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# Plot Style

- Scatter Plots
  - Scatter
- Vector Plots
  - Vector2D
  - Vector3D
  - ReferenceVector
  - ReferenceVectorLabel
- Legends
  - ContourLegend
  - LineLegend
- Contours
  - ContourGroup
  - ContourColorCutoff
  - ContourColormapFilter
  - ContourColormapOverride
  - ContourColormapZebraShade
  - ContourLabels
  - ContourLevels
  - ContourLines
- Isosurface
  - IsosurfaceGroup
  - IsosurfaceContour
  - IsosurfaceEffects
  - IsosurfaceMesh
  - IsosurfaceShade
- Slice
  - SliceGroup
  - SliceContour
  - SliceEdge
  - SliceEffects
  - SliceMesh
  - SliceShade
  - SliceVector
- Streamtraces
  - Streamtraces
  - StreamtraceRodRibbon
  - StreamtraceTiming
  - StreamtraceRodRibbonContour
  - StreamtraceRodRibbonEffects
  - StreamtraceRodRibbonMesh
  - StreamtraceRodRibbonShade
  - StreamtraceTerminationLine
- Text
  - TextBox
  - Font
  - LabelFormat
- Viewport
  - ReadOnlyViewport
  - Viewport
  - Cartesian2DViewport
  - PolarViewport
- View
  - Cartesian2DView
  - Cartesian3DView
  - LineView
  - PolarView

## Scatter Plots

### Scatter

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

  Plot-local scatter style settings.

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

  Attributes

  `variable` | The `Variable` to be used when sizing scatter symbols.
  `variable_index` | Zero-based index of the `Variable` used for size of scatter symbols.

`Scatter.``variable`

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

  The variable must belong to the `Dataset` attached to 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

## 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 *

      exdir = tp.session.tecplot_examples_directory()
      infile = path.join(exdir, '2D', '3element.lpk')
      tp.load_layout(infile)

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

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

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

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

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

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

      for fmap in plot.fieldmaps:
          fmap.show = False

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

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

  Attributes

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

`Vector2D.``arrowhead_angle`

  Angle between the vector body and the head line.

  Type: | `float` (degrees)

  Example usage:

      >>> plot.vector.arrowhead_angle = 10

`Vector2D.``arrowhead_fraction`

  Size of the arrowhead when sizing by fraction.

  Type: | `float` (ratio)

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

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

`Vector2D.``arrowhead_size`

  Size of arrowhead when sizing by frame height.

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

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

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

`Vector2D.``length`

  Length of all vectors when not using relative sizing.

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

  Example usage:

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

`Vector2D.``reference_vector`

  Vector field reference vector.

  Type: | `ReferenceVector`

  Example usage:

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

`Vector2D.``relative_length`

  Magnitude-varying length of the vector line.

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

  When `use_relative` 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

`Vector2D.``size_arrowhead_by_fraction`

  Base arrowhead size on length of vector line.

  Type: | `bool`

  Example usage:

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

`Vector2D.``u_variable`

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

  Type: | `Variable`

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

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

`Vector2D.``u_variable_index`

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

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

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

      >>> plot.vector.u_variable_index = 3

`Vector2D.``use_grid_units`

  Use grid-units when determining the relative length.

  Type: | `bool`

  This takes effect only if `use_relative` 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

`Vector2D.``use_relative`

  Use relative sizing for vector lines.

  Type: | `bool`

  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

`Vector2D.``v_variable`

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

  Type: | `Variable`

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

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

`Vector2D.``v_variable_index`

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

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

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

      >>> plot.vector.v_variable_index = 4

### 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.

  Type: | `float` (degrees)

  Example usage:

      >>> plot.vector.arrowhead_angle = 10

`Vector3D.``arrowhead_fraction`

  Size of the arrowhead when sizing by fraction.

  Type: | `float` (ratio)

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

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

`Vector3D.``arrowhead_size`

  Size of arrowhead when sizing by frame height.

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

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

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

`Vector3D.``length`

  Length of all vectors when not using relative sizing.

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

  Example usage:

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

`Vector3D.``reference_vector`

  Vector field reference vector.

  Type: | `ReferenceVector`

  Example usage:

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

`Vector3D.``relative_length`

  Magnitude-varying length of the vector line.

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

  When `use_relative` 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

`Vector3D.``size_arrowhead_by_fraction`

  Base arrowhead size on length of vector line.

  Type: | `bool`

  Example usage:

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

`Vector3D.``u_variable`

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

  Type: | `Variable`

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

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

`Vector3D.``u_variable_index`

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

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

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

      >>> plot.vector.u_variable_index = 3

`Vector3D.``use_grid_units`

  Use grid-units when determining the relative length.

  Type: | `bool`

  This takes effect only if `use_relative` 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

`Vector3D.``use_relative`

  Use relative sizing for vector lines.

  Type: | `bool`

  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

`Vector3D.``v_variable`

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

  Type: | `Variable`

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

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

`Vector3D.``v_variable_index`

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

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

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

      >>> plot.vector.v_variable_index = 4

`Vector3D.``w_variable`

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

  Type: | `Variable`

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

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

`Vector3D.``w_variable_index`

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

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

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

      >>> plot.vector.w_variable_index = 5

### 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 *

      exdir = tp.session.tecplot_examples_directory()
      infile = path.join(exdir, '2D', 'Flow.lpk')
      tp.load_layout(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('R/RFR')
      vector_contour.colormap_name = 'Magma'
      vector_contour.colormap_filter.reversed = True

      vorticity_contour = plot.contour(1)
      vorticity_contour.variable = dataset.variable('Vorticity')
      vorticity_contour.colormap_name = 'GrayScale'
      vorticity_contour.colormap_filter.reversed = True

      vector = plot.vector
      vector.u_variable = dataset.variable('U/RFC')
      vector.v_variable = dataset.variable('V/RFC')
      vector.relative_length = 0.4
      vector.arrowhead_size = 0.3
      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

      fmap = plot.fieldmap(0)
      fmap.contour.flood_contour_group = vorticity_contour
      fmap.points.step = (4, 4)
      fmap.vector.color = vector_contour
      fmap.vector.line_thickness = 0.4

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

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

  Attributes

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

`ReferenceVector.``angle`

  Degrees counter-clockwise to rotate the reference vector.

  Type: | `float` (degrees)

  Example usage:

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

`ReferenceVector.``color`

  `Color` of the reference vector.

  Type: | `Color`

  Example usage:

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

`ReferenceVector.``label`

  reference vector label style control.

  Type: | `ReferenceVectorLabel`

  Example usage:

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

`ReferenceVector.``line_thickness`

  reference vector line thickness.

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

  Example usage:

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

`ReferenceVector.``magnitude`

  Length of the reference vector.

  Type: | `float` (data units)

  Example usage:

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

`ReferenceVector.``position`

  \((x,y)\) of the reference vector.

  Type: | `2-tuple` of `floats` (\((x,y)\) percent of frame height)

  Example usage:

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

`ReferenceVector.``show`

  Draw the reference vector.

  Type: | `boolean`

  Example usage:

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

### 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` | `Font` of the reference vector label.
  `format` | Number formatting control for the reference vector label.
  `offset` | Distance from the reference vector to the associated label.
  `show` | Print a label next to the reference vector.

`ReferenceVectorLabel.``color`

  `Color` of the reference vector label.

  Type: | `Color`

  Example usage:

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

`ReferenceVectorLabel.``font`

  `Font` of the reference vector label.

  Type: | `Font`

  Example usage:

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

`ReferenceVectorLabel.``format`

  Number formatting control for the reference vector label.

  Type: | `LabelFormat`

  Example usage:

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

`ReferenceVectorLabel.``offset`

  Distance from the reference vector to the associated label.

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

  Example usage:

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

`ReferenceVectorLabel.``show`

  Print a label next to the reference vector.

  Type: | `boolean`

  Example usage:

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

## Legends

- ContourLegend
- LineLegend

### 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.

      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, '3D', '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=5)

  Attributes

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

`ContourLegend.``anchor_alignment`

  Anchor location of the legend.

  Type: | `AnchorAlignment`

  Example usage:

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

`ContourLegend.``auto_resize`

  Automatically skip some levels to create a reasonably sized legend.

  Type: | `boolean`

  Example usage:

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

`ContourLegend.``box`

  Legend box attributes.

  Type: | `text.TextBox`

  Example usage:

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

`ContourLegend.``header_font`

  Font used to display the name of the contour variable.

  Note

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

  Type: | `text.Font`

  Example usage:

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

`ContourLegend.``include_cutoff_levels`

  Show color bands and labels for levels affected by color cutoff.

  Type: | `boolean`

  Example usage:

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

`ContourLegend.``label_format`

  Number formatting for labels along the legend.

  Type: | `LabelFormat`

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

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

`ContourLegend.``label_increment`

  Spacing between labels along the contour legend.

  Type: | `float`

  Labels will be placed on the contour variable range from min to max. The smaller the increment value the more legend labels will be created. If the `label_location` 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`

`ContourLegend.``label_location`

  Placement of labels on the legend.

  Type: | `ContLegendLabelLocation`

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

  - `ContLegendLabelLocation.ContourLevels` - This option places one label for each contour level. See Contour Levels and Color.
  - `ContLegendLabelLocation.Increment` - 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`

`ContourLegend.``label_step`

  Step size between labels along the legend.

  Type: | `integer`

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

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

`ContourLegend.``number_font`

  Font used to display numbers in the legend.

  Note

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

  Type: | `text.Font`

  Example usage:

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

`ContourLegend.``overlay_bar_grid`

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

  Type: | `boolean`

  Example usage:

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

`ContourLegend.``position`

  Position of the legend as a percentage of frame width/height.

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

  Type: | 2-`tuple` of `floats`

  Example usage:

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

`ContourLegend.``row_spacing`

  Spacing between rows in the legend.

  Type: | `float`

  Example usage:

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

`ContourLegend.``show`

  Show or hide the legend.

  Type: | `boolean`

  Example usage:

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

`ContourLegend.``show_header`

  Show the name of the contour variable in the legend.

  Type: | `boolean`

  Example usage:

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

`ContourLegend.``text_color`

  Color of legend text.

  Type: | `Color`

  Example usage:

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

`ContourLegend.``vertical`

  Orientation of the legend.

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

  Type: | `boolean`

  Example usage:

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

### LineLegend

class `tecplot.legend.``LineLegend`(plot, *svargs)

  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.

      import os

      import tecplot
      from tecplot.constant import *

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, 'XY', 'rainfall.plt')
      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=4)

  Attributes

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

`LineLegend.``anchor_alignment`

  Anchor location of the legend.

