Data

tecplot.data

Dataset access and manipulation.

A Dataset consists of a matrix of Zones and Variables. Each Zone - Variable pair corresponds to a data object which can always be treated as a 1D array, but which may be interpreted as 2D or 3D in the case of ijk-ordered data. In general, the Zone defines the size, shape and connectivity of the data while the Variable defines the underlying data type and whether the data is nodal or cell-centered.

Warning

Zero-based Indexing

It is important to know that all indexing in PyTecplot scripts are zero-based. This is a departure from the macro language which is one-based. This is to keep with the expectations when working in the python language. However, PyTecplot does not modify strings that are passed to the Tecplot Engine. This means that one-based indexing should be used when running macro commands from python or when using execute_equation().

Loading Data

data.load_tecplot()

tecplot.data.load_tecplot(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_first_zone_only=None, initial_plot_type=None, zones=None, variables=None, collapse=None, skip=None, assign_strand_ids=True, add_zones_to_existing_strands=None, include_text=None, include_geom=None, include_custom_labels=None, include_data=None)[source]

Read a tecplot data file.

Parameters:

  • filenames (pathlib.Path, str or list) – Files to be read. (See note below concerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame to attach the resulting Dataset. If None, the currently active Frame is used and the zones are appended by default.

  • read_data_option (ReadDataOption, optional) –

    Specify how the data is loaded into Tecplot. (default: ReadDataOption.Append)

    Possible values are:

    Default: ReadDataOption.Append

  • reset_style (bool, optional) – Reset the style for destination Frame. If False, the Frame’s current style is preserved. (default: True)

  • initial_plot_first_zone_only (bool, optional) – Informs the Tecplot Engine that after the data is loaded it only needs to activate the first enabled Zone for the initial plot. This option is particularly useful if you have many Zones and want to get the data into the Tecplot Engine and the first Zone drawn as fast as possible. The inactive Zones can always be activated when needed. (default: False)

  • initial_plot_type (PlotType, optional) – Forces a specific type of plot upon loading of the data. Only used if resetstyle is True. To have Tecplot 360 determine the most appropriate plot type for the data, use PlotType.Automatic. Possible values are: PlotType.Automatic (default), Cartesian3D, Cartesian2D, XYLine, PlotType.Sketch, PolarLine.

  • zones (set of integers, optional) – Set of Zones to load. Use None to load all zones. (default: None)

  • variables (set of strings or integers, optional) – Set of Variables to load. If variable names are specified as strings and appending then variables will be aligned with existing variables. If variable offsets are supplied then the variables at those offsets will be loaded and aligned based on the value of the collapse parameter. Use None to load all variables. (default: None)

  • collapse (bool, optional) – Variables if any are disabled. (default: False)

  • skip – (3-tuple of integers, optional) The ijk-skip. A value of (1,1,1) loads every data point in the (i,j,k) directions. A value of (2,2,2) loads every other data point and so forth. This only applies to ordered data. (default: (1,1,1))

  • assign_strand_ids (bool, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default: True)

  • add_zones_to_existing_strands (bool, optional) – Add the Zones to matching strands, if they exist. Otherwise, if the new data specifies strands, new ones will be created beginning after the last strand in the Dataset. (default: False)

  • include_text (bool, optional) – Load any text, geometries, or custom labels (default: True)

  • include_geom (bool, optional) – Load geometries. (default: True)

  • include_custom_labels (bool, optional) – (default: True)

  • include_data (bool, optional) – Load data. Set this to False if you only want annotations such as text or geometries. (default: True)

Returns:

Dataset – The Dataset holding the loaded data.

Raises:

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_tecplot_szl()

tecplot.data.load_tecplot_szl(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_first_zone_only=None, initial_plot_type=None, assign_strand_ids=True, add_zones_to_existing_strands=None, server=None, connection_method=None, user=None, authentication_method=None, ssh_private_keyfile=PosixPath('~/.ssh/id_rsa'), szlserver_path=None)[source]

Read tecplot SZL data file.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_cfx()

tecplot.data.load_cfx(filename, frame=None, append=True, assign_strand_ids=True, add_zones_to_existing_strands=True, initial_plot_type=PlotType.Automatic)[source]

Read an ANSYS CFX data file.

Parameters:

  • filename (pathlib.Path or str) – The data file to be read. (See note below concerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame to attach the resulting Dataset. If None, the currently active Frame is used and the zones are appended by default.

  • append (bool, optional) – Append the data to the existing Dataset. If False, the existing data attached to the Frame is deleted and replaced. (default: True)

  • assign_strand_ids (bool, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default: True)

  • add_zones_to_existing_strands (bool, optional) – Add the Zones to matching strands, if they exist. Otherwise, if the new data specifies strands, new ones will be created beginning after the last strand in the Dataset. (default: True)

  • initial_plot_type (PlotType, optional) – Set the initial plot type upon loading of the data. Must be one of PlotType.Automatic (default), PlotType.Cartesian3D or PlotType.Cartesian2D.

Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Note

The CFX loader is not available on macOS.

data.load_cgns()

tecplot.data.load_cgns(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_first_zone_only=None, initial_plot_type=None, zones=None, variables=None, load_convergence_history=None, combine_fe_sections=None, average_to_nodes='Arithmetic', uniform_grid=None, assign_strand_ids=None, add_zones_to_existing_strands=None, include_boundary_conditions=True)[source]

Read CGNS data files.

Parameters:

  • filenames (pathlib.Path, str or list) – CGNS data files to be read. (See note below concerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame to attach the resulting Dataset. If None, the currently active Frame is used and the zones are appended by default.

  • read_data_option (ReadDataOption, optional) –

    Specify how the data is loaded into Tecplot. (default: ReadDataOption.Append)

    Possible values are:

  • reset_style (bool, optional) – Reset the style for destination Frame. If False, the Frame’s current style is preserved. (default: True)

  • initial_plot_first_zone_only (bool, optional) – Informs the Tecplot Engine that after the data is loaded it only needs to activate the first enabled Zone for the initial plot. This option is particularly useful if you have many Zones and want to get the data into the Tecplot Engine and the first Zone drawn as fast as possible. The inactive Zones can always be activated when needed. (default: False)

  • initial_plot_type (PlotType, optional) – Forces a specific type of plot upon loading of the data. Only used if resetstyle is True. To have Tecplot 360 determine the most appropriate plot type for the data, use PlotType.Automatic. Possible values are: PlotType.Automatic (default), Cartesian3D, Cartesian2D, XYLine, PlotType.Sketch, PolarLine.

  • zones (list of integers, optional) – List of zone indexes to load starting from zero. None implies loading all zones. (default: None)

  • variables (list of integers, optional) – List of variable indexes, beyond the first coordinate variables, to load starting from zero. None implies loading all variables. The grid will always be loaded and an index of zero indicates the first non-coordinate variable. (default: None)

  • load_convergence_history (bool, optional) – Load the global convergence history rather than any grid or solution data. (default: False)

  • combine_fe_sections (bool, optional) – Combine all finite-element sections with the zone cell-dimension into one zone. (default: False)

  • average_to_nodes (str, optional) – Average cell-centered data to grid nodes using the specified method. (Options: None, “Arithmetic”, “Laplacian”, default: “Arithmetic”)

  • uniform_grid (bool, optional) – Indicates the grid structure is the same for all time steps. (default: True)

  • assign_strand_ids (bool, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default: True)

  • add_zones_to_existing_strands (bool, optional) – Add the Zones to matching strands, if they exist. Otherwise, if the new data specifies strands, new ones will be created beginning after the last strand in the Dataset. (default: False)

  • include_boundary_conditions (bool, optional) – Load the boundary conditions along with the data. Upon loading, the associated fieldmaps will remain inactive. For unstructured data, boundary conditions are always loaded and this option is ignored. (default: True)

Returns:

Dataset – The Dataset holding the loaded data.

Raises:

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_converge_cgns()

tecplot.data.load_converge_cgns(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None)[source]

Read CONVERGE CGNS data files.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_converge_hdf5()

tecplot.data.load_converge_hdf5(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_type=None)[source]

Read CONVERGE HDF5 data files.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

New in version 2019.1: Loading CONVERGE HDF5 data requires Tecplot 360 2019 R1 or later.

New in version 1.2.

data.load_converge_output()

tecplot.data.load_converge_output(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None)[source]

Read CONVERGE Output data files.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_ensight()

tecplot.data.load_ensight(filename, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_first_zone_only=None, initial_plot_type=None, assign_strand_ids=True, add_zones_to_existing_strands=None)[source]

Read Ensight data files and/or boundary file.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_fluent()

tecplot.data.load_fluent(case_filenames=None, data_filenames=None, frame=None, append=True, zones=None, variables=None, all_poly_zones=None, average_to_nodes='Arithmetic', time_interval=None, assign_strand_ids=True, add_zones_to_existing_strands=None, include_particle_data=None, include_additional_quantities=True, save_uncompressed_files=None)[source]

Read Fluent data files.

Parameters:

  • case_filenames (pathlib.Path, str or list, optional) – Case (.cas, .cas.gz) files to be read. Compressed files with extension .gz are supported. (See note below concerning absolute and relative paths.)

  • data_filenames (pathlib.Path, str or list, optional) – Data (.dat, .xml, .dat.gz, .fdat, .fdat.gz, etc.) files to be read. Compressed files with extension .gz are supported.

  • frame (Frame, optional) – The Frame to attach the resulting Dataset. If None, the currently active Frame is used and the zones are appended by default.

  • append (bool, optional) – Append the data to the existing Dataset. If False, the existing data attached to the Frame is deleted and replaced. (default: True)

  • zones (str or list of integers, optional) – List of zone indexes (zero-based) to load or string specifying the type of zones to load. Possible values are: “CellsAndBoundaries”, “CellsOnly” and “BoundariesOnly”. Specifying one of these options is mutually exclusive with the variables option. (default: “CellsAndBoundaries”)

  • variables (list of strings, optional) – List of variable names to load. None implies loading all variables. (default: None)

  • all_poly_zones (bool, optional) – Converts all zones to Tecplot polytope (polyhedral or polygonal) zones. (default: False)

  • average_to_nodes (str, optional) – Average cell-centered data to grid nodes using the specified method. (Options: None, “Arithmetic”, “Laplacian”, default: “Arithmetic”)

  • time_interval (float, optional) – Use a constant time interval between each data (.dat) file. If None, the flow-data parameter of each solution data (.dat) file is used. (default: None)

  • assign_strand_ids (bool, optional) –

    Assign strand ID’s to zones that have a strand ID of -1. (default: True)

    Note

    assign_strand_ids only applies if you have also provided a time_interval, otherwise it will be ignored.

  • add_zones_to_existing_strands (bool, optional) – Add the Zones to matching strands, if they exist. Otherwise, if the new data specifies strands, new ones will be created beginning after the last strand in the Dataset. (default: False)

  • include_particle_data (bool, optional) – Load particle data from the data (.dat) files. If loading particle data from an XML file, the XML file should be included in the data_filenames list. (default: False)

  • include_additional_quantities (bool, optional) – Load quantities that were derived from the FLUENT’s standard quantities. (default: True) New in Tecplot 360 2017 R2.

  • save_uncompressed_files (bool, optional) – Save the uncompressed files to the compressed files’ location.

Returns:

Dataset – The Dataset holding the loaded data.

Raises:

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Notes

The zones option takes either a list of zone indexes to be imported or one of “CellsAndBoundaries”, “CellsOnly” or “BoundariesOnly” to indicate the type of zones the user wants to load, however these options are mutually exclusive with the variables option:

>>> dataset = tecplot.data.load_fluent(['one.cas', 'two.cas'],
...     data_filenames=['one.dat', 'two.dat'],
...     variables = ['Pressure','Velocity'],
...     zones = [0,1,3])

data.load_fluent_cff()

tecplot.data.load_fluent_cff(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_type=None, solution_time_source=None, include_interior_face_zones=None, include_particle_zones=None)[source]

Read Fluent Common Fluids Format files.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Note

The Fluent CFF loader is not available on macOS.

The Fluent CFF loader takes one or more case files, followed by zero or more data files in a single list:

>>> dataset = tecplot.data.load_fluent_cff(
...     ['one.cas.h5', 'one.dat.h5', 'two.dat.h5', 'three.cas.h5'],
...     read_data_option = ReadDataOption.Replace,
...     include_interior_face_zones = True)

New in version 2021.1: Loading Fluent CFF files requires Tecplot 360 2021 R1 or later.

New in version 1.4.

data.load_fvcom()

tecplot.data.load_fvcom(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_type=None)[source]

Read FVCOM netCDF data files.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

New in version 2018.2: Loading FVCOM data requires Tecplot 360 2018 R2 or later.

data.load_openfoam()

tecplot.data.load_openfoam(filename, frame=None, append=True, boundary_zone_construction=None, assign_strand_ids=True, add_zones_to_existing_strands=True, initial_plot_type=PlotType.Automatic, initial_plot_first_zone_only=False)[source]

Read an OpenFOAM data file.

Parameters:

  • filename (pathlib.Path or str) – The data file to be read. (See note below concerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame to attach the resulting Dataset. If None, the currently active Frame is used and the zones are appended by default.

  • append (bool, optional) – Append the data to the existing Dataset. If False, the existing data attached to the Frame is deleted and replaced. (default: True)

  • boundary_zone_construction (OpenFOAMLoader.BoundaryZoneConstruction, optional) – Set how the boundary zones are constructed. This may be either OpenFOAMLoader.BoundaryZoneConstruction.Reconstructed (default) or OpenFOAMLoader.BoundaryZoneConstruction.Decomposed. This option requires Tecplot 360 2019 R1 or later.

  • assign_strand_ids (bool, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default: True)

  • add_zones_to_existing_strands (bool, optional) – Add the Zones to matching strands, if they exist. Otherwise, if the new data specifies strands, new ones will be created beginning after the last strand in the Dataset. (default: True)

  • initial_plot_type (PlotType, optional) – Set the initial plot type upon loading of the data. Must be one of PlotType.Automatic (default), PlotType.Cartesian3D or PlotType.Cartesian2D.

  • initial_plot_first_zone_only (bool, optional) – Informs the Tecplot Engine that after the data is loaded it only needs to activate the first enabled Zone for the initial plot. This option is particularly useful if you have many Zones and want to get the data into the Tecplot Engine and the first Zone drawn as fast as possible. The inactive Zones can always be activated when needed. (default: False)

Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

data.load_plot3d()

tecplot.data.load_plot3d(grid_filenames=None, solution_filenames=None, function_filenames=None, name_filename=None, frame=None, append=True, data_structure=None, is_multi_grid=None, style=None, ascii_is_double=None, ascii_has_blanking=None, uniform_grid=None, assign_strand_ids=True, add_zones_to_existing_strands=True, append_function_variables=None, include_boundaries=True)[source]

Read Plot3D data files.

Parameters:

  • grid_filenames (pathlib.Path, str or list, optional) – One or more grid file names to be read. (See note below concerning absolute and relative paths.)

  • solution_filenames (pathlib.Path, str or list, optional) – One or more solution data file names to be read.

  • function_filenames (pathlib.Path, str or list, optional) – One or more function file names.

  • name_filename (pathlib.Path or str, optional) – Path to the name file.

  • frame (Frame, optional) – The Frame to attach the resulting Dataset. If None, the currently active Frame is used and the zones are appended by default.

  • append (bool, optional) – Append the data to the existing Dataset. If False, the existing data attached to the Frame is deleted and replaced. (default: True)

  • data_structure (str, optional) – Specifies the data structure and overrides the automatic detection. Options are: 1D, 2D, 3DP, 3DW, UNSTRUCTURED. Setting this requires is_multi_grid and style to be set as well.

  • is_multi_grid (bool, optional) – Sets data as multi-grid and overrides the automatic data structure detection. Setting this requires data_structure and style to be set as well.

  • style (bool, optional) – Specifies the data style and overrides the automatic data structure detection. Options are: PLOT3DCLASSIC, PLOT3DFUNCTION, OVERFLOW. Setting this requires data_structure and is_multi_grid to be set as well.

  • ascii_is_double (bool, optional) – Indicates that floating-point numbers found in the text data files should be store with 64-bit precision. (default: False)

  • ascii_has_blanking (bool, optional) – Indicates that the text data files contain blanking. (default: False)

  • uniform_grid (bool, optional) – Indicates the grid structure is the same for all time steps. (default: True)

  • assign_strand_ids (bool, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default: True)

  • add_zones_to_existing_strands (bool, optional) – Add the Zones to matching strands, if they exist. Otherwise, if the new data specifies strands, new ones will be created beginning after the last strand in the Dataset. (default: True)

  • append_function_variables (bool, optional) – Append variables in function files to those found in solution files. (default: False)

  • include_boundaries (bool, optional) – Loads boundary zones found in the “.g.fvbnd” file located in the same directory as the grid file, if available. (default: True)

Returns:

Dataset – The Dataset holding the loaded data.

Raises:

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Note

Data structure is automatically detected by default.

The options data_structure, is_multi_grid and style must be supplied together or not at all. When all of these are None, the data structure is automatically detected.

The variables from the function files can be appended to the dataset upon loading:

>>> dataset = tecplot.data.load_plot3d(
...     grid_filenames = 'data.g',
...     solution_filenames = ['t0.q', 't1.q'],
...     function_filenames = ['t0.f', 't1.f'],
...     append_function_variables = True)

data.load_telemac()

tecplot.data.load_telemac(filenames=None, boundary_filename=None, frame=None, read_data_option=ReadDataOption.Append, reset_style=None, initial_plot_type=None)[source]

Read Telemac data files and/or boundary file.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

New in version 2019.1: Loading Telemac data requires Tecplot 360 2019 R1 or later.

data.load_stl()

tecplot.data.load_stl(filename, frame=None, append=True, subdivide_zones=SubdivideZones.DoNotSubdivide, assign_strand_ids=True, add_zones_to_existing_strands=True, initial_plot_type=PlotType.Automatic, initial_plot_first_zone_only=False)[source]

Read a 3D Systems STL data file.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

data.load_vtk()

tecplot.data.load_vtk(filenames, frame=None, read_data_option=ReadDataOption.Append, reset_style=True, initial_plot_type=PlotType.Automatic, assign_strand_ids=True, add_zones_to_existing_strands=False, solution_time_source=SolutionTimeSource.Auto)[source]

Read VTK data files.

Parameters:
Returns:

Dataset – The Dataset holding the loaded data.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Saving Data

data.save_tecplot_ascii()

tecplot.data.save_tecplot_ascii(filename, frame=None, dataset=None, zones=None, variables=None, include_text=None, precision=None, include_geom=None, include_data=None, include_data_share_linkage=None, include_autogen_face_neighbors=None, use_point_format=None)[source]

Write Tecplot ASCII data file.

Parameters:

  • filename (pathlib.Path or str) – Name of the data file to write. (See note below conerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame which holds the Dataset to be written. If this option and dataset are both None, the currently active Frame is used. (default: None)

  • dataset (Dataset, optional) – The Dataset to write out. If this and frame are both None, the Dataset of the currently active Frame is used. (default: None)

  • include_text (bool, optional) – Write out all text, geometries and custom labels. (default: True)

  • include_geom (bool, optional) – Write out all geometries. (default: True)

  • include_data (bool, optional) – Write out the data. Set this to False if you only want to write out annotations. (default: True)

  • include_data_share_linkage (bool, optional) – Conserve space and write the variable and connectivity linkage wherever possible. If False, this will write out all data, losing the connectivity sharing linkage for future dataset reads of the file. (default: False)

  • include_autogen_face_neighbors (bool, optional) – Save the face neighbor connectivity. This may produce very large data files. (default: False)

  • use_point_format (bool, optional) – Write out point format, otherwise use block format. (default: False)

  • zones (list of Zones, optional) – Zones to write out. Use None to write out all Zones. (default: None)

  • variables (list of Variables, optional) – Variables to write out. Use None to write out all Variables. (default: None)

  • precision (int, optional) – ASCII decimal precision to use. (default: 12)

Returns:

Dataset – The Dataset read from when saving.

Raises:

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Example

In this example, we load sample data and save the data in Tecplot ASCII format:

from os import path
import tecplot
examples_directory = tecplot.session.tecplot_examples_directory()
infile = path.join(examples_directory,
                   'OneraM6wing', 'OneraM6_SU2_RANS.plt')
dataset = tecplot.data.load_tecplot(infile)
variables_to_save = [dataset.variable(V)
                     for V in ('x','y','z','Pressure_Coefficient')]

zone_to_save = dataset.zone('WingSurface')
# write data out to an ascii file
tecplot.data.save_tecplot_ascii('wing.dat', dataset=dataset,
                                variables=variables_to_save,
                                zones=[zone_to_save])

data.save_tecplot_plt()

tecplot.data.save_tecplot_plt(filename, frame=None, dataset=None, zones=None, variables=None, version=None, include_text=None, include_geom=None, include_data=None, include_data_share_linkage=None, include_autogen_face_neighbors=None, associate_with_layout=None)[source]

Write Tecplot binary PLT data file.

Parameters:

  • filename (pathlib.Path or str) – Name of the data file to write. (See note below conerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame which holds the Dataset to be written. If this option and dataset are both None, the currently active Frame is used. (default: None)

  • dataset (Dataset, optional) – The Dataset to write out. If this and frame are both None, the Dataset of the currently active Frame is used. (default: None)

  • zones (list of Zones, optional) – Zones to write out. If None, all Zones will be saved.

  • variables (list of Variables, optional) – Variables to write out. If None, all Variables will be saved.

  • include_text (bool, optional) – Write out all text, geometries and custom labels. (default: True)

  • include_geom (bool, optional) – Write out all geometries. (default: True)

  • include_data (bool, optional) – Write out the data. Set this to False if you only want to write out annotations. (default: True)

  • include_data_share_linkage (bool, optional) – Conserve space and write the variable and connectivity linkage wherever possible. If False, this will write out all data, losing the connectivity sharing linkage for future dataset reads of the file. (default: False)

  • include_autogen_face_neighbors (bool, optional) – Save the face neighbor connectivity. This may produce very large data files. (default: False)

  • associate_with_layout (bool, optional) – Associate this data file with the current layout. Set to False to write the datafile without modifying Tecplot’s current data file to layout association. If version is set to anything other than BinaryFileVersion.Current, this association is not possible, and this parameter will be ignored. (default: True)

  • version (BinaryFileVersion, optional) – Specifies the file version to write. Note that some data may be excluded from the file if it cannot be supported in the specified version. Possible values are: Tecplot2006, Tecplot2008, Tecplot2009 and BinaryFileVersion.Current. (default: BinaryFileVersion.Current)

Returns:

Dataset – The Dataset read from when saving.

