from collections.abc import Iterable
from contextlib import contextmanager
from ..tecutil import _tecutil
from ..constant import *
from ..exception import *
from .. import layout, tecutil
from ..tecutil import IndexSet, Index, flatten_args, sv, optional
[docs]
@tecutil.lock()
def 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):
"""The execute_equation function operates on a data set within the
|Tecplot Engine| using FORTRAN-like equations.
Parameters:
equation (`str`): String containing the equation.
Multiple equations can be processed by separating each equation
with a newline. See Section 20 - 1 "Data Alteration through
Equations" in the |Tecplot User's Manual| for more information on
using equations. Iterable container of `Zone <data_access>` 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 <data_access>` objects, optional):
Iterable container of `Zone <data_access>` objects to operate on.
May be a list, set, tuple, or any iterable container. If `None`,
the equation will be applied to all zones.
i_range (`tuple` of `integers <int>`, optional):
Tuple of integers for I: (min, max, step). If `None`, then the
equation will operate on the entire range.
Not used for finite element nodal data.
j_range (`tuple` of `integers <int>`, optional):
Tuple of integers for J: (min, max, step). If `None`, then the
equation will operate on the entire range.
Not used for finite element nodal data.
k_range (`tuple` of `integers <int>`, optional):
Tuple of integers for K: (min, max, step). If `None`, then the
equation will operate on the entire range.
Not used for finite element nodal data.
value_location (`ValueLocation`, optional):
Variable `ValueLocation` for the variable on the left hand side.
This is used only if this variable is being created for the first
time.
If `None`, |Tecplot Engine| will choose the location for you.
variable_data_type (`FieldDataType`, optional):
Data type for the variable on the left hand side.
This is used only if this variable is being created for the first
time.
If `None`, |Tecplot Engine| will choose the type for you.
ignore_divide_by_zero (`bool`, optional):
`bool` value which instructs |Tecplot Engine| to ignore
divide by zero errors. The result is clamped
such that 0/0 is clamped to zero and (+/-N)/0
where N != 0 clamps to +/-maximum value for the given type.
.. warning:: Zero-based Indexing
It is important to know that all indexing in |PyTecplot| scripts are
zero-based. This is a departure from the macro language which is
one-based. This is to keep with the expectations when working in the
python language. However, |PyTecplot| does not modify strings that are
passed to the |Tecplot Engine|. This means that one-based indexing
should be used when running macro commands from python or when using
`execute_equation() <tecplot.data.operate.execute_equation>`.
Add one to variable 'X' for zones 'Rectangular' and 'Circular' for every
data point:
>>> dataset = tecplot.active_frame().dataset
>>> execute_equation('{X} = {X} + 1', zones=[dataset.zone('Rectangular'),
>>> dataset.zone('Circular')])
Create a new, double precision variable called DIST:
>>> execute_equation('{DIST} = SQRT({X}**2 + {Y}**2)',
... variable_data_type=FieldDataType.Double)
Set a variable called **P** to zero along the boundary of an IJ-ordered
zone:
>>> execute_equation('{P} = 0', i_range=(0, -1, 0), j_range=(0, -1, 0))
Using 1-based indexing in equations and 0-based indexing in parameters.
Zone 4 is subtracted from zone 3 and the result is placed in zone 2:
>>> execute_equation('{T} = {T}[3]-{T}[4]', zones=[ds.zone(1)])
"""
if __debug__:
if not isinstance(equation, str):
raise TecplotTypeError('Equation must be a string')
elif len(equation) == 0:
raise TecplotValueError('Equation can not be empty')
if not isinstance(value_location, (ValueLocation, type(None))):
msg = 'value_location must be a ValueLocation'
raise TecplotTypeError(msg)
if not isinstance(variable_data_type, (FieldDataType, type(None))):
msg = 'variable_data_type must be a FieldDataType'
raise TecplotTypeError(msg)
if not isinstance(ignore_divide_by_zero, (bool, type(None))):
raise TecplotTypeError('ignore_divide_by_zero must be a bool')
# Check that all zones belong to the active dataset
# (which is currently the only dataset option available)
if zones:
def dataset_uid(zone):
'''return the dataset uid if zone is a Zone object.'''
try:
return zone.dataset.uid
except AttributeError:
return None
if isinstance(zones, Iterable):
# converting generator to list to allow
# iterating over it more than once
zones = list(zones)
parent_ids = map(dataset_uid, zones)
else:
parent_ids = {dataset_uid(zones)}
parent_ids = set(filter(None, parent_ids))
active_id = layout.active_frame().dataset.uid
if parent_ids and parent_ids != {active_id}:
msg = 'All zones must have the same parent dataset'
raise TecplotValueError(msg)
with tecutil.ArgList() as arglist:
arglist[sv.EQUATION] = equation
with optional(IndexSet, zones) as zoneset:
arglist[sv.ZONESET] = zoneset
for dim, rng in zip(['I', 'J', 'K'], [i_range, j_range, k_range]):
if rng is not None:
rng = tecutil.IndexRange(*rng)
if rng.min is not None:
arglist[dim + 'MIN'] = Index(rng.min)
if rng.max is not None:
arglist[dim + 'MAX'] = Index(rng.max)
if rng.step is not None:
step = int(rng.step)
if step > 0:
arglist[dim + 'SKIP'] = step
elif step < 0:
msg = 'Negative step not supported.'
