>>> plot.solution_timestep += 1
>>> print(plot.solution_time)
1.0

Note

Possible side-effect when connected to Tecplot 360.

Changing the solution times in the dataset or modifying the active fieldmaps in a frame may trigger a change in the active plot’s solution time by the Tecplot 360 interface. This is done to keep the GUI controls consistent. In batch mode, no such side-effect will take place and the user must take care to set the plot’s solution time with the plot.solution_time or plot.solution_timestep properties.

New in version 2017.2: Solution time manipulation requires Tecplot 360 2017 R2 or later.

Type:

int

Cartesian3DFieldPlot.streamtraces

Plot-local streamtrace attributes.

Example usage:

>>> streamtraces = frame.plot().streamtraces
>>> streamtraces.color = Color.Blue
Type:

Streamtraces

Cartesian3DFieldPlot.symbol_lift_fraction

Lift symbols above plot by percentage distance to the eye.

Example usage:

>>> plot.symbol_lift_fraction = 0.6
Type:

float

Cartesian3DFieldPlot.transient_zone_visibility

Policy to determine transient visibility of zones.

Possible values: ZonesAtOrBeforeSolutionTime, ZonesAtSolutionTime.

Example usage:

>>> from tecplot.constant import TransientZoneVisibility
>>> plot.transient_zone_visibility = \
...     TransientZoneVisibility.ZonesAtSolutionTime

New in version 2021.2: Transient zone visibility requires Tecplot 360 2021 R2 or later.

Type:

TransientZoneVisibility

Cartesian3DFieldPlot.use_lighting_effect

Enable lighting effect for all objects within this plot.

Example usage:

>>> frame.plot().use_lighting_effect = True
Type:

bool

Cartesian3DFieldPlot.use_translucency

Enable translucent effect for all objects within this plot.

Example usage:

>>> frame.plot().use_translucency = True
Type:

bool

Cartesian3DFieldPlot.value_blanking

Mask off cells by value.

Example usage:

>>> plot.value_blanking.constraint(0).comparison_value = 3.14
>>> plot.value_blanking.constraint(0).active = True
Type:

ValueBlanking

Cartesian3DFieldPlot.vector

Vector variable and style control for this plot.

Example usage:

>>> plot.vector.u_variable = dataset.variable('U')
Type:

Vector3D

Cartesian3DFieldPlot.vector_lift_fraction

Lift vectors above plot by percentage distance to the eye.

Example usage:

>>> plot.vector_lift_fraction = 0.6
Type:

float

Cartesian3DFieldPlot.view

Viewport, axes orientation and limits adjustments.

Example usage:

>>> plot.view.fit()
Type:

Cartesian3DView

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 *

frame = tp.active_frame()

npoints = 300
r = np.linspace(0, 2000, npoints)
theta = np.linspace(0, 10, npoints)

dataset = frame.create_dataset('Data', ['R', 'Theta'])
zone = dataset.add_ordered_zone('Zone', (300,))
zone.values('R')[:] = r
zone.values('Theta')[:] = theta

plot = frame.plot(PlotType.PolarLine)
plot.activate()
plot.axes.r_axis.max = 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
lmap.line.color = Color.Green

plot.view.fit()

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

Attributes

active_linemap_indices

set of all active linemaps by index.

active_linemaps

Active linemaps in this plot.

axes

Axes style control for this plot.

base_font

Default typeface style control.

data_labels

Node and cell labels.

legend

Line plot legend style and placement control.

linking_between_frames

Style linking between frames.

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.

value_blanking

Mask off points by value.

view

View control of the plot relative to the frame.

Methods

activate()

Make this the active plot type on the parent frame.

activated()

Context to ensure this plot is active.

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(*keys)

PolarLinemapCollection by index or name.

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.activated()

Context to ensure this plot is active.

Example usage:

>>> from tecplot.constant import PlotType
>>> frame = tecplot.active_frame()
>>> frame.plot_type = PlotType.XYLine  # set active plot type
>>> plot = frame.plot(PlotType.Cartesian3D)  # get inactive plot
>>> print(frame.plot_type)
PlotType.XYLine
>>> with plot.activated():
...     print(frame.plot_type)  # 3D plot temporarily active
PlotType.Cartesian3D
>>> print(frame.plot_type)  # original plot type restored
PlotType.XYLine
PolarLinePlot.active_linemap_indices

set of all active linemaps by index.

Numbers are zero-based indices to the linemaps:

>>> active_indices = plot.active_linemap_indices
>>> active_lmaps = [plot.linemap(i) for i in active_indices]
Type:

set of integers

PolarLinePlot.active_linemaps

Active linemaps in this plot.

Example usage:

>>> plot.active_linemaps.show_symbols = True
Type:

PolarLinemapCollection

PolarLinePlot.add_linemap(name=None, zone=None, r=None, theta=None, show=True)[source]

Add a linemap using the specified zone and variables.

Parameters:
  • name (str) – Name of the linemap which can be used for retrieving with PolarLinePlot.linemap. If None, then the linemap will not have a name. Default: None.

  • zone (Zone) – The data to be used when drawing this linemap. If None, then Tecplot Engine will select a Zone. Default: None.

  • r (Variable) – The r variable which must be from the same Dataset as theta and zone. If None, then Tecplot Engine will select a variable. Default: None.

  • theta (Variable) – The theta variable which must be from the same Dataset as r and zone. If None, then Tecplot Engine will select a variable. Default: None.

  • show (bool, optional) – Enable this linemap as soon as it’s added. (default: True)

Returns:

PolarLinemap

Example usage:

>>> lmap = plot.add_linemap('Line 1', dataset.zone('Zone'),
...                         dataset.variable('R'),
...                         dataset.variable('Theta'))
>>> lmap.line.line_thickness = 0.8
PolarLinePlot.axes

Axes style control for this plot.

Example usage:

>>> from tecplot.constant import PlotType, ThetaMode
>>> frame.plot_type = PlotType.PolarLine
>>> axes = frame.plot().axes
>>> axes.theta_mode = ThetaMode.Radians
Type:

PolarLineAxes

PolarLinePlot.base_font

Default typeface style control.

Example usage:

>>> plot.base_font.typeface = 'Times'
Type:

BaseFont

PolarLinePlot.data_labels

Node and cell labels.

This object controls displaying labels for every node and/or cell in the dataset. Example usage:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.step_index = 10
Type:

LinePlotDataLabels

PolarLinePlot.delete_linemaps(*linemaps)

Clear all linemaps within this plot.

Parameters:

*linemaps (Linemaps, int or str) – One or more of the following: Linemaps objects, linemap indices (zero-based) or linemap names. If none are given, all linemaps will be deleted.

Example usage:

>>> plot.delete_linemaps()
>>> print(plot.num_linemaps)
0
PolarLinePlot.legend

Line plot legend style and placement control.

Example usage:

>>> plot.legend.show = True
Type:

LineLegend

PolarLinePlot.linemap(pattern)[source]

Returns a specific linemap within this plot.

Parameters:

pattern (int, str or re.Pattern) – Zero-based index, case-insensitive glob-style pattern string or a compiled regex pattern instance used to match the linemaps by name. A negative index is interpreted as counting from the end of the available linemaps.

Returns:

PolarLinemap corresponding to pattern or None if pattern was passed in as a str or regex pattern instance and no matching linemap was found.

Note

Plots can contain linemaps with identical names and only the first match found is returned. This is not guaranteed to be deterministic and care should be taken to have only linemaps with unique names when this feature is used.

Example usage:

>>> plot.linemap(0).error_bar.show = True
PolarLinePlot.linemaps(*keys)[source]

PolarLinemapCollection by index or name.

Parameters:

keys (int, str or re.Pattern) – Zero-based index, case-insensitive glob-style pattern string or a compiled regex pattern instance used to match the linemaps by name. A negative index is interpreted as counting from the end of the available linemaps.

Example usage, adjusting the line thickness for all lines in the plot:

>>> plot.linemaps().line.line_thickness = 1.4
PolarLinePlot.linking_between_frames

Style linking between frames.

Example usage:

>>> plot.linking_between_frames.group = 1
>>> plot.linking_between_frames.link_solution_time = True
Type:

PolarPlotLinkingBetweenFrames

PolarLinePlot.num_linemaps

Number of linemaps held by this plot.

Example usage:

>>> print(plot.num_linemaps)
3
Type:

int

PolarLinePlot.show_lines

Enable lines for this plot.

Example usage:

>>> plot.show_lines = True
Type:

bool

PolarLinePlot.show_symbols

Enable symbols at line vertices for this plot.

Example usage:

>>> plot.show_symbols = True
Type:

bool

PolarLinePlot.value_blanking

Mask off points by value.

Example usage:

>>> plot.value_blanking.constraint(0).comparison_value = 3.14
>>> plot.value_blanking.constraint(0).active = True
Type:

ValueBlanking

PolarLinePlot.view

View control of the plot relative to the frame.

Example usage:

>>> plot.view.fit()
Type:

PolarView

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, 'SimpleData', 'SunSpots.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).symbols.fill_mode = FillMode.UseLineColor
plot.linemap(0).symbols.size = 1

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

Attributes

active_linemap_indices

set of all active linemaps by index.

active_linemaps

Active linemaps in this plot.

axes

Axes style control for this plot.

base_font

Default typeface style control.

data_labels

Node and cell labels.

legend

Line plot legend style and placement control.

linking_between_frames

Style linking between frames.

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.

value_blanking

Mask off points by value.

view

View control of the plot relative to the frame.

Methods

activate()

Make this the active plot type on the parent frame.

activated()

Context to ensure this plot is active.

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(*keys)

XYLinemapCollection by index or name.

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.activated()

Context to ensure this plot is active.

Example usage:

>>> from tecplot.constant import PlotType
>>> frame = tecplot.active_frame()
>>> frame.plot_type = PlotType.XYLine  # set active plot type
>>> plot = frame.plot(PlotType.Cartesian3D)  # get inactive plot
>>> print(frame.plot_type)
PlotType.XYLine
>>> with plot.activated():
...     print(frame.plot_type)  # 3D plot temporarily active
PlotType.Cartesian3D
>>> print(frame.plot_type)  # original plot type restored
PlotType.XYLine
XYLinePlot.active_linemap_indices

set of all active linemaps by index.

