Data¶
tecplot.data¶
Dataset
access and manipulation.
A Dataset
consists of a matrix of Zones and Variables
. Each Zone <data_access>
-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: 1>, 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 (
string
orlist
ofstrings
) – Files to be read. (See note below conerning absolute and relative paths.) - frame (
Frame
, optional) – TheFrame
to attach the resultingDataset
. IfNone
, the currently activeFrame
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:
ReadDataOption.ReplaceInActiveFrame
- The
DataSet
in the active frame is replaced by the newDataSet
. If other frames were using the sameDataSet
originally in the active frame, they will continue to use it.
ReadDataOption.Append
- Append the new
DataSet
to the existingDataSet
.
ReadDataOption.Replace
- Replace the
DataSet
attached to the active frame, and to all other frames that use the sameDataSet
.
Default:
ReadDataOption.Append
- reset_style (
boolean
, optional) – Reset the style for destinationFrame
, ifFalse
, theFrame
’s current style is preserved. (default:True
) - initial_plot_first_zone_only (
boolean
, 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 isTrue
. To have Tecplot 360 determine the most appropriate plot type for the data, usePlotType.Automatic
. Possible values are:PlotType.Automatic
(default),Cartesian3D
,Cartesian2D
,XYLine
,PlotType.Sketch
,PolarLine
. - zones (
set
ofintegers
, optional) – Set of Zones to load. UseNone
to load all zones. (default:None
) - variables (
set
ofstrings
orintegers
, optional) – Set ofVariables
to load. UseNone
to load all variables. (default:None
) - collapse (
boolean
, optional) – Reindex Zones andVariables
if any are disabled. (default:False
) - skip – (3-
tuple
ofintegers
, 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 (
boolean
, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default:True
) - add_zones_to_existing_strands (
boolean
, 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 theDataset
. (default:False
) - include_text (
boolean
, optional) – Load any text, geometries, or custom labels (default:True
) - include_geom (
boolean
, optional) – Load geometries. (default:True
) - include_custom_labels (
boolean
, optional) – (default:True
) - include_data (
boolean
, optional) – Load data. Set this toFalse
if you only want annotations such as text or geometries. (default:True
)
Returns: Raises: TecplotSystemError
– Internal error when loading data.TecplotTypeError
– In-valid input.
Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.- filenames (
data.load_tecplot_szl()¶
-
tecplot.data.
load_tecplot_szl
(filenames, frame=None, read_data_option=<ReadDataOption.Append: 1>, 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, ssh_private_keyfile=None)[source]¶ Read tecplot SZL data file.
Parameters: - filenames (
string
orlist
ofstrings
) – Files to be read. (See note below conerning absolute and relative paths.) - frame (
Frame
, optional) – TheFrame
to attach the resultingDataset
. IfNone
, the currently activeFrame
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:
ReadDataOption.ReplaceInActiveFrame
- Remove the dataset from the active frame prior to reading
in the new dataset. If other frames use the same
DataSet
in the active frame, they will continue to use the old one.
ReadDataOption.Append
- Append the new
DataSet
to the dataset to the existing dataset.
ReadDataOption.Replace
- Replace the
DataSet
attached to the active frame and to all other frames that use the sameDataSet
.
- reset_style (
boolean
, optional) – Reset the style for destinationFrame
, ifFalse
, theFrame
’s current style is preserved. (default:True
) - initial_plot_first_zone_only (
boolean
, 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 isTrue
. To have Tecplot 360 determine the most appropriate plot type for the data, usePlotType.Automatic
. Possible values are:PlotType.Automatic
(default),Cartesian3D
,Cartesian2D
,XYLine
,PlotType.Sketch
,PolarLine
. - assign_strand_ids (
boolean
, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default:True
) - add_zones_to_existing_strands (
boolean
, 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 theDataset
. (default:False
) - server (
string
, optional) – Load the data remotely from this server address. (default:None
) - connection_method (
RemoteConnectionMethod
, optional) – When server is given, this specifies the type of connection to be made. Possible values are:RemoteConnectionMethod.Tunneled
(default),RemoteConnectionMethod.Direct
,RemoteConnectionMethod.Manual
. - user (
string
, optional) – When server is given, this specifies the username to use when logging into the server. This will default to the client’s user name. - private_ssh_keyfile (
string
, optional) – When server is given, this specifies the full path to the private SSH keyfile which defaults to~/.ssh/id_rsa
where~
expands out to the local user’s home directory.
Returns: Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.- filenames (
data.load_cgns()¶
-
tecplot.data.
load_cgns
(filenames, frame=None, read_data_option=<ReadDataOption.Append: 1>, 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 (
string
orlist
ofstrings
) – CGNS data files to be read. (See note below conerning absolute and relative paths.) - frame (
Frame
, optional) – TheFrame
to attach the resultingDataset
. IfNone
, the currently activeFrame
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:
ReadDataOption.ReplaceInActiveFrame
- Remove the dataset from the active frame prior to reading
in the new dataset. If other frames use the same
DataSet
in the active frame, they will continue to use the old one.
ReadDataOption.Append
- Append the new
DataSet
to the dataset to the existing dataset.
ReadDataOption.Replace
- Replace the
DataSet
attached to the active frame and to all other frames that use the sameDataSet
.
- reset_style (
boolean
, optional) – Reset the style for destinationFrame
, ifFalse
, theFrame
’s current style is preserved. (default:True
) - initial_plot_first_zone_only (
boolean
, 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 isTrue
. To have Tecplot 360 determine the most appropriate plot type for the data, usePlotType.Automatic
. Possible values are:PlotType.Automatic
(default),Cartesian3D
,Cartesian2D
,XYLine
,PlotType.Sketch
,PolarLine
. - zones (
list
ofintegers
, optional) – List of zone indexes to load starting from zero.None
implies loading all zones. (default:None
) - variables (
list
ofintegers
, 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 (
boolean
, optional) – Load the global convergence history rather than any grid or solution data. (default:False
) - combine_fe_sections (
boolean
, optional) – Combine all finite-element sections with the zone cell-dimension into one zone. (default:False
) - average_to_nodes (
string
, optional) – Average cell-centered data to grid nodes using the specified method. (Options:None
, “Arithmetic”, “Laplacian”, default: “Arithmetic”) - uniform_grid (
boolean
, optional) – Indicates the grid structure is the same for all time steps. (default:True
) - assign_strand_ids (
boolean
, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default:True
) - add_zones_to_existing_strands (
boolean
, 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 theDataset
. (default:False
) - include_boundary_conditions (
boolean
, 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: Raises: TecplotSystemError
– Internal error when loading data.TecplotTypeError
– Invalid input.
Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.- filenames (
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 (
string
orlist
ofstrings
, optional) – Case (.cas, .cas.gz) files to be read. Compressed files with extension .gz are supported. (See note below conerning absolute and relative paths.) - data_filenames (
string
orlist
ofstrings
, optional) – Data (.dat, .xml, .dat.gz, .fdat, .fdat.gz, etc.) files to be read. Compressed files with extension .gz are supported. - frame (
Frame
, optional) – TheFrame
to attach the resultingDataset
. IfNone
, the currently activeFrame
is used and the zones are appended by default. - append (
boolean
, optional) – Append the data to the existingDataset
. IfFalse
, the existing data attached to theFrame
is deleted and replaced. (default:True
) - zones (
string
orlist
ofintegers
, 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 thevariables
option. (default: “CellsAndBoundaries”) - variables (
list
ofstrings
, optional) – List of variable names to load.None
implies loading all variables. (default:None
) - all_poly_zones (
boolean
, optional) – Converts all zones to Tecplot polytope (polyhedral or polygonal) zones. (default:False
) - average_to_nodes (
string
, 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 .dat file. IfNone
, the flow-data parameter of each solution .dat file is used. (default:None
) - assign_strand_ids (
boolean
, 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 (
boolean
, 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 theDataset
. (default:False
) - include_particle_data (
boolean
, optional) – Load particle data from the .dat files. If loading particle data from an XML file, the XML file should be included in thedata_filenames
list. (default:False
) - include_additional_quantities (
boolean
, optional) – Load quantities that were derived from the FLUENT’s standard quantities. (default:True
) New in Tecplot 360 2017 R2. - save_uncompressed_files (
boolean
, optional) – Save the uncompressed files to the compressed files’ location.
Returns: Raises: TecplotSystemError
– Internal error when loading data.TecplotTypeError
– In-valid input.
Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.Notes
The
zones
option takes either alist
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 thevariables
option:>>> import tecplot >>> dataset = tecplot.data.load_fluent(['one.cas', 'two.cas'], ... data_filenames=['one.dat', 'two.dat'], ... variables = ['Pressure','Velocity'], ... zones = [0,1,3])
- case_filenames (
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 (
list
ofstrings
, optional) – One or more grid file names to be read. (See note below conerning absolute and relative paths.) - solution_filenames (
list
ofstrings
, optional) – One or more solution data file names to be read. - function_filenames (
list
ofstrings
, optional) – One or more function file names. - name_filename (
string
, optional) – Path to the name file. - frame (
Frame
, optional) – TheFrame
to attach the resultingDataset
. IfNone
, the currently activeFrame
is used and the zones are appended by default. - append (
boolean
, optional) – Append the data to the existingDataset
. IfFalse
, the existing data attached to theFrame
is deleted and replaced. (default:True
) - data_structure (
string
, optional) – Specifies the data structure and overrides the automatic detection. Options are:1D
,2D
,3DP
,3DW
,UNSTRUCTURED
. Setting this requiresis_multi_grid
andstyle
to be set as well. - is_multi_grid (
boolean
, optional) – Sets data as multi-grid and overrides the automatic data structure detection. Setting this requiresdata_structure
andstyle
to be set as well. - style (
boolean
, optional) – Specifies the data style and overrides the automatic data structure detection. Options are:PLOT3DCLASSIC
,PLOT3DFUNCTION
,OVERFLOW
. Setting this requiresdata_structure
andis_multi_grid
to be set as well. - ascii_is_double (
boolean
, optional) – Indicates that floating-point numbers found in the text data files should be store with 64-bit precision. (default:False
) - ascii_has_blanking (
boolean
, optional) – Indicates that the text data files contain blanking. (default:False
) - uniform_grid (
boolean
, optional) – Indicates the grid structure is the same for all time steps. (default:True
) - assign_strand_ids (
boolean
, optional) – Assign strand ID’s to zones that have a strand ID of -1. (default:True
) - add_zones_to_existing_strands (
boolean
, 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 theDataset
. (default:True
) - append_function_variables (
boolean
, optional) – Append variables in function files to those found in solution files. (default:False
) - include_boundaries (
boolean
, optional) – Loads boundary zones found in the “.g.fvbnd” file located in the same directory as the grid file, if available. (default:True
)
Returns: Raises: TecplotSystemError
– Internal error when loading data.TecplotValueError
– In-valid input.
Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.Note
Data structure is automatically detected by default.