  Type: | `AnchorAlignment`

  Example usage:

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

`LineLegend.``box`

  Legend box attributes.

  Type: | `text.TextBox`

  Example usage:

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

`LineLegend.``font`

  Legend font attributes.

  Note

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

  Type: | `text.Font`

  Example usage:

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

`LineLegend.``position`

  Position of the legend as a percentage of frame width/height.

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

  Type: | 2-`tuple` of `floats`

  Example usage:

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

`LineLegend.``row_spacing`

  Spacing between rows in the legend.

  Type: | `float`

  Example usage:

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

`LineLegend.``show`

  Show or hide the legend.

  Type: | `boolean`

  Example usage:

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

`LineLegend.``show_text`

  Show/hide mapping names in the legend.

  Type: | `boolean`

  Example usage:

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

`LineLegend.``text_color`

  Color of legend text.

  Type: | `Color`

  Example usage:

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

## Contours

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

### 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
      exdir = tp.session.tecplot_examples_directory()
      datafile = path.join(exdir,'2D','polarplot.plt')
      dataset = tp.data.load_tecplot(datafile)
      plot = dataset.frame.plot()
      plot.show_contour = True

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

      # save image to file
      tp.export.save_png('polarplot_magma.png', 600)

  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.

  Type: | `ContourColorCutoff`

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

`ContourGroup.``colormap_filter`

  `ContourColormapFilter` object controlling colormap style properties.

  Type: | `ContourColormapFilter`

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

`ContourGroup.``colormap_name`

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

  Type: | `string`

  Example:

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

`ContourGroup.``default_num_levels`

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

  Type: | `integer`

  Example:

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

`ContourGroup.``labels`

  `ContourLabels` object controlling contour line labels.

  Type: | `ContourLabels`

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

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

`ContourGroup.``legend`

  `ContourLegend` associated with this `ContourGroup`.

  Type: | `ContourLegend`

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

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

`ContourGroup.``levels`

  `ContourLevels` holding the list of contour levels.

  Type: | `ContourLevels`

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

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

`ContourGroup.``lines`

  `ContourLines` object controlling contour line style.

  Type: | `ContourLines`

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

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

`ContourGroup.``variable`

  The `Variable` being contoured.

  The variable must belong to the `Dataset` attached to 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,'3D','pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

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

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

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

  Attributes

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

`ContourColorCutoff.``include_max`

  Use the maximum cutoff value.

  Type: | `boolean`

  Thie example turns off the maximum cutoff:

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

`ContourColorCutoff.``include_min`

  Use the minimum cutoff value.

  Type: | `boolean`

  Example usage:

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

`ContourColorCutoff.``inverted`

  Cuts values outside the range instead of inside.

  Type: | `bool`

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

`ContourColorCutoff.``max`

  The maximum cutoff value.

  Type: | `float` or `None`

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

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

`ContourColorCutoff.``min`

  The minimum cutoff value.

  Type: | `float` or `None`

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

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

### 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
      exdir = tp.session.tecplot_examples_directory()
      datafile = path.join(exdir,'2D','MultiPoly2D.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')

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

      # save image to file
      tp.export.save_png('poly2d_filtered.png', 600)

  Attributes

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

  Methods

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

`ContourColormapFilter.``continuous_max`

  Upper limit for continuous colormap flooding.

  Type: | `float`

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

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

`ContourColormapFilter.``continuous_min`

  Lower limit for continuous colormap flooding.

  Type: | `float`

  Example usage:

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

`ContourColormapFilter.``distribution`

  Rendering style of the colormap.

  Type: | `ColorMapDistribution`

  Possible values:

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

  Example:

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

`ContourColormapFilter.``fast_continuous_flood`

  Use a fast approximation to continuously flood the colormap.

  Type: | `bool`

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

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

`ContourColormapFilter.``num_cycles`

  Number of cycles to repeat the colormap.

  Type: | `integer`

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

`ContourColormapFilter.``override`(index)

  Returns a `ContourColormapOverride` object by index.

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

  Example:

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

`ContourColormapFilter.``reversed`

  Reverse the colormap.

  Type: | `bool`

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

`ContourColormapFilter.``show_overrides`

  Enable the colormap overrides in this contour group.

  Type: | `boolean`

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

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

`ContourColormapFilter.``zebra_shade`

  Returns a `ContourColormapZebraShade` filtering object.

  Type: | `ContourColormapZebraShade`

  Example usage:

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

### ContourColormapOverride

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

  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
      exdir = tp.session.tecplot_examples_directory()
      datafile = path.join(exdir,'2D','cstream.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('v3')
      contour.colormap_name = 'Sequential - Yellow/Green/Blue'
      contour.levels.reset(10)

      # 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 = 0
      contour_override.end_level = 4

      # save image to file
      tp.export.save_png('cstream_contours.png', 600)

  Attributes

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

`ContourColormapOverride.``color`

  Color which will override the colormap.

  Type: | `Color`

  Example usage:

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

`ContourColormapOverride.``end_level`

  Last level to override.

  Type: | `integer`

  Example usage:

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

`ContourColormapOverride.``show`

  Include this colormap override when filter is shown.

  Type: | `boolean`

  Example usage:

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

`ContourColormapOverride.``start_level`

  First level to override.

  Type: | `integer`

  Example usage:

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

### 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,'3D','pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

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

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

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

  Attributes

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

`ContourColormapZebraShade.``color`

  Color of the zebra shading.

  Type: | `Color`

  Example usage:

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

`ContourColormapZebraShade.``show`

  Show zebra shading in this `ContourGroup`.

  Type: | `boolean`

  Example usage:

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

`ContourColormapZebraShade.``transparent`

  Set the the zebra bands to be transparent.

  Type: | `boolean`

  Example usage:

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

### 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,'3D','pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

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

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

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

  Attributes

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

`ContourLabels.``auto_align`

  Automatically align the labels with the contour lines.

  Type: | `bool`

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

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

`ContourLabels.``auto_generate`

  Automatically generate labels along contour lines.

  Type: | `bool`

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

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

`ContourLabels.``background_color`

  Background fill color behind the text labels.

  Type: | `Color`

  The `filled` attribute must be set 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

`ContourLabels.``color`

  Text color of the labels.

  Type: | `Color`

  Example:

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

`ContourLabels.``filled`

  Fill the background area behind the text labels.

  Type: | `boolean`

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

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

`ContourLabels.``font`

  `text.Font` used to show the labels.

  Type: | `text.Font`

  Example:

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

`ContourLabels.``format`

  Number formatting for contour labels.

  Type: | `LabelFormat`

  Example usage:

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

`ContourLabels.``label_by_level`

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

  Type: | `bool`

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

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

`ContourLabels.``margin`

  Spacing around the text and the filled background area.

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

  Contour numbers start from one when drawn. Example usage:

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

`ContourLabels.``show`

  Show the contour line labels.

  Type: | `bool`

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

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

`ContourLabels.``spacing`

  Spacing between labels along the contour lines.

  Type: | `float`

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

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

`ContourLabels.``step`

  Number of contour lines from one label to the next.

  Type: | `int`

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

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

### ContourLevels

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

  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,'2D','3element.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('3element_adjusted_levels.png', 600)

  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 (`integer`) – Number of levels. (default: 10)

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

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

`ContourLevels.``reset_levels`(*values)

  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 (`integer`) – Approximate number of levels to create. (default: 10)

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

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

### ContourLines

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

  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,'3D','pyramid.plt')
      dataset = tp.data.load_tecplot(datafile)

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

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

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

  Attributes

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

`ContourLines.``mode`

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

  Type: | `ContourLineMode`

  Possible values:

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

`ContourLines.``pattern_length`

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

  Type: | `float`

  The length is in percentage of the frame height:

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

`ContourLines.``step`

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

  Type: | `int`

  Example:

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

## Isosurface

- IsosurfaceGroup
- IsosurfaceContour
- IsosurfaceEffects
- IsosurfaceMesh
- IsosurfaceShade

### IsosurfaceGroup

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

  Isosurfaces style control.

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

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, '3D_Volume', '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)  # type: IsosurfaceGroup

      iso.isosurface_selection = IsoSurfaceSelection.ThreeSpecificValues
      iso.isosurface_values = (135.674706817, 264.930212259, 394.185717702)

      iso.shade.use_lighting_effect = True
      iso.effects.lighting_effect = LightingEffect.Paneled
      iso.contour.show = True

      iso.mesh.show = True
      iso.mesh.line_thickness = .1
      iso.mesh.color = Color.Black
      iso.effects.use_translucency = True
      iso.effects.surface_translucency = 50

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

  Attributes

  `contour` | Contour attributes for this isosurface group.
  `definition_contour_group` | Contour group from which isosurfaces are based.
  `definition_contour_group_index` | Contour group index from which isosurfaces are based.
  `effects` | Settings for isosurface effects.
  `isosurface_selection` | Select where to draw isosurfaces.
  `isosurface_values` | Query/Assign up to 3 values at which to draw isosurfaces.
  `mesh` | Mesh attributes for this isosurface group.
  `obey_source_zone_blanking` | Obey source zone blanking.
  `shade` | Shade attributes for this isosurface group.
  `show` | Show isosurfaces for this isosurface group.
  `surface_generation_method` | Determines how the surface is generated.

`IsosurfaceGroup.``contour`

  Contour attributes for this isosurface group.

  Type: | `IsosurfaceContour`

  Example usage:

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

`IsosurfaceGroup.``definition_contour_group`

  Contour group from which isosurfaces are based.

  Type: | `ContourGroup`

  Example usage:

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

`IsosurfaceGroup.``definition_contour_group_index`

  Contour group index from which isosurfaces are based.

  Type: | `Index`

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

  Example usage:

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

`IsosurfaceGroup.``effects`

  Settings for isosurface effects.

  Type: | `IsosurfaceEffects`

  Example usage:

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

`IsosurfaceGroup.``isosurface_selection`

  Select where to draw isosurfaces.

  Type: | `IsoSurfaceSelection`

  Iso-surfaces may be drawn at:
    - Contour group levels
    - At specified value(s) - Specify up to three values of the contour variable at which to draw isosurfaces.
  To draw isosurfaces at contour group lines:
    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)

`IsosurfaceGroup.``isosurface_values`

  Query/Assign up to 3 values at which to draw isosurfaces.