Numbers are zero-based indices to the linemaps:

>>> active_indices = plot.active_linemap_indices
>>> active_lmaps = [plot.linemap(i) for i in active_indices]
Type:

set of integers

XYLinePlot.active_linemaps

Active linemaps in this plot.

Example usage:

>>> plot.active_linemaps.show_symbols = True
Type:

XYLinemapCollection

XYLinePlot.add_linemap(name=None, zone=None, x=None, y=None, show=True)[source]

Add a linemap using the specified zone and variables.

Parameters:
  • name (str) – Name of the linemap which can be used for retrieving with XYLinePlot.linemap. If None, then the linemap will not have a name. Default: None.

  • zone (Zone) – The data to be used when drawing this linemap. If None, then Tecplot Engine will select a zone. Default: None.

  • x (Variable) – The x variable which must be from the same Dataset as y and zone. If None, then Tecplot Engine will select an x variable. Default: None.

  • y (Variable) – The y variable which must be from the same Dataset as x and zone. If None, then Tecplot Engine will select a y variable. Default: None.

  • show (bool, optional) – Enable this linemap as soon as it’s added. (default: True). If None, then Tecplot Engine will determine if the linemap should be enabled.

Returns:

XYLinemap

Example usage:

>>> lmap = plot.add_linemap('Line 1', dataset.zone('Zone'),
...                         dataset.variable('X'),
...                         dataset.variable('Y'))
>>> lmap.line.line_thickness = 0.8
XYLinePlot.axes

Axes style control for this plot.

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
Type:

XYLineAxes

XYLinePlot.base_font

Default typeface style control.

Example usage:

>>> plot.base_font.typeface = 'Times'
Type:

BaseFont

XYLinePlot.data_labels

Node and cell labels.

This object controls displaying labels for every node and/or cell in the dataset. Example usage:

>>> plot.data_labels.show_node_labels = True
>>> plot.data_labels.step_index = 10
Type:

LinePlotDataLabels

XYLinePlot.delete_linemaps(*linemaps)

Clear all linemaps within this plot.

Parameters:

*linemaps (Linemaps, int or str) – One or more of the following: Linemaps objects, linemap indices (zero-based) or linemap names. If none are given, all linemaps will be deleted.

Example usage:

>>> plot.delete_linemaps()
>>> print(plot.num_linemaps)
0
XYLinePlot.legend

Line plot legend style and placement control.

Example usage:

>>> plot.legend.show = True
Type:

LineLegend

XYLinePlot.linemap(pattern)[source]

Returns a specific linemap within this plot.

Parameters:

pattern (int, str or re.Pattern) – Zero-based index, case-insensitive glob-style pattern string or a compiled regex pattern instance used to match the linemaps by name. A negative index is interpreted as counting from the end of the available linemaps.

Returns:

XYLinemap corresponding to pattern or None if pattern was passed in as a str or regex pattern instance and no matching linemap was found.

Note

Plots can contain linemaps with identical names and only the first match found is returned. This is not guaranteed to be deterministic and care should be taken to have only linemaps with unique names when this feature is used.

Example usage:

>>> plot.linemap(0).error_bar.show = True
XYLinePlot.linemaps(*keys)[source]

XYLinemapCollection by index or name.

Parameters:

keys (int, str or re.Pattern) – Zero-based index, case-insensitive glob-style pattern string or a compiled regex pattern instance used to match the linemaps by name. A negative index is interpreted as counting from the end of the available linemaps.

Example usage, adjusting the line thickness for all lines in the plot:

>>> plot.linemaps().line.line_thickness = 1.4
XYLinePlot.linking_between_frames

Style linking between frames.

Example usage:

>>> plot.linking_between_frames.group = 1
>>> plot.linking_between_frames.link_solution_time = True
Type:

XYLinePlotLinkingBetweenFrames

XYLinePlot.num_linemaps

Number of linemaps held by this plot.

Example usage:

>>> print(plot.num_linemaps)
3
Type:

int

XYLinePlot.show_bars

Enable bar chart drawing mode for this plot.

Example usage:

>>> plot.show_bars = True
Type:

bool

XYLinePlot.show_error_bars

Enable error bars for this plot.

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
Type:

bool

XYLinePlot.show_lines

Enable lines for this plot.

Example usage:

>>> plot.show_lines = True
Type:

bool

XYLinePlot.show_symbols

Enable symbols at line vertices for this plot.

Example usage:

>>> plot.show_symbols = True
Type:

bool

XYLinePlot.value_blanking

Mask off points by value.

Example usage:

>>> plot.value_blanking.constraint(0).comparison_value = 3.14
>>> plot.value_blanking.constraint(0).active = True
Type:

ValueBlanking

XYLinePlot.view

View control of the plot relative to the frame.

Example usage:

>>> plot.view.fit()
Type:

XYLineView

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, supersample=3)
../_images/plot_sketch.png

Attributes

axes

Axes (x and y) for the sketch plot.

linking_between_frames

Style linking between frames.

Methods

activate()

Make this the active plot type on the parent frame.

activated()

Context to ensure this plot is active.

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.activated()

Context to ensure this plot is active.

Example usage:

>>> from tecplot.constant import PlotType
>>> frame = tecplot.active_frame()
>>> frame.plot_type = PlotType.XYLine  # set active plot type
>>> plot = frame.plot(PlotType.Cartesian3D)  # get inactive plot
>>> print(frame.plot_type)
PlotType.XYLine
>>> with plot.activated():
...     print(frame.plot_type)  # 3D plot temporarily active
PlotType.Cartesian3D
>>> print(frame.plot_type)  # original plot type restored
PlotType.XYLine
SketchPlot.axes

Axes (x and y) for the sketch plot.

Example usage:

>>> from tecplot.constant import PlotType
>>> frame.plot_type = PlotType.Sketch
>>> frame.plot().axes.x_axis.show = True
Type:

SketchAxes

SketchPlot.linking_between_frames

Style linking between frames.

Example usage:

>>> plot.linking_between_frames.group = 1
>>> plot.linking_between_frames.link_solution_time = True
Type:

SketchPlotLinkingBetweenFrames

Fieldmaps

Cartesian2DFieldmap

class tecplot.plot.Cartesian2DFieldmap(plot, index)[source]

Style control for a single 2D fieldmap.

Attributes

contour

Style including flooding, lines and line coloring.

edge

Style control for boundary lines.

effects

Style control for clipping and blanking effects.

fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

group

Zero-based group number for this Fieldmaps.

mesh

Style lines connecting neighboring data points.

points

Control which points to draw.

scatter

Style for scatter plots.

shade

Style control for surface shading.

show

Display this fieldmap on the plot.

surfaces

Control which surfaces to draw.

vector

Style for vector field plots using arrows.

zones

List of zones used by this fieldmap.

Cartesian2DFieldmap.contour

Style including flooding, lines and line coloring.

Type:

FieldmapContour

Cartesian2DFieldmap.edge

Style control for boundary lines.

Type:

FieldmapEdge

Cartesian2DFieldmap.effects

Style control for clipping and blanking effects.

Type:

FieldmapEffects

Cartesian2DFieldmap.fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

Cartesian2DFieldmap.group

Zero-based group number for this Fieldmaps.

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.fieldmaps(0, 3).group = 4
>>> for fmap in filter(lambda f: f.group == 4, plot.fieldmaps()):
...     print(fmap.index)
0
3
Type:

int

Cartesian2DFieldmap.mesh

Style lines connecting neighboring data points.

Type:

FieldmapMesh

Cartesian2DFieldmap.points

Control which points to draw.

Type:

FieldmapPoints

Cartesian2DFieldmap.scatter

Style for scatter plots.

Type:

FieldmapScatter

Cartesian2DFieldmap.shade

Style control for surface shading.

Type:

FieldmapShade

Cartesian2DFieldmap.show

Display this fieldmap on the plot.

Example usage:

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

See also

Cartesian2DFieldmapCollection or Cartesian3DFieldmapCollection

For optimized style control of several fieldmaps, it is recommended to use Cartesian2DFieldmapCollection or Cartesian3DFieldmapCollection objects.

Type:

bool

Cartesian2DFieldmap.surfaces

Control which surfaces to draw.

Type:

FieldmapSurfaces

Cartesian2DFieldmap.vector

Style for vector field plots using arrows.

Type:

FieldmapVector

Cartesian2DFieldmap.zones

List of zones used by this fieldmap.

Example usage:

>>> for zone in fieldmap.zones:
...     print(zone.name)
Zone 1
Zone 2

Cartesian2DFieldmapCollection

class tecplot.plot.Cartesian2DFieldmapCollection(plot, *indices)[source]

Style control for one or more 2D fieldmaps.

This class behaves like Cartesian2DFieldmap except that setting any underlying style will do so for all of the represented fieldmaps. The style properties are then always returned as a tuple of properties, one for each fieldmap, ordered by index number. This means there is an asymmetry between setting and getting any property under this object, illustrated by the following example:

>>> fmaps = plot.fieldmaps(0, 1, 2)
>>> fmaps.show = True
>>> print(fmaps.show)
(True, True, True)

This is the preferred way to control the style of many fieldmaps as it is much faster to execute. All examples that set style on a single fieldmap like the following:

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

may be converted to setting the same style on all fieldmaps like so:

>>> plot.fieldmaps().contour.show = True

New in version 1.1: Fieldmap collection objects.

Attributes

contour

Style including flooding, lines and line coloring.

edge

Style control for boundary lines.

effects

Style control for clipping and blanking effects.

fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

group

Zero-based group number for this Fieldmaps.

mesh

Style lines connecting neighboring data points.

points

Control which points to draw.

scatter

Style for scatter plots.

shade

Style control for surface shading.

show

Display the fielmaps in this collection on the plot.

surfaces

Control which surfaces to draw.

vector

Style for vector field plots using arrows.

