# Plot Style

- Scatter Plots
  - Scatter
  - ScatterReferenceSymbol
- Vector Plots
  - Vector2D
  - Vector3D
  - ReferenceVector
  - ReferenceVectorLabel
- Legends
  - ContourLegend
  - LineLegend
  - RGBColoringLegend
  - ScatterLegend
- Contours
  - ContourGroup
  - ContourColorCutoff
  - ContourColormapFilter
  - ContourColormapOverride
  - ContourColormapZebraShade
  - ContourLabels
  - ContourLevels
  - ContourLines
  - RGBColoring
- Isosurface
  - IsosurfaceGroup
  - IsosurfaceContour
  - IsosurfaceEffects
  - IsosurfaceMesh
  - IsosurfaceShade
  - IsosurfaceVector
- Slice
  - SliceGroup
  - SliceContour
  - SliceEdge
  - SliceEffects
  - SliceMesh
  - SliceShade
  - SliceVector
- Streamtraces
  - Streamtraces
  - StreamtraceRodRibbon
  - StreamtraceTiming
  - StreamtraceRodRibbonContour
  - StreamtraceRodRibbonEffects
  - StreamtraceRodRibbonMesh
  - StreamtraceRodRibbonShade
  - StreamtraceTerminationLine
- Text
  - Font
  - BaseFont
  - TextBox
  - LabelFormat
- Data Labels
  - FieldPlotDataLabels
  - LinePlotDataLabels
- Viewport
  - ReadOnlyViewport
  - Viewport
  - Cartesian2DViewport
  - PolarViewport
- View and Lighting
  - Cartesian2DFieldView
  - Cartesian3DView
  - XYLineView
  - PolarView
  - LightSource
- Frame Linking
  - SketchPlotLinkingBetweenFrames
  - Cartesian2DPlotLinkingBetweenFrames
  - Cartesian3DPlotLinkingBetweenFrames
  - XYLinePlotLinkingBetweenFrames
  - PolarPlotLinkingBetweenFrames

## Scatter Plots

- Scatter
- ScatterReferenceSymbol

### Scatter

class `tecplot.plot.``Scatter`(plot)

  Plot-local scatter style settings.

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

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

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

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

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

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

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

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

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

  Attributes

  `base_font` | Default typeface to use for text scatter symbols.
  `legend` | Scatter symbol legend.
  `reference_symbol` | Reference symbol for scatter plots.
  `relative_size` | Relative size of the reference symbol.
  `relative_size_units` | Use grid or page units for relative size.
  `sphere_render_quality` | render quality of spheres
  `variable` | The `Variable` to be used when sizing scatter symbols.
  `variable_index` | Zero-based index of the `Variable` used for size of scatter symbols.

`Scatter.``base_font`

  Default typeface to use for text scatter symbols.

  Example usage:

      >>> plot.scatter.base_font.typeface = 'Times'

  Type: | `BaseFont`

`Scatter.``legend`

  Scatter symbol legend.

  Example usage:

      >>> plot.scatter.legend.show = True

  Type: | `ScatterLegend`

`Scatter.``reference_symbol`

  Reference symbol for scatter plots.

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

      >>> plot.fieldmap(0).scatter.size_by_variable = True
      >>> plot.scatter.variable = dataset.variable('s')
      >>> plot.scatter.reference_symbol.show = True

  Type: | `ScatterReferenceSymbol`

`Scatter.``relative_size`

  Relative size of the reference symbol.

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

      >>> plot.scatter.relative_size = 20

  Type: | `float`

`Scatter.``relative_size_units`

  Use grid or page units for relative size.

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

      >>> from tecplot.constant import RelativeSizeUnits
      >>> plot.scatter.relative_size_units = RelativeSizeUnits.Grid
      >>> plot.scatter.relative_size = 2.0

  Type: | `RelativeSizeUnits`

`Scatter.``sphere_render_quality`

  render quality of spheres

  Example usage:

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

  Type: | `SphereScatterRenderQuality`

`Scatter.``variable`

  The `Variable` to be used when sizing scatter symbols.

  The variable must belong to the `Dataset` attached to the `Frame` that holds this `ContourGroup`. Example usage:

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

`Scatter.``variable_index`

  Zero-based index of the `Variable` used for size of scatter symbols.

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

  The `Dataset` attached to this contour group’s `Frame` is used, and the variable itself can be obtained through it:

      >>> scatter = plot.scatter
      >>> scatter_var = dataset.variable(scatter.variable_index)
      >>> scatter_var.index == scatter.variable_index
      True

### ScatterReferenceSymbol

class `tecplot.plot.``ScatterReferenceSymbol`(scatter)

  Reference symbol for scatter plots.

  Note

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

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

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

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

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

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

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

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

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

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

  Attributes

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

  Methods

  `symbol`([symbol_type]) | `TextSymbol` or `GeometrySymbol`: Style control the displayed symbol.

`ScatterReferenceSymbol.``color`

  The `Color` of the reference symbol.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.scatter.reference_symbol.color = Color.Blue

  Type: | `Color`

`ScatterReferenceSymbol.``fill_color`

  The fill `Color` of the reference symbol.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.scatter.reference_symbol.fill_color = Color.Blue

  Type: | `Color`

`ScatterReferenceSymbol.``filled`

  Fill the background area behind the reference symbol.

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

      >>> plot.scatter.reference_symbol.filled = True

  Type: | `bool`

`ScatterReferenceSymbol.``line_thickness`

  Edge line thickness for geometry reference symbols.

  Example usage:

      >>> plot.scatter.reference_symbol.line_thickness = 2.5

  Type: | `float`

`ScatterReferenceSymbol.``magnitude`

  Symbol size relative to data variable ranges.

  Example usage:

      >>> plot.scatter.reference_symbol.magnitude = 10.0

  Type: | `float`

`ScatterReferenceSymbol.``position`

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

  This position is in `Frame` percentage units::’

      >>> plot.scatter.reference_symbol.position = (50, 50)

  Type: | `tuple`

`ScatterReferenceSymbol.``show`

  Display a reference scatter symbol on the plot.

  Example usage:

      >>> plot.fieldmap(0).scatter.size_by_variable = True
      >>> plot.scatter.variable = dataset.variable('s')
      >>> plot.scatter.reference_symbol.show = True

  Type: | `bool`

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

  `TextSymbol` or `GeometrySymbol`: Style control the displayed symbol.

  Example usage:

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

`ScatterReferenceSymbol.``symbol_type`

  The type of symbol to display.

  Example usage:

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

  Type: | `SymbolType`

## Vector Plots

- Vector2D
- Vector3D
- ReferenceVector
- ReferenceVectorLabel

### Vector2D

class `tecplot.plot.``Vector2D`(plot)

  Vector field style control for Cartesian 2D plots.

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

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

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

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

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

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

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

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

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

  >     for fmap in plot.fieldmaps():
  >         fmap.show = False

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

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

  Attributes

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

`Vector2D.``arrowhead_angle`

  Angle between the vector body and the head line.

  Example usage:

      >>> plot.vector.arrowhead_angle = 10

  Type: | `float` (degrees)

`Vector2D.``arrowhead_fraction`

  Size of the arrowhead when sizing by fraction.

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

      >>> plot.vector.size_arrowhead_by_fraction = True
      >>> plot.vector.arrowhead_fraction = 0.4

  Type: | `float` (ratio)

`Vector2D.``arrowhead_size`

  Size of arrowhead when sizing by frame height.

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

      >>> plot.vector.size_arrowhead_by_fraction = False
      >>> plot.vector.arrowhead_size = 4

  Type: | `float` (percent of frame height)

`Vector2D.``length`

  Length of all vectors when not using relative sizing.

  Example usage:

      >>> plot.vector.use_relative = False
      >>> plot.vector.length = 5

  Type: | `float` (percent of plot height)

`Vector2D.``reference_vector`

  Vector field reference vector.

  Example usage:

      >>> plot.vector.reference_vector.show = True

  Type: | `ReferenceVector`

`Vector2D.``relative_length`

  Magnitude-varying length of the vector line.

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

      >>> plot.vector.use_relative = True
      >>> plot.vector.use_grid_units = True
      >>> plot.vector.relative_length = 0.003

  Type: | `float` (grid units or cm per magnitude)

`Vector2D.``size_arrowhead_by_fraction`

  Base arrowhead size on length of vector line.

  Example usage:

      >>> plot.vector.size_arrowhead_by_fraction = True
      >>> plot.vector.relative_length = 0.1

  Type: | `bool`

`Vector2D.``u_variable`

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

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

      >>> plot.vector.u_variable = dataset.variable('Pressure X')

  Type: | `Variable`

`Vector2D.``u_variable_index`

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

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

      >>> plot.vector.u_variable_index = 3

  Type: | `int` (Zero-based index)

`Vector2D.``use_grid_units`

  Use grid-units when determining the relative length.

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

      >>> plot.vector.use_relative = True
      >>> plot.vector.use_grid_units = False
      >>> plot.vector.relative_length = 0.010

  Type: | `bool`

`Vector2D.``use_relative`

  Use relative sizing for vector lines.

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

      >>> plot.vector.use_relative = False
      >>> plot.vector.relative_length = 0.5

  Type: | `bool`

`Vector2D.``v_variable`

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

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

      >>> plot.vector.v_variable = dataset.variable('Pressure Y')

  Type: | `Variable`

`Vector2D.``v_variable_index`

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

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

      >>> plot.vector.v_variable_index = 4

  Type: | `int` (Zero-based index)

### Vector3D

class `tecplot.plot.``Vector3D`(plot)

  Vector field style control for Cartesian 3D plots.

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

  Attributes

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

`Vector3D.``arrowhead_angle`

  Angle between the vector body and the head line.

  Example usage:

      >>> plot.vector.arrowhead_angle = 10

  Type: | `float` (degrees)

`Vector3D.``arrowhead_fraction`

  Size of the arrowhead when sizing by fraction.

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

      >>> plot.vector.size_arrowhead_by_fraction = True
      >>> plot.vector.arrowhead_fraction = 0.4

  Type: | `float` (ratio)

`Vector3D.``arrowhead_size`

  Size of arrowhead when sizing by frame height.

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

      >>> plot.vector.size_arrowhead_by_fraction = False
      >>> plot.vector.arrowhead_size = 4

  Type: | `float` (percent of frame height)

`Vector3D.``length`

  Length of all vectors when not using relative sizing.

  Example usage:

      >>> plot.vector.use_relative = False
      >>> plot.vector.length = 5

  Type: | `float` (percent of plot height)

`Vector3D.``reference_vector`

  Vector field reference vector.

  Example usage:

      >>> plot.vector.reference_vector.show = True

  Type: | `ReferenceVector`

`Vector3D.``relative_length`

  Magnitude-varying length of the vector line.

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

      >>> plot.vector.use_relative = True
      >>> plot.vector.use_grid_units = True
      >>> plot.vector.relative_length = 0.003

  Type: | `float` (grid units or cm per magnitude)

`Vector3D.``size_arrowhead_by_fraction`

  Base arrowhead size on length of vector line.

  Example usage:

      >>> plot.vector.size_arrowhead_by_fraction = True
      >>> plot.vector.relative_length = 0.1

  Type: | `bool`

`Vector3D.``u_variable`

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

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

      >>> plot.vector.u_variable = dataset.variable('Pressure X')

  Type: | `Variable`

`Vector3D.``u_variable_index`

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

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

      >>> plot.vector.u_variable_index = 3

  Type: | `int` (Zero-based index)

`Vector3D.``use_grid_units`

  Use grid-units when determining the relative length.

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

      >>> plot.vector.use_relative = True
      >>> plot.vector.use_grid_units = False
      >>> plot.vector.relative_length = 0.010

  Type: | `bool`

`Vector3D.``use_relative`

  Use relative sizing for vector lines.

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

      >>> plot.vector.use_relative = False
      >>> plot.vector.relative_length = 0.5

  Type: | `bool`

`Vector3D.``v_variable`

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

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

      >>> plot.vector.v_variable = dataset.variable('Pressure Y')

  Type: | `Variable`

`Vector3D.``v_variable_index`

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

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

      >>> plot.vector.v_variable_index = 4

  Type: | `int` (Zero-based index)

`Vector3D.``w_variable`

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

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

      >>> plot.vector.w_variable = dataset.variable('Pressure Z')

  Type: | `Variable`

`Vector3D.``w_variable_index`

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

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

      >>> plot.vector.w_variable_index = 5

  Type: | `int` (Zero-based index)

### ReferenceVector

class `tecplot.plot.``ReferenceVector`(vector)

  Vector field reference vector.

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

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

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

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

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

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

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

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

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

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

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

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

  Attributes

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

`ReferenceVector.``angle`

  Degrees counter-clockwise to rotate the reference vector.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.angle = 90  # vertical, up

  Type: | `float` (degrees)

`ReferenceVector.``color`

  `Color` of the reference vector.

  Example usage:

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

  Type: | `Color`

`ReferenceVector.``label`

  reference vector label style control.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.label.show = True

  Type: | `ReferenceVectorLabel`

`ReferenceVector.``line_thickness`

  reference vector line thickness.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.line_thickness = 0.3

  Type: | `float` (percentage of frame height)

`ReferenceVector.``magnitude`

  Length of the reference vector.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.magnitude = 2

  Type: | `float` (data units)

`ReferenceVector.``position`

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

  Example usage:

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

  Type: | `tuple`

`ReferenceVector.``show`

  Draw the reference vector.

  Example usage:

      >>> plot.vector.reference_vector.show = True

  Type: | `bool`

### ReferenceVectorLabel

class `tecplot.plot.``ReferenceVectorLabel`(ref_vector)

  Label for the reference vector.

  See the example under `ReferenceVector`.

  Attributes

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

`ReferenceVectorLabel.``color`

  `Color` of the reference vector label.

  Example usage:

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

  Type: | `Color`

`ReferenceVectorLabel.``font`

  Typeface of the reference vector label.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.label.show = True
      >>> ref_vector.label.font.size = 6

  Type: | `text.Font`

`ReferenceVectorLabel.``format`

  Number formatting control for the reference vector label.

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.label.show = True
      >>> ref_vector.label.format.format_type = NumberFormat.Exponential

  Type: | `LabelFormat`

`ReferenceVectorLabel.``offset`

  Distance from the reference vector to the associated label.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.label.show = True
      >>> ref_vector.label.offset = 10

  Type: | `float` (percent of frame height)

`ReferenceVectorLabel.``show`

  Print a label next to the reference vector.

  Example usage:

      >>> ref_vector = plot.vector.reference_vector
      >>> ref_vector.show = True
      >>> ref_vector.label.show = True

  Type: | `bool`

## Legends

- ContourLegend
- LineLegend
- RGBColoringLegend
- ScatterLegend

### ContourLegend

class `tecplot.legend.``ContourLegend`(contour, *svargs)

  Contour legend attributes.

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

      import os
      import numpy as np

      import tecplot
      from tecplot.constant import *

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

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

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

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

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

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

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

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

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

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

  Attributes

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

`ContourLegend.``anchor_alignment`

  Anchor location of the legend.

  Example usage:

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

  Type: | `AnchorAlignment`

`ContourLegend.``auto_resize`

  Automatically skip some levels to create a reasonably sized legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.auto_resize = True

  Type: | `bool`

`ContourLegend.``box`

  Legend box attributes.

  Example usage:

      >>> from tecplot.constant import PlotType, Color
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.box.color = Color.Blue

  Type: | `text.TextBox`

`ContourLegend.``header_font`

  Font used to display the name of the contour variable.

  Note

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

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.header_font.italic = True

  Type: | `text.Font`

`ContourLegend.``label_format`

  Number formatting for labels along the legend.

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

      >>> contour = frame.plot().contour(0)
      >>> contour.legend.label_format.precision = 3
      >>> print(contour.labels.format.precision)
      3

  Type: | `LabelFormat`

`ContourLegend.``label_increment`

  Spacing between labels along the contour legend.

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

  Note

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

  Example usage:

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

  See also

  `label_location`

  Type: | `float`

`ContourLegend.``label_location`

  Placement of labels on the legend.

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

  - `ContLegendLabelLocation.ContourLevels` - This option places one label for each contour level. See Contour Levels and Color.
  - `ContLegendLabelLocation.Increment` - Set `label_increment` to the increment value.
  - `ContLegendLabelLocation.ColorMapDivisions` - Places one label for each control point on the color map.

  Example usage:

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

  See also

  `label_increment`

  Type: | `ContLegendLabelLocation`

`ContourLegend.``label_step`

  Step size between labels along the legend.

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

      >>> contour = frame.plot().contour(0)
      >>> contour.legend.label_step = 3
      >>> print(contour.labels.step)
      3

  Type: | `int`

`ContourLegend.``number_font`

  Font used to display numbers in the legend.

  Note

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

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.number_font.italic = True

  Type: | `text.Font`

`ContourLegend.``overlay_bar_grid`

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

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.overlay_bar_grid = False

  Type: | `bool`

`ContourLegend.``position`

  Position as a percentage of frame width/height.

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

  Example usage:

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

  Type: | `tuple`

`ContourLegend.``row_spacing`

  Spacing between rows in the legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.row_spacing = 1.5

  Type: | `float`

`ContourLegend.``show`

  Show or hide the legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.show = True

  Type: | `bool`

`ContourLegend.``show_cutoff_levels`

  Show color bands for levels affected by color cutoff.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.show_cutoff_levels = True

  Type: | `bool`

`ContourLegend.``show_header`

  Show the name of the contour variable in the legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.Cartesian3D).contour(0).legend
      >>> legend.show_header = True

  Type: | `bool`

`ContourLegend.``text_color`

  Color of legend text.

  Example usage:

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

  Type: | `Color`

`ContourLegend.``vertical`

  Orientation of the legend.

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

  Example usage:

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

  Type: | `bool`

### LineLegend

class `tecplot.legend.``LineLegend`(plot)

  Line plot legend attributes.

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

      import os

      import tecplot
      from tecplot.constant import *

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

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

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

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

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

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

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

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

  Attributes

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

`LineLegend.``anchor_alignment`

  Anchor location of the legend.

  Example usage:

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

  Type: | `AnchorAlignment`

`LineLegend.``box`

  Legend box attributes.

  Example usage:

      >>> from tecplot.constant import PlotType, Color
      >>> plot = frame.plot(PlotType.XYLine)
      >>> plot.legend.box.color = Color.Blue

  Type: | `text.TextBox`

`LineLegend.``font`

  Legend font attributes.

  Note

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

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> plot = frame.plot(PlotType.XYLine)
      >>> plot.legend.font.italic = True

  Type: | `text.Font`

`LineLegend.``position`

  Position as a percentage of frame width/height.

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

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.XYLine).legend
      >>> legend.position = (10, 30)

  Type: | `tuple`

`LineLegend.``row_spacing`

  Spacing between rows in the legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.XYLine).legend
      >>> legend.row_spacing = 1.5

  Type: | `float`

`LineLegend.``show`

  Show or hide the legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> legend = frame.plot(PlotType.XYLine).legend
      >>> legend.show = True

  Type: | `bool`

`LineLegend.``show_text`

  Show/hide mapping names in the legend.

  Example usage:

      >>> from tecplot.constant import PlotType
      >>> plot = frame.plot(PlotType.XYLine)
      >>> plot.legend.show_text = True

  Type: | `bool`

`LineLegend.``text_color`

  Color of legend text.

  Example usage:

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

  Type: | `Color`

### RGBColoringLegend

class `tecplot.legend.``RGBColoringLegend`(rgb_coloring)

  Legend for RGB coloring (multivariate contour) plots.

  Note

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

      import os
      import numpy as np

      import tecplot as tp
      from tecplot.constant import *

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

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

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

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

      plot.rgb_coloring.mode = RGBMode.SpecifyGB

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

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

      for fmap in plot.fieldmaps():
          fmap.contour.flood_contour_group = plot.rgb_coloring

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

  Attributes

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

`RGBColoringLegend.``anchor_alignment`

  Anchor location of the legend.

  Example usage:

      >>> from tecplot.constant import AnchorAlignment
      >>> legend = plot.rgb_coloring.legend
      >>> legend.anchor_alignment = AnchorAlignment.BottomCenter

  Type: | `AnchorAlignment`

`RGBColoringLegend.``blue_label`

  Label to use for the blue channel.