  Type: | 1, 2, or 3-`tuple` of `floats`, or 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(plot.isosurface(0).isosurface_values)
      3

`IsosurfaceGroup.``mesh`

  Mesh attributes for this isosurface group.

  Type: | `IsosurfaceMesh`

  Example usage:

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

`IsosurfaceGroup.``obey_source_zone_blanking`

  Obey source zone blanking.

  Type: | `boolean` 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

`IsosurfaceGroup.``shade`

  Shade attributes for this isosurface group.

  Type: | `IsosurfaceShade`

  Example usage:

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

`IsosurfaceGroup.``show`

  Show isosurfaces for this isosurface group.

  Type: | `bool`

  Example usage:

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

`IsosurfaceGroup.``surface_generation_method`

  Determines how the surface is generated.

  Type: | `SurfaceGenerationMethod`

  May be one of:

  - `SurfaceGenerationMethod.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. Preferred when the source zone is FE-Brink or IJK-Ordered and the surface is aligned with the source cells.

  Example usage:

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

### IsosurfaceContour

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

  Contour attributes of the isosurface group.

      import tecplot as tp
      from os import path
      from tecplot.plot import IsosurfaceGroup
      from tecplot.constant import ContourType

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, '3D_Volume', '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.use_lighting_effect = True

      iso.contour.show = True
      iso.contour.contour_type = ContourType.AverageCell
      iso.contour.flood_contour_group_index = 5  # T(K)

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

  Attributes

  `contour_type` | Contour display type.
  `flood_contour_group` | Contour group to use for flooding.
  `flood_contour_group_index` | The `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 `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.

  Type: | `ContourType`

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

  Example usage:

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

`IsosurfaceContour.``flood_contour_group`

  Contour group to use for flooding.

  Type: | `ContourGroup`

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

  Example usage:

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

`IsosurfaceContour.``flood_contour_group_index`

  The `Index` of the `ContourGroup` to use for flooding.

  Type: | `Index` (zero-based index)

  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

`IsosurfaceContour.``line_color`

  `Color` of contour lines.

  Type: | `Color` or `ContourGroup`

  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

`IsosurfaceContour.``line_contour_group`

  The contour group to use for contour lines.

  Type: | `ContourGroup`

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

  Example usage:

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

`IsosurfaceContour.``line_contour_group_index`

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

  Type: | `integer` (zero-based index)

  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

`IsosurfaceContour.``line_thickness`

  Contour line thickness as a percentage of frame width.

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

  Type: | `float`

  Example usage:

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

`IsosurfaceContour.``show`

  Show contours on isosurfaces.

  Type: | `boolean`

  Example usage:

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

`IsosurfaceContour.``use_lighting_effect`

  Enable lighting effect.

  Type: | `Boolean`

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

  Note

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

  Example usage:

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

### IsosurfaceEffects

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

  Effects of the isosurface group.

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

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tp.data.load_tecplot(datafile)

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

      plot.show_isosurfaces = True
      iso = plot.isosurface(0)
      iso.shade.use_lighting_effect = True

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

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

  Attributes

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

`IsosurfaceEffects.``lighting_effect`

  Lighting effect.

  Type: | `LightingEffect`

  Isosurface lighting effects must be enabled by setting `IsosurfaceShade.use_lighting_effect` 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

`IsosurfaceEffects.``surface_translucency`

  Surface translucency of the isosurface group.

  Type: | `integer`

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

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

  Example usage:

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

`IsosurfaceEffects.``use_translucency`

  Enable surface translucency for this isosurface group.

  Type: | `boolean`

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

  Example usage:

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

### IsosurfaceMesh

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

  Mesh attributes of the isosurface group.

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

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tp.data.load_tecplot(datafile)

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

      iso = plot.isosurface(0)
      iso.shade.use_lighting_effect = True
      iso.contour.show = True
      iso.mesh.show = True
      iso.mesh.color = Color.Black
      iso.mesh.line_thickness = 0.4

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

  Attributes

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

`IsosurfaceMesh.``color`

  Isosurface mesh color.

  Type: | `Color` or `ContourGroup`

  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

`IsosurfaceMesh.``line_thickness`

  Isosurface mesh line thickness.

  Type: | `float`

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

  Example usage:

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

`IsosurfaceMesh.``show`

  Display the mesh on isosurfaces.

  Type: | `boolean`

  Example usage:

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

### IsosurfaceShade

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

  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, '3D_Volume', '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)

  Attributes

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

`IsosurfaceShade.``color`

  Shade color.

  Type: | `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

`IsosurfaceShade.``show`

  Show shade attributes.

  Type: | `boolean`

  Example usage:

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

`IsosurfaceShade.``use_lighting_effect`

  Enable lighting effect.

  Type: | `Boolean`

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

  Note

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

  Example usage:

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

## 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 SliceSurface, ContourType

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tp.data.load_tecplot(datafile)

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

      vector_3D = tp.plot.Vector3D(plot)
      vector_3D.u_variable_index = 3  # U(M/S)
      vector_3D.v_variable_index = 4  # V(M/S)
      vector_3D.w_variable_index = 5  # 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

      slice_0.show_start_and_end_slices = True

      # Show an arbitrary slice
      slice_0.orientation = SliceSurface.Arbitrary
      slice_0.arbitrary_normal = (1, -.25, 0)
      slice_0.origin = (.19, .5, .25)  # AKA Primary Slice Position

      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.vector.show = True

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

      tp.export.save_png('slice_example.png', 300)

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

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

  Attributes

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

  Methods

  `set_arbitrary_from_points`(p1, p2, p3) | Set an arbitrary slice from 3 points.

`SliceGroup.``arbitrary_normal`

  Normal for arbitrary slices.

  Type: | 3-`tuple` of floats

  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

`SliceGroup.``contour`

  Contour attributes for the slice group.

  Type: | `SliceContour`

  Example usage:

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

`SliceGroup.``edge`

  Edge attributes for this slice group.

  Type: | `SliceEdge`

  Example usage:

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

`SliceGroup.``effects`

  Effects attributes for this slice group.

  Type: | `SliceEffects`

  Example usage:

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

`SliceGroup.``end_position`

  Position of the end slice.

  `SliceGroup.show_start_and_end_slices` must be `True` to show the end slice.

  Type: | 3-`tuple` of floats

  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

`SliceGroup.``mesh`

  Mesh attributes for this slice group.

  Type: | `SliceMesh`

  Example usage:

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

`SliceGroup.``num_intermediate_slices`

  Number of intermediate slicing planes.

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

  Type: | `integer`

  Example usage:

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

`SliceGroup.``obey_source_zone_blanking`

  Obey source zone blanking.

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

  Type: | `boolean`

  Example usage:

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

`SliceGroup.``orientation`

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

  Type: | `SliceSurface`

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

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

  Example usage:

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

`SliceGroup.``origin`

  Origin of the slice.

  Type: | 3-`tuple` of floats

  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

`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.

  Type: | `SliceShade`

  Example usage:

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

`SliceGroup.``show`

  Show slices for this slice group.

  Type: | `bool`

  Example usage:

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

`SliceGroup.``show_intermediate_slices`

  Show intermediate slices.

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

  Example usage:

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

`SliceGroup.``show_primary_slice`

  Include the primary slice (first slice placed) in the Plot.

  Type: | `bool`

  Example usage:

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

`SliceGroup.``show_start_and_end_slices`

  Include start and end slices.

  Type: | `boolean`

  Example usage:

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

`SliceGroup.``slice_source`

  Zones to slice through.

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

  Type: | `SliceSource`

  Example usage:

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

`SliceGroup.``start_position`

  Position of the start slice.

  `SliceGroup.show_start_and_end_slices` must be `True` to show the start slice.

  Type: | 3-`tuple` of floats

  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

`SliceGroup.``surface_generation_method`

  Determines how the surface is generated.

  Type: | `SurfaceGenerationMethod`

  May be one of:

  - `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. Preferred when the source zone is FE-Brink or IJK-Ordered and the surface is aligned with the source cells.

  Example usage:

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

`SliceGroup.``vector`

  Vector attributes for this slice group.

  Type: | `SliceVector`

  Example usage:

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

### SliceContour

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

  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, '3D_Volume', '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.contour.contour_type = ContourType.Overlay  # AKA "Both lines and flood"

      slice_0.show_start_and_end_slices = True

      # Show an arbitrary slice
      slice_0.orientation = SliceSurface.Arbitrary
      slice_0.arbitrary_normal = (1, -.25, 0)
      slice_0.origin = (.19, .5, .25)  # AKA Primary Slice Position

      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

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

  Attributes

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

`SliceContour.``contour_type`

  Contour type for the slice contours.

  Type: | `ContourType`

  Example usage:

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

`SliceContour.``flood_contour_group`

  Contour group to use for flooding.

  Type: | `ContourGroup`

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

  Example usage:

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

`SliceContour.``flood_contour_group_index`

  The `Index` of the `ContourGroup` to use for flooding.

  Type: | `Index` (zero-based index)

  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

`SliceContour.``line_color`

  `Color` of contour lines.

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

  Type: | `Color`

  Example usage:

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

`SliceContour.``line_contour_group`

  Contour group to use for contour lines.

  Type: | `ContourGroup`

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

  Example usage:

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

`SliceContour.``line_contour_group_index`

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

  Type: | `integer` (zero-based index)

  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

`SliceContour.``line_thickness`

  Contour line thickness as a percentage of frame width.

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

  Type: | `float`

  Example usage:

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

`SliceContour.``show`

  Show contours on the slice.

  Type: | `bool`

  Example usage:

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

`SliceContour.``use_lighting_effect`

  Enable lighting effect.

  Note

  Setting `SliceContour.use_lighting_effect` will also set the same value 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`.

  Type: | `boolean`

  Example usage:

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

### SliceEdge

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

  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, '3D_Volume', 'ductflow.plt')
      dataset = tp.data.load_tecplot(datafile)

      plot = tp.active_frame().plot()

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

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

      slice_0.edge.show = True
      slice_0.edge.line_thickness = 0.8

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

  Attributes

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

`SliceEdge.``color`

  Edge color.

  Type: | `Color`

  Example usage:

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

`SliceEdge.``edge_type`

  Edge type.

  Type: | `EdgeType`

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

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

  Example usage:

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

`SliceEdge.``line_thickness`

  Edge line thickness as a percentage of frame width.

  Type: | `float`

  Example usage:

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

`SliceEdge.``show`

  Show edges.