Raises:

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Example

In this example, we load sample data and save the data in Tecplot binary PLT format:

from os import path
import tecplot
examples_directory = tecplot.session.tecplot_examples_directory()
infile = path.join(examples_directory,
                   'OneraM6wing', 'OneraM6_SU2_RANS.plt')
dataset = tecplot.data.load_tecplot(infile)
variables_to_save = [dataset.variable(V)
                     for V in ('x', 'y', 'z',
                               'Pressure_Coefficient')]

zone_to_save = dataset.zone('WingSurface')
# write data out to a binary file
tecplot.data.save_tecplot_plt('wing.plt', dataset=dataset,
                                variables=variables_to_save,
                                zones=[zone_to_save])

data.save_tecplot_szl()

tecplot.data.save_tecplot_szl(filename, frame=None, dataset=None)[source]

Write Tecplot SZL data file.

Parameters:

  • filename (pathlib.Path or str) – Name of the data file to write. (See note below conerning absolute and relative paths.)

  • frame (Frame, optional) – The Frame which holds the Dataset to be written. If this option and dataset are both None, the currently active Frame is used. (default: None)

  • dataset (Dataset, optional) – The Dataset to write out. If this and frame are both None, the Dataset of the currently active Frame is used. (default: None)

Returns:

Dataset – The Dataset read from when saving.

Note

Absolute and relative paths with PyTecplot

Relative paths, when used within the PyTecplot API are always from Python’s current working directory which can be obtained by calling os.getcwd(). This is true for batch and connected modes. One exception to this is paths within a macro command or file which will be relative to the Tecplot Engine’s home directory, which is typically the Tecplot 360 installation directory. Finally, when connected to a remote (non-local) instance of Tecplot 360, only absolute paths are allowed.

Note that backslashes must be escaped which is especially important for windows paths such as "C:\\Users" or "\\\\server\\path" which will resolve to "C:\Users" and "\\server\path" respectively. Alternatively, one may use Python’s raw strings: r"C:\Users" and r"\\server\path"

Example

In this example, we load sample data and save it in Tecplot SZL format:

from os import path
import tecplot
examples_directory = tecplot.session.tecplot_examples_directory()
infile = path.join(examples_directory,
                   'OneraM6wing', 'OneraM6_SU2_RANS.plt')
dataset = tecplot.data.load_tecplot(infile)
tecplot.data.save_tecplot_szl('wing.szplt')

Data Queries

data.query.probe_at_position()

tecplot.data.query.probe_at_position(x, y, z=None, nearest=False, starting_cell=None, starting_zone=None, zones=None, dataset=None, frame=None)[source]

Returns field values at a point in space.

Note

The position is taken according to the axis assignments of the Frame which may be any of the associated variables in the Dataset and not necessarily (X, Y, Z). See: Cartesian3DFieldAxis.variable.

Parameters:

  • x (float, z is optional) – position to probe for field values.

  • y (float, z is optional) – position to probe for field values.

  • z (float, z is optional) – position to probe for field values.

  • nearest (bool) – Returns the values at the nearest node to the given position. Probe position must be inside the volume of the data being queried, otherwise this will return None.

  • starting_cell (3-tuple of integers, optional) – The (i,j,k)-index of the cell to start looking for the given position. This must be used with starting_zone.

  • starting_zone (Zone, optional) – The first zone to start searching. This is required only when starting_cell is specified.

  • zones (list of Zones, optional) – Limits the search to the given zones. None implies searching all zones. (default: None)

  • dataset (Dataset, optional) – The Dataset to probe. (defaults to the active Dataset.)

  • frame (Frame, optional) – The Frame which determines the spatial variable assignment (X,Y,Z). (defaults to the active Frame.)

Returns:

namedtuple –

(data, cell, zone):

data (list of floats)

The values of each variable in the dataset at the given position.

cell (3-tuple of integers)

(i,j,k) of the cell containing the given position.

zone (Zone)

Zone containing the given position

Note

Returns None if the position can’t be probed.

This method will return None if the position is outside the volume of the data being queried. This means one should capture the results in a single variable and test it against None before proceeding:

result = tp.data.query.probe_at_position(1.0, 2.0, 3.0)
if result is None:
    print('probe failed.')
else:
    data, cell, zone = result

Additionally, with Tecplot 360 versions 2018 R1 and later, this function will raise an exception if Tecplot 360 was interrupted via the GUI during the probe operation.

data.query.probe_on_surface()

tecplot.data.query.probe_on_surface(positions=((0,), (0,), (0,)), zones=None, variables=None, probe_nearest=ProbeNearest.Position, obey_blanking=True, num_nearest_nodes=20, tolerance=1e-05, dataset=None, frame=None)[source]

Returns field values at points on a surface closest the points given.

Note

The positions are processed according to the axis assignments of the Frame which may be any of the associated variables in the Dataset and not necessarily (but usually) (X, Y, Z). See: Cartesian3DFieldAxis.variable.

Parameters:

  • positions (2D float array) – Array of points to probe dimensioned by (3, N) where the first dimension corresponds to (x, y, z). A 1D float array is accepted for single point probes, however this should be avoided when probing several positions as the internal algorithm is optimized for probing many positions at once.

  • zones (list of Zones, optional) – Limits the search to the given zones. None implies searching all active relevant surface zones including surfaces of ordered volume zones. To search FE or polygonal volume boundaries, include the volume zones in this list. (default: None)

  • variables (list of Variables, optional) – The variables within the dataset to probe. None implies all variables. (default: None)

  • probe_nearest (ProbeNearest, optional) – Probe at the nodal location (ProbeNearest.Node) or interpolate to nearest location on the surface (ProbeNearest.Position, default). The return parameter cells_or_nodes will be cells if set to ProbeNearest.Position (default), or nodes if set to ProbeNearest.Node.

  • obey_blanking (bool, optional) – Do not search blanked cells according the frame’s style settings. (default: True)

  • num_nearest_nodes (int, optional) – Only consider surface cells that contain one of the closest N nodes to the probed position. For highly varying surfaces, the nearest cell may or may not contain the nearest nodes to the probe position and so this value should be increased accordingly, however doing so increases the search-space linearly. (default: 20)

  • tolerance (float, optional) – The percentage of the longest cartesian (x, y, z) dimension subtended by the polygons of the surface. This is used in several parts of the algorithm to find the nearest position on the surface zones and should be increased when probing imprecise nodal position data. (default: 1e-5)

  • dataset (Dataset, optional) – The Dataset to probe. (defaults to the active Dataset.)

  • frame (Frame, optional) – The Frame which determines the spatial variable assignment (X,Y,Z). (defaults to the active Frame.)

Returns:

namedtuple – (data, cells_or_nodes, planes, zone):

data (list of floats)

Flattened float array which can be reshaped to (V, N) where V is the number of variables returned (either the number of variables in the dataset or the length of variables input parameter) and N is the number of points probed.

cells_or_nodes (list of integers)

The index to the cells (or nodes if ProbeNearest.Node was passed in to probe_nearest) containing the returned positions.

planes (list of IJKPlanes)

For ordered zones, these are the plane-orientations of the cells for each probed position.

zones (list of Zones)

Zones containing the given positions.

New in version 2018.1: Probe on surface requires Tecplot 360 2018 R1 or later.

Note

The frame’s plot type must be set to PlotType.Cartesian3D

Probe on surface requires the spatial variables to be set according to the frame’s style. This can be done by setting the plot type to PlotType.Cartesian3D. Example:

tp.active_frame().plot_type = tp.constant.PlotType.Cartesian3D

For probing on 2D data, use probe_at_position().

Note

Linear zones will always return nearest nodal values.

If linear zones, which are ignored by default, are included in the zones parameter, the resulting values on that zone will always be nodal and no interpolation on the position will be done.

Note

Irregular or jaggged surfaces may behave poorly.

For performance reasons, this algorithm has the potential to miss the closest position on highly varying surfaces. This can be addressed by first increasing num_nearest_nodes to search more of the zones and then by increasing the tolerance to allow for imprecise position data - skewed polygons for example.

All nodes of each cell considered are checked for co-planarity. This check can be relaxed slightly by increasing the tolerance parameter. The nearest position calculation will then be made assuming the cells are planar and the resulting positions may be imprecise.

Example usage:

from os import path
import numpy as np

import tecplot as tp
from tecplot.constant import PlotType

examples = tp.session.tecplot_examples_directory()
datafile = path.join(examples, 'SimpleData', 'F18.plt')
ds = tp.data.load_tecplot(datafile)
fr = tp.active_frame()
fr.plot_type = PlotType.Cartesian3D

# probe a single point
res = tp.data.query.probe_on_surface((13.5, 4.0, 0.6 ))

'''
The following line will print:
    (13.499723788684996, 3.9922783797612795, 0.49241572276992346,
    0.0018958827755862578, 0.07313805429221854, 0.997276718375976,
    0.06335166319722907)
'''
print(res.data)

# probe multiple points
points = np.array([[13.5,  4.0, 0.6],  # just above starboard wing
                   [13.5, -4.0, 0.6]]) # just above port wing

res = tp.data.query.probe_on_surface(points.transpose())
values = np.array(res.data).reshape((-1, len(points))).transpose()

'''
The following will print the probed position and the result of the probe
    [ 13.5   4.    0.6] [  1.34997238e+01   3.99227838e+00   4.92415723e-01
       1.89588278e-03   7.31380543e-02   9.97276718e-01   6.33516632e-02]
    [ 13.5  -4.    0.6] [  1.34997238e+01  -3.99227838e+00   4.92415723e-01
       1.89588278e-03   7.31380543e-02   9.97276718e-01   6.33516632e-02]
'''
for pt, v in zip(points, values):
    print(pt, v)

Data Operations

data.operate.execute_equation()

tecplot.data.operate.execute_equation(equation, zones=None, i_range=None, j_range=None, k_range=None, value_location=None, variable_data_type=None, ignore_divide_by_zero=None)[source]

The execute_equation function operates on a data set within the Tecplot Engine using FORTRAN-like equations.

Parameters:

  • equation (str) –

    String containing the equation. Multiple equations can be processed by separating each equation with a newline. See Section 20 - 1 “Data Alteration through Equations” in the Tecplot User’s Manual for more information on using equations. Iterable container of Zone objects to operate on. May be a list, set, tuple, or any iterable container. If None, the equation will be applied to all zones.

    Note

    In the equation string, variable names should be enclosed in curly braces. For example, ‘{X} = {X} + 1’

  • zones – (Iterable container of Zone objects, optional): Iterable container of Zone objects to operate on. May be a list, set, tuple, or any iterable container. If None, the equation will be applied to all zones.

  • i_range (tuple of integers, optional) – Tuple of integers for I: (min, max, step). If None, then the equation will operate on the entire range. Not used for finite element nodal data.

  • j_range (tuple of integers, optional) – Tuple of integers for J: (min, max, step). If None, then the equation will operate on the entire range. Not used for finite element nodal data.

  • k_range (tuple of integers, optional) – Tuple of integers for K: (min, max, step). If None, then the equation will operate on the entire range. Not used for finite element nodal data.

  • value_location (ValueLocation, optional) – Variable ValueLocation for the variable on the left hand side. This is used only if this variable is being created for the first time. If None, Tecplot Engine will choose the location for you.

  • variable_data_type (FieldDataType, optional) – Data type for the variable on the left hand side. This is used only if this variable is being created for the first time. If None, Tecplot Engine will choose the type for you.

  • ignore_divide_by_zero (bool, optional) – bool value which instructs Tecplot Engine to ignore divide by zero errors. The result is clamped such that 0/0 is clamped to zero and (+/-N)/0 where N != 0 clamps to +/-maximum value for the given type.

Warning

Zero-based Indexing

It is important to know that all indexing in PyTecplot scripts are zero-based. This is a departure from the macro language which is one-based. This is to keep with the expectations when working in the python language. However, PyTecplot does not modify strings that are passed to the Tecplot Engine. This means that one-based indexing should be used when running macro commands from python or when using execute_equation().

Add one to variable ‘X’ for zones ‘Rectangular’ and ‘Circular’ for every data point:

>>> dataset = tecplot.active_frame().dataset
>>> execute_equation('{X} = {X} + 1', zones=[dataset.zone('Rectangular'),
>>>                  dataset.zone('Circular')])

Create a new, double precision variable called DIST:

>>> execute_equation('{DIST} = SQRT({X}**2 + {Y}**2)',
...                  variable_data_type=FieldDataType.Double)

Set a variable called P to zero along the boundary of an IJ-ordered zone:

>>> execute_equation('{P} = 0', i_range=(0, -1, 0), j_range=(0, -1, 0))

Using 1-based indexing in equations and 0-based indexing in parameters. Zone 4 is subtracted from zone 3 and the result is placed in zone 2:

>>> execute_equation('{T} = {T}[3]-{T}[4]', zones=[ds.zone(1)])

data.operate.interpolate_inverse_distance()

tecplot.data.operate.interpolate_inverse_distance(destination_zone, source_zones=None, variables=None, exponent=3.5, min_radius=0.0, point_selection=PtSelection.OctantN, num_points=8, plot=None)[source]

Inverse-Distance interpolation onto a destination zone.

Parameters:

  • destination_zone (zone or int) – The destination zone (or zone index) for interpolation.

  • source_zones (zones or integers, optional) – Zones (or zone indices) used to obtain the field values for interpolation. By default, all zones except the destination_zone will be used. All source zones must be FE-Tetra, FE-Brick or be IJK-ordered when doing linear interpolation in 3D.

  • variables (variables or integers, optional) – Variables (or variable indices) to interpolate. By default, all variables except those assigned to the axes will be used and is in general dependent on the active plot type of the frame.

  • exponent (float, optional) – Exponent for the inverse-distance weighting. (default: 3.5)

  • min_radius (float, optional) – Minimum distance used for the inverse-distance weighting. (default: 0.0)

  • point_selection (PtSelection, optional) – Method for determining which source points to consider for each destination data point. Possible values: PtSelection.OctantN (default) closest num_points selected by coordinate-system octants, PtSelection.NearestN closest num_points to the destination point, PtSelection.All all points in the source zone.

  • num_points (int, optional) – Number of source points to consider for each destination data point if point_selection is PtSelection.OctantN or PtSelection.NearestN. (default: 8)

  • plot (Plot, optional) – The plot to use when interpolating which determines the dimensionality and spatial variables. By default, the active plot on the active frame will be used.

Note

Cartesian 2D and 3D plots only.

This interpolation method relies on the coordinates, \((x, y)\) for 2D or \((x, y, z)\) for 3D, set for the active (or given) plot which must be either Cartesian2D or Cartesian3D.

The following example loads a 2D dataset and interpolates the first zone to a new one with a larger grid spacing:

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

# Use interpolation to merge information from two independent zones
examples_dir = tp.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'RainierElevation.plt')
dataset = tp.data.load_tecplot(datafile)
# Get list of source zones to use later
srczones = list(dataset.zones())

fr = tp.active_frame()
plot = fr.plot(PlotType.Cartesian2D)
plot.activate()
plot.show_contour = True
plot.show_edge = True

# Show two section of the plot independently
plot.contour(0).legend.show = False
plot.contour(1).legend.show = False
plot.contour(1).colormap_name = 'Diverging - Blue/Red'
for scrzone in srczones:
    plot.fieldmap(scrzone).edge.line_thickness = 0.4
plot.fieldmap(0).contour.flood_contour_group = plot.contour(1)

# export image of original data
tp.export.save_png('interpolate_2d_source.png', 600, supersample=3)

# use the first zone as the source, and get the range of (x, y)
xvar = plot.axes.x_axis.variable
yvar = plot.axes.y_axis.variable
ymin, xmin = 99999,99999
ymax, xmax = -99999,-99999
for scrzone in srczones:
    curxmin, curxmax = scrzone.values(xvar.index).minmax()
    curymin, curymax = scrzone.values(yvar.index).minmax()
    ymin = min(curymin,ymin)
    ymax = max(curymax,ymax)
    xmin = min(curxmin,xmin)
    xmax = max(curxmax,xmax)

# create new zone with a coarse grid
# onto which we will interpolate from the source zone
xpoints = 40
ypoints = 40
newzone = dataset.add_ordered_zone('Interpolated', (xpoints, ypoints))

# setup the (x, y) positions of the new grid
xx = np.linspace(xmin, xmax, xpoints)
yy = np.linspace(ymin, ymax, ypoints)
YY, XX = np.meshgrid(yy, xx, indexing='ij')
newzone.values(xvar.index)[:] = XX.ravel()
newzone.values(yvar.index)[:] = YY.ravel()

# perform linear interpolation from the source to the new zone
tp.data.operate.interpolate_inverse_distance(newzone, source_zones=srczones)

# show the new zone's data, hide the source
plot.fieldmap(newzone).show = True
plot.fieldmap(newzone).contour.show = True
plot.fieldmap(newzone).contour.flood_contour_group = plot.contour(0)
plot.fieldmap(newzone).edge.show = True
plot.fieldmap(newzone).edge.line_thickness = .4
plot.fieldmap(newzone).edge.color = Color.Orange

for scrzone in srczones:
    plot.fieldmap(scrzone).show = False

# export image of interpolated data
tp.export.save_png('interpolate_invdst_2d_dest.png', 600, supersample=3)
../_images/interpolate_2d_source.png

Source data.

../_images/interpolate_invdst_2d_dest.png

Interpolated data.

data.operate.interpolate_kriging()

tecplot.data.operate.interpolate_kriging(destination_zone, source_zones=None, variables=None, krig_range=0.3, zero_value=0.0, drift=Drift.Linear, point_selection=PtSelection.OctantN, num_points=8, plot=None)[source]

Kriging interpolation onto a destination zone.

Parameters:

  • destination_zone (zone or int) – The destination zone (or zone index) for interpolation.

  • source_zones (zones or integers, optional) – Zones (or zone indices) used to obtain the field values for interpolation. By default, all zones except the destination_zone will be used. All source zones must be FE-Tetra, FE-Brick or IJK-ordered when doing kriging interpolation in 3D.

  • variables (variables or integers, optional) – Variables (or variable indices) to interpolate. By default, all variables except those assigned to the axes will be used and is in general dependent on the active plot type of the frame.

  • krig_range (float, optional) – Distance beyond which source points become insignificant. Must be between zero and one, inclusive. (default: 0.3)

  • zero_value (float, optional) – Semi-variance at each source data point on a normalized scale from zero to one. (default: 0.0)

  • drift (Drift, optional) – Overall trend for the data. Possible values: Drift.None_ no trend, Drift.Linear (default) linear trend, Drift.Quad quadratic trend.

  • point_selection (PtSelection, optional) – Method for determining which source points to consider for each destination data point. Possible values: PtSelection.OctantN (default) closest num_points selected by coordinate-system octants, PtSelection.NearestN closest num_points to the destination point, PtSelection.All all points in the source zone.

  • num_points (int, optional) – Number of source points to consider for each destination data point if point_selection is PtSelection.OctantN or PtSelection.NearestN. (default: 8)

  • plot (Plot, optional) – The plot to use when interpolating which determines the dimensionality and spatial variables. By default, the active plot on the active frame will be used.

Note

Cartesian 2D and 3D plots only.

This interpolation method relies on the coordinates, \((x, y)\) for 2D or \((x, y, z)\) for 3D, set for the active (or given) plot which must be either Cartesian2D or Cartesian3D.

The following example loads a 2D dataset and interpolates the first zone to a new one with a larger grid spacing:

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

# Use interpolation to merge information from two independent zones
examples_dir = tp.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData',
                        'RainierElevation.plt')
dataset = tp.data.load_tecplot(datafile)
# Get list of source zones to use later
srczones = list(dataset.zones())

fr = tp.active_frame()
plot = fr.plot(PlotType.Cartesian2D)
plot.activate()
plot.show_contour = True
plot.show_edge = True

# Show two section of the plot independently
plot.contour(0).legend.show = False
plot.contour(1).legend.show = False
plot.contour(1).colormap_name = 'Diverging - Blue/Red'
for scrzone in srczones:
    plot.fieldmap(scrzone).edge.line_thickness = 0.4
plot.fieldmap(0).contour.flood_contour_group = plot.contour(1)

# export image of original data
tp.export.save_png('interpolate_2d_source.png', 600, supersample=3)

# use the first zone as the source, and get the range of (x, y)
xvar = plot.axes.x_axis.variable
yvar = plot.axes.y_axis.variable
ymin, xmin = 99999,99999
ymax, xmax = -99999,-99999
for scrzone in srczones:
    curxmin, curxmax = scrzone.values(xvar.index).minmax()
    curymin, curymax = scrzone.values(yvar.index).minmax()
    ymin = min(curymin,ymin)
    ymax = max(curymax,ymax)
    xmin = min(curxmin,xmin)
    xmax = max(curxmax,xmax)

# create new zone with a coarse grid
# onto which we will interpolate from the source zone
xpoints = 20
ypoints = 20
newzone = dataset.add_ordered_zone('Interpolated', (xpoints, ypoints))

# setup the (x, y) positions of the new grid
xx = np.linspace(xmin, xmax, xpoints)
yy = np.linspace(ymin, ymax, ypoints)
YY, XX = np.meshgrid(yy, xx, indexing='ij')
newzone.values(xvar.index)[:] = XX.ravel()
newzone.values(yvar.index)[:] = YY.ravel()

# perform linear interpolation from the source to the new zone
tp.data.operate.interpolate_kriging(newzone, source_zones=srczones,
                                    drift=Drift.None_, num_points=1)

# show the new zone's data, hide the source
plot.fieldmap(newzone).show = True
plot.fieldmap(newzone).contour.show = True
plot.fieldmap(newzone).contour.flood_contour_group = plot.contour(0)
plot.fieldmap(newzone).edge.show = True
plot.fieldmap(newzone).edge.line_thickness = .4
plot.fieldmap(newzone).edge.color = Color.Orange

for scrzone in srczones:
    plot.fieldmap(scrzone).show = False

# export image of interpolated data
tp.export.save_png('interpolate_krig_2d_dest.png', 600, supersample=3)
../_images/interpolate_2d_source.png

Source data.

../_images/interpolate_krig_2d_dest.png

Interpolated data.

data.operate.interpolate_linear()

tecplot.data.operate.interpolate_linear(destination_zone, source_zones=None, variables=None, fill_value=None, plot=None)[source]

Linear interpolation onto a destination zone.

Parameters:

  • destination_zone (zone or int) – The destination zone (or zone index) for interpolation.

  • source_zones (zones or integers, optional) – Zones (or zone indices) used to obtain the field values for interpolation. By default, all zones except the destination_zone will be used. All source zones must be FE-Tetra, FE-Brick or be IJK-ordered when doing linear interpolation in 3D.

  • variables (variables or integers, optional) – Variables (or variable indices) to interpolate. By default, all variables except those assigned to the axes will be used and is in general dependent on the active plot type of the frame.