The options
data_structure
,is_multi_grid
andstyle
must be supplied together or not at all. When all of these areNone
, 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)
- grid_filenames (
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 (
string
) – Name of the data file to write. (See note below conerning absolute and relative paths.) - frame (
Frame
, optional) – TheFrame
which holds theDataset
to be written. If this option and dataset are bothNone
, the currently activeFrame
is used. (default:None
) - dataset (
Dataset
, optional) – TheDataset
to write out. If this and frame are bothNone
, theDataset
of the currently activeFrame
is used. (default:None
) - include_text (
boolean
, optional) – Write out all text, geometries and custom labels. (default:True
) - include_geom (
boolean
, optional) – Write out all geometries. (default:True
) - include_data (
boolean
, optional) – Write out the data. Set this toFalse
if you only want to write out annotations. (default:True
) - include_data_share_linkage (
boolean
, optional) – Conserve space and write the variable and connectivity linkage wherever possible. IfFalse
, this will write out all data, losing the connectivity sharing linkage for future dataset reads of the file. (default:True
) - include_autogen_face_neighbors (
boolean
, optional) – Save the face neighbor connectivity. This may produce very large data files. (default:False
) - use_point_format (
boolean
, optional) – Write out point format, otherwise use block format. (default:False
) - zones (
list
of Zones, optional) – Zones to write out. UseNone
to write out all Zones. (default:None
) - variables (
list
ofVariables
, optional) –Variables
to write out. UseNone
to write out allVariables
. (default:None
) - precision (
integer
, optional) – ASCII decimal precision to use. (default: 12)
Returns: Raises: Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.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])
- filename (
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 (
string
) – Name of the data file to write. (See note below conerning absolute and relative paths.) - frame (
Frame
, optional) – TheFrame
which holds theDataset
to be written. If this option and dataset are bothNone
, the currently activeFrame
is used. (default:None
) - dataset (
Dataset
, optional) – TheDataset
to write out. If this and frame are bothNone
, theDataset
of the currently activeFrame
is used. (default:None
) - zones (
list
of Zones, optional) – Zones to write out. IfNone
, all Zones will be saved. - variables (
list
ofVariables
, optional) –Variables
to write out. IfNone
, allVariables
will be saved. - include_text (
boolean
, optional) – Write out all text, geometries and custom labels. (default:True
) - include_geom (
boolean
, optional) – Write out all geometries. (default:True
) - include_data (
boolean
, optional) – Write out the data. Set this toFalse
if you only want to write out annotations. (default:True
) - include_data_share_linkage (
boolean
, optional) – Conserve space and write the variable and connectivity linkage wherever possible. IfFalse
, this will write out all data, losing the connectivity sharing linkage for future dataset reads of the file. (default:True
) - include_autogen_face_neighbors (
boolean
, optional) – Save the face neighbor connectivity. This may produce very large data files. (default:False
) - associate_with_layout (
boolean
, optional) – Associate this data file with the current layout. Set toFalse
to write the datafile without modifying Tecplot’s current data file to layout association. If version is set to anything other thanBinaryFileVersion.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
andBinaryFileVersion.Current
. (default:BinaryFileVersion.Current
)
Returns: Raises: Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.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])
- filename (
data.save_tecplot_szl()¶
-
tecplot.data.
save_tecplot_szl
(filename, frame=None, dataset=None)[source]¶ Write Tecplot SZL data file.
Parameters: - filename (
string
) – Name of the data file to write. (See note below conerning absolute and relative paths.) - frame (
Frame
, optional) – TheFrame
which holds theDataset
to be written. If this option and dataset are bothNone
, the currently activeFrame
is used. (default:None
) - dataset (
Dataset
, optional) – TheDataset
to write out. If this and frame are bothNone
, theDataset
of the currently activeFrame
is used. (default:None
)
Returns: Note
Absolute and relative paths with PyTecplot
Unless file paths are absolute, saving and loading files will be relative to the current working directory of the parent process. This is different when running the PyTecplot script in batch mode and when running in connected mode with
tecplot.session.connect()
. In batch mode, paths will be relative to Python’s current working directory as obtained byos.getcwd()
. When connected to an instance of Tecplot 360, paths will be relative to Tecplot 360’s‘ start-up folder which is typically the Tecplot 360 installation “bin” folder.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')
- filename (
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 theDataset
and not necessarily(X, Y, Z)
. See:Cartesian3DFieldAxis.variable
.Parameters: - x,y,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 returnNone
. - starting_cell (3-
tuple
ofintegers
, optional) – The(i,j,k)
-index of the cell to start looking for the given position. This must be used withstarting_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) – TheDataset
to probe. (defaults to the activeDataset
.) - frame (
Frame
, optional) – TheFrame
which determines the spatial variable assignment(X,Y,Z)
. (defaults to the activeFrame
.)
Returns: (data, cell, zone)
: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 againstNone
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.
- x,y,z (
data.query.probe_on_surface()¶
-
tecplot.data.query.
probe_on_surface
(positions=((0, ), (0, ), (0, )), zones=None, variables=None, probe_nearest=<ProbeNearest.Position: 0>, 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 theDataset
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 1Dfloat
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
ofVariables
, 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 toProbeNearest.Position
(default), or nodes if set toProbeNearest.Node
. - obey_blanking (
bool
, optional) – Do not search blanked cells according the frame’s style settings. (default:True
) - num_nearest_nodes (
integer
, optional) – Only consider surface cells that contain one of the closestN
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) – TheDataset
to probe. (defaults to the activeDataset
.) - frame (
Frame
, optional) – TheFrame
which determines the spatial variable assignment(X,Y,Z)
. (defaults to the activeFrame
.)
Returns: namedtuple
–(data, cells_or_nodes, planes, zone)
:data
(list
offloats
)Flattened
float
array which can be reshaped to(V, N)
whereV
is the number of variables returned (either the number of variables in the dataset or the length of variables input parameter) andN
is the number of points probed.cells_or_nodes
(list
ofintegers
)The index to the cells (or nodes if
ProbeNearest.Node
was passed in to probe_nearest) containing the returned positions.planes
(list
ofIJKPlanes
)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.
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 )) >>> print(res.data) (13.499723788684996, 3.9922783797612795, 0.49241572276992346, 0.0018958827755862578, 0.07313805429221854, 0.997276718375976, 0.06335166319722907) >>> >>> # 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() >>> >>> # print probed position and the result of the probe >>> for pt, v in zip(points, values): >>> print(pt, v) [ 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]
- positions (2D
Data Operations¶
data.operate.Range()¶
-
tecplot.data.operate.
Range
(min, max, step)¶ Limit the data altered by the
execute_equation
function.The Range specification of I,J,K range indices for
execute_equation
follow these rules:- All indices start with 0 and go to some maximum index m.
- Negative values represent the indexes starting with the maximum at -1 and continuing back to the beginning of the range.
