# Plot

- Plots

  - Cartesian2DFieldPlot

  - Cartesian3DFieldPlot

  - PolarLinePlot

  - XYLinePlot

  - SketchPlot

- Fieldmaps

  - Cartesian2DFieldmap

  - Cartesian2DFieldmapCollection

  - Cartesian3DFieldmap

  - Cartesian3DFieldmapCollection

  - FieldmapContour

  - FieldmapEdge

  - FieldmapEffects

  - FieldmapEffects3D

  - FieldmapMesh

  - FieldmapPoints

  - FieldmapScatter

  - GeometryScatterSymbol

  - TextScatterSymbol

  - FieldmapShade

  - FieldmapShade3D

  - FieldmapSurfaces

  - FieldmapVector

- Linemaps

  - PolarLinemap

  - PolarLinemapCollection

  - XYLinemap

  - XYLinemapCollection

  - LinemapLine

  - LinemapCurve

  - LinemapBars

  - LinemapErrorBars

  - LinemapIndices

  - LinemapSymbols

  - GeometrySymbol

  - TextSymbol

## Plots

- Cartesian2DFieldPlot

- Cartesian3DFieldPlot

- PolarLinePlot

- XYLinePlot

- SketchPlot

### Cartesian2DFieldPlot

class `tecplot.plot.``Cartesian2DFieldPlot`(frame)

  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)

  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.
  `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.
  `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`()

  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)

  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)

  `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.``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.``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)

  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)

  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.
  `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.
  `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`()

  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)

  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)

  `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.``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)

  `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)

  `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)

  `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.``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)

  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)

  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`()

  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)

  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)

  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)

  `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)

  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)

  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`()

  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)

  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)

  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)

  `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)

  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)

  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`()

  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

- Cartesian2DFieldmapCollection

- Cartesian3DFieldmap

- Cartesian3DFieldmapCollection

- FieldmapContour

- FieldmapEdge

- FieldmapEffects

- FieldmapEffects3D

- FieldmapMesh

- FieldmapPoints

- FieldmapScatter

- GeometryScatterSymbol

- TextScatterSymbol

- FieldmapShade

- FieldmapShade3D

- FieldmapSurfaces

- FieldmapVector

### Cartesian2DFieldmap

class `tecplot.plot.``Cartesian2DFieldmap`(plot, index)

  Style control for a single 2D fieldmap.

  See also

  `Cartesian2DFieldmapCollection`

  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)

  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

  See also

  `Cartesian2DFieldmap`

  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)

  Style control for a single 3D fieldmap.

  See also

  `Cartesian3DFieldmapCollection`

  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)

  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

  See also

  `Cartesian3DFieldmap`

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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')

  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)

  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')

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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

- PolarLinemapCollection

- XYLinemap

- XYLinemapCollection

- LinemapLine

- LinemapCurve

- LinemapBars

- LinemapErrorBars

- LinemapIndices

- LinemapSymbols

- GeometrySymbol

- TextSymbol

### PolarLinemap

class `tecplot.plot.``PolarLinemap`(plot, *indices)

  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)

  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_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_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)

  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)

  See also

  `XYLinemapCollection`

  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)

  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)

  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_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_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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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 `integers` (min, max, step)

`LinemapIndices.``j_range`

  `IndexRange` for the J dimension of ordered data.

  This example shows all `J`-lines starting with `j = 10` up to the maximum `J`-line of the associated Zone:

      >>> plot.linemap(0).indices.j_range = 10, None, 1

  Type

    `tuple` of `integers` (min, max, step)

`LinemapIndices.``k_range`

  `IndexRange` for the K dimension of ordered data.

  This example shows all `K`-lines starting with the first up to 5 from the last `K`-line of the associated Zone:

      >>> plot.linemap(0).indices.k_range = None, -5

  Type

    `tuple` of `integers` (min, max, step)

`LinemapIndices.``varying_index`

  Family of lines to be drawn.

  This is the order which varies along the lines drawn. K-varying is only available if the mapping is using an IJK-ordered zone:

      >>> from tecplot.constant import IJKLines
      >>> plot.linemap(0).indices.varying_index = IJKLines.J

  Type

    `IJKLines`

### LinemapSymbols

class `tecplot.plot.``LinemapSymbols`(linemap)

  Style control for markers placed along lines.