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

      >>> plot.rgb_coloring.legend.use_variable_for_blue_label = False
      >>> plot.rgb_coloring.legend.blue_label = 'water'

  Type: | `str`

`RGBColoringLegend.``box`

  Legend box attributes.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.rgb_coloring.legend.box.fill_color = Color.Yellow

  Type: | `text.TextBox`

`RGBColoringLegend.``font`

  Legend font attributes.

  Note

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

  Example usage:

      >>> plot.rgb_coloring.legend.font.italic = True

  Type: | `text.Font`

`RGBColoringLegend.``green_label`

  Label to use for the green channel.

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

      >>> plot.rgb_coloring.legend.use_variable_for_green_label = False
      >>> plot.rgb_coloring.legend.green_label = 'oil'

  Type: | `str`

`RGBColoringLegend.``height`

  Size of RGB coloring legend

  Example usage:

      >>> plot.rgb_coloring.legend.height = 20

  Type: | `float`

`RGBColoringLegend.``orientation`

  Placement of the RGB channels on the legend.

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

      >>> from tecplot.constant import RGBLegendOrientation
      >>> legend = plot.rgb_coloring.legend
      >>> legend.orientation = RGBLegendOrientation.RBG

  Type: | `RGBLegendOrientation`

`RGBColoringLegend.``position`

  Position as a percentage of frame width/height.

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

  Example usage:

      >>> plot.rgb_coloring.legend.position = (20, 80)

  Type: | `tuple`

`RGBColoringLegend.``red_label`

  Label to use for the red channel.

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

      >>> plot.rgb_coloring.legend.use_variable_for_red_label = False
      >>> plot.rgb_coloring.legend.red_label = 'gas'

  Type: | `str`

`RGBColoringLegend.``show`

  Display the RGB coloring legend.

  Example usage:

      >>> plot.rgb_coloring.legend.show = True

  Type: | `bool`

`RGBColoringLegend.``show_labels`

  Show the RGB channel labels.

  Example usage:

      >>> legend = plot.rgb_coloring.legend
      >>> legend.show_labels = True
      >>> legend.red_label = 'Variable A'
      >>> legend.green_label = 'Variable B'
      >>> legend.blue_label = 'Variable C'

  Type: | `bool`

`RGBColoringLegend.``text_color`

  Color of legend text.

  Example usage:

      >>> from tecplot.constant import Color
      >>> legend = plot.rgb_coloring.legend
      >>> legend.text_color = Color.Blue

  Type: | `Color`

`RGBColoringLegend.``use_variable_for_blue_label`

  Use the `Variable` name for the blue channel.

  Example usage:

      >>> plot.rgb_coloring.legend.use_variable_for_blue_label = False
      >>> plot.rgb_coloring.legend.blue_label = 'gas'

  Type: | `bool`

`RGBColoringLegend.``use_variable_for_green_label`

  Use the `Variable` name for the green channel.

  Example usage:

      >>> plot.rgb_coloring.legend.use_variable_for_green_label = False
      >>> plot.rgb_coloring.legend.green_label = 'gas'

  Type: | `bool`

`RGBColoringLegend.``use_variable_for_red_label`

  Use the `Variable` name for the red channel.

  Example usage:

      >>> plot.rgb_coloring.legend.use_variable_for_red_label = False
      >>> plot.rgb_coloring.legend.red_label = 'gas'

  Type: | `bool`

### ScatterLegend

class `tecplot.legend.``ScatterLegend`(scatter)

  Legend style for scatter plots.

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

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

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

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

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

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

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

  Attributes

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

`ScatterLegend.``anchor_alignment`

  Anchor location of the legend.

  Example usage:

      >>> from tecplot.constant import AnchorAlignment
      >>> legend = plot.scatter.legend
      >>> legend.anchor_alignment = AnchorAlignment.BottomCenter

  Type: | `AnchorAlignment`

`ScatterLegend.``box`

  Legend box attributes.

  Example usage:

      >>> from tecplot.constant import PlotType, Color
      >>> plot.scatter.legend.box.color = Color.Blue

  Type: | `text.TextBox`

`ScatterLegend.``font`

  Legend font attributes.

  Note

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

  Example usage:

      >>> plot.scatter.legend.font.italic = True

  Type: | `text.Font`

`ScatterLegend.``position`

  Position as a percentage of frame width/height.

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

  Example usage:

      >>> plot.scatter.legend.position = (10, 30)

  Type: | `tuple`

`ScatterLegend.``row_spacing`

  Spacing between rows in the legend.

  Example usage:

      >>> plot.scatter.legend.row_spacing = 1.5

  Type: | `float`

`ScatterLegend.``show`

  Show or hide the legend.

  Example usage:

      >>> plot.scatter.legend.show = True

  Type: | `bool`

`ScatterLegend.``show_text`

  Show/hide mapping names in the legend.

  Example usage:

      >>> plot.scatter.legend.show_text = True

  Type: | `bool`

`ScatterLegend.``text_color`

  Color of legend text.

  Example usage:

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

  Type: | `Color`

## Contours

- ContourGroup
- ContourColorCutoff
- ContourColormapFilter
- ContourColormapOverride
- ContourColormapZebraShade
- ContourLabels
- ContourLevels
- ContourLines
- RGBColoring

### ContourGroup

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

  Contouring of a variable using a colormap.

  This object controls the style for a specific contour group within a `Frame`. Contour levels, colormap and contour lines are accessed through this class:

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

      # load data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir,'SimpleData','CircularContour.plt')
      dataset = tp.data.load_tecplot(datafile)
      plot = dataset.frame.plot()
      plot.show_contour = True

      contour = plot.contour(0)
      contour.variable = dataset.variable('Mix')
      contour.colormap_name = 'Magma'

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

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

  There are a fixed number of contour groups available for each plot. Others can be enabled and modified by specifying an index other than zero:

      >>> contour3 = plot.contour(3)
      >>> contour3.variable = dataset.variable('U')

  Attributes

  `color_cutoff` | `ContourColorCutoff` object controlling color cutoff min/max.
  `colormap_filter` | `ContourColormapFilter` object controlling colormap style properties.
  `colormap_name` | The name of the colormap (`str`) to be used.
  `default_num_levels` | Default target number (`int`) of levels used when resetting.
  `labels` | `ContourLabels` object controlling contour line labels.
  `legend` | `ContourLegend` associated with this `ContourGroup`.
  `levels` | `ContourLevels` holding the list of contour levels.
  `lines` | `ContourLines` object controlling contour line style.
  `variable` | The `Variable` being contoured.
  `variable_index` | Zero-based index of the `Variable` being contoured.

`ContourGroup.``color_cutoff`

  `ContourColorCutoff` object controlling color cutoff min/max.

      >>> cutoff = plot.contour(0).color_cutoff
      >>> cutoff.min = 3.14

  Type: | `ContourColorCutoff`

`ContourGroup.``colormap_filter`

  `ContourColormapFilter` object controlling colormap style properties.

      >>> plot.contour(0).colormap_filter.reverse = True

  Type: | `ContourColormapFilter`

`ContourGroup.``colormap_name`

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

  Example:

      >>> plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'

  Type: | `str`

`ContourGroup.``default_num_levels`

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

  Example:

      >>> plot.contour(0).default_num_levels = 20

  Type: | `int`

`ContourGroup.``labels`

  `ContourLabels` object controlling contour line labels.

  Lines must be turned on through the associated fieldmap object for style changes to be meaningful:

      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True

  Type: | `ContourLabels`

`ContourGroup.``legend`

  `ContourLegend` associated with this `ContourGroup`.

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

  Example usage:

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

  Type: | `ContourLegend`

`ContourGroup.``levels`

  `ContourLevels` holding the list of contour levels.

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

      >>> plot.contour(0).levels.reset_to_nice(15)

  Type: | `ContourLevels`

`ContourGroup.``lines`

  `ContourLines` object controlling contour line style.

  Lines must be turned on through the associated fieldmap object for style changes to be meaningful:

      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).lines.mode = ContourLineMode.DashNegative

  Type: | `ContourLines`

`ContourGroup.``variable`

  The `Variable` being contoured.

  The variable must belong to the `Dataset` attached to the `Frame` that holds this `ContourGroup`. Example usage:

      >>> plot.contour(0).variable = dataset.variable('P')

`ContourGroup.``variable_index`

  Zero-based index of the `Variable` being contoured.

      >>> plot.contour(0).variable_index = dataset.variable('P').index

  The `Dataset` attached to this contour group’s `Frame` is used:

      >>> contour = plot.contour(0)
      >>> contour_var = frame.dataset.variable(contour.variable_index)
      >>> contour_var.index == contour.variable_index
      True

### ContourColorCutoff

class `tecplot.plot.``ContourColorCutoff`(contour)

  Color-mapped value limits to display.

  This lets you specify a range within which contour flooding and multi-colored objects, such as scatter symbols, are displayed:

      import os
      import tecplot as tp
      from tecplot.constant import PlotType, SurfacesToPlot

      # load the data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir,'SimpleData','Pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

      # show boundary faces and contours
      plot = tp.active_frame().plot()
      surfaces = plot.fieldmap(0).surfaces
      surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_contour = True

      # cutoff contour flooding outside min/max range
      cutoff = plot.contour(0).color_cutoff
      cutoff.include_min = True
      cutoff.min = 0.5
      cutoff.include_max = True
      cutoff.max = 1.0
      cutoff.inverted = True

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

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

  Attributes

  `include_max` | Use the maximum cutoff value.
  `include_min` | Use the minimum cutoff value.
  `inverted` | Cuts values outside the range instead of inside.
  `max` | The maximum cutoff value.
  `min` | The minimum cutoff value.

`ContourColorCutoff.``include_max`

  Use the maximum cutoff value.

  Thie example turns off the maximum cutoff:

      >>> plot.contour(0).color_cutoff.include_max = False

  Type: | `bool`

`ContourColorCutoff.``include_min`

  Use the minimum cutoff value.

  Example usage:

      >>> plot.contour(0).color_cutoff.include_min = True
      >>> plot.contour(0).color_cutoff.min = 3.14

  Type: | `bool`

`ContourColorCutoff.``inverted`

  Cuts values outside the range instead of inside.

      >>> plot.contour(0).color_cutoff.inverted = True

  Type: | `bool`

`ContourColorCutoff.``max`

  The maximum cutoff value.

  The `include_max` must be set to `True`:

      >>> plot.contour(0).color_cutoff.include_max = True
      >>> plot.contour(0).color_cutoff.max = None

  Type: | `float` or `None`

`ContourColorCutoff.``min`

  The minimum cutoff value.

  The `include_min` must be set to `True`:

      >>> plot.contour(0).color_cutoff.include_min = True
      >>> plot.contour(0).color_cutoff.min = 3.14

  Type: | `float` or `None`

### ContourColormapFilter

class `tecplot.plot.``ContourColormapFilter`(contour)

  Controls how the colormap is rendered for a given contour.

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

      # load the data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir,'SimpleData','HeatExchanger.plt')
      ds = tp.data.load_tecplot(datafile)

      # set plot type to 2D field plot
      frame = tp.active_frame()
      frame.plot_type = PlotType.Cartesian2D
      plot = frame.plot()

      # show boundary faces and contours
      surfaces = plot.fieldmap(0).surfaces
      surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_contour = True

      # by default, contour 0 is the one that's shown,
      # set the contour's variable, colormap and number of levels
      contour = plot.contour(0)
      contour.variable = ds.variable('P(N)')

      # cycle through the colormap three times and reversed
      # show a faithful (non-approximate) continuous distribution
      contour_filter = contour.colormap_filter
      contour_filter.num_cycles = 3
      contour_filter.reversed = True
      contour_filter.fast_continuous_flood = False
      contour_filter.distribution = ColorMapDistribution.Continuous

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

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

  Attributes

  `continuous_max` | Upper limit for continuous colormap flooding.
  `continuous_min` | Lower limit for continuous colormap flooding.
  `distribution` | Rendering style of the colormap.
  `fast_continuous_flood` | Use a fast approximation to continuously flood the colormap.
  `num_cycles` | Number of cycles to repeat the colormap.
  `reversed` | Reverse the colormap.
  `show_overrides` | Enable the colormap overrides in this contour group.
  `zebra_shade` | Returns a `ContourColormapZebraShade` filtering object.

  Methods

  `override`(index) | Returns a `ContourColormapOverride` object by index.

`ContourColormapFilter.``continuous_max`

  Upper limit for continuous colormap flooding.

  Example set the limits to the (min, max) of a variable in a specific zone:

      >>> from tecplot.constant import ColorMapDistribution
      >>> cmap_filter = plot.contour(0).colormap_filter
      >>> cmap_filter.distribution = ColorMapDistribution.Continuous
      >>> pressure = dataset.variable('Pressure').values('My Zone')
      >>> cmap_filter.continuous_min = pressure.min()
      >>> cmap_filter.continuous_max = pressure.max()

  Type: | `float`

`ContourColormapFilter.``continuous_min`

  Lower limit for continuous colormap flooding.

  Example usage:

      >>> from tecplot.constant import ColorMapDistribution
      >>> cmap_filter = plot.contour(0).colormap_filter
      >>> cmap_filter.distribution = ColorMapDistribution.Continuous
      >>> cmap_filter.continuous_min = 3.1415

  Type: | `float`

`ContourColormapFilter.``distribution`

  Rendering style of the colormap.

  Possible values:

  `Banded`
    A solid color is assigned for all values within the band between two levels.
  `Continuous`
    The color distribution assigns linearly varying colors to all multi-colored objects or contour flooded regions.

  Example:

      >>> from tecplot.constant import ColorMapDistribution
      >>> cmap_filter = plot.contour(0).colormap_filter
      >>> cmap_filter.distribution = ColorMapDistribution.Banded

  Type: | `ColorMapDistribution`

`ContourColormapFilter.``fast_continuous_flood`

  Use a fast approximation to continuously flood the colormap.

  Causes each cell to be flooded using interpolation between the color values at each node. When the transition from a color at one node to another node crosses over the boundary between control points in the color spectrum, fast flooding may produce colors not in the spectrum. Setting this to `False` is slower, but more accurate:

      >>> cmap_filter = plot.contour(0).colormap_filter
      >>> cmap_filter.fast_continuous_flood = True

  Type: | `bool`

`ContourColormapFilter.``num_cycles`

  Number of cycles to repeat the colormap.

      >>> plot.contour(0).colormap_filter.num_cycles = 3

  Type: | `int`

`ContourColormapFilter.``override`(index)

  Returns a `ContourColormapOverride` object by index.

  Parameters: | index (`int`) – The index of the colormap override object.
  Returns: | `ContourColormapOverride` – The class controlling the specific contour colormap override requested by index.

  Example:

      >>> cmap_override = plot.contour(0).colormap_filter.override(0)
      >>> cmap_override.show = True

`ContourColormapFilter.``reversed`

  Reverse the colormap.

      >>> plot.contour(0).colormap_filter.reversed = True

  Type: | `bool`

`ContourColormapFilter.``show_overrides`

  Enable the colormap overrides in this contour group.

  The overrides themselves must be turned on as well for this to have an effect on the resulting plot:

      >>> contour = plot.contour(0)
      >>> cmap_filter = contour.colormap_filter
      >>> cmap_filter.show_overrides = True
      >>> cmap_filter.override(0).show = True

  Type: | `bool`

`ContourColormapFilter.``zebra_shade`

  Returns a `ContourColormapZebraShade` filtering object.

  Example usage:

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

  Type: | `ContourColormapZebraShade`

### ContourColormapOverride

class `tecplot.plot.``ContourColormapOverride`(index, colormap_filter)

  Assigns contour bands to specific color.

  Specific contour bands can be assigned a unique basic color. This is useful for forcing a particular region to use blue, for example, to designate an area of water. You can define up to 16 color overrides:

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

      # load the data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, 'SimpleData', 'HeatExchanger.plt')
      dataset = tp.data.load_tecplot(datafile)

      # set plot type to 2D field plot
      frame = tp.active_frame()
      frame.plot_type = PlotType.Cartesian2D
      plot = frame.plot()

      # show boundary faces and contours
      surfaces = plot.fieldmap(0).surfaces
      surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_contour = True

      # by default, contour 0 is the one that's shown,
      # set the contour's variable, colormap and number of levels
      contour = plot.contour(0)
      contour.variable = dataset.variable('T(K)')
      contour.colormap_name = 'Sequential - Yellow/Green/Blue'
      contour.levels.reset(9)

      # turn on colormap overrides for this contour
      contour_filter = contour.colormap_filter
      contour_filter.show_overrides = True

      # turn on override 0, coloring the first 4 levels red
      contour_override = contour_filter.override(0)
      contour_override.show = True
      contour_override.color = Color.Red
      contour_override.start_level = 7
      contour_override.end_level = 8

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

  Attributes

  `color` | Color which will override the colormap.
  `end_level` | Last level to override.
  `show` | Include this colormap override when filter is shown.
  `start_level` | First level to override.

`ContourColormapOverride.``color`

  Color which will override the colormap.

  Example usage:

      >>> from tecplot.constant import Color
      >>> colormap_filter = plot.contour(0).colormap_filter
      >>> cmap_override = colormap_filter.override(0)
      >>> cmap_override.color = Color.Blue

  Type: | `Color`

`ContourColormapOverride.``end_level`

  Last level to override.

  Example usage:

      >>> colormap_filter = plot.contour(0).colormap_filter
      >>> cmap_override = colormap_filter.override(0)
      >>> cmap_override.end_level = 2

  Type: | `int`

`ContourColormapOverride.``show`

  Include this colormap override when filter is shown.

  Example usage:

      >>> colormap_filter = plot.contour(0).colormap_filter
      >>> cmap_override = colormap_filter.override(0)
      >>> cmap_override.show = True

  Type: | `bool`

`ContourColormapOverride.``start_level`

  First level to override.

  Example usage:

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

  Type: | `int`

### ContourColormapZebraShade

class `tecplot.plot.``ContourColormapZebraShade`(colormap_filter)

  This filter sets a uniform color for every other band.

  Setting the color to `None` turns the bands off and makes them transparent:

      from os import path
      import numpy as np
      import tecplot as tp
      from tecplot.constant import Color, SurfacesToPlot

      # load the data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir,'SimpleData','Pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

      # show boundary faces and contours
      plot = tp.active_frame().plot()
      surfaces = plot.fieldmap(0).surfaces
      surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_contour = True
      plot.show_shade = False

      # set zebra filter on and make the zebra contours transparent
      cont0 = plot.contour(0)
      zebra = cont0.colormap_filter.zebra_shade
      zebra.show = True
      zebra.transparent = True

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

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

  Attributes

  `color` | Color of the zebra shading.
  `show` | Show zebra shading in this `ContourGroup`.
  `transparent` | Set the the zebra bands to be transparent.

`ContourColormapZebraShade.``color`

  Color of the zebra shading.

  Example usage:

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

  Type: | `Color`

`ContourColormapZebraShade.``show`

  Show zebra shading in this `ContourGroup`.

  Example usage:

      >>> cmap_filter = plot.contour(0).colormap_filter
      >>> cmap_filter.zebra_shade.show = True

  Type: | `bool`

`ContourColormapZebraShade.``transparent`

  Set the the zebra bands to be transparent.

  Example usage:

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

  Type: | `bool`

### ContourLabels

class `tecplot.plot.``ContourLabels`(contour)

  Contour line label style, position and alignment control.

  These are labels that identify particular contour levels either by value or optionally, by number starting from one. The plot type must be lines or lines and flood in order to see them:

      from os import path
      import tecplot as tp
      from tecplot.constant import Color, ContourType, SurfacesToPlot

      # load the data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir,'SimpleData','Pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

      # show boundary faces and contours
      plot = tp.active_frame().plot()
      plot.fieldmap(0).contour.contour_type = ContourType.Lines
      surfaces = plot.fieldmap(0).surfaces
      surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_contour = True

      # set contour label style
      contour_labels = plot.contour(0).labels
      contour_labels.show = True
      contour_labels.auto_align = False
      contour_labels.color = Color.Blue
      contour_labels.background_color = Color.White
      contour_labels.margin = 20

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

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

  Attributes

  `auto_align` | Automatically align the labels with the contour lines.
  `auto_generate` | Automatically generate labels along contour lines.
  `background_color` | Background fill color behind the text labels.
  `color` | Text color of the labels.
  `filled` | Fill the background area behind the text labels.
  `font` | `text.Font` used to show the labels.
  `format` | Number formatting for contour labels.
  `label_by_level` | Use the contour numbers as the label instead of the data value.
  `margin` | Spacing around the text and the filled background area.
  `show` | Show the contour line labels.
  `spacing` | Spacing between labels along the contour lines.
  `step` | Number of contour lines from one label to the next.