- A step of None, 0 and 1 are all equivalent and mean that no elements are skipped.
- A negative step indicates a skip less than the maximum.
Example
Add one to variable ‘X’ for a zone ‘Rectangular’ for data points in I Range 1 to max, skipping every three points:
>>> execute_equation('{X} = {X}+1', i_range=Range(1, None, 3), ... zone_set='Rectangular')
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 (
string
) –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 (
Range
, optional) – Tuple of integers for I: (min, max, step). IfNone
, then the equation will operate on the entire range. Not used for finite element nodal data. - j_range (
Range
, optional) – Tuple of integers for J: (min, max, step). IfNone
, then the equation will operate on the entire range. Not used for finite element nodal data. - k_range (
Range
, optional) – Tuple of integers for K: (min, max, step). IfNone
, then the equation will operate on the entire range. Not used for finite element nodal data. - value_location (
ValueLocation
, optional) – VariableValueLocation
for the variable on the left hand side. This is used only if this variable is being created for the first time. IfNone
, 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. IfNone
, 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=Range(step=-1)) >>> execute_equation('{P} = 0', j_range=Range(step=-1))
- equation (
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
integer
) – 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
orintegers
, 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)
- destination_zone (zone or
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: 2>, num_points=8, plot=None)[source]¶ Inverse-Distance interpolation onto a destination zone.
Parameters: - destination_zone (zone or
integer
) – 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
orintegers
, 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) - num_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 (
integer
, optional) – Number of source points to consider for each destination data point if point_selection isPtSelection.OctantN
orPtSelection.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)
- destination_zone (zone or
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: 1>, point_selection=<PtSelection.OctantN: 2>, num_points=8, plot=None)[source]¶ Kriging interpolation onto a destination zone.
Parameters: - destination_zone (zone or
integer
) – 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
orintegers
, 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 (
integer
, optional) – Number of source points to consider for each destination data point if point_selection isPtSelection.OctantN
orPtSelection.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)
- destination_zone (zone or
Data Extractions¶
data.extract.extract_slice()¶
-
tecplot.data.extract.
extract_slice
(origin=(0, 0, 0), normal=(0, 0, 1), source=None, multiple_zones=None, copy_cell_centers=None, assign_strand_ids=None, frame=None, dataset=None)[source]¶ Create new zone slices from the zones in a dataset.
Parameters: - origin (array of three
floats
) – Point in space, \((x, y, z)\), that lies on the slice plane. - normal (array of three
floats
) – Vector direction, \((x, y, z)\), indicating the normal of the slice plane. - source (
SliceSource
) – Source zone types to consider when extracting the slice. Possible values:SliceSource.LinearZones
,SliceSource.SurfaceZones
,SliceSource.SurfacesOfVolumeZones
,SliceSource.VolumeZones
(default). - multiple_zones (
boolean
) – IfTrue
, this allows the extracted slice to consist of one zone per contiguous region. By default, only a single zone is created. (default:False
) - copy_cell_centers (
boolean
) – IfTrue
, cell-center values will be copied when possible to the extracted slice plane. Cell-centers are copied when a variable is cell-centered for all the source zones through which the slice passes. Otherwise, extracted planes use node-centered data, which is calculated by interpolation. (default:False
) - assign_strand_ids (
boolean
) – automatically assign strand IDs to the data extracted from transient sources. This is only available if multiple_zones isFalse
. (default:True
)
Returns: Warning
Slicing is only available when the plot type is set to 3D:
>>> from tecplot.constant import PlotType >>> frame.plot_type = PlotType.Cartesian3D
Note
This function returns a list of zones if multiple_zones is set to
True
. Otherwise, a single zone is returned.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)
- origin (array of three
Data Access¶
Dataset¶
-
class
tecplot.data.
Dataset
(uid, frame)[source]¶ Table of
Arrays
identified by Zone andVariable
.This is the primary data container within the Tecplot Engine. A
Dataset
can be shared among severalFrames
, though any particularDataset
object will have a handle to at least one of them. Any modification of a sharedDataset
will be reflected in allFrames
that use it.Though a
Dataset
is usually attached to aFrame
and the plot style associated with that, it can be thought of as independent from any style or plotting representation. EachDataset
consists of a list ofVariables
which are used by one or more of a list of Zones. TheVariable
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 anArray
. 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 andvariables
.Attributes
VariablesNamedTuple
A collections.namedtuple
object using variable names.aux_data
Auxiliary data for this dataset. num_solution_times
Number of solution times for all zones in the dataset. num_variables
Number of Variables
in thisDataset
.num_zones
Number of Zones in this Dataset
.solution_times
List
of solution times for all zones in the dataset.title
Title of this Dataset
.Methods
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 activeDataset
.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 thisDataset
.delete_zones
(*zones)Remove Zones from 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) >>> msg = '(RHO, E) = ({:.2f}, {:.2f})' >>> print(msg.format(data.RHO, data.E)) (RHO, E) = (1.17, 252930.37)
-
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
) – The type of Zone to be created. Possible values are:FETriangle
,FEQuad
,FETetra
,FEBrick
andFELineSeg
. - name (
string
) – Name of the new Zone. This does not have to be unique. - num_points (
integer
) – Number of points (nodes) in this zone. - num_elements (
integer
) – 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
Keyword arguments are passed to the parent zone creation method
Dataset.add_zone
.Warning
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
orNodemap
objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset toPlotType.Sketch
before creating the zone and only going toPlotType.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
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. See the documentation fortecplot.session.suspend()
for more information.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.
- zone_type (
-
Dataset.
add_ordered_zone
(name, shape, **kwargs)[source]¶ Add a single ordered Zone to this
Dataset
.Parameters: See also
Keyword arguments are passed to the parent zone creation method
Dataset.add_zone
.Note
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. See the documentation fortecplot.session.suspend()
for more information.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), ... dtype=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)
-
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
) – The type of Zone to be created. Possible values are:FEPolyhedron
andFEPolygon
. - name (
string
) – Name of the new Zone. This does not have to be unique. - num_points (
integer
) – Number of points in this zone. - num_elements (
integer
) – Number of elements in this zone. - num_faces (
integer
) – Number of faces in this zone. - **kwargs – These arguments are passed to
Dataset.add_zone
.