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

  Type: | `boolean`

  Example usage:

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

### 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, '3D_Volume', 'ductflow.plt')
      dataset = tp.data.load_tecplot(datafile)

      plot = tp.active_frame().plot()

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

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

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

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

  Attributes

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

`SliceEffects.``lighting_effect`

  Lighting effect.

  Type: | `LightingEffect`

  Slice lighting effects must be enabled by setting `SliceContour.use_lighting_effect` 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

`SliceEffects.``surface_translucency`

  Surface translucency of the slice group.

  Type: | `integer`

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

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

  Example usage:

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

`SliceEffects.``use_translucency`

  Enable surface translucency for this slice group.

  Type: | `boolean`

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

  Example usage:

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

### SliceMesh

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

  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, '3D_Volume', 'ductflow.plt')
      dataset = tp.data.load_tecplot(datafile)
      plot = tp.active_frame().plot()
      plot.show_slices = True
      plot.contour(0).variable = dataset.variable('U(M/S)')

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

      tp.export.save_png('slice_mesh.png', 300)

  Attributes

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

`SliceMesh.``color`

  Slice mesh line `Color` or `ContourGroup`.

  Type: | `Color` or `ContourGroup`

  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

`SliceMesh.``line_thickness`

  Mesh line thickness.

  Type: | `float`

  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

`SliceMesh.``show`

  Show mesh lines.

  Type: | `boolean`

  Example usage:

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

### 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, '3D', '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', 300)

  Attributes

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

`SliceShade.``color`

  Shade color.

  Type: | `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

`SliceShade.``show`

  Show shade attributes.

  Type: | `boolean`

  Example usage:

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

`SliceShade.``use_lighting_effect`

  Use lighting effect.

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

  Type: | `Boolean`

  Example usage:

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

### SliceVector

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

  Vector attributes of the slice group.

      from os import path
      import tecplot as tp

      examples_dir = tp.session.tecplot_examples_directory()
      datafile = path.join(examples_dir, '3D', 'Arrow.plt')
      dataset = tp.data.load_tecplot(datafile)

      plot = tp.active_frame().plot()
      plot.contour(0).variable_index = dataset.variable('Z').index

      plot.show_slices = True

      # Vector variables must be assigned before displaying
      vector_3D = tp.plot.Vector3D(plot)
      vector_3D.u_variable_index = 1
      vector_3D.v_variable_index = 2
      vector_3D.w_variable_index = 3

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

      tp.export.save_png('slice_vector.png', 300)

  Attributes

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

`SliceVector.``arrowhead_style`

  Arrowhead style of slice vectors.

  Type: | `ArrowheadStyle`

  Example usage:

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

`SliceVector.``color`

  Set slice vector color.

  Type: | `Color`

  Example usage:

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

`SliceVector.``is_tangent`

  Use tangent vectors for slices.

  Type: | `boolean`

  Example usage:

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

`SliceVector.``line_thickness`

  Vector line thickness as a percentage of the frame height.

  Type: | `float`

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

  Example usage:

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

`SliceVector.``show`

  Show vectors on slices.

  Type: | `boolean`

  Example usage:

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

`SliceVector.``vector_type`

  Type of vector for slices in this slice group.

  Type: | `VectorType`

  Example usage:

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

## Streamtraces

- Streamtraces
- 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 *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', '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 = 3
      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)

      tecplot.export.save_png('streamtrace_example.png', 600)

  Attributes

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

  Methods

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

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

  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`.

  Raises: | `TecplotSystemError` – The streamtraces could not be added.

      import tecplot
      from tecplot.constant import *
      import os

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', '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
      plot.streamtraces.add([3, 4, 5], Streamtrace.VolumeLine)
      tecplot.export.save_png('streamtrace_add_xyz.png', 600)

`Streamtraces.``add_on_zone_surface`(zones, stream_type, 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: | zones (`Set displays` of `integers`) – Set of Zones on which to add streamtraces. stream_type – (`Streamtrace`): Type of streamtraces to add. num_seed_points – (`integer`, optional): Number of seed points for distributing along a rake or on defined surfaces. direction – (`StreamDir`, optional): Direction of propagation of the streamtraces being added.

      import tecplot
      from tecplot.constant import *
      import os

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '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 = 3
      plot.vector.v_variable_index = 4
      plot.vector.w_variable_index = 5
      plot.show_streamtraces = True

      plot.streamtraces.add_on_zone_surface(
          zones=[1],  # Add streamtraces on 2nd zone only
          stream_type=Streamtrace.VolumeLine)
      tecplot.export.save_png('streamtrace_add_on_zone_surface.png', 600)

`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 – (`integer`, optional): Number of seed points for distributing along a rake or on defined surfaces. direction – (`StreamDir`, optional): Direction of propagation of the streamtraces being added.

      import tecplot
      from tecplot.constant import *
      import os

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', '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)

`Streamtraces.``are_active`

  Determine if there are active streamtraces in the current plot type.

  Note

  This property is read-only.

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

  Example usage:

      >>> streamtraces_are_active = plot.streamtraces.are_active

`Streamtraces.``arrowhead_size`

  Arrowhead size as a percentage of frame height.

  Type: | `float`

  Recommend values are one of 1, 3, 5, 8, or 12.

  Example usage:

      >>> plot.streamtraces.show_arrows = True
      >>> plot.streamtraces.arrowhead_size = 1.0

`Streamtraces.``arrowhead_spacing`

  Distance between arrowheads in terms of Y-frame units.

  Type: | `float`

  For example, a value of 10 will space arrowheads approximately ten percent of the frame height apart from each other along each streamline.

  Example usage:

      >>> plot.streamtraces.show_arrows = True
      >>> plot.streamtraces.arrowhead_spacing = 10

`Streamtraces.``color`

  Color of streamtraces line (not rods or ribbons).

  Type: | `Color` or `ContourGroup`

  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

`Streamtraces.``count`

  Query the number of active streamtraces for the current plot type.

  Returns: | `integer`

  Note

  This property is read-only.

      >>> num_active_streamtraces = plot.streamtraces.count

`Streamtraces.``dash_skip`

  Number of time deltas used for the “off” sections of the streamlines.

  Note

  The `dash_skip` value must be greater than 0.

  Type: | `integer`

  Example usage:

      >>> plot.streamtraces.dash_skip = 2

`Streamtraces.``delete_all`()

  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 – (`integer`): 0-based start streamtrace number to delete. range_end – (`integer`): 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.``has_terminating_line`

  Determine if the streamtraces have the terminating line.

  Note

  This property is read-only.

  Returns: | `boolean`. `True` if the streamtraces have the terminating line, `False` otherwise.

  Example usage:

      >>> has_terminating_line = plot.streamtraces.has_terminating_line

`Streamtraces.``line_thickness`

  Streamtrace line thickness.

  Type: | `float`

  Line thickness as a percentage of the frame height for 2D lines, or a percentage of the median axis length for 3D surface lines and volume lines.

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

  Example usage:

      >>> plot.streamtraces.line_thickness = 1.1

`Streamtraces.``marker_color`

  `Color` of the streamline markers.

  Type: | `Color` or `ContourGroup`

  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

`Streamtraces.``marker_size`

  Size of streamline markers.

  Type: | `float`

  Example usage:

      >>> plot.streamtraces.marker_size = 1.1

`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: `SymbolType`

  Example usage:

      >>> from tecplot.constant import SymbolType
      >>> streamtrace = plot.streamtraces
      >>> streamtraces.marker_symbol_type = SymbolType.Text
      >>> symbol = streamtraces.marker_symbol(SymbolType.Text)
      >>> symbol.text = 'a'

`Streamtraces.``marker_symbol_type`

  The `SymbolType` to use for stream markers.

  Type: | `SymbolType`

  This sets the active symbol type for streamtrace markers. Use `Streamtraces.marker_symbol` to access the symbol:

      >>> from tecplot.constant import SymbolType
      >>> streamtrace = plot.streamtraces
      >>> streamtraces.marker_symbol_type = SymbolType.Text
      >>> symbol = streamtraces.marker_symbol(SymbolType.Text)
      >>> symbol.text = 'a'

`Streamtraces.``max_steps`

  Maximum number of steps before the streamtrace is terminated.

  Type: | `integer`

  `max_steps` prevents streamtraces from spinning forever in a vortex, or from wandering into a region where the vector components are very small, very random, or both.

  If a small `step_size` is selected, the `max_steps` should be a large value.

  Example usage:

      >>> plot.streamtraces.max_steps = 5000

`Streamtraces.``min_step_size`

  Smallest step size to use as a percentage of cell distance.

  A typical minimum step size value is 0.00001, which is the default.

  Type: | `float`

  Warning

  Setting this too small results in integration problems. Setting this greater than or equal to the `step_size` results in a constant step size.

  Example usage:

      >>> plot.streamtraces.min_step_size = .0002

`Streamtraces.``obey_source_zone_blanking`

  Obey source zone blanking.

  Type: | `boolean`

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

  Example usage:

      >>> plot.streamtraces.obey_source_zone_blanking = True

`Streamtraces.``position`(stream_number)

  Query the starting position of a streamtrace.

  Parameters: | stream_number – (`integer`): 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.

  Type: | `StreamtraceRodRibbon`

  Example usage:

      >>> streamtraces.rod_ribbon.mesh.show = True

`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.

  Type: | `boolean`

  Example usage:

      >>> plot.streamtraces.show_arrows = True

`Streamtraces.``show_dashes`

  Display streamtrace dashes.

  The lengths of the dashes and the spaces between the dashes are controlled by the value of `StreamtraceTiming.delta`. Set the `Streamtraces.dash_skip` attribute to control the number of time deltas used for the “off” sections of the streamtraces.

  Type: | `boolean`

  Example usage:

      >>> plot.streamtraces.show_dashes = True

`Streamtraces.``show_markers`

  Display streamtrace markers.

  Type: | `boolean`

  Stream markers are only available for surface and volume type streamlines.

  You may also specify the `size`, `color`, and `shape` of the markers.

  Example usage:

      >>> plot.streamtraces.show_markers = True

`Streamtraces.``show_paths`

  Draw streamtrace paths (lines, ribbons, or rods).

  Type: | `boolean`

  A streamtrace path may be a line, ribbon or rod.

  Example usage:

      >>> plot.streamtraces.show_paths = True

  See also `Streamtraces.show_markers`

`Streamtraces.``step_size`

  Maximum fraction of the distance across a cell that a streamtrace moves in one step.