`ContourLabels.``auto_align`

  Automatically align the labels with the contour lines.

  This causes the flow of the text to be aligned with the contour lines. Otherwise, the labels are aligned with the frame:

      >>> from tecplot.constant import ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.auto_align = False

  Type: | `bool`

`ContourLabels.``auto_generate`

  Automatically generate labels along contour lines.

  This causes a new set of contour labels to be created at each redraw:

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.auto_generate = True

  Type: | `bool`

`ContourLabels.``background_color`

  Background fill color behind the text labels.

  The `filled` attribute must be set to `True`:

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.background_color = Color.Blue

  Type: | `Color`

`ContourLabels.``color`

  Text color of the labels.

  Example:

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.color Color.Blue

  Type: | `Color`

`ContourLabels.``filled`

  Fill the background area behind the text labels.

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

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.filled = True
      >>> plot.contour(0).labels.background_color = Color.Blue
      >>> plot.contour(1).labels.show = True
      >>> plot.contour(1).labels.filled = False

  Type: | `bool`

`ContourLabels.``font`

  `text.Font` used to show the labels.

  Example:

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.font.size = 3.5

  Type: | `text.Font`

`ContourLabels.``format`

  Number formatting for contour labels.

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> contour = plot.contour(0)
      >>> contour.labels.format.format_type = NumberFormat.Integer

  Type: | `LabelFormat`

`ContourLabels.``label_by_level`

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

  Contour level numbers start from one when drawn. Example usage:

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.label_by_level = True

  Type: | `bool`

`ContourLabels.``margin`

  Spacing around the text and the filled background area.

  Contour numbers start from one when drawn. Example usage:

      >>> from tecplot.constant import Color, ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.background_color = Color.Yellow
      >>> plot.contour(0).labels.margin = 20

  Type: | `float` in percentage of the text height.

`ContourLabels.``show`

  Show the contour line labels.

  Contour lines must be on for this to have any effect:

      >>> from tecplot.constant import ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True

  Type: | `bool`

`ContourLabels.``spacing`

  Spacing between labels along the contour lines.

  This is the distance between each label along each contour line in percentage of the frame height:

      >>> from tecplot.constant import ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.spacing = 20

  Type: | `float`

`ContourLabels.``step`

  Number of contour lines from one label to the next.

  This is the number of contour bands between lines that are to be labeled:

      >>> from tecplot.constant import ContourType
      >>> plot.fieldmap(0).contour.contour_type = ContourType.Lines
      >>> plot.contour(0).labels.show = True
      >>> plot.contour(0).labels.step = 4

  Type: | `int`

### ContourLevels

class `tecplot.plot.``ContourLevels`(contour)

  List of contour level values.

  A contour level is a value at which contour lines are drawn, or for banded contour flooding, the border between different colors of flooding. Initially, each contour group consists of approximately 10 levels evenly spaced over the z coordinate in the `Frame`’s `Dataset`. These values can be manipulated with the `ContourLevels` object obtained via the `ContourGroup.levels` attribute:

      from os import path
      import numpy as np
      import tecplot as tp

      # load layout
      examples_dir = tp.session.tecplot_examples_directory()
      example_layout = path.join(examples_dir,'SimpleData','3ElementWing.lpk')
      tp.load_layout(example_layout)
      frame = tp.active_frame()

      levels = frame.plot().contour(0).levels
      levels.reset_levels(np.linspace(55000,115000,61))

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

  Note

  The streamtraces in the plot above is a side-effect of settings in layout file used. For more information about streamtraces, see the `plot.Streamtraces` class reference.

  Methods

  `add`(*values) | Adds new levels to the existing list.
  `delete_nearest`(value) | Removes the level closest to the specified value.
  `delete_range`(min_value, max_value) | Inclusively, deletes all levels within a specified range.
  `reset`([num_levels]) | Resets the levels to the number specified.
  `reset_levels`(*values) | Resets the levels to the values specified.
  `reset_to_nice`([num_levels]) | Approximately resets the levels to the number specified.

`ContourLevels.``add`(*values)

  Adds new levels to the existing list.

  Parameters: | *values (`floats`) – The level values to be added to the `ContourGroup`.

  The values added are inserted into the list of levels in ascending order:

      >>> levels = plot.contour(0).levels
      >>> list(levels)
      [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
      >>> levels.add(3.14159)
      >>> list(levels)
      [0.0, 1.0, 2.0, 3.0, 3.14159, 4.0, 5.0]

`ContourLevels.``delete_nearest`(value)

  Removes the level closest to the specified value.

  Parameters: | value (`float`) – Value of the level to remove.

  This method deletes the contour level with the value nearest the supplied value:

      >>> plot.contour(0).levels.delete_nearest(3.14)

`ContourLevels.``delete_range`(min_value, max_value)

  Inclusively, deletes all levels within a specified range.

  Parameters: | min_value (`float`) – Minimum value to remove. max_value (`float`) – Maximum value to remove.

  This method deletes all contour levels between the specified minimum and maximum values of the contour variable (inclusive):

      >>> plot.contour(0).levels.delete_range(0.5, 1.5)

`ContourLevels.``reset`(num_levels=15)

  Resets the levels to the number specified.

  Parameters: | num_levels (`int`) – Number of levels. (default: 10)

  This will reset the contour levels to a set of evenly distributed values spanning the entire range of the currently selected contouring variable:

      >>> plot.contour(0).levels.reset(30)

`ContourLevels.``reset_levels`(*values)

  Resets the levels to the values specified.

  Parameters: | *values (`floats`) – The level values to be added to the `ContourGroup`.

  This method replaces the current set of contour levels with a new set. Here, we set the levels to go from 0 to 100 in steps of 5:

      >>> plot.contour(0).levels.reset_levels(*range(0,101,5))

`ContourLevels.``reset_to_nice`(num_levels=15)

  Approximately resets the levels to the number specified.

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

  This will reset the contour levels to a set of evenly distributed values that approximately spans the range of the currently selected contouring variable. Exact range and number of levels will be adjusted to make the contour levels have “nice” values:

      >>> plot.contour(0).levels.reset_to_nice(50)

### ContourLines

class `tecplot.plot.``ContourLines`(contour)

  Contour line style.

  This object sets the style of the contour lines once turned on:

      from os import path
      import tecplot as tp
      from tecplot.constant import (ContourLineMode, ContourType,
                                    SurfacesToPlot)

      # load the data
      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir,'SimpleData','Pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

      # show boundary faces and contours
      plot = tp.active_frame().plot()
      plot.fieldmap(0).contour.contour_type = ContourType.Lines
      surfaces = plot.fieldmap(0).surfaces
      surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_contour = True

      # set contour line style
      contour_lines = plot.contour(0).lines
      contour_lines.mode = ContourLineMode.SkipToSolid
      contour_lines.step = 4
      contour_lines.pattern_length = 2

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

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

  Attributes

  `mode` | Type of lines to draw on the plot (`ContourLineMode`).
  `pattern_length` | Length of dashed lines and space between dashes (`float`).
  `step` | Number of lines to step for `SkipToSolid` line mode (`int`).

`ContourLines.``mode`

  Type of lines to draw on the plot (`ContourLineMode`).

  Possible values:

  > `UseZoneLineType`
  >   For each zone, draw the contour lines using the line pattern and pattern length specified in the `FieldmapContour` for the parent Fieldmaps. If you are adding contour lines to polyhedral zones, the patterns will not be continuous from one cell to the next and the pattern will restart at every cell boundary.
  > `SkipToSolid`
  >   Draw dashed lines between each pair of solid lines which are spaced out by the `ContourLines.step` property. This will override any line pattern or thickness setting in the parent Fieldmaps’s `FieldmapContour` object.
  > `DashNegative`
  >   Draw lines of positive contour variable value as solid lines and lines of negative contour variable value as dashed lines. This will override any line pattern or thickness setting in the parent Fieldmaps’s `FieldmapContour` object.

  Example:

      >>> from tecplot.constant import ContourLineMode
      >>> lines = plot.contour(0).lines
      >>> lines.mode = ContourLineMode.DashNegative

  Type: | `ContourLineMode`

`ContourLines.``pattern_length`

  Length of dashed lines and space between dashes (`float`).

  The length is in percentage of the frame height:

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

  Type: | `float`

`ContourLines.``step`

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

  Example:

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

  Type: | `int`

### RGBColoring

class `tecplot.plot.``RGBColoring`(plot)

  RGB coloring (multivariate contour) style control.

      import os
      import numpy as np

      import tecplot as tp
      from tecplot.constant import *

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

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

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

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

      plot.rgb_coloring.mode = RGBMode.SpecifyGB

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

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

      for fmap in plot.fieldmaps():
          fmap.contour.flood_contour_group = plot.rgb_coloring

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

  Attributes

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

`RGBColoring.``blue_variable`

  `Variable` to use for the blue channel.

  Example usage:

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

  Note

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

  Type: | `Variable`

`RGBColoring.``blue_variable_index`

  `Variable` `Index` to use for the blue channel.

  Example usage:

      >>> plot.rgb_coloring.blue_variable_index = 4

  Note

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

  Type: | `Index`

`RGBColoring.``green_variable`

  `Variable` to use for the green channel.

  Example usage:

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

  Note

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

  Type: | `Variable`

`RGBColoring.``green_variable_index`

  `Variable` `Index` to use for the green channel.

  Example usage:

      >>> plot.rgb_coloring.green_variable_index = 3

  Note

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

  Type: | `Index`

`RGBColoring.``legend`

  Legend placement and style control.

  Example usage:

      >>> plot.rgb_coloring.legend.show = True

  Type: | `RGBColoringLegend`

`RGBColoring.``max_intensity`

  `Variable` value at maximum intensity for each channel.

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

      >>> plot.rgb_coloring.max_intensity = dataset.variable('P').max()

  Type: | `float`

`RGBColoring.``min_intensity`

  `Variable` value at minimum intensity for each channel.

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

      >>> plot.rgb_coloring.min_intensity = dataset.variable('P').min()

  Type: | `float`

`RGBColoring.``mode`

  Which channels to use for RGB coloring.

  Example usage:

      >>> from tecplot.constant import RGBMode
      >>> plot.rgb_coloring.mode = RGBMode.SpecifyRB

  Type: | `RGBMode`

`RGBColoring.``red_variable`

  `Variable` to use for the red channel.

  Example usage:

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

  Note

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

  Type: | `Variable`

`RGBColoring.``red_variable_index`

  `Variable` `Index` to use for the red channel.

  Example usage:

      >>> plot.rgb_coloring.red_variable_index = 5

  Note

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

  Type: | `Index`

## Isosurface

- IsosurfaceGroup
- IsosurfaceContour
- IsosurfaceEffects
- IsosurfaceMesh
- IsosurfaceShade
- IsosurfaceVector

### IsosurfaceGroup

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

  Isosurfaces style control.

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

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

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

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

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

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

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

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

  Attributes

  `contour` | Contour attributes for this isosurface group.
  `definition_contour_group` | Contour group from which isosurfaces are based.
  `definition_contour_group_index` | Contour group index from which isosurfaces are based.
  `effects` | Settings for isosurface effects.
  `isosurface_selection` | Select where to draw isosurfaces.
  `isosurface_values` | Values at which to draw isosurfaces.
  `mesh` | Mesh attributes for this isosurface group.
  `obey_source_zone_blanking` | Obey source zone blanking.
  `shade` | Shade attributes for this isosurface group.
  `show` | Show isosurfaces for this isosurface group.
  `surface_generation_method` | Determines how the surface is generated.
  `vector` | Vector attributes for this isosurface group.

  Methods

  `extract`([mode, assign_strand_ids]) | Create new zones from this isosurface.

`IsosurfaceGroup.``contour`

  Contour attributes for this isosurface group.

  Example usage:

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

  Type: | `IsosurfaceContour`

`IsosurfaceGroup.``definition_contour_group`

  Contour group from which isosurfaces are based.

  Example usage:

      >>> group = plot.contour(1)
      >>> plot.isosurface(0).definition_contour_group = group

  Type: | `ContourGroup`

`IsosurfaceGroup.``definition_contour_group_index`

  Contour group index from which isosurfaces are based.

  Contour group settings can be changed from `plot.ContourGroup`.

  Example usage:

      >>> plot.isosurface(0).show = True
      >>> plot.isosurface(0).definition_contour_group_index = 1

  Type: | `Index`

`IsosurfaceGroup.``effects`

  Settings for isosurface effects.

  Example usage:

      >>> plot.isosurface(0).show = True
      >>> plot.isosurface(0).effects.use_translucency = True

  Type: | `IsosurfaceEffects`

`IsosurfaceGroup.``extract`(mode=<ExtractMode.SingleZone: 0>, assign_strand_ids=True)

  Create new zones from this isosurface.

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

  Parameters: | mode (`ExtractMode`, optional) – Determines how many zones are created upon extraction. Possible values are: `ExtractMode.SingleZone` (default) and `ExtractMode.OneZonePerConnectedRegion`. assign_strand_ids (`bool`, optional) – Automatically assign strand ID’s to the created zones. (default: `True`)
  Returns: | The extracted zone if mode is `ExtractMode.SingleZone`, otherwise a generator of the extracted zones.

  Example usage:

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

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

`IsosurfaceGroup.``isosurface_selection`

  Select where to draw isosurfaces.

  Iso-surfaces may be drawn at:
    - Contour group levels
    - At specified value(s) - Specify up to three values of the contour variable at which to draw isosurfaces.
  To draw isosurfaces at contour group lines:
    1. Set `isosurface_selection` to `IsoSurfaceSelection.AllContourLevels`.
    2. Optional: Change `tecplot.plot.ContourLevels`
  To draw isosurfaces at up to 3 values:
    1. Set `isosurface_selection` to one of the following: `IsoSurfaceSelection.OneSpecificValue` `IsoSurfaceSelection.TwoSpecificValues` `IsoSurfaceSelection.ThreeSpecificValues`
    2. Set `isosurface_values` to a 1, 2, or 3 `tuple` of `floats`

  See also `isosurface_values`.

  Example usage:

      >>> plot.isosurface(0).show = True
      >>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.TwoSpecificValues
      >>> plot.isosurface(0).isosurface_values = (.3, .8)

  Type: | `IsoSurfaceSelection`

`IsosurfaceGroup.``isosurface_values`

  Values at which to draw isosurfaces.

  This may be a 1, 2, or 3-`tuple` of `floats`, or a single scalar `float`.

  To draw isosurfaces at up to 3 values:
    1. Set `isosurface_selection` to one of the following: `IsoSurfaceSelection.OneSpecificValue` `IsoSurfaceSelection.TwoSpecificValues` `IsoSurfaceSelection.ThreeSpecificValues`
    2. Set `isosurface_values` to a 1, 2, or 3 `tuple` or `list` of `floats`, or set to a scalar `float` to assign the first value only.

  When queried, this property will always return a 3 tuple of `floats`.

  See also `isosurface_selection`.

  Assign first isosurface value using a scalar `float`:

      >>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.OneSpecificValue
      >>> plot.isosurface(0).isosurface_values = 0.5
      >>> plot.isosurface(0).isosurface_values[0]
      0.5

  Assign first isosurface value using a 1-`tuple`:

      >>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.OneSpecificValue
      >>> plot.isosurface(0).isosurface_values = (.5,) # 1-tuple
      >>> plot.isosurface(0).isosurface_values
      0.5

  Assign all three isosurface values:

      >>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
      >>> plot.isosurface(0).isosurface_values = (.5, .7, 9)

  Assign the third isosurface values after assigning the first two:

      >>> plot.isosurface(0).isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
      >>> # Assign first and second isosurface value using a tuple
      >>> plot.isosurface(0).isosurface_values = (0.0, 0.1)
      >>> # Assign third isosurface value
      >>> plot.isosurface(0).isosurface_values[2] = .3
      >>> plot.isosurface(0).isosurface_values[2]
      .3
      >>> plot.isosurface(0).isosurface_values
      (0.0, 0.1, .3)

  Query the three isosurface values:

      >>> # isosurface_values always returns a
      >>> # list-like object of 3 floats with of current
      >>> # isosurface values, even if fewer than three have been set.
      >>> values = plot.isosurface(0).isosurface_values
      >>> values
      (0.1, 0.2, 0.3)
      >>> values[0]
      0.1
      >>> values[1]
      0.2
      >>> values[2]
      0.3
      >>> len(values)
      3

  Type: | `tuple` or `float`

`IsosurfaceGroup.``mesh`

  Mesh attributes for this isosurface group.

  Example usage:

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

  Type: | `IsosurfaceMesh`

`IsosurfaceGroup.``obey_source_zone_blanking`

  Obey source zone blanking.

  > - When `True`, isosurfaces are generated for non-blanked regions only.
  > - When `False`, isosurfaces are generated for blanked and unblanked. regions.

  Example usage:

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

  Type: | `bool`

`IsosurfaceGroup.``shade`

  Shade attributes for this isosurface group.

  Example usage:

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

  Type: | `IsosurfaceShade`

`IsosurfaceGroup.``show`

  Show isosurfaces for this isosurface group.

  Example usage:

      >>> plot.isosurface(1).show = True

  Type: | `bool`

`IsosurfaceGroup.``surface_generation_method`

  Determines how the surface is generated.

  May be one of:

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

  Example usage:

      >>> from tecplot.constant import SurfaceGenerationMethod
      >>> AllowQuads = SurfaceGenerationMethod.AllowQuads
      >>> plot.isosurface(0).surface_generation_method = AllowQuads

  Type: | `SurfaceGenerationMethod`

`IsosurfaceGroup.``vector`

  Vector attributes for this isosurface group.

  Example usage:

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

  Type: | `IsosurfaceVector`

### IsosurfaceContour

class `tecplot.plot.``IsosurfaceContour`(isosurface)

  Contour attributes of the isosurface group.

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

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

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

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

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

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

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

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

  Attributes

  `contour_type` | Contour display type.
  `flood_contour_group` | Contour group to use for flooding.
  `flood_contour_group_index` | The zero-based `Index` of the `ContourGroup` to use for flooding.
  `line_color` | `Color` of contour lines.
  `line_contour_group` | The contour group to use for contour lines.
  `line_contour_group_index` | The zero-based `Index` of the `ContourGroup` to use for contour lines.
  `line_thickness` | Contour line thickness as a percentage of frame width.
  `show` | Show contours on isosurfaces.
  `use_lighting_effect` | Enable lighting effect.

`IsosurfaceContour.``contour_type`

  Contour display type.

  - `ContourType.Lines` - Draws lines of constant value of the specified contour variable.
  - `ContourType.Flood` - Floods regions between contour lines with colors from a color map. The distribution of colors used for contour flooding may be banded or continuous. When banded distribution is used for flooding, a solid color is used between contour levels. If continuous color distribution is used, the flood color will vary linearly in all directions.
  - `ContourType.Overlay` - Combines the above two options.
  - `ContourType.AverageCell` - Floods cells or finite elements with colors from a color map according to the average value of the contour variable over the data points bounding the cell. If the variables are located at the nodes, the values at the nodes are averaged. If the variables are cell-centered, the cell-centered values are averaged to the nodes and the nodes are then averaged.
  - `ContourType.PrimaryValue` - Floods cells or finite elements with colors from a color map according to the primary value of the contour variable for each cell. If the variable is cell centered, the primary value is the value assigned to the cell. If the variable is node located, the primary value comes from the lowest index node in the cell. If the variables are located at the nodes, the value of the lowest indexed node in the cell is used. When plotting IJK-ordered, FE-brick or FE-tetra cells, each face is considered independently of the other faces. You may get different colors on the different faces of the same cell. If the variables are cell-centered, the cell-centered value is used directly. When plotting I, J, or K-planes in 3D, the cell on the positive side of the plane supplies the value, except in the case of the last plane, where the cell on the negative side supplies the value.

  Example usage:

      >>> plot.isosurface(0).contour.show = True
      >>> plot.isosurface(0).contour.contour_type = ContourType.Flood

  Type: | `ContourType`

`IsosurfaceContour.``flood_contour_group`

  Contour group to use for flooding.