See also
Keyword arguments are passed to the parent zone creation method
Dataset.add_zone
.Warning
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
orNodemap
objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset toPlotType.Sketch
before creating the zone and only going toPlotType.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
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. See the documentation fortecplot.session.suspend()
for more information.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.
- zone_type (
-
Dataset.
add_variable
(name, dtypes=None, locations=None)[source]¶ Add a single
Variable
to the activeDataset
.Parameters: - name (
string
) – The name of the newVariable
. This does not have to be unique. - dtypes (
FieldDataType
orlist
ofFieldDataType
, optional) – Data types of thisVariable
for each Zone in the currently activeDataset
. Options are:FieldDataType.Float
,Double
,Int32
,Int16
,Byte
andBit
. If a single value, this will be duplicated for all Zones. (default:None
) - locations (
ValueLocation
orlist
ofValueLocation
, optional) – Point locations of thisVariable
for each Zone in the currently activeDataset
. Options are:Nodal
andCellCentered
. If a single value, this will be duplicated for all Zones. (default:None
)
Returns: Note
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. See the documentation fortecplot.session.suspend()
for more information.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 asDataset.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)
- name (
-
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)[source]¶ Add a single Zone to this
Dataset
.Parameters: - zone_type (
ZoneType
) – The type of Zone to be created. Possible values are:Ordered
,FETriangle
,FEQuad
,FETetra
,FEBrick
,FELineSeg
,FEPolyhedron
andFEPolygon
. - name (
string
) – Name of the new Zone. This does not have to be unique. - shape (
integer
orlist
ofintegers
) – Specifies the length and dimension (up to three) of the new Zone. A 1D Zone is assumed if a singleint
is given. This is (i, j, k) for ordered Zones, (num_points, num_elements) for finite-element Zones and (num_points, num_elements, num_faces) for polytope Zones where the number of faces is known. - dtypes (
FieldDataType
,list
ofFieldDataType
, optional) – Data types of this Zone for eachVariable
in the currently activeDataset
. Options are:Float
,Double
,Int32
,Int16
,Byte
andBit
. If a single value, this will be duplicated for allVariables
. IfNone
then the type of the firstVariable
, defaulting toFieldDataType.Float
, is used for all. (default:None
) - locations (
ValueLocation
,list
ofValueLocation
, optional) – Point locations of this Zone for eachVariable
in the currently activeDataset
. Options are:Nodal
andCellCentered
. If a single value, this will be duplicated for allVariables
. IfNone
then the type of the firstVariable
, defaulting toNodal
, is used for all. (default:None
) - face_neighbor_mode (
FaceNeighborMode
, optional) – Specifies the face-neighbor mode for this zone. Options are:FaceNeighborMode.LocalOneToOne
(default),FaceNeighborMode.LocalOneToMany
,FaceNeighborMode.GlobalOneToOne
orFaceNeighborMode.GlobalOneToMany
. - parent_zone (Zone, optional) – A parent Zone to be used when generating surface-restricted streamtraces.
- solution_time (
float
, optional) – Solution time for this zone. (default: 0) - strand_id (
integer
, optional) – Associate this new Zone with a particular strand. - index (
integer
, optional) – Number of the zone to add or replace. If omitted or set toNone
, the new zone will be appended to the dataset. This value can be set to the number of a zone that already exists thereby replacing the existing zone. (default:None
)
Returns: Warning
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
orNodemap
objects. This can be achieved by setting the plot type of the frame(s) holding on to the dataset toPlotType.Sketch
before creating the zone and only going toPlotType.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
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. See the documentation fortecplot.session.suspend()
for more information.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 asFrame.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:
- zone_type (
-
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
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: See also
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: Returns: Note
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. See the documentation fortecplot.session.suspend()
for more information.Example usage:
>>> new_zones = dataset.copy_zones()
-
Dataset.
delete_variables
(*variables)[source]¶ Remove
Variables
from thisDataset
.Parameters: *variables ( Variable
or indexinteger
) – Variables to remove from this dataset.>>> print([v.name for v in dataset.variables()]) ['X','Y','Z'] >>> dataset.delete_variables(dataset.variable('Z')) >>> print([v.name for v in dataset.variables()]) ['X','Y']
Warning
Deleting
Variables
invalidates iterators referencing them in the containingDataset
such as those obtained fromDataset.variables()
. It is recommended to create a list of theVariables
you want to delete and to pass that into a single call toDataset.delete_variables()
Notes
Multiple
Variables
can be deleted at once, though the lastVariable
can not be deleted. The following example deletes all but the firstVariable
in theDataset
(usuallyX
):>>> # 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([v.name for v in dataset.variables()]) ['X']
-
Dataset.
delete_zones
(*zones)[source]¶ Remove Zones from this
Dataset
.Parameters: *zones (Zones or index integers
) – Zones to remove from this dataset.>>> print([z.name for z in dataset.zones()]) ['Zone 1', 'Zone 2'] >>> dataset.delete_zones(dataset.zone('Zone 2')) >>> print([z.name for z in dataset.zones()]) ['Zone 1']
Warning
Deleting Zones invalidates iterators referencing them in the containing
Dataset
such as those obtained fromDataset.zones()
. It is recommended to create a list of the Zones you want to delete and to pass that into a single call toDataset.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.
num_solution_times
¶ Number of solution times for all zones in the dataset.
Type: int
(read-only)Example usage:
>>> print(dataset.num_solution_times) 10
New in version 2017.3: Solution time manipulation requires Tecplot 360 2017 R3 or later.
-
Dataset.
num_variables
¶ Number of
Variables
in thisDataset
.Type: integer
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
¶ Number of Zones in this
Dataset
.Type: integer
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)
Share connectivity between zones.