raise TecplotLogicError(msg)
arglist[sv.VALUELOCATION] = value_location
arglist[sv.VARDATATYPE] = variable_data_type
arglist[sv.IGNOREDIVIDEBYZERO] = ignore_divide_by_zero
if not _tecutil.DataAlterX(arglist):
raise TecplotSystemError()
@contextmanager
def _interpolate_process_args(dest_zone, src_zones, variables, plot):
if plot is None:
plot = layout.active_frame().plot()
if isinstance(dest_zone, int):
dest_zone = plot.frame.dataset.zone(dest_zone)
if not isinstance(src_zones, (Iterable, type(None))):
src_zones = [src_zones]
if not isinstance(variables, (Iterable, type(None))):
variables = [variables]
if __debug__:
if plot.frame.dataset != dest_zone.dataset:
msg = 'Plot and destination zone do not share the same dataset.'
raise TecplotLogicError(msg)
if src_zones is not None:
def is_invalid_zone(z):
if isinstance(z, int):
return z >= plot.frame.dataset.num_zones
else:
return plot.frame.dataset != z.dataset
if any(map(is_invalid_zone, src_zones)):
msg = 'Source zones are not part of the same dataset.'
raise TecplotLogicError(msg)
if variables is not None:
def is_invalid_variable(v):
if isinstance(v, int):
return v >= plot.frame.dataset.num_variables
else:
return plot.frame.dataset != v.dataset
if any(map(is_invalid_variable, variables)):
msg = 'Variables are not part of the same dataset.'
raise TecplotLogicError(msg)
with plot.activated():
with optional(IndexSet, src_zones) as src:
with optional(IndexSet, variables) as varset:
dest = dest_zone.index + 1
yield src, dest, varset
[docs]
@tecutil.lock()
def interpolate_linear(destination_zone, source_zones=None, variables=None,
fill_value=None, plot=None):
"""Linear interpolation onto a destination zone.
Parameters:
destination_zone (`zone <data_access>` or `int`): The
destination zone (or zone index) for interpolation.
source_zones (`zones <data_access>` or `integers <int>`, 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 <Variable>` or `integers <int>`, 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 (:ref:`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, :math:`(x, y)` for
2D or :math:`(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:
.. code-block:: python
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)
.. figure:: /_static/images/interpolate_2d_source.png
:width: 300px
:figwidth: 300px
Source data.
.. figure:: /_static/images/interpolate_linear_2d_dest.png
:width: 300px
:figwidth: 300px
Interpolated data.
"""
with _interpolate_process_args(destination_zone, source_zones, variables,
plot) as (src, dest, varset):
if fill_value is None:
interp_const = 0.0
interp_mode = LinearInterpMode.DontChange
else:
interp_const = float(fill_value)
interp_mode = LinearInterpMode.SetToConst
if not _tecutil.LinearInterpolate(src, dest, varset, interp_const,
interp_mode.value):
raise TecplotSystemError()
[docs]
@tecutil.lock()
def interpolate_kriging(destination_zone, source_zones=None, variables=None,
krig_range=0.3, zero_value=0.0, drift=Drift.Linear,
point_selection=PtSelection.OctantN, num_points=8,
plot=None):
"""Kriging interpolation onto a destination zone.
Parameters:
destination_zone (`zone <data_access>` or `int`): The
destination zone (or zone index) for interpolation.
source_zones (`zones <data_access>` or `integers <int>`, optional):
Zones (or zone indices) used to obtain the field values for
interpolation. By default, all zones except the *destination_zone*
will be used. All source zones must be FE-Tetra, FE-Brick or
IJK-ordered when doing kriging interpolation in 3D.
variables (`variables <Variable>` or `integers <int>`, optional):
Variables (or variable indices) to interpolate. By default, all
variables except those assigned to the axes will be used and is in
general dependent on the active plot type of the frame.
krig_range (`float`, optional): Distance beyond which source points
become insignificant. Must be between zero and one, inclusive.