Cartesian2DFieldmapCollection.contour

Style including flooding, lines and line coloring.

Type:

FieldmapContour

Cartesian2DFieldmapCollection.edge

Style control for boundary lines.

Type:

FieldmapEdge

Cartesian2DFieldmapCollection.effects

Style control for clipping and blanking effects.

Type:

FieldmapEffects

Cartesian2DFieldmapCollection.fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

Cartesian2DFieldmapCollection.group

Zero-based group number for this Fieldmaps.

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.fieldmaps(0, 3).group = 4
>>> for fmap in filter(lambda f: f.group == 4, plot.fieldmaps()):
...     print(fmap.index)
0
3
Type:

int

Cartesian2DFieldmapCollection.mesh

Style lines connecting neighboring data points.

Type:

FieldmapMesh

Cartesian2DFieldmapCollection.points

Control which points to draw.

Type:

FieldmapPoints

Cartesian2DFieldmapCollection.scatter

Style for scatter plots.

Type:

FieldmapScatter

Cartesian2DFieldmapCollection.shade

Style control for surface shading.

Type:

FieldmapShade

Cartesian2DFieldmapCollection.show

Display the fielmaps in this collection on the plot.

Example turning on all fieldmaps on the plot:

>>> plot.fieldmaps().show = True
Type:

bool

Cartesian2DFieldmapCollection.surfaces

Control which surfaces to draw.

Type:

FieldmapSurfaces

Cartesian2DFieldmapCollection.vector

Style for vector field plots using arrows.

Type:

FieldmapVector

Cartesian3DFieldmap

class tecplot.plot.Cartesian3DFieldmap(plot, index)[source]

Style control for a single 3D fieldmap.

Attributes

contour

Style including flooding, lines and line coloring.

edge

Style control for boundary lines.

effects

Style control for blanking and lighting effects.

fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

group

Zero-based group number for this Fieldmaps.

mesh

Style lines connecting neighboring data points.

points

Control which points to draw.

scatter

Style for scatter plots.

shade

Style control for surface shading.

show

Display this fieldmap on the plot.

show_isosurfaces

Enable drawing of Iso-surfaces.

show_slices

Enable drawing of slice surfaces.

show_streamtraces

Enable drawing of streamtraces.

surfaces

Control which surfaces to draw.

vector

Style for vector field plots using arrows.

zones

List of zones used by this fieldmap.

Cartesian3DFieldmap.contour

Style including flooding, lines and line coloring.

Type:

FieldmapContour

Cartesian3DFieldmap.edge

Style control for boundary lines.

Type:

FieldmapEdge

Cartesian3DFieldmap.effects

Style control for blanking and lighting effects.

Type:

FieldmapEffects3D

Cartesian3DFieldmap.fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

Cartesian3DFieldmap.group

Zero-based group number for this Fieldmaps.

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.fieldmaps(0, 3).group = 4
>>> for fmap in filter(lambda f: f.group == 4, plot.fieldmaps()):
...     print(fmap.index)
0
3
Type:

int

Cartesian3DFieldmap.mesh

Style lines connecting neighboring data points.

Type:

FieldmapMesh

Cartesian3DFieldmap.points

Control which points to draw.

Type:

FieldmapPoints

Cartesian3DFieldmap.scatter

Style for scatter plots.

Type:

FieldmapScatter

Cartesian3DFieldmap.shade

Style control for surface shading.

Type:

FieldmapShade3D

Cartesian3DFieldmap.show

Display this fieldmap on the plot.

Example usage:

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

See also

Cartesian2DFieldmapCollection or Cartesian3DFieldmapCollection

For optimized style control of several fieldmaps, it is recommended to use Cartesian2DFieldmapCollection or Cartesian3DFieldmapCollection objects.

Type:

bool

Cartesian3DFieldmap.show_isosurfaces

Enable drawing of Iso-surfaces.

Type:

bool

Cartesian3DFieldmap.show_slices

Enable drawing of slice surfaces.

Type:

bool

Cartesian3DFieldmap.show_streamtraces

Enable drawing of streamtraces.

Type:

bool

Cartesian3DFieldmap.surfaces

Control which surfaces to draw.

Type:

FieldmapSurfaces

Cartesian3DFieldmap.vector

Style for vector field plots using arrows.

Type:

FieldmapVector

Cartesian3DFieldmap.zones

List of zones used by this fieldmap.

Example usage:

>>> for zone in fieldmap.zones:
...     print(zone.name)
Zone 1
Zone 2

Cartesian3DFieldmapCollection

class tecplot.plot.Cartesian3DFieldmapCollection(plot, *indices)[source]

Style control for one or more 3D fieldmaps.

This class behaves like Cartesian3DFieldmap except that setting any underlying style will do so for all of the represented fieldmaps. The style properties are then always returned as a tuple of properties, one for each fieldmap, ordered by index number. This means there is an asymmetry between setting and getting any property under this object, illustrated by the following example:

>>> fmaps = plot.fieldmaps(0, 1, 2)
>>> fmaps.show = True
>>> print(fmaps.show)
(True, True, True)

This is the preferred way to control the style of many fieldmaps as it is much faster to execute. All examples that set style on a single fieldmap like the following:

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

may be converted to setting the same style on all fieldmaps like so:

>>> plot.fieldmaps().contour.show = True

New in version 1.1: Fieldmap collection objects.

The following example illustrates manipulating the style for a selection of fieldmaps associated with specific zones:

import os
import numpy
import tecplot

examples_dir = tecplot.session.tecplot_examples_directory()
infile = os.path.join(examples_dir, 'SimpleData', 'F18.lay')
tecplot.load_layout(infile)
frame = tecplot.active_frame()
plot = frame.plot()
dataset = frame.dataset

plot.contour(0).colormap_name = 'GrayScale'
plot.contour(0).legend.show = False

wings = [dataset.zone(name) for name in ['left wing', 'right wing']]
fmaps = frame.plot().fieldmaps(wings)
fmaps.contour.flood_contour_group = plot.contour(1)

plot.contour(1).colormap_name = 'Sequential - Yellow/Green/Blue'
plot.contour(1).levels.reset_levels(numpy.linspace(-0.07, 0.07, 50))

tecplot.export.save_png('F18_wings.png', 600, supersample=3)
../_images/F18_wings.png

Attributes

contour

Style including flooding, lines and line coloring.

edge

Style control for boundary lines.

effects

Style control for blanking and lighting effects.

fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

group

Zero-based group number for this Fieldmaps.

mesh

Style lines connecting neighboring data points.

points

Control which points to draw.

scatter

Style for scatter plots.

shade

Style control for surface shading.

show

Display the fielmaps in this collection on the plot.

show_isosurfaces

Enable drawing of Iso-surfaces.

show_slices

Enable drawing of slice surfaces.

show_streamtraces

Enable drawing of streamtraces.

surfaces

Control which surfaces to draw.

vector

Style for vector field plots using arrows.

Cartesian3DFieldmapCollection.contour

Style including flooding, lines and line coloring.

Type:

FieldmapContour

Cartesian3DFieldmapCollection.edge

Style control for boundary lines.

Type:

FieldmapEdge

Cartesian3DFieldmapCollection.effects

Style control for blanking and lighting effects.

Type:

FieldmapEffects3D

Cartesian3DFieldmapCollection.fieldmap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

Cartesian3DFieldmapCollection.group

Zero-based group number for this Fieldmaps.

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.fieldmaps(0, 3).group = 4
>>> for fmap in filter(lambda f: f.group == 4, plot.fieldmaps()):
...     print(fmap.index)
0
3
Type:

int

Cartesian3DFieldmapCollection.mesh

Style lines connecting neighboring data points.

Type:

FieldmapMesh

Cartesian3DFieldmapCollection.points

Control which points to draw.

Type:

FieldmapPoints

Cartesian3DFieldmapCollection.scatter

Style for scatter plots.

Type:

FieldmapScatter

Cartesian3DFieldmapCollection.shade

Style control for surface shading.

Type:

FieldmapShade3D

Cartesian3DFieldmapCollection.show

Display the fielmaps in this collection on the plot.

Example turning on all fieldmaps on the plot:

>>> plot.fieldmaps().show = True
Type:

bool

Cartesian3DFieldmapCollection.show_isosurfaces

Enable drawing of Iso-surfaces.

Type:

bool

Cartesian3DFieldmapCollection.show_slices

Enable drawing of slice surfaces.

Type:

bool

Cartesian3DFieldmapCollection.show_streamtraces

Enable drawing of streamtraces.

Type:

bool

Cartesian3DFieldmapCollection.surfaces

Control which surfaces to draw.

Type:

FieldmapSurfaces

Cartesian3DFieldmapCollection.vector

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):

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.values(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

# assign flooding to the first contour group
contour.flood_contour_group = plot.contour(0)
contour.flood_contour_group.variable = dataset.variable('P')
contour.flood_contour_group.colormap_name = 'Sequential - Yellow/Green/Blue'
contour.flood_contour_group.legend.show = False

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

Attributes

contour_type

ContourType to plot.

flood_contour_group

The ContourGroup to use for flooding.

flood_contour_group_index

Zero-based Index of the ContourGroup to use for flooding.

line_color

The Color or ContourGroup for lines.

line_group

ContourGroup to use for line placement and style.

line_group_index

Zero-based Index of the ContourGroup 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.

use_lighting_effect

Enable lighting effect on this contour.

FieldmapContour.contour_type

ContourType to plot.

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
Type:

ContourType

FieldmapContour.flood_contour_group

The ContourGroup to use for flooding.