  Type: | `float`

  The step size is the maximum fraction of the distance across a cell that a streamtrace moves in one step. A streamtrace adjusts its step size between `step_size` 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

`Streamtraces.``streamtrace_type`(stream_number)

  Query the type of a streamtrace by streamtrace number.

  Parameters: | stream_number – (`integer`): 0-based stream number to query.
  Returns: | `Streamtrace`

  Get the type of streamtrace 3. Note 0-based stream number:

      >>> streamtrace_type = plot.streamtraces.streamtrace_type(2) # streamtrace 3
      >>> streamtrace_type
      <Streamtrace.VolumeLine: 2>

`Streamtraces.``termination_line`

  Streamtraces termination line attributes.

  A streamtrace termination line is a polyline that terminates any streamtraces that cross it. The termination line is useful for stopping streamtraces before they spiral or stall.

  Type: | `StreamtraceTerminationLine`

  Example usage:

      >>> term_line = plot.streamtraces.termination_line
      >>> term_line.show = True

`Streamtraces.``timing`

  Streamtraces timing attributes.

  Type: | `StreamtraceTiming`

  Example usage:

      >>> timing = plot.streamtraces.timing
      >>> timing.start = 0.01

### StreamtraceRodRibbon

class `tecplot.plot.``StreamtraceRodRibbon`(streamtrace)

  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 *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

      plot = frame.plot()  # type: Cartesian3DFieldPlot
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.contour(0).variable = dataset.variable(3)
      plot.contour(0).levels.reset_to_nice()

      plot.show_mesh = True
      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 = .05
      plot.streamtraces.rod_ribbon.contour.show = True

      plot.streamtraces.add_rake(start_position=(0, 0, 0),
                                 end_position=(.05, .05, .175),
                                 stream_type=Streamtrace.VolumeRibbon,
                                 num_seed_points=26)

      tecplot.export.save_png('streamtrace_ribbon.png', 600)

  Attributes

  `contour` | Streamtraces rod/ribbon contour attributes.
  `effects` | Streamtraces rod/ribbon effects.
  `mesh` | Streamtraces rod/ribbon mesh attributes.
  `num_rod_points` | Numer of rod points.
  `shade` | Streamtraces rod/ribbon color and lighting attributes.
  `width` | Rod/ribbon width in grid units.

`StreamtraceRodRibbon.``contour`

  Streamtraces rod/ribbon contour attributes.

  Type: | `StreamtraceRodRibbonContour`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.contour.show = True

`StreamtraceRodRibbon.``effects`

  Streamtraces rod/ribbon effects.

  Type: | `StreamtraceRodRibbonEffects`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.effects.use_translucency = True

`StreamtraceRodRibbon.``mesh`

  Streamtraces rod/ribbon mesh attributes.

  Type: | `StreamtraceRodRibbonMesh`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.mesh.show = True

`StreamtraceRodRibbon.``num_rod_points`

  Numer of rod points.

  Type: | `integer`, valid range 3-100

  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

`StreamtraceRodRibbon.``shade`

  Streamtraces rod/ribbon color and lighting attributes.

  Type: | `StreamtraceRodRibbonShade`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.shade.color = Color.Magenta

`StreamtraceRodRibbon.``width`

  Rod/ribbon width in grid units.

  Type: | `float`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.width = 0.01

### 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, '2D', 'cylinder.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_index = 3
      plot.vector.v_variable_index = 4
      plot.show_streamtraces = True

      streamtraces = plot.streamtraces
      streamtraces.show_markers = True
      timing = streamtraces.timing
      timing.anchor = 0
      timing.start = 0.01
      timing.end = 0.2
      timing.delta = 0.1

      streamtraces.add(seed_point=[1, 1], stream_type=Streamtrace.TwoDLine)

      tecplot.export.save_png('streamtrace_timing.png', 600)

  Attributes

  `anchor` | Time that a dash is guaranteed to start.
  `delta` | Time between stream markers.
  `end` | Time after which no stream markers are drawn.
  `start` | Time at which the first marker should be drawn.

  Methods

  `reset_delta`() | Reset the time delta for dashed streamtraces.

`StreamtraceTiming.``anchor`

  Time that a dash is guaranteed to start.

  Type: | `float`

  A dash is guaranteed to start at `anchor`, provided the start and end time surround the dash.

  Example usage:

      >>> plot.streamtraces.timing.anchor = 1.1

`StreamtraceTiming.``delta`

  Time between stream markers.

  Type: | `float`

  `delta` is the time interval that measures the time between stream markers. The actual distance between markers is the product of this number and the local `Vector` magnitude.

  Call `StreamtraceTiming.reset_delta()` to reset this to the default.

  Example usage:

      >>> plot.streamtraces.timing.delta = 0.1

`StreamtraceTiming.``end`

  Time after which no stream markers are drawn.

  Type: | `float`

  Example usage:

      >>> plot.streamtraces.timing.end = 3.0

`StreamtraceTiming.``reset_delta`()

  Reset the time delta for dashed streamtraces.

  The delta time is reset such that a stream dash in the vicinity of the maximum vector magnitude will have a length approximately equal to 10 percent of the frame width.

  Raises: | `TecplotSystemError` – Streamtraces time delta could not be reset.

  Example usage:

      >>> plot.streamtraces.timing.reset_delta()

`StreamtraceTiming.``start`

  Time at which the first marker should be drawn.

  Type: | `float`

  A start time of zero means that the first marker is drawn at the starting point. A start time of 2.5 means that the first stream marker is drawn 2.5 time units downstream of the starting point.

  Example usage:

      >>> plot.streamtraces.timing.start = 2.5

### StreamtraceRodRibbonContour

class `tecplot.plot.``StreamtraceRodRibbonContour`(streamtrace)

  Contour flooding display for streamtrace rod/ribbons.

      import os
      import tecplot
      from tecplot.constant import *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

      plot = frame.plot()  # type: Cartesian3DFieldPlot
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.contour(0).variable = dataset.variable(3)
      plot.contour(0).levels.reset_to_nice()

      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

      rod_ribbon = plot.streamtraces.rod_ribbon
      rod_ribbon.contour.show = True
      rod_ribbon.contour.use_lighting_effect = True

      plot.streamtraces.add(seed_point=[0, 0, 0],
                            stream_type=Streamtrace.VolumeRibbon)

      plot.streamtraces.rod_ribbon.width = .5

      tecplot.export.save_png('streamtrace_ribbon_contour.png', 600)

  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.

  Type: | `ContourGroup`

  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

`StreamtraceRodRibbonContour.``flood_contour_group_index`

  The `Index` of the `ContourGroup` to use for flooding.

  Type: | `Index` (zero-based index)

  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

`StreamtraceRodRibbonContour.``show`

  Enable or disable contour flooding display.

  Type: | `boolean`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.contour.show = True

`StreamtraceRodRibbonContour.``use_lighting_effect`

  Enable lighting effect for streamtrace rod/ribbons.

  Note

  Setting `StreamtraceRodRibbonContour.use_lighting_effect` will also set the same value 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`.

  Type: | `boolean`

  Example usage:

      >>> ribbon = plot.streamtraces.rod_ribbon
      >>> contour = ribbon.contour
      >>> contour.use_lighting_effect = True
      >>> ribbon.effects.lighting_effect = LightingEffect.Paneled

### StreamtraceRodRibbonEffects

class `tecplot.plot.``StreamtraceRodRibbonEffects`(streamtrace)

  Controls how lighting and translucency interacts with streamtrace rods and ribbons.

      import os
      import tecplot
      from tecplot.constant import *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

      plot = frame.plot()  # type: Cartesian3DFieldPlot
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.contour(0).variable = dataset.variable(3)
      plot.contour(0).levels.reset_to_nice()

      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=(0, 0, 0),
                                 end_position=(.05, .05, .175),
                                 stream_type=Streamtrace.VolumeRibbon)

      tecplot.export.save_png('streamtrace_ribbon_effects.png', 600)

  Attributes

  `lighting_effect` | Get/set the lighting algorithm used when lighting streamtrace rods and ribbons.
  `surface_translucency` | Surface translucency of the streamtraces ribbon.
  `use_translucency` | Enable surface translucency.

`StreamtraceRodRibbonEffects.``lighting_effect`
  Get/set the lighting algorithm used when lighting
    streamtrace rods and ribbons.

  Type: | `LightingEffect`

  Ribbon lighting effects must be enabled by setting `StreamtraceRodRibbonShade.use_lighting_effect` 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

`StreamtraceRodRibbonEffects.``surface_translucency`

  Surface translucency of the streamtraces ribbon.

  Type: | `integer`

  Surface translucency must be enabled by setting `StreamtraceRodRibbonEffects.use_translucency` = `True` when setting this value.

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

  Example usage:

      >>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
      >>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20

`StreamtraceRodRibbonEffects.``use_translucency`

  Enable surface translucency.

  Type: | `boolean`

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

  Example usage:

      >>> plot.streamtraces.rod_ribbon.effects.use_translucency = True
      >>> plot.streamtraces.rod_ribbon.effects.surface_translucency = 20

### StreamtraceRodRibbonMesh

class `tecplot.plot.``StreamtraceRodRibbonMesh`(streamtrace)

  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 *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', 'jetflow.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

      plot = frame.plot()  # type: Cartesian3DFieldPlot
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.show_mesh = False
      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

      ribbon = plot.streamtraces.rod_ribbon
      ribbon.width = .018
      ribbon.mesh.show = True

      plot.streamtraces.add_rake(
          start_position=(0.323, 0.103, 0.491),
          end_position=(-0.367, 0.229, 0.116),
          stream_type=Streamtrace.VolumeRibbon)

      tecplot.export.save_png('streamtrace_ribbon_mesh.png', 600)

  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

  Type: | `float`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.mesh.line_thickness = 0.2

`StreamtraceRodRibbonMesh.``show`

  Display mesh.

  Note

  The mesh color for streamtraces is determined by the line color.

  Type: | `boolean`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.mesh.show = True

### StreamtraceRodRibbonShade

class `tecplot.plot.``StreamtraceRodRibbonShade`(streamtrace)

  Color and lighting display for rod/ribbons.

      import os
      import tecplot
      from tecplot.constant import *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', 'ductflow.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

      plot = frame.plot()  # type: Cartesian3DFieldPlot
      plot.fieldmap(0).surfaces.surfaces_to_plot = SurfacesToPlot.BoundaryFaces
      plot.contour(0).variable = dataset.variable(3)
      plot.contour(0).levels.reset_to_nice()

      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.show_paths = True

      plot.streamtraces.rod_ribbon.shade.show = True
      plot.streamtraces.rod_ribbon.shade.color = Color.Blue
      plot.streamtraces.rod_ribbon.shade.use_lighting_effect = False

      plot.streamtraces.add_rake(start_position=(0, 0, 0),
                                 end_position=(.05, .05, .175),
                                 stream_type=Streamtrace.VolumeRibbon)

      tecplot.export.save_png('streamtrace_ribbon_shade.png', 600)

  Attributes

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

`StreamtraceRodRibbonShade.``color`

  Shade color.