  • fill_value (float, optional) – Constant value to which all points outside the data field are set. By default, the values outside the field are preserved.

  • plot (Plot, optional) – The plot to use when interpolating which determines the dimensionality and spatial variables. By default, the active plot on the active frame will be used.

Note

Cartesian 2D and 3D plots only.

This interpolation method relies on the coordinates, \((x, y)\) for 2D or \((x, y, z)\) for 3D, set for the active (or given) plot which must be either Cartesian2D or Cartesian3D.

The following example loads a 2D dataset and uses interpolation to merge information from two independent zones:

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

# Use interpolation to merge information from two independent zones
examples_dir = tp.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'RainierElevation.plt')
dataset = tp.data.load_tecplot(datafile)
# Get list of source zones to use later
srczones = list(dataset.zones())

fr = tp.active_frame()
plot = fr.plot(PlotType.Cartesian2D)
plot.activate()
plot.show_contour = True
plot.show_edge = True

# Show two section of the plot independently
plot.contour(0).legend.show = False
plot.contour(1).legend.show = False
plot.contour(1).colormap_name = 'Diverging - Blue/Red'
for scrzone in srczones:
    plot.fieldmap(scrzone).edge.line_thickness = 0.4
plot.fieldmap(0).contour.flood_contour_group = plot.contour(1)

# export image of original data
tp.export.save_png('interpolate_2d_source.png', 600, supersample=3)

# use the first zone as the source, and get the range of (x, y)
xvar = plot.axes.x_axis.variable
yvar = plot.axes.y_axis.variable
ymin, xmin = 99999,99999
ymax, xmax = -99999,-99999
for scrzone in srczones:
    curxmin, curxmax = scrzone.values(xvar.index).minmax()
    curymin, curymax = scrzone.values(yvar.index).minmax()
    ymin = min(curymin,ymin)
    ymax = max(curymax,ymax)
    xmin = min(curxmin,xmin)
    xmax = max(curxmax,xmax)

# create new zone with a coarse grid
# onto which we will interpolate from the source zone
xpoints = 40
ypoints = 40
newzone = dataset.add_ordered_zone('Interpolated', (xpoints, ypoints))

# setup the (x, y) positions of the new grid
xx = np.linspace(xmin, xmax, xpoints)
yy = np.linspace(ymin, ymax, ypoints)
YY, XX = np.meshgrid(yy, xx, indexing='ij')
newzone.values(xvar.index)[:] = XX.ravel()
newzone.values(yvar.index)[:] = YY.ravel()

# perform linear interpolation from the source to the new zone
tp.data.operate.interpolate_linear(newzone, source_zones=srczones)

# show the new zone's data, hide the source
plot.fieldmap(newzone).show = True
plot.fieldmap(newzone).contour.show = True
plot.fieldmap(newzone).contour.flood_contour_group = plot.contour(0)
plot.fieldmap(newzone).edge.show = True
plot.fieldmap(newzone).edge.line_thickness = .4
plot.fieldmap(newzone).edge.color = Color.Orange

for scrzone in srczones:
    plot.fieldmap(scrzone).show = False

# export image of interpolated data
tp.export.save_png('interpolate_linear_2d_dest.png', 600, supersample=3)
../_images/interpolate_2d_source.png

Source data.

../_images/interpolate_linear_2d_dest.png

Interpolated data.

data.operate.smooth()

tecplot.data.operate.smooth(array, num_passes=1, weight=0.8, boundary_condition=BoundaryCondition.Fixed, frame=None)[source]

Smooth a field data Array in the dataset.

Parameters:

  • array (Array) – The field data to smooth.

  • num_passes (int, optional) – The number of smoothing passes to perform. (default: 1)

  • weight (float, optional) – The relaxation factor for each pass of smoothing. Must be a number between zero and one exclusively. Higher numbers indicate a greater smoothing effect. (default: 0.8)

  • boundary_condition (BoundaryCondition, optional) – The boundary condition by which to smooth. Possible values are BoundaryCondition.Fixed (default), BoundaryCondition.ZeroGradient and BoundaryCondition.Zero2nd.

  • frame (Frame, optional) – The Frame that specifies the spatial variables to smooth over via the active plot. By default, the frame associated with the input Array object will be used.

The data will be smoothed over the spatial variables set by the plot and is dependent on the active plot type of the associated Frame (Cartesian2D or Cartesian3D). Example usage:

>>> tp.data.operate.smooth(dataset.zone('Zone').values('Pressure'))

data.operate.transform_polar_to_rectangular()

tecplot.data.operate.transform_polar_to_rectangular(r, theta, x=None, y=None, angle_units=AngleUnits.Radians, zones=None)[source]

Transform all points from polar to rectangular coordinates.

Parameters:

  • r (Variable) – The radial input variable.

  • theta (Variable) – The angular input variable in angle_units from the \(x\)-axis.

  • x (Variable, optional) – The rectangular output \(x\) variable. By default, a new variable will be created. Both x and y must be specified together as either existing variables or as None.

  • y (Variable, optional) – The rectangular output \(y\) variable. By default, a new variable will be created. Both x and y must be specified together as either existing variables or as None.

  • angle_units (AngleUnits, optional) – The units of the angular variables. This may be either AngleUnits.Radians (default) or AngleUnits.Degrees.

  • zones (list of Zones, optional) – Specific zones to transform. By default, all zones are transformed.

This example will create two new variables in the dataset corresponding to the \(x\) and \(y\) equivalents of the \(r\) and \(theta\) variables:

>>> dataset = tp.active_frame().dataset
>>> r = dataset.variable('R (m)')
>>> theta = dataset.variable('Theta (rad)')
>>> tp.data.operate.transform_polar_to_rectangular(r, theta)
>>> x, y = dataset.variable(-2), dataset.variable(-1)

Warning

Source variables must have the same location as the destination variables.

The variables involved with this transformation must all be either nodal or cell-centered.

data.operate.transform_rectangular_to_polar()

tecplot.data.operate.transform_rectangular_to_polar(x, y, r=None, theta=None, angle_units=AngleUnits.Radians, zones=None)[source]

Transform all points from rectangular to polar coordinates.

Parameters:

  • x (Variable) – The rectangular input \(x\) variable.

  • y (Variable) – The rectangular input \(y\) variable.

  • r (Variable, optional) – The radial output \(r\) variable. By default, a new variable will be created. Both r and theta must be specified together as either existing variables or as None.

  • theta (Variable, optional) – The angular output \(theta\) variable. By default, a new variable will be created. Both r and theta must be specified together as either existing variables or as None.

  • angle_units (AngleUnits, optional) – The units of the angular variables. This may be either AngleUnits.Radians (default) or AngleUnits.Degrees.

  • zones (list of Zones, optional) – Specific zones to transform. By default, all zones are transformed.

This example will create two new variables in the dataset corresponding to the \(r\) and \(theta\) equivalents of the \(x\) and \(y\) variables:

>>> dataset = tp.active_frame().dataset
>>> x = dataset.variable('X (m)')
>>> y = dataset.variable('Y (m)')
>>> tp.data.operate.transform_rectangular_to_polar(x, y)
>>> theta, r = dataset.variable(-2), dataset.variable(-1)

Warning

Source variables must have the same location as the destination variables.

The variables involved with this transformation must all be either nodal or cell-centered.

data.operate.transform_rectangular_to_spherical()

tecplot.data.operate.transform_rectangular_to_spherical(x, y, z, r=None, theta=None, psi=None, angle_units=AngleUnits.Radians, zones=None)[source]

Transform all points from rectangular to spherical coordinates.

Parameters:

  • x (Variable) – The rectangular input \(x\) variable.

  • y (Variable) – The rectangular input \(y\) variable.

  • z (Variable) – The rectangular input \(z\) variable.

  • r (Variable, optional) – The radial output \(r\) variable. By default, a new variable will be created. All of r, theta and psi must be specified together as either existing variables or as None.

  • theta (Variable, optional) – The angular output \(theta\) variable from the \(x\)-axis. By default, a new variable will be created. All of r, theta and psi must be specified together as either existing variables or as None.

  • psi (Variable, optional) – The angular output \(psi\) variable from the \(z\)-axis. By default, a new variable will be created. All of r, theta and psi must be specified together as either existing variables or as None.

  • angle_units (AngleUnits, optional) – The units of the angular variables. This may be either AngleUnits.Radians (default) or AngleUnits.Degrees.

  • zones (list of Zones, optional) – Specific zones to transform. By default, all zones are transformed.

This example will create three new variables in the dataset corresponding to the \((r, theta, psi)\) equivalents of the \((x, y, z)\) variables:

>>> dataset = tp.active_frame().dataset
>>> x = dataset.variable('X (m)')
>>> y = dataset.variable('Y (m)')
>>> z = dataset.variable('Z (m)')
>>> tp.data.operate.transform_rectangular_to_spherical(x, y, z)
>>> theta = dataset.variable(-3)
>>> r = dataset.variable(-2)
>>> psi = dataset.variable(-1)

Warning

Source variables must have the same location as the destination variables.

The variables involved with this transformation must all be either nodal or cell-centered.

data.operate.transform_spherical_to_rectangular()

tecplot.data.operate.transform_spherical_to_rectangular(r, theta, psi, x=None, y=None, z=None, angle_units=AngleUnits.Radians, zones=None)[source]

Transform all points from spherical to rectangular coordinates.

Parameters:

  • r (Variable) – The radial input variable.

  • theta (Variable) – The angular input variable in angle_units from the \(x\)-axis.

  • psi (Variable) – The angular input variable in angle_units from the \(z\)-axis.

  • x (Variable, optional) – The rectangular output \(x\) variable. By default, a new variable will be created. All of x, y and z must be specified together as either existing variables or as None.

  • y (Variable, optional) – The rectangular output \(y\) variable. By default, a new variable will be created. All of x, y and z must be specified together as either existing variables or as None.

  • z (Variable, optional) – The rectangular output \(z\) variable. By default, a new variable will be created. All of x, y and z must be specified together as either existing variables or as None.

  • angle_units (AngleUnits, optional) – The units of the angular variables. This may be either AngleUnits.Radians (default) or AngleUnits.Degrees.

  • zones (list of Zones, optional) – Specific zones to transform. By default, all zones are transformed.

This example will create three new variables in the dataset corresponding to the \((x, y, z)\) equivalents of the \((r, theta, psi)\) variables:

>>> dataset = tp.active_frame().dataset
>>> r = dataset.variable('R (m)')
>>> theta = dataset.variable('Theta (rad)')
>>> psi = dataset.variable('Psi (rad)')
>>> tp.data.operate.transform_spherical_to_rectangular(r, theta, psi)
>>> x = dataset.variable(-3)
>>> y = dataset.variable(-2)
>>> z = dataset.variable(-1)

Warning

Source variables must have the same location as the destination variables.

The variables involved with this transformation must all be either nodal or cell-centered.

Data Extractions

data.extract.extract_blanked_zones()

tecplot.data.extract.extract_blanked_zones(*zones, **kwargs)[source]

Extract subsets of Zones based on blanking conditions of the plot.

Parameters:

*zones (Zones, required) – Set of source zones to extract.

Keyword Arguments:

plot (Cartesian3DFieldPlot or other data-backed plot type, optional) – The plot that defines the blanking conditions. By default, the active plot will be used.

Returns:

Zones – A list of the extracted zones.

A new zone will be created for each zone specified unless blanking dictates that all cells and points in the entire zone are blanked. The following example extracts all zones in the dataset:

import os

import tecplot as tp
from tecplot.constant import *

examples_dir = tp.session.tecplot_examples_directory()
datafile = os.path.join(examples_dir, 'SimpleData', 'VortexShedding.plt')

dataset = tp.data.load_tecplot(datafile)

plot = tp.active_frame().plot()
plot.show_contour = True

xax = plot.axes.x_axis
xax.min = -0.005
xax.max = 0.015
yax = plot.axes.y_axis
yax.min = -0.01
yax.max = 0.002

# Setup value blanking
vblank = plot.value_blanking
constraint = vblank.constraint(0)
constraint.variable_index = 1
constraint.comparison_operator = RelOp.GreaterThan
constraint.active = True
vblank.active = True

# Use list comprehension to get all zones assocaitated with
# a specific strand, in this case strand 1
in_zns = [zn for zn in dataset.zones() if zn.strand == 1]

# Extract all zones assocaitated with strand 1
ext_zns = tp.data.extract.extract_blanked_zones(in_zns)

# Place all extracted zones into the same strand
for zn in ext_zns:
    zn.strand = 2

# Turn off plotting for the original zone and turn on plotting
# of the extracted zones.
plot.fieldmap(0).show = False
plot.fieldmap(1).show = True

tp.export.save_time_animation_mpeg4('extract_blanked_zones.mp4',
                                    width=400, end_time=0.0004,
                                    supersample=3)

New in version 2020.1: Extracting blanked zones requires Tecplot 360 2020 R1 or later.

data.extract.extract_line()

tecplot.data.extract.extract_line(points, num_points=None, frame=None, dataset=None)[source]

Create new zone from a line in the dataset.

Parameters:

  • points (2D numeric array) – The points defining the line in two or three dimensions. This array must be of the shape (N, D) where N is the number points and D is the number of dimensions. That is, it must take the form [(x0, y0, z0), (x1, y1, z1) ...]. Points that do not lie within the dataset volume will be removed from the resulting zone.

  • num_points (int, optional) – The number of points to evenly distribute along the polyline. (default: length of points or N)

  • frame (Frame, optional) – A Frame that holds the Dataset to operate on which must match dataset if given. (default: currently active Frame)

  • dataset (Dataset, optional) – The Dataset to operate on which must be attached to frame if given. (default: currently active Dataset)

Returns:

A 1D OrderedZone representing a line through the data. Points outside of the dataset will be removed from the extracted zone resulting in fewer points than input.

Warning

Line extraction is only available when the plot type is set to Cartesian2D or Cartesian3D:

>>> from tecplot.constant import PlotType
>>> frame.plot_type = PlotType.Cartesian3D

This example shows how to extract a zone along a line, overlaying the result in a new frame:

import numpy as np
from os import path

import tecplot as tp
from tecplot.constant import *

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

frame = tp.active_frame()
frame.activate()
plot = frame.plot()
plot.contour(0).variable = dataset.variable("P(N/M2)")
plot.show_contour = True
plot.contour(0).levels.reset(num_levels=11)
plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'

plot.axes.y_axis.min = -0.01
plot.axes.y_axis.max = 0.01
plot.axes.x_axis.min = -0.005
plot.axes.x_axis.max = 0.015

xx = np.linspace(0, 0.01, 100)
yy = np.zeros(100)
line = tp.data.extract.extract_line(zip(xx, yy))

plot.show_mesh = True
plot.fieldmap(0).mesh.show = False

frame = tp.active_page().add_frame()
frame.position = (3.0, 0.5)
frame.height = 2
frame.width = 4
plot = tp.active_frame().plot(PlotType.XYLine)
plot.activate()

plot.delete_linemaps()
lmap = plot.add_linemap('data', line, x=dataset.variable('P(N/M2)'),
                        y=dataset.variable('T(K)'))
lmap.line.line_thickness = 2.0
plot.axes.x_axis(0).title.font.size = 10
plot.axes.y_axis(0).title.font.size = 10
plot.axes.viewport.left = 20
plot.axes.viewport.bottom = 20
plot.view.fit()

tp.export.save_png("extract_line.png", region=ExportRegion.AllFrames,
                   width=600, supersample=3)
../_images/extract_line.png

data.extract.extract_slice()

tecplot.data.extract.extract_slice(origin=(0, 0, 0), normal=(0, 0, 1), source=None, mode=ExtractMode.SingleZone, copy_cell_centers=None, assign_strand_ids=None, transient_mode=TransientOperationMode.SingleSolutionTime, frame=None, dataset=None, resulting_1d_zone_type=Resulting1DZoneType.FELineSegment, **kw)[source]

Create new zone from a plane in the dataset.

Parameters:
Returns:

One or a list of Zones representing a planar slice.

Warning

Slicing is only available when the plot type is set to 3D:

>>> from tecplot.constant import PlotType
>>> frame.plot_type = PlotType.Cartesian3D

Note

The extracted zone is returned if mode is ExtractMode.SingleZone and transient_mode is TransientOperationMode.SingleSolutionTime, otherwise a generator of the extracted zones.

See also

tecplot.plot.SliceGroup.extract()

This example shows extracting a slice zone from the surface a wing:

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

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

frame = tp.active_frame()
frame.plot_type = PlotType.Cartesian3D

# set active plot to 3D and extract
# an arbitrary slice from the surface
# data on the wing
extracted_slice = tp.data.extract.extract_slice(
    origin=(0, 0.25, 0),
    normal=(0, 1, 0),
    source=SliceSource.SurfaceZones,
    dataset=dataset)

# switch plot type in current frame, clear plot
plot = frame.plot(PlotType.XYLine)
plot.activate()
plot.delete_linemaps()

# create line plot from extracted zone data
cp_linemap = plot.add_linemap(
    name='Quarter-chord C_p',
    zone=extracted_slice,
    x=dataset.variable('x'),
    y=dataset.variable('Pressure_Coefficient'))

# set style of linemap plot and
# update axes limits to show data
cp_linemap.line.color = tp.constant.Color.Blue
cp_linemap.line.line_thickness = 0.8
cp_linemap.y_axis.reverse = True
plot.view.fit()

# export image of pressure coefficient as a function of x
tp.export.save_png('wing_slice_pressure_coeff.png', 600, supersample=3)
../_images/wing_slice_pressure_coeff.png

data.extract.extract_connected_regions()

tecplot.data.extract.extract_connected_regions(*zones, **kwargs)[source]

Create new zones from regions of connected cells.

Parameters:

*zones (Zones, required) – Source zones from which to extract connected regions. All source zones must be finite-element zones, either classic FE or polygonal/polyhedral.

Keyword Arguments:

  • assign_strand_ids (bool, optional) – Automatically assign strand IDs to the zones extracted from transient sources. If True the resulting zones will be all be assigned to the same newly created strand id. (default: True)

  • frame (Frame, optional) – A Frame that holds the Dataset to operate on which must match dataset if given. (default: currently active Frame)

  • dataset (Dataset, optional) – The Dataset to operate on which must be attached to frame if given. (default: currently active Dataset)

Returns:

A list of the extracted Zones.

New in version 2020.2: Extracting connected regions requires Tecplot 360 2020 R2 or later.

data.extract.triangulate()

tecplot.data.extract.triangulate(*zones, **kwargs)[source]

Create a new zone by forming triangles from points in 2D zones.

Parameters:

*zones (Zones) – Set of 2-dimensional source zones to triangulate.

Keyword Arguments:

  • boundary_zones (list of \(I\)-Ordered Zones, optional) – Set of \(I\)-ordered zones that define the boundaries across which no triangles can be created. (default: None)

  • include_boundary_points (bool, optional) – If True, boundary points will be used to create triangles. (default: False)

  • keep_factor (float in the range [0.0, 0.5], optional) – The smaller the number, the more likely it will be that highly obtuse triangles will be created opening toward the outside of the triangulated zone.

  • plot (Cartesian2DFieldPlot, optional) – The plot defining the \((x, y)\) variables in the dataset. If not set, the active plot will be used which must be of type Cartesian2DFieldPlot.

Returns:

ClassicFEZone – The resulting triangulated zone.

The active plot or the plot specified must be of type Cartesian2DFieldPlot and the boundary zones, if supplied, may only be \(I\)-ordered zones. This example creates a new zone by triangulating data points from two other Zones:

>>> zone0 = dataset.zone('Zone 0')
>>> zone1 = dataset.zone('Zone 1')
>>> new_zone = tp.data.extract.triangulate(zone0, zone1)

Data Access

Dataset

class tecplot.data.Dataset(uid, frame)[source]

Table of Arrays identified by Zone and Variable.

This is the primary data container within the Tecplot Engine. A Dataset can be shared among several Frames, though any particular Dataset object will have a handle to at least one of them. Any modification of a shared Dataset will be reflected in all Frames that use it.

Though a Dataset is usually attached to a Frame and the plot style associated with that, it can be thought of as independent from any style or plotting representation. Each Dataset consists of a list of Variables which are used by one or more of a list of Zones. The Variable determines the data type, while the Zone determines the layout such as shape and ordered vs unordered.

The actual data are found at the intersection of a Zone and Variable and the resulting object is an Array. The data array can be obtained using either path:

>>> # These two lines obtain the same object "x"
>>> x = dataset.zone('My Zone').values('X')
>>> x = dataset.variable('X').values('My Zone')

A Dataset is the object returned by most data-loading operations in PyTecplot:

>>> dataset = tecplot.data.load_tecplot('my_data.plt')

Under Dataset, there are a number methods to create and delete Zones and variables.

Attributes

VariablesNamedTuple

A collections.namedtuple object using variable names.

aux_data

Auxiliary data for this dataset.

num_solution_times

Number of solution times in the dataset.

num_variables

int: Number of Variables in this Dataset.

num_zones

int: Number of Zones in this Dataset.

solution_time_clustering

Solution time clustering options.

solution_times

Solution times in the dataset.

title

str: Title of this Dataset.

variable_names

A list of names for all variables in the dataset.

zone_names

A list of names for all zones in the dataset.

Methods

add_fe_mixed_zone(name, num_points, ...)

Add a finite-element Zone consisting of one or more blocks of uniform cell-type (sections).

add_fe_zone(zone_type, name, num_points, ...)

Add a single finite-element Zone to this Dataset.

add_ordered_zone(name, shape, **kwargs)

Add a single ordered Zone to this Dataset.

add_poly_zone(zone_type, name, num_points, ...)

Add a single polygonal Zone to this Dataset.

add_variable(name[, dtypes, locations])

Add a single Variable to the active Dataset.

add_zone(zone_type, name, shape[, dtypes, ...])

Add a single Zone to this Dataset.

branch_connectivity(zones)

Breaks connectivity sharing between zones.

branch_variables(zones, variables[, copy_data])

Breaks data sharing between zones.

copy_zones(*zones, **kwargs)

Copies Zones within this Dataset.

delete_variables(*variables)

Remove Variables from this Dataset.

delete_zones(*zones)

Remove Zones from this Dataset.

mirror_zones(mirror_variables, *zones)

Mirrors Zones within this Dataset.

share_connectivity(source_zone, ...)

Share connectivity between zones.

share_variables(source_zone, ...)

Share field data between zones.

variable(pattern)

Returns the Variable by index or string pattern.

variables([pattern])

Yields all Variables matching a pattern.

zone(pattern)

Returns Zone by index or string pattern.

zones([pattern])

Yields all Zones matching a pattern.
Dataset.VariablesNamedTuple

A collections.namedtuple object using variable names.