This method links the connectivity (
nodemap
orfacemap
) 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: See also
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]
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: See also
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_times
¶ List
of solution times for all zones in the dataset.Type: list
offloats
(read-only)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.
-
Dataset.
title
¶ Title of this
Dataset
.Type: string
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 ( integer
orstring
) – Zero-based index orglob-style pattern
in which case, the first match is returned.Raises: TecplotIndexError
The
Variable.name
attribute is used to match the pattern to the desiredVariable
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
-
Dataset.
variables
(pattern=None)[source]¶ Yields all
Variables
matching a pattern.Parameters: pattern ( string
pattern, optional) –glob-style pattern
used to match variable names orNone
which will return all variables. (default:None
)Example usage:
>>> for variable in dataset.variables('A*'): ... array = variable.values('My Zone')
-
Dataset.
zone
(pattern)[source]¶ Returns Zone by index or string pattern.
Parameters: pattern ( integer
orstring
) – Zero-based index orglob-style pattern
in which case, the first match is returned.Returns: OrderedZone
,ClassicFEZone
orPolyFEZone
depending on the zone type.Raises: TecplotIndexError
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
-
Dataset.
zones
(pattern=None)[source]¶ Yields all Zones matching a pattern.
Parameters: pattern ( string
pattern, optional) –glob-style pattern
used to match zone names orNone
which will return all zones. (default:None
)Returns: OrderedZone
,ClassicFEZone
orPolyFEZone
depending on the zone type.Example usage:
>>> for zone in dataset.zones('A*'): ... x_array = zone.variable('X')
Use list comprehension to construct a list of all zones with ‘Wing’ in the zone name:
>>> wing_zones = [Z for Z in dataset.zones() if 'Wing' in Z.name]
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 parentDataset
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
Zero-based position within the parent Dataset
.name
Returns or sets the name. num_zones
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
¶ Zero-based position within the parent
Dataset
.Type: Index
Example usage:
>>> plot.contour(0).variable_index = dataset.variable('S').index
-
Variable.
max
()[source]¶ Upper bound of the values stored in this variable across all zones.
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.
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.
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.
Type: string
Example usage:
>>> print(dataset.variable(0).name) X
-
Variable.
num_zones
¶ Number of Zones in the parent
Dataset
.Type: integer
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 ( integer
orstring
) – Zero-based index orglob-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 desiredArray
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
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 aDataset
.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, aVariable
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
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
Number of Variables
in the parentDataset
.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])Duplicate this Zone in the parent Dataset
.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)¶ Duplicate this Zone in the parent
Dataset
.The name is also copied but can be changed after duplication.
Parameters: share_variables ( bool
orlist
ofVariables
) – Variables to be shared between original and generated zones. If variables are not shared they will be created as passive 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
-
OrderedZone.
dimensions
¶ Nodal dimensions along
(i, j, k)
.Returns: tuple
ofintegers
–(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.
Type: FaceNeighbors
Example usage:
>>> zone = dataset.zone(0) >>> print(zone.face_neighbors.mode) FaceNeighborMode.LocalOneToOne
-
OrderedZone.
index
¶ Zero-based position within the parent
Dataset
.Type: Index
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.
name
¶ The name of the zone.
Type: string
Example usage:
>>> dataset.zone(0).name = 'Zone 0'
-
OrderedZone.
num_elements
¶ Number of cells in this zone.
Type: integer
Example usage:
>>> zone = dataset.zone('My Zone') >>> print(zone.dimensions) (128, 128, 128) >>> print(zone.num_elements) 2048383
-
OrderedZone.
num_faces
¶ Number of faces in this zone.
Type: integer
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
-
OrderedZone.
num_faces_per_element
¶ Number of faces per element in this ordered zone.
Type: integer
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
-
OrderedZone.
num_points
¶ Total number of nodes within this zone.
Type: integer
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
-
OrderedZone.
num_points_per_element
¶ Points per cell for ordered zones.
Type: integer
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
-
OrderedZone.
num_variables
¶ Number of
Variables
in the parentDataset
.Type: integer
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.
Type: integer
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
-
OrderedZone.
solution_time
¶ The solution time for this zone.
Type: float
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
orplot.solution_timestep
properties.
-
OrderedZone.
strand
¶ The strand ID number.
Type: integer
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
orplot.solution_timestep
properties.
-
OrderedZone.
values
(pattern)¶ Returns an
Array
by index or string pattern.Parameters: pattern ( integer
orstring
) – Zero-based index,glob-style pattern
in which case, the first match is returned, or aVariable
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 desiredArray
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
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, aVariable
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
Zero-based position within the parent Dataset
.name
The name of the zone. nodemap
The connectivity Nodemap
for this classic 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
Number of Variables
in the parentDataset
.rank
Number of dimensions of the data array. shared_connectivity
list
of 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
.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
orlist
ofVariables
) – Variables to be shared between original and generated zones. If variables are not shared they will be created as passive 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.
Type: FaceNeighbors
Example usage:
>>> zone = dataset.zone(0) >>> print(zone.face_neighbors.mode) FaceNeighborMode.LocalOneToMany
-
ClassicFEZone.
index
¶ Zero-based position within the parent
Dataset
.Type: Index
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.
name
¶ The name of the zone.
Type: string
Example usage:
>>> dataset.zone(0).name = 'Zone 0'
-
ClassicFEZone.
nodemap
¶ The connectivity
Nodemap
for this classic finite-element zone.Type: Nodemap
Example usage:
>>> zone = dataset.zone(0) >>> print(zone.nodemap.num_points_per_element) 4
-
ClassicFEZone.
num_elements
¶ Number of cells in this finite-element zone.
Type: integer
Example usage:
>>> print(dataset.zone('My Zone').num_elements) 1048576
-
ClassicFEZone.
num_faces
¶ Number of faces in this zone.
Type: integer
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
-
ClassicFEZone.
num_faces_per_element
¶ Number of faces per element.
Type: integer
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
-
ClassicFEZone.
num_points
¶ Total number of nodes within this zone.