  Changing style on this `ContourGroup` will affect all fieldmaps on the same `Frame` that use it.

  Example usage:

      >>> group = plot.contour(1)
      >>> contour = plot.isosurface(1).contour
      >>> contour.flood_contour_group = group

  Type: | `ContourGroup`

`IsosurfaceContour.``flood_contour_group_index`

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

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

  Example usage:

      >>> contour = plot.isosurface(0).contour
      >>> contour.flood_contour_group_index = 1

  Type: | `Index`

`IsosurfaceContour.``line_color`

  `Color` of contour lines.

  Contour lines can be a solid color or be colored by a `ContourGroup` as obtained through the `plot.contour` property.

  Example usage:

      >>> plot.show_isosurfaces = True
      >>> plot.isosurface(0).contour.line_color = Color.Blue

  Type: | `Color` or `ContourGroup`

`IsosurfaceContour.``line_contour_group`

  The contour group to use for contour lines.

  Note that changing style on this `ContourGroup` will affect all other fieldmaps on the same `Frame` that use it.

  Example usage:

      >>> contour = plot.isosurface(0).contour
      >>> group = plot.contour(1)
      >>> contour.line_contour_group = group

  Type: | `ContourGroup`

`IsosurfaceContour.``line_contour_group_index`

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

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

  Example usage:

      >>> contour = plot.isosurface(0).contour
      >>> contour.line_contour_group_index = 2

  Type: | `int`

`IsosurfaceContour.``line_thickness`

  Contour line thickness as a percentage of frame width.

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

  Example usage:

      >>> plot.show_isosurfaces = True
      >>> plot.isosurface(0).contour.line_thickness = .4

  Type: | `float`

`IsosurfaceContour.``show`

  Show contours on isosurfaces.

  Example usage:

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

  Type: | `bool`

`IsosurfaceContour.``use_lighting_effect`

  Enable lighting effect.

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

  Example usage:

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

  Type: | `bool`

### IsosurfaceEffects

class `tecplot.plot.``IsosurfaceEffects`(isosurface)

  Effects of the isosurface group.

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

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

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

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

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

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

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

  Attributes

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

`IsosurfaceEffects.``lighting_effect`

  Surface lighting effect.

  Isosurface lighting effects must be enabled by setting `IsosurfaceShade.use_lighting_effect` to `True` when setting this value.

  There are two types of lighting effects: Paneled and Gouraud:

  > - `Paneled`: Within each cell, the color assigned to each area by
  >     shading or contour flooding is tinted by a shade constant across the cell. This shade is based on the orientation of the cell relative to your 3D light source.
  > - `Gouraud`: This offers smoother, more continuous shading than
  >     Paneled shading, but it also results in slower plotting and larger print files. `Gouraud` shading is not continuous across zone boundaries unless face neighbors are specified in the data. `Gouraud` shading is not available for finite element volume Zone when blanking is active. The zone’s lighting effect reverts to `Paneled` shading in this case.

  Example usage:

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

  Type: | `LightingEffect`

`IsosurfaceEffects.``surface_translucency`

  Surface translucency of the isosurface group.

  Iso-surface surface translucency must be enabled by setting `IsosurfaceEffects.use_translucency` = `True` when setting this value.

  Valid translucency values range from one (opaque) to 99 (translucent).

  Example usage:

      >>> plot.isosurface(0).effects.use_translucency = True
      >>> plot.isosurface(0).effects.surface_translucency = 20

  Type: | `int`

`IsosurfaceEffects.``use_translucency`

  Enable surface translucency for this isosurface group.

  The surface translucency value can be changed by setting `IsosurfaceEffects.surface_translucency`.

  Example usage:

      >>> plot.isosurface(0).effects.use_translucency = True
      >>> plot.isosurface(0).effects.surface_translucency = 20

  Type: | `bool`

### IsosurfaceMesh

class `tecplot.plot.``IsosurfaceMesh`(isosurface)

  Mesh attributes of the isosurface group.

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

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

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

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

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

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

  Attributes

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

`IsosurfaceMesh.``color`

  Isosurface mesh color.

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

  Example usage:

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

  Type: | `Color` or `ContourGroup`

`IsosurfaceMesh.``line_thickness`

  Isosurface mesh line thickness.

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

  Example usage:

      >>> plot.isosurface(0).mesh.show = True
      >>> plot.isosurface(0).mesh.line_thickness = .4

  Type: | `float`

`IsosurfaceMesh.``show`

  Display the mesh on isosurfaces.

  Example usage:

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

  Type: | `bool`

### IsosurfaceShade

class `tecplot.plot.``IsosurfaceShade`(isosurface)

  Shade attributes of the isosurface group.

      import tecplot as tp
      from os import path
      from tecplot.plot import IsosurfaceGroup
      from tecplot.constant import Color, LightingEffect

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

      plot = tp.active_frame().plot()
      plot.show_isosurfaces = True
      plot.contour(0).variable = dataset.variable('U(M/S)')
      iso = plot.isosurface(0)

      iso.contour.show = False  # Hiding the contour will reveal the shade.

      iso.shade.show = True
      iso.shade.color = Color.Red
      iso.shade.use_lighting_effect = True

      iso.effects.lighting_effect = LightingEffect.Paneled

      tp.export.save_png('isosurface_shade.png', 600, supersample=3)

  Attributes

  `color` | Shade color.
  `show` | Show shade attributes.
  `use_lighting_effect` | Enable lighting effect.

`IsosurfaceShade.``color`

  Shade color.

  `Color.MultiColor` and `Color.RGBColor` coloring are not available. Use flooded contours for multi-color or RGB flooding

  Example usage:

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

  Type: | `Color`

`IsosurfaceShade.``show`

  Show shade attributes.

  Example usage:

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

  Type: | `bool`

`IsosurfaceShade.``use_lighting_effect`

  Enable lighting effect.

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

  Example usage:

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

  Type: | `bool`

### IsosurfaceVector

class `tecplot.plot.``IsosurfaceVector`(isosurface)

  Isosurface vector field control.

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

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

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

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

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

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

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

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

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

  Attributes

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

`IsosurfaceVector.``arrowhead_style`

  Arrowhead style of isosurface vectors.

  Example usage:

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

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

  Type: | `ArrowheadStyle`

`IsosurfaceVector.``color`

  Isosurface vector color.

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

  Example usage:

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

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

  Type: | `Color` or `ContourGroup`

`IsosurfaceVector.``is_tangent`

  Use tangent vectors for isosurfaces.

  Example usage:

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

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

  Type: | `bool`

`IsosurfaceVector.``line_thickness`

  Vector line thickness as a percentage of the frame height.

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

  Example usage:

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

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

  Type: | `float`

`IsosurfaceVector.``show`

  Show vectors on isosurfaces.

  Example usage:

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

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

  Type: | `bool`

`IsosurfaceVector.``vector_type`

  Type of vector for isosurfaces.

  Example usage:

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

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

  Type: | `VectorType`

## Slice

- SliceGroup
- SliceContour
- SliceEdge
- SliceEffects
- SliceMesh
- SliceShade
- SliceVector

### SliceGroup

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

  Change attributes associated with slices.

  Slices can include lighting effects, contours, meshes, and more. To customize these and other attributes of slices, use this object.

  This object controls the style for a specific slice group within a `Frame`. Slice `contour`, vector, edge, effects, mesh, visibility and position information are accessed through this class:

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

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

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

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

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

      slice_0.contour.show = True
      slice_0.contour.contour_type = ContourType.Overlay  # AKA "Both lines and flood"

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

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

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

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

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

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

  Up to eight different slice groups can be set. Each slice group can use different slice planes or different ranges for the same slice plane.

      >>> slice_3 = plot.slice(3)
      >>> slice_3.contour.show = True

  Attributes

  `arbitrary_normal` | Normal for arbitrary slices.
  `contour` | Contour attributes for the slice group.
  `edge` | Edge attributes for this slice group.
  `effects` | Effects attributes for this slice group.
  `end_position` | Position of the end slice.
  `mesh` | Mesh attributes for this slice group.
  `num_intermediate_slices` | Number of intermediate slicing planes.
  `obey_source_zone_blanking` | Obey source zone blanking.
  `orientation` | Select which plane the slice is drawn on (X,Y,Z, I, J, K or arbitrary).
  `origin` | Origin of the slice.
  `shade` | Shade attributes for this slice group.
  `show` | Show slices for this slice group.
  `show_intermediate_slices` | Show intermediate slices.
  `show_primary_slice` | `bool`; Include the primary slice (first slice placed) in the Plot.
  `show_start_and_end_slices` | Include start and end slices.
  `slice_source` | Zones to slice through.
  `start_position` | Position of the start slice.
  `surface_generation_method` | Determines how the surface is generated.
  `vector` | Vector attributes for this slice group.

  Methods

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

`SliceGroup.``arbitrary_normal`

  Normal for arbitrary slices.

  Example usage:

      >>> plot.slice(0).orientation = SliceSurface.Arbitrary
      >>> plot.slice(0).arbitrary_normal = (0.1, 0.2, 0.3)
      >>> plot.slice(0).arbitrary_normal.x
      0.1
      >>> plot.slice(0).arbitrary_normal.y
      0.2
      >>> plot.slice(0).arbitrary_normal.z
      0.3

  Type: | `tuple`

`SliceGroup.``contour`

  Contour attributes for the slice group.

  Example usage:

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

  Type: | `SliceContour`

`SliceGroup.``edge`

  Edge attributes for this slice group.

  Example usage:

      >>> plot.slice(0).edge.show = True

  Type: | `SliceEdge`

`SliceGroup.``effects`

  Effects attributes for this slice group.

  Example usage:

      >>> plot.slice(0).effects.use_translucency = True

  Type: | `SliceEffects`

`SliceGroup.``end_position`

  Position of the end slice.

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

  Example usage:

      >>> plot.slice(0).show_start_and_end_slices = True
      >>> plot.slice(0).end_position = (1, 1, 1)
      >>> plot.slice(0).end_position.i
      1

  Type: | `tuple` or `int`

`SliceGroup.``extract`(mode=<ExtractMode.SingleZone: 0>, assign_strand_ids=True, resulting_1d_zone_type=<Resulting1DZoneType.IOrderedIfPossible: 0>, transient_mode=<TransientOperationMode.SingleSolutionTime: 0>)

  Create new zones from this slice.

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

  Parameters: | mode (`ExtractMode`, optional) – Determines how many zones are created upon extraction. Possible values are: `ExtractMode.SingleZone` (default) and `ExtractMode.OneZonePerConnectedRegion`. assign_strand_ids (`bool`, optional) – Automatically assign strand ID’s to the created zones. (default: `True`) resulting_1d_zone_type (`Resulting1DZoneType`, optional) – The type of zone to create when the result is one-dimensional. Possible values are: `Resulting1DZoneType.IOrderedIfPossible` (default) and `Resulting1DZoneType.FELineSegment`. transient_mode (`TransientOperationMode`) – Determines which solution times are used to extract slices when transient data is available in the dataset. Possible values are `TransientOperationMode.SingleSolutionTime` (default) or `TransientOperationMode.AllSolutionTimes`.
  Returns: | The extracted zone is returned if mode is `ExtractMode.SingleZone` and transient_mode is `TransientOperationMode.SingleSolutionTime`, otherwise a generator of the extracted zones.

  See also

  `tecplot.data.extract.extract_slice()`

  Example usage:

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

`SliceGroup.``mesh`

  Mesh attributes for this slice group.

  Example usage:

      >>> plot.slice(0).mesh.show = True

  Type: | `SliceMesh`

`SliceGroup.``num_intermediate_slices`

  Number of intermediate slicing planes.

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

  Example usage:

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

  Type: | `int`

`SliceGroup.``obey_source_zone_blanking`

  Obey source zone blanking.

  When set to `True`, slices are subject to any blanking used for the data. When set to `False`, slices are generated for blanked and unblanked regions.

  Example usage:

      >>> plot.slice(0).obey_source_zone_blanking = True

  Type: | `bool`

`SliceGroup.``orientation`

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

  You may also choose `SliceSurface.Arbitrary` to place the slice on an arbitrary plane.

  To orient slices in an arbitrary direction, choose `SliceSurface.Arbitrary`. As with other slices, you may specify origin points for a primary slice and/or for start and end slices. Slices pass through the indicated origin point(s), so you can easily align the edge of a slice or group of slices along some other feature of the plot, such as an axis. If intermediate slices are activated, they are drawn equally spaced between the slices defined by the start and end origins.

  Example usage:

      >>> plot.slice(0).orientation = SliceSurface.XPlanes

  Type: | `SliceSurface`

`SliceGroup.``origin`

  Origin of the slice.

  This will be a 3-`tuple` of `float` if `orientation` is `X,Y,Z` or zero-based `int` if `orientation` is `I,J,K`. For arbitrary slice orientation, the origin can be any location. For axis orientations (`XPlanes`, `YPlanes`, etc.) two of the three components are not used.

  Example usage:

      >>> slice_0 = plot.slice(0)
      >>> slice_0.orientation = SliceSurface.IPlanes
      >>> slice_0.origin = (1, 0, 0)
      >>> dx = (1, 1, 1)
      >>> slice_0.origin += dx
      >>> slice_0.origin.i
      2
      >>> slice_0.origin.j
      1
      >>> slice_0.origin.k
      1

      >>> slice_0.orientation = SliceSurface.Arbitrary
      >>> slice_0.origin = (.5, .1, .1)
      >>> slice_0.origin += dx
      >>> slice_0.origin.x
      1.5
      >>> slice_0.origin.y
      .1
      >>> slice_0.origin.z
      .1

  Type: | `tuple` or `int`

`SliceGroup.``set_arbitrary_from_points`(p1, p2, p3)

  Set an arbitrary slice from 3 points.

  Set the normal and origin of an arbitrary slice using three points. The origin will be set to the 3rd point.

  The three points must not be coincident or collinear. The slice’s origin is set to the third point and its normal is recalculated such that the cutting plane passes through all three points.

  Parameters:

      p1:  3-`tuple` of floats
      p2:  3-`tuple` of floats
      p3:  3-`tuple` of floats

  Example usage:

      >>> slice_0 = plot.slice(0)
      >>> slice_0.set_arbitrary_from_points((0,0,0), (.1,.2,.3), (.1,.1,.1))

`SliceGroup.``shade`

  Shade attributes for this slice group.

  Example usage:

      >>> plot.slice(0).shade.show = True

  Type: | `SliceShade`

`SliceGroup.``show`

  Show slices for this slice group.

  Example usage:

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

  Type: | `bool`

`SliceGroup.``show_intermediate_slices`

  Show intermediate slices.

  Intermediate slices are evenly distributed between the start and end slices.

  Example usage:

      >>> plot.slice(0).show_intermediate_slices = True

`SliceGroup.``show_primary_slice`

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

  Example usage:

      >>> plot.slice(0).show = True
      >>> plot.slice(0).show_primary_slice = True

`SliceGroup.``show_start_and_end_slices`

  Include start and end slices.

  Example usage:

      >>> plot.slice(0).show_start_and_end_slices = True

  Type: | `bool`

`SliceGroup.``slice_source`

  Zones to slice through.

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

  Example usage:

      >>> plot.slice(0).slice_source = SliceSource.SurfaceZones

  Type: | `SliceSource`

`SliceGroup.``start_position`

  Position of the start slice.

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

  Example usage:

      >>> plot.slice(0).show_start_and_end_slices = True
      >>> plot.slice(0).start_position = (1, 1, 1)
      >>> plot.slice(0).start_position.i
      1

  Type: | `tuple` or `int`

`SliceGroup.``surface_generation_method`

  Determines how the surface is generated.

  May be one of:

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

  Example usage:

      >>> from tecplot.constant import SurfaceGenerationMethod
      >>> plot.slice(0).surface_generation_method = SurfaceGenerationMethod.AllowQuads

  Type: | `SurfaceGenerationMethod`

`SliceGroup.``vector`

  Vector attributes for this slice group.

  Example usage:

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

  Type: | `SliceVector`

### SliceContour

class `tecplot.plot.``SliceContour`(parent_slice)

  Contour attributes of the slice group.

      from os import path
      import tecplot as tp
      from tecplot.constant import SliceSurface, ContourType

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

      plot = tp.active_frame().plot()

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

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

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

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

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

  Attributes

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

`SliceContour.``contour_type`

  Contour type for the slice contours.

  Example usage:

      >>> plot.show_slices = True
      >>> plot.slice(0).contour.contour_type = ContourType.AverageCell

  Type: | `ContourType`

`SliceContour.``flood_contour_group`

  Contour group to use for flooding.

  Changing style on this `ContourGroup` will affect all fieldmaps on the same `Frame` that use it.

  Example usage:

      >>> group = plot.contour(1)
      >>> contour = plot.slice(1).contour
      >>> contour.flood_contour_group = group

  Type: | `ContourGroup`

`SliceContour.``flood_contour_group_index`

  Zero-based `Index` of the flodding `ContourGroup`.

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

  Example usage:

      >>> plot.show_slices = True
      >>> contour = plot.slice(0).contour
      >>> contour.flood_contour_group_index = 1

  Type: | `Index`

`SliceContour.``line_color`

  `Color` of contour lines.

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

  Example usage:

      >>> plot.show_slices = True
      >>> plot.slice(0).contour.line_color = Color.Blue

  Type: | `Color`

`SliceContour.``line_contour_group`

  Contour group to use for contour lines.

  Changing style on this `ContourGroup` will affect all fieldmaps on the same `Frame` that use it.

  Example usage:

      >>> group = plot.contour(1)
      >>> contour = plot.slice(1).contour
      >>> contour.line_contour_group = group

  Type: | `ContourGroup`

`SliceContour.``line_contour_group_index`

  Zero-based `Index` of the `ContourGroup` for contour lines.

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

  Example usage:

      >>> plot.show_slices = True
      >>> contour = plot.slice(0).contour
      >>> contour.line_contour_group_index = 2

  Type: | `int`

`SliceContour.``line_thickness`

  Contour line thickness as a percentage of frame width.

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

  Example usage:

      >>> plot.show_slices = True
      >>> plot.slice(0).contour.line_thickness = .4

  Type: | `float`

`SliceContour.``show`

  Show contours on the slice.

  Example usage:

      >>> plot.show_slices = True
      >>> plot.slice(1).contour.show = True

  Type: | `bool`

`SliceContour.``use_lighting_effect`

  `bool`; Enable lighting effect.

  Note

  Setting `SliceContour.use_lighting_effect` will also set the same value for `SliceShade.use_lighting_effect`, and vice-versa.

  The lighting effect is set with `SliceEffects.lighting_effect`, and may be one of `LightingEffect.Gouraud` or `LightingEffect.Paneled`.

  Example usage:

      >>> plot.show_slices = True
      >>> contour = plot.slice(0).contour
      >>> contour.use_lighting_effect = True
      >>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled

### SliceEdge

class `tecplot.plot.``SliceEdge`(vector)

  Edge attributes of the slice group.

  When enabled, selected edge lines of all slices in this group will be shown:

      from os import path
      import tecplot as tp

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

      plot = tp.active_frame().plot()
      plot.show_slices = True
      plot.contour(0).variable = dataset.variable('U(M/S)')

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

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

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

  Attributes

  `color` | Edge color.
  `edge_type` | Edge type.
  `line_thickness` | Edge line thickness as a percentage of frame width.
  `show` | Show edges.

`SliceEdge.``color`

  Edge color.

  Example usage:

      >>> plot.slice(0).edge.show = True
      >>> plot.slice(0).edge.color = Color.Blue

  Type: | `Color`

`SliceEdge.``edge_type`

  Edge type.

  There are two types of edges in Tecplot 360: creases and borders.

  An edge border is the boundary of a Zone. An edge crease appears when the inside angle between two cells is less than a user-defined limit. The inside angle can range from 0-180 degrees (where 180 degrees indicates coplanar surfaces). The default inside angle for determining an edge crease is 135 degrees.

  Example usage:

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

  Type: | `EdgeType`

`SliceEdge.``line_thickness`

  Edge line thickness as a percentage of frame width.

  Example usage:

      >>> plot.slice(0).edge.show = True
      >>> plot.slice(0).edge.line_thickness = .8

  Type: | `float`

`SliceEdge.``show`

  Show edges.