(default: 0.3)
zero_value (`float`, optional): Semi-variance at each source data point
on a normalized scale from zero to one. (default: 0.0)
drift (`Drift`, optional): Overall trend for the data. Possible values:
`Drift.None_` no trend, `Drift.Linear` (default) linear trend,
`Drift.Quad` quadratic trend.
point_selection (`PtSelection`, optional): Method for determining which
source points to consider for each destination data point. Possible
values: `PtSelection.OctantN` (default) closest *num_points*
selected by coordinate-system octants, `PtSelection.NearestN`
closest *num_points* to the destination point, `PtSelection.All`
all points in the source zone.
num_points (`int`, optional): Number of source points to
consider for each destination data point if *point_selection* is
`PtSelection.OctantN` or `PtSelection.NearestN`. (default: 8)
plot (:ref:`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, :math:`(x, y)` for
2D or :math:`(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:
.. code-block:: python
:emphasize-lines: 58-59
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)
.. figure:: /_static/images/interpolate_2d_source.png
:width: 300px
:figwidth: 300px
Source data.
.. figure:: /_static/images/interpolate_krig_2d_dest.png
:width: 300px
:figwidth: 300px
Interpolated data.
"""
with _interpolate_process_args(destination_zone, source_zones, variables,
plot) as (src, dest, varset):
if not _tecutil.Krig(
src, dest, varset, krig_range, zero_value, Drift(drift).value,
PtSelection(point_selection).value, num_points):
raise TecplotSystemError()
[docs]
@tecutil.lock()
def interpolate_inverse_distance(destination_zone, source_zones=None,
variables=None, exponent=3.5, min_radius=0.0,
point_selection=PtSelection.OctantN,
num_points=8, plot=None):
"""Inverse-Distance interpolation onto a destination zone.
Parameters:
destination_zone (`zone <data_access>` or `int`): The
destination zone (or zone index) for interpolation.
source_zones (`zones <data_access>` or `integers <int>`, 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 <Variable>` or `integers <int>`, optional):
Variables (or variable indices) to interpolate. By default, all
variables except those assigned to the axes will be used and is in
general dependent on the active plot type of the frame.
exponent (`float`, optional): Exponent for the inverse-distance
weighting. (default: 3.5)
min_radius (`float`, optional): Minimum distance used for the
inverse-distance weighting. (default: 0.0)
point_selection (`PtSelection`, optional): Method for determining which
source points to consider for each destination data point. Possible
values: `PtSelection.OctantN` (default) closest *num_points*
selected by coordinate-system octants, `PtSelection.NearestN`
closest *num_points* to the destination point, `PtSelection.All`
all points in the source zone.
num_points (`int`, optional): Number of source points to
consider for each destination data point if *point_selection* is
`PtSelection.OctantN` or `PtSelection.NearestN`. (default: 8)
plot (:ref:`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, :math:`(x, y)` for
2D or :math:`(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:
.. code-block:: python
:emphasize-lines: 57
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)
.. figure:: /_static/images/interpolate_2d_source.png
:width: 300px
:figwidth: 300px
Source data.
.. figure:: /_static/images/interpolate_invdst_2d_dest.png
:width: 300px
:figwidth: 300px
Interpolated data.
"""
with _interpolate_process_args(destination_zone, source_zones, variables,
plot) as (src, dest, varset):
if not _tecutil.InverseDistInterpolation(
src, dest, varset, exponent, min_radius,
PtSelection(point_selection).value, num_points):
raise TecplotSystemError()
[docs]
@tecutil.lock()
def smooth(array, num_passes=1, weight=0.8,
boundary_condition=BoundaryCondition.Fixed, frame=None):
"""Smooth a field data `Array` in the dataset.
Parameters:
array (`Array`): The field data to smooth.
num_passes (`int`, optional): The number of smoothing passes to
perform. (default: 1)
weight (`float`, optional): The relaxation factor for each pass of
smoothing. Must be a number between zero and one exclusively.
Higher numbers indicate a greater smoothing effect. (default: 0.8)
boundary_condition (`BoundaryCondition`, optional): The boundary
condition by which to smooth. Possible values are
`BoundaryCondition.Fixed` (default),
`BoundaryCondition.ZeroGradient` and `BoundaryCondition.Zero2nd`.
frame (`Frame <layout.Frame>`, optional): The `Frame <layout.Frame>` that specifies the spatial
variables to smooth over via the active plot. By default, the frame
associated with the input `Array` object will be used.
The data will be smoothed over the spatial variables set by the plot and is
dependent on the active plot type of the associated `Frame <layout.Frame>` (`Cartesian2D`
or `Cartesian3D`). Example usage::
>>> tp.data.operate.smooth(dataset.zone('Zone').values('Pressure'))
"""
frame = frame or array.zone.dataset.frame
with frame.activated():
if not _tecutil.Smooth(array.zone.index + 1, array.variable.index + 1,
int(num_passes), float(weight),
BoundaryCondition(boundary_condition).value):
raise TecplotSystemError()
def _dataset(*objlists):
for objs in objlists:
if objs:
for obj in objs:
if obj and hasattr(obj, 'dataset'):
return obj.dataset
return layout.active_frame().dataset