Type: integer
This is the total number of nodes in the zone. Example usage:
>>> print(dataset.zone('My Zone').num_points) 2048
-
ClassicFEZone.
num_points_per_element
¶ Points per element for classic finite-element zones.
Type: integer
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 (\(N\), \(8 N\)) Example usage:
>>> zone = dataset.zone('My Zone') >>> print(zone.zone_type) ZoneType.FETriangle >>> print(zone.num_points_per_element) 3
-
ClassicFEZone.
num_variables
¶ Number of
Variables
in the parentDataset
.Type: integer
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.
Type: integer
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
list
of Zones sharing connectivity.Type: list
of ZonesExample usage:
>>> dataset.zone('My Zone').copy() >>> for zone in dataset.zone('My Zone').shared_connectivity: ... print(zone.index) 0 1
-
ClassicFEZone.
solution_time
¶ The solution time for this zone.
Type: float
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
orplot.solution_timestep
properties.
-
ClassicFEZone.
strand
¶ The strand ID number.
Type: integer
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
orplot.solution_timestep
properties.
-
ClassicFEZone.
values
(pattern)¶ Returns an
Array
by index or string pattern.Parameters: pattern ( integer
orstring
) – Zero-based index,glob-style pattern
in which case, the first match is returned, or aVariable
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 desiredArray
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
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, aVariable
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
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
Number of Variables
in the parentDataset
.rank
Number of dimensions of the data array. shared_connectivity
list
of 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
.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
orlist
ofVariables
) – Variables to be shared between original and generated zones. If variables are not shared they will be created as passive 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.Type: Facemap
Example usage:
>>> zone = dataset.zone(0) >>> print(zone.facemap.num_faces) 4500
-
PolyFEZone.
index
¶ Zero-based position within the parent
Dataset
.Type: Index
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.
name
¶ The name of the zone.
Type: string
Example usage:
>>> dataset.zone(0).name = 'Zone 0'
-
PolyFEZone.
num_elements
¶ Number of cells in this finite-element zone.
Type: integer
Example usage:
>>> print(dataset.zone('My Zone').num_elements) 1048576
-
PolyFEZone.
num_faces
¶ Number of faces in this finite-element zone.
Type: integer
The number of faces may be
0
if unknown or facemap creation is deferred. Example usage:>>> print(dataset.zone('My Zone').num_faces) 1048576
-
PolyFEZone.
num_points
¶ Total number of nodes within this zone.
Type: integer
This is the total number of nodes in the zone. Example usage:
>>> print(dataset.zone('My Zone').num_points) 2048
-
PolyFEZone.
num_variables
¶ Number of
Variables
in the parentDataset
.Type: integer
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.
Type: integer
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
list
of Zones sharing connectivity.Type: list
of ZonesExample usage:
>>> dataset.zone('My Zone').copy() >>> for zone in dataset.zone('My Zone').shared_connectivity: ... print(zone.index) 0 1
-
PolyFEZone.
solution_time
¶ The solution time for this zone.
Type: float
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
orplot.solution_timestep
properties.
-
PolyFEZone.
strand
¶ The strand ID number.
Type: integer
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
orplot.solution_timestep
properties.
-
PolyFEZone.
values
(pattern)¶ Returns an
Array
by index or string pattern.Parameters: pattern ( integer
orstring
) – Zero-based index,glob-style pattern
in which case, the first match is returned, or aVariable
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 desiredArray
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
Array¶
-
class
tecplot.data.
Array
(zone, variable)[source]¶ Low-level accessor for underlying data within a
Dataset
.Note
The data manipulation context referred to below is currently being developed and should show up in an up-coming revision.
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 data manipulation context for a significant performance increase on large datasets.
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:
>>> import tecplot as tp >>> ds = tp.active_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
andtecplot.extension.numpy
. Please refer to these for details. Continuing with the example above, we could accomplish the same thing with either of the following usingexecute_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.
Attributes
c_type
ctypes
compatible data type of this array.data_type
FieldDataType
indicating the underlying value type of this array.location
The location of the data points with respect to the elements. passive
Boolean
indicating an unallocated zone-variable combination.shape
(i, j, k)
shape for this array.shared_zones
list
of all Zones sharing this array.Methods
as_numpy_array
(arr)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
(arr)¶
-
Array.
c_type
¶ ctypes
compatible data type of this array.This is the
ctypes
equivalent ofArray.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: 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 >>> print(x[:]) [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]
-
Array.
data_type
¶ FieldDataType
indicating the underlying value type of this array.Type: FieldDataType
Example usage:
>>> print(dataset.zone('Zone').values('X').data_type) FieldDataType.Float
-
Array.
location
¶ The location of the data points with respect to the elements.
Type: ValueLocation
Possible values are
ValueLocation.CellCentered
andValueLocation.Nodal
. Example usage:>>> print(dataset.zone(0).values('X').location) ValueLocation.Nodal
-
Array.
max
()[source]¶ Upper bound of the values stored in this array.
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.
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.
Type: 2-tuple of floats
This always returns
floats
regardless of the underlying data type:>>> print(dataset.zone('Zone').values('x').minmax()) (0, 10)
-
Array.
passive
¶ Boolean
indicating an unallocated zone-variable combination.Type: bool
Passive variables are placeholders where no data is defined for a zone variable combination. Passive variables will always return zero when queried.
Example:
>>> import tecplot as tp >>> ds = tp.active_page().add_frame().create_dataset('D', ['x','y']) >>> z = ds.add_ordered_zone('Z1', (3,)) >>> z.values(0).passive False
-
Array.
shape
¶ (i, j, k)
shape for this array.Type: tuple
offloats
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 thenumpy
-ownedarray
:>>> 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)) >>> x = np.array(zone.values('X')[:]) >>> print(x) [ 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) [[[ 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.]]]
Nodemap¶
-
class
tecplot.data.