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

  Example usage:

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

  Type: | `bool`

### SliceEffects

class `tecplot.plot.``SliceEffects`(effects)

  Slice effects for this slice.

      from os import path
      import tecplot as tp

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

      plot = tp.active_frame().plot()

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

      plot.contour(0).variable = dataset.variable('U(M/S)')
      slice_0.contour.show = True

      slice_0.effects.use_translucency = True
      slice_0.effects.surface_translucency = 70

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

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

  Attributes

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

`SliceEffects.``lighting_effect`

  Surface lighting effect.

  Slice lighting effects must be enabled by setting `SliceContour.use_lighting_effect` or `SliceShade.use_lighting_effect` to `True` when setting this value.

  There are two types of lighting effects: Paneled and Gouraud:

  > - `Paneled`: Within each cell, the color assigned to each area by
  >     shading or contour flooding is tinted by a shade constant across the cell. This shade is based on the orientation of the cell relative to your 3D light source.
  > - `Gouraud`: This offers smoother, more continuous shading than
  >     Paneled shading, but it also results in slower plotting and larger print files. `Gouraud` shading is not continuous across zone boundaries unless face neighbors are specified in the data. `Gouraud` shading is not available for finite element volume Zones when blanking is active. The zone’s lighting effect reverts to `Paneled` shading in this case.

  Example usage:

      >>> plot.slice(0).contour.use_lighting_effect = True
      >>> plot.slice(0).effects.lighting_effect = LightingEffect.Paneled

  Type: | `LightingEffect`

`SliceEffects.``surface_translucency`

  Surface translucency of the slice group.

  Slice surface translucency must be enabled by setting `SliceEffects.use_translucency` = `True` when setting this value.

  Valid slice translucency values range from one (opaque) to 99 (translucent).

  Example usage:

      >>> plot.slice(0).effects.use_translucency = True
      >>> plot.slice(0).effects.surface_translucency = 20

  Type: | `int`

`SliceEffects.``use_translucency`

  Enable surface translucency for this slice group.

  The surface translucency value can be changed by setting `SliceEffects.surface_translucency`.

  Example usage:

      >>> plot.slice(0).effects.use_translucency = True
      >>> plot.slice(0).effects.surface_translucency = 20

  Type: | `bool`

### SliceMesh

class `tecplot.plot.``SliceMesh`(mesh)

  Mesh attributes of the slice group.

      from os import path
      import tecplot as tp
      from tecplot.constant import SliceSurface, ContourType

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, 'SimpleData', 'DuctFlow.plt')
      dataset = tp.data.load_tecplot(datafile)
      plot = tp.active_frame().plot()
      plot.show_slices = True
      plot.contour(0).variable = dataset.variable('U(M/S)')

      plot.slice(0).mesh.show = True

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

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

  Attributes

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

`SliceMesh.``color`

  Slice mesh line Color.

  Slice mesh lines can be a solid color or be colored by a `ContourGroup` as obtained through the `plot.contour` property.

  Example usage:

      >>> plot.slice(0).mesh.show = True
      >>> plot.slice(0).mesh.color = Color.Green

  Type: | `Color` or `ContourGroup`

`SliceMesh.``line_thickness`

  Mesh line thickness.

  The mesh line thickness is specified as a percentage of the frame width.

  Example usage:

      >>> plot.slice(0).mesh.show = True
      >>> plot.slice(0).mesh.line_thickness = 0.8

  Type: | `float`

`SliceMesh.``show`

  Show mesh lines.

  Example usage:

      >>> plot.slice(0).mesh.show = True

  Type: | `bool`

### SliceShade

class `tecplot.plot.``SliceShade`(shade)

  Shade attributes of the slice group.

  Show shading on the slice when `SliceContour.show` has not been selected or is set to `ContourType.Lines`:

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

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

      plot = tp.active_frame().plot()
      plot.show_slices = True
      plot.slice(0).contour.show = False
      shade = plot.slice(0).shade
      shade.show = True
      shade.color = Color.Red  # Slice will be colored solid red.
      tp.export.save_png('slice_shade.png', 600, supersample=3)

  Attributes

  `color` | Shade color.
  `show` | Show shade attributes.
  `use_lighting_effect` | Use lighting effect.

`SliceShade.``color`

  Shade color.

  `Color.MultiColor` and `Color.RGBColor` coloring are not available. Use flooded contours for multi-color or RGB flooding

  Example usage:

      >>> plot.slice(0).shade.show = True
      >>> plot.slice(0).shade.color = Color.Blue

  Type: | `Color`

`SliceShade.``show`

  Show shade attributes.

  Example usage:

      >>> plot.slice(0).shade.show = True

  Type: | `bool`

`SliceShade.``use_lighting_effect`

  Use lighting effect.

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

  Note

  Setting `SliceShade.use_lighting_effect` will also set the same value for `SliceContour.use_lighting_effect`, and vice-versa.

  Example usage:

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

  Type: | `bool`

### SliceVector

class `tecplot.plot.``SliceVector`(vector)

  Vector attributes of the slice group.

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

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

      plot = tp.active_frame().plot()

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

      plot.contour(0).variable = dataset.variable('T(K)')

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

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

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

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

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

  Attributes

  `arrowhead_style` | Arrowhead style of slice vectors.
  `color` | Set slice vector color.
  `is_tangent` | Use tangent vectors for slices.
  `line_thickness` | Vector line thickness as a percentage of the frame height.
  `show` | Show vectors on slices.
  `vector_type` | Type of vector for slices in this slice group.

`SliceVector.``arrowhead_style`

  Arrowhead style of slice vectors.

  Example usage:

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

  Type: | `ArrowheadStyle`

`SliceVector.``color`

  Set slice vector color.

  Example usage:

      >>> plot.slice(0).vector.show = True
      >>> plot.slice(0).vector.color = Color.Red

  Type: | `Color`

`SliceVector.``is_tangent`

  Use tangent vectors for slices.

  Example usage:

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

  Type: | `bool`

`SliceVector.``line_thickness`

  Vector line thickness as a percentage of the frame height.

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

  Example usage:

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

  Type: | `float`

`SliceVector.``show`

  Show vectors on slices.

  Example usage:

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

  Type: | `bool`

`SliceVector.``vector_type`

  Type of vector for slices in this slice group.

  Example usage:

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

  Type: | `VectorType`

## Streamtraces

- Streamtraces
- StreamtraceRodRibbon
- StreamtraceTiming
- StreamtraceRodRibbonContour
- StreamtraceRodRibbonEffects
- StreamtraceRodRibbonMesh
- StreamtraceRodRibbonShade
- StreamtraceTerminationLine

### Streamtraces

class `tecplot.plot.``Streamtraces`(plot)

  Streamtrace attributes for the plot.

  A streamtrace is the path traced by a massless particle placed at an arbitrary location in a steady-state vector field. Streamtraces may be used to illustrate the nature of the vector field flow in a particular region of the Plot.

  Note

  Because streamtraces are dependent upon a vector field, you must define vector components before creating streamtraces. However, it is not necessary to activate the Vector zone layer to use streamtraces.

      import os
      import tecplot
      from tecplot.constant import *

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

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

      plot = frame.plot()
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_mesh = True
      plot.show_shade = False

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

      streamtraces = plot.streamtraces
      streamtraces.color = Color.Blue

      streamtraces.show_arrows = True
      streamtraces.arrowhead_size = 2
      streamtraces.step_size = .25
      streamtraces.line_thickness = .2
      streamtraces.max_steps = 100

      streamtraces.add_rake(start_position=(45.49, 15.32, 59.1),
                            end_position=(48.89, 53.2, 47.6),
                            stream_type=Streamtrace.SurfaceLine,
                            num_seed_points=4)

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

  Attributes

  `active` | Determine if there are active streamtraces.
  `arrowhead_size` | Arrowhead size as a percentage of frame height.
  `arrowhead_spacing` | Distance between arrowheads in terms of Y-frame units.
  `color` | Color of streamtraces line (not rods or ribbons).
  `count` | Query the number of active streamtraces for the current plot type.
  `dash_skip` | Number of time deltas used for the “off” sections of the streamlines.
  `has_terminating_line` | Determine if the streamtraces have the terminating line.
  `line_thickness` | Streamtrace line thickness.
  `marker_color` | `Color` of the streamline markers.
  `marker_size` | Size of streamline markers.
  `marker_symbol_type` | The `SymbolType` to use for stream markers.
  `max_steps` | Maximum number of steps before the streamtrace is terminated.
  `min_step_size` | Smallest step size to use as a percentage of cell distance.
  `obey_source_zone_blanking` | Obey source zone blanking.
  `rod_ribbon` | Streamtrace rod/ribbon attributes.
  `show_arrows` | Display arrowheads along all streamlines.
  `show_dashes` | Display streamtrace dashes.
  `show_markers` | Display streamtrace markers.
  `show_paths` | Draw streamtrace paths (lines, ribbons, or rods).
  `step_size` | Maximum fraction of the distance across a cell that a streamtrace moves in one step.
  `termination_line` | Streamtraces termination line attributes.
  `timing` | Streamtraces timing attributes.

  Methods

  `add`(seed_point, stream_type[, direction]) | Add a single streamtrace to the plot of the current frame.
  `add_on_zone_surface`(stream_type[, zones, …]) | Add streamtraces to one or more zones in a plot.
  `add_rake`(start_position, end_position, …) | Add a rake of streamtraces to the plot of the current frame.
  `delete_all`() | Delete all streamtraces for the current plot type.
  `delete_range`(range_start, range_end) | Delete a range of streamtraces.
  `extract`([concatenate, assign_strand_ids]) | Create new zones from streamtraces
  `marker_symbol`([symbol_type]) | Returns a streamline symbol style object.
  `position`(stream_number) | Query the starting position of a streamtrace.
  `set_termination_line`(line_points) | Set the position of the termination line for streamtraces.
  `streamtrace_type`(stream_number) | Query the type of a streamtrace by streamtrace number.

`Streamtraces.``active`

  Determine if there are active streamtraces.

  Note

  This property is read-only.

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

  Example usage:

      >>> streamtraces_are_active = plot.streamtraces.active

  Type: | `bool`

`Streamtraces.``add`(seed_point, stream_type, direction=<StreamDir.Both: 2>)

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

  The plot type must be either `Cartesian2D` or `Cartesian3D`.

  Parameters: | seed_point – (2- or 3- `tuple` of `floats`): Pass a 2-`tuple` of `float` for a `Cartesian2DFieldPlot`, or a 3-`tuple` of `float` for a `Cartesian3DFieldPlot`. stream_type – (`Streamtrace`): Type of streamtraces to add. direction – (`StreamDir`, optional): Direction of propagation of the streamtraces being added.

  Note

  stream_type is automatically set to `Streamtrace.SurfaceLine` if the plot type is `Cartesian2DFieldPlot`. The only stream type available for 2D plots is `Streamtrace.SurfaceLine`.

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

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

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

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

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

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

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

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

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

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

      tecplot.export.save_png('streamtrace_add_xyz.png', 600, supersample=3)

`Streamtraces.``add_on_zone_surface`(stream_type, zones=None, num_seed_points=10, direction=<StreamDir.Both: 2>)

  Add streamtraces to one or more zones in a plot.

  The plot type must be either `Cartesian2D` or `Cartesian3D`.

  Note

  For volume zones the streamtraces are propagated from the surfaces of the volume.

  Parameters: | stream_type – (`Streamtrace`): Type of streamtraces to add. zones (`set` of `integers`, optional) – Set of Zones on which to add streamtraces. If `None`, then streamtraces will be added to the currently active zones. num_seed_points – (`int`, optional): Number of seed points for distributing along a rake or on defined surfaces. direction – (`StreamDir`, optional): Direction of propagation of the streamtraces being added.

      import os

      import tecplot
      from tecplot.constant import *

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

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

      plot = frame.plot()

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

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

      tecplot.export.save_png('streamtrace_add_on_zone_surface.png', 600, supersample=3)

`Streamtraces.``add_rake`(start_position, end_position, stream_type, num_seed_points=10, direction=<StreamDir.Both: 2>)

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

  The plot type must be either `Cartesian2D` or `Cartesian3D`.

  Parameters: | start_position – (2- or 3- `tuple` of `floats`): Pass a 2-`tuple` of `float` for a `Cartesian2DFieldPlot`, or a 3-`tuple` of `float` for a `Cartesian3DFieldPlot`. end_position – (2- or 3- `tuple` of `floats`): Pass a 2-`tuple` of `float` for a `Cartesian2DFieldPlot`, or a 3-`tuple` of `float` for a `Cartesian3DFieldPlot`. stream_type – (`Streamtrace`): Type of streamtraces to add. num_seed_points – (`int`, optional): Number of seed points for distributing along a rake or on defined surfaces. direction – (`StreamDir`, optional): Direction of propagation of the streamtraces being added.

      import os

      import tecplot
      from tecplot.constant import *

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

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

      plot = frame.plot()
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_mesh = True
      plot.show_shade = False

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

      streamtraces = plot.streamtraces
      streamtraces.add_rake(start_position=[.5, .5, .5],
                            end_position=[20, 20, 20],
                            stream_type=Streamtrace.VolumeLine)

      tecplot.export.save_png('streamtrace_add_rake.png', 600, supersample=3)

`Streamtraces.``arrowhead_size`

  Arrowhead size as a percentage of frame height.

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

  Example usage:

      >>> plot.streamtraces.show_arrows = True
      >>> plot.streamtraces.arrowhead_size = 1.0

  Type: | `float`

`Streamtraces.``arrowhead_spacing`

  Distance between arrowheads in terms of Y-frame units.

  For example, a value of 10 will space arrowheads approximately ten percent of the frame height apart from each other along each streamline.

  Example usage:

      >>> plot.streamtraces.show_arrows = True
      >>> plot.streamtraces.arrowhead_spacing = 10

  Type: | `float`

`Streamtraces.``color`

  Color of streamtraces line (not rods or ribbons).

  Streamtraces can be a solid color or be colored by a `ContourGroup` as obtained through the `plot.contour` property.

  Example usage:

      >>> plot.streamtraces.color = Color.Red

  Type: | `Color` or `ContourGroup`

`Streamtraces.``count`

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

  Returns: | `int`

  Note

  This property is read-only.

      >>> num_active_streamtraces = plot.streamtraces.count

`Streamtraces.``dash_skip`

  Number of time deltas used for the “off” sections of the streamlines.

  Note

  The `dash_skip` value must be greater than 0.

  Example usage:

      >>> plot.streamtraces.dash_skip = 2

  Type: | `int`

`Streamtraces.``delete_all`()

  Delete all streamtraces for the current plot type.

  2D and 3D streamtraces are independent of each other.

  If the plot type is `Cartesian2D`, all 2D streamtraces are deleted. If the plot type is `Cartesian3D`, all 3D streamtraces are deleted.

  Raises: | `TecplotSystemError` – The streamtraces could not be deleted.

  Example usage:

      >>> plot.streamtraces.delete_all()

`Streamtraces.``delete_range`(range_start, range_end)

  Delete a range of streamtraces.

  Parameters: | range_start – (`int`): 0-based start streamtrace number to delete. range_end – (`int`): 0-based end streamtrace number to delete.
  Raises: | `TecplotSystemError` – The streamtraces in the range could not be deleted.

  Example usage:

      >>> # Delete the first 100 streamtraces
      >>> plot.streamtraces.delete_range(0, 99)

`Streamtraces.``extract`(concatenate=False, assign_strand_ids=True)

  Create new zones from streamtraces

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

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

  Example usage:

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

`Streamtraces.``has_terminating_line`

  Determine if the streamtraces have the terminating line.

  Note

  This property is read-only.

  Returns: | `bool`. `True` if the streamtraces have the terminating line, `False` otherwise.

  Example usage:

      >>> has_terminating_line = plot.streamtraces.has_terminating_line

`Streamtraces.``line_thickness`

  Streamtrace line thickness.

  Line thickness as a percentage of the frame height for 2D lines, or a percentage of the median axis length for 3D surface lines and volume lines.

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

  Example usage:

      >>> plot.streamtraces.line_thickness = 1.1

  Type: | `float`

`Streamtraces.``marker_color`

  `Color` of the streamline markers.

  Streamtrace markers can be a solid color or be colored by a `ContourGroup` as obtained through the `plot.contour` property.

  Example usage:

      >>> plot.streamtraces.marker_color = Color.Blue

  Type: | `Color` or `ContourGroup`

`Streamtraces.``marker_size`

  Size of streamline markers.

  Example usage:

      >>> plot.streamtraces.marker_size = 1.1

  Type: | `float`

`Streamtraces.``marker_symbol`(symbol_type=None)

  Returns a streamline symbol style object.

  Parameters: | symbol_type (`SymbolType`, optional) – The type of symbol to return. By default, this will return the active marker symbol type which is obtained from `Streamtraces.marker_symbol_type`.

  Returns: `TextSymbol` or `GeometrySymbol`, depending on `marker_symbol_type`

  Example usage:

      >>> from tecplot.constant import SymbolType
      >>> streamtrace = plot.streamtraces
      >>> streamtraces.marker_symbol_type = SymbolType.Text
      >>> symbol = streamtraces.marker_symbol(SymbolType.Text)
      >>> symbol.text = 'a'

`Streamtraces.``marker_symbol_type`

  The `SymbolType` to use for stream markers.

  This sets the active symbol type for streamtrace markers. Use `Streamtraces.marker_symbol` to access the symbol:

      >>> from tecplot.constant import SymbolType
      >>> streamtrace = plot.streamtraces
      >>> streamtraces.marker_symbol_type = SymbolType.Text
      >>> symbol = streamtraces.marker_symbol(SymbolType.Text)
      >>> symbol.text = 'a'

  Type: | `SymbolType`

`Streamtraces.``max_steps`

  Maximum number of steps before the streamtrace is terminated.

  `max_steps` prevents streamtraces from spinning forever in a vortex, or from wandering into a region where the vector components are very small, very random, or both.

  If a small `step_size` is selected, the `max_steps` should be a large value.

  Example usage:

      >>> plot.streamtraces.max_steps = 5000

  Type: | `int`

`Streamtraces.``min_step_size`

  Smallest step size to use as a percentage of cell distance.

  A typical minimum step size value is 0.00001, which is the default.

  Warning

  Setting this too small results in integration problems. Setting this greater than or equal to the `step_size` results in a constant step size.

  Example usage:

      >>> plot.streamtraces.min_step_size = .0002

  Type: | `float`

`Streamtraces.``obey_source_zone_blanking`

  Obey source zone blanking.

  When `True`, streamtraces are generated for non-blanked regions only. When `False`, streamtraces are generated for both blanked and unblanked regions.

  Example usage:

      >>> plot.streamtraces.obey_source_zone_blanking = True

  Type: | `bool`

`Streamtraces.``position`(stream_number)

  Query the starting position of a streamtrace.

  Parameters: | stream_number – (`int`): 0-based stream number to query.
  Returns: | `tuple` of `floats`

  Get the position of streamtrace number 3:

      >>> position = plot.streamtraces.position(2) # Note: 0-based
      >>> position.x  # == position[0]
      0.1
      >>> position.y  # == position[1]
      0.2
      >>> position.z  # == position[2]
      0.3

`Streamtraces.``rod_ribbon`

  Streamtrace rod/ribbon attributes.

  Example usage:

      >>> streamtraces.rod_ribbon.mesh.show = True

  Type: | `StreamtraceRodRibbon`

`Streamtraces.``set_termination_line`(line_points)

  Set the position of the termination line for streamtraces.

  Parameters: | line_points – (array of `float` `tuple`) Points of the termination line.
  Raises: | `TecplotSystemError` – Termination line could not be set.

  Example usage:

      >>> # Multi-segment line between points (0,0)-(5,8)-(3,6)
      >>> line_points = [(0, 0), (5, 8), (3,6)]
      >>> plot.streamtraces.set_termination_line(line_points)

`Streamtraces.``show_arrows`

  Display arrowheads along all streamlines.

  Example usage:

      >>> plot.streamtraces.show_arrows = True

  Type: | `bool`

`Streamtraces.``show_dashes`

  Display streamtrace dashes.

  The lengths of the dashes and the spaces between the dashes are controlled by the value of `StreamtraceTiming.delta`. Set the `Streamtraces.dash_skip` attribute to control the number of time deltas used for the “off” sections of the streamtraces.