The variable names are transformed to be unique, valid identifiers suitable for use as the key-list for a collections.namedtuple. This means that all invalid characters such as spaces and dashes are converted to underscores, Python keywords are appended by an underscore, leading numbers or empty names are prepended with a “v” and duplicate variable names are indexed starting with zero, padded left with zeros variable names duplicated more than nine times. The following table gives some specific examples:

Variable names

Resulting namedtuple fields

'x', 'y'

'x', 'y'

'x', 'x'

'x0', 'x1'

'X', 'Y=f(X)'

'X', 'Y_f_X_'

'x 2', '_', '_'

'x_2', 'v0', 'v1'

'def', 'if'

'def_', 'if_'

'1', '2', '3'

'v1', 'v2', 'v3'

This example shows how one can use this n-tuple type with the result from a call to tecplot.data.query.probe_at_position:

from os import path
import tecplot as tp

examples_dir = tp.session.tecplot_examples_directory()
datafile = path.join(examples_dir,'SimpleData','DownDraft.plt')
dataset = tp.data.load_tecplot(datafile)
result = tp.data.query.probe_at_position(0,0.1,0.3)
data = dataset.VariablesNamedTuple(*result.data)

# prints: (RHO, E) = (1.17, 252930.37)
msg = '(RHO, E) = ({:.2f}, {:.2f})'
print(msg.format(data.RHO, data.E))
Dataset.add_fe_mixed_zone(name, num_points, sections, **kwargs)[source]

Add a finite-element Zone consisting of one or more blocks of uniform cell-type (sections).

Parameters:

  • name (str) – have to be unique.

  • num_points (int) – Number of points (nodes) in this zone.

  • sections (list or tuples) – A list of the properties of the sections in the form: (num_elements, cell_shape, grid_order, basis_function). The grid_order and basis_func items may be omitted and will default to 1 (linear) and FECellBasisFunction.Lagrangian respectively.

  • **kwargs – These arguments are passed to Dataset.add_zone.

See also

Dataset.add_zone

Keyword arguments are passed to the parent zone creation method Dataset.add_zone.

Warning

Setting connectivity in connected mode.

When connected to a running instance of Tecplot 360 using the TecUtil Server, care must be taken to ensure that the GUI does not try to render the data between the creation of the zone and the setting of the connectivity, through the Facemap or Nodemap objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset to PlotType.Sketch before creating the zone and only going to PlotType.Cartesian3D after the connectivity is set. Tecplot 360 may get into a bad state, corrupting loaded data, if it attempts to render (especially polytope) data without connectivity.

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

For more details, see the “working with datasets” examples shipped with PyTecplot in the Tecplot 360 distribution.

Dataset.add_fe_zone(zone_type, name, num_points, num_elements, **kwargs)[source]

Add a single finite-element Zone to this Dataset.

Parameters:

  • zone_type (ZoneType) – created. Possible values are: FETriangle, FEQuad, FETetra, FEBrick and FELineSeg.

  • name (str) – does not have to be unique.

  • num_points (int) – Number of points (nodes) in this zone.

  • num_elements (int) – Number of elements in this zone. The nodemap will have the shape (num_points, num_elements).

  • **kwargs – These arguments are passed to Dataset.add_zone.

See also

Dataset.add_zone

Keyword arguments are passed to the parent zone creation method Dataset.add_zone.

Warning

Setting connectivity in connected mode.

When connected to a running instance of Tecplot 360 using the TecUtil Server, care must be taken to ensure that the GUI does not try to render the data between the creation of the zone and the setting of the connectivity, through the Facemap or Nodemap objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset to PlotType.Sketch before creating the zone and only going to PlotType.Cartesian3D after the connectivity is set. Tecplot 360 may get into a bad state, corrupting loaded data, if it attempts to render (especially polytope) data without connectivity.

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

The number of points (also known as nodes) per finite-element is determined from the zone_type parameter. The follow table shows the number of points per element for the available zone types along with the resulting shape of the nodemap based on the number of points specified (\(N\)):

Zone Type

Points/Element

Nodemap Shape

FELineSeg

2

(\(N\), \(2 N\))

FETriangle

3

(\(N\), \(3 N\))

FEQuad

4

(\(N\), \(4 N\))

FETetra

4

(\(N\), \(4 N\))

FEBrick

8

(\(N\), \(8 N\))

For more details, see the “working with datasets” examples shipped with PyTecplot in the Tecplot 360 distribution.

Dataset.add_ordered_zone(name, shape, **kwargs)[source]

Add a single ordered Zone to this Dataset.

Parameters:

  • name (str) – does not have to be unique.

  • shape (int or list of integers) – Specifies the length and dimension (i, j, k) of the new Zone. A 1D Zone is assumed if a single int is given.

  • **kwargs – These arguments are passed to Dataset.add_zone.

See also

Dataset.add_zone

Keyword arguments are passed to the parent zone creation method Dataset.add_zone.

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

This example creates a 10x10x10 ordered zone of double-precision floating-point numbers:

>>> from tecplot.constant import FieldDataType
>>> my_zone = dataset.add_zone('My Zone', (10, 10, 10),
...                            dtypes=FieldDataType.Double)

Here is a full example:

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

# Generate data
x = np.linspace(-4, 4, 100)

# Setup Tecplot dataset
dataset = tp.active_frame().create_dataset('Data', ['x', 'y'])

# Create a zone
zone = dataset.add_ordered_zone('sin(x)', len(x))
zone.values('x')[:] = x
zone.values('y')[:] = np.sin(x)

# Create another zone
zone = dataset.add_ordered_zone('cos(x)', len(x))
zone.values('x')[:] = x
zone.values('y')[:] = np.cos(x)

# And one more zone
zone = dataset.add_ordered_zone('tan(x)', len(x))
zone.values('x')[:] = x
zone.values('y')[:] = np.tan(x)

# Set plot type to XYLine
plot = tp.active_frame().plot(PlotType.XYLine)
plot.activate()

# Show all linemaps and make the lines a bit thicker
for lmap in plot.linemaps():
    lmap.show = True
    lmap.line.line_thickness = 0.6

plot.legend.show = True

tp.export.save_png('add_ordered_zones.png', 600, supersample=3)
../_images/add_ordered_zones.png
Dataset.add_poly_zone(zone_type, name, num_points, num_elements, num_faces, **kwargs)[source]

Add a single polygonal Zone to this Dataset.

Parameters:

  • zone_type (ZoneType) – created. Possible values are: FEPolyhedron and FEPolygon.

  • name (str) – does not have to be unique.

  • num_points (int) – Number of points in this zone.

  • num_elements (int) – Number of elements in this zone.

  • num_faces (int) – Number of faces in this zone.

  • **kwargs – These arguments are passed to Dataset.add_zone.

See also

Dataset.add_zone

Keyword arguments are passed to the parent zone creation method Dataset.add_zone.

Warning

Setting connectivity in connected mode.

When connected to a running instance of Tecplot 360 using the TecUtil Server, care must be taken to ensure that the GUI does not try to render the data between the creation of the zone and the setting of the connectivity, through the Facemap or Nodemap objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset to PlotType.Sketch before creating the zone and only going to PlotType.Cartesian3D after the connectivity is set. Tecplot 360 may get into a bad state, corrupting loaded data, if it attempts to render (especially polytope) data without connectivity.

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

Note

The num_faces is the number of unique faces.

The number of unique faces, given an element map can be obtained using the following function for polygon data:

def num_unique_faces(elementmap):
    return len(set( tuple(sorted([e[i], e[(i+1)%len(e)]]))
                for e in elementmap for i in range(len(e)) ))

This function creates a unique set of node pairs (edges around the polygons) and counts them. For polyhedron data, the following can be used:

def num_unique_faces(elementmap):
    return len(set( tuple(sorted(f)) for e in elementmap
                                     for f in e ))

which merely counts the number of unique faces defined in the element map.

For more details, see the “working with datasets” examples shipped with PyTecplot in the Tecplot 360 distribution.

Dataset.add_variable(name, dtypes=None, locations=None)[source]

Add a single Variable to the active Dataset.

Parameters:
Returns:

Variable

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

The added Variable will be available for use in each Zone of the dataset. This method should be used in conjunction with other data creation methods such as Dataset.add_zone:

import math
import tecplot as tp
from tecplot.constant import PlotType

# Setup Tecplot dataset
dataset = tp.active_frame().create_dataset('Data')
dataset.add_variable('x')
dataset.add_variable('s')
zone = dataset.add_ordered_zone('Zone', 100)

# Fill the dataset
x = [0.1 * i for i in range(100)]
zone.values('x')[:] = x
zone.values('s')[:] = [math.sin(i) for i in x]

# Set plot type to XYLine
tp.active_frame().plot(PlotType.XYLine).activate()

tp.export.save_png('add_variables.png', 600, supersample=3)
../_images/add_variables.png
Dataset.add_zone(zone_type, name, shape, dtypes=None, locations=None, face_neighbor_mode=None, parent_zone=None, solution_time=None, strand_id=None, index=None, sections=None)[source]

Add a single Zone to this Dataset.

Parameters:
Returns:

Zone

Warning

Setting connectivity in connected mode.

When connected to a running instance of Tecplot 360 using the TecUtil Server, care must be taken to ensure that the GUI does not try to render the data between the creation of the zone and the setting of the connectivity, through the Facemap or Nodemap objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset to PlotType.Sketch before creating the zone and only going to PlotType.Cartesian3D after the connectivity is set. Tecplot 360 may get into a bad state, corrupting loaded data, if it attempts to render (especially polytope) data without connectivity.

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

The added Zone will be able to use all Variables defined in the dataset. This method should be used in conjunction with other data creation methods such as Frame.create_dataset. Example usage:

>>> from tecplot.constant import ZoneType
>>> zone = dataset.add_zone(ZoneType.Ordered, 'Zone', (10, 10, 10))

Note

The relationship and meaning of this method’s parameters change depending on the type of zone being created. Therefore, it is recommended to use the more specific zone creation methods:

Dataset.aux_data

Auxiliary data for this dataset.

Returns:

AuxData

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

>>> frame = tp.active_frame()
>>> aux = frame.dataset.aux_data
>>> aux['Result'] = '3.14159'
>>> print(aux['Result'])
3.14159
Dataset.branch_connectivity(zones)[source]

Breaks connectivity sharing between zones.

Parameters:

zones (list of Zones) – Zones to be branched.

See also

Dataset.share_connectivity()

Example usage:

>>> z = dataset.zone('My Zone')
>>> zcopy = z.copy()
>>> print([zn.index for zn in z.shared_connectivity])
[0,1]
>>> dataset.branch_connectivity(zcopy)
>>> print([zn.index for zn in z.shared_connectivity])
[]
Dataset.branch_variables(zones, variables, copy_data=True)[source]

Breaks data sharing between zones.

Parameters:

  • zones (list of Zones) – Zones to be branched.

  • variables (list of Variables) – Variables to be branched.

  • copy_data (bool, optional) – Allocate space for the branched values and copy the data. If False, the new variables will be passive. (default: True)

See also

Dataset.share_variables()

Variable sharing allows you to lower the use of physical memory (RAM). When sharing a variable the memory used by the source zone is shared with the copied zone. Alterations to the variable in one zone will affect the other. Example usage:

>>> z = dataset.zone('My Zone')
>>> zcopy = z.copy(share_variables=True)
>>> print([zn.index for zn in z.values(0).shared_zones])
[0,1]
>>> dataset.branch_variables(zcopy,dataset.variable(0))
>>> print([zn.index for zn in z.values(0).shared_zones])
[]
>>> print([zn.index for zn in z.values(1).shared_zones])
[0,1]
Dataset.copy_zones(*zones, **kwargs)[source]

Copies Zones within this Dataset.

Parameters:

  • *zones (Zone, optional) – Specific Zones to copy. All zones will be copied if none are supplied.

  • shared_variables (bool or list of Variables) – Share all variables between the original and generated zones if True or the list of Variables to be shared. Variable sharing allows you to lower the use of physical memory (RAM). When sharing a variable the memory used by the source zone is shared with the copied zone. Alterations to the variable in one zone will affect the other. See also Dataset.branch_variables(). Default: False.

  • i_range (tuple of integers, optional) – Range (min, max, step) along the i dimension for ordered data. Min and max are zero-based indicies where max is inclusive. If step causes max to be skipped, max will be included. If None (default), the entire range will be copied.

  • j_range (tuple of integers, optional) – Range (min, max, step) along the j dimension for ordered data. Min and max are zero-based indicies where max is inclusive. If step causes max to be skipped, max will be included. If None (default), the entire range will be copied.

  • k_range (tuple of integers, optional) – Range (min, max, step) along the k dimension for ordered data. Min and max are zero-based indicies where max is inclusive. If step causes max to be skipped, max will be included. If None (default), the entire range will be copied.

Returns:

list of the newly created Zones.

Note

Performance considerations for data manipulations.

When performing many data-manipulation operations including adding zones, adding variables, modifying field data or connectivity, and especially in connected mode, it is recommended to do this all with the tecplot.session.suspend(). This will prevent the Tecplot engine from trying to “keep up” with the changes. Tecplot will be notified of all changes made upon exit of this context. This may result in significant performance gains for long operations.

import tecplot as tp

ds = tp.active_page().add_frame().create_dataset('D', ['x','y','z'])
z = ds.add_ordered_zone('Z1', (3,3,3))
ds.copy_zones(z, i_range=(1, -1, 2), share_variables=True)
Dataset.delete_variables(*variables)[source]

Remove Variables from this Dataset.

Parameters:

*variables (Variable or index int) – Variables to remove from this dataset.

>>> print(dataset.variable_names)
['X','Y','Z']
>>> dataset.delete_variables(dataset.variable('Z'))
>>> print(dataset.variable_names)
['X','Y']

Warning

Deleting Variables invalidates iterators referencing them in the containing Dataset such as those obtained from Dataset.variables(). It is recommended to create a list of the Variables you want to delete and to pass that into a single call to Dataset.delete_variables()

Notes

Multiple Variables can be deleted at once, though the last Variable can not be deleted. The following example deletes all but the first Variable in the Dataset (usually X):

>>> # Try to delete all variables:
>>> dataset.delete_variables(dataset.variables())
>>> # Dataset requires at least one variable to
>>> # exist, so it leaves the first one:
>>> print(dataset.variable_names)
['X']
Dataset.delete_zones(*zones)[source]

Remove Zones from this Dataset.

Parameters:

*zones (Zones or index integers) – Zones to remove from this dataset.

>>> print(dataset.zone_names)
['Zone 1', 'Zone 2']
>>> dataset.delete_zones(dataset.zone('Zone 2'))
>>> print(dataset.zone_names)
['Zone 1']

Warning

Deleting Zones invalidates iterators referencing them in the containing Dataset such as those obtained from Dataset.zones(). It is recommended to create a list of the Zones you want to delete and to pass that into a single call to Dataset.delete_zones()

Notes

Multiple Zones can be deleted at once, though the last Zone can not be deleted. The following example deletes all but the first Zone in the Dataset:

>>> dataset.delete_zones(dataset.zones())
Dataset.mirror_zones(mirror_variables, *zones)[source]

Mirrors Zones within this Dataset.

Each mirror zone has a name of the form “Mirror of zone sourcezone”, where sourcezone is the number of the zone from which the mirrored zone was created. The variables in the newly created zones are shared with their corresponding source zones, except for variables to be mirrored as specified.

Parameters:

  • mirror_variables (Variable or list of Variables) – Variables in the new zone to be multiplied by \(-1\) after the zone is copied. the variables may be Variable objects, str names or int indices.

  • *zones (Zones, optional) – Specific Zones to mirror. All zones will be mirrored if none are supplied. The values may also be str names or int indices.

Returns:

Generator of mirrored zones.

This example show how to mirror all zones across the xy-plane in 3D:

>>> mirrored_zones = dataset.mirror_zones('Z')
Dataset.num_solution_times

Number of solution times in the dataset.

This property is read-only. Solution times are grouped according to the current solution time clustering settings (see Dataset.solution_time_clustering). Example usage:

>>> print(dataset.num_solution_times)
10

New in version 2017.3: Solution time manipulation requires Tecplot 360 2017 R3 or later.

Type:

int (read-only)

Dataset.num_variables

int: Number of Variables in this Dataset.

This count includes disabled variables which were skipped when the data was loaded. Example usage:

>>> for i in range(dataset.num_variables):
...     variable = dataset.variable(i)
Dataset.num_zones

int: Number of Zones in this Dataset.

This count includes disabled zones which were skipped when loading the data. Example usage:

>>> for i in range(dataset.num_zones):
...     zone = dataset.zone(i)
Dataset.share_connectivity(source_zone, destination_zones)[source]

Share connectivity between zones.

This method links the connectivity (nodemap or facemap) of the destination zones to the connectivity of the source zone. Modifying the connectivity of one zone will affect all others in this group.

Parameters:

  • source_zone (Zone) – Zone which provides data to be shared.

  • destination_zones (list of Zones) – Zones where connectivity list will be overwritten.

See also

Dataset.branch_connectivity()

Example usage:

>>> z = dataset.zone('My Zone')
>>> zcopy = z.copy()
>>> print([zn.index for zn in z.shared_connectivity])
[0,1]
>>> dataset.branch_connectivity(zcopy)
>>> print([zn.index for zn in z.shared_connectivity])
[]
>>> dataset.share_connectivity(z,zcopy)
>>> print([zn.index for zn in z.shared_connectivity])
[0,1]
Dataset.share_variables(source_zone, destination_zones, variables)[source]

Share field data between zones.

This method links the underlying data arrays of the destination zones to the data of the source zone. Modifying the array data of one zone will affect all others in this group.

Parameters:

  • source_zone (Zone) – Zone which provides data to be shared.

  • destination_zones (list of Zones) – Zones where data will be overwritten.

  • variables (list of Variables) – Variables to be shared.

See also

Dataset.branch_variables()

Example usage:

>>> z = dataset.zone('My Zone')
>>> zcopy = z.copy(share_variables=False)
>>> print([zn.index for zn in z.values(0).shared_zones])
[]
>>> dataset.share_variables(zcopy,[z],[dataset.variable(0)])
>>> print([zn.index for zn in z.values(0).shared_zones])
[0,1]
>>> print([zn.index for zn in z.values(1).shared_zones])
[]
Dataset.solution_time_clustering

Solution time clustering options.

Example usage:

>>> from tecplot.constant import TimeScaling
>>> dataset.solution_time_clustering.time_scaling = \
...     TimeScaling.Logarithmic

New in version 2021.2: Solution time clustering requires Tecplot 360 2021 R2 or later.

Type:

SolutionTimeClustering

Dataset.solution_times

Solution times in the dataset.

This property is read-only. Solution times are grouped according to the current solution time clustering settings (see Dataset.solution_time_clustering). Example usage:

>>> print(dataset.solution_times)
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]

New in version 2017.3: Solution time manipulation requires Tecplot 360 2017 R3 or later.

Type:

list of floats

Dataset.title

str: Title of this Dataset.

Example usage:

>>> dataset.title = 'My Data'

Changed in version 2017.3: of Tecplot 360 The dataset title property requires Tecplot 360 2017 R3 or later.

Dataset.variable(pattern)[source]

Returns the Variable by index or string pattern.

Parameters:

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

Returns:

Variable or None if no matching Variable name was found.

Raises:

TecplotIndexError –

Note

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

The Variable.name attribute is used to match the pattern to the desired Variable though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: ['Rectangular zone']
  Variables: ['x', 'y', 'z']
>>> x = ds.variable('x')
>>> x == ds.variable(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

Dataset.variable_names

A list of names for all variables in the dataset.

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> print(dataset.variable_names)
['x', 'y', 'z', 's']
Dataset.variables(pattern=None)[source]

Yields all Variables matching a pattern.

Parameters:

pattern (str or re.Pattern, optional) – Case-insensitive glob-style pattern string or a compiled regex pattern instance used to match variable names.

Returns:

Generator of Variables. All Variables if pattern is not specified.

Example using case-insensitive glob-style matching:

>>> for variable in dataset.variables('A*'):
...     array = variable.values('My Zone')

Example using (case-sensitive) regex:

>>> import re
>>> for variable in dataset.variables(re.compile(r'A.*')):
...     array = variable.values('My Zone')

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

Dataset.zone(pattern)[source]

Returns Zone by index or string pattern.

Parameters:

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

Returns:

OrderedZone, ClassicFEZone or PolyFEZone depending on the zone type, None no matching zone name was found.

Raises:

TecplotIndexError –

Note

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

The Zone.name attribute is used to match the pattern to the desired Zone though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: ['Rectangular zone']
  Variables: ['x', 'y', 'z']
>>> rectzone = ds.zone('Rectangular zone')
>>> rectzone == ds.zone(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

Dataset.zone_names

A list of names for all zones in the dataset.

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> print(dataset.zone_names)
['Zone A', 'Zone B', 'Zone C']
Dataset.zones(pattern=None)[source]

Yields all Zones matching a pattern.

Parameters:

pattern (str or re.Pattern, optional) – Case-insensitive glob-style pattern string or a compiled regex pattern instance used to match zone names.

Returns:

Generator of OrderedZones, ClassicFEZones or PolyFEZones depending on the zone types. All zones if pattern is not specified.

If pattern is a string, this will be interpreted as a case-insensitive glob-style pattern. Example using glob which will match all zones starting with “A” or “a”:

>>> for zone in dataset.zones('A*'):
...     x_array = zone.variable('X')

Alternatively, a regular-expression may be compiled using re.compile which will be used directly. This example shows a case-sensitive regex search:

>>> import re
>>> pattern = re.compile('[A-Z]*')
>>> for zone in dataset.zones(pattern):
...     x_array = zone.variable('X')

Use list comprehension to construct a list of all zones with ‘Wing’ in the zone name. In contrast to the Dataset.zones() method, this is case-sensitive:

>>> wing_zones = [Z for Z in dataset.zones() if 'Wing' in Z.name]

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

SolutionTimeClustering

class tecplot.data.SolutionTimeClustering(dataset)[source]

Settings for controlling solution time clustering of solution times.

By default, a dataset’s zones solution times are organized into time steps with the assumption that they are distributed linearly, and are grouped together if they are within the following solution time tolerance:

(tolerance_factor)*(max_time - min_time) + absolute_tolerance

Where by default the tolerance_factor is 1.0e-6 and the absolute_tolerance is 0.0.

Attributes

absolute_tolerance

Absolute tolerance used to cluster zones by their solution times.

time_scaling

Scaling used to cluster zones by their solution times.

tolerance_factor

Tolerance factor used to cluster zones by solution times.
SolutionTimeClustering.absolute_tolerance

Absolute tolerance used to cluster zones by their solution times.