  Type: | `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

`StreamtraceRodRibbonShade.``show`

  Show shade attributes.

  Type: | `boolean`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.shade.show = True

`StreamtraceRodRibbonShade.``use_lighting_effect`

  Use lighting effect.

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

  Type: | `Boolean`

  Example usage:

      >>> plot.streamtraces.rod_ribbon.shade.use_lighting_effect = True
      >>> plot.streamtraces.rod_ribbon.effects.lighting_effect = LightingEffect.Paneled

### StreamtraceTerminationLine

class `tecplot.plot.``StreamtraceTerminationLine`(streamtrace)

  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 os
      import tecplot
      from tecplot.constant import *
      from tecplot.plot import Cartesian3DFieldPlot

      examples_dir = tecplot.session.tecplot_examples_directory()
      datafile = os.path.join(examples_dir, '3D_Volume', 'eddy.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

      plot = frame.plot()  # type: Cartesian3DFieldPlot
      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.set_termination_line([(18.2293706294, 50.5561212121),
                                      (51.7714125874, -19.9307785548)])

      term_line = streamtraces.termination_line
      term_line.is_active = True
      term_line.show = True
      term_line.color = Color.Red
      term_line.line_pattern = LinePattern.Dashed
      term_line.pattern_length = 3
      term_line.line_thickness = .5

      plot.streamtraces.add_rake(
          start_position=(45.49, 15.32, 59.1),
          end_position=(48.89, 53.2, 47.6),
          stream_type=Streamtrace.VolumeLine)

      tecplot.export.save_png('streamtrace_term_line.png', 600)

  Attributes

  `color` | Color of the termination line.
  `is_active` | Activate/disable the streamtrace termination line.
  `line_pattern` | Pattern of the terminating line.
  `line_thickness` | Thickness of the termination line as a percentage of frame height.
  `pattern_length` | Length of the pattern as a percentage of frame height.
  `show` | Display the termination line.

`StreamtraceTerminationLine.``color`

  Color of the termination line.

  Type: | `Color`

  Example usage:

      >>> plot.streamtraces.termination_line.color = Color.Red

`StreamtraceTerminationLine.``is_active`

  Activate/disable the streamtrace termination line.

  Type: | `boolean`

  Set to `True` to activate the termination line and terminate any streamtraces that cross it. Set 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.is_active = True

`StreamtraceTerminationLine.``line_pattern`

  Pattern of the terminating line.

  Type: | `LinePattern`

  Example usage:

      >>> plot.streamtraces.termination_line.line_pattern = LinePattern.Dotted

`StreamtraceTerminationLine.``line_thickness`

  Thickness of the termination line as a percentage of frame height.

  Type: | `float`

  Example usage:

      >>> plot.streamtraces.termination_line.line_thickness = 0.1

`StreamtraceTerminationLine.``pattern_length`

  Length of the pattern as a percentage of frame height.

  Type: | `float`

  Example usage:

      >>> plot.streamtraces.termination_line.pattern_length = 2

`StreamtraceTerminationLine.``show`

  Display the termination line.

  Type: | `boolean`

  Set to `True` to display the termination line. Set to `False` and redraw the plot to display terminated streamlines (if `is_active` is set to `True`), but not the termination line itself.

  Note

  To display terminated streamtraces, `is_active` must be set to `True`.

  Example usage:

      >>> plot.streamtraces.termination_line.show = True

## Text

- TextBox
- Font
- LabelFormat

### 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.

  Type: | `constant.TextBox`

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.box_type = constant.TextBox.None_

`TextBox.``color`

  Color of the box surrounding the text.

  Type: | `Color`

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.color = Color.Blue

`TextBox.``fill_color`

  Fill color of the box surrounding the text.

  Type: | `Color`

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.fill_color = Color.Blue

`TextBox.``line_thickness`

  Line thickness of the box surrounding the text.

  Type: | `float`

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.line_thickness = 0.2

`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.

  Type: | `float`

  Example usage:

      >>> plot = frame.plot()
      >>> plot.legend.box.margin = 0.3

### 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

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

  Attributes

  `bold` | Use the bold version of the current typeface.
  `italic` | Use the italic version of the current typeface.
  `size` | Height of the font.
  `size_units` | Units used by the size attribute.
  `typeface` | Specific font (or typeface) to use for text.

`Font.``bold`

  Use the bold version of the current typeface.

  Type: | `boolean`

  Example:

      >>> axis.title.font.bold = True

`Font.``italic`

  Use the italic version of the current typeface.

  Type: | `boolean`

  Example:

      >>> axis.title.font.italic = True

`Font.``size`

  Height of the font.

  Type: | `float` in units of `Font.size_units`

  Example usage:

      >>> axis.title.font.size = 10

`Font.``size_units`

  Units used by the size attribute.

  Type: | `Units`

  Possible values: `Units.Point`, `Units.Frame` (percentage of frame height). This example sets the axis title to 10% of the frame height:

      >>> from tecplot.constant import Units
      >>> axis.title.font.size_units = Units.Frame
      >>> axis.title.font.size = 10

`Font.``typeface`

  Specific font (or typeface) to use for text.

  Type: | `string`

  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'

### 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)

  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.

  Type: | `Index` (zero-based)

  Example usage:

      >>> axis.tick_labels.format.custom_labels_index = 0

`LabelFormat.``datetime_format`

  The date/time format to be used.

  Type: | `string`

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> axis.tick_labels.format.format_type = NumberFormat.TimeDate
      >>> axis.tick_labels.format.datetime_format = 'mmm d, yyyy'

  The format can be any combination of the following codes. Placing a backslash in front of a y, m, d, or s in the Time/Date formula will keep it from being processed as part of the formula. All characters not part of the Time/Date formula will appear as entered. For example, “\year yyyy” will appear as “year 2008”, as the backslash keeps the first y from being processed as part of the formula. If you use “m” immediately after the “h” or “hh” code or immediately before the “ss” code, the minutes instead of the month will be displayed.

  Years:
  `yy` | 00-99
  `yyyy` | 1800-9999
  Months:
  `m` | 1-12
  `mm` | 01-12
  `mmm` | Jan-Dec
  `mmmm` | January-December
  `mmmmm` | first letter of the month
  Days:
  `[d]` | elapsed days
  `d` | 1-31
  `dd` | 01-31
  `ddd` | Sun-Sat
  `dddd` | Sunday-Saturday
  `ddddd` | S,M,T,W,T,F,S
  Hours:
  `[h]` | elapsed hours
  `h` | 0-23 or 1-12
  `hh` | 00-23 or 1-12
  `AM/PM` | AM or PM
  `A/P` | AM or PM as “A” or “P”
  Minutes:
  `[m]` | elapsed minutes
  `m` | 0-59
  `mm` | 00-59
  Seconds: |
  `s` | 0-59
  `ss` | 00-59
  `.0` | Tenths
  `.00` | Hundredths
  `.000` | Thousandths

  To display the time and date on your plot as a “Sat-Jan-05-2008”, enter the following code:

      "ddd-mmm-dd-yyyy"

  To display the time and date on your plot as a “1-3-08”, enter the following code:

      "m-d-yy"

  To display the time and date on your plot as a “9:30:05 AM”, enter the following code:

      "h:mm:ss AM"

  To display an elapsed time, such as “3:10:15”, enter the following code:

      "[d]:hh:mm"

`LabelFormat.``format_type`

  Type of number formatting to use.

  Type: | `NumberFormat`

  Possible values: `Integer`, `FixedFloat`, `Exponential`, `BestFloat`, `SuperScript`, `CustomLabel`, `LogSuperScript`, `RangeBestFloat`, `DynamicLabel`, `TimeDate`.

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> axis.tick_labels.format.format_type = NumberFormat.BestFloat

`LabelFormat.``negative_prefix`

  Prefix string to use for negative valued tick labels.

  Type: | `string`

  This example shows how to use parentheses instead of a negative sign:

      >>> axis.tick_labels.format.show_negative_sign = False
      >>> axis.tick_labels.format.negative_prefix = '('
      >>> axis.tick_labels.format.negative_suffix = ')'

`LabelFormat.``negative_suffix`

  Suffix string to use for negative valued tick labels.

  Type: | `string`

  This example shows how to use parentheses instead of a negative sign:

      >>> axis.tick_labels.format.show_negative_sign = False
      >>> axis.tick_labels.format.negative_prefix = '('
      >>> axis.tick_labels.format.negative_suffix = ')'

`LabelFormat.``num_custom_labels`

  Number of custom label sets available to use.

  Type: | `int`

  Example usage:

      >>> print(axis.tick_labels.format.num_custom_labels)
      1

`LabelFormat.``positive_prefix`

  Prefix string to use for positive valued tick labels.

  Type: | `string`

  Example usage:

      >>> axis.tick_labels.format.positive_prefix = 'increase: '

`LabelFormat.``positive_suffix`

  Suffix string to use for positive valued tick labels.

  Type: | `string`

  Example usage:

      >>> axis.tick_labels.format.positive_suffix = ' (m)'

`LabelFormat.``precision`

  Number digits after decimal for fixed floating point format.

  Type: | `integer`

  Example usage:

      >>> from tecplot.constant import NumberFormat
      >>> axis.tick_labels.format.format_type = NumberFormat.FixedFloat
      >>> axis.tick_labels.format.precision = 3

`LabelFormat.``remove_leading_zeros`

  Strip leading zeros in the formatted number.

  Type: | `boolean`

  Example usage:

      >>> axis.tick_labels.format.remove_leading_zeros = True

`LabelFormat.``show_decimals_on_whole_numbers`

  Include trailing decimal character with whole numbers.

  Type: | `boolean`

  Example usage:

      >>> axis.tick_labels.format.show_decimals_on_whole_numbers = True

`LabelFormat.``show_negative_sign`

  Include negative sign for negative values.

  Type: | `boolean`

  Example usage:

      >>> axis.tick_labels.format.show_negative_sign = True

`LabelFormat.``zero_prefix`

  Prefix string to use for zero valued tick labels.