  Example usage:

      >>> plot.streamtraces.show_dashes = True

  Type: | `bool`

`Streamtraces.``show_markers`

  Display streamtrace markers.

  Stream markers are only available for surface and volume type streamlines.

  You may also specify the `size`, `color`, and `shape` of the markers.

  Example usage:

      >>> plot.streamtraces.show_markers = True

  Type: | `bool`

`Streamtraces.``show_paths`

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

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

  Example usage:

      >>> plot.streamtraces.show_paths = True

  See also `Streamtraces.show_markers`

  Type: | `bool`

`Streamtraces.``step_size`

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

  The step size is the maximum fraction of the distance across a cell that a streamtrace moves in one step. A streamtrace adjusts its step size between `step_size` and `min_step_size`, depending on local curvature of the streamtrace.

  A typical value (and the default) is 0.25, which results in four integration steps through each cell or element. The value for Step Size affects the accuracy of the integration.

  Warning

  Setting step size too small can result in round-off errors, while setting it too large can result in truncation errors and missed cells.

  Example usage:

      >>> plot.streamtraces.step_size = .25

  Type: | `float`

`Streamtraces.``streamtrace_type`(stream_number)

  Query the type of a streamtrace by streamtrace number.

  Parameters: | stream_number – (`int`): 0-based stream number to query.
  Returns: | `Streamtrace`

  Get the type of streamtrace 3. Note 0-based stream number:

      >>> streamtrace_type = plot.streamtraces.streamtrace_type(2) # streamtrace 3
      >>> streamtrace_type
      <Streamtrace.VolumeLine: 2>

`Streamtraces.``termination_line`

  Streamtraces termination line attributes.

  A streamtrace termination line is a polyline that terminates any streamtraces that cross it. The termination line is useful for stopping streamtraces before they spiral or stall.

  Example usage:

      >>> term_line = plot.streamtraces.termination_line
      >>> term_line.show = True

  Type: | `StreamtraceTerminationLine`

`Streamtraces.``timing`

  Streamtraces timing attributes.

  Example usage:

      >>> timing = plot.streamtraces.timing
      >>> timing.start = 0.01

  Type: | `StreamtraceTiming`

### StreamtraceRodRibbon

class `tecplot.plot.``StreamtraceRodRibbon`(streamtrace)

  Get/Set streamtrace rod/ribbon attributes.

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

  - `Streamtrace.SurfaceRibbon`
  - `Streamtrace.VolumeRibbon`
  - `Streamtrace.VolumeRod`

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

  - `Rod/ribbon mesh`
  - `Rod/ribbon contour`
  - `Rod/ribbon shade`
  - `Rod/ribbon effects`

  Note

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

      import os
      import tecplot
      from tecplot.constant import *

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

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

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

      plot.vector.u_variable_index = 3
      plot.vector.v_variable_index = 4
      plot.vector.w_variable_index = 5

      plot.show_streamtraces = True
      plot.streamtraces.rod_ribbon.width = .03
      plot.streamtraces.rod_ribbon.contour.show = True

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

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

  Attributes

  `contour` | Streamtraces rod/ribbon contour attributes.
  `effects` | Streamtraces rod/ribbon effects.
  `mesh` | Streamtraces rod/ribbon mesh attributes.
  `num_rod_points` | Number of rod points.
  `shade` | Streamtraces rod/ribbon color and lighting attributes.
  `width` | Rod/ribbon width in grid units.

`StreamtraceRodRibbon.``contour`

  Streamtraces rod/ribbon contour attributes.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.contour.show = True

  Type: | `StreamtraceRodRibbonContour`

`StreamtraceRodRibbon.``effects`

  Streamtraces rod/ribbon effects.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.effects.use_translucency = True

  Type: | `StreamtraceRodRibbonEffects`

`StreamtraceRodRibbon.``mesh`

  Streamtraces rod/ribbon mesh attributes.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.mesh.show = True

  Type: | `StreamtraceRodRibbonMesh`

`StreamtraceRodRibbon.``num_rod_points`

  Number of rod points.

  Volume rods have a polygonal cross-section; this parameter tells Tecplot 360 what that cross-section should be. (Three is an equilateral triangle, four is a square, five is a regular pentagon, and so on.) If you want two sets of volume rods with different cross-sections, you must create one set and then extract the set as a zone, then configure a new set of streamtraces with the second cross-section.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.num_rod_points = 10

  Type: | `int`, valid range 3-100

`StreamtraceRodRibbon.``shade`

  Streamtraces rod/ribbon color and lighting attributes.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.shade.color = Color.Magenta

  Type: | `StreamtraceRodRibbonShade`

`StreamtraceRodRibbon.``width`

  Rod/ribbon width in grid units.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.width = 0.01

  Type: | `float`

### StreamtraceTiming

class `tecplot.plot.``StreamtraceTiming`(streamtrace)

  Timed markers for streamlines.

  Use `StreamtraceTiming` to control timed markers for streamlines, and timed dashes for all types of streamtraces. Stream markers are drawn at time locations along streamlines. The spacing between stream markers is proportional to the magnitude of the local vector field:

      import tecplot
      from tecplot.constant import *
      import os

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

      frame = tecplot.active_frame()
      frame.plot_type = tecplot.constant.PlotType.Cartesian2D

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

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

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

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

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

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

  Attributes

  `anchor` | Time that a dash is guaranteed to start.
  `delta` | Time between stream markers.
  `end` | Time after which no stream markers are drawn.
  `start` | Time at which the first marker should be drawn.

  Methods

  `reset_delta`() | Reset the time delta for dashed streamtraces.

`StreamtraceTiming.``anchor`

  Time that a dash is guaranteed to start.

  A dash is guaranteed to start at `anchor`, provided the start and end time surround the dash.

  Example usage:

      >>> plot.streamtraces.timing.anchor = 1.1

  Type: | `float`

`StreamtraceTiming.``delta`

  Time between stream markers.

  `delta` is the time interval that measures the time between stream markers. The actual distance between markers is the product of this number and the local `Vector` magnitude.

  Call `StreamtraceTiming.reset_delta()` to reset this to the default.

  Example usage:

      >>> plot.streamtraces.timing.delta = 0.1

  Type: | `float`

`StreamtraceTiming.``end`

  Time after which no stream markers are drawn.

  Example usage:

      >>> plot.streamtraces.timing.end = 3.0

  Type: | `float`

`StreamtraceTiming.``reset_delta`()

  Reset the time delta for dashed streamtraces.

  The delta time is reset such that a stream dash in the vicinity of the maximum vector magnitude will have a length approximately equal to 10 percent of the frame width.

  Raises: | `TecplotSystemError` – Streamtraces time delta could not be reset.

  Example usage:

      >>> plot.streamtraces.timing.reset_delta()

`StreamtraceTiming.``start`

  Time at which the first marker should be drawn.

  A start time of zero means that the first marker is drawn at the starting point. A start time of 2.5 means that the first stream marker is drawn 2.5 time units downstream of the starting point.

  Example usage:

      >>> plot.streamtraces.timing.start = 2.5

  Type: | `float`

### StreamtraceRodRibbonContour

class `tecplot.plot.``StreamtraceRodRibbonContour`(streamtrace)

  Contour flooding display for streamtrace rod/ribbons.

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

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

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

      plot = frame.plot()
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.contour(0).variable = dataset.variable(3)
      plot.contour(0).levels.reset_levels(np.linspace(1.15,1.25,11))
      plot.show_mesh = False
      plot.show_shade = False
      plot.show_edge = True

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

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

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

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

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

  Attributes

  `flood_contour_group` | Contour group to use for flooding.
  `flood_contour_group_index` | The `Index` of the `ContourGroup` to use for flooding.
  `show` | Enable or disable contour flooding display.
  `use_lighting_effect` | Enable lighting effect for streamtrace rod/ribbons.

`StreamtraceRodRibbonContour.``flood_contour_group`

  Contour group to use for flooding.

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

  Example usage:

      >>> group = plot.contour(1)
      >>> contour = plot.streamtraces.rod_ribbon.contour
      >>> contour.flood_contour_group = group

  Type: | `ContourGroup`

`StreamtraceRodRibbonContour.``flood_contour_group_index`

  The `Index` of the `ContourGroup` to use for flooding.

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

  Example usage:

      >>> contour = plot.streamtraces.rod_ribbon.contour
      >>> contour.flood_contour_group_index = 0  # First contour group

  Type: | `Index` (zero-based index)

`StreamtraceRodRibbonContour.``show`

  Enable or disable contour flooding display.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.contour.show = True

  Type: | `bool`

`StreamtraceRodRibbonContour.``use_lighting_effect`

  Enable lighting effect for streamtrace rod/ribbons.

  Note

  Setting `StreamtraceRodRibbonContour.use_lighting_effect` will also set the same value for `StreamtraceRodRibbonShade.use_lighting_effect`, and vice-versa.

  The lighting effect is set with `StreamtraceRodRibbonEffects.lighting_effect`, and may be one of `LightingEffect.Gouraud` or `LightingEffect.Paneled`.

  Example usage:

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

  Type: | `bool`

### StreamtraceRodRibbonEffects

class `tecplot.plot.``StreamtraceRodRibbonEffects`(streamtrace)

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

      import os
      import tecplot
      from tecplot.constant import *

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

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

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

      plot.show_mesh = False
      plot.show_shade = False

      plot.vector.u_variable_index = 3
      plot.vector.v_variable_index = 4
      plot.vector.w_variable_index = 5
      plot.show_streamtraces = True
      plot.streamtraces.rod_ribbon.width = .03
      plot.streamtraces.rod_ribbon.shade.color = Color.Green

      plot.streamtraces.rod_ribbon.effects.use_translucency = True
      plot.streamtraces.rod_ribbon.effects.surface_translucency = 80

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

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

  Attributes

  `lighting_effect` | Get/set the lighting algorithm used when lighting
  `surface_translucency` | Surface translucency of the streamtraces ribbon.
  `use_translucency` | Enable surface translucency.

`StreamtraceRodRibbonEffects.``lighting_effect`
  Get/set the lighting algorithm used when lighting
    streamtrace rods and ribbons.

  Ribbon lighting effects must be enabled by setting `StreamtraceRodRibbonShade.use_lighting_effect` to `True` when setting this value.

  Note that setting `StreamtraceRodRibbonShade.use_lighting_effect` will also set this value for `ribbon contours`.

  There are two types of lighting effects: Paneled and Gouraud:

  > - `Paneled`: Within each cell, the color assigned to each area by
  >     shading or contour flooding is tinted by a shade constant across the cell. This shade is based on the orientation of the cell relative to your 3D light source.
  > - `Gouraud`: This offers smoother, more continuous shading than
  >     Paneled shading, but it also results in slower plotting and larger print files. `Gouraud` shading is not continuous across zone boundaries unless face neighbors are specified in the data. `Gouraud` shading is not available for finite element volume Zone when blanking is active. The zone’s lighting effect reverts to `Paneled` shading in this case.

  Example usage:

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

  Type: | `LightingEffect`

`StreamtraceRodRibbonEffects.``surface_translucency`

  Surface translucency of the streamtraces ribbon.

  Surface translucency must be enabled by setting `StreamtraceRodRibbonEffects.use_translucency` = `True` when setting this value.

  Valid translucency values range from one (opaque) to 99 (translucent).

  Example usage:

      >>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
      >>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20

  Type: | `int`

`StreamtraceRodRibbonEffects.``use_translucency`

  Enable surface translucency.

  The surface translucency value can be changed by setting `StreamtraceRodRibbonEffects.surface_translucency`.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
      >>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20

  Type: | `bool`

### StreamtraceRodRibbonMesh

class `tecplot.plot.``StreamtraceRodRibbonMesh`(robribbon)

  Streamtraces rod/ribbon mesh attributes.

  Note

  To set the mesh color or line thickness, see `Streamtraces.color` and `Streamtraces.line_thickness`.

      import os
      import tecplot
      from tecplot.constant import *

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

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

      plot = frame.plot()
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_mesh = False
      plot.show_shade = False
      plot.show_edge = True

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

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

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

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

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

  Attributes

  `line_thickness` | Get/Set streamtrace rod/ribbon mesh line thickness as a percentage of frame height.
  `show` | Display mesh.

`StreamtraceRodRibbonMesh.``line_thickness`

  Get/Set streamtrace rod/ribbon mesh line thickness as a percentage of frame height.

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

  Example usage:

      >>> plot.streamtraces.rod_ribbon.mesh.line_thickness = 0.2

  Type: | `float`

`StreamtraceRodRibbonMesh.``show`

  Display mesh.

  Note

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

  Example usage:

      >>> plot.streamtraces.rod_ribbon.mesh.show = True

  Type: | `bool`

### StreamtraceRodRibbonShade

class `tecplot.plot.``StreamtraceRodRibbonShade`(streamtrace)

  Color and lighting display for rod/ribbons.

      import os
      import tecplot
      from tecplot.constant import *

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

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

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

      plot.vector.u_variable_index = 3
      plot.vector.v_variable_index = 4
      plot.vector.w_variable_index = 5

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

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

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

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

  Attributes

  `color` | Shade color.
  `show` | Show shade attributes.
  `use_lighting_effect` | Use lighting effect.

`StreamtraceRodRibbonShade.``color`

  Shade color.

  `Color.MultiColor` and `Color.RGBColor` coloring are not available. Use flooded contours for multi-color or RGB flooding.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.shade.show = True
      >>> plot.streamtraces.rod_ribbon.shade.color = Color.Blue

  Type: | `Color`

`StreamtraceRodRibbonShade.``show`

  Show shade attributes.

  Example usage:

      >>> plot.streamtraces.rod_ribbon.shade.show = True

  Type: | `bool`

`StreamtraceRodRibbonShade.``use_lighting_effect`

  Use lighting effect.

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

  Note

  Setting `SliceShade.use_lighting_effect` will also set the same value for `SliceContour.use_lighting_effect`, and vice-versa.

  Example usage:

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

  Type: | `bool`

### StreamtraceTerminationLine

class `tecplot.plot.``StreamtraceTerminationLine`(streamtrace)

  Streamtraces termination line attributes.

  A streamtrace termination line is a polyline that terminates any streamtraces that cross it. The termination line is useful for stopping streamtraces before they spiral or stall.

  Note

  Before setting any `StreamtraceTerminationLine` properties, you must `add a termination line`.

  Streamtraces are terminated whenever any of the following occur:

  > - The maximum number of integration steps is reached.
  > - Any point where a streamtrace passes outside the available data.
  > - The streamtrace reaches a point where the velocity magnitude is zero.

      import tecplot
      from tecplot.constant import *
      import os

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

      frame = tecplot.active_frame()
      frame.plot_type = tecplot.constant.PlotType.Cartesian2D

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

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

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

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

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

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

  Attributes

  `active` | Activate/disable the streamtrace termination line.
  `color` | Color of the termination line.
  `line_pattern` | Pattern of the terminating line.
  `line_thickness` | Thickness of the termination line as a percentage of frame height.
  `pattern_length` | Length of the pattern as a percentage of frame height.
  `show` | Display the termination line.

`StreamtraceTerminationLine.``active`

  Activate/disable the streamtrace termination line.

  Set to `True` to activate the termination line and terminate any streamtraces that cross it. Set to `False` and redraw the plot with unterminated streamtraces.

  Note

  To display the termination line itself, set `show` to `True`.

  Example usage:

      >>> plot.streamtraces.termination_line.active = True

  Type: | `bool`

`StreamtraceTerminationLine.``color`

  Color of the termination line.

  Example usage:

      >>> plot.streamtraces.termination_line.color = Color.Red

  Type: | `Color`

`StreamtraceTerminationLine.``line_pattern`

  Pattern of the terminating line.

  Example usage:

      >>> plot.streamtraces.termination_line.line_pattern = LinePattern.Dotted

  Type: | `LinePattern`

`StreamtraceTerminationLine.``line_thickness`

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

  Example usage:

      >>> plot.streamtraces.termination_line.line_thickness = 0.1

  Type: | `float`

`StreamtraceTerminationLine.``pattern_length`

  Length of the pattern as a percentage of frame height.

  Example usage:

      >>> plot.streamtraces.termination_line.pattern_length = 2

  Type: | `float`

`StreamtraceTerminationLine.``show`

  Display the termination line.

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

  Note

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

  Example usage:

      >>> plot.streamtraces.termination_line.show = True

  Type: | `bool`

## Text

- Font
- BaseFont
- TextBox
- LabelFormat

### Font

class `tecplot.text.``Font`(parent, sv_textshape='TEXTSHAPE')

  Style of text objects such as titles and labels.

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

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

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

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

      plot.show_contour = True

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

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

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

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

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

  Attributes

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

`Font.``bold`

  Use the bold version of the current typeface.

  Example:

      >>> axis.title.font.bold = True

  Type: | `bool`

`Font.``italic`

  Use the italic version of the current typeface.

  Example:

      >>> axis.title.font.italic = True

  Type: | `bool`

`Font.``size`

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

  Example usage:

      >>> axis.title.font.size = 10

  Type: | `float`

`Font.``size_units`

  Used by the size attribute.

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

      >>> from tecplot.constant import Units
      >>> axis.title.font.size_units = Units.Frame
      >>> axis.title.font.size = 10

  Type: | `constant.Units`

`Font.``typeface`

  Specific font (or typeface) to use for text.

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

      >>> axis.title.font.typeface = 'Times'

  Type: | `str`

### BaseFont

class `tecplot.text.``BaseFont`(*svargs, **kwargs)

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

  Note

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

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

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

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

  Attributes

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

`BaseFont.``bold`

  Use the bold version of the current typeface.

  Example:

      >>> line_plot.base_font.bold = True
      >>> field_plot.scatter.base_font.bold = True

  Type: | `bool`

`BaseFont.``italic`

  Use the italic version of the current typeface.

  Example:

      >>> line_plot.base_font.italic = True
      >>> field_plot.scatter.base_font.italic = True

  Type: | `bool`

`BaseFont.``typeface`

  Specific font (or typeface) to use for text.

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

      >>> line_plot.base_font.typeface = 'Times'
      >>> field_plot.scatter.base_font.typeface = 'Times'

  Type: | `str`

### TextBox

class `tecplot.text.``TextBox`(parent)

  Rectangular frame around a text element.

  Warning

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

  Attributes

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

`TextBox.``box_type`

  Type of box surrounding the text.

  Example usage:

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

  Type: | `constant.TextBox`

`TextBox.``color`

  Color of the box surrounding the text.

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.color = Color.Blue

  Type: | `Color`

`TextBox.``fill_color`

  Fill color of the box surrounding the text.

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.fill_color = Color.Blue

  Type: | `Color`

`TextBox.``line_thickness`

  Line thickness of the box surrounding the text.

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.line_thickness = 0.2

  Type: | `float`

`TextBox.``margin`

  Margin of the box surrounding the text.

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

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.margin = 0.3

  Type: | `float`

### LabelFormat

class `tecplot.text.``LabelFormat`(labels)

  Formatting of numbers shown along in axes and in legends.

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

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

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

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

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

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

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

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

  Attributes

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

  Methods

  `add_custom_labels`(*labels) | Append a list of custom labels as a new set.
  `custom_labels`(index) | List of labels for custom labels for set specified by index.

`LabelFormat.``add_custom_labels`(*labels)

  Append a list of custom labels as a new set.

  Example usage:

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

`LabelFormat.``custom_labels`(index)

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

  Example usage:

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

`LabelFormat.``custom_labels_index`

  Index of the custom label to use.

  Example usage:

      >>> axis.tick_labels.format.custom_labels_index = 0

  Type: | `Index` (zero-based)

`LabelFormat.``datetime_format`

  The date/time format to be used.

  Example usage:

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

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

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

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

      "ddd-mmm-dd-yyyy"

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

      "m-d-yy"

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

      "h:mm:ss AM"

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

      "[d]:hh:mm"

  Type: | `str`

`LabelFormat.``format_type`

  Type of number formatting to use.

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

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> axis.tick_labels.format.format_type = NumberFormat.BestFloat

  Type: | `NumberFormat`

`LabelFormat.``negative_prefix`

  Prefix string to use for negative valued tick labels.

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

      >>> axis.tick_labels.format.show_negative_sign = False
      >>> axis.tick_labels.format.negative_prefix = '('
      >>> axis.tick_labels.format.negative_suffix = ')'

  Type: | `str`

`LabelFormat.``negative_suffix`

  Suffix string to use for negative valued tick labels.

