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# Source code for tecplot.data.operate

    from builtins import int

    from collections import Iterable, namedtuple
    from contextlib import contextmanager

    from six import string_types

    from ..tecutil import _tecutil
    from .. import layout
    from ..constant import *
    from ..exception import *
    from ..tecutil import (ArgList, IndexSet, Index, flatten_args, sv, lock,
                           optional)

    Range = namedtuple('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')
    """
    Range.__new__.__defaults__ = (None, None, None)

    @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 (`string <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 (`Range`, 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 (`Range`, 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 (`Range`, 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.

        Raises:
            `TecplotSystemError`

        .. 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:
        >>> import tecplot
        >>> 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))
        """
        if __debug__:
            if not isinstance(equation, string_types):
                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:
                try:
                    current_dataset = layout.active_frame().dataset
                    parent_ids = {U.dataset.uid for U in zones} if isinstance(
                        zones, Iterable) else {zones.dataset.uid}

                    if {current_dataset.uid} != parent_ids:
                        raise TecplotValueError(
                            'All zones must have the same parent dataset')
                except AttributeError:
                    pass  # integer indices do not care or know about parent dataset

        with 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:
                        if not isinstance(rng, Range):
                            rng = Range(*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'] = rng.step

                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

    @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 `integer <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()

    @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 `integer <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.
            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 <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

            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()

    @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 `integer <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)
            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 <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

            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()

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