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

>>> cmap_name = 'Sequential - Yellow/Green/Blue'
>>> contour = plot.fieldmap(0).contour
>>> contour.flood_contour_group = plot.contour(1)
>>> contour.flood_contour_group.variable = dataset.variable('P')
>>> contour.flood_contour_group.colormap_name = cmap_name

Setting this to the RGBColoring instance floods this fieldmap contour by the plot’s RGB coloring settings. This requires that variables are assigned to the red, green and blue color channels:

>>> plot.rgb_coloring.red_variable = dataset.variable('x')
>>> plot.rgb_coloring.green_variable = dataset.variable('y')
>>> plot.rgb_coloring.blue_variable = dataset.variable('z')
>>> contour = plot.fieldmap(0).contour
>>> contour.flood_contour_group = plot.rgb_coloring

See RGBColoring for more details.

Type:

ContourGroup or RGBColoring

FieldmapContour.flood_contour_group_index

Zero-based Index of the ContourGroup to use for flooding.

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

>>> contour = plot.fieldmap(0).contour
>>> contour.flood_contour_group_index = 1
>>> contour.flood_contour_group.variable = dataset.variable('P')

Note

To set the flood contour to RGB (multivariate) coloring, you must set the FieldmapContour.flood_contour_group property to plot.rgb_coloring. See FieldmapContour.flood_contour_group for more details.

Type:

int

FieldmapContour.line_color

The Color or ContourGroup for lines.

FieldmapContour lines can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property. Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame 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')

Setting this to the RGBColoring instance colors the lines by the plot’s multivariate contour settings. This requires that variables are assigned to the red, green and blue color channels:

>>> plot.rgb_coloring.red_variable = dataset.variable('x')
>>> plot.rgb_coloring.green_variable = dataset.variable('y')
>>> plot.rgb_coloring.blue_variable = dataset.variable('z')
>>> plot.fieldmap(0).contour.line_color = plot.rgb_coloring

See RGBColoring for more details.

Type:

Color or ContourGroup

FieldmapContour.line_group

ContourGroup to use for line placement and style.

This property sets and gets the ContourGroup used for line placement and though all properties of the ContourGroup can be manipulated through this object, many of them such as color will not effect the lines unless the FieldmapContour.line_color is set to the same ContourGroup. Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame that use it. Example usage:

>>> contour = plot.fieldmap(0).contour
>>> contour.line_group = plot.contour(2)
>>> contour.line_group.variable = dataset.variable('Z')
Type:

ContourGroup

FieldmapContour.line_group_index

Zero-based Index of the ContourGroup for contour lines.

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

>>> contour = plot.fieldmap(0).contour
>>> contour.line_group_index = 2
>>> contour.line_group.variable = dataset.variable('Z')
Type:

int

FieldmapContour.line_pattern

LinePattern type to use for contour lines.

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
Type:

LinePattern

FieldmapContour.line_thickness

Thickness (float) of the drawn lines.

This is the line thickness in percentage of the Frame’s height. Example usage:

>>> contour = plot.fieldmap(0).contour
>>> contour.line_thickness = 0.7
Type:

float

FieldmapContour.pattern_length

Length (float) of the pattern segment for non-solid lines.

This is the pattern length in percentage of the Frame’s height. Example usage:

>>> contour = plot.fieldmap(0).contour
>>> contour.pattern_length = 3.5
Type:

float

FieldmapContour.show

Enable drawing the contours.

Example usage:

>>> contour = plot.fieldmap(0).contour
>>> contour.show = True
Type:

bool

FieldmapContour.use_lighting_effect

Enable lighting effect on this contour.

Example usage:

>>> contour = plot.fieldmap(0).contour
>>> contour.use_lighting_effect = False
Type:

bool

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, 'SimpleData', 'F18.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

contour = plot.contour(0)
contour.colormap_name = 'Sequential - Blue'
contour.variable = dataset.variable('S')

# adjust effects for every fieldmap in this dataset
fmaps = plot.fieldmaps()
fmaps.contour.flood_contour_group = contour
fmaps.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces

edge = fmaps.edge
edge.edge_type = EdgeType.Creases
edge.color = Color.RedOrange
edge.line_thickness = 0.7

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

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

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.color

Line Color.

Type:

Color

FieldmapEdge.edge_type

Where to draw edge lines.

Possible values: Borders, Creases, BordersAndCreases.

Example usage:

>>> from tecplot.constant import EdgeType
>>> plot.show_edge = True
>>> plot.fieldmap(0).edge.edge_type = EdgeType.Creases
Type:

EdgeType

FieldmapEdge.i_border

Which border lines to draw in the I-dimension.

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
Type:

BorderLocation

FieldmapEdge.j_border

Which border lines to draw in the J-dimension.

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
Type:

BorderLocation

FieldmapEdge.k_border

Which border lines to draw in the K-dimension.

Possible values: None, Min, Max, Both.

Example usage:

>>> from tecplot.constant import BorderLocation
>>> plot.show_edge = True
>>> plot.fieldmap(0).edge.k_border = None
Type:

BorderLocation

FieldmapEdge.line_thickness

Thickness of the edge lines drawn.

This is the line thickness in percentage of Frame height. Example usage:

>>> plot.fieldmap(0).edge.line_thickness = 0.4
Type:

float

FieldmapEdge.show

Draw the mesh for this fieldmap.

Example usage:

>>> plot.show_edge = True
>>> plot.fieldmap(0).edge.show = True
Type:

bool

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

value_blanking

Enable value blanking effect for this fieldmap.

FieldmapEffects.clip_planes
FieldmapEffects.value_blanking

Enable value blanking effect for this fieldmap.

Example usage:

>>> plot.fieldmap(0).effects.value_blanking = True
Type:

bool

FieldmapEffects3D

class tecplot.plot.FieldmapEffects3D(fieldmap)[source]

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, 'SimpleData', 'F18.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.use_translucency = True

plot.contour(0).variable = dataset.variable('S')

# adjust effects for every fieldmap in this dataset
fmaps = plot.fieldmaps()
fmaps.contour.flood_contour_group = plot.contour(0)
fmaps.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces

eff = fmaps.effects
eff.lighting_effect = LightingEffect.Paneled
eff.surface_translucency = 30

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

# save image to file
tp.export.save_png('fieldmap_effects3d.png', 600, supersample=3)
../_images/fieldmap_effects3d.png

Attributes

clip_planes

Slice groups to use for clipping.

lighting_effect

The type of lighting effect to render.

surface_translucency

int Translucency of all surfaces for this fieldmap in percent.

use_translucency

Enable translucency of all drawn surfaces for this fieldmap.

value_blanking

Enable value blanking effect for this fieldmap.

FieldmapEffects3D.clip_planes

Slice groups to use for clipping.

Only slice groups 0 to 5 are available for clipping. Example usage:

>>> plot.fieldmap(0).effects.clip_planes = [0, 1, 2]

See also

SliceGroup.clip

Type:

list of integers [0-5]

FieldmapEffects3D.lighting_effect

The type of lighting effect to render.

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 to Paneled shading in this case.

If IJK-ordered data with FieldmapSurfaces.surfaces_to_plot is set to SurfacesToPlot.ExposedCellFaces, faces exposed by blanking will revert to Paneled shading.

Example usage:

>>> from tecplot.constant import LightingEffect
>>> effects = plot.fieldmap(0).effects
>>> effects.lighting_effect = LightingEffect.Paneled
Type:

LightingEffect

FieldmapEffects3D.surface_translucency

int Translucency of all surfaces for this fieldmap in percent.

The use_translucency attribute must be set to True:

>>> effects = plot.fieldmap(0).effects
>>> effects.use_translucency = True
>>> effects.surface_translucency = 50
FieldmapEffects3D.use_translucency

Enable translucency of all drawn surfaces for this fieldmap.

This enables translucency controlled by the surface_translucency attribute:

>>> effects = plot.fieldmap(0).effects
>>> effects.use_translucency = True
>>> effects.surface_translucency = 50
Type:

bool

FieldmapEffects3D.value_blanking

Enable value blanking effect for this fieldmap.

Example usage:

>>> plot.fieldmap(0).effects.value_blanking = True
Type:

bool

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. For I-ordered data, the mesh is a single line connecting all of the points in order of increasing I-index. For IJ-ordered data, the mesh consists of two families of lines connecting adjacent data points of increasing I-index and increasing J-index. For IJK-ordered data, the mesh consists of three families of lines, one connecting points of increasing I-index, one connecting points of increasing J-index, and one connecting points of increasing K-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, 'SimpleData', 'F18.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
mesh = plot.fieldmaps().mesh
mesh.mesh_type = MeshType.HiddenLine
mesh.color = contour

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

Attributes

color

The Color or ContourGroup for 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 or ContourGroup for lines.

FieldmapContour lines can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property. Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame 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)
Type:

Color or ContourGroup

FieldmapMesh.line_pattern

LinePattern type to use for mesh lines.

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
Type:

LinePattern

FieldmapMesh.line_thickness

Thickness (float) of the drawn lines.

This is the line thickness in percentage of the Frame’s height. Example usage:

>>> plot.fieldmap(0).mesh.line_thickness = 0.7
Type:

float

FieldmapMesh.mesh_type

MeshType to show.

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 with mesh.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 with mesh.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.

Type:

MeshType

FieldmapMesh.pattern_length

Length (float) of the pattern segment for non-solid lines.

This is the pattern length in percentage of the Frame’s height. Example usage:

>>> plot.fieldmap(0).mesh.pattern_length = 3.5
Type:

float

FieldmapMesh.show

Draw the mesh for this fieldmap.

Example usage:

>>> from tecplot.constant import SurfacesToPlot
>>> plot.show_mesh = True
>>> surfaces = plot.fieldmap(0).surfaces
>>> surfaces.surfaces_to_plot = SurfacesToPlot.IPlanes
>>> plot.fieldmap(0).mesh.show = True
Type:

bool

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 and FieldmapScatter 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, 'SimpleData', 'HeatExchanger.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

points = plot.fieldmaps().points
points.points_to_plot = PointsToPlot.SurfaceCellCenters
points.step = (2,2)

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

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.

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
Type:

PointsToPlot

FieldmapPoints.step

Step along the dimensions (I, J, K).