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

      >>> axis.tick_labels.format.show_negative_sign = False
      >>> axis.tick_labels.format.negative_prefix = '('
      >>> axis.tick_labels.format.negative_suffix = ')'

  Type: | `str`

`LabelFormat.``num_custom_labels`

  Number of custom label sets available to use.

  Example usage:

      >>> print(axis.tick_labels.format.num_custom_labels)
      1

  Type: | `int`

`LabelFormat.``positive_prefix`

  Prefix string to use for positive valued tick labels.

  Example usage:

      >>> axis.tick_labels.format.positive_prefix = 'increase: '

  Type: | `str`

`LabelFormat.``positive_suffix`

  Suffix string to use for positive valued tick labels.

  Example usage:

      >>> axis.tick_labels.format.positive_suffix = ' (m)'

  Type: | `str`

`LabelFormat.``precision`

  Number digits after decimal for fixed floating point format.

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> axis.tick_labels.format.format_type = NumberFormat.FixedFloat
      >>> axis.tick_labels.format.precision = 3

  Type: | `int`

`LabelFormat.``remove_leading_zeros`

  Strip leading zeros in the formatted number.

  Example usage:

      >>> axis.tick_labels.format.remove_leading_zeros = True

  Type: | `bool`

`LabelFormat.``show_decimals_on_whole_numbers`

  Include trailing decimal character with whole numbers.

  Example usage:

      >>> axis.tick_labels.format.show_decimals_on_whole_numbers = True

  Type: | `bool`

`LabelFormat.``show_negative_sign`

  Include negative sign for negative values.

  Example usage:

      >>> axis.tick_labels.format.show_negative_sign = True

  Type: | `bool`

`LabelFormat.``zero_prefix`

  Prefix string to use for zero valued tick labels.

  Example usage:

      >>> axis.tick_labels.format.zero_prefix = 'origin: '

  Type: | `str`

`LabelFormat.``zero_suffix`

  Suffix string to use for zero valued tick labels.

  Example usage:

      >>> axis.tick_labels.format.zero_suffix = ' (origin)'

  Type: | `str`

## Data Labels

- FieldPlotDataLabels
- LinePlotDataLabels

### FieldPlotDataLabels

class `tecplot.plot.``FieldPlotDataLabels`(plot)

  Node and cell labels for field plots.

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

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

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

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

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

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

  Attributes

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

`FieldPlotDataLabels.``cell_label_type`

  The value to be displayed for cell labels.

  Possible values are `LabelType.Index` or `LabelType.VarValue`:

      >>> plot.data_labels.show_cell_labels = True
      >>> plot.data_labels.cell_label_type = LabelType.VarValue

  Type: | `LabelType`

`FieldPlotDataLabels.``cell_variable`

  `Variable` to use for cell labels.

  Example usage:

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

  Type: | `Variable`

`FieldPlotDataLabels.``cell_variable_index`

  `Index` of the variable to use for cell labels.

  Example usage:

      >>> from tecplot.constant import LabelType
      >>> plot.data_labels.show_cell_labels = True
      >>> plot.data_labels.cell_label_type = LabelType.VarValue
      >>> plot.data_labels.cell_variable_index = 3

  Type: | `Index`

`FieldPlotDataLabels.``color`

  The `Color` of the data labels.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.color = Color.LightBlue

  Type: | `Color`

`FieldPlotDataLabels.``color_by_map`

  Inherit `Color` from the symbol or scatter mapping style.

  Example usage for linemaps:

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

  Example usage for fieldmaps:

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

  Type: | `bool`

`FieldPlotDataLabels.``font`

  Typeface control for all data labels.

  Example usage:

      >>> plot.data_labels.font.typeface = 'Times'

  Type: | `text.Font`

`FieldPlotDataLabels.``index_step`

  Step interval between labels.

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

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

  See also

  `LinePlotDataLabels.step_mode` for line plots.

  Type: | `int`

`FieldPlotDataLabels.``label_format`

  Floating-point number format control.

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> labels = plot.data_labels
      >>> labels.label_format.format_type = NumberFormat.Integer

  Type: | `text.LabelFormat`

`FieldPlotDataLabels.``node_label_type`

  The value to be displayed for node labels.

  Possible values are `LabelType.Index` or `LabelType.VarValue`:

      >>> from tecplot.constant import LabelType
      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.node_label_type = LabelType.VarValue

  Type: | `LabelType`

`FieldPlotDataLabels.``node_variable`

  `Variable` to use for node labels.

  Example usage:

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

  Type: | `Variable`

`FieldPlotDataLabels.``node_variable_index`

  `Index` of the variable to use for node labels.

  Example usage:

      >>> from tecplot.constant import LabelType
      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.node_label_type = LabelType.VarValue
      >>> plot.data_labels.node_variable_index = 3

  Type: | `Index`

`FieldPlotDataLabels.``show_box`

  Show a box around each label.

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

      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.show_box = False

  Type: | `bool`

`FieldPlotDataLabels.``show_cell_labels`

  Display labels at each cell.

  Example usage:

      >>> plot.data_labels.show_cell_labels = True

  Type: | `bool`

`FieldPlotDataLabels.``show_node_labels`

  Display labels at each node.

  Example usage:

      >>> plot.data_labels.show_node_labels = True

  Type: | `bool`

### LinePlotDataLabels

class `tecplot.plot.``LinePlotDataLabels`(plot)

  Node labels for line plots.

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

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

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

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

  Attributes

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

`LinePlotDataLabels.``color`

  The `Color` of the data labels.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.color = Color.LightBlue

  Type: | `Color`

`LinePlotDataLabels.``color_by_map`

  Inherit `Color` from the symbol or scatter mapping style.

  Example usage for linemaps:

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

  Example usage for fieldmaps:

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

  Type: | `bool`

`LinePlotDataLabels.``font`

  Typeface control for all data labels.

  Example usage:

      >>> plot.data_labels.font.typeface = 'Times'

  Type: | `text.Font`

`LinePlotDataLabels.``index_step`

  Step interval between labels.

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

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

  See also

  `LinePlotDataLabels.step_mode` for line plots.

  Type: | `int`

`LinePlotDataLabels.``label_format`

  Floating-point number format control.

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> labels = plot.data_labels
      >>> labels.label_format.format_type = NumberFormat.Integer

  Type: | `text.LabelFormat`

`LinePlotDataLabels.``node_label_type`

  The value to be displayed for node labels.

  Possible values are `LabelType.Index` or `LabelType.VarValue`:

      >>> from tecplot.constant import LabelType
      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.node_label_type = LabelType.VarValue

  Type: | `LabelType`

`LinePlotDataLabels.``show_box`

  Show a box around each label.

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

      >>> plot.data_labels.show_node_labels = True
      >>> plot.data_labels.show_box = False

  Type: | `bool`

`LinePlotDataLabels.``show_node_labels`

  Display labels at each node.

  Example usage:

      >>> plot.data_labels.show_node_labels = True

  Type: | `bool`

`LinePlotDataLabels.``step_distance`

  Distance between labels when stepping by frame units.

  Example usage:

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

  Type: | `float`

`LinePlotDataLabels.``step_mode`

  The scale to use when stepping through elements.

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

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

  Type: | `StepMode`

## Viewport

- ReadOnlyViewport
- Viewport
- Cartesian2DViewport
- PolarViewport

### ReadOnlyViewport

class `tecplot.plot.``ReadOnlyViewport`(axes)

  Attributes

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

`ReadOnlyViewport.``bottom`

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

  Example usage:

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

`ReadOnlyViewport.``left`

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

  Example usage:

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

`ReadOnlyViewport.``right`

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

  Example usage:

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

`ReadOnlyViewport.``top`

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

  Example usage:

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

### Viewport

class `tecplot.plot.``Viewport`(axes)

  Attributes

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

`Viewport.``bottom`

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

  Example usage:

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

`Viewport.``left`

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

  Example usage:

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

`Viewport.``right`

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

  Example usage:

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

`Viewport.``top`

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

  Example usage:

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

### Cartesian2DViewport

class `tecplot.plot.``Cartesian2DViewport`(axes)

  Attributes

  `bottom` | `float` in percentage of frame height from the bottom of frame
  `left` | `float` in percentage of frame width from the left of the frame.
  `nice_fit_buffer` | Tolerance for viewport/frame fit niceness.
  `right` | `float` in percentage of frame width from the left of the frame.
  `top` | `float` in percentage of frame height from the bottom of the frame.
  `top_snap_target` | Target value for top when being adjusted or dragged.
  `top_snap_tolerance` | Tolerance for snapping to target value for top.

`Cartesian2DViewport.``bottom`

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

  Example usage:

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

`Cartesian2DViewport.``left`

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

  Example usage:

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

`Cartesian2DViewport.``nice_fit_buffer`

  Tolerance for viewport/frame fit niceness.

  Example usage:

      >>> plot.axes.viewport.nice_fit_buffer = 20

  Type: | `float`

`Cartesian2DViewport.``right`

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

  Example usage:

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

`Cartesian2DViewport.``top`

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

  Example usage:

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

`Cartesian2DViewport.``top_snap_target`

  Target value for top when being adjusted or dragged.

  Example usage:

      >>> plot.axes.viewport.top_snap_target = 90

  Type: | `float`

`Cartesian2DViewport.``top_snap_tolerance`

  Tolerance for snapping to target value for top.

  Example usage:

      >>> plot.axes.viewport.top_snap_tolerance = 8

  Type: | `float`

### PolarViewport

class `tecplot.plot.``PolarViewport`(axes)

  Attributes

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

`PolarViewport.``border_color`

  Border line color around the viewport.

  Example usage:

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

  Type: | `Color`

`PolarViewport.``border_thickness`

  Border line thickness around the viewport.

  Example usage:

      >>> plot.axes.viewport.show_border = True
      >>> plot.axes.viewport.border_thickness = 0.8

  Type: | `float`

`PolarViewport.``bottom`

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

  Example usage:

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

`PolarViewport.``fill_color`

  Background fill color of the entire viewport.

  Example usage:

      >>> from tecplot.constant import Color
      >>> plot.axes.viewport.fill_color = Color.Blue

  Type: | `Color`

`PolarViewport.``left`

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

  Example usage:

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

`PolarViewport.``right`

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

  Example usage:

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

`PolarViewport.``show_border`

  Draw a border line around the viewport.

  Example usage:

      >>> plot.axes.viewport.show_border = True

  Type: | `bool`

`PolarViewport.``top`

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

  Example usage:

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

## View and Lighting

- Cartesian2DFieldView
- Cartesian3DView
- XYLineView
- PolarView
- LightSource

### Cartesian2DFieldView

class `tecplot.plot.``Cartesian2DFieldView`(plot, *svargs)

  Adjust the way cartesian 2D data is displayed.

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

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

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

      plot.view.fit_to_nice()

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

      tp.export.save_png('view_2D.png', 600, supersample=3)

  Attributes

  `magnification` | Magnification for the data being plotted.

  Methods

  `adjust_to_nice`() | Shifts axes to make axis-line values “nice”
  `center`([consider_blanking]) | Center the data within the axis grid area.
  `fit`([consider_blanking]) | Fit the data being plotted within the axis grid area.
  `fit_data`([consider_blanking]) | Fit data zones or line mappings within the grid area.
  `fit_to_nice`([consider_blanking]) | Set axis range to begin/end on major axis increments.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, ymin, xmax, ymax) | Zoom the view to a rectangular region of the plot.

`Cartesian2DFieldView.``adjust_to_nice`()

  Shifts axes to make axis-line values “nice”

  Modifies the axis range to fit the minimum and maximum of the variable assigned to that axis, then snaps the major tick marks to the ends of the axis. If axis dependency is not independent, this may affect the range on another axis.

  In other words, given an existing range of values RMin, RMax and an initial delta, D (such as axis ranges with grid spacing or contour levels), determine a new delta (ND) that:

  - Is 1,2, or 5 times 10 to some power that is the “best” alternative to D.
  - Produces new range min and max values that are some multiple of ND that are nearest the original RMin and RMax

  Axes are shifted without changing the extents of the window.

`Cartesian2DFieldView.``center`(consider_blanking=True)

  Center the data within the axis grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

`Cartesian2DFieldView.``fit`(consider_blanking=True)

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

`Cartesian2DFieldView.``fit_data`(consider_blanking=True)

  Fit data zones or line mappings within the grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

  Note

  This does not take into consideration text and geometries that are plotted using the grid coordinate system.

`Cartesian2DFieldView.``fit_to_nice`(consider_blanking=True)

  Set axis range to begin/end on major axis increments.

  Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

`Cartesian2DFieldView.``magnification`

  Magnification for the data being plotted.

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10

  Type: | `float`

`Cartesian2DFieldView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`Cartesian2DFieldView.``zoom`(xmin, ymin, xmax, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.

  Zoom so the rectangular region with corners at `(1, 0)` and `(7, 9)` is in view:

      >>> view.zoom(1, 7, 0, 9)

### Cartesian3DView

class `tecplot.plot.``Cartesian3DView`(plot)

  Adjust the way cartesian 3D data is displayed.

      import tecplot
      import os
      from tecplot.constant import *
      examples_dir = tecplot.session.tecplot_examples_directory()
      infile = os.path.join(examples_dir, 'SimpleData', 'F18.plt')
      ds = tecplot.data.load_tecplot(infile)
      plot = tecplot.active_frame().plot(PlotType.Cartesian3D)
      plot.activate()
      plot.view.width = 17.5
      plot.view.alpha = 0
      plot.view.theta = 125
      plot.view.psi   = 65
      plot.view.position = (-100, 80, 65)

      tecplot.export.save_jpeg('view_3D.jpeg', 600, supersample=3)

  Attributes

  `alpha` | Eye origin view Alpha angle in degrees.
  `distance` | Get or set the view distance.
  `field_of_view` | Amount of the plot which is displayed.
  `magnification` | Magnification for the data being plotted.
  `position` | 3D viewer position.
  `projection` | Projection type (`Perspective` or `Orthographic`).
  `psi` | Eye origin view Psi angle in degrees.
  `rotation_origin` | Center of rotation for `rotate_axes` and `rotate_viewer`.
  `theta` | Eye origin view Theta angle in degrees.
  `width` | 3D view width.

  Methods

  `center`([consider_blanking]) | Center the data within the axis grid area.
  `fit`([consider_blanking]) | Fit the data being plotted within the axis grid area.
  `fit_data`([consider_blanking]) | Fit data zones or line mappings within the grid area.
  `fit_surfaces`([consider_blanking]) | Fit 3D plot surfaces to the grid area.
  `rotate_axes`(angle[, normal]) | Adjust the view so the axes are rotated about some normal vector.
  `rotate_to_angles`(psi, theta, alpha) | Rotate the plot to specific angles.
  `rotate_viewer`(angle[, normal]) | Rotate the camera or viewer about some normal vector.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, ymin, xmax, ymax) | Zoom the view to a rectangular region of the plot.

`Cartesian3DView.``alpha`

  Eye origin view Alpha angle in degrees.

  The Alpha angle is the twist about the eye origin ray:

      >>> plot.view.alpha = 95.0

  Type: | `float`

`Cartesian3DView.``center`(consider_blanking=True)

  Center the data within the axis grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

`Cartesian3DView.``distance`

  Get or set the view distance.

  The view distance is the distance from the viewer to the plane that is parallel to the screen and passes through the 3-D rotation origin.

  Note

  Changing this value will also change the viewer `position`.

  See also

  `position`

  Example usage:

      >>> plot.view.distance
      13.5
      >>> plot.view.distance = 10.0
      >>> plot.view.distance
      10.0

  Type: | `float`

`Cartesian3DView.``field_of_view`

  Amount of the plot which is displayed.

  Get or set the amount of the plot (in terms of spherical arc) in front of the viewer that may be seen.

  Warning

  `field_of_view` cannot be set if `projection` is `Projection.Orthographic`.

  Example usage:

      >>> from tecplot.constant import Projection
      >>> plot.view.projection = Projection.Perspective
      >>> plot.view.field_of_view = 9.6

  Type: | `float`

`Cartesian3DView.``fit`(consider_blanking=True)

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

`Cartesian3DView.``fit_data`(consider_blanking=True)

  Fit data zones or line mappings within the grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

  Note

  This does not take into consideration text and geometries that are plotted using the grid coordinate system.

`Cartesian3DView.``fit_surfaces`(consider_blanking=True)

  Fit 3D plot surfaces to the grid area.

  Parameters: | consider_blanking (`bool`, optional) – If `True` and blanking is enabled, the resulting view excludes blanked cells at the edges of the plot. If ‘False`, then the resulting view will ignore blanked cells at the edges of the plot. (default: `True`)

  Note

  3D volume zones are excluded when `surfaces_to_plot` is `SurfacesToPlot.None_`.

`Cartesian3DView.``magnification`

  Magnification for the data being plotted.

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10

  Type: | `float`

`Cartesian3DView.``position`

  3D viewer position.

  The viewer position is the viewer’s relation to the image:

      >>> plot.view.position
      (1.25, 3.2, 0.74)
      >>> plot.view.position.x
      1.25
      >>> plot.view.position = (2.5, 0.0, 1.0)
      >>> plot.view.position.y
      0.0
      >>> plot.view.position.z
      1.0

  See also

  `distance`

  Type: | `tuple`

`Cartesian3DView.``projection`

  Projection type (`Perspective` or `Orthographic`).

  When set to `Perspective`, Tecplot 360 draws the plot in perspective. When set to `Orthographic`, the plot is drawn with orthographic projection where the shape of the object does not change with distance.

  Note

  Requires Tecplot version 2017.2 or later.

  Example usage:

      >>> from tecplot.constant import Projection
      >>> plot.view.projection = Projection.Orthographic

  Type: | `Projection`

`Cartesian3DView.``psi`

  Eye origin view Psi angle in degrees.

  The Psi angle is the tilt of the eye origin ray away from the Z-axis:

      >>> plot.view.psi = 90.0

  Type: | `float`

`Cartesian3DView.``rotate_axes`(angle, normal=(0, 0, 1))

  Adjust the view so the axes are rotated about some normal vector.

  This effectively rotates the axes around the axes origin about some normal using the right-hand rule by the specified angle in degrees. The rotation is performed about the position: `Cartesian3DView.rotation_origin`.

  Parameters: | angle (`float`) – The angle in degrees to rotate. normal (`tuple`, optional) – The direction vector \((x, y, z)\) around which the rotation will be done. (default: `(0, 0, 1)`)

  Example of rotating 30 degrees about the \(x\)-axis and around the data’s origin:

      >>> plot.view.rotation_origin = (0, 0, 0)
      >>> plot.view.rotate_axes(30, (1, 0, 0))

`Cartesian3DView.``rotate_to_angles`(psi, theta, alpha)

  Rotate the plot to specific angles.

  Parameters: | psi – (`float`): Tilt, in degrees, of the eye origin ray away from the Z-axis. theta – (`float`): Rotation, in degrees, of the eye origin ray about the Z-axis. alpha – (`float`): Twist, in degrees, about the eye origin ray.

`Cartesian3DView.``rotate_viewer`(angle, normal=(0, 0, 1))

  Rotate the camera or viewer about some normal vector.

  This rotates the viewr about some normal using the right-hand rule by the specified angle in degrees. The rotation is performed about the position: `Cartesian3DView.rotation_origin`.

  Parameters: | angle (`float`) – The angle in degrees to rotate. normal (`tuple`, optional) – The direction vector \((x, y, z)\) around which the rotation will be done. (default: `(0, 0, 1)`)

  Example of rotating 2 degrees about the \(x\)-axis and around the data’s origin:

      >>> plot.view.rotation_origin = (0, 0, 0)
      >>> plot.view.rotate_viewer(2, (1, 0, 0))

`Cartesian3DView.``rotation_origin`

  Center of rotation for `rotate_axes` and `rotate_viewer`.

  Example of rotating 30 degrees about the \(y\)-axis and around the data-position \((x, y, z) = (1, 2, 3)\):

      >>> plot.view.rotation_origin = (1, 2, 3)
      >>> plot.view.rotate_axes(30, (0, 1, 0))

  Type: | `tuple`

`Cartesian3DView.``theta`

  Eye origin view Theta angle in degrees.