Absolute tolerance applied to each solution time when clustering with nearby times. (Default: 0.0)

Example usage:

>>> dataset.solution_time_clustering.absolute_tolerance = 0.0025

New in version 2021.2: Solution time clustering requires Tecplot 360 2021 R2 or later.

Type:

float

SolutionTimeClustering.time_scaling

Scaling used to cluster zones by their solution times.

Identifies if the dataset’s zone solution times are distributed linearly or logarithmically. (Default: TimeScaling.Linear)

Possible values: TimeScaling.Linear, TimeScaling.Logarithmic.

Example usage:

>>> from tecplot.constant import TimeScaling
>>> dataset.solution_time_clustering.time_scaling = \
...     TimeScaling.Logarithmic

New in version 2021.2: Solution time clustering requires Tecplot 360 2021 R2 or later.

Type:

TimeScaling

SolutionTimeClustering.tolerance_factor

Tolerance factor used to cluster zones by solution times.

Tolerance factor, which is multiplied by the overall solution time delta and applied to each solution time when clustering nearby times. (Default: 1.0e-6)

Example usage:

>>> dataset.solution_time_clustering.tolerance_factor = 1.0e-4

New in version 2021.2: Solution time clustering requires Tecplot 360 2021 R2 or later.

Type:

float

Variable

class tecplot.data.Variable(uid, dataset)[source]

Key value for a data array within a Dataset.

Variables can be identified (uniquely) by the index within their parent Dataset or (non-uniquely) by name. In general, a Zone must also be selected to access the underlying data array. This object is used by several style controlling classes such as contours and vectors. The following example sets the contour variable for the first contour group to the first variable named ‘S’:

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

Attributes

aux_data

Auxiliary data for this variable.

index

Index: Zero-based position within the parent Dataset.

lock_mode

Type of lock or None (read-only).

name

Returns or sets the name.

num_zones

int: Number of Zones in the parent Dataset.

Methods

max()

Upper bound of the values stored in this variable across all zones.

min()

Lower bound of the values stored in this variable across all zones.

minmax()

Limits of the values stored in this variable across all zones.

values(pattern)

Returns Array by index or string pattern.
Variable.aux_data

Auxiliary data for this variable.

Returns:

AuxData

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

>>> frame = tp.active_frame()
>>> aux = frame.dataset.variable('X').aux_data
>>> aux['X_weighted_avg'] = '3.14159'
>>> print(aux['X_weighted_avg'])
3.14159
Variable.index

Index: Zero-based position within the parent Dataset.

Example usage:

>>> plot.contour(0).variable_index = dataset.variable('S').index
Variable.lock_mode

Type of lock or None (read-only).

Variables may be locked as a result of other operations, typically through the use of the CFD Analyzer. This read-only property returns None if the variable is not locked, VarLockMode.ValueChange if the variable may not be modified or VarLockMode.Delete if the variable may not be deleted.

This example modifies variable s only if it is not locked:

>>> variable_s = dataset.variable('s')
>>> if variable_s.lock_mode is None:
...     tp.data.operate.execute_equation('{s} = {p}**2')

New in version 2019.1: Variable lock mode property requires Tecplot 360 2019 R1 or later.

Type:

VarLockMode

Variable.max()[source]

Upper bound of the values stored in this variable across all zones.

Return type:

float

This always returns a float regardless of the underlying data type:

>>> print(dataset.variable('x').max())
10
Variable.min()[source]

Lower bound of the values stored in this variable across all zones.

Return type:

float

This always returns a float regardless of the underlying data type:

>>> print(dataset.variable('x').min())
0
Variable.minmax()[source]

Limits of the values stored in this variable across all zones.

Return type:

2-tuple of floats

This always returns floats regardless of the underlying data type:

>>> print(dataset.variable('x').minmax())
(0, 10)
Variable.name

Returns or sets the name.

Return type:

string

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> print(dataset.variable(0).name)
X
Variable.num_zones

int: Number of Zones in the parent Dataset.

Example usage, looping over all zones by index:

>>> for zindex in range(dataset.num_zones):
...     zone = dataset.zone(zindex)
Variable.values(pattern)[source]

Returns Array by index or string pattern.

Parameters:

pattern (int, str or Zone) – Zero-based index or glob-style pattern in which case, the first match is returned, or a Zone object.

Note

Data operations can make use of Numpy when installed.

When doing large data transfers into and out of Tecplot using PyTecplot, it is recommended to install the Python array-processing module Numpy. PyTecplot will automatically use this to optimize data transfers which may result in significant performance gains.

The Zone.name attribute is used to match the pattern to the desired Array though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: 'Rectangular zone'
  Variables: 'x', 'y', 'z'
>>> x = ds.variable('x')
>>> rectzone = x.values('Rectangular zone')
>>> rectzone == x.values(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

Zones

tecplot.data.zone

Zones describe the size, shape, element (cell) geometry, connectivity and solution time of arrays in a dataset. Zones created as “ordered,” “classic finite-element,” or “polytopal finite-element” along with the number of nodes and elements which can not be changed (without creating a new zone). Ordered zones are always considered to be logically-rectangular grids of one, two or three dimensions depending on the shape. Classic finite-element zones must use a fixed-type element throughout. This means that each element has the same number of faces and nodes. Polytopal zones can have a varying number of faces and nodes for each element. The connectivity is implied in ordered zones and explicitly provided by the user for finite-element zones.

In PyTecplot, there are three zone class objects: OrderedZone, ClassicFEZone and PolyFEZone. The OrderedZone and ClassicFEZone classes use the FaceNeighbors class to handle “global” face-neighbor connections from one zone to another. The connectivity for the ClassicFEZone objects are accessed through the Nodemap class. The PolyFEZone objects provide element definition and connectivity access through the Facemap class.

OrderedZone

class tecplot.data.OrderedZone(uid, dataset)[source]

An ordered (i, j, k) zone within a Dataset.

Ordered zones contain nodal or cell-centered arrays where the connectivity is implied by the dimensions and ordering of the data.

Zones can be identified (uniquely) by the index with their parent Dataset or (non-uniquely) by name. In general, a Variable must be selected to access the underlying data array. This object is used by fieldmaps and linemaps to apply style to specific zones. Here we obtain the fieldmap associated with the zone named ‘My Zone’:

>>> fmap = plot.fieldmap(dataset.zone('My Zone'))

Attributes

aux_data

Auxiliary data for this zone.

dimensions

Nodal dimensions along (i, j, k).

face_neighbors

The face neighbor list for this ordered zone.

index

Index: Zero-based position within the parent Dataset.

name

The name of the zone.

num_elements

Number of cells in this zone.

num_faces

Number of faces in this zone.

num_faces_per_element

Number of faces per element in this ordered zone.

num_points

Total number of nodes within this zone.

num_points_per_element

Points per cell for ordered zones.

num_variables

int: Number of Variables in the parent Dataset.

rank

Number of dimensions of the data array.

solution_time

The solution time for this zone.

strand

The strand ID number.

zone_type

The ZoneType indicating structure of the data contained.

Methods

copy([share_variables, i_range, j_range, ...])

Duplicate this Zone in the parent Dataset.

mirror(mirror_variables)

Mirror this Zone.

values(pattern)

Returns an Array by index or string pattern.
OrderedZone.aux_data

Auxiliary data for this zone.

Returns:

AuxData

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

>>> frame = tp.active_frame()
>>> aux = frame.dataset.zone('My Zone').aux_data
>>> aux['X_weighted_avg'] = '3.14159'
>>> print(aux['X_weighted_avg'])
3.14159
OrderedZone.copy(share_variables=False, i_range=None, j_range=None, k_range=None)[source]

Duplicate this Zone in the parent Dataset.

The name is also copied but can be changed after duplication.

Parameters:

  • share_variables (bool or list of Variables) – Share all variables between the original and generated zones if True or the list of Variables to be shared. Variable sharing allows you to lower the use of physical memory (RAM). When sharing a variable the memory used by the source zone is shared with the copied zone. Alterations to the variable in one zone will affect the other. See also Dataset.branch_variables(). Default: False.

  • i_range (tuple of integers, optional) – Range (min, max, step) along the i dimension for ordered data. Min and max are zero-based indicies where max is inclusive. If step causes max to be skipped, max will be included. If None (default), the entire range will be copied.

  • j_range (tuple of integers, optional) – Range (min, max, step) along the j dimension for ordered data. Min and max are zero-based indicies where max is inclusive. If step causes max to be skipped, max will be included. If None (default), the entire range will be copied.

  • k_range (tuple of integers, optional) – Range (min, max, step) along the k dimension for ordered data. Min and max are zero-based indicies where max is inclusive. If step causes max to be skipped, max will be included. If None (default), the entire range will be copied.

Returns:

Zone – The newly created zone.

import tecplot as tp

ds = tp.active_page().add_frame().create_dataset('D', ['x','y','z'])
z = ds.add_ordered_zone('Z1', (3,3,3))
zcopy = z.copy(i_range=(1, -1, 2))
OrderedZone.dimensions

Nodal dimensions along (i, j, k).

Returns:

tuple of integers – (i, j, k) for ordered data.

Example usage:

>>> print(zone.dimensions)
(128, 128, 128)
OrderedZone.face_neighbors

The face neighbor list for this ordered zone.

Example usage:

>>> zone = dataset.zone(0)
>>> print(zone.face_neighbors.mode)
FaceNeighborMode.LocalOneToOne
Type:

FaceNeighbors

OrderedZone.index

Index: Zero-based position within the parent Dataset.

This is the value used to obtain a specific zone if you have duplicately named zones in the dataset:

>>> tp.new_layout()
>>> frame = tp.active_frame()
>>> dataset = frame.create_dataset('Dataset', ['x', 'y'])
>>> dataset.add_ordered_zone('Zone', (10,10,10))
>>> dataset.add_ordered_zone('Zone', (3,3,3))
>>> # getting zone by name always returns first match
>>> print(dataset.zone('Zone').index)
0
>>> # use index to get specific zone
>>> print(dataset.zone(1).dimensions)
(3, 3, 3)
OrderedZone.mirror(mirror_variables)

Mirror this Zone.

The name of the resulting zone will be “Mirror of zone sourcezone”, where sourcezone is the number of the zone from which the mirrored zone was created. The variables in the newly created zones are shared with their corresponding source zones, except for variables to be mirrored as specified.

Parameters:

mirror_variables (Variable or list of Variables) – Variables in the new zone to be multiplied by \(-1\) after the zone is copied. the variables may be Variable objects, str names or int indices.

Returns:

A zone of the same type as the source.

This example show how to mirror the first zone across the xy-plane in 3D:

>>> mirrored_zone = dataset.zone(0).mirror('Z')
OrderedZone.name

The name of the zone.

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> dataset.zone(0).name = 'Zone 0'
Type:

str

OrderedZone.num_elements

Number of cells in this zone.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.dimensions)
(128, 128, 128)
>>> print(zone.num_elements)
2048383
Type:

int

OrderedZone.num_faces

Number of faces in this zone.

This is the same as the number of elements times the number of faces per element. Example usage:

>>> print(dataset.zone('My Zone').num_faces)
1048576
Type:

int

OrderedZone.num_faces_per_element

Number of faces per element in this ordered zone.

This is determined by the rank of the zone:

Rank

Faces Per Element

0

0

1

1

2

4

3

6

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.dimensions)
(1, 10, 10)
>>> print(zone.rank)
2
>>> print(zone.num_faces_per_element)
4
Type:

int

OrderedZone.num_points

Total number of nodes within this zone.

This is number of nodes within the zone and is equivalent to the product of the values in OrderedZone.dimensions. Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.dimensions)
(128, 128, 128)
>>> print(zone.num_points)
2097152
Type:

int

OrderedZone.num_points_per_element

Points per cell for ordered zones.

For ordered zones, this is \(2^{d}\) where \(d\) is the number of dimensions greater than one:

Rank

Faces Per Element

0

0

1

2

2

4

3

8

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.dimensions)
(10, 10, 1)
>>> print(zone.rank)
2
>>> print(zone.num_points_per_element)
4
Type:

int

OrderedZone.num_variables

int: Number of Variables in the parent Dataset.

Example usage, iterating over all variables by index:

>>> for i in range(dataset.num_variables):
...     variable = dataset.variable(i)
OrderedZone.rank

Number of dimensions of the data array.

This will return the number of dimensions which contain more than one value:

>>> zone = dataset.zone('My Zone')
>>> print(zone.dimensions)
(10, 10, 1)
>>> print(zone.rank)
2
Type:

int

OrderedZone.solution_time

The solution time for this zone.

Example usage:

>>> dataset.zone('My Zone').solution_time = 3.14

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

float

OrderedZone.strand

The strand ID number.

Example usage:

>>> dataset.zone('My Zone').strand = 2

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

int

OrderedZone.values(pattern)

Returns an Array by index or string pattern.

Parameters:

pattern (int, str or Variable) – Zero-based index, glob-style pattern in which case, the first match is returned, or a Variable object.

Note

Data operations can make use of Numpy when installed.

When doing large data transfers into and out of Tecplot using PyTecplot, it is recommended to install the Python array-processing module Numpy. PyTecplot will automatically use this to optimize data transfers which may result in significant performance gains.

The Variable.name attribute is used to match the pattern to the desired Array though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: ['Rectangular zone']
  Variables: ['x', 'y', 'z']
>>> zone = ds.zone('Rectangular zone')
>>> x = zone.values('x')
>>> x == zone.values(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

OrderedZone.zone_type

The ZoneType indicating structure of the data contained.

The specific type of zone this object represents:

>>> print(dataset.zone(0).zone_type)
ZoneType.Ordered
Type:

ZoneType

ClassicFEZone

class tecplot.data.ClassicFEZone(uid, dataset)[source]

A classic finite-element zone within a Dataset.

Classic finite-element zones are arrays of nodes that are connected explicitly into pre-defined geometric shapes called “elements.” The geometry is consistent across the whole zone so that the number of nodes per element is constant.

Zones can be identified (uniquely) by the index with their parent Dataset or (non-uniquely) by name. In general, a Variable must be selected to access the underlying data array. This object is used by fieldmaps and linemaps to apply style to specific zones. Here we obtain the fieldmap associated with the zone named ‘My Zone’:

>>> fmap = plot.fieldmap(dataset.zone('My Zone'))

Attributes

aux_data

Auxiliary data for this zone.

face_neighbors

The face neighbor list for this finite-element zone.

index

Index: Zero-based position within the parent Dataset.

name

The name of the zone.

nodemap

The connectivity for this finite-element zone.

num_elements

Number of cells in this finite-element zone.

num_faces

Number of faces in this zone.

num_faces_per_element

Number of faces per element.

num_points

Total number of nodes within this zone.

num_points_per_element

Points per element for classic finite-element zones.

num_variables

int: Number of Variables in the parent Dataset.

rank

Number of dimensions of the data array.

shared_connectivity

Zones sharing connectivity.

solution_time

The solution time for this zone.

strand

The strand ID number.

zone_type

The ZoneType indicating structure of the data contained.

Methods

copy([share_variables])

Duplicate this Zone in the parent Dataset.

mirror(mirror_variables)

Mirror this Zone.

values(pattern)

Returns an Array by index or string pattern.
ClassicFEZone.aux_data

Auxiliary data for this zone.

Returns:

AuxData

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

>>> frame = tp.active_frame()
>>> aux = frame.dataset.zone('My Zone').aux_data
>>> aux['X_weighted_avg'] = '3.14159'
>>> print(aux['X_weighted_avg'])
3.14159
ClassicFEZone.copy(share_variables=False)

Duplicate this Zone in the parent Dataset.

The name is also copied but can be changed after duplication.

Parameters:

share_variables (bool or list of Variables) – Share all variables between the original and generated zones if True or the list of Variables to be shared. Variable sharing allows you to lower the use of physical memory (RAM). When sharing a variable the memory used by the source zone is shared with the copied zone. Alterations to the variable in one zone will affect the other. See also Dataset.branch_variables(). Default: False.

Returns:

Zone – The newly created zone.

Example:

>>> new_zone = dataset.zone('My Zone').copy()
>>> print(new_zone.name)
My Zone
>>> new_zone.name = 'My Zone Copy'
>>> print(new_zone.name)
My Zone Copy
ClassicFEZone.face_neighbors

The face neighbor list for this finite-element zone.

Example usage:

>>> zone = dataset.zone(0)
>>> print(zone.face_neighbors.mode)
FaceNeighborMode.LocalOneToMany
Type:

FaceNeighbors

ClassicFEZone.index

Index: Zero-based position within the parent Dataset.

This is the value used to obtain a specific zone if you have duplicately named zones in the dataset:

>>> tp.new_layout()
>>> frame = tp.active_frame()
>>> dataset = frame.create_dataset('Dataset', ['x', 'y'])
>>> dataset.add_ordered_zone('Zone', (10,10,10))
>>> dataset.add_ordered_zone('Zone', (3,3,3))
>>> # getting zone by name always returns first match
>>> print(dataset.zone('Zone').index)
0
>>> # use index to get specific zone
>>> print(dataset.zone(1).dimensions)
(3, 3, 3)
ClassicFEZone.mirror(mirror_variables)

Mirror this Zone.

The name of the resulting zone will be “Mirror of zone sourcezone”, where sourcezone is the number of the zone from which the mirrored zone was created. The variables in the newly created zones are shared with their corresponding source zones, except for variables to be mirrored as specified.

Parameters:

mirror_variables (Variable or list of Variables) – Variables in the new zone to be multiplied by \(-1\) after the zone is copied. the variables may be Variable objects, str names or int indices.

Returns:

A zone of the same type as the source.

This example show how to mirror the first zone across the xy-plane in 3D:

>>> mirrored_zone = dataset.zone(0).mirror('Z')
ClassicFEZone.name

The name of the zone.

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> dataset.zone(0).name = 'Zone 0'
Type:

str

ClassicFEZone.nodemap

The connectivity for this finite-element zone.

Example usage:

>>> zone = dataset.zone(0)
>>> print(zone.nodemap.num_points_per_element)
4
Type:

ClassicNodemap

ClassicFEZone.num_elements

Number of cells in this finite-element zone.

Example usage:

>>> print(dataset.zone('My Zone').num_elements)
1048576
Type:

int

ClassicFEZone.num_faces

Number of faces in this zone.

This is the same as the number of elements times the number of faces per element. Example usage:

>>> print(dataset.zone('My Zone').num_faces)
1048576
Type:

int

ClassicFEZone.num_faces_per_element

Number of faces per element.

This is dependent on the type of element this zone contains:

Zone Type

Faces Per Element

FELineSeg

1

FETriangle

3

FEQuad

4

FETetra

4

FEBrick

6

Example usage:

>>> print(dataset.zone('My Zone').num_faces_per_element)
4
Type:

int

ClassicFEZone.num_points

Total number of nodes within this zone.

This is the total number of nodes in the zone. Example usage:

>>> print(dataset.zone('My Zone').num_points)
2048
Type:

int

ClassicFEZone.num_points_per_element

Points per element for classic finite-element zones.

The number of points (also known as nodes) per finite-element is determined from the zone_type parameter. The following table shows the number of points per element for the available zone types along with the resulting shape of the nodemap based on the number of elements specified (\(N\)):

Zone Type

Points/Element

Nodemap Shape

FELineSeg

2

(\(N\), \(2\))

FETriangle

3

(\(N\), \(3\))

FEQuad

4

(\(N\), \(4\))

FETetra

4

(\(N\), \(4\))

FEBrick

8

(\(N\), \(8\))

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.zone_type)
ZoneType.FETriangle
>>> print(zone.num_points_per_element)
3
Type:

int

ClassicFEZone.num_variables

int: Number of Variables in the parent Dataset.

Example usage, iterating over all variables by index:

>>> for i in range(dataset.num_variables):
...     variable = dataset.variable(i)
ClassicFEZone.rank

Number of dimensions of the data array.

This indicates the dimensionality of the data and is dependent on the type of element this zone contains.

Zone Type

Rank

FELineSeg

1

FETriangle

2

FEQuad

2

FETetra

3

FEBrick

3

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.zone_type)
ZoneType.FEBrick
>>> print(zone.rank)
3
Type:

int

ClassicFEZone.shared_connectivity

Zones sharing connectivity.

Example usage:

>>> dataset.zone('My Zone').copy()
>>> for zone in dataset.zone('My Zone').shared_connectivity:
...     print(zone.index)
0
1
Type:

list of Zones

ClassicFEZone.solution_time

The solution time for this zone.

Example usage:

>>> dataset.zone('My Zone').solution_time = 3.14

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

float

ClassicFEZone.strand

The strand ID number.

Example usage:

>>> dataset.zone('My Zone').strand = 2

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

int

ClassicFEZone.values(pattern)

Returns an Array by index or string pattern.

Parameters:

pattern (int, str or Variable) – Zero-based index, glob-style pattern in which case, the first match is returned, or a Variable object.

Note

Data operations can make use of Numpy when installed.

When doing large data transfers into and out of Tecplot using PyTecplot, it is recommended to install the Python array-processing module Numpy. PyTecplot will automatically use this to optimize data transfers which may result in significant performance gains.

The Variable.name attribute is used to match the pattern to the desired Array though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: ['Rectangular zone']
  Variables: ['x', 'y', 'z']
>>> zone = ds.zone('Rectangular zone')
>>> x = zone.values('x')
>>> x == zone.values(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

ClassicFEZone.zone_type

The ZoneType indicating structure of the data contained.

The specific type of zone this object represents:

>>> print(dataset.zone(0).zone_type)
ZoneType.FEBrick
Type:

ZoneType

MixedFEZone

class tecplot.data.MixedFEZone(uid, dataset)[source]

A finite-element zone capable of storing high-order elements.

The nodemap for mixed finite-element zones consists of sections of uniform cell types:

>>> zone = dataset.zone('My Zone')

See also

Dataset.add_fe_mixed_zone()

Attributes

aux_data

Auxiliary data for this zone.

face_neighbors

The face neighbor list for this finite-element zone.

index

Index: Zero-based position within the parent Dataset.

name

The name of the zone.

nodemap

The connectivity Nodemap for this finite-element zone.

num_elements

Number of cells in this finite-element zone.

num_faces

Number of faces in this zone.

num_points

Total number of nodes within this zone.

num_sections

The number of sections of uniform cell type.

num_variables

int: Number of Variables in the parent Dataset.

rank

Number of dimensions of the data array.

shared_connectivity

Zones sharing connectivity.

solution_time

The solution time for this zone.

strand

The strand ID number.

zone_type

The ZoneType indicating structure of the data contained.

Methods

copy([share_variables])

Duplicate this Zone in the parent Dataset.

mirror(mirror_variables)

Mirror this Zone.

section_metrics(section_index)

Returns the type and number of cells within a specific section.

values(pattern)

Returns an Array by index or string pattern.
MixedFEZone.aux_data

Auxiliary data for this zone.