This property specifies the I, J, and K-step intervals. For irregular and finite element data, only the first parameter or I-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)
Type:

tuple

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, FillMode

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

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

scatter = plot.fieldmaps().scatter
scatter.symbol_type = SymbolType.Geometry
scatter.fill_mode = FillMode.UseSpecificColor
scatter.fill_color = plot.contour(0)
scatter.size = 1

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

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

Attributes

color

Line Color or ContourGroup of the drawn symbols.

fill_color

Fill or background color.

fill_mode

Mode for the 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 or ContourGroup of the drawn symbols.

This can be a solid color or a ContourGroup as obtained through the plot.contour property. Note that changing style on the ContourGroup will affect all other fieldmaps in the same Frame 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)
Type:

Color or ContourGroup

FieldmapScatter.fill_color

Fill or background color.

The fill_mode attribute must be set accordingly:

>>> from tecplot.constant import Color, SymbolType, FillMode
>>> scatter = plot.fieldmap(0).scatter
>>> scatter.symbol_type = SymbolType.Geometry
>>> scatter.fill_mode = FillMode.UseSpecificColor
>>> scatter.fill_color = Color.Red
Type:

Color or ContourGroup

FieldmapScatter.fill_mode

Mode for the background color.

Options include: FillMode.UseSpecificColor, FillMode.UseLineColor, FillMode.UseBackgroundColor and FillMode.None_.

Example usage:

>>> from tecplot.constant import Color, SymbolType, FillMode
>>> scatter = plot.fieldmap(0).scatter
>>> scatter.symbol_type = SymbolType.Geometry
>>> scatter.fill_mode = FillMode.UseSpecificColor
>>> scatter.fill_color = Color.Red
Type:

FillMode

FieldmapScatter.line_thickness

Width of the lines when drawing symbols.

Example usage:

>>> from tecplot.constant import SymbolType
>>> scatter = plot.fieldmap(0).scatter
>>> scatter.symbol_type = SymbolType.Geometry
>>> scatter.line_thickness = 1
Type:

float

FieldmapScatter.show

Show the scatter symbols.

Example usage:

>>> plot.show_scatter = True
>>> plot.fieldmap(2).scatter.show = True
Type:

bool

FieldmapScatter.size

Size of the symbols to draw.

Example usage:

>>> plot.fieldmap(0).scatter.size = 4
Type:

float

FieldmapScatter.size_by_variable

Use a variable to determine relative size of symbols.

Example usage:

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

See also

Scatter.variable

Type:

bool

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 from FieldmapScatter.symbol_type.

Returns: TextScatterSymbol or GeometryScatterSymbol

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.

Possible values are SymbolType.Geometry or SymbolType.Text. Example usage:

>>> from tecplot.constant import SymbolType
>>> plot.fieldmap(0).scatter.symbol_type = SymbolType.Text
Type:

SymbolType

GeometryScatterSymbol

class tecplot.plot.GeometryScatterSymbol(parent, svarg='SYMBOLSHAPE')[source]

Geometric shape for scatter plots.

from os import path
import tecplot as tp
from tecplot.constant import (Color, PlotType, PointsToPlot, SymbolType,
                              GeomShape, FillMode)

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

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

# get handle to a collection of all fieldmaps
fmaps = plot.fieldmaps()

points = fmaps.points
points.points_to_plot = PointsToPlot.SurfaceCellCenters
points.step = (2,2)

scatter = fmaps.scatter
scatter.fill_mode = FillMode.UseSpecificColor
scatter.size = 2
scatter.line_thickness = 0.5
scatter.symbol_type = SymbolType.Geometry

for i, fmap in enumerate(fmaps):
    fmap.scatter.symbol().shape = GeomShape(i%7)
    fmap.scatter.color = Color(i)
    fmap.scatter.fill_color = Color(i + plot.num_fieldmaps)

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

Attributes

shape

Geometric shape to use when plotting scatter points.

GeometryScatterSymbol.shape

Geometric shape to use when plotting scatter points.

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
Type:

GeomShape

TextScatterSymbol

class tecplot.plot.TextScatterSymbol(parent, svarg='SYMBOLSHAPE')[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, FillMode)

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

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

# get handle to a collection of all fieldmaps
fmaps = plot.fieldmaps()

fmaps.points.points_to_plot = PointsToPlot.SurfaceCellCenters
fmaps.points.step = (4,4)
fmaps.shade.color = Color.LightBlue

fmaps.scatter.fill_mode = FillMode.UseSpecificColor
fmaps.scatter.fill_color = Color.Yellow
fmaps.scatter.size = 3
fmaps.scatter.symbol_type = SymbolType.Text

for i, fmap in enumerate(fmaps):
    fmap.scatter.color = Color((i % 4) + 13)
    fmap.scatter.symbol().text = hex(i)[-1]

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

Attributes

font_override

Typeface to use when rendering text-based scatter.

text

The ASCII character to use as the symbol to show

use_base_font

Use the base typeface when rendering text-based scatter.

TextScatterSymbol.font_override

Typeface to use when rendering text-based scatter.

Possible values: constant.Font.Greek, constant.Font.Math or constant.Font.UserDefined.

The use_base_font attribute must be set to False:

>>> from tecplot.constant import SymbolType, Font
>>> scatter = plot.fieldmap(0).scatter
>>> scatter.symbol_type = SymbolType.Text
>>> scatter.symbol().use_base_font = False
>>> scatter.symbol().font_override = Font.Math
Type:

constant.Font

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.use_base_font

Use the base typeface when rendering text-based scatter.

When False, the font_override attribute takes effect:

>>> from tecplot.constant import SymbolType, Font
>>> scatter = plot.fieldmap(0).scatter
>>> scatter.symbol_type = SymbolType.Text
>>> scatter.symbol().use_base_font = False
>>> scatter.symbol().font_override = Font.Greek
Type:

bool

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, 'SimpleData', 'F18.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))
    plot.fieldmap(zone).shade.color = color

tecplot.export.save_png('fieldmap_shade2d.png', 600, supersample=3)
../_images/fieldmap_shade2d.png

Attributes

color

Fill Color of the shade.

show

FieldmapShade the drawn surfaces.

FieldmapShade.color

Fill Color of the shade.

Example usage:

>>> from tecplot.constant import Color
>>> plot.fieldmap(0).shade.color = Color.Blue
Type:

Color

FieldmapShade.show

FieldmapShade the drawn surfaces.

Example usage:

>>> plot.fieldmap(0).shade.show = False
Type:

bool

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, 'SimpleData', 'F18.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.use_lighting_effect = False

tecplot.export.save_png('fieldmap_shade3d.png', 600, supersample=3)
../_images/fieldmap_shade3d.png

Attributes

color

Fill Color of the shade.

show

FieldmapShade the drawn surfaces.

use_lighting_effect

Draw a lighting effect on the shaded surfaces.

FieldmapShade3D.color

Fill Color of the shade.

Example usage:

>>> from tecplot.constant import Color
>>> plot.fieldmap(0).shade.color = Color.Blue
Type:

Color

FieldmapShade3D.show

FieldmapShade the drawn surfaces.

Example usage:

>>> plot.fieldmap(0).shade.show = False
Type:

bool

FieldmapShade3D.use_lighting_effect

Draw a lighting effect on the shaded surfaces.

Example usage:

>>> plot.fieldmap(0).shade.use_lighting_effect = False
Type:

bool

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.values(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_contour_group.variable = dataset.variable('P')
plot.contour(0).levels.reset_to_nice()

# 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, supersample=3)
../_images/fieldmap_surfaces_ij.png

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.

This example shows I-planes at i = [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
Type:

tuple of integers (min, max, step)

FieldmapSurfaces.j_range

IndexRange for the J dimension of ordered data.

This example shows all J-planes starting with j = 10 up to the maximum J-plane of the associated Zone:

>>> from tecplot.constant import SurfacesToPlot
>>> srf = frame.plot().fieldmap(0).surfaces
>>> srf.surfaces_to_plot = SurfacesToPlot.JPlanes
>>> srf.j_range = 10, None, 1
Type:

tuple of integers (min, max, step)

FieldmapSurfaces.k_range

IndexRange for the K dimension of ordered data.

This example shows all K-planes starting with the first up to 5 from the last K-plane of the associated Zone:

>>> from tecplot.constant import SurfacesToPlot
>>> srf = frame.plot().fieldmap(0).surfaces
>>> srf.surfaces_to_plot = SurfacesToPlot.KPlanes
>>> srf.k_range = None, -5
Type:

tuple of integers (min, max, step)

FieldmapSurfaces.surfaces_to_plot

The surfaces to show.

Possible values: BoundaryFaces, ExposedCellFaces, IPlanes, JPlanes, KPlanes, IJPlanes, JKPlanes, IKPlanes, IJKPlanes, All, the python built-in None.

Options such as IJKPlanes show planes from multiple dimensions. For example, the IJPlanes value shows both the I-planes and the J-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
Type:

SurfacesToPlot

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.values(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.contour(0).levels.reset_to_nice()
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, supersample=3)
../_images/fieldmap_vector.png

Attributes

arrowhead_style

The ArrowheadStyle drawn.

color

The Color or ContourGroup 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.

Possible values: Plain, Filled, Hollow.

Example usage:

>>> from tecplot.constant import ArrowheadStyle
>>> plot.fieldmap(0).vector.arrowhead_style = ArrowheadStyle.Filled
Type:

ArrowheadStyle

FieldmapVector.color

The Color or ContourGroup to use when drawing vectors.

FieldmapVectors can be a solid color or be colored by a ContourGroup as obtained through the plot.contour property. Note that changing style on this ContourGroup will affect all other fieldmaps on the same Frame 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)
Type:

Color or ContourGroup

FieldmapVector.line_pattern

The LinePattern used to draw the arrow line.

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
Type:

LinePattern

FieldmapVector.line_thickness

The width of the arrow line.

Example usage:

>>> from tecplot.constant import LinePattern
>>> vector = plot.fieldmap(0).vector.line_thickness = 0.7
Type:

float (percentage of Frame height)

FieldmapVector.pattern_length

Length of the pattern used when drawing vector lines.