  This class allows the user to set the style of the symbols to be shown including setting a geometric shape, text character, line and fill colors and spacing. The plot-level `show_symbols` attribute must be enabled to show symbols in any specific linemap within the plot:

      from os import path
      import tecplot as tp
      from tecplot.constant import PlotType, Color, FillMode, GeomShape

      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_symbols = True

      lmaps = plot.linemaps(0, 1, 2)

      lmaps.symbols.show = True
      lmaps.symbols.symbol().shape = GeomShape.Square
      lmaps.symbols.size = 2.5
      lmaps.symbols.color = Color.Blue
      lmaps.symbols.line_thickness = 0.4
      lmaps.symbols.fill_mode = FillMode.UseSpecificColor
      lmaps.symbols.fill_color = Color.Azure

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

  Attributes

  `color` | Edge or text `Color` of the drawn symbols.
  `fill_color` | The fill or background color.
  `fill_mode` | The fill mode for the background.
  `line_thickness` | Width of the lines when drawing geometry symbols.
  `show` | Display symbols along the lines to be drawn.
  `size` | Size of the symbols to draw.
  `step` | Space between symbols to be shown.
  `step_mode` | Space the symbols by index or frame height.
  `symbol_type` | The `SymbolType` to use for this linemap.

  Methods

  `symbol`([symbol_type]) | Returns a linemap symbol style object.

`LinemapSymbols.``color`

  Edge or text `Color` of the drawn symbols.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.linemap(1).symbols.color = Color.Blue

  Type

    `Color`

`LinemapSymbols.``fill_color`

  The fill or background color.

  The `fill_mode` attribute must be set to `FillMode.UseSpecificColor`:

      >>> from tecplot.constant import Color, FillMode
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.fill_mode = FillMode.UseSpecificColor
      >>> symbols.fill_color = Color.Yellow

  Type

    `Color`

`LinemapSymbols.``fill_mode`

  The fill mode for the background.

  Possible values: `FillMode.UseSpecificColor`, `FillMode.UseLineColor`,

    `FillMode.UseBackgroundColor` or `FillMode.None_`.

  Example usage:

      >>> from tecplot.constant import Color, FillMode
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.fill_mode = FillMode.UseBackgroundColor

  Type

    `FillMode`

`LinemapSymbols.``line_thickness`

  Width of the lines when drawing geometry symbols.

  Example usage:

      >>> from tecplot.constant import SymbolType
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.symbol_type = SymbolType.Geometry
      >>> symbols.line_thickness = 0.8

  Type

    `float`

`LinemapSymbols.``show`

  Display symbols along the lines to be drawn.

  The parent plot object must have symbols enabled as well:

      >>> plot.show_symbols = True
      >>> plot.linemap(0).symbols.show = True

  Type

    `bool`

`LinemapSymbols.``size`

  Size of the symbols to draw.

  Example usage:

      >>> plot.linemap(0).symbols.size = 3.5

  Type

    `float`

`LinemapSymbols.``step`

  Space between symbols to be shown.

  The step is specified either as a percentage of the frame height or as a number of indices to skip depending on the value of `LinemapSymbols.step_mode`. This example will add symbols to every third point:

      >>> plot.linemap(0).symbols.step = 3

  Type

    `float`

`LinemapSymbols.``step_mode`

  Space the symbols by index or frame height.

  This example will make sure all symbols are no closer than 10% of the frame height to each other:

      >>> from tecplot.constant import StepMode
      >>> sym = plot.linemap(0).symbols
      >>> sym.step_mode = StepMode.ByFrameUnits
      >>> sym.step = 10

  Type

    `StepMode`

`LinemapSymbols.``symbol`(symbol_type=None)

  Returns a linemap symbol style object.

  Parameters

    symbol_type (`SymbolType`, optional) – The type of symbol to return. By default, this will return the active symbol type which is obtained from `LinemapSymbols.symbol_type`.

  Returns: `TextSymbol` or `GeometrySymbol`

  Example usage:

      >>> from tecplot.constant import SymbolType
      >>> plot.linemap(0).symbols.symbol_type = SymbolType.Text
      >>> symbol = plot.linemap(0).symbols.symbol()
      >>> symbol.text = 'a'

`LinemapSymbols.``symbol_type`

  The `SymbolType` to use for this linemap.