  Type: | `string`

  Example usage:

      >>> axis.tick_labels.format.zero_prefix = 'origin: '

`LabelFormat.``zero_suffix`

  Suffix string to use for zero valued tick labels.

  Type: | `string`

  Example usage:

      >>> axis.tick_labels.format.zero_suffix = ' (origin)'

## Viewport

- ReadOnlyViewport
- Viewport
- Cartesian2DViewport
- PolarViewport

### ReadOnlyViewport

class `tecplot.plot.``ReadOnlyViewport`(axes)

  Attributes

  `bottom` | (`float`) Bottom position of viewport relative to the `Frame`.
  `left` | (`float`) Left position of viewport relative to the `Frame`.
  `right` | (`float`) Right position of viewport relative to the `Frame`.
  `top` | (`float`) Top position of viewport relative to the `Frame`.

`ReadOnlyViewport.``bottom`

  (`float`) Bottom position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame height from the bottom of the frame.

  Example usage:

      >>> print(plot.axes.viewport.bottom)
      10.0

`ReadOnlyViewport.``left`

  (`float`) Left position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.left)
      10.0

`ReadOnlyViewport.``right`

  (`float`) Right position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.right)
      90.0

`ReadOnlyViewport.``top`

  (`float`) Top position of viewport relative to the `Frame`.

  Type: | `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`) Bottom position of viewport relative to the `Frame`.
  `left` | (`float`) Left position of viewport relative to the `Frame`.
  `right` | (`float`) Right position of viewport relative to the `Frame`.
  `top` | (`float`) Top position of viewport relative to the `Frame`.

`Viewport.``bottom`

  (`float`) Bottom position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame height from the bottom of the frame.

  Example usage:

      >>> print(plot.axes.viewport.bottom)
      10.0

`Viewport.``left`

  (`float`) Left position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.left)
      10.0

`Viewport.``right`

  (`float`) Right position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.right)
      90.0

`Viewport.``top`

  (`float`) Top position of viewport relative to the `Frame`.

  Type: | `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`) Bottom position of viewport relative to the `Frame`.
  `left` | (`float`) Left position of viewport relative to the `Frame`.
  `nice_fit_buffer` | Tolerance for viewport/frame fit niceness.
  `right` | (`float`) Right position of viewport relative to the `Frame`.
  `top` | (`float`) Top position of viewport relative to 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`) Bottom position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame height from the bottom of the frame.

  Example usage:

      >>> print(plot.axes.viewport.bottom)
      10.0

`Cartesian2DViewport.``left`

  (`float`) Left position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.left)
      10.0

`Cartesian2DViewport.``nice_fit_buffer`

  Tolerance for viewport/frame fit niceness.

  Type: | `float`

  Example usage:

      >>> plot.axes.viewport.nice_fit_buffer = 20

`Cartesian2DViewport.``right`

  (`float`) Right position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.right)
      90.0

`Cartesian2DViewport.``top`

  (`float`) Top position of viewport relative to the `Frame`.

  Type: | `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.

  Type: | `float`

  Example usage:

      >>> plot.axes.viewport.top_snap_target = 90

`Cartesian2DViewport.``top_snap_tolerance`

  Tolerance for snapping to target value for top.

  Type: | `float`

  Example usage:

      >>> plot.axes.viewport.top_snap_tolerance = 8

### PolarViewport

class `tecplot.plot.``PolarViewport`(axes)

  Attributes

  `border_color` |
  `border_thickness` |
  `bottom` | (`float`) Bottom position of viewport relative to the `Frame`.
  `fill_color` |
  `left` | (`float`) Left position of viewport relative to the `Frame`.
  `right` | (`float`) Right position of viewport relative to the `Frame`.
  `show_border` |
  `top` | (`float`) Top position of viewport relative to the `Frame`.

`PolarViewport.``border_color`

`PolarViewport.``border_thickness`

`PolarViewport.``bottom`

  (`float`) Bottom position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame height from the bottom of the frame.

  Example usage:

      >>> print(plot.axes.viewport.bottom)
      10.0

`PolarViewport.``fill_color`

`PolarViewport.``left`

  (`float`) Left position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.left)
      10.0

`PolarViewport.``right`

  (`float`) Right position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame width from the left of the frame.

  Example usage:

      >>> print(plot.axes.viewport.right)
      90.0

`PolarViewport.``show_border`

`PolarViewport.``top`

  (`float`) Top position of viewport relative to the `Frame`.

  Type: | `float` in percentage of frame height from the bottom of the frame.

  Example usage:

      >>> print(plot.axes.viewport.top)
      90.0

## View

- Cartesian2DView
- Cartesian3DView
- LineView
- PolarView

### Cartesian2DView

class `tecplot.plot.``Cartesian2DView`(plot)

  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, '2D', 'intflow.lpk')
      dataset = tp.load_layout(infile)

      frame = tp.active_frame()
      plot = frame.plot(PlotType.Cartesian2D)
      plot.activate()
      plot.show_edge = False
      plot.show_mesh = False

      plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'
      plot.view.fit_to_nice()
      tp.export.save_png('view_2D.png', 600, supersample=3)

  Attributes

  `magnification` | Magnification for the data being plotted.

  Methods

  `adjust_to_nice`() | Shifts axes to make axis-line values “nice”
  `center`([consider_blanking]) | Center the data within the axis grid area.
  `fit`([consider_blanking]) | Fit the data being plotted within the axis grid area.
  `fit_data`([consider_blanking]) | Fit data zones or line mappings within the grid area.
  `fit_to_nice`([consider_blanking]) | Set axis range to begin/end on major axis increments.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, xmax, ymin, ymax) | Zoom the view to a rectangular region of the plot.

`Cartesian2DView.``adjust_to_nice`()

  Shifts axes to make axis-line values “nice”

  Modifies the axis range to fit the minimum and maximum of the variable assigned to that axis, then snaps the major tick marks to the ends of the axis. If axis dependency is not independent, this may affect the range on another axis.

  In other words, given an existing range of values RMin, RMax and an initial delta, D (such as axis ranges with grid spacing or contour levels), determine a new delta (ND) that:

  - Is 1,2, or 5 times 10 to some power that is the “best” alternative to D.
  - Produces new range min and max values that are some multiple of ND that are nearest the original RMin and RMax

  Axes are shifted without changing the extents of the window.

  Raises: | `TecplotSystemError` – Internal error.

`Cartesian2DView.``center`(consider_blanking=True)

  Center the data within the axis grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – 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`)
  Raises: | `TecplotSystemError` – View could not be centered.

`Cartesian2DView.``fit`(consider_blanking=True)

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

  Parameters: | consider_blanking (`Boolean`, optional) – 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`)
  Raises: | `TecplotSystemError` – Internal error.

`Cartesian2DView.``fit_data`(consider_blanking=True)

  Fit data zones or line mappings within the grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – 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.

  Raises: | `TecplotSystemError` – Internal error.

`Cartesian2DView.``fit_to_nice`(consider_blanking=True)

  Set axis range to begin/end on major axis increments.

  Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

  Parameters: | consider_blanking (`Boolean`, optional) – 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`)
  Raises: | `TecplotSystemError` – Internal error.

`Cartesian2DView.``magnification`

  Magnification for the data being plotted.

  Type: | `float`

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area.

  Raises: | `TecplotSystemError` – Magnification could not be queried or set. Possible cases where `TecplotSystemError` is raised include XY plots where no mappings are active or floating point out of range error

  Scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10
      >>> view.magnification
      0.10

`Cartesian2DView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)
  Raises: | `TecplotSystemError` – View could not be translated.

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`Cartesian2DView.``zoom`(xmin, xmax, ymin, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.
  Raises: | `TecplotSystemError` – The view could not be zoomed.

  Zoom so the rectangular region with corners at (xmin, ymin)=(1,0) and (xmax, ymax)=(7,9) is in view:

      >>> view.zoom(1, 7, 0, 9)

### Cartesian3DView

class `tecplot.plot.``Cartesian3DView`(plot)

  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, '3D', 'jet_surface.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('jet_surface.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.
  `theta` | Eye origin view Theta angle in degrees.
  `width` | 3D view width.

  Methods

  `center`([consider_blanking]) | Center the data within the axis grid area.
  `fit`([consider_blanking]) | Fit the data being plotted within the axis grid area.
  `fit_data`([consider_blanking]) | Fit data zones or line mappings within the grid area.
  `fit_surfaces`([consider_blanking]) | Fit 3D plot surfaces to the grid area.
  `fit_to_nice`([consider_blanking]) | Set axis range to begin/end on major axis increments.
  `rotate_to_angles`(psi, theta, alpha) | Rotate the plot to specific angles.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, xmax, ymin, ymax) | Zoom the view to a rectangular region of the plot.

`Cartesian3DView.``alpha`

  Eye origin view Alpha angle in degrees.

  The Alpha angle is the twist about the eye origin ray.

  Type: | `float`

  Example usage:

      >>> plot.view.alpha = 95.0

`Cartesian3DView.``center`(consider_blanking=True)

  Center the data within the axis grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – 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`)
  Raises: | `TecplotSystemError` – View could not be centered.

`Cartesian3DView.``distance`

  Get or set the view distance.

  Type: | `float`

  The view distance is the distance from the viewer to the plane that is parallel to the screen and passes through the 3-D rotation origin.

  Note

  Changing this value will also change the viewer `position`.

  See also

  `position`

  Example usage:

      >>> plot.view.distance
      13.5
      >>> plot.view.distance = 10.0
      >>> plot.view.distance
      10.0

`Cartesian3DView.``field_of_view`

  Amount of the plot which is displayed.

  Type: | `float`

  Get or set the amount of the plot (in terms of spherical arc) in front of the viewer that may be seen.

  Warning

  `field_of_view` cannot be set if `projection` is `Projection.Orthographic`.

  Example usage:

      >>> from tecplot.constant import Projection
      >>> plot.view.projection = Projection.Perspective
      >>> plot.view.field_of_view = 9.6

`Cartesian3DView.``fit`(consider_blanking=True)

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system. Axes are also included.

  Parameters: | consider_blanking (`Boolean`, optional) – 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`)
  Raises: | `TecplotSystemError` – Internal error.

`Cartesian3DView.``fit_data`(consider_blanking=True)

  Fit data zones or line mappings within the grid area.

  Parameters: | consider_blanking (`Boolean`, optional) – 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.