  The Theta angle is the rotation of the eye origin ray about the Z-axis:

      >>> plot.view.theta = 24.3

  Type: | `float`

`Cartesian3DView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`Cartesian3DView.``width`

  3D view width.

  The 3D view width is the amount of the plot (in X-axis units) in front of the viewer that may be seen.

  Warning

  `width` cannot be set if `projection` is `Perspective`.

  Example usage:

      >>> plot.view.width = 1.5

  Type: | `float`

`Cartesian3DView.``zoom`(xmin, ymin, xmax, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.

  Zoom so the rectangular region with corners at `(1, 0)` and `(7, 9)` is in view:

      >>> view.zoom(1, 7, 0, 9)

### XYLineView

class `tecplot.plot.``XYLineView`(plot)

  Adjust the way XY Line data is displayed.

      import os
      import tecplot
      from tecplot.constant import *

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

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

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

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

      tecplot.export.save_png('view_line.png', 600, supersample=3)

  Attributes

  `extents` | Viewport extents in data units.
  `magnification` | Magnification for the data being plotted.

  Methods

  `adjust_to_nice`() | Shifts axes to make axis-line values “nice”
  `center`() | Center the data within the axis grid area.
  `fit`() | Fit the data being plotted within the axis grid area.
  `fit_data`() | Fit data zones or line mappings within the grid area.
  `fit_to_nice`() | Set axis range to begin/end on major axis increments.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, ymin, xmax, ymax) | Zoom the view to a rectangular region of the plot.

`XYLineView.``adjust_to_nice`()

  Shifts axes to make axis-line values “nice”

  Modifies the axis range to fit the minimum and maximum of the variable assigned to that axis, then snaps the major tick marks to the ends of the axis. If axis dependency is not independent, this may affect the range on another axis.

  In other words, given an existing range of values RMin, RMax and an initial delta, D (such as axis ranges with grid spacing or contour levels), determine a new delta (ND) that:

  - Is 1,2, or 5 times 10 to some power that is the “best” alternative to D.
  - Produces new range min and max values that are some multiple of ND that are nearest the original RMin and RMax

  Axes are shifted without changing the extents of the window.

`XYLineView.``center`()

  Center the data within the axis grid area.

  Raises: | `TecplotSystemError` – View could not be centered.

`XYLineView.``extents`

  Viewport extents in data units.

  Extents are represented by the `tuple`: `(x1, y1, x2, y2)` and setting this effectively zooms the view of the data within the viewport. The values `(x1, y1)` and `(x2, y2)` are the lower-left and upper-right edges respectively and are in data units. The following example will set the bottom and left edges of the viewport to the value of `-3` and the top and right edges to the value of 5:

      >>> plot.view.extents = -3, -3, 5, 5

  Type: | `tuple`

`XYLineView.``fit`()

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

`XYLineView.``fit_data`()

  Fit data zones or line mappings within the grid area.

  Note

  This does not take into consideration text and geometries that are plotted using the grid coordinate system.

`XYLineView.``fit_to_nice`()

  Set axis range to begin/end on major axis increments.

  Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

`XYLineView.``magnification`

  Magnification for the data being plotted.

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10

  Type: | `float`

`XYLineView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`XYLineView.``zoom`(xmin, ymin, xmax, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.

  Zoom so the rectangular region with corners at `(1, 0)` and `(7, 9)` is in view:

      >>> view.zoom(1, 7, 0, 9)

### PolarView

class `tecplot.plot.``PolarView`(plot)

  Adjust the way polar data is displayed.

      import numpy as np
      import tecplot as tp
      from tecplot.constant import PlotType, ThetaMode

      frame = tp.active_frame()

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

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

      plot = frame.plot(PlotType.PolarLine)
      plot.activate()

      plot.axes.r_axis.max = np.max(r)
      plot.axes.theta_axis.mode = ThetaMode.Radians

      plot.delete_linemaps()
      lmap = plot.add_linemap('Linemap', zone, dataset.variable('R'),
                              dataset.variable('Theta'))
      lmap.line.line_thickness = 0.8

      plot.view.fit()

      tp.export.save_png('view_polar.png', 600, supersample=3)

  Attributes

  `extents` | Viewport extents in radial data units.
  `magnification` | Magnification for the data being plotted.

  Methods

  `center`() | Center the data within the axis grid area.
  `fit`() | Fit the data being plotted within the axis grid area.
  `fit_data`() | Fit data zones or line mappings within the grid area.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, ymin, xmax, ymax) | Zoom the view to a rectangular region of the plot.

`PolarView.``center`()

  Center the data within the axis grid area.

  Raises: | `TecplotSystemError` – View could not be centered.

`PolarView.``extents`

  Viewport extents in radial data units.

  Extents are represented by the `tuple`: `(x1, y1, x2, y2)` and setting this effectively zooms the view of the data within the viewport. The values `(x1, y1)` and `(x2, y2)` are the lower-left and upper-right edges respectively and are in radial (data) units. The following example will set the bottom and left edges of the viewport to the radial value of `-3` and the top and right edges to the radial value of 5:

      >>> plot.view.extents = -3, -3, 5, 5

  Type: | `tuple`

`PolarView.``fit`()

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

`PolarView.``fit_data`()

  Fit data zones or line mappings within the grid area.

  Note

  This does not take into consideration text and geometries that are plotted using the grid coordinate system.

`PolarView.``magnification`

  Magnification for the data being plotted.

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area. The following example will scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10

  Type: | `float`

`PolarView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`PolarView.``zoom`(xmin, ymin, xmax, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.

  Zoom so the rectangular region with corners at `(1, 0)` and `(7, 9)` is in view:

      >>> view.zoom(1, 7, 0, 9)

### LightSource

class `tecplot.plot.``LightSource`(plot)

  Three-dimensional light source style control.

  The light source is a point of light infinitely far from the drawing area.

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

      examples_dir = tp.session.tecplot_examples_directory()
      infile = path.join(examples_dir, 'SimpleData', 'F18.plt')
      dataset = tp.data.load_tecplot(infile)

      frame = tp.active_frame()
      plot = frame.plot(PlotType.Cartesian3D)
      plot.activate()

      plot.light_source.direction = (0., -0.7, 0.9)
      plot.light_source.intensity = 70
      plot.light_source.specular_intensity = 80
      plot.light_source.specular_shininess = 50

      tp.export.save_png('light_source.png')

  Attributes

  `background_light` | Percentage intensity of the omni-directional fill light.
  `direction` | \((x, y, z)\) direction of the light rays.
  `force_gouraud_for_contour_flood` | Force gouraud effects for shaded continuous flooding.
  `force_paneled_for_cell_flood` | Force paneled effects for shaded cell flooding.
  `intensity` | Percentage intensity of the light source.
  `specular_intensity` | Percentage intensity of specular highlights.
  `specular_shininess` | Percentage of shininess for specular highlights.
  `surface_color_contrast` | Percentage of contrast for surface colors.

`LightSource.``background_light`

  Percentage intensity of the omni-directional fill light.

  Example usage:

      >>> plot.light_source.background_light = 70.0

  Type: | `float`

`LightSource.``direction`

  \((x, y, z)\) direction of the light rays.

  The direction is in the view coordinate system where \(z\) goes into the page and the origin of \((x, y)\) is in the lower left corner. The default is \((-0.2, -0.2, 0.959)\):

      >>> plot.light_source.direction = (0, -0.7, 0.9)

  Type: | `tuple`

`LightSource.``force_gouraud_for_contour_flood`

  Force gouraud effects for shaded continuous flooding.

  Example usage:

      >>> plot.light_source.force_gouraud_for_contour_flood = True

  Type: | `bool`

`LightSource.``force_paneled_for_cell_flood`

  Force paneled effects for shaded cell flooding.

  Example usage:

      >>> plot.light_source.force_paneled_for_cell_flood = True

  Type: | `bool`

`LightSource.``intensity`

  Percentage intensity of the light source.

  Example usage:

      >>> plot.light_source.intensity = 50.0

  Type: | `float`

`LightSource.``specular_intensity`

  Percentage intensity of specular highlights.

  Set this to zero to turn off specular effects:

      >>> plot.light_source.specular_intensity = 0

  Type: | `float`

`LightSource.``specular_shininess`

  Percentage of shininess for specular highlights.

  Example usage:

      >>> plot.light_source.specular_shininess = 80.0

  Type: | `float`

`LightSource.``surface_color_contrast`

  Percentage of contrast for surface colors.

  Example usage:

      >>> plot.light_source.surface_color_contrast = 80.0

  Type: | `float`

## Frame Linking

- SketchPlotLinkingBetweenFrames
- Cartesian2DPlotLinkingBetweenFrames
- Cartesian3DPlotLinkingBetweenFrames
- XYLinePlotLinkingBetweenFrames
- PolarPlotLinkingBetweenFrames

### SketchPlotLinkingBetweenFrames

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

  `SketchPlot` `Frame` style linking control.

  See also

  `Cartesian3DPlotLinkingBetweenFrames` for details on how to link style across multiple frames.

  Attributes

  `group` | Group number (1-32).
  `link_frame_size_and_position` | Match frame geometry.
  `link_solution_time` | Match current solution time.

`SketchPlotLinkingBetweenFrames.``group`

  Group number (1-32).

  Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 5
      >>> frame_linking.link_frame_size_and_position = True
      >>> frame_linking.link_solution_time = True

  Type: | `int`

`SketchPlotLinkingBetweenFrames.``link_frame_size_and_position`

  Match frame geometry.

  Keeps the same geometry across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_frame_size_and_position = True

  Type: | `bool`

`SketchPlotLinkingBetweenFrames.``link_solution_time`

  Match current solution time.

  Keeps the same active solution time across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_solution_time = True

  Type: | `bool`

### Cartesian2DPlotLinkingBetweenFrames

class `tecplot.plot.``Cartesian2DPlotLinkingBetweenFrames`(frame)

  `Cartesian2DFieldPlot` `Frame` style linking control.

  See also

  `Cartesian3DPlotLinkingBetweenFrames` for details on how to link style across multiple frames.

  Attributes

  `group` | Group number (1-32).
  `link_axis_position` | Match axis position within the frames.
  `link_contour_levels` | Match all contour levels.
  `link_frame_size_and_position` | Match frame geometry.
  `link_solution_time` | Match current solution time.
  `link_value_blanking` | Match all value blanking style settings.
  `link_x_axis_range` | Match x-axis range.
  `link_y_axis_range` | Match y-axis range.

`Cartesian2DPlotLinkingBetweenFrames.``group`

  Group number (1-32).

  Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 5
      >>> frame_linking.link_frame_size_and_position = True
      >>> frame_linking.link_solution_time = True

  Type: | `int`

`Cartesian2DPlotLinkingBetweenFrames.``link_axis_position`

  Match axis position within the frames.

  Keeps the same axis position across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_axis_position = True

  Type: | `bool`

`Cartesian2DPlotLinkingBetweenFrames.``link_contour_levels`

  Match all contour levels.

  Keeps the same contour levels across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_contour_levels = True

  Type: | `bool`

`Cartesian2DPlotLinkingBetweenFrames.``link_frame_size_and_position`

  Match frame geometry.

  Keeps the same geometry across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_frame_size_and_position = True

  Type: | `bool`

`Cartesian2DPlotLinkingBetweenFrames.``link_solution_time`

  Match current solution time.

  Keeps the same active solution time across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_solution_time = True

  Type: | `bool`

`Cartesian2DPlotLinkingBetweenFrames.``link_value_blanking`

  Match all value blanking style settings.

  Keeps the same value blanking across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_value_blanking = True

  Type: | `bool`

`Cartesian2DPlotLinkingBetweenFrames.``link_x_axis_range`

  Match x-axis range.

  Keeps the same x-axis range across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_x_axis_range = True

  Type: | `bool`

`Cartesian2DPlotLinkingBetweenFrames.``link_y_axis_range`

  Match y-axis range.

  Keeps the same y-axis range across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_y_axis_range = True

  Type: | `bool`

### Cartesian3DPlotLinkingBetweenFrames

class `tecplot.plot.``Cartesian3DPlotLinkingBetweenFrames`(frame)

  `Cartesian3DFieldPlot` `Frame` style linking control.

  The following example shows how to set up a series of transparent overlay frames where each overlay shows one component of the \((U, V, W)\) vector from the `Dataset`. All four frames are linked to each other (group 1) so they have the same size, position and view.

      import os

      import tecplot as tp
      from tecplot.constant import *

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

      # Create a "blank" (zeroed-out) variable to use when plotting
      # only one component of the (U, V, W) vectors
      tp.data.operate.execute_equation(r'{blank} = 0')

      # Setup the background frame and plot style
      frame = tp.active_frame()
      frame.background_color = Color.Black

      plot = frame.plot(PlotType.Cartesian3D)
      plot.activate()

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

      plot.use_translucency = True
      plot.show_contour = True
      plot.show_edge = True
      plot.axes.orientation_axis.color = Color.White
      plot.view.width = 2.43

      fmap = plot.fieldmap(0)
      fmap.edge.edge_type = EdgeType.Creases
      fmap.edge.color = Color.White
      fmap.surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces

      frame_linking = plot.linking_between_frames
      frame_linking.group = 1
      frame_linking.link_view = True
      frame_linking.link_frame_size_and_position = True

      def add_transparent_overlay(frame):
          '''Creates a transparent frame overlay with "blank" vector variables.'''
          overlay_frame = frame.page.add_frame()
          overlay_frame.transparent = True

          plot = overlay_frame.plot(frame.plot_type)
          plot.activate()
          plot.show_shade = False
          plot.axes.orientation_axis.show = False

          blank_var = overlay_frame.dataset.variable('blank')
          plot.vector.u_variable = blank_var
          plot.vector.v_variable = blank_var
          plot.vector.w_variable = blank_var
          plot.show_vector = True

          fmap = plot.fieldmap(0)
          fmap.vector.line_thickness = 0.35
          fmap.points.step = 80
          fmap.points.points_to_plot = PointsToPlot.AllCellCenters

          frame_linking = plot.linking_between_frames
          frame_linking.group = 1
          frame_linking.link_view = True
          frame_linking.link_frame_size_and_position = True

          return plot

      # Create three overlays - one for each vector component we want to show
      u_plot = add_transparent_overlay(frame)
      u_plot.vector.u_variable = dataset.variable('U(M/S)')
      u_plot.fieldmap(0).vector.color = Color.Red

      v_plot = add_transparent_overlay(frame)
      v_plot.vector.v_variable = dataset.variable('V(M/S)')
      v_plot.fieldmap(0).vector.color = Color.Green

      w_plot = add_transparent_overlay(frame)
      w_plot.vector.w_variable = dataset.variable('W(M/S)')
      w_plot.fieldmap(0).vector.color = Color.Blue

      # Now that all plots have been linked,
      # movement in one will affect all three plots.
      u_plot.view.translate(x=5)

      tp.export.save_png('plot3d_linking_between_frames.png')

  Attributes

  `group` | Group number (1-32).
  `link_contour_levels` | Match all contour levels.
  `link_frame_size_and_position` | Match frame geometry.
  `link_isosurface_values` | Match isosurface values.
  `link_slice_positions` | Match slice positions.
  `link_solution_time` | Match current solution time.
  `link_value_blanking` | Match all value blanking style settings.
  `link_view` | Match the view orientation and position.

`Cartesian3DPlotLinkingBetweenFrames.``group`

  Group number (1-32).

  Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 5
      >>> frame_linking.link_frame_size_and_position = True
      >>> frame_linking.link_solution_time = True

  Type: | `int`

`Cartesian3DPlotLinkingBetweenFrames.``link_contour_levels`

  Match all contour levels.

  Keeps the same contour levels across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_contour_levels = True

  Type: | `bool`

`Cartesian3DPlotLinkingBetweenFrames.``link_frame_size_and_position`

  Match frame geometry.

  Keeps the same geometry across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_frame_size_and_position = True

  Type: | `bool`

`Cartesian3DPlotLinkingBetweenFrames.``link_isosurface_values`

  Match isosurface values.

  Keeps the same isosurfaces across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_isosurface_values = True

  Type: | `bool`

`Cartesian3DPlotLinkingBetweenFrames.``link_slice_positions`

  Match slice positions.

  Keeps the same slice positions across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_slice_positions = True

  Type: | `bool`

`Cartesian3DPlotLinkingBetweenFrames.``link_solution_time`

  Match current solution time.

  Keeps the same active solution time across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_solution_time = True

  Type: | `bool`

`Cartesian3DPlotLinkingBetweenFrames.``link_value_blanking`

  Match all value blanking style settings.

  Keeps the same value blanking across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_value_blanking = True

  Type: | `bool`

`Cartesian3DPlotLinkingBetweenFrames.``link_view`

  Match the view orientation and position.

  Keeps the same view across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_view = True

  Type: | `bool`

### XYLinePlotLinkingBetweenFrames

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

  `XYLinePlot` `Frame` style linking control.

  See also

  `Cartesian3DPlotLinkingBetweenFrames` for details on how to link style across multiple frames.

  Attributes

  `group` | Group number (1-32).
  `link_axis_position` | Match axis position within the frames.
  `link_frame_size_and_position` | Match frame geometry.
  `link_solution_time` | Match current solution time.
  `link_value_blanking` | Match all value blanking style settings.
  `link_x_axis_range` | Match x-axis range.
  `link_y_axis_range` | Match y-axis range.

`XYLinePlotLinkingBetweenFrames.``group`

  Group number (1-32).

  Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 5
      >>> frame_linking.link_frame_size_and_position = True
      >>> frame_linking.link_solution_time = True

  Type: | `int`

`XYLinePlotLinkingBetweenFrames.``link_axis_position`

  Match axis position within the frames.

  Keeps the same axis position across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_axis_position = True

  Type: | `bool`

`XYLinePlotLinkingBetweenFrames.``link_frame_size_and_position`

  Match frame geometry.

  Keeps the same geometry across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_frame_size_and_position = True

  Type: | `bool`

`XYLinePlotLinkingBetweenFrames.``link_solution_time`

  Match current solution time.

  Keeps the same active solution time across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_solution_time = True

  Type: | `bool`

`XYLinePlotLinkingBetweenFrames.``link_value_blanking`

  Match all value blanking style settings.

  Keeps the same value blanking across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_value_blanking = True

  Type: | `bool`

`XYLinePlotLinkingBetweenFrames.``link_x_axis_range`

  Match x-axis range.

  Keeps the same x-axis range across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_x_axis_range = True

  Type: | `bool`

`XYLinePlotLinkingBetweenFrames.``link_y_axis_range`

  Match y-axis range.

  Keeps the same y-axis range across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_y_axis_range = True

  Type: | `bool`

### PolarPlotLinkingBetweenFrames

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

  `PolarLinePlot` `Frame` style linking control.

  See also

  `Cartesian3DPlotLinkingBetweenFrames` for details on how to link style across multiple frames.

  Attributes

  `group` | Group number (1-32).
  `link_frame_size_and_position` | Match frame geometry.
  `link_solution_time` | Match current solution time.
  `link_value_blanking` | Match all value blanking style settings.
  `link_view` | Match polar view settings.

`PolarPlotLinkingBetweenFrames.``group`

  Group number (1-32).

  Each frame may be a member of a single group and may opt in or out of linking each specific style to other frames within this group. Once the group is set, the frame may opt in and out of specific attributes:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 5
      >>> frame_linking.link_frame_size_and_position = True
      >>> frame_linking.link_solution_time = True

  Type: | `int`

`PolarPlotLinkingBetweenFrames.``link_frame_size_and_position`

  Match frame geometry.

  Keeps the same geometry across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_frame_size_and_position = True

  Type: | `bool`

`PolarPlotLinkingBetweenFrames.``link_solution_time`

  Match current solution time.

  Keeps the same active solution time across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_solution_time = True

  Type: | `bool`

`PolarPlotLinkingBetweenFrames.``link_value_blanking`

  Match all value blanking style settings.

  Keeps the same value blanking across all frames in the specified `group`. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_value_blanking = True

  Type: | `bool`

`PolarPlotLinkingBetweenFrames.``link_view`

  Match polar view settings.

  Keeps the same view across all frames in the specified `group` showing a polar plot. Example usage:

      >>> frame_linking = plot.linking_between_frames
      >>> frame_linking.group = 1
      >>> frame_linking.link_view = True

  Type: | `bool`