Example usage:

>>> from tecplot.constant import LinePattern
>>> vector = plot.fieldmap(0).vector
>>> vector.line_pattern = LinePattern.Dashed
>>> vector.pattern_length = 3.5
Type:

float (percentage of Frame height)

FieldmapVector.show

Enable drawing vectors on the plot.

Example usage:

>>> plot.show_vector = True
>>> plot.fieldmap(0).vector.show = True
Type:

bool

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 volume IJK-ordered zone where both the I and J-planes are shown. If tangent vectors cannot be drawn, then regular vectors are plotted instead.

Example usage:

>>> plot.fieldmap(0).vector.tangent_only = True
Type:

bool

FieldmapVector.vector_type

Anchor point of the drawn vectors.

Possible values: TailAtPoint, HeadAtPoint, MidAtPoint, HeadOnly.

Example usage:

>>> from tecplot.constant import VectorType
>>> plot.fieldmap(0).vector.vector_type = VectorType.MidAtPoint
Type:

VectorType

Linemaps

PolarLinemap

class tecplot.plot.PolarLinemap(plot, *indices)[source]

Data mapping and style control for polar line plots.

Attributes

aux_data

Auxiliary data for this linemap.

curve

Style and fitting-method control for lines.

function_dependency

The independent variable for function evalulation.

index

Zero-based integer identifier for this Linemaps.

indices

Object controlling which lines are shown.

line

Style for lines to be drawn.

linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

name

Name identifier of this Linemaps.

r_axis

Radial axis used by this linemap.

r_variable

\(r\)-component Variable of the plotted line.

r_variable_index

\(r\)-component Variable index of the plotted line.

show

Display this linemap on the plot.

show_in_legend

Show this Linemaps in the legend.

sort_mode

Control which Variable to use when sorting lines.

sort_variable

Specific Variable used when listing lines.

sort_variable_index

Zero-based index of the specific Variable used for sorting.

symbols

Style for markers at points along the lines.

theta_axis

Angular axis used by this linemap.

theta_variable

:math:` heta`-component Variable of the plotted line.

theta_variable_index

:math:` heta`-component Variable index of the plotted line.

zone

Data source (Zone) for this Linemaps.

zone_index

Zero-based index of the Zone this Linemaps will draw.

PolarLinemap.aux_data

Auxiliary data for this linemap.

Returns: AuxData

This is the auxiliary data attached to the linemap. Such data is written to the layout file by default and can be retrieved later. Example usage:

>>> from tecplot.constant import PlotType
>>> plot = tp.active_frame().plot(PlotType.XYLine)
>>> aux = plot.linemap(0).aux_data
>>> aux['Result'] = '3.14159'
>>> print(aux['Result'])
3.14159
PolarLinemap.curve

Style and fitting-method control for lines.

Type:

LinemapCurve

PolarLinemap.function_dependency

The independent variable for function evalulation.

Possible values: RIndependent, ThetaIndependent. Example usage:

>>> from tecplot.constant import FunctionDependency, PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> lmap = plot.linemap(0)
>>> lmap.function_dependency = FunctionDependency.ThetaIndependent
Type:

FunctionDependency

PolarLinemap.index

Zero-based integer identifier for this Linemaps.

Example:

>>> lmap = plot.linemap(1)
>>> print(lmap.index)
1
Type:

int

PolarLinemap.indices

Object controlling which lines are shown.

Type:

LinemapIndices

PolarLinemap.line

Style for lines to be drawn.

Type:

LinemapLine

PolarLinemap.linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

PolarLinemap.name

Name identifier of this Linemaps.

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.

Type:

str

PolarLinemap.r_axis

Radial axis used by this linemap.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).r_axis.title = 'distance (m)'
Type:

RadialLineAxis

PolarLinemap.r_variable

\(r\)-component Variable of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).r_variable = dataset.variable('R')
Type:

Variable

PolarLinemap.r_variable_index

\(r\)-component Variable index of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).r_variable_index = 0
Type:

int (Zero-based index)

PolarLinemap.show

Display this linemap on the plot.

Example usage for turning on all linemaps:

>>> for lmap in plot.linemaps():
...     lmap.show = True
Type:

bool

PolarLinemap.show_in_legend

Show this Linemaps in the legend.

Possible values:

LegendShow.Always

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.Never

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.

Type:

LegendShow

PolarLinemap.sort_mode

Control which Variable to use when sorting lines.

Possible values: LineMapSort.BySpecificVar, LineMapSort.ByIndependentVar, LineMapSort.ByDependentVar or LineMapSort.None_.

Example usage:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_mode = LineMapSort.ByDependentVar
Type:

LineMapSort

PolarLinemap.sort_variable

Specific Variable used when listing lines.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable = dataset.variable('P')
Type:

Variable

PolarLinemap.sort_variable_index

Zero-based index of the specific Variable used for sorting.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable_index = 3
Type:

int

PolarLinemap.symbols

Style for markers at points along the lines.

Type:

LinemapSymbols

PolarLinemap.theta_axis

Angular axis used by this linemap.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).theta_axis.title = 'angle (deg)'
Type:

PolarAngleLineAxis

PolarLinemap.theta_variable

:math:` heta`-component Variable of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).theta_variable = dataset.variable('Theta')
Type:

Variable

PolarLinemap.theta_variable_index

:math:` heta`-component Variable index of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).theta_variable_index = 1
Type:

int (Zero-based index)

PolarLinemap.zone

Data source (Zone) for this Linemaps.

Example usage:

>>> plot.linemap(0).zone = dataset.zone('Zone 1')
PolarLinemap.zone_index

Zero-based index of the Zone this Linemaps will draw.

Example usage:

>>> plot.linemap(0).zone_index = 2
Type:

int

PolarLinemapCollection

class tecplot.plot.PolarLinemapCollection(plot, *indices)[source]

Data mapping and style control for one or more polar line plots.

New in version 1.1: Linemap collection objects.

Attributes

curve

Style and fitting-method control for lines.

function_dependency

The independent variable for function evalulation.

indices

Object controlling which lines are shown.

line

Style for lines to be drawn.

linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

name

Name identifier of this Linemaps.

r_axis

Radial axis used by this linemap.

r_variable

\(r\)-component Variable of the plotted line.

r_variable_index

\(r\)-component Variable index of the plotted line.

show

Display this linemap on the plot.

show_in_legend

Show this Linemaps in the legend.

sort_mode

Control which Variable to use when sorting lines.

sort_variable

Specific Variable used when listing lines.

sort_variable_index

Zero-based index of the specific Variable used for sorting.

symbols

Style for markers at points along the lines.

theta_axis

Angular axis used by this linemap.

theta_variable

:math:` heta`-component Variable of the plotted line.

theta_variable_index

:math:` heta`-component Variable index of the plotted line.

zone

Zones of this LinemapCollection.

zone_index

Zero-based index of the Zone this Linemaps will draw.

PolarLinemapCollection.curve

Style and fitting-method control for lines.

Type:

LinemapCurve

PolarLinemapCollection.function_dependency

The independent variable for function evalulation.

Possible values: RIndependent, ThetaIndependent. Example usage:

>>> from tecplot.constant import FunctionDependency, PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> lmap = plot.linemap(0)
>>> lmap.function_dependency = FunctionDependency.ThetaIndependent
Type:

FunctionDependency

PolarLinemapCollection.indices

Object controlling which lines are shown.

Type:

LinemapIndices

PolarLinemapCollection.line

Style for lines to be drawn.

Type:

LinemapLine

PolarLinemapCollection.linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

PolarLinemapCollection.name

Name identifier of this Linemaps.

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.

Type:

str

PolarLinemapCollection.r_axis

Radial axis used by this linemap.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).r_axis.title = 'distance (m)'
Type:

RadialLineAxis

PolarLinemapCollection.r_variable

\(r\)-component Variable of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).r_variable = dataset.variable('R')
Type:

Variable

PolarLinemapCollection.r_variable_index

\(r\)-component Variable index of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).r_variable_index = 0
Type:

int (Zero-based index)

PolarLinemapCollection.show

Display this linemap on the plot.

Example usage for turning on all linemaps:

>>> plot.linemaps().show = True
Type:

bool

PolarLinemapCollection.show_in_legend

Show this Linemaps in the legend.

Possible values:

LegendShow.Always

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.Never

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.

Type:

LegendShow

PolarLinemapCollection.sort_mode

Control which Variable to use when sorting lines.

Possible values: LineMapSort.BySpecificVar, LineMapSort.ByIndependentVar, LineMapSort.ByDependentVar or LineMapSort.None_.

Example usage:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_mode = LineMapSort.ByDependentVar
Type:

LineMapSort

PolarLinemapCollection.sort_variable

Specific Variable used when listing lines.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable = dataset.variable('P')
Type:

Variable

PolarLinemapCollection.sort_variable_index

Zero-based index of the specific Variable used for sorting.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable_index = 3
Type:

int

PolarLinemapCollection.symbols

Style for markers at points along the lines.

Type:

LinemapSymbols

PolarLinemapCollection.theta_axis

Angular axis used by this linemap.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).theta_axis.title = 'angle (deg)'
Type:

PolarAngleLineAxis

PolarLinemapCollection.theta_variable

:math:` heta`-component Variable of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).theta_variable = dataset.variable('Theta')
Type:

Variable

PolarLinemapCollection.theta_variable_index

:math:` heta`-component Variable index of the plotted line.

Example usage:

>>> from tecplot.constant import PlotType
>>> plot = frame.plot(PlotType.PolarLine)
>>> plot.linemap(0).theta_variable_index = 1
Type:

int (Zero-based index)

PolarLinemapCollection.zone

Zones of this LinemapCollection.

Example usage:

>>> print([z.name for z in plot.linemaps().zone])
['Zone 1', 'Zone 2']

New in version 1.6: Linemap collection zone property.

Type:

tuple of Zones

PolarLinemapCollection.zone_index

Zero-based index of the Zone this Linemaps will draw.