Returns:

AuxData

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

>>> frame = tp.active_frame()
>>> aux = frame.dataset.zone('My Zone').aux_data
>>> aux['X_weighted_avg'] = '3.14159'
>>> print(aux['X_weighted_avg'])
3.14159
MixedFEZone.copy(share_variables=False)

Duplicate this Zone in the parent Dataset.

The name is also copied but can be changed after duplication.

Parameters:

share_variables (bool or list of Variables) – Share all variables between the original and generated zones if True or the list of Variables to be shared. Variable sharing allows you to lower the use of physical memory (RAM). When sharing a variable the memory used by the source zone is shared with the copied zone. Alterations to the variable in one zone will affect the other. See also Dataset.branch_variables(). Default: False.

Returns:

Zone – The newly created zone.

Example:

>>> new_zone = dataset.zone('My Zone').copy()
>>> print(new_zone.name)
My Zone
>>> new_zone.name = 'My Zone Copy'
>>> print(new_zone.name)
My Zone Copy
MixedFEZone.face_neighbors

The face neighbor list for this finite-element zone.

Example usage:

>>> zone = dataset.zone(0)
>>> print(zone.face_neighbors.mode)
FaceNeighborMode.LocalOneToMany
Type:

FaceNeighbors

MixedFEZone.index

Index: Zero-based position within the parent Dataset.

This is the value used to obtain a specific zone if you have duplicately named zones in the dataset:

>>> tp.new_layout()
>>> frame = tp.active_frame()
>>> dataset = frame.create_dataset('Dataset', ['x', 'y'])
>>> dataset.add_ordered_zone('Zone', (10,10,10))
>>> dataset.add_ordered_zone('Zone', (3,3,3))
>>> # getting zone by name always returns first match
>>> print(dataset.zone('Zone').index)
0
>>> # use index to get specific zone
>>> print(dataset.zone(1).dimensions)
(3, 3, 3)
MixedFEZone.mirror(mirror_variables)

Mirror this Zone.

The name of the resulting zone will be “Mirror of zone sourcezone”, where sourcezone is the number of the zone from which the mirrored zone was created. The variables in the newly created zones are shared with their corresponding source zones, except for variables to be mirrored as specified.

Parameters:

mirror_variables (Variable or list of Variables) – Variables in the new zone to be multiplied by \(-1\) after the zone is copied. the variables may be Variable objects, str names or int indices.

Returns:

A zone of the same type as the source.

This example show how to mirror the first zone across the xy-plane in 3D:

>>> mirrored_zone = dataset.zone(0).mirror('Z')
MixedFEZone.name

The name of the zone.

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> dataset.zone(0).name = 'Zone 0'
Type:

str

MixedFEZone.nodemap

The connectivity Nodemap for this finite-element zone.

Example usage:

>>> zone = dataset.zone(0)
>>> print(zone.nodemap.num_points_per_element)
4
Type:

Nodemap

MixedFEZone.num_elements

Number of cells in this finite-element zone.

Example usage:

>>> print(dataset.zone('My Zone').num_elements)
1048576
Type:

int

MixedFEZone.num_faces

Number of faces in this zone.

This is the same as the number of elements times the number of faces per element. Example usage:

>>> print(dataset.zone('My Zone').num_faces)
1048576
Type:

int

MixedFEZone.num_points

Total number of nodes within this zone.

This is the total number of nodes in the zone. Example usage:

>>> print(dataset.zone('My Zone').num_points)
2048
Type:

int

MixedFEZone.num_sections

The number of sections of uniform cell type.

Note

This property is read-only.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.num_sections)
1
MixedFEZone.num_variables

int: Number of Variables in the parent Dataset.

Example usage, iterating over all variables by index:

>>> for i in range(dataset.num_variables):
...     variable = dataset.variable(i)
MixedFEZone.rank

Number of dimensions of the data array.

This indicates the dimensionality of the data and is dependent on the type of element this zone contains. All sections within an “FE-mixed zone” (a zone of type ZoneType.FEMixed), must consist of sections with the same dimension.

Note

All sections within a zone are required to have the same dimensionality and therefore only the first section is queried when fetching the rank of the zone.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.zone_type)
ZoneType.FEMixed
>>> print(zone.section_metrics(0).cell_shape)
FECellShape.Tetrahedron
>>> print(zone.rank)
3
Type:

int

MixedFEZone.section_metrics(section_index)[source]

Returns the type and number of cells within a specific section.

Returns:

collections.namedtuple
cell_shape: FECellShape

The shape of all cells within this section.

grid_order: int

The grid-order for all cell in this section. A value of one indicates a “linear” cell with corners only and no high-order nodes.

basis_func: FECellBasisFunction

This determines the number of high-order nodes for a given shape and grid order as well as the winding (ordering) of the nodes. Currently, only FECellBasisFunction.Lagrangian is supported.

num_elements: int

The total number of elements or cells within this section.

num_corners_per_elem: int

The number of linear nodes (those nodes at the corners) for all cells within this section.

num_nodes_per_elem: int

The total number of nodes for each cell within this section. This will be the same as num_corners_per_elem for grid-order one cells.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> metrics = zone.section_metrics(0)  # first section
>>> print(metrics.cell_shape)
FECellShape.Tetrahedron
>>> print(metrics.grid_order)
2
>>> print(metrics.basis_func)
FECellBasisFunction.Lagrangian
>>> print(metrics.num_elements)
1024
MixedFEZone.shared_connectivity

Zones sharing connectivity.

Example usage:

>>> dataset.zone('My Zone').copy()
>>> for zone in dataset.zone('My Zone').shared_connectivity:
...     print(zone.index)
0
1
Type:

list of Zones

MixedFEZone.solution_time

The solution time for this zone.

Example usage:

>>> dataset.zone('My Zone').solution_time = 3.14

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

float

MixedFEZone.strand

The strand ID number.

Example usage:

>>> dataset.zone('My Zone').strand = 2

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

int

MixedFEZone.values(pattern)

Returns an Array by index or string pattern.

Parameters:

pattern (int, str or Variable) – Zero-based index, glob-style pattern in which case, the first match is returned, or a Variable object.

Note

Data operations can make use of Numpy when installed.

When doing large data transfers into and out of Tecplot using PyTecplot, it is recommended to install the Python array-processing module Numpy. PyTecplot will automatically use this to optimize data transfers which may result in significant performance gains.

The Variable.name attribute is used to match the pattern to the desired Array though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: ['Rectangular zone']
  Variables: ['x', 'y', 'z']
>>> zone = ds.zone('Rectangular zone')
>>> x = zone.values('x')
>>> x == zone.values(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

MixedFEZone.zone_type

The ZoneType indicating structure of the data contained.

This will always return ZoneType.FEMixed for MixedFEZone:

>>> print(dataset.zone(0).zone_type)
ZoneType.FEMixed
Type:

ZoneType

PolyFEZone

class tecplot.data.PolyFEZone(uid, dataset)[source]

A polygonal finite-element zone within a Dataset.

A polygonal zone consists of arrays of nodes which are connected explicitly into arbitrary and varying geometric elements. These elements are 2D or 3D in nature and have a number of faces (connections between nodes) which hold the concept of a left and right neighbor.

Zones can be identified (uniquely) by the index with their parent Dataset or (non-uniquely) by name. In general, a Variable must be selected to access the underlying data array. This object is used by fieldmaps and linemaps to apply style to specific zones. Here we obtain the fieldmap associated with the zone named ‘My Zone’:

>>> fmap = plot.fieldmap(dataset.zone('My Zone'))

Attributes

aux_data

Auxiliary data for this zone.

facemap

The connectivity Facemap for this polygonal finite-element zone.

index

Index: Zero-based position within the parent Dataset.

name

The name of the zone.

num_elements

Number of cells in this finite-element zone.

num_faces

Number of faces in this finite-element zone.

num_points

Total number of nodes within this zone.

num_variables

int: Number of Variables in the parent Dataset.

rank

Number of dimensions of the data array.

shared_connectivity

Zones sharing connectivity.

solution_time

The solution time for this zone.

strand

The strand ID number.

zone_type

The ZoneType indicating structure of the data contained.

Methods

copy([share_variables])

Duplicate this Zone in the parent Dataset.

mirror(mirror_variables)

Mirror this Zone.

values(pattern)

Returns an Array by index or string pattern.
PolyFEZone.aux_data

Auxiliary data for this zone.

Returns:

AuxData

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

>>> frame = tp.active_frame()
>>> aux = frame.dataset.zone('My Zone').aux_data
>>> aux['X_weighted_avg'] = '3.14159'
>>> print(aux['X_weighted_avg'])
3.14159
PolyFEZone.copy(share_variables=False)

Duplicate this Zone in the parent Dataset.

The name is also copied but can be changed after duplication.

Parameters:

share_variables (bool or list of Variables) – Share all variables between the original and generated zones if True or the list of Variables to be shared. Variable sharing allows you to lower the use of physical memory (RAM). When sharing a variable the memory used by the source zone is shared with the copied zone. Alterations to the variable in one zone will affect the other. See also Dataset.branch_variables(). Default: False.

Returns:

Zone – The newly created zone.

Example:

>>> new_zone = dataset.zone('My Zone').copy()
>>> print(new_zone.name)
My Zone
>>> new_zone.name = 'My Zone Copy'
>>> print(new_zone.name)
My Zone Copy
PolyFEZone.facemap

The connectivity Facemap for this polygonal finite-element zone.

Example usage:

>>> zone = dataset.zone(0)
>>> print(zone.facemap.num_faces)
4500
Type:

Facemap

PolyFEZone.index

Index: Zero-based position within the parent Dataset.

This is the value used to obtain a specific zone if you have duplicately named zones in the dataset:

>>> tp.new_layout()
>>> frame = tp.active_frame()
>>> dataset = frame.create_dataset('Dataset', ['x', 'y'])
>>> dataset.add_ordered_zone('Zone', (10,10,10))
>>> dataset.add_ordered_zone('Zone', (3,3,3))
>>> # getting zone by name always returns first match
>>> print(dataset.zone('Zone').index)
0
>>> # use index to get specific zone
>>> print(dataset.zone(1).dimensions)
(3, 3, 3)
PolyFEZone.mirror(mirror_variables)

Mirror this Zone.

The name of the resulting zone will be “Mirror of zone sourcezone”, where sourcezone is the number of the zone from which the mirrored zone was created. The variables in the newly created zones are shared with their corresponding source zones, except for variables to be mirrored as specified.

Parameters:

mirror_variables (Variable or list of Variables) – Variables in the new zone to be multiplied by \(-1\) after the zone is copied. the variables may be Variable objects, str names or int indices.

Returns:

A zone of the same type as the source.

This example show how to mirror the first zone across the xy-plane in 3D:

>>> mirrored_zone = dataset.zone(0).mirror('Z')
PolyFEZone.name

The name of the zone.

Warning

Newlines in string identifiers may affect performance.

When iterating over many items by name, such as must be done when fetching an item via pattern matching, PyTecplot will optimize the search only if there are no newline characters in the searched items. Iterating over strings that contain newlines will be slower and therefore, it is best to avoid using newlines in string identifiers or names of objects such as Zones or Variables.

Example usage:

>>> dataset.zone(0).name = 'Zone 0'
Type:

str

PolyFEZone.num_elements

Number of cells in this finite-element zone.

Example usage:

>>> print(dataset.zone('My Zone').num_elements)
1048576
Type:

int

PolyFEZone.num_faces

Number of faces in this finite-element zone.

The number of faces may be 0 if unknown or facemap creation is deferred. Example usage:

>>> print(dataset.zone('My Zone').num_faces)
1048576
Type:

int

PolyFEZone.num_points

Total number of nodes within this zone.

This is the total number of nodes in the zone. Example usage:

>>> print(dataset.zone('My Zone').num_points)
2048
Type:

int

PolyFEZone.num_variables

int: Number of Variables in the parent Dataset.

Example usage, iterating over all variables by index:

>>> for i in range(dataset.num_variables):
...     variable = dataset.variable(i)
PolyFEZone.rank

Number of dimensions of the data array.

This indicates the dimensionality of the data and is dependent on the type of element this zone contains:

Zone Type

Rank

FEPolygon

2

FEPolyhedron

3

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.zone_type)
ZoneType.FEPolygon
>>> print(zone.rank)
2
Type:

int

PolyFEZone.shared_connectivity

Zones sharing connectivity.

Example usage:

>>> dataset.zone('My Zone').copy()
>>> for zone in dataset.zone('My Zone').shared_connectivity:
...     print(zone.index)
0
1
Type:

list of Zones

PolyFEZone.solution_time

The solution time for this zone.

Example usage:

>>> dataset.zone('My Zone').solution_time = 3.14

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

float

PolyFEZone.strand

The strand ID number.

Example usage:

>>> dataset.zone('My Zone').strand = 2

Note

Possible side-effect when connected to Tecplot 360.

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

Type:

int

PolyFEZone.values(pattern)

Returns an Array by index or string pattern.

Parameters:

pattern (int, str or Variable) – Zero-based index, glob-style pattern in which case, the first match is returned, or a Variable object.

Note

Data operations can make use of Numpy when installed.

When doing large data transfers into and out of Tecplot using PyTecplot, it is recommended to install the Python array-processing module Numpy. PyTecplot will automatically use this to optimize data transfers which may result in significant performance gains.

The Variable.name attribute is used to match the pattern to the desired Array though this is not necessarily unique:

>>> ds = frame.dataset
>>> print(ds)
Dataset:
  Zones: ['Rectangular zone']
  Variables: ['x', 'y', 'z']
>>> zone = ds.zone('Rectangular zone')
>>> x = zone.values('x')
>>> x == zone.values(0)
True

Warning

Zone and variable ordering may change between releases

Due to possible changes in data loaders or data formats over time, the ordering of zones and variables may be different between versions of Tecplot 360. Therefore it is recommended to always reference zones and variables by name instead of by index.

PolyFEZone.zone_type

The ZoneType indicating structure of the data contained.

The specific type of zone this object represents:

>>> print(dataset.zone(0).zone_type)
ZoneType.FEPolygon
Type:

ZoneType

Array

class tecplot.data.Array(zone, variable)[source]

Low-level accessor for underlying data within a Dataset.

This object exposes a list-like interface to the underlying data array. Using it, values can be directly queried and modified. After any modification to the data, the Tecplot Engine will have to be notified of the change. This notification will happen automatically in most cases, but can be turned off using the suspend context for a significant performance increase on large data sets.

Accessing values within an Array is done through the standard [] syntax:

>>> print(array[3])
3.1415

The numbers passed are interpreted just like Python’s built-in slice object:

>>> # print the values at indices: 5, 7, 9
>>> print(array[5:10:2])
[1.0, 1.0, 1.0]

Elements within an array can be manipulated in-place with the assignment operator:

>>> array[3] = 5.0
>>> print(array[3])
5.0

Element-by-element access is not guaranteed to be performant and users should avoid writing loops over indices in Python. Instead, whole arrays should be used. This will effectively push the loop down to the underlying native library and will be much faster in virtually all cases.

Consider this array of 10k elements:

>>> ds = frame.create_dataset('Dataset', ['x'])
>>> zn = ds.add_ordered_zone('Zone', 10000)
>>> array = zn.values('x')

The following loop, which takes the sine of all values in the array will require several Python function calls per element which is a tremendous overhead:

>>> import math
>>> for i in range(len(ar)):
...     ar[i] = math.sin(ar[i])

An immediate improvement on this can be made by looping over the elements in Python only when reading the values, but assigning them using the whole array. This will be several times faster for even modest arrays:

>>> ar[:] = [math.sin(x) for x in ar]

But there is still a large performance penalty for looping over elements directly in Python and PyTecplot supports two solutions for large arrays: tecplot.data.operate.execute_equation and Array.as_numpy_array(). Please refer to these for details. Continuing with the example above, we could accomplish the same thing with either of the following using execute_equation() (assuming the array is identified by the first zone, first variable):

>>> from tecplot.data.operate import execute_equation
>>> execute_equation('V1 = SIN(V1)', zones=[dataset.zone(0)])

or by using the numpy library:

>>> import numpy as np
>>> ar[:] = np.sin(ar[:])

In both of these cases, the calculation of the sine and loop over elements is pushed to the low level library and is much faster. Note that only the execute_equation() solution does the calculation within Tecplot and does not require the data to copied out to Python so it will typically be the fastest option.

Note

When modifying data using this class, it may be necessary to update the range of any associated contouring with a call to ContourLevels.reset() or similar. This will ensure that the total range of the new values is presented in the plot.

Attributes

c_type

ctypes compatible data type of this array.

data_type

Indicating the underlying value type of this array.

location

Data points location with respect to the elements.

passive

An unallocated zone-variable combination.

shape

(i, j, k) shape for this array.

shared_zones

All Zones sharing this array.

Methods

as_numpy_array([offset, size, copy])

Present the underlying data array as a numpy.ndarray.

copy([offset, size])

Copy the whole or part of the array into a ctypes array.

max()

Upper bound of the values stored in this array.

min()

Lower bound of the values stored in this array.

minmax()

Limits of the values stored in this array.
Array.as_numpy_array(offset=0, size=None, copy=True)[source]

Present the underlying data array as a numpy.ndarray.

If the copy parameter is False, this method will attempt to return an array pointing to the actual data stored in the Tecplot Engine. This will fail in connected mode or if the loader does not support immediate loading of the entire array into memory. Care should be taken to ensure the validity of the pointers to the data.

Parameters:

  • offset (int, optional) – Zero-based offset into the array. This will be the starting point of the resulting data. (default: 0)

  • size (int, optional) – Number of elements in the resulting array. The default (a value of None) is to go to the end of the data.

  • copy (bool, optional) – Copy the data out from the Tecplot Engine. If False, an attempt is made to point to the underlying raw data and an exception is thrown on error. (default: True)

Returns:

numpy.ndarray

Example usage:

>>> x = dataset.zone('Zone').values('X').as_numpy_array()
Array.c_type

ctypes compatible data type of this array.

This is the ctypes equivalent of Array.data_type and will return one of the following:

and can be used to create a ctypes array to store a copy of the data:

import tecplot as tp
frame = tp.active_frame()
dataset = frame.create_dataset('Dataset', ['x'])
dataset.add_ordered_zone('Zone', (3,3,3))
x = dataset.zone('Zone').values('x')
# allocate array using Python's ctypes
x_array = (x.c_type * len(x))()
# copy values from Dataset into ctypes array
x_array[:] = x[:]
Array.copy(offset=0, size=None)[source]

Copy the whole or part of the array into a ctypes array.

Parameters:

  • offset (int, optional) – Zero-based offset for starting index to copy. (default: 0)

  • size (int, optional) – Number of values to copy into the resulting array. A value of None will copy to the end of the array. (default: None)

Here we will copy out chunks of the data, do some operation and set the values back into the dataset:

import tecplot as tp

tp.new_layout()
frame = tp.active_frame()
dataset = frame.create_dataset('Dataset', ['x'])
dataset.add_ordered_zone('Zone', (2, 2, 2))
x = dataset.zone('Zone').values('x')

# loop over array copying out 4 values at a time
for i, offset in enumerate(range(0, len(x), 4)):
    x_array = x.copy(offset, 4)
    x_array[:] = [i] * 4
    x[offset:offset + 4] = x_array

# will print: [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]
print(x[:])
Array.data_type

Indicating the underlying value type of this array.

Example usage:

>>> print(dataset.zone('Zone').values('X').data_type)
FieldDataType.Float
Type:

FieldDataType

Array.location

Data points location with respect to the elements.

Possible values are ValueLocation.CellCentered and ValueLocation.Nodal. Example usage:

>>> print(dataset.zone(0).values('X').location)
ValueLocation.Nodal
Type:

ValueLocation

Array.max()[source]

Upper bound of the values stored in this array.

Return type:

float

This always returns a float regardless of the underlying data type:

>>> print(dataset.zone('Zone').values('x').max())
10
Array.min()[source]

Lower bound of the values stored in this array.

Return type:

float

This always returns a float regardless of the underlying data type:

>>> print(dataset.zone('Zone').values('x').min())
0
Array.minmax()[source]

Limits of the values stored in this array.

Return type:

tuple of floats

This always returns floats regardless of the underlying data type:

>>> print(dataset.zone('Zone').values('x').minmax())
(0, 10)
Array.passive

An unallocated zone-variable combination.

Passive variables are placeholders where no data is defined for a zone variable combination. Passive variables will always return zero when queried:

import tecplot as tp

ds = tp.active_page().add_frame().create_dataset('D', ['x','y'])
z = ds.add_ordered_zone('Z1', (3,))
assert not z.values(0).passive
Type:

bool

Array.shape

(i, j, k) shape for this array.

This is defined by the parent zone and can be used to reshape arrays. The following example assumes 32-bit floating point array and copies the Tecplot-owned data into the numpy-owned array:

>>> import numpy as np
>>> data = dataset.zone('Zone').values('X')
>>> array = np.empty(data.shape, dtype=np.float32)
>>> arr_ptr = array.ctypes.data_as(POINTER(data.c_type))
>>> memmove(arr_ptr, data.copy(), sizeof(data.c_type) * len(data))

The data array presented is normally one-dimensional. For ordered data, you may wish to reshape the array indexing according to the dimensionality given by the shape attribute:

import numpy as np
import tecplot as tp

frame = tp.active_frame()
dataset = frame.create_dataset('Dataset', ['X'])
zone = dataset.add_ordered_zone('Zone', shape=(3,3,3))

'''
the following will print:
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
'''
x = np.array(zone.values('X')[:])
print(x)

'''
the following will print:
[[[ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]]

 [[ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]]

 [[ 0.  0.  0.]
  [ 0.  0.  0.]
  [ 0.  0.  0.]]]
'''
x.shape = zone.values('X').shape
print(x)
Type:

tuple of floats

Array.shared_zones

All Zones sharing this array.

Example usage:

>>> dataset.zone('My Zone').copy(share_variables=True)
>>> for z in dataset.zone('My Zone').values(0).shared_zones:
...     print(z.index)
0
1
Type:

list of Zones

FECellType

class tecplot.data.FECellType(cell_shape, grid_order, basis_func)[source]

Attributes

basis_func

The basis function used for this cell.

grid_order

Grid order of the nodes in the cell (order 1 is the linear case).

num_corners

Number of nodes at the corners of the cell.

num_high_order_nodes

Number of high-order nodes for the cell (not the corners).

num_nodes

Total number of nodes for the cell including corners and high-order nodes.

shape

Geometric shape of the cell.
FECellType.basis_func

The basis function used for this cell.

Type:

FECellBasisFunction

FECellType.grid_order

Grid order of the nodes in the cell (order 1 is the linear case).