Nodemap
(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
Nodemap.array
property as well as reverse look-up withNodemap.num_elements()
andNodemap.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:
- Setup the data
- Create the tecplot dataset and variables
- Create the zone
- Set the node locations and connectivity lists
- Set the (scalar) data
- Write out data file
- 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 * # (x, y, z) locations of nodes nodes = ( (0, 0, 0 ), # node 0 (1, 0, 0.5), # node 1 (0, 1, 0.5), # ... (1, 1, 1 ), ) scalar_data = [0, 1, 2, 3] # Connectivity list # (n0, n1, n2) node indexes which make up the triangles conn = ( (0, 1, 2), # element 0 consisting of faces # node connections: 0-1, 1-2, 2-0 (1, 3, 2), # element 1 ) # Setup dataset and zone ds = tp.active_frame().create_dataset('Data', ['x','y','z','s']) z = ds.add_fe_zone(ZoneType.FETriangle, name='FE Triangle Float (4,2) Nodal', num_points=len(nodes), num_elements=len(conn)) # Fill in node locations 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] # Set the nodemap z.nodemap[:] = conn # Set the scalar data z.values('s')[:] = scalar_data ### Now we setup a nice 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 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) # Showing mesh, we can see all the individual triangles plot.show_mesh = True plot.fieldmap(z).mesh.line_pattern = LinePattern.Dashed tp.export.save_png('fe_triangles1.png', 600, supersample=3)
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. num_elements
(node)The number of elements that use a given node.
-
Nodemap.
array
¶ Flattened array accessor for this nodemap.
Type: 1D view of the 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]
-
Nodemap.
assignment
()[source]¶ Context manager for assigning to the nodemap.
When setting values to the nodemap, a state change 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(): ... for elem, nodes in enumerate(mydata): ... nodemap[elem] = nodes
-
Nodemap.
c_type
¶ The underlying data type for this nodemap.
Type: One of: ctypes.c_int32
,ctypes.c_int64
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.
-
Nodemap.
element
(node, offset)[source]¶ The element containing a given node.
Parameters: Returns: integer
- Zero-based index of the element.Example usage:
>>> nodemap = dataset.zone('My Zone').nodemap >>> print(nodemap.element(3, 7)) 324
-
Nodemap.
num_elements
(node)[source]¶ The number of elements that use a given node.
Parameters: node – ( integer
): Zero-based index of a node.Returns: integer
- The number of elements that use this node.Example usage:
>>> nodemap = dataset.zone('My Zone').nodemap >>> nodemap.num_elements(3) 8
-
Nodemap.
num_points_per_element
¶ Points per element for classic finite-element zones.
Type: integer
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
-
Nodemap.
shape
¶ Shape of the nodemap array.
Type: tuple
ofintegers
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)
-
Nodemap.
size
¶ Total number of nodes stored in the nodemap array.
Type: integer
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
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 tp.export.save_png('polygons1.png', 600, supersample=3)
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 andface_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: 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 theFacemap.set_nodes()
andFacemap.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.
- face_nodes (
-
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()
andFacemap.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: Returns: (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: Returns: integer
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: Returns: 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: Returns: integer
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: Returns: integer
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 ( integer
, optional) – Zero-based index of an element. If no element is given, the total number of faces in the map are returned.Returns: integer
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 (
integer
, 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 (
integer
, 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: integer
The number of nodes.Example usage:
>>> print(zone.facemap.num_nodes(face=1)) 4
- face (
-
Facemap.
num_unique_nodes
¶ The number of unique nodes in this
Facemap
.Type: integer
Note
This property is read-only.
Example usage:
>>> print(zone.facemap.num_unique_nodes) 4194304
-
Facemap.
right_element
(face, element=None)[source]¶ The element to the right of a specific face.
Parameters: Returns: 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 aFacemap.assignment()
context, and should follow calls toFacemap.set_nodes()
andFacemap.set_elements()
to complete the connectivity map information needed for rendering. UsingFacemap.set_mapping()
is recommended, which does all the required book keeping.- elements (2D array of
-
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()
andFacemap.assignment()
,Facemap.set_nodes()
,Facemap.set_elements()
andFacemap.set_boundary_connections()
family of methods. The size of the underlying arrays are calculated based on the elementmap given and a call toFacemap.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: The facemap must first be allocated with
Facemap.alloc()
and must be called from within aFacemap.assignment()
context and should follow a call toFacemap.set_nodes()
to complete the connectivity map information needed for rendering. UsingFacemap.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
) – Thelist
oflists
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.- facemap (2D array of zero-based
-
Facemap.
set_nodes
(facemap)[source]¶ Sets the polytope connectivity.
Parameters: facemap (2D array of zero-based integers
) – Thelist
oflists
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 aFacemap.assignment()
context and should be followed by a call toFacemap.set_elements()
to complete the connectivity map information needed for rendering. It is recomended to use theFacemap.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) for fmap in plot.fieldmaps(): fmap.surfaces.surfaces_to_plot = SurfacesToPlot.All fmap.effects.surface_translucency = 40 # Turning on mesh, we can see all the individual triangles plot.show_mesh = True for fmap in plot.fieldmaps(): fmap.mesh.line_pattern = LinePattern.Dashed tp.export.save_png('fe_triangles1.png', 600, supersample=3)
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
FaceNeighborMode
of this zone.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: 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
ofintegers
orNone
) – 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 asneighbors
. UseNone
to indicate these are local neighbors. (default:None
) - obscure (
boolean
, 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.- element (
-
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/orFaceNeighbors.add_neighbors()
methods. See theFaceNeighbors
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: Returns: 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
¶ FaceNeighborMode
of this zone.Type: FaceNeighborMode
- 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
- Possible values:
-
FaceNeighbors.
neighbors
(element, face)[source]¶ Get the neighboring elements and zones of a specific face.
Parameters: Returns: list
ofnamedtuples
–(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 \(-1\) orNone
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 forFaceNeighbors
class object for details on how to use this method.- neighbors (array of
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.' >>> for k, v in aux.items(): ... print('{}: {}'.format(k,v)) info: Here is some information. note: Aux data values are always converted to strings. Xavg: 3.14159
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