  Raises: | `TecplotSystemError` – Internal error.

`Cartesian3DView.``fit_surfaces`(consider_blanking=True)

  Fit 3D plot surfaces to 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

  3D volume zones are excluded when `surfaces_to_plot` is `SurfacesToPlot.None_`.

  Raises: | `TecplotSystemError` – Internal error.

`Cartesian3DView.``fit_to_nice`(consider_blanking=True)

  Set axis range to begin/end on major axis increments.

  Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

  Parameters: | consider_blanking (`Boolean`, optional) – 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`)
  Raises: | `TecplotSystemError` – Internal error.

`Cartesian3DView.``magnification`

  Magnification for the data being plotted.

  Type: | `float`

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area.

  Raises: | `TecplotSystemError` – Magnification could not be queried or set. Possible cases where `TecplotSystemError` is raised include XY plots where no mappings are active or floating point out of range error

  Scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10
      >>> view.magnification
      0.10

`Cartesian3DView.``position`

  3D viewer position.

  The viewer position is the viewer’s relation to the image.

  Type: | 3-`tuple` of `floats`

  Example usage:

      >>> plot.view.position
      (1.25, 3.2, 0.74)
      >>> plot.view.position.x
      1.25
      >>> plot.view.position = (2.5, 0.0, 1.0)
      >>> plot.view.position.y
      0.0
      >>> plot.view.position.z
      1.0

  See also

  `distance`

`Cartesian3DView.``projection`

  Projection type (`Perspective` or `Orthographic`).

  Type: | `Projection`

  When set to Perspective, Tecplot 360 draws the plot in perspective. When set to Orthographic, the plot is drawn with orthographic projection where the shape of the object does not change with distance.

  Note

  Requires Tecplot version 2017.2 or later.

  Example usage:

      >>> from tecplot.constant import Projection
      >>> plot.view.projection = Projection.Orthographic

`Cartesian3DView.``psi`

  Eye origin view Psi angle in degrees.

  The Psi angle is the tilt of the eye origin ray away from the Z-axis.

  Type: | `float`

  Example usage:

      >>> plot.view.psi = 90.0

`Cartesian3DView.``rotate_to_angles`(psi, theta, alpha)

  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.``theta`

  Eye origin view Theta angle in degrees.

  The Theta angle is the rotation of the eye origin ray about the Z-axis.

  Type: | `float`

  Example usage:

      >>> plot.view.theta = 24.3

`Cartesian3DView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)
  Raises: | `TecplotSystemError` – View could not be translated.

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`Cartesian3DView.``width`

  3D view width.

  Type: | `float`

  The 3D view width is the amount of the plot (in X-axis units) in front of the viewer that may be seen.

  Warning

  `width` cannot be set if `projection` is `Perspective`.

  Example usage:

      >>> plot.view.width = 1.5

`Cartesian3DView.``zoom`(xmin, xmax, ymin, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.
  Raises: | `TecplotSystemError` – The view could not be zoomed.

  Zoom so the rectangular region with corners at (xmin, ymin)=(1,0) and (xmax, ymax)=(7,9) is in view:

      >>> view.zoom(1, 7, 0, 9)

### LineView

class `tecplot.plot.``LineView`(plot)

  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, 'XY', 'rainfall.plt')
      dataset = tecplot.data.load_tecplot(datafile)

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

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

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

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

  Attributes

  `magnification` | Magnification for the data being plotted.

  Methods

  `adjust_to_nice`() | Shifts axes to make axis-line values “nice”
  `center`() | Center the data within the axis grid area.
  `fit`() | Fit the data being plotted within the axis grid area.
  `fit_data`() | Fit data zones or line mappings within the grid area.
  `fit_to_nice`() | Set axis range to begin/end on major axis increments.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.
  `zoom`(xmin, xmax, ymin, ymax) | Zoom the view to a rectangular region of the plot.

`LineView.``adjust_to_nice`()

  Shifts axes to make axis-line values “nice”

  Modifies the axis range to fit the minimum and maximum of the variable assigned to that axis, then snaps the major tick marks to the ends of the axis. If axis dependency is not independent, this may affect the range on another axis.

  In other words, given an existing range of values RMin, RMax and an initial delta, D (such as axis ranges with grid spacing or contour levels), determine a new delta (ND) that:

  - Is 1,2, or 5 times 10 to some power that is the “best” alternative to D.
  - Produces new range min and max values that are some multiple of ND that are nearest the original RMin and RMax

  Axes are shifted without changing the extents of the window.

  Raises: | `TecplotSystemError` – Internal error.

`LineView.``center`()

  Center the data within the axis grid area.

  Raises: | `TecplotSystemError` – View could not be centered.

`LineView.``fit`()

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

  Raises: | `TecplotSystemError` – Internal error.

`LineView.``fit_data`()

  Fit data zones or line mappings within the grid area.

  Note

  This does not take into consideration text and geometries that are plotted using the grid coordinate system.

  Raises: | `TecplotSystemError` – Internal error.

`LineView.``fit_to_nice`()

  Set axis range to begin/end on major axis increments.

  Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

  Raises: | `TecplotSystemError` – Internal error.

`LineView.``magnification`

  Magnification for the data being plotted.

  Type: | `float`

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area.

  Raises: | `TecplotSystemError` – Magnification could not be queried or set. Possible cases where `TecplotSystemError` is raised include XY plots where no mappings are active or floating point out of range error

  Scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10
      >>> view.magnification
      0.10

`LineView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)
  Raises: | `TecplotSystemError` – View could not be translated.

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

`LineView.``zoom`(xmin, xmax, ymin, ymax)

  Zoom the view to a rectangular region of the plot.

  Change the view by “zooming” into the data. Ranges on the axes are adjusted to view the region defined by the rectangle with corners at (`xmin`, `ymin`) and (`xmax`, `ymax`).

  Note

  All position values are defined in units of the X- and Y- axis (that is, grid coordinates).

  Parameters: | xmin – (`float`) X min corner of the rectangle to be viewed. xmax – (`float`) X max corner of the rectangle to be viewed. ymin – (`float`) Y min corner of the rectangle to be viewed. ymax – (`float`) Y max corner of the rectangle to be viewed.
  Raises: | `TecplotSystemError` – The view could not be zoomed.

  Zoom so the rectangular region with corners at (xmin, ymin)=(1,0) and (xmax, ymax)=(7,9) is in view:

      >>> view.zoom(1, 7, 0, 9)

### PolarView

class `tecplot.plot.``PolarView`(plot)

  Adjust the way polar data is displayed.

      import numpy as np
      import tecplot as tp
      from tecplot.constant import PlotType, ThetaMode

      frame = tp.active_frame()

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

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

      plot = frame.plot(PlotType.PolarLine)
      plot.activate()

      plot.axes.r_axis.max = np.max(r)
      plot.axes.theta_axis.mode = ThetaMode.Radians

      plot.delete_linemaps()
      lmap = plot.add_linemap('Linemap', zone, dataset.variable('R'),
                              dataset.variable('Theta'))
      lmap.line.line_thickness = 0.8

      plot.view.reset_to_entire_circle()
      plot.view.fit()

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

  Attributes

  `extents` | View extents in grid units of transformed X & Y.
  `magnification` | Magnification for the data being plotted.

  Methods

  `center`() | Center the data within the axis grid area.
  `fit`() | Fit the data being plotted within the axis grid area.
  `fit_data`() | Fit data zones or line mappings within the grid area.
  `fit_to_nice`() | Set axis range to begin/end on major axis increments.
  `reset_to_entire_circle`() | Set the range of Theta to encompass an entire circle.
  `translate`([x, y]) | Shift the data being plotted in the X and/or Y direction.

`PolarView.``center`()

  Center the data within the axis grid area.

  Raises: | `TecplotSystemError` – View could not be centered.

`PolarView.``extents`

  View extents in grid units of transformed X & Y.

  Type: | 4-`tuple` of `float` (x1, y1, x2, y2)

  - x1, y1: Upper left corner of the extents.
  - x2, y2: Lower right corner of the extents.

  Set the view of the polar plot to view the full extents of the plot area:

      >>> plot.view.extents = (10, 10, 90, 90)
      >>> plot.view.extents.x1
      10.0
      >>> plot.view.extents.y1
      10.0
      >>> plot.view.extents.x2
      90.0
      >>> plot.view.extents.y2
      90.0

`PolarView.``fit`()

  Fit the data being plotted within the axis grid area.

  Note

  This also takes into consideration text and geometries that are plotted using the grid coordinate system.

  Raises: | `TecplotSystemError` – Internal error.

`PolarView.``fit_data`()

  Fit data zones or line mappings within the grid area.

  Note

  This does not take into consideration text and geometries that are plotted using the grid coordinate system.

  Raises: | `TecplotSystemError` – Internal error.

`PolarView.``fit_to_nice`()

  Set axis range to begin/end on major axis increments.

  Changes the view to make the extents of the frame neatly hold the plot with integer values for axis labels.

  Raises: | `TecplotSystemError` – Internal error.

`PolarView.``magnification`

  Magnification for the data being plotted.

  Type: | `float`

  The `magnification` value is a decimal percent and must be greater than 0. A `magnification` size of 1.0 (100%) will size the plot so that it can fit within the grid area.

  Raises: | `TecplotSystemError` – Magnification could not be queried or set. Possible cases where `TecplotSystemError` is raised include XY plots where no mappings are active or floating point out of range error

  Scale the view to ten percent of the size at which the data would fit the full frame:

      >>> view.magnification = 0.10
      >>> view.magnification
      0.10

`PolarView.``reset_to_entire_circle`()

  Set the range of Theta to encompass an entire circle.

      >>> plot.view.reset_to_entire_circle()

  Raises: | `TecplotSystemError` – Internal error.

`PolarView.``translate`(x=0.0, y=0.0)

  Shift the data being plotted in the X and/or Y direction.

  Note

  The amount translated is in frame units.

  Parameters: | x (`float`, optional) – Amount to shift in the X direction as a percentage of the frame width. Positive values shift right, negative values shift left. (default: 0.0) y (`float`, optional) – Amount to shift in the Y direction as a percentage of the frame height. Positive values shift up, negative values shift down. (default: 0.0)
  Raises: | `TecplotSystemError` – View could not be translated.

  Translate the view 10 percent of the frame width to the right:

      >>> view.translate(x=10)

  Translate the view 5 percent of the frame width to the right, 20 of the frame height down:

      >>> view.translate(x=5, y=-20)

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