Type:

int

FECellType.num_corners

Number of nodes at the corners of the cell.

Type:

int

FECellType.num_high_order_nodes

Number of high-order nodes for the cell (not the corners).

Type:

int

FECellType.num_nodes

Total number of nodes for the cell including corners and high-order nodes.

Type:

int

FECellType.shape

Geometric shape of the cell.

Type:

FECellShape

Nodemap

class tecplot.data.Nodemap(zone)[source]

Element to node map for a mixed-FE zone.

This object maps elements (cells) to specific nodes (points) in the dataset. The elements are grouped by cell type into sections and each section stores the corners (linear part of the cell) separate from the high-order nodes of the cells into two different arrays.

For more details, see the “working with datasets” examples shipped with PyTecplot in the Tecplot 360 distribution.

Attributes

c_type

The underlying data type for this nodemap.

Methods

assignment()

Context manager for assigning to the nodemap.

element(node, offset)

The element containing a given node.

nodes(element[, section])

Returns node values for a specific element.

num_elements(node)

The number of elements that use a given node.

section(index)

Returns a NodemapSection of the nodemap.

section_element(element)

Returns the section and element within that section for a globally-indexed element.
Nodemap.assignment()

Context manager for assigning to the nodemap.

When setting values to the nodemap, a State Changes is emitted to the engine after every statement. This can degrade performance if in the script the nodemap is being set many times. This context provides a way to suspend the state change notification until all assignments have been completed. In the following example, the state change is emitted only after the nodemap.assignment() context exits:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> with nodemap.assignment():
...     nodemap[:] = node_data
Nodemap.c_type

The underlying data type for this nodemap.

Note

This property is read-only.

This is the ctypes integer type used by the Tecplot Engine to store the nodemap data. This is used internally and is not normally needed for simple nodemap access.

Type:

ctypes.c_int32 or ctypes.c_int64

Nodemap.element(node, offset)[source]

The element containing a given node.

Parameters:

  • node (int) – Zero-based index of a node.

  • offset (int) – Zero-based index of the element that uses the given node.

Returns:

int - Zero-based index of the element.

Example usage:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> print(nodemap.element(3, 7))
324
Nodemap.nodes(element, section=None)[source]

Returns node values for a specific element.

Parameters:

  • element (int) – Element index with in the section if specified, otherwise this is the element index across all sections.

  • section (int, optional) – Section index. If no section is speficied, the element index will span across all sections.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.nodemap.nodes(1))
(0, 1, 3, 4)
Nodemap.num_elements(node)[source]

The number of elements that use a given node.

Parameters:

node – (int): Zero-based index of a node.

Returns:

int - The number of elements that use this node.

Example usage:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> nodemap.num_elements(3)
8
Nodemap.section(index)[source]

Returns a NodemapSection of the nodemap.

Each section of a Nodemap consists of a mapping for a specific cell type. The data may be linear or higher-order.

Parameters:

index (int) – The zero-based section index.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> nmap_section = zone.nodemap.section(0)
>>> print(nmap_section.cell_shape)
FECellShape.Tetrahedron
Nodemap.section_element(element)[source]

Returns the section and element within that section for a globally-indexed element.

Parameters:

element (int) – The zero-based element index spanning all sections in the nodemap.

Returns: tuple of int (zero-based) indices: (section, element).

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.nodemap.section_element(1))
(0, 1)

NodemapSection

class tecplot.data.NodemapSection(nodemap, index)[source]

A section of uniform cell-type in a Nodemap.

A MixedFEZone contains a Nodemap that is made of one or more sections of uniform cell type (NodemapSection). Within these sections, the nodemap data is stored in an array consisting of the node indices.

For more details, see the “working with datasets” examples shipped with PyTecplot in the Tecplot 360 distribution.

Attributes

array

Flattened array accessor for node data.

basis_func

The basis function used to determine the node winding for elements in this NodemapSection.

cell_shape

The geometric shape of elements in this NodemapSection.

cell_type

The cell type of this NodemapSection.

grid_order

The grid order of the cell type in this NodemapSection.

num_elements

The total number of elements in this NodemapSection.

num_points_per_element

Points per element for this NodemapSection.

shape

Shape of the nodemap array.

size

Total number of nodes stored in the nodemap array.

Methods

nodes(element)

Returns node values for a specific element in this section.
NodemapSection.array

Flattened array accessor for node data.

A section of the nodemap is normally dimensioned by \((N_e, N_{npe})\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. This property represents a flattened view into the array containing the nodes of each element.

Standard Python list slicing works for both fetching values and assignments. Example usage:

>>> nmap_section = dataset.zone('My Zone').nodemap.section(0)
>>> nmap_section.array[:] = mydata
>>> print(nmap_section.array[:10])
[1, 10, 8, 0, 5, 18, 6, 12, 18, 11]
Type:

list-like array

NodemapSection.basis_func

The basis function used to determine the node winding for elements in this NodemapSection.

Note

This property is read-only.

Currently, Tecplot only supports the Lagrangian basis function.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> nmap_section = zone.nodemap.section(0)
>>> print(nmap_section.basis_func)
FECellBasisFunction.Lagrangian
Type:

FECellBasisFunction

NodemapSection.cell_shape

The geometric shape of elements in this NodemapSection.

Note

This property is read-only

Example usage:

>>> zone = dataset.zone('My Zone')
>>> nmap_section = zone.nodemap.section(0)
>>> print(nmap_section.cell_shape)
FECellShape.Tetrahedron
Type:

FECellShape

NodemapSection.cell_type

The cell type of this NodemapSection.

Note

This property is read-only.

The cell type encapsulates the shape, grid order and basis function for the elemens in the section of the zone.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> cell_type = zone.nodemap.section(0).cell_type
>>> print(cell_type.shape)
FECellShape.Tetrahedron
>>> print(cell_type.grid_order)
2
Type:

FECellType

NodemapSection.grid_order

The grid order of the cell type in this NodemapSection.

Note

This property is read-only.

A grid order of 1 is the classic case with linear cells where the nodes are exclusively on the corners of the elements. Note that the high_order_array and high-order node data is only available for grid orders 2 or greater.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> nmap_section = zone.nodemap.section(0)
>>> print(nmap_section.grid_order)
2
Type:

int

NodemapSection.nodes(element)[source]

Returns node values for a specific element in this section.

Parameters:

element (int) – The element index.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.nodemap.section(0).nodes(1))
(0, 1, 3, 4)
NodemapSection.num_elements

The total number of elements in this NodemapSection.

Note

This property is read-only.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> nmap_section = zone.nodemap.section(0)
>>> print(nmap_section.num_elements)
1024
Type:

int

NodemapSection.num_points_per_element

Points per element for this NodemapSection.

Note

This property is read-only.

The number of points (also known as nodes) per finite-element is determined from the cell shape, grid order and the basis function used.

This example shows the output for a high-order Tet-10 section:

>>> zone = dataset.zone('My Zone')
>>> nmap_section = zone.nodemap.section(0)
>>> print(nmap_section.cell_shape)
FECellShape.Tetrahedron
>>> print(nmap_section.grid_order)
2
>>> print(nmap_section.num_points_per_element)
10
Type:

int

NodemapSection.shape

Shape of the nodemap array.

Note

This property is read-only.

This is defined by the zone type and is equal to \((N_e, N_{npe})\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. Example usage:

>>> print(dataset.zone(0).nodemap.section(0).shape)
(1024, 4)
Type:

tuple of integers

NodemapSection.size

Total number of nodes stored in the nodemap array.

Note

This property is read-only.

This is defined by the cell type and is equal to \(N_e \times N_{npe}\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. Example usage:

>>> print(dataset.zone(0).nodemap.section(0).shape)
(1024, 4)
>>> print(dataset.zone(0).nodemap.section(0).size)
4096
Type:

int

ClassicNodemap

class tecplot.data.ClassicNodemap(zone)[source]

Connectivity list definition and control for classic FE zones.

A nodemap holds the connectivity between nodes and elements for classic finite-element zones. It is nominally a two-dimensionaly array of shape \((N_e, N_{npe})\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. The nodemap interface has flat-array access through the ClassicNodemap.array property as well as reverse look-up with Nodemap.num_elements() and Nodemap.element().

The nodemap behaves mostly like a two-dimensional array and can be treated as such:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> print('nodes in the first element:', nodemap[0])
nodes in the first element: [0, 1, 2, 3]
>>> print(nodemap[:3])
[[0, 1, 2, 3], [2, 3, 4, 5], [4, 5, 6, 7]]
>>> nodemap[0] = [6, 7, 8, 9]
>>> print(nodemap[0])
[6, 7, 8, 9]

Just for clarity, the nodemap indexing is by element first, then offset within that element:

>>> element = 6
>>> offset = 2
>>> node = nodemap[element][offset]
>>> print(node)
21

Setting node indices must be done for an entire element because getting values out of the nodemap and into Python always creates a copy. For example, this will not work:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> # Trying to set the 3rd node of the element 10
>>> nodemap[10][2] = 5  # Error: nodemap[10] returns a copy

To modify a single node in a nodemap, it is neccessary to do a round trip like so:

nodemap = dataset.zone('My Zone').nodemap
>>> nodes = nodemap[10]
>>> nodes[2] = 5
>>> nodemap[10] = nodes  # OK: setting whole element at a time
>>> print(nodemap[10])
[20, 21, 5, 22]

The following script creates a quad of two triangles from scratch using the PyTecplot low-level data creation interface. The general steps are:

  1. Setup the data

  2. Create the tecplot dataset and variables

  3. Create the zone

  4. Set the node locations and connectivity lists

  5. Set the (scalar) data

  6. Write out data file

  7. Adjust plot style and export image

The data created looks like this:

Node positions (x,y,z):

               (1,1,1)
              3
             / \
            /   \
 (0,1,.5)  2-----1  (1,0,.5)
            \   /
             \ /
              0
               (0,0,0)

Breaking up the two triangular elements, the faces look like this. Notice the first element (index: 0) is on the bottom:

Element 1 Faces:
                   *
   (nodes 3-2)  1 / \ 0  (nodes 1-3)
                 /   \
                *-----*
                   2
                    (nodes 2-1)

Element 0 Faces:
                    (nodes 1-2)
                   1
                *-----*
                 \   /
   (nodes 2-0)  2 \ / 0  (nodes 0-1)
                   *

The nodes are created as a list of \((x, y, z)\) positions:

[(x0, y0, z0), (x1, y1, z1)...]

which are transposed to lists of \(x\), \(y\) and \(z\)-positions:

[(x0, x1, x2...), (y0, y1, y2...)...]

and passed to the \((x, y, z)\) arrays. The nodemap, or connectivity list, is given as an array of dimensions \((N, D)\) where \(N\) is the number of elements and \(D\) is the number of nodes per element. The order of the node locations determines the indices used when specifying the connectivity list. The Nodemap can be set individually and separately or all at once as shown here:

import tecplot as tp
from tecplot.constant import *

# Triangle 0
nodes0 = (
    (0, 0, 0  ),
    (1, 0, 0.5),
    (0, 1, 0.5))
scalar_data0 = (0, 1, 2)
conn0 = ((0, 1, 2),)
neighbors0 = ((None, 0, None),)
neighbor_zones0 = ((None, 1, None),)

# Triangle 1
nodes1 = (
    (1, 0, 0.5),
    (0, 1, 0.5),
    (1, 1, 1  ))
scalar_data1 = (1, 2, 3)
conn1 = ((0, 1, 2),)
neighbors1 = ((0, None, None),)
neighbor_zones1 = ((0, None, None),)

# Create the dataset and zones
ds = tp.active_frame().create_dataset('Data', ['x','y','z','s'])
z0 = ds.add_fe_zone(ZoneType.FETriangle,
                    name='FE Triangle Float (3,1) Nodal 0',
                    num_points=len(nodes0), num_elements=len(conn0),
                    face_neighbor_mode=FaceNeighborMode.GlobalOneToOne)
z1 = ds.add_fe_zone(ZoneType.FETriangle,
                    name='FE Triangle Float (3,1) Nodal 1',
                    num_points=len(nodes1), num_elements=len(conn1),
                    face_neighbor_mode=FaceNeighborMode.GlobalOneToOne)

# Fill in and connect first triangle
z0.values('x')[:] = [n[0] for n in nodes0]
z0.values('y')[:] = [n[1] for n in nodes0]
z0.values('z')[:] = [n[2] for n in nodes0]
z0.nodemap[:] = conn0
z0.values('s')[:] = scalar_data0

# Fill in and connect second triangle
z1.values('x')[:] = [n[0] for n in nodes1]
z1.values('y')[:] = [n[1] for n in nodes1]
z1.values('z')[:] = [n[2] for n in nodes1]
z1.nodemap[:] = conn1
z1.values('s')[:] = scalar_data1

# Set face neighbors
z0.face_neighbors.set_neighbors(neighbors0, neighbor_zones0, obscures=True)
z1.face_neighbors.set_neighbors(neighbors1, neighbor_zones1, obscures=True)


### Setup a view of the data
plot = tp.active_frame().plot(PlotType.Cartesian3D)
plot.activate()

plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'
plot.contour(0).colormap_filter.distribution = ColorMapDistribution.Continuous

for ax in plot.axes:
    ax.show = True

plot.show_mesh = False
plot.show_contour = True
plot.show_edge = True
plot.use_translucency = True

# View parameters obtained interactively from Tecplot 360
plot.view.distance = 10
plot.view.width = 2
plot.view.psi = 80
plot.view.theta = 30
plot.view.alpha = 0
plot.view.position = (-4.2, -8.0, 2.3)

fmaps = plot.fieldmaps()
fmaps.surfaces.surfaces_to_plot = SurfacesToPlot.All
fmaps.effects.surface_translucency = 40

# Turning on mesh, we can see all the individual triangles
plot.show_mesh = True
fmaps.mesh.line_pattern = LinePattern.Dashed

plot.contour(0).levels.reset_to_nice()
tp.export.save_png('fe_triangles1.png', 600, supersample=3)
../_images/fe_triangles1.png

Attributes

array

Flattened array accessor for this nodemap.

c_type

The underlying data type for this nodemap.

num_points_per_element

Points per element for classic finite-element zones.

shape

Shape of the nodemap array.

size

Total number of nodes stored in the nodemap array.

Methods

assignment()

Context manager for assigning to the nodemap.

element(node, offset)

The element containing a given node.

nodes(element)

Returns node values for a specific element.

num_elements(node)

The number of elements that use a given node.
ClassicNodemap.array

Flattened array accessor for this nodemap.

The nodemap is normally dimensioned by \((N_e, N_{npe})\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. This property represents a flattened view into the array which is of length \(N_e \times N_{npe}\). This may be more convenient than flattening the array in your script using a looping construct.

Standard Python list slicing works for both fetching values and assignments. Example usage:

>>> nmap = dataset.zone('My Zone').nodemap
>>> nmap.array[:] = mydata
>>> print(nmap.array[:10])
[1, 10, 8, 0, 5, 18, 6, 12, 18, 11]
Type:

list-like array

ClassicNodemap.assignment()

Context manager for assigning to the nodemap.

When setting values to the nodemap, a State Changes is emitted to the engine after every statement. This can degrade performance if in the script the nodemap is being set many times. This context provides a way to suspend the state change notification until all assignments have been completed. In the following example, the state change is emitted only after the nodemap.assignment() context exits:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> with nodemap.assignment():
...     nodemap[:] = node_data
ClassicNodemap.c_type

The underlying data type for this nodemap.

Note

This property is read-only.

This is the ctypes integer type used by the Tecplot Engine to store the nodemap data. This is used internally and is not normally needed for simple nodemap access.

Type:

ctypes.c_int32 or ctypes.c_int64

ClassicNodemap.element(node, offset)

The element containing a given node.

Parameters:

  • node (int) – Zero-based index of a node.

  • offset (int) – Zero-based index of the element that uses the given node.

Returns:

int - Zero-based index of the element.

Example usage:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> print(nodemap.element(3, 7))
324
ClassicNodemap.nodes(element)[source]

Returns node values for a specific element.

Parameters:

element (int) – The element index.

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.nodemap.nodes(1))
(0, 1, 3, 4)
ClassicNodemap.num_elements(node)

The number of elements that use a given node.

Parameters:

node – (int): Zero-based index of a node.

Returns:

int - The number of elements that use this node.

Example usage:

>>> nodemap = dataset.zone('My Zone').nodemap
>>> nodemap.num_elements(3)
8
ClassicNodemap.num_points_per_element

Points per element for classic finite-element zones.

Note

This property is read-only.

The number of points (also known as nodes) per finite-element is determined from the zone_type parameter. The following table shows the number of points per element for the available zone types along with the resulting shape of the nodemap based on the number of points specified (\(N\)):

Zone Type

Points/Element

Nodemap Shape

FELineSeg

2

\((N, 2 N)\)

FETriangle

3

\((N, 3 N)\)

FEQuad

4

\((N, 4 N)\)

FETetra

4

\((N, 4 N)\)

FEBrick

8

\((N, 8 N)\)

Example usage:

>>> zone = dataset.zone('My Zone')
>>> print(zone.zone_type)
ZoneType.FETriangle
>>> print(zone.nodemap.num_points_per_element)
3
Type:

int

ClassicNodemap.shape

Shape of the nodemap array.

Note

This property is read-only.

This is defined by the zone type and is equal to \((N_e, N_{npe})\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. Example usage:

>>> print(dataset.zone(0).nodemap.shape)
(1024, 4)
Type:

tuple of integers

ClassicNodemap.size

Total number of nodes stored in the nodemap array.

Note

This property is read-only.

This is defined by the zone type and is equal to \(N_e \times N_{npe}\) where \(N_e\) is the number of elements and \(N_{npe}\) is the number of nodes per element. Example usage:

>>> print(dataset.zone(0).nodemap.shape)
(1024, 4)
>>> print(dataset.zone(0).nodemap.size)
4096
Type:

int

Facemap

class tecplot.data.Facemap(zone)[source]

Connectivity list definition and control.

A facemap holds the connectivity for a polytopal finite-element zone. This includes node-to-element and element-to-element connections. The following script creates a quad of two triangles from scratch using the PyTecplot low-level data creation interface. The data created looks like this:

Node positions (x,y,z):

               (1,1,1)
              3
             / \
            /   \
 (0,1,.5)  2-----1  (1,0,.5)
            \   /
             \ /
              0
               (0,0,0)

Element indices are used when identifying the left and right of each face, where \(-1\) is used to indicate no element:

    *
-1 / \ -1
  / 1 \
 *-----*
  \ 0 /
-1 \ / -1
    *

import tecplot as tp
from tecplot.constant import *

nodes = ((0, 0, 0  ),
         (1, 0, 0.5),
         (0, 1, 0.5),
         (1, 1, 1  ))
faces = ((0, 1),
         (1, 2),
         (2, 0),
         (1, 3),
         (3, 2))
elements = (( 0, 0,  0,  1,  1),  # elements to the left of each face
            (-1, 1, -1, -1, -1))  # elements to the right of each face
num_elements = 2
scalar_data = (0, 1, 2, 3)

ds = tp.active_frame().create_dataset('Data', ['x','y','z','s'])
z = ds.add_poly_zone(ZoneType.FEPolygon,
                     name='FE Polygon Float (4,2,5) Nodal',
                     num_points=len(nodes),
                     num_elements=num_elements,
                     num_faces=len(faces))

z.values('x')[:] = [n[0] for n in nodes]
z.values('y')[:] = [n[1] for n in nodes]
z.values('z')[:] = [n[2] for n in nodes]
z.facemap.set_mapping(faces, elements)
z.values('s')[:] = scalar_data

### setup a view of the data
plot = tp.active_frame().plot(PlotType.Cartesian3D)
plot.activate()

cont = plot.contour(0)
cont.colormap_name = 'Sequential - Yellow/Green/Blue'
cont.colormap_filter.distribution = ColorMapDistribution.Continuous

for ax in plot.axes:
    ax.show = True

plot.show_mesh = False
plot.show_contour = True
plot.show_edge = True
plot.use_translucency = True

fmap = plot.fieldmap(z)
fmap.surfaces.surfaces_to_plot = SurfacesToPlot.All
fmap.effects.surface_translucency = 40

# View parameters obtained interactively from Tecplot 360
plot.view.distance = 10
plot.view.width = 2
plot.view.psi = 80
plot.view.theta = 30
plot.view.alpha = 0
plot.view.position = (-4.2, -8.0, 2.3)

# Turning on mesh, we can see all the individual triangles
plot.show_mesh = True
plot.fieldmap(z).mesh.line_pattern = LinePattern.Dashed

cont.levels.reset_to_nice()
tp.export.save_png('polygons1.png', 600, supersample=3)
../_images/polygons1.png

Two triangle polygons showing edge and mesh lines.

Attributes

element_c_type

The data type of the element indices.

node_c_type

The data type of the node indices.

num_unique_nodes

The number of unique nodes in this Facemap.

Methods

alloc(face_nodes[, boundary_faces, ...])

Allocate space for the facemap.

assignment()

Context manager for assigning facemap connections.

boundary_connection(face, offset[, element])

The connected element and zone along a boundary face.

face(element, offset)

Face index on a specific element.

left_element(face[, element])

The element to the left of a specific face.

node(face, offset[, element])

The node index along a specific face.

num_boundary_connections([face, element])

The number of boundary connections for a given face.

num_faces([element])

The number of faces of an element in this Facemap.

num_nodes([face, element])

The number nodes for a given face in this Facemap.

right_element(face[, element])

The element to the right of a specific face.

set_boundary_connections(elements, zones)

Set the boundary connections.

set_elementmap(elementmap)

Define connectivity per element.

set_elements(left_elements, right_elements)

Sets the polytope connectivity.

set_mapping(facemap, elements[, ...])

Set the node and element connectivity for this polytope zone.

set_nodes(facemap)

Sets the polytope connectivity.
Facemap.alloc(face_nodes, boundary_faces=0, boundary_connections=0)[source]

Allocate space for the facemap.

Parameters:

  • face_nodes (int) – Total number of nodes for all faces. This is not the number of unique nodes but the total number. For example if a facemap defines two triangle polygons that share a common face, faces would be 5 and face_nodes would be 6, not 4.

  • boundary_faces (int, optional) – Total number of boundary faces. (default: 0)

  • boundary_connections (int, optional) – Total number of boundary face elements or boundary face element/zone pairs. (default: 0)

Returns:

Facemap

This is called when using the Facemap.set_mapping() method which is the preferred method for filling the connectivity of polytope zones. If the zone does not already have space allocated for a facemap and if you wish to use the Facemap.set_nodes() and Facemap.set_elements() methods to fill in the connectivity, then this must be called first:

>>> facemap = zone.facemap.alloc(400, 25, 50)

Note

Tecplot version 2017.2 or later.