Example usage:

>>> plot.linemap(0).zone_index = 2
Type:

int

XYLinemap

class tecplot.plot.XYLinemap(plot, *indices)[source]

Data mapping and style control 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 with XYLinemap.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, 'SimpleData', 'Rainfall.dat')
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 = lmap.line.color

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

Attributes

aux_data

Auxiliary data for this linemap.

bars

LinemapBars style for bar charts.

curve

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

Object controlling which lines are shown.

line

Style for lines to be drawn.

linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

name

Name identifier of this Linemaps.

show

Display this linemap on the plot.

show_in_legend

Show this Linemaps in the legend.

sort_mode

Control which Variable to use when sorting lines.

sort_variable

Specific Variable used when listing lines.

sort_variable_index

Zero-based index of the specific Variable used for sorting.

symbols

Style for markers at points along the lines.

x_axis

The 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

Data source (Zone) for this Linemaps.

zone_index

Zero-based index of the Zone this Linemaps will draw.

XYLinemap.aux_data

Auxiliary data for this linemap.

Returns: AuxData

This is the auxiliary data attached to the linemap. Such data is written to the layout file by default and can be retrieved later. Example usage:

>>> from tecplot.constant import PlotType
>>> plot = tp.active_frame().plot(PlotType.XYLine)
>>> aux = plot.linemap(0).aux_data
>>> aux['Result'] = '3.14159'
>>> print(aux['Result'])
3.14159
XYLinemap.bars

LinemapBars style for bar charts.

Type:

LinemapBars

XYLinemap.curve

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.

Possible values: XIndependent, YIndependent.

Example usage:

>>> from tecplot.constant import FunctionDependency
>>> lmap = plot.linemap(0)
>>> lmap.function_dependency = FunctionDependency.YIndependent
Type:

FunctionDependency

XYLinemap.index

Zero-based integer identifier for this Linemaps.

Example:

>>> lmap = plot.linemap(1)
>>> print(lmap.index)
1
Type:

int

XYLinemap.indices

Object controlling which lines are shown.

Type:

LinemapIndices

XYLinemap.line

Style for lines to be drawn.

Type:

LinemapLine

XYLinemap.linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

XYLinemap.name

Name identifier of this Linemaps.

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.

Type:

str

XYLinemap.show

Display this linemap on the plot.

Example usage for turning on all linemaps:

>>> for lmap in plot.linemaps():
...     lmap.show = True
Type:

bool

XYLinemap.show_in_legend

Show this Linemaps in the legend.

Possible values:

LegendShow.Always

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.Never

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.

Type:

LegendShow

XYLinemap.sort_mode

Control which Variable to use when sorting lines.

Possible values: LineMapSort.BySpecificVar, LineMapSort.ByIndependentVar, LineMapSort.ByDependentVar or LineMapSort.None_.

Example usage:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_mode = LineMapSort.ByDependentVar
Type:

LineMapSort

XYLinemap.sort_variable

Specific Variable used when listing lines.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable = dataset.variable('P')
Type:

Variable

XYLinemap.sort_variable_index

Zero-based index of the specific Variable used for sorting.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable_index = 3
Type:

int

XYLinemap.symbols

Style for markers at points along the lines.

Type:

LinemapSymbols

XYLinemap.x_axis

The X-axis used by this linemap.

Example usage:

>>> plot.linemap(0).x_axis = plot.axes.x_axis(2)
Type:

XYLineAxis

XYLinemap.x_axis_index

Zero-based index of the x-axis used by this linemap.

Example usage:

>>> plot.linemap(0).x_axis_index = 2
Type:

int

XYLinemap.x_variable

Variable used for x-positions of this linemap.

Example usage:

>>> plot.linemap(0).x_variable = dataset.variable('P')
Type:

Variable

XYLinemap.x_variable_index

Zero-based index of the Variable used for x-positions.

Example usage:

>>> plot.linemap(0).x_variable_index = 2
Type:

int

XYLinemap.y_axis

Y-axis used by this linemap.

Example usage:

>>> plot.linemap(0).x_axis = plot.axes.y_axis(2)
Type:

XYLineAxis

XYLinemap.y_axis_index

Zero-based index of the y-axis used by this linemap.

Example usage:

>>> plot.linemap(0).y_axis_index = 2
Type:

int

XYLinemap.y_variable

Variable used for y-positions of this linemap.

Example usage:

>>> plot.linemap(0).y_variable = dataset.variable('Q')
Type:

Variable

XYLinemap.y_variable_index

Zero-based index of the Variable used for y-positions.

Example usage:

>>> plot.linemap(0).y_variable_index = 2
Type:

int

XYLinemap.zone

Data source (Zone) for this Linemaps.

Example usage:

>>> plot.linemap(0).zone = dataset.zone('Zone 1')
XYLinemap.zone_index

Zero-based index of the Zone this Linemaps will draw.

Example usage:

>>> plot.linemap(0).zone_index = 2
Type:

int

XYLinemapCollection

class tecplot.plot.XYLinemapCollection(plot, *indices)[source]

Data mapping and style control for one or more line plots.

This class behaves like XYLinemap except that setting any underlying style will do so for all of the represented linemaps. The style properties are then always returned as a tuple of properties, one for each linemap, ordered by index number. This means there is an asymmetry between setting and getting any property under this object, illustrated by the following example:

>>> lmaps = plot.linemaps(0, 1, 2)
>>> lmaps.show = True
>>> print(lmaps.show)
(True, True, True)

This is the preferred way to control the style of many linemaps as it is much faster to execute. All examples that set style on a single linemap like the following:

>>> plot.linemap(0).line.color = Color.Blue

may be converted to setting the same style on all linemaps like so:

>>> plot.linemaps().line.color = Color.Blue

New in version 1.1: Linemap collection objects.

The Linemap layer controls how ordered or connected data is represented. This may be either a set of line segments connecting all the data points, or a curve fitted to the original data. This object represents one or more linemaps and can conveniently control the style for each one.

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

# load data from examples directory
examples_dir = tp.session.tecplot_examples_directory()
infile = path.join(examples_dir, 'SimpleData', 'Rainfall.dat')
dataset = tp.data.load_tecplot(infile)

# get handle to the active frame and set plot type to XY Line
frame = tp.active_frame()
frame.plot_type = PlotType.XYLine
plot = frame.plot()

# We will set the name, color and a few other properties
# for the first three linemaps in the dataset.
names = ['Seattle', 'Dallas', 'Miami']
colors = [Color.Blue, Color.DeepRed, Color.Khaki]

lmaps = plot.linemaps()

# set common style for all linemaps in the collection
lmaps.show = True
lmaps.line.line_thickness = 1
lmaps.line.line_pattern = LinePattern.LongDash
lmaps.line.pattern_length = 2

# loop over the linemaps, setting name and color for each
for lmap, name, color in zip(lmaps, names, colors):
    lmap.name = name
    lmap.line.color = color

# Set the y-axis label
plot.axes.y_axis(0).title.title_mode = AxisTitleMode.UseText
plot.axes.y_axis(0).title.text = 'Rainfall'

# Turn on legend
plot.legend.show = True

# Adjust the axes limits to show all the data
plot.view.fit()

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

Attributes

bars

LinemapBars style for bar charts.

curve

Style and fitting-method control for lines.

error_bars

LinemapErrorBars style for error bars.

function_dependency

The independent variable for function evalulation.

indices

Object controlling which lines are shown.

line

Style for lines to be drawn.

linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

name

Name identifier of this Linemaps.

show

Display this linemap on the plot.

show_in_legend

Show this Linemaps in the legend.

sort_mode

Control which Variable to use when sorting lines.

sort_variable

Specific Variable used when listing lines.

sort_variable_index

Zero-based index of the specific Variable used for sorting.

symbols

Style for markers at points along the lines.

x_axis

The 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

Zones of this LinemapCollection.

zone_index

Zero-based index of the Zone this Linemaps will draw.

XYLinemapCollection.bars

LinemapBars style for bar charts.

Type:

LinemapBars

XYLinemapCollection.curve

Style and fitting-method control for lines.

Type:

LinemapCurve

XYLinemapCollection.error_bars

LinemapErrorBars style for error bars.

Type:

LinemapErrorBars

XYLinemapCollection.function_dependency

The independent variable for function evalulation.

Possible values: XIndependent, YIndependent.

Example usage:

>>> from tecplot.constant import FunctionDependency
>>> lmap = plot.linemap(0)
>>> lmap.function_dependency = FunctionDependency.YIndependent
Type:

FunctionDependency

XYLinemapCollection.indices

Object controlling which lines are shown.

Type:

LinemapIndices

XYLinemapCollection.line

Style for lines to be drawn.

Type:

LinemapLine

XYLinemapCollection.linemap_indices

Read-only, sorted list of zero-based fieldmap indices.

Type:

list

XYLinemapCollection.name

Name identifier of this Linemaps.

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.

Type:

str

XYLinemapCollection.show

Display this linemap on the plot.

Example usage for turning on all linemaps:

>>> plot.linemaps().show = True
Type:

bool

XYLinemapCollection.show_in_legend

Show this Linemaps in the legend.

Possible values:

LegendShow.Always

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.Never

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.

Type:

LegendShow

XYLinemapCollection.sort_mode

Control which Variable to use when sorting lines.

Possible values: LineMapSort.BySpecificVar, LineMapSort.ByIndependentVar, LineMapSort.ByDependentVar or LineMapSort.None_.

Example usage:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_mode = LineMapSort.ByDependentVar
Type:

LineMapSort

XYLinemapCollection.sort_variable

Specific Variable used when listing lines.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable = dataset.variable('P')
Type:

Variable

XYLinemapCollection.sort_variable_index

Zero-based index of the specific Variable used for sorting.

The sort_mode attribute must be set to LineMapSort.BySpecificVar:

>>> from tecplot.constant import LineMapSort
>>> plot.linemap(0).sort_by = LineMapSort.BySpecificVar
>>> plot.linemap(0).sort_variable_index = 3
Type:

int

XYLinemapCollection.symbols

Style for markers at points along the lines.

Type:

LinemapSymbols

XYLinemapCollection.x_axis

The X-axis used by this linemap.

Example usage:

>>> plot.linemap(0).x_axis = plot.axes.x_axis(2)
Type:

XYLineAxis

XYLinemapCollection.x_axis_index

Zero-based index of the x-axis used by this linemap.

Example usage:

>>> plot.linemap(0).x_axis_index = 2
Type:

int

XYLinemapCollection.x_variable

Variable used for x-positions of this linemap.

Example usage:

>>> plot.linemap(0).x_variable = dataset.variable('P')
Type:

Variable

XYLinemapCollection.x_variable_index

Zero-based index of the Variable used for x-positions.

Example usage:

>>> plot.linemap(0).x_variable_index = 2
Type:

int

XYLinemapCollection.y_axis

Y-axis used by this linemap.

Example usage:

>>> plot.linemap(0).x_axis = plot.axes.y_axis(2)
Type:

XYLineAxis

XYLinemapCollection.y_axis_index

Zero-based index of the y-axis used by this linemap.

Example usage:

>>> plot.linemap(0).y_axis_index = 2
Type:

int

XYLinemapCollection.y_variable

Variable used for y-positions of this linemap.

Example usage:

>>> plot.linemap(0).y_variable = dataset.variable('Q')
Type:

Variable

XYLinemapCollection.y_variable_index

Zero-based index of the Variable used for y-positions.

Example usage:

>>> plot.linemap(0).y_variable_index = 2
Type:

int

XYLinemapCollection.zone

Zones of this LinemapCollection.

Example usage:

>>> print([z.name for z in plot.linemaps().zone])
['Zone 1', 'Zone 2']

New in version 1.6: Linemap collection zone property.

Type:

tuple of Zones

XYLinemapCollection.zone_index

Zero-based index of the Zone this Linemaps will draw.

Example usage:

>>> plot.linemap(0).zone_index = 2
Type:

int

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, 'SimpleData', 'Rainfall.dat')
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

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

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.

Example usage:

>>> from tecplot.constant import Color
>>> plot.linemap(0).line.color = Color.Blue
Type:

Color

LinemapLine.line_pattern

Pattern style of the line to be drawn.

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
Type:

LinePattern

LinemapLine.line_thickness

Width of the line to be drawn.

Example usage:

>>> plot.linemap(0).line.line_thickness = 0.5
Type:

float

LinemapLine.pattern_length

Segment length of the repeated line pattern.

Example usage:

>>> from tecplot.constant import LinePattern
>>> lmap = plot.linemap(0)
>>> lmap.line.line_pattern = LinePattern.LongDash
>>> lmap.line.pattern_length = 3.5
Type:

float

LinemapLine.show

Display this point-to-point line on the plot.

Example usage:

>>> plot.linemap(0).line.show = True
Type:

bool

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. Setting curve_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, 'SimpleData', 'Rainfall.dat')
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.values('Error 1')
wvar = zone.values('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()

lmaps = plot.linemaps()

lmaps.show = True
lmaps.x_variable = dataset.variable(0)

for lmap, var in zip(lmaps, list(dataset.variables())[1:4]):
    lmap.y_variable = var

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].use_fit_range = True
curves[1].fit_range = 4,8
curves[1].weight_variable = dataset.variable('Weight')
curves[1].use_weight_variable = True

curves[2].curve_type = CurveType.Spline
curves[2].clamp_spline = True
curves[2].spline_derivative_at_ends = 0,0

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

Attributes

clamp_spline

Enable derivative clamping for spline fits.

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.

use_fit_range

Limit the fit to the fit_range specified.

use_weight_variable

Use the specified variable for curve-fit weighting.

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.clamp_spline

Enable derivative clamping for spline fits.

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.clamp_spline = True
>>> curve.spline_derivative_at_ends = 0, 0
Type:

bool

LinemapCurve.curve_type

Type of curve to draw or fit.

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
Type:

CurveType

LinemapCurve.fit_range

The range to fit and display a fitted curve.

Example showing how to set the limits of a polynomial fit to [5,10]. The use_fit_range attribute must be set to True:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.PolynomialFit
>>> curve.use_fit_range = True
>>> curve.fit_range = 5, 10
Type:

tuple

LinemapCurve.num_points

Number of points to use when drawing a fitted curve.

Example usage:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.PolynomialFit
>>> curve.num_points = 100
Type:

int

LinemapCurve.polynomial_order

Order of the fit when set to polynomial.

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
Type:

int (1 to 10)

LinemapCurve.spline_derivative_at_ends

Clamp the derivative of the spline fit at the edges of the range.

Example showing how to set the derivative at the limits of a spline curve to zero. Notice the clamp_spline attribute must be set to True:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.Spline
>>> curve.clamp_spline = True
>>> curve.spline_derivative_at_ends = 0, 0
Type:

tuple

LinemapCurve.use_fit_range

Limit the fit to the fit_range specified.

Example usage:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.PolynomialFit
>>> curve.use_fit_range = True
>>> curve.fit_range = 5, 10
Type:

bool

LinemapCurve.use_weight_variable

Use the specified variable for curve-fit weighting.

Example usage:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.PolynomialFit
>>> curve.use_weight_variable = True
>>> curve.weight_variable_index = 3
Type:

bool

LinemapCurve.weight_variable

Variable to use for curve-fit weighting.

Example usage:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.PolynomialFit
>>> curve.use_weight_variable = True
>>> curve.weight_variable = dataset.variable('P')
Type:

Variable

LinemapCurve.weight_variable_index

Zero-based index of the variable to use for curve-fit weighting.

The use_weight_variable attribute must be set to True:

>>> from tecplot.constant import CurveType
>>> curve = plot.linemap(0).curve
>>> curve.curve_type = CurveType.PolynomialFit
>>> curve.use_weight_variable = True
>>> curve.weight_variable_index = 3
Type:

int

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, 'SimpleData', 'Rainfall.dat')
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).num_points)
bars.fill_color = Color.Red
bars.line_color = Color.Red
bars.line_thickness = 0.01

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

Attributes

fill_color

Fill color of the bars.

fill_mode

fill mode for 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.

The fill_mode attribute must be set to FillMode.UseSpecificColor:

>>> from tecplot.constant import Color, FillMode
>>> bars = plot.linemap(0).bars
>>> bars.fill_mode = FillMode.UseSpecificColor
>>> bars.fill_color = Color.Red
Type:

Color or ContourGroup.

LinemapBars.fill_mode

fill mode for the bars.

Possible values: FillMode.UseSpecificColor, FillMode.UseLineColor,

FillMode.UseBackgroundColor or FillMode.None_.

Example usage:

>>> from tecplot.constant import FillMode
>>> bars = plot.linemap(0).bars
>>> bars.fill_mode = FillMode.UseBackgroundColor
Type:

FillMode

LinemapBars.line_color

Edge line color of the bars.

Example usage:

>>> from tecplot.constant import Color
>>> plot.linemap(0).bars.line_color = Color.Red
Type:

Color

LinemapBars.line_thickness

Edge line thickness of the bars.

Example usage:

>>> plot.linemap(0).bars.line_thickness = 0.1
Type:

float

LinemapBars.show

Display bars on the plot for this Linemaps.

The parent plot object must have bars enabled as well:

>>> plot.show_bars = True
>>> plot.linemap(0).bars.show = True
Type:

bool

LinemapBars.size

Width of the bars.

Example usage:

>>> plot.linemap(0).bars.size = 0.10
Type:

float (percentange of Frame width)

LinemapErrorBars

class tecplot.plot.LinemapErrorBars(linemap)[source]

Error bar style and variable assignment control.

A single XYLinemap holds a single Variable 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.values('x')[:] = [0,1,2,3,4]
zone.values('y')[:] = [1,2,4,8,10]

# error in x is a constant
zone.values('xerr')[:] = [0.2]*5

# error in y is the square-root of the value
zone.values('yerr')[:] = [sqrt(y) for y in zone.values('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, supersample=3)
../_images/linemap_error_bars.png

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.

Possible values: Up, Down, Left, Right, Horz, Vert, Cross.

Example usage:

>>> from tecplot.constant import ErrorBar
>>> plot.linemap(0).error_bars.bar_type = ErrorBar.Cross
Type:

ErrorBar

LinemapErrorBars.color

Color of the error bars.

Example usage:

>>> from tecplot.constant import Color
>>> plot.linemap(0).error_bars.color = Color.Red
Type:

Color

LinemapErrorBars.endcap_size

Length of the endcaps of the error bars.

Example usage:

>>> plot.linemap(0).error_bars.endcap_size = 2.5
Type:

float

LinemapErrorBars.line_thickness

Width of the error bar lines.

Example usage:

>>> plot.linemap(0).error_bars.line_thickness = 0.8
Type:

float

LinemapErrorBars.show

Display error bars on the plot for this Linemaps.

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
Type:

bool

LinemapErrorBars.step

Space between points to show error bars.

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
Type:

float

LinemapErrorBars.step_mode

Space the error bars by index or frame height.

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
Type:

StepMode

LinemapErrorBars.variable

Variable to use for error bar sizes.

Example usage:

>>> ebars = plot.linemap(0).error_bars
>>> ebars.variable = dataset.variable('Err')
Type:

Variable

LinemapErrorBars.variable_index

Zero-based variable index to use for error bar sizes.

Example usage:

>>> plot.linemap(0).error_bars.variable_index = 3
Type:

int

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, 'SimpleData', 'Rainfall.dat')
dataset = tp.data.load_tecplot(infile)

frame = tp.active_frame()
frame.plot_type = PlotType.XYLine
plot = frame.plot()

lmaps = plot.linemaps(0, 1, 2)
lmaps.show = True
lmaps.indices.varying_index = IJKLines.I
lmaps.indices.i_range = 0,0,3

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

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.

This example shows I-lines at i = [0, 2, 4, 6, 8, 10]:

>>> plot.linemap(0).indices.i_range = 0, 10, 2
Type:

tuple of