  Possible values are: `SymbolType.Geometry`, `SymbolType.Text`.

  This sets the active symbol type. Use LinemapSymbols.symbol` to access the symbol:

      >>> from tecplot.constant import SymbolType
      >>> linemap = plot.linemap(0)
      >>> linemap.symbols.symbol_type = SymbolType.Text
      >>> symbol = linemap.symbols.symbol(SymbolType.Text)
      >>> symbol.text = 'a'

  Type

    `SymbolType`

### GeometrySymbol

class `tecplot.plot.``GeometrySymbol`(parent, svarg='SYMBOLSHAPE')

  Geometric shape for linemap symbols.

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

      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_symbols = True

      cols = [Color.DeepRed, Color.Blue, Color.Fern]
      shapes = [GeomShape.Square, GeomShape.Circle, GeomShape.Del]

      lmaps = plot.linemaps()

      lmaps.show = True
      lmaps.symbols.show = True
      lmaps.symbols.size = 4.5
      lmaps.symbols.fill_mode = FillMode.UseSpecificColor
      lmaps.symbols.symbol_type = SymbolType.Geometry

      for lmap, color, shape in zip(lmaps, cols, shapes):
          lmap.line.color = color
          lmap.symbols.color = color
          lmap.symbols.fill_color = color
          lmap.symbols.symbol().shape = shape

      plot.view.fit()

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

  Attributes

  `shape` | Geometric shape to use when plotting linemap symbols.

`GeometrySymbol.``shape`

  Geometric shape to use when plotting linemap symbols.

  Possible values: `Square`, `Del`, `Grad`, `RTri`, `LTri`, `Diamond`, `Circle`, `Cube`, `Sphere`, `Octahedron`, `Point`.

  Example usage:

      >>> from tecplot.constant import SymbolType, GeomShape
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.symbol_type = SymbolType.Geometry
      >>> symbols.symbol().shape = GeomShape.Diamond

  Type

    `GeomShape`

### TextSymbol

class `tecplot.plot.``TextSymbol`(parent, svarg='SYMBOLSHAPE')

  Text character for linemap symbols.

  Only a single character can be used.

      from os import path
      import tecplot as tp
      from tecplot.constant import PlotType, Color, SymbolType, FillMode

      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_symbols = True

      cols = [Color.DeepRed, Color.Blue, Color.Fern]
      chars = ['S','D','M']

      lmaps = plot.linemaps()
      lmaps.show = True
      lmaps.symbols.show = True
      lmaps.symbols.size = 2.5
      lmaps.symbols.color = Color.White
      lmaps.symbols.fill_mode = FillMode.UseSpecificColor
      lmaps.symbols.symbol_type = SymbolType.Text

      for lmap, color, character in zip(lmaps, cols, chars):
          lmap.line.color = color
          lmap.symbols.fill_color = color
          lmap.symbols.symbol().text = character

      plot.view.fit()

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

  Attributes

  `font_override` | Typeface to use when rendering text-based symbols.
  `text` | The ASCII character to use as the symbol to show
  `use_base_font` | Use the base typeface when rendering text-based symbols.

`TextSymbol.``font_override`

  Typeface to use when rendering text-based symbols.

  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
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.symbol_type = SymbolType.Text
      >>> symbols.symbol().use_base_font = False
      >>> symbols.symbol().font_override = Font.Greek

  Type

    `constant.Font`

`TextSymbol.``text`

  The ASCII character to use as the symbol to show

  Note

  This is limited to a single character.

  Example usage:

      >>> from tecplot.constant import SymbolType
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.symbol_type = SymbolType.Text
      >>> symbols.symbol().text = 'X'

`TextSymbol.``use_base_font`

  Use the base typeface when rendering text-based symbols.

  When `False`, the `font_override` attribute takes effect:

      >>> from tecplot.constant import SymbolType, Font
      >>> symbols = plot.linemap(0).symbols
      >>> symbols.symbol_type = SymbolType.Text
      >>> symbols.symbol().use_base_font = False
      >>> symbols.symbol().font_override = Font.Greek

  Type

    `bool`