Setting the boundary faces and boundary connections using PyTecplot requires Tecplot version 2017.2 or later.

Facemap.assignment()[source]

Context manager for assigning facemap connections.

This context ensures the proper book keeping is done when setting the connectivity list and must be used with Facemap.set_nodes(), Facemap.set_elements() and Facemap.set_boundary_connections(), which are used to define the connectivity of the zone.

Facemap.boundary_connection(face, offset, element=None)[source]

The connected element and zone along a boundary face.

Parameters:

  • face (int) – The zero-based index of the face.

  • offset (int) – The zero-based index of the node being requested.

  • element (int, optional) – Zero-based index of an element. If given, face will be locally indexed within this element, otherwise face is globally indexed over the whole zone.

Returns:

namedtuple –

(element, zone):
element

The zero-based index of the neighboring element.

zone

The zone holding the neighboring element.

Example usage:

>>> bconn = zone.facemap.boundary_connection(0, 2)
>>> print(bconn.element)
128
>>> print(bconn.zone.index)
2
Facemap.element_c_type

The data type of the element indices.

Possible values: ctypes.c_int32, ctypes.c_int64

Note

This property is read-only.

Facemap.face(element, offset)[source]

Face index on a specific element.

Parameters:

  • element (int) – The zero-based index of the element.

  • offset (int) – The zero-based index of the face being requested.

Returns:

int Zero-based index of the face at the specified location.

Example usage:

>>> print(zone.facemap.face(0, 2))
128
Facemap.left_element(face, element=None)[source]

The element to the left of a specific face.

Parameters:

  • face (int) – The zero-based index of the face.

  • element (int, optional) – Zero-based index of an element. If given, face will be locally indexed within this element, otherwise face is globally indexed over the whole zone.

Returns:

int

A negative number indicates there is no element to the left of this face. Example usage:

>>> print(zone.facemap.left_element(0))
128
Facemap.node(face, offset, element=None)[source]

The node index along a specific face.

Parameters:

  • face (int) – The zero-based index of the face.

  • offset (int) – The zero-based index of the node being requested.

  • element (int, optional) – Zero-based index of an element. If given, face will be locally indexed within this element, otherwise face is globally indexed over the whole zone.

Returns:

int The node at the specified location.

Example usage:

>>> print(zone.facemap.face_node(0, 2))
128
Facemap.node_c_type

The data type of the node indices.

Possible values: ctypes.c_int32, ctypes.c_int64

Note

This property is read-only.

Facemap.num_boundary_connections(face=None, element=None)[source]

The number of boundary connections for a given face.

Parameters:

  • face (int, required if no element is given) – The zero-based index of the face.

  • element (int, optional) – Zero-based index of an element. If given, face will be locally indexed within this element, otherwise face is globally indexed over the whole zone.

Returns:

int The number of boundary connections.

Example usage:

>>> print(zone.facemap.num_boundary_connections(1))
1
Facemap.num_faces(element=None)[source]

The number of faces of an element in this Facemap.

Parameters:

element (int, optional) – Zero-based index of an element. If no element is given, the total number of faces in the map are returned.

Returns:

int

Example usage:

>>> print(zone.facemap.num_faces())
1048576
Facemap.num_nodes(face=None, element=None)[source]

The number nodes for a given face in this Facemap.

Parameters:

  • face (int, required if no element is given) – The zero-based index of the face either within the given element or globally. If no face is given, the number of nodes on the given element are returned.

  • element (int, required if no face is given) – The zero-based index of an element. If no element is given, then face is globally indexed from zero over the whole zone.

Returns:

int The number of nodes.

Example usage:

>>> print(zone.facemap.num_nodes(face=1))
4
Facemap.num_unique_nodes

The number of unique nodes in this Facemap.

Note

This property is read-only.

Example usage:

>>> print(zone.facemap.num_unique_nodes)
4194304
Type:

int

Facemap.right_element(face, element=None)[source]

The element to the right of a specific face.

Parameters:

  • face (int) – The zero-based index of the face.

  • element (int, optional) – Zero-based index of an element. If given, face will be locally indexed within this element, otherwise face is globally indexed over the whole zone.

Returns:

int

A negative number indicates there is no element to the right of this face. Example usage:

>>> print(zone.facemap.right_element(0))
-1
Facemap.set_boundary_connections(elements, zones)[source]

Set the boundary connections.

Parameters:

  • elements (2D array of integers) – Zero-based indices of the connected elements. This is a “ragged” array of dimension \((N,E_i)\) where \(N\) is the number of boundary faces and \(E_i\) is the number of boundary connected elements for the \(i^{th}\) face.

  • zones (2D array of integers) – Zero-based indices of the zones for each entry given in elements. This must be the same shape as elements.

The facemap must first be allocated with Facemap.alloc() and must be called from within a Facemap.assignment() context, and should follow calls to Facemap.set_nodes() and Facemap.set_elements() to complete the connectivity map information needed for rendering. Using Facemap.set_mapping() is recommended, which does all the required book keeping.

Facemap.set_elementmap(elementmap)[source]

Define connectivity per element.

Parameters:

elementmap (integers) – Zero-based indices of the nodes that make up each face of each element. For polygons, the map is a list of elements, each made of up a list of nodes. For polyhedrons, this is a list of elements, made up a list of faces, each made up of a list of nodes.

Warning

This method is mutually exclusive with the Facemap.set_mapping() and Facemap.assignment(), Facemap.set_nodes(), Facemap.set_elements() and Facemap.set_boundary_connections() family of methods. The size of the underlying arrays are calculated based on the elementmap given and a call to Facemap.alloc() is made which will override any previous allocation.

This may be a more convenient way to describe the connectivity of a polytope zone, however it does not support boundary face connections to other zones (see Facemap.set_mapping()).

Here is an example of an elementmap for two triangles (polygons):

nodes = ((0, 0, 0  ),
         (1, 0, 0.5),
         (0, 1, 0.5),
         (1, 1, 1  ))
elementmap = ((0, 1, 2),  # polygon 0, 3 faces
              (1, 3, 2))  # polygon 1, 3 faces

This is an example of an elementmap for two tetrahedrons (polyhedrons):

nodes = ((0, 0, 0),
         (1, 1, 0),
         (1, 0, 1),
         (0, 1, 1),
         (0, 0, 1))
elementmap = (((0, 1, 2),  # polyhedron 0, 4 faces
               (0, 1, 3),
               (1, 3, 2),
               (0, 2, 3)),
              ((0, 2, 3),  # polyhedron 1, 4 faces
               (2, 3, 4),
               (0, 2, 4),
               (0, 4, 3)))
Facemap.set_elements(left_elements, right_elements)[source]

Sets the polytope connectivity.

Parameters:

  • left_elements (array of zero-based integers) – This is an array of the elements to the left of each face in the facemap and must be the same length as the number of faces.

  • right_elements (array of zero-based integers) – Same as left_elements for the right side of each face.

The facemap must first be allocated with Facemap.alloc() and must be called from within a Facemap.assignment() context and should follow a call to Facemap.set_nodes() to complete the connectivity map information needed for rendering. Using Facemap.set_mapping() is recommended, which does all the required book keeping.

Facemap.set_mapping(facemap, elements, boundary_elements=None, boundary_zones=None)[source]

Set the node and element connectivity for this polytope zone.

Parameters:

  • facemap (2D array of zero-based integers) – The list of lists which need not all be the same length, defining the individual elements by their nodes.

  • elements (2D array of zero-based integers) – This is a \((2,N)\) array where \(N\) is the number of faces and the items are a list of the elements to the left and right of the face respectively.

  • boundary_elements (2D array of integers, optional) – Zero-based indices of the connected elements. This is a “ragged” array of dimension \((N, E_i)\) where \(N\) is the number of boundary faces and \(E_i\) is the number of boundary connected elements for the \(i^{th}\) face.

  • boundary_zones (2D array of integers, optional) – Zero-based indices of the zones for each entry given in elements. This must be the same shape as elements.

See the code example for the Facemap class object for details.

Note

boundary_elements and boundary_zones

The two parameters, boundary_elements and boundary_zones, must be supplied together or not at all.

Facemap.set_nodes(facemap)[source]

Sets the polytope connectivity.

Parameters:

facemap (2D array of zero-based integers) – The list of lists which need not all be the same length, defining the individual elements by their nodes.

The facemap must first be allocated with Facemap.alloc() and must be called from within a Facemap.assignment() context and should be followed by a call to Facemap.set_elements() to complete the connectivity map information needed for rendering. It is recomended to use the Facemap.set_mapping() which does all the required book keeping.

FaceNeighbors

class tecplot.data.FaceNeighbors(zone)[source]

Face neighbor definition and control.

Face neighbors are used when the face of an element overlaps with another face from another element. Specifying these two (or more) overlapping faces as “face neighbors” indicates element connections outside the implicit faces of the nodemap. By specifying face neighbors it ensures that plot elements like shading, creases and edges are treated continously even if there is a zone or cell boundry.

The neighbors can be completely “local”, within a single zone, or “global” conntecting two or more zones together. Furthermore, the connections made can be one-to-one meaning there any given face can only neighbor one other face, or one-to-many where a single face can neighbor several other faces.

This example creates two triangles in two different zones. Global one-to-one face neighbors are then used to stitch the two triangles into a quad. The data created looks like this:

Node positions (x,y,z):

               (1,1,1)
              *
             / \
            /   \
 (0,1,.5)  *-----*  (1,0,.5)
            \   /
             \ /
              *
               (0,0,0)

The two triangles will have separate nodes at the shared locations:

Nodes:
                   2
    Zone 1:       / \
                 /   \
                1-----0
                2-----1
                 \   /
    Zone 0:       \ /
                   0

import tecplot as tp
from tecplot.constant import *

# Triangle 0
nodes0 = (
    (0, 0, 0  ),
    (1, 0, 0.5),
    (0, 1, 0.5))
scalar_data0 = (0, 1, 2)
conn0 = ((0, 1, 2),)
neighbors0 = ((None, 0, None),)
neighbor_zones0 = ((None, 1, None),)

# Triangle 1
nodes1 = (
    (1, 0, 0.5),
    (0, 1, 0.5),
    (1, 1, 1  ))
scalar_data1 = (1, 2, 3)
conn1 = ((0, 1, 2),)
neighbors1 = ((0, None, None),)
neighbor_zones1 = ((0, None, None),)

# Create the dataset and zones
ds = tp.active_frame().create_dataset('Data', ['x','y','z','s'])
z0 = ds.add_fe_zone(ZoneType.FETriangle,
                    name='FE Triangle Float (3,1) Nodal 0',
                    num_points=len(nodes0), num_elements=len(conn0),
                    face_neighbor_mode=FaceNeighborMode.GlobalOneToOne)
z1 = ds.add_fe_zone(ZoneType.FETriangle,
                    name='FE Triangle Float (3,1) Nodal 1',
                    num_points=len(nodes1), num_elements=len(conn1),
                    face_neighbor_mode=FaceNeighborMode.GlobalOneToOne)

# Fill in and connect first triangle
z0.values('x')[:] = [n[0] for n in nodes0]
z0.values('y')[:] = [n[1] for n in nodes0]
z0.values('z')[:] = [n[2] for n in nodes0]
z0.nodemap[:] = conn0
z0.values('s')[:] = scalar_data0

# Fill in and connect second triangle
z1.values('x')[:] = [n[0] for n in nodes1]
z1.values('y')[:] = [n[1] for n in nodes1]
z1.values('z')[:] = [n[2] for n in nodes1]
z1.nodemap[:] = conn1
z1.values('s')[:] = scalar_data1

# Set face neighbors
z0.face_neighbors.set_neighbors(neighbors0, neighbor_zones0, obscures=True)
z1.face_neighbors.set_neighbors(neighbors1, neighbor_zones1, obscures=True)


### Setup a view of the data
plot = tp.active_frame().plot(PlotType.Cartesian3D)
plot.activate()

plot.contour(0).colormap_name = 'Sequential - Yellow/Green/Blue'
plot.contour(0).colormap_filter.distribution = ColorMapDistribution.Continuous

for ax in plot.axes:
    ax.show = True

plot.show_mesh = False
plot.show_contour = True
plot.show_edge = True
plot.use_translucency = True

# View parameters obtained interactively from Tecplot 360
plot.view.distance = 10
plot.view.width = 2
plot.view.psi = 80
plot.view.theta = 30
plot.view.alpha = 0
plot.view.position = (-4.2, -8.0, 2.3)

fmaps = plot.fieldmaps()
fmaps.surfaces.surfaces_to_plot = SurfacesToPlot.All
fmaps.effects.surface_translucency = 40

# Turning on mesh, we can see all the individual triangles
plot.show_mesh = True
fmaps.mesh.line_pattern = LinePattern.Dashed

plot.contour(0).levels.reset_to_nice()
tp.export.save_png('fe_triangles1.png', 600, supersample=3)
../_images/fe_triangles1.png

Two triangles in two separate zones, stitched together using global face neighbors, showing the edge and mesh.

Attributes

c_type

Underlying storage type used by the Tecplot Engine.

mode

Relative locality of the face neighbors.

Methods

add_local_neighbors(neighbors[, offset])

Assign all local one-to-one face neighbors at once.

add_neighbors(element, face, neighbors[, ...])

Connect boundary of an element's face to a neighboring face.

assignment()

Context manager for assigning face neighbors.

is_obscured(element, face[, active_zones])

Obscuration of the specified face.

neighbors(element, face)

Get the neighboring elements and zones of a specific face.

set_neighbors(neighbors[, zones, obscures])

Clear and set face neighbors from the given array.
FaceNeighbors.add_local_neighbors(neighbors, offset=0)[source]

Assign all local one-to-one face neighbors at once.

Parameters:

  • neighbors (2D array of integers) – \((E,F)\) Array of the face neighbors where \(E\) is the number of elements and \(F\) is the number of faces per element.

  • offset (int, optional) – Offset in Tecplot’s face neighbor array to begin assigning the supplied neighbor elements. (default: 0)

This method must be called from within a FaceNeighbors.assignment() context which will clear any previously existing face neighbor data:

>>> with zone.face_neighbors.assigment():
...     zone.face_neighbors.add_local_neighbors(neighbors)

See the example code for FaceNeighbors class object for more details on how to set up user-defined face neighbors.

FaceNeighbors.add_neighbors(element, face, neighbors, zones=None, obscure=False)[source]

Connect boundary of an element’s face to a neighboring face.

This sets the boundary connection face neighbors within an open face neighbor assignment sequence for the specified element and face.

Parameters:

  • element (int) – The element number (zero-based).

  • face (int) – The face number on the element (zero-based).

  • neighbors (list of integers or None) – List of zero-based indices of the neighboring faces.

  • zones (list of zone objects, optional) – List of zones for global neighbors. This must be the same length as neighbors. Use None to indicate these are local neighbors. (default: None)

  • obscure (bool, optional) – Indicates that the neighbors completely obscure the face. (default: False)

This method must be called from within a FaceNeighbors.assignment() context which will clear any previously existing face neighbor data:

>>> with zone.face_neighbors.assigment():
...     for elem, face, neighbors, zn in face_neighbor_data:
...         zone.face_neighbors.add_neighbors(elem, face,
...                                           neighbors, zn)

See the example code for FaceNeighbors class object for more details on how to set up user-defined face neighbors.

FaceNeighbors.assignment()[source]

Context manager for assigning face neighbors.

This context ensures the proper book keeping is done when setting face neighbors. After the face neighbors are specified, this context will valid the connections and make appropriate changes to the zone metadata. It must be used with the FaceNeighbors.add_local_neighbors() and/or FaceNeighbors.add_neighbors() methods. See the FaceNeighbors example code for more details on how to set up user-defined face neighbors.

FaceNeighbors.c_type

Underlying storage type used by the Tecplot Engine.

Possible values: ctypes.c_int32, ctypes.c_int64`.

Note

This property is read-only.

FaceNeighbors.is_obscured(element, face, active_zones=None)[source]

Obscuration of the specified face.

Parameters:

  • element (int) – The zero-based index of the element.

  • face (int) – The zero-based index of the face on the element.

  • active_zones (list of Zones) – List of zones to consider when global face neighbors are present. If None, the active zones of the dataset’s parent frame will be used.

Returns:

bool

Note

Because datasets can be shared between frames, the default frame used to identify the active zones may not be the one you want. In this case, you can use the Frame.active_zones() method to provide the active zones for a specific frame. Furthermore, the plot type of the frame must have the concept of active zones - i.e. it must not be in “sketch” mode.

Example usage:

>>> zone.face_neighbors.is_obscured(element=0, face=1)
True
FaceNeighbors.mode

Relative locality of the face neighbors.

Possible values: FaceNeighborMode.LocalOneToOne,

FaceNeighborMode.LocalOneToMany, FaceNeighborMode.GlobalOneToOne, FaceNeighborMode.GlobalOneToMany.

Note

This property is read-only.

Face neighbors are used when the face of an element overlaps with another face from another element. The neighbors can be completely “local”, within a single zone, or “global” conntecting two or more zones together. Furthermore, the connections made can be one-to-one meaning there any given face can only neighbor one other face, or one-to-many where a single face can neighbor several other faces. The face neighbor mode is set on zone creation and can not be changed afterwards:

>>> zone = dataset.add_fe_zone(ZoneType.FETriangle, 'Zone', 4, 2,
...    face_neighbor_mode=FaceNeighborMode.LocalOneToMany)
>>> print(zone.face_neighbors.mode)
FaceNeighborMode.LocalOneToMany
Type:

FaceNeighborMode

FaceNeighbors.neighbors(element, face)[source]

Get the neighboring elements and zones of a specific face.

Parameters:

  • element (int) – The zero-based index of the element.

  • face (int) – The zero-based index of the face on this element.

Returns:

list of namedtuples –

(element, zone):
element:

The zero-based index of the neighboring element.

zone:

The zone holding the neighboring element. A value of None indicates this is a local (intra-zone) neighbor connection.

Example getting the neighboring faces of a zone’s first element, second face:

>>> neighbors = zone.face_neighbors.neighbors(element=0, face=1)
>>> for neighbor in neighbors:
...     elem, zn = neighbor
...     print(elem, zn.index)
21 2
FaceNeighbors.set_neighbors(neighbors, zones=None, obscures=False)[source]

Clear and set face neighbors from the given array.

Parameters:

  • neighbors (array of integers) – Zero-based Element indices of the neighbors for each face in the zone. A value of \(-1\) or None indicates no neighbor.

  • zones (array of Zones, optional) – This parameter is only used when the face neighbor mode is global one-to-one or global one-to-many. (default: None)

  • obscures (array of booleans, optional) – Indicates that the neighbors completely obscure the face. (default: False)

This method uses the FaceNeighbors.assignment() context internally to ensure the proper book keeping is done. See the example code for FaceNeighbors class object for details on how to use this method.

Auxiliary Data

AuxData

class tecplot.session.AuxData(parent, object_type, object_index=None)[source]

Auxiliary data.

The Tecplot Engine can hold auxiliary data attached to one of the following objects:

Auxiliary data is an ordered key-value pair that behaves like an ordered dictionary, or OrderedDict. Keys are strings which are ordered alphabetically and values can additionally be access by index. The keys must be alphanumeric (special characters “.” and “_” are allowed), must not contain spaces, and must begin with a non-numeric character or underscore. Values, on the other hand, are arbitrary strings and can contain anything except the null character. In this example, we query the auxiliary data attached to the dataset and add some information to it. Notice that the stored order is alphabetical:

import tecplot as tp

aux = tp.active_frame().aux_data
aux['info'] = 'Here is some information.'
aux['Xavg'] = 3.14159
aux['note'] = 'Aux data values are always converted to strings.'

'''
The following code will print:
    info: Here is some information.
    note: Aux data values are always converted to strings.
    Xavg: 3.14159
'''
for k, v in aux.items():
    print('{}: {}'.format(k,v))

Methods

as_dict()

Returns a Python dict of the Aux Data attached to the parent.

clear()

Deletes all Aux Data from the associated object.

index(key)

Returns the zero-based index of the element based on key.

items()

Yields all key/value pairs of the Aux Data attached to the parent.

key(index)

Returns the key at a specific zero-based index.

keys()

Yields all keys of the Aux Data attached to the parent.

update(*other, **kwargs)

Update Aux Data with key/value pairs from another Aux Data or dict.

values()

Yields all values of the Aux Data attached to the parent.
AuxData.as_dict()[source]

Returns a Python dict of the Aux Data attached to the parent.

Note that this will remove the alphabetical ordering guarantee that Aux Data has since Python dict objects are unordered. Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data['result'] = '3.1415'
>>> frame.aux_data['other_info'] = '128'
>>> aux = frame.aux_data.as_dict()
>>> print(aux)
{'result': '3.1415', 'other_info': '128'}
AuxData.clear()[source]

Deletes all Aux Data from the associated object.

Example usage:

>>> print(frame.aux_data)
{'bb': 'test bb', 'cc': 'test cc', 'aa': 'test aa'}
>>> frame.aux_data.clear()
>>> print(frame.aux_data)
{}
AuxData.index(key)[source]

Returns the zero-based index of the element based on key.

Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data['result'] = '3.1415'
>>> frame.aux_data['other_info'] = '128'
>>> print(frame.aux_data.index('other_info'))
0
>>> print(frame.aux_data.index('result'))
1
AuxData.items()[source]

Yields all key/value pairs of the Aux Data attached to the parent.

Elements are always ordered alphabetically by the keys. Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data['result'] = '3.1415'
>>> frame.aux_data['other_info'] = '128'
>>> for key, value in frame.aux_data.items():
...     print(key, value)
other_info 128
result 3.1415
AuxData.key(index)[source]

Returns the key at a specific zero-based index.

Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data['result'] = '3.1415'
>>> frame.aux_data['other_info'] = '128'
>>> print(frame.aux_data.key(0))
other_info
>>> print(frame.aux_data.key(1))
result
AuxData.keys()[source]

Yields all keys of the Aux Data attached to the parent.

Elements are always ordered alphabetically by the keys. Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data['result'] = '3.1415'
>>> frame.aux_data['other_info'] = '128'
>>> for value in frame.aux_data.keys():
...     print(value)
other_info
result
AuxData.update(*other, **kwargs)[source]

Update Aux Data with key/value pairs from another Aux Data or dict.

Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data.update({'result': '3.1415', 'other_info': '128'})
>>> print(frame.aux_data)
{'result': '3.1415', 'other_info': '128'}
AuxData.values()[source]

Yields all values of the Aux Data attached to the parent.

Elements are always ordered alphabetically by the keys. Example usage:

>>> frame = tp.active_frame()
>>> frame.aux_data['result'] = '3.1415'
>>> frame.aux_data['other_info'] = '128'
>>> for value in frame.aux_data.values():
...     print(value)
